From bcf616a4d891205666bb550d1990573d4a24a525 Mon Sep 17 00:00:00 2001 From: Anh-Khoa NGO-HO Date: Wed, 20 Sep 2023 17:07:08 +0200 Subject: [PATCH 01/99] feat: import TabDDPM and TabDDPMTS in api.rst --- docs/api.rst | 10 ++++ docs/imputers.rst | 4 +- examples/tutorials/tuto_diffusion_models.py | 8 +++ qolmat/utils/data.py | 54 +++++++-------------- 4 files changed, 37 insertions(+), 39 deletions(-) create mode 100644 examples/tutorials/tuto_diffusion_models.py diff --git a/docs/api.rst b/docs/api.rst index 876a4db5..3469923e 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -93,3 +93,13 @@ EM engine imputations.em_sampler.MultiNormalEM imputations.em_sampler.VARpEM + +Diffusion engine +================ + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + imputations.diffusions.diffusions.TabDDPM + imputations.diffusions.diffusions.TabDDPMTS \ No newline at end of file diff --git a/docs/imputers.rst b/docs/imputers.rst index dbbbe7fd..5c51460c 100644 --- a/docs/imputers.rst +++ b/docs/imputers.rst @@ -74,14 +74,14 @@ Two cases are considered: 8. TabDDPM ----------- -:class:`qolmat.diffusions.TabDDPM` is a deep learning imputer based on Denoising Diffusion Probabilistic Models (DDPMs) [7] for handling multivariate tabular data. Our implementation mainly follows the works of [8, 9]. Diffusion models focus on modeling the process of data transitions from noisy and incomplete observations to the underlying true data. They include two main processes: +:class:`~qolmat.imputations.diffusions.diffusions.TabDDPM` is a deep learning imputer based on Denoising Diffusion Probabilistic Models (DDPMs) [7] for handling multivariate tabular data. Our implementation mainly follows the works of [8, 9]. Diffusion models focus on modeling the process of data transitions from noisy and incomplete observations to the underlying true data. They include two main processes: * Forward process perturbs observed data to noise until all the original data structures are lost. The pertubation is done over a series of steps. Let :math:`X_{obs}` be observed data, :math:`T` be the number of steps that noises :math:`\epsilon \sim \mathcal{N}(0,I)` are added into the observed data. Therefore, :math:`X_{obs}^t = \bar{\alpha}_t \times X_{obs} + \sqrt{1-\bar{\alpha}_t} \times \epsilon` where :math:`\bar{\alpha}_t` controls the right amount of noise. * Reverse process removes noise and reconstructs the observed data. At each step :math:`t`, we train an autoencoder :math:`\epsilon_\theta` based on ResNet [9] to predict the added noise :math:`\epsilon_t` based on the rest of the observed data. The objective function is the error between the noise added in the forward process and the noise predicted by :math:`\epsilon_\theta`. In training phase, we use the self-supervised learning method of [8] to train incomplete data. In detail, our model randomly masks a part of observed data and computes loss from these masked data. Moving on to the inference phase, (1) missing data are replaced by Gaussian noises :math:`\epsilon \sim \mathcal{N}(0,I)`, (2) at each noise step from :math:`T` to 0, our model denoises these missing data based on :math:`\epsilon_\theta`. -In the case of time-series data, we also propose :class:`qolmat.diffusions.TabDDPMTS` (built on top of :class:`qolmat.diffusions.TabDDPM`) to capture time-based relationships between data points in a dataset. In fact, the dataset is pre-processed by using sliding window method to obtain a set of data partitions. The noise prediction of the model :math:`\epsilon_\theta` takes into account not only the observed data at the current time step but also data from previous time steps. These time-based relationships are encoded by using a transformer-based architecture [8]. +In the case of time-series data, we also propose :class:`~qolmat.imputations.diffusions.diffusions.TabDDPMTS` (built on top of :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM`) to capture time-based relationships between data points in a dataset. In fact, the dataset is pre-processed by using sliding window method to obtain a set of data partitions. The noise prediction of the model :math:`\epsilon_\theta` takes into account not only the observed data at the current time step but also data from previous time steps. These time-based relationships are encoded by using a transformer-based architecture [8]. References ---------- diff --git a/examples/tutorials/tuto_diffusion_models.py b/examples/tutorials/tuto_diffusion_models.py new file mode 100644 index 00000000..4e5f0d7d --- /dev/null +++ b/examples/tutorials/tuto_diffusion_models.py @@ -0,0 +1,8 @@ +""" +=============================================== +Tutorial for imputers based on diffusion models +=============================================== + +In this tutorial, we show how to use :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM` +and :class:`~qolmat.imputations.diffusions.diffusions.TabDDPMTS` classes. +""" diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index 02d178c8..1d8a1154 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -77,23 +77,29 @@ def get_data( """ urllink1 = "https://zenodo.org/record/" if name_data == "Beijing": - # urllink = "https://archive.ics.uci.edu/static/public/381/" - # zipname = "beijing+pm2+5+data" urllink = "https://archive.ics.uci.edu/static/public/501/" zipname = "beijing+multi+site+air+quality+data" - list_df = download_data(zipname, urllink, datapath=datapath) + path_zip = os.path.join(datapath, zipname) + path_zip_ext = path_zip + ".zip" + url = os.path.join(urllink, zipname) + ".zip" + os.makedirs(datapath, exist_ok=True) + if not os.path.exists(path_zip_ext) and not os.path.exists(path_zip): + request.urlretrieve(url, path_zip_ext) + if not os.path.exists(path_zip): + with zipfile.ZipFile(path_zip_ext, "r") as zip_ref: + zip_ref.extractall(path_zip) + + zipname = "PRSA2017_Data_20130301-20170228" + path_zip = os.path.join(path_zip, zipname) + path_zip_ext = path_zip + ".zip" + if not os.path.exists(path_zip): + with zipfile.ZipFile(path_zip_ext, "r") as zip_ref: + zip_ref.extractall(path_zip) + list_df = get_dataframes_in_folder(path_zip, ".csv") list_df = [preprocess_data_beijing(df) for df in list_df] df = pd.concat(list_df) return df - elif name_data == "Beijing_offline": - # urllink = "https://archive.ics.uci.edu/dataset/381/beijing+pm2+5+data" - folder = "PRSA2017_Data_20130301-20170228" - path = os.path.join(datapath, folder) - list_df = get_dataframes_in_folder(path, ".csv") - list_df = [preprocess_data_beijing_offline(df) for df in list_df] - df = pd.concat(list_df) - return df elif name_data == "Artificial": city = "Wonderland" n_samples = 1000 @@ -172,32 +178,6 @@ def get_data( def preprocess_data_beijing(df: pd.DataFrame) -> pd.DataFrame: """Preprocess data from the "Beijing" datset - Parameters - ---------- - df : pd.DataFrame - dataframe with some specific column names - - Returns - ------- - pd.DataFrame - preprocessed dataframe - """ - df["datetime"] = pd.to_datetime(df[["year", "month", "day", "hour"]]) - df["station"] = "Beijing" - df.set_index(["station", "datetime"], inplace=True) - df.drop( - columns=["year", "month", "day", "hour", "No", "cbwd", "Iws", "Is", "Ir"], inplace=True - ) - df.sort_index(inplace=True) - df = df.groupby( - ["station", df.index.get_level_values("datetime").floor("d")], group_keys=False - ).mean() - return df - - -def preprocess_data_beijing_offline(df: pd.DataFrame) -> pd.DataFrame: - """Preprocess data from the "Beijing" datset - Parameters ---------- df : pd.DataFrame From 89b7be09ca50dfd1b928a1ff8922a1cb3c802bc3 Mon Sep 17 00:00:00 2001 From: Anh-Khoa NGO-HO Date: Thu, 21 Sep 2023 14:28:47 +0200 Subject: [PATCH 02/99] feat: add tutorial for diffusion models --- docs/api.rst | 1 + docs/index.rst | 1 + examples/tutorials/tuto_diffusion_models.py | 214 ++++++++++++++++++++ 3 files changed, 216 insertions(+) diff --git a/docs/api.rst b/docs/api.rst index 3469923e..06973517 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -101,5 +101,6 @@ Diffusion engine :toctree: generated/ :template: class.rst + imputations.imputers_pytorch.ImputerDiffusion imputations.diffusions.diffusions.TabDDPM imputations.diffusions.diffusions.TabDDPMTS \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index a7c9580b..d41195b5 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -16,6 +16,7 @@ imputers examples/tutorials/tuto_benchmark_TS + examples/tutorials/tuto_diffusion_models .. toctree:: :maxdepth: 2 diff --git a/examples/tutorials/tuto_diffusion_models.py b/examples/tutorials/tuto_diffusion_models.py index 4e5f0d7d..d35f578c 100644 --- a/examples/tutorials/tuto_diffusion_models.py +++ b/examples/tutorials/tuto_diffusion_models.py @@ -6,3 +6,217 @@ In this tutorial, we show how to use :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM` and :class:`~qolmat.imputations.diffusions.diffusions.TabDDPMTS` classes. """ + +# %% +import numpy as np +import matplotlib.pyplot as plt + +from qolmat.utils import data +from qolmat.benchmark import comparator, missing_patterns + +from qolmat.imputations.imputers_pytorch import ImputerDiffusion +from qolmat.imputations.diffusions.diffusions import TabDDPM, TabDDPMTS + +# %% +# 1. Data +# --------------------------------------------------------------- +# We use the public Beijing Multi-Site Air-Quality Data Set. +# It consists in hourly air pollutants data from 12 chinese nationally-controlled air-quality +# monitoring sites. The original data from which the features were extracted comes from +# https://archive.ics.uci.edu/static/public/501/beijing+multi+site+air+quality+data.zip + +df_data = data.get_data_corrupted("Beijing") + +print("Number of nan at each column:") +print(df_data.isna().sum()) + +# %% +# 2. Hyperparameters for the wapper ImputerDiffusion +# --------------------------------------------------------------- +# We use the wapper :class:`~qolmat.imputations.imputers_pytorch.ImputerDiffusion` for our +# diffusion models (e.g., :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM`, +# :class:`~qolmat.imputations.diffusions.diffusions.TabDDPMTS`). The most important hyperparameter +# is ``model`` where we select a diffusion base model for the task of imputation +# (e.g., ``model=TabDDPM()``). +# Other hyperparams are for training the selected diffusion model. +# +# * ``cols_imputed``: list of columns that need to be imputed. Recall that we train the model on +# incomplete data by using the self-supervised learning method. We can set which columns to be +# masked during training. Its defaut value is ``None``. +# +# * ``epochs`` : a number of iterations, its defaut value ``epochs=10``. In practice, we should +# set a larger number of epochs e.g., ``epochs=100``. +# +# * ``batch_size`` : a size of batch, its defaut value ``batch_size=100``. +# +# The following hyperparams are for validation: +# +# * ``x_valid``: a validation set. +# +# * ``metrics_valid``: a list validation metrics (see all metrics :doc:`imputers`). Its default +# value ``metrics_valid=(metrics.mean_absolute_error, metrics.dist_wasserstein,)`` +# +# * ``print_valid``: a boolean to display/hide a training progress (including epoch_loss, +# remaining training duration and performance scores computed by the metrics above). + +df_data_valid = df_data.iloc[:5000] + +tabddpm = ImputerDiffusion( + model=TabDDPM(), epochs=50, batch_size=100, x_valid=df_data_valid, print_valid=True +) +tabddpm = tabddpm.fit(df_data) + +# %% +# We can see the architecture of the TabDDPM with ``get_summary_architecture()`` + +print(tabddpm.get_summary_architecture()) + + +# %% +# We also get the summary of the training progress with ``get_summary_training()`` + +summary = tabddpm.get_summary_training() + +print(f"Performance metrics: {list(summary.keys())}") + +metric = "mean_absolute_error" +metric_scores = summary[metric] + +fig, ax = plt.subplots() +ax.plot(range(len(metric_scores)), metric_scores) +ax.set_xlabel("Epoch") +ax.set_ylabel(metric) + +plt.show() + + +# %% +# We display the imputations for the variable TEMP. + +df_imputed = tabddpm.transform(df_data) + +station = df_data.index.get_level_values("station")[0] +col = "TEMP" + +values_orig = df_data.loc[station, col] +values_imp = df_imputed.loc[station, col].copy() + +fig, ax = plt.subplots(figsize=(10, 3)) +plt.plot(values_orig, ".", color="black", label="original") + +values_imp[values_orig.notna()] = np.nan + +plt.plot(values_imp, ".", color="blue", label="TabDDPM") +plt.ylabel(col, fontsize=10) +plt.legend(loc=[1.01, 0], fontsize=10) +ax.tick_params(axis="both", which="major", labelsize=10) +plt.show() + +# %% +# 3. Hyperparameters for TabDDPM +# --------------------------------------------------------------- +# :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM` is a diffusion model based on +# Denoising Diffusion Probabilistic Models [1] for imputing tabular data. Several important +# hyperparameters are +# +# * ``num_noise_steps``: the number of step in the forward/reverse process. +# It is T in the equation 1 of [1]. Its default value ``num_noise_steps=50``. +# Note that a larger value can improve imputation quality but also increases inference time. +# +# * ``beta_start`` and ``beta_end``: the minimum and the maximum value +# for the linear variance schedule (equation 2 of [1]). +# Their default values ``beta_start=1e-4``, ``beta_end=0.02`` +# +# * ``num_sampling``: for each missing value, the model generates n imputation variants. +# The mean value of these variants is returned. +# Based on our experiments, a large n (n > 5) often improves reconstruction scores (e.g., MAE). +# Its default value ``num_sampling=1``. +# +# * ``ratio_nan=0.1``: in the self-supervised learning method, we need to randomly mask partial +# observed data based on this ratio of missing values. +# +# Other hyperparams for building this deep learning model are +# +# * ``lr``: learning rate (``float = 0.001``) +# +# * ``num_blocks``: number of residual blocks (``int = 1)`` +# +# * ``dim_embedding``: dimension of hidden layers in residual blocks (``int = 128``) +# +# Let see an example below. We can observe that a large ``num_sampling`` generally improves +# reconstruction errors (mae, wmape) but increases distribution distance (KL_columnwise, +# wasserstein_columnwise). + +dict_imputers = { + "num_sampling=5": ImputerDiffusion(model=TabDDPM(num_sampling=5), epochs=10, batch_size=100), + "num_sampling=20": ImputerDiffusion(model=TabDDPM(num_sampling=10), epochs=10, batch_size=100), +} + +comparison = comparator.Comparator( + dict_imputers, + selected_columns=df_data.columns, + generator_holes=missing_patterns.UniformHoleGenerator(n_splits=4), + metrics=["mae", "wmape", "KL_columnwise", "wasserstein_columnwise"], + max_evals=10, +) +results = comparison.compare(df_data.iloc[:5000]) + +results.groupby(axis=0, level=0).mean().groupby(axis=0, level=0).mean() + +# %% +# 4. Hyperparameters for TabDDPMTS +# --------------------------------------------------------------- +# :class:`~qolmat.imputations.diffusions.diffusions.TabDDPMTS` is built on top of +# :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM` to capture time-based relationships +# between data points in a dataset. +# +# Two important hyperparameters for processing time-series data are ``index_datetime`` +# and ``freq_str``. +# E.g., ``ImputerDiffusion(model=TabDDPM(), index_datetime='datetime', freq_str='1D')``, +# +# * ``index_datetime``: the column name of datetime in index. +# +# * ``freq_str``: the time-series frequency for splitting data into a list of chunks (each chunk +# has the same number of rows). These chunks are fetched up in batches. +# A large frequency e.g., ``6M``, ``1Y`` can cause the out of memory. +# Its default value ``freq_str: str = "1D"``. Time series frequencies can be found in this +# `link `_ +# +# For TabDDPMTS, we have two options for splitting data: +# +# * ``is_rolling=False`` (default value): the data is splited by using +# pandas.DataFrame.resample(rule=freq_str). There is no duplication of row between chunks, +# leading a smaller number of chunks than the number of rows in the original data. +# +# * ``is_rolling=True``: the data is splited by using pandas.DataFrame.rolling(window=freq_str). +# The number of chunks is also the number of rows in the original data. +# Note that setting ``is_rolling=True`` always produces better quality of imputations +# but requires a longer training/inference time. + +dict_imputers = { + "tabddpm": ImputerDiffusion(model=TabDDPM(num_sampling=5), epochs=10, batch_size=100), + "tabddpmts": ImputerDiffusion( + model=TabDDPMTS(num_sampling=5, is_rolling=True), + epochs=10, + batch_size=100, + index_datetime="datetime", + freq_str="10D", + ), +} + +comparison = comparator.Comparator( + dict_imputers, + selected_columns=df_data.columns, + generator_holes=missing_patterns.UniformHoleGenerator(n_splits=4), + metrics=["mae", "wmape", "KL_columnwise", "wasserstein_columnwise"], + max_evals=10, +) +results = comparison.compare(df_data.iloc[:5000]) + +results.groupby(axis=0, level=0).mean().groupby(axis=0, level=0).mean() + +# %% +# [1] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. `Denoising diffusion probabilistic models. +# `_ +# Advances in neural information processing systems 33 (2020): 6840-6851. From b75ef49da7ea9dd020bc94a01c51452cd9706517 Mon Sep 17 00:00:00 2001 From: Anh-Khoa NGO-HO Date: Fri, 22 Sep 2023 12:32:04 +0200 Subject: [PATCH 03/99] feat: remove beijing_offline from data.py and test_data.py --- tests/utils/test_data.py | 70 ++++++++++------------------------------ 1 file changed, 17 insertions(+), 53 deletions(-) diff --git a/tests/utils/test_data.py b/tests/utils/test_data.py index 3246ca99..0761ce0e 100644 --- a/tests/utils/test_data.py +++ b/tests/utils/test_data.py @@ -16,26 +16,18 @@ ], columns=columns, ) -index_preprocess_beijing = pd.MultiIndex.from_tuples( + +index_beijing_preprocess = pd.MultiIndex.from_tuples( [ - ("Beijing", datetime.datetime(2013, 3, 1)), - ("Beijing", datetime.datetime(2014, 3, 1)), - ("Beijing", datetime.datetime(2015, 3, 1)), + ("Gucheng", datetime.datetime(2013, 3, 1)), + ("Gucheng", datetime.datetime(2014, 3, 1)), + ("Gucheng", datetime.datetime(2015, 3, 1)), ], names=["station", "datetime"], ) -df_preprocess_beijing = pd.DataFrame( - [[1, 2], [3, np.nan], [np.nan, 6]], columns=["a", "b"], index=index_preprocess_beijing -) -columns = ["No", "year", "month", "day", "hour", "a", "b", "wd", "station"] -df_offline = pd.DataFrame( - [ - [1, 2013, 3, 1, 0, 1, 2, "NW", "Gucheng"], - [2, 2014, 3, 1, 0, 3, np.nan, "NW", "Gucheng"], - [3, 2015, 3, 1, 0, np.nan, 6, "NW", "Gucheng"], - ], - columns=columns, +df_beijing_preprocess = pd.DataFrame( + [[1, 2], [3, np.nan], [np.nan, 6]], columns=["a", "b"], index=index_beijing_preprocess ) df_monach_weather = pd.DataFrame( @@ -94,18 +86,6 @@ index=pd.date_range(start="2002-01-01", periods=3, freq="30T"), ) -index_preprocess_offline = pd.MultiIndex.from_tuples( - [ - ("Gucheng", datetime.datetime(2013, 3, 1)), - ("Gucheng", datetime.datetime(2014, 3, 1)), - ("Gucheng", datetime.datetime(2015, 3, 1)), - ], - names=["station", "datetime"], -) -df_preprocess_offline = pd.DataFrame( - [[1, 2], [3, np.nan], [np.nan, 6]], columns=["a", "b"], index=index_preprocess_offline -) - urllink = "https://archive.ics.uci.edu/ml/machine-learning-databases/00501/" zipname = "PRSA2017_Data_20130301-20170228" @@ -122,7 +102,6 @@ "name_data, df", [ ("Beijing", df_beijing), - ("Beijing_offline", df_offline), ("Monach_weather", df_monach_weather), ("Monach_electricity_australia", df_monach_elec), ("Artificial", None), @@ -131,17 +110,13 @@ ) def test_utils_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFixture) -> None: mock_download = mocker.patch("qolmat.utils.data.download_data", return_value=[df]) - mocker.patch( - "qolmat.utils.data.preprocess_data_beijing_offline", return_value=df_preprocess_offline - ) - mocker.patch("qolmat.utils.data.preprocess_data_beijing", return_value=df_preprocess_beijing) + mocker.patch("qolmat.utils.data.preprocess_data_beijing", return_value=df_beijing_preprocess) mock_get = mocker.patch("qolmat.utils.data.get_dataframes_in_folder", return_value=[df]) try: df_result = data.get_data(name_data=name_data) except ValueError: assert name_data not in [ "Beijing", - "Beijing_offline", "Monach_weather", "Monach_electricity_australia", "Artificial", @@ -150,12 +125,9 @@ def test_utils_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFix return if name_data == "Beijing": - assert mock_download.call_count == 1 - pd.testing.assert_frame_equal(df_result, df_preprocess_beijing) - elif name_data == "Beijing_offline": assert mock_download.call_count == 0 assert mock_get.call_count == 1 - pd.testing.assert_frame_equal(df_result, df_preprocess_offline) + pd.testing.assert_frame_equal(df_result, df_beijing_preprocess) elif name_data == "Artificial": expected_columns = ["signal", "X", "A", "E"] assert isinstance(df_result, pd.DataFrame) @@ -172,13 +144,7 @@ def test_utils_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFix assert False -@pytest.mark.parametrize("df", [df_offline]) -def test_utils_data_preprocess_data_beijing_offline(df: pd.DataFrame) -> None: - result = data.preprocess_data_beijing_offline(df) - pd.testing.assert_frame_equal(result, df_preprocess_offline, atol=1e-3) - - -@pytest.mark.parametrize("df", [df_preprocess_offline]) +@pytest.mark.parametrize("df", [df_beijing_preprocess]) def test_utils_data_add_holes(df: pd.DataFrame) -> None: df_out = data.add_holes(df, 0.0, 1) assert df_out.isna().sum().sum() == 2 @@ -188,19 +154,17 @@ def test_utils_data_add_holes(df: pd.DataFrame) -> None: @pytest.mark.parametrize("name_data", ["Beijing"]) def test_utils_data_get_data_corrupted(name_data: str, mocker: MockerFixture) -> None: - mock_download = mocker.patch("qolmat.utils.data.download_data", return_value=[df_beijing]) - mocker.patch("qolmat.utils.data.preprocess_data_beijing", return_value=df_preprocess_beijing) + mocker.patch("qolmat.utils.data.get_data", return_value=df_beijing_preprocess) df_out = data.get_data_corrupted() df_result = pd.DataFrame( - [[1, 2], [np.nan, np.nan], [np.nan, 6]], columns=["a", "b"], index=index_preprocess_beijing + [[1, 2], [np.nan, np.nan], [np.nan, 6]], columns=["a", "b"], index=index_beijing_preprocess ) - assert mock_download.call_count == 1 pd.testing.assert_frame_equal(df_result, df_out) -@pytest.mark.parametrize("df", [df_preprocess_beijing]) +@pytest.mark.parametrize("df", [df_beijing_preprocess]) def test_utils_data_add_station_features(df: pd.DataFrame) -> None: - columns_out = ["a", "b"] + ["station=Beijing"] + columns_out = ["a", "b"] + ["station=Gucheng"] expected = pd.DataFrame( [ [1, 2, 1.0], @@ -208,13 +172,13 @@ def test_utils_data_add_station_features(df: pd.DataFrame) -> None: [np.nan, 6, 1.0], ], columns=columns_out, - index=index_preprocess_beijing, + index=index_beijing_preprocess, ) result = data.add_station_features(df) pd.testing.assert_frame_equal(result, expected, atol=1e-3) -@pytest.mark.parametrize("df", [df_preprocess_beijing]) +@pytest.mark.parametrize("df", [df_beijing_preprocess]) def test_utils_data_add_datetime_features(df: pd.DataFrame) -> None: columns_out = ["a", "b"] + ["time_cos"] expected = pd.DataFrame( @@ -224,7 +188,7 @@ def test_utils_data_add_datetime_features(df: pd.DataFrame) -> None: [np.nan, 6, 0.512], ], columns=columns_out, - index=index_preprocess_beijing, + index=index_beijing_preprocess, ) result = data.add_datetime_features(df) pd.testing.assert_frame_equal(result, expected, atol=1e-3) From facfa1f0cdf476e89974ed0420f25cb76fe49184 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 12 Oct 2023 10:38:36 +0200 Subject: [PATCH 04/99] =?UTF-8?q?Bump=20version:=200.0.15=20=E2=86=92=200.?= =?UTF-8?q?1.0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 2 +- docs/conf.py | 2 +- qolmat/_version.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index 460da476..3765caca 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.0.15 +current_version = 0.1.0 commit = True tag = True diff --git a/docs/conf.py b/docs/conf.py index 01521320..1d6d7bd7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "Quantmetry" # The full version, including alpha/beta/rc tags -version = "0.0.15" +version = "0.1.0" release = version # -- General configuration --------------------------------------------------- diff --git a/qolmat/_version.py b/qolmat/_version.py index 6561790f..3dc1f76b 100644 --- a/qolmat/_version.py +++ b/qolmat/_version.py @@ -1 +1 @@ -__version__ = "0.0.15" +__version__ = "0.1.0" diff --git a/setup.py b/setup.py index 7e52c507..915ff331 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import find_packages, setup DISTNAME = "qolmat" -VERSION = "0.0.15" +VERSION = "0.1.0" DESCRIPTION = "A Python library for optimal data imputation." LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst" with codecs.open("README.rst", encoding="utf-8-sig") as f: From ee9ef760dcb517e560646b9c039fd40df0108c33 Mon Sep 17 00:00:00 2001 From: Anh-Khoa NGO-HO Date: Wed, 25 Oct 2023 14:39:43 +0200 Subject: [PATCH 05/99] docs --- .../tutorials/plot_tuto_diffusion_models.py | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/examples/tutorials/plot_tuto_diffusion_models.py b/examples/tutorials/plot_tuto_diffusion_models.py index d35f578c..ba64ef98 100644 --- a/examples/tutorials/plot_tuto_diffusion_models.py +++ b/examples/tutorials/plot_tuto_diffusion_models.py @@ -3,8 +3,8 @@ Tutorial for imputers based on diffusion models =============================================== -In this tutorial, we show how to use :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM` -and :class:`~qolmat.imputations.diffusions.diffusions.TabDDPMTS` classes. +In this tutorial, we show how to use :class:`~qolmat.imputations.diffusions.ddpms.TabDDPM` +and :class:`~qolmat.imputations.diffusions.ddpms.TsDDPM` classes. """ # %% @@ -15,7 +15,7 @@ from qolmat.benchmark import comparator, missing_patterns from qolmat.imputations.imputers_pytorch import ImputerDiffusion -from qolmat.imputations.diffusions.diffusions import TabDDPM, TabDDPMTS +from qolmat.imputations.diffusions.ddpms import TabDDPM, TsDDPM # %% # 1. Data @@ -34,8 +34,8 @@ # 2. Hyperparameters for the wapper ImputerDiffusion # --------------------------------------------------------------- # We use the wapper :class:`~qolmat.imputations.imputers_pytorch.ImputerDiffusion` for our -# diffusion models (e.g., :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM`, -# :class:`~qolmat.imputations.diffusions.diffusions.TabDDPMTS`). The most important hyperparameter +# diffusion models (e.g., :class:`~qolmat.imputations.diffusions.ddpms.TabDDPM`, +# :class:`~qolmat.imputations.diffusions.ddpms.TsDDPM`). The most important hyperparameter # is ``model`` where we select a diffusion base model for the task of imputation # (e.g., ``model=TabDDPM()``). # Other hyperparams are for training the selected diffusion model. @@ -62,7 +62,7 @@ df_data_valid = df_data.iloc[:5000] tabddpm = ImputerDiffusion( - model=TabDDPM(), epochs=50, batch_size=100, x_valid=df_data_valid, print_valid=True + model=TabDDPM(), epochs=10, batch_size=100, x_valid=df_data_valid, print_valid=True ) tabddpm = tabddpm.fit(df_data) @@ -115,7 +115,7 @@ # %% # 3. Hyperparameters for TabDDPM # --------------------------------------------------------------- -# :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM` is a diffusion model based on +# :class:`~qolmat.imputations.diffusions.ddpms.TabDDPM` is a diffusion model based on # Denoising Diffusion Probabilistic Models [1] for imputing tabular data. Several important # hyperparameters are # @@ -164,10 +164,10 @@ results.groupby(axis=0, level=0).mean().groupby(axis=0, level=0).mean() # %% -# 4. Hyperparameters for TabDDPMTS +# 4. Hyperparameters for TsDDPM # --------------------------------------------------------------- -# :class:`~qolmat.imputations.diffusions.diffusions.TabDDPMTS` is built on top of -# :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM` to capture time-based relationships +# :class:`~qolmat.imputations.diffusions.ddpms.TsDDPM` is built on top of +# :class:`~qolmat.imputations.diffusions.ddpms.TabDDPM` to capture time-based relationships # between data points in a dataset. # # Two important hyperparameters for processing time-series data are ``index_datetime`` @@ -183,7 +183,7 @@ # `link `_ # -# For TabDDPMTS, we have two options for splitting data: +# For TsDDPM, we have two options for splitting data: # # * ``is_rolling=False`` (default value): the data is splited by using # pandas.DataFrame.resample(rule=freq_str). There is no duplication of row between chunks, @@ -196,8 +196,8 @@ dict_imputers = { "tabddpm": ImputerDiffusion(model=TabDDPM(num_sampling=5), epochs=10, batch_size=100), - "tabddpmts": ImputerDiffusion( - model=TabDDPMTS(num_sampling=5, is_rolling=True), + "TsDDPM": ImputerDiffusion( + model=TsDDPM(num_sampling=5, is_rolling=True), epochs=10, batch_size=100, index_datetime="datetime", From 0c1806aecb6a849329084d9c8bcd95c138febfa6 Mon Sep 17 00:00:00 2001 From: Anh-Khoa NGO-HO Date: Wed, 25 Oct 2023 14:41:35 +0200 Subject: [PATCH 06/99] docs --- docs/api.rst | 4 ++-- docs/imputers.rst | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/api.rst b/docs/api.rst index 06973517..2382a666 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -102,5 +102,5 @@ Diffusion engine :template: class.rst imputations.imputers_pytorch.ImputerDiffusion - imputations.diffusions.diffusions.TabDDPM - imputations.diffusions.diffusions.TabDDPMTS \ No newline at end of file + imputations.diffusions.ddpms.TabDDPM + imputations.diffusions.ddpms.TsDDPM \ No newline at end of file diff --git a/docs/imputers.rst b/docs/imputers.rst index 111187d6..fb8217e2 100644 --- a/docs/imputers.rst +++ b/docs/imputers.rst @@ -83,14 +83,14 @@ Two cases are considered: 9. TabDDPM ----------- -:class:`~qolmat.imputations.diffusions.diffusions.TabDDPM` is a deep learning imputer based on Denoising Diffusion Probabilistic Models (DDPMs) [7] for handling multivariate tabular data. Our implementation mainly follows the works of [8, 9]. Diffusion models focus on modeling the process of data transitions from noisy and incomplete observations to the underlying true data. They include two main processes: +:class:`~qolmat.imputations.diffusions.ddpms.TabDDPM` is a deep learning imputer based on Denoising Diffusion Probabilistic Models (DDPMs) [7] for handling multivariate tabular data. Our implementation mainly follows the works of [8, 9]. Diffusion models focus on modeling the process of data transitions from noisy and incomplete observations to the underlying true data. They include two main processes: * Forward process perturbs observed data to noise until all the original data structures are lost. The pertubation is done over a series of steps. Let :math:`X_{obs}` be observed data, :math:`T` be the number of steps that noises :math:`\epsilon \sim \mathcal{N}(0,I)` are added into the observed data. Therefore, :math:`X_{obs}^t = \bar{\alpha}_t \times X_{obs} + \sqrt{1-\bar{\alpha}_t} \times \epsilon` where :math:`\bar{\alpha}_t` controls the right amount of noise. * Reverse process removes noise and reconstructs the observed data. At each step :math:`t`, we train an autoencoder :math:`\epsilon_\theta` based on ResNet [9] to predict the added noise :math:`\epsilon_t` based on the rest of the observed data. The objective function is the error between the noise added in the forward process and the noise predicted by :math:`\epsilon_\theta`. In training phase, we use the self-supervised learning method of [8] to train incomplete data. In detail, our model randomly masks a part of observed data and computes loss from these masked data. Moving on to the inference phase, (1) missing data are replaced by Gaussian noises :math:`\epsilon \sim \mathcal{N}(0,I)`, (2) at each noise step from :math:`T` to 0, our model denoises these missing data based on :math:`\epsilon_\theta`. -In the case of time-series data, we also propose :class:`~qolmat.imputations.diffusions.diffusions.TabDDPMTS` (built on top of :class:`~qolmat.imputations.diffusions.diffusions.TabDDPM`) to capture time-based relationships between data points in a dataset. In fact, the dataset is pre-processed by using sliding window method to obtain a set of data partitions. The noise prediction of the model :math:`\epsilon_\theta` takes into account not only the observed data at the current time step but also data from previous time steps. These time-based relationships are encoded by using a transformer-based architecture [8]. +In the case of time-series data, we also propose :class:`~qolmat.imputations.diffusions.ddpms.TsDDPM` (built on top of :class:`~qolmat.imputations.diffusions.ddpms.TabDDPM`) to capture time-based relationships between data points in a dataset. In fact, the dataset is pre-processed by using sliding window method to obtain a set of data partitions. The noise prediction of the model :math:`\epsilon_\theta` takes into account not only the observed data at the current time step but also data from previous time steps. These time-based relationships are encoded by using a transformer-based architecture [8]. References ---------- From b6c98f1e6cd228d0374563d41d045598c493d6b4 Mon Sep 17 00:00:00 2001 From: Anh-Khoa NGO-HO Date: Wed, 25 Oct 2023 16:42:53 +0200 Subject: [PATCH 07/99] fix: reduce computation time --- .../tutorials/plot_tuto_diffusion_models.py | 45 ++++++++++--------- 1 file changed, 24 insertions(+), 21 deletions(-) diff --git a/examples/tutorials/plot_tuto_diffusion_models.py b/examples/tutorials/plot_tuto_diffusion_models.py index ba64ef98..8bcbc21e 100644 --- a/examples/tutorials/plot_tuto_diffusion_models.py +++ b/examples/tutorials/plot_tuto_diffusion_models.py @@ -8,6 +8,7 @@ """ # %% +import pandas as pd import numpy as np import matplotlib.pyplot as plt @@ -18,14 +19,20 @@ from qolmat.imputations.diffusions.ddpms import TabDDPM, TsDDPM # %% -# 1. Data +# 1. Time-series data # --------------------------------------------------------------- # We use the public Beijing Multi-Site Air-Quality Data Set. # It consists in hourly air pollutants data from 12 chinese nationally-controlled air-quality # monitoring sites. The original data from which the features were extracted comes from -# https://archive.ics.uci.edu/static/public/501/beijing+multi+site+air+quality+data.zip +# https://archive.ics.uci.edu/static/public/501/beijing+multi+site+air+quality+data.zip. +# For this tutorial, we only use a small subset of this data +# 1000 rows and 2 features (TEMP, PRES). df_data = data.get_data_corrupted("Beijing") +df_data = df_data[["TEMP", "PRES"]].iloc[:1000] +df_data.index = df_data.index.set_levels( + [df_data.index.levels[0], pd.to_datetime(df_data.index.levels[1])] +) print("Number of nan at each column:") print(df_data.isna().sum()) @@ -59,7 +66,7 @@ # * ``print_valid``: a boolean to display/hide a training progress (including epoch_loss, # remaining training duration and performance scores computed by the metrics above). -df_data_valid = df_data.iloc[:5000] +df_data_valid = df_data.iloc[:500] tabddpm = ImputerDiffusion( model=TabDDPM(), epochs=10, batch_size=100, x_valid=df_data_valid, print_valid=True @@ -71,7 +78,6 @@ print(tabddpm.get_summary_architecture()) - # %% # We also get the summary of the training progress with ``get_summary_training()`` @@ -144,22 +150,20 @@ # * ``dim_embedding``: dimension of hidden layers in residual blocks (``int = 128``) # # Let see an example below. We can observe that a large ``num_sampling`` generally improves -# reconstruction errors (mae, wmape) but increases distribution distance (KL_columnwise, -# wasserstein_columnwise). +# reconstruction errors (mae) but increases distribution distance (KL_columnwise). dict_imputers = { "num_sampling=5": ImputerDiffusion(model=TabDDPM(num_sampling=5), epochs=10, batch_size=100), - "num_sampling=20": ImputerDiffusion(model=TabDDPM(num_sampling=10), epochs=10, batch_size=100), + "num_sampling=10": ImputerDiffusion(model=TabDDPM(num_sampling=10), epochs=10, batch_size=100), } comparison = comparator.Comparator( dict_imputers, selected_columns=df_data.columns, - generator_holes=missing_patterns.UniformHoleGenerator(n_splits=4), - metrics=["mae", "wmape", "KL_columnwise", "wasserstein_columnwise"], - max_evals=10, + generator_holes=missing_patterns.UniformHoleGenerator(n_splits=2), + metrics=["mae", "KL_columnwise"], ) -results = comparison.compare(df_data.iloc[:5000]) +results = comparison.compare(df_data) results.groupby(axis=0, level=0).mean().groupby(axis=0, level=0).mean() @@ -174,7 +178,7 @@ # and ``freq_str``. # E.g., ``ImputerDiffusion(model=TabDDPM(), index_datetime='datetime', freq_str='1D')``, # -# * ``index_datetime``: the column name of datetime in index. +# * ``index_datetime``: the column name of datetime in index. It must be a pandas datetime object. # # * ``freq_str``: the time-series frequency for splitting data into a list of chunks (each chunk # has the same number of rows). These chunks are fetched up in batches. @@ -196,23 +200,22 @@ dict_imputers = { "tabddpm": ImputerDiffusion(model=TabDDPM(num_sampling=5), epochs=10, batch_size=100), - "TsDDPM": ImputerDiffusion( - model=TsDDPM(num_sampling=5, is_rolling=True), + "tsddpm": ImputerDiffusion( + model=TsDDPM(num_sampling=5, is_rolling=False), epochs=10, - batch_size=100, - index_datetime="datetime", - freq_str="10D", + batch_size=5, + index_datetime="date", + freq_str="5D", ), } comparison = comparator.Comparator( dict_imputers, selected_columns=df_data.columns, - generator_holes=missing_patterns.UniformHoleGenerator(n_splits=4), - metrics=["mae", "wmape", "KL_columnwise", "wasserstein_columnwise"], - max_evals=10, + generator_holes=missing_patterns.UniformHoleGenerator(n_splits=2), + metrics=["mae", "KL_columnwise"], ) -results = comparison.compare(df_data.iloc[:5000]) +results = comparison.compare(df_data) results.groupby(axis=0, level=0).mean().groupby(axis=0, level=0).mean() From 86350df02b0a675f230bccfb8a2bd0fac1600cad Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Mon, 30 Oct 2023 15:40:05 +0100 Subject: [PATCH 08/99] remove unused imports --- qolmat/imputations/imputers_pytorch.py | 1 - 1 file changed, 1 deletion(-) diff --git a/qolmat/imputations/imputers_pytorch.py b/qolmat/imputations/imputers_pytorch.py index bbaaadf6..c339160b 100644 --- a/qolmat/imputations/imputers_pytorch.py +++ b/qolmat/imputations/imputers_pytorch.py @@ -9,7 +9,6 @@ from qolmat.imputations.imputers import _Imputer, ImputerRegressor from qolmat.utils.exceptions import EstimatorNotDefined, PyTorchExtraNotInstalled -from qolmat.imputations.diffusions.ddpms import TabDDPM, TsDDPM from qolmat.benchmark import metrics try: From 5a85b0d8a1c31fe26bdb3797863fb8daec20e486 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Mon, 30 Oct 2023 16:24:08 +0100 Subject: [PATCH 09/99] add pytorch to doc environment --- environment.doc.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.doc.yml b/environment.doc.yml index 30458c93..7e7f4cda 100644 --- a/environment.doc.yml +++ b/environment.doc.yml @@ -12,3 +12,4 @@ dependencies: - pip - pip: - sphinx-markdown-tables==0.0.17 + - torch From e0c5100bb97a2eb8b2ae219e737437dccf32da2e Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Mon, 30 Oct 2023 16:52:51 +0100 Subject: [PATCH 10/99] add extra_requirements pytorch --- .readthedocs.yml | 2 ++ environment.doc.yml | 1 - 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index b932bdad..d33a0ffc 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -9,6 +9,8 @@ python: install: - method: pip path: . + extra_requirements: + - pytorch conda: environment: environment.doc.yml diff --git a/environment.doc.yml b/environment.doc.yml index 7e7f4cda..30458c93 100644 --- a/environment.doc.yml +++ b/environment.doc.yml @@ -12,4 +12,3 @@ dependencies: - pip - pip: - sphinx-markdown-tables==0.0.17 - - torch From 84e41059d3f5129893d9f08420c490903a90d140 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Mon, 30 Oct 2023 17:40:22 +0100 Subject: [PATCH 11/99] fix __init__.py name --- qolmat/imputations/diffusions/{__init__py => __init__.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename qolmat/imputations/diffusions/{__init__py => __init__.py} (100%) diff --git a/qolmat/imputations/diffusions/__init__py b/qolmat/imputations/diffusions/__init__.py similarity index 100% rename from qolmat/imputations/diffusions/__init__py rename to qolmat/imputations/diffusions/__init__.py From 19dd48d9dbd9159c4cfd47daed8c5510a3baa273 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Mon, 30 Oct 2023 18:20:50 +0100 Subject: [PATCH 12/99] cosmetic on tuto mean median --- docs/api.rst | 12 +++++++++++- examples/tutorials/plot_tuto_diffusion_models.py | 1 - examples/tutorials/plot_tuto_mean_median.py | 13 +++---------- 3 files changed, 14 insertions(+), 12 deletions(-) diff --git a/docs/api.rst b/docs/api.rst index 2382a666..08e091dc 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -103,4 +103,14 @@ Diffusion engine imputations.imputers_pytorch.ImputerDiffusion imputations.diffusions.ddpms.TabDDPM - imputations.diffusions.ddpms.TsDDPM \ No newline at end of file + imputations.diffusions.ddpms.TsDDPM + + +Utils +================ + +.. autosummary:: + :toctree: generated/ + :template: function.rst + + utils.data.add_holes diff --git a/examples/tutorials/plot_tuto_diffusion_models.py b/examples/tutorials/plot_tuto_diffusion_models.py index 8bcbc21e..1c24dea7 100644 --- a/examples/tutorials/plot_tuto_diffusion_models.py +++ b/examples/tutorials/plot_tuto_diffusion_models.py @@ -7,7 +7,6 @@ and :class:`~qolmat.imputations.diffusions.ddpms.TsDDPM` classes. """ -# %% import pandas as pd import numpy as np import matplotlib.pyplot as plt diff --git a/examples/tutorials/plot_tuto_mean_median.py b/examples/tutorials/plot_tuto_mean_median.py index 7d0146a1..52a0d625 100644 --- a/examples/tutorials/plot_tuto_mean_median.py +++ b/examples/tutorials/plot_tuto_mean_median.py @@ -1,6 +1,6 @@ """ ======================================================================================== -Tutorial for comparison between mean and median imputations with uniform hole generation +Comparison of basic imputers ======================================================================================== In this tutorial, we show how to use the Qolmat comparator @@ -31,11 +31,10 @@ # the 82nd column contains the critical temperature which is used as the # target variable. # The data does not contain missing values; so for the purpose of this notebook, -# we corrupt the data, with the ``qolmat.utils.data.add_holes`` function. +# we corrupt the data, with the :func:`qolmat.utils.data.add_holes` function. # In this way, each column has missing values. -df_data = data.get_data("Superconductor") -df = data.add_holes(df_data, ratio_masked=0.2, mean_size=120) +df = data.add_holes(data.get_data("Superconductor"), ratio_masked=0.2, mean_size=120) # %% # The dataset contains 82 columns. For simplicity, @@ -76,10 +75,6 @@ imputer_median = imputers.ImputerMedian() dict_imputers = {"mean": imputer_mean, "median": imputer_median} -generator_holes = missing_patterns.UniformHoleGenerator( - n_splits=2, subset=cols_to_impute, ratio_masked=0.1 -) - metrics = ["mae", "wmape", "KL_columnwise"] # %% @@ -88,9 +83,7 @@ # (those previously mentioned), # a list with the columns names to impute, # a generator of holes specifying the type of holes to create. -# Just a few words about hole generation. # in this example, we have chosen the uniform hole generator. -# You can see what this looks like. # For example, by imposing that 10% of missing data be created # ``ratio_masked=0.1`` and creating missing values in columns # ``subset=cols_to_impute``: From ca43e3aaa80282ee6f1d2d29ee8d8b987c83aa40 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Mon, 30 Oct 2023 19:09:02 +0100 Subject: [PATCH 13/99] cosmetic changes on explanation.rst --- docs/explanation.rst | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/docs/explanation.rst b/docs/explanation.rst index 1ff3c84b..fbf3e6f6 100644 --- a/docs/explanation.rst +++ b/docs/explanation.rst @@ -99,7 +99,7 @@ We compute the associated complete dataset :math:`\hat{X}^{(k)}` for the partial ----------------- Evaluating the imputers requires to generate holes that are representative of the holes at hand. -The missingness mechanisms have been classified by Rubin [1] into MCAR, MAR and MNAR. +The missingness mechanisms have been classified by :ref:`Rubin [1]` into MCAR, MAR and MNAR. Suppose we have :math:`X_{obs}`, a subset of a complete data model :math:`X = (X_{obs}, X_{mis})`, which is not fully observable (:math:`X_{mis}` is the missing part). We define the matrix :math:`M` such that :math:`M_{ij}=1` if :math:`X_{ij}` is missing, and 0 otherwise, and we assume distribution of :math:`M` is parametrised by :math:`\psi`. @@ -108,14 +108,14 @@ The observations are said to be Missing Completely at Random (MCAR) if the proba Formally, .. math:: - P(M | X_{obs}, X_{mis}, \psi) = P(M, \psi), \quad \forall \psi. + P(M | X_{obs}, X_{mis}, \psi) = P(M | \psi), \quad \forall \psi. The observations are said to be Missing at Random (MAR) if the probability of an observation to be missing only depends on the observations. Formally, .. math:: P(M | X_{obs}, X_{mis}, \psi) = P(M | X_{obs}, \psi), \quad \forall \psi, X_{mis}. -Finally, the observations are said to be Missing Not at Random (MNAR) in all other cases, i.e. if P(M | X_{obs}, X_{mis}, \psi) does not simplify. +Finally, the observations are said to be Missing Not at Random (MNAR) in all other cases, i.e. if :math:`P(M | X_{obs}, X_{mis}, \psi)` does not simplify. Qolmat allows to generate new missing values on a an existing dataset, but only in the MCAR case. @@ -140,4 +140,7 @@ Qolmat can be used to search for hyperparameters in imputation functions. Let sa References ---------- -[1] Rubin, Donald B. `Inference and missing data. `_ Biometrika 63.3 (1976): 581-592. \ No newline at end of file + +.. _rubin-article: + +[1] Rubin, Donald B. `Inference and missing data. `_ Biometrika 63.3 (1976): 581-592. From 428ec66348433f13a550a9a6b02c33672eeddd9e Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Tue, 31 Oct 2023 11:28:03 +0100 Subject: [PATCH 14/99] cosmetic on README --- README.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.rst b/README.rst index ce2de698..003e3759 100644 --- a/README.rst +++ b/README.rst @@ -47,7 +47,7 @@ Qolmat can be installed in different ways: .. code:: sh $ pip install qolmat # installation via `pip` - $ pip install qolmat[pytorch] # if you need pytorch + $ pip install qolmat[pytorch] # if you need ImputerDiffusion relying on pytorch $ pip install git+https://github.com/Quantmetry/qolmat # or directly from the github repository ⚡️ Quickstart @@ -105,8 +105,8 @@ The full documentation can be found `on this link `_, +| Qolmat allows model selection for scikit-learn compatible imputation algorithms, by performing three steps pictured below: +1) For each of the K folds, Qolmat artificially masks a set of observed values using a default or user specified `hole generator `_. 2) For each fold and each compared `imputation method `_, Qolmat fills both the missing and the masked values, then computes each of the default or user specified `performance metrics `_. 3) For each compared imputer, Qolmat pools the computed metrics from the K folds into a single value. @@ -117,7 +117,7 @@ This is very similar in spirit to the `cross_val_score `_ on Wikipedia. .. list-table:: :widths: 25 70 15 15 From ba60fdb7d8a971b9ce94af2e40c24bd27799b985 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Tue, 31 Oct 2023 12:25:03 +0100 Subject: [PATCH 15/99] simpler worflow names --- .github/workflows/test.yml | 2 +- .github/workflows/test_quick.yml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index aa4e4578..fb022df8 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -1,4 +1,4 @@ -name: Unit test on many environments +name: Unit tests on: push: diff --git a/.github/workflows/test_quick.yml b/.github/workflows/test_quick.yml index 47bb2db7..f6814104 100644 --- a/.github/workflows/test_quick.yml +++ b/.github/workflows/test_quick.yml @@ -1,4 +1,4 @@ -name: Unit test Qolmat +name: Unit tests fast on: push: From 0e8034d452b21702e21cbdebcf6c1cae67c9adc4 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Tue, 31 Oct 2023 14:19:20 +0100 Subject: [PATCH 16/99] use mamba to setup ci environment with cache --- .github/workflows/test_quick.yml | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/.github/workflows/test_quick.yml b/.github/workflows/test_quick.yml index f6814104..89f282c4 100644 --- a/.github/workflows/test_quick.yml +++ b/.github/workflows/test_quick.yml @@ -7,6 +7,9 @@ on: - main workflow_dispatch: +env: + CACHE_NUMBER: 0 # increase to reset cache manually + jobs: basic-testing: runs-on: ${{matrix.os}} @@ -21,11 +24,28 @@ jobs: steps: - name: Git clone uses: actions/checkout@v3 - - name: Set up venv for ci + + - name: Setup Mambaforge uses: conda-incubator/setup-miniconda@v2 with: - python-version: ${{matrix.python-version}} - environment-file: environment.ci.yml + miniforge-variant: Mambaforge + miniforge-version: latest + activate-environment: env_qolmat_ci + use-mamba: true + + - name: Set cache date + run: echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV + + - uses: actions/cache@v2 + with: + path: ${{ matrix.prefix }} + key: ${{ matrix.label }}-conda-${{ hashFiles('environment.ci.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} + id: cache + + - name: Update environment + run: mamba env update -n env_qolmat_ci -f environment.ci.yml + if: steps.cache.outputs.cache-hit != 'true' + - name: Lint with flake8 run: | conda install flake8 From 6f81a91e75c01017f26c9cb33746fc2dca987898 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Tue, 31 Oct 2023 14:41:02 +0100 Subject: [PATCH 17/99] fix with.path cache argument --- .github/workflows/test_quick.yml | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/.github/workflows/test_quick.yml b/.github/workflows/test_quick.yml index 89f282c4..b17ad74b 100644 --- a/.github/workflows/test_quick.yml +++ b/.github/workflows/test_quick.yml @@ -7,9 +7,6 @@ on: - main workflow_dispatch: -env: - CACHE_NUMBER: 0 # increase to reset cache manually - jobs: basic-testing: runs-on: ${{matrix.os}} @@ -25,6 +22,8 @@ jobs: - name: Git clone uses: actions/checkout@v3 + # See caching environments + # https://github.com/conda-incubator/setup-miniconda#caching-environments - name: Setup Mambaforge uses: conda-incubator/setup-miniconda@v2 with: @@ -33,13 +32,22 @@ jobs: activate-environment: env_qolmat_ci use-mamba: true - - name: Set cache date - run: echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV + - name: Get Date + id: get-date + run: echo "today=$(/bin/date -u '+%Y%m%d')" >> $GITHUB_OUTPUT - - uses: actions/cache@v2 + - name: Cache Conda env + uses: actions/cache@v2 with: - path: ${{ matrix.prefix }} - key: ${{ matrix.label }}-conda-${{ hashFiles('environment.ci.yml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }} + path: ${{ env.CONDA }}/envs + key: + conda-${{ runner.os }}--${{ runner.arch }}--${{ + steps.get-date.outputs.today }}-${{ + hashFiles('environment.ci.yml') }}-${{ env.CACHE_NUMBER + }} + env: + # Increase this value to reset cache if environment.ci.yml has not changed + CACHE_NUMBER: 0 id: cache - name: Update environment From 801263a34d8d5895f9724288c86e7518b69ad42d Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Tue, 31 Oct 2023 14:51:29 +0100 Subject: [PATCH 18/99] remove useless lines in workflows --- .github/workflows/test.yml | 2 -- .github/workflows/test_quick.yml | 3 --- 2 files changed, 5 deletions(-) diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index fb022df8..9081326e 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -31,11 +31,9 @@ jobs: environment-file: environment.ci.yml - name: Lint with flake8 run: | - conda install flake8 flake8 - name: Test with pytest run: | - conda install pytest make coverage - name: typing with mypy run: | diff --git a/.github/workflows/test_quick.yml b/.github/workflows/test_quick.yml index b17ad74b..40f58f5a 100644 --- a/.github/workflows/test_quick.yml +++ b/.github/workflows/test_quick.yml @@ -56,12 +56,9 @@ jobs: - name: Lint with flake8 run: | - conda install flake8 flake8 - name: Test with pytest run: | - conda install pytest - pip install -e .[pytorch] make coverage - name: Test docstrings run: make doctest From 89e812911af585a0927bf3674a4c57cbd51c7fc6 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Tue, 31 Oct 2023 15:13:53 +0100 Subject: [PATCH 19/99] assert False in unit test for testing purpose --- tests/imputations/test_imputers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index 694288c2..8d660055 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -37,7 +37,7 @@ def test_hyperparameters_get_hyperparameters() -> None: imputer = imputers.ImputerKNN(n_neighbors=3) hyperparams = imputer.get_hyperparams("col") - + assert False assert hyperparams == {"n_neighbors": 3, "weights": "distance"} From 4cce62885671fbd45b6129a63d109f9acb837f73 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Tue, 31 Oct 2023 15:17:35 +0100 Subject: [PATCH 20/99] Revert "assert False in unit test for testing purpose" This reverts commit 89e812911af585a0927bf3674a4c57cbd51c7fc6. --- tests/imputations/test_imputers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index 8d660055..694288c2 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -37,7 +37,7 @@ def test_hyperparameters_get_hyperparameters() -> None: imputer = imputers.ImputerKNN(n_neighbors=3) hyperparams = imputer.get_hyperparams("col") - assert False + assert hyperparams == {"n_neighbors": 3, "weights": "distance"} From cac3bd8aa4fbc07a64c8729e785c5390428dabd6 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 3 Nov 2023 15:29:41 +0100 Subject: [PATCH 21/99] =?UTF-8?q?Bump=20version:=200.1.0=20=E2=86=92=200.1?= =?UTF-8?q?.1?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 2 +- docs/conf.py | 2 +- qolmat/_version.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index 3765caca..e62aab6e 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.1.0 +current_version = 0.1.1 commit = True tag = True diff --git a/docs/conf.py b/docs/conf.py index 1d6d7bd7..38e22bd6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "Quantmetry" # The full version, including alpha/beta/rc tags -version = "0.1.0" +version = "0.1.1" release = version # -- General configuration --------------------------------------------------- diff --git a/qolmat/_version.py b/qolmat/_version.py index 3dc1f76b..485f44ac 100644 --- a/qolmat/_version.py +++ b/qolmat/_version.py @@ -1 +1 @@ -__version__ = "0.1.0" +__version__ = "0.1.1" diff --git a/setup.py b/setup.py index 915ff331..19bf6b11 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import find_packages, setup DISTNAME = "qolmat" -VERSION = "0.1.0" +VERSION = "0.1.1" DESCRIPTION = "A Python library for optimal data imputation." LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst" with codecs.open("README.rst", encoding="utf-8-sig") as f: From 92cdc795f88ee2281829d2ce21fc33c118a840d1 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 3 Nov 2023 15:34:46 +0100 Subject: [PATCH 22/99] history updated --- HISTORY.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/HISTORY.rst b/HISTORY.rst index e4c91209..089d5255 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -6,6 +6,7 @@ History ------------------- * Hotfix reference to tensorflow in the documentation, when it should be pytorch +* Metrics KL forest has been removed from package 0.1.0 (2023-10-11) ------------------- From b59c50a251940a745d080e783363277fc463f83a Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 3 Nov 2023 15:39:56 +0100 Subject: [PATCH 23/99] mend --- HISTORY.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/HISTORY.rst b/HISTORY.rst index e4c91209..3deae21f 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,10 +2,13 @@ History ======= -0.1.1 (2023-??-??) +0.1.1 (2023-11-03) ------------------- * Hotfix reference to tensorflow in the documentation, when it should be pytorch +* Metrics KL forest has been removed from package +* EM imputer made more robust to colinearity, and transform bug patched +* CICD made faster with mamba and a quick test setting 0.1.0 (2023-10-11) ------------------- From bb64d3a4e4c68ac4f93c181d12fa6dbc99fa6979 Mon Sep 17 00:00:00 2001 From: Charles-Henri Prat Date: Mon, 6 Nov 2023 12:47:17 +0100 Subject: [PATCH 24/99] fix rst syntax error --- README.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 003e3759..1fda8987 100644 --- a/README.rst +++ b/README.rst @@ -105,7 +105,8 @@ The full documentation can be found `on this link `_. 2) For each fold and each compared `imputation method `_, Qolmat fills both the missing and the masked values, then computes each of the default or user specified `performance metrics `_. 3) For each compared imputer, Qolmat pools the computed metrics from the K folds into a single value. From d24486397c94697717b416dfaf5ec1a5835dda15 Mon Sep 17 00:00:00 2001 From: mariemabi Date: Mon, 6 Nov 2023 15:37:17 +0100 Subject: [PATCH 25/99] fix codecov path in README --- README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.rst b/README.rst index 003e3759..d78790e6 100644 --- a/README.rst +++ b/README.rst @@ -23,7 +23,7 @@ .. |Commits| image:: https://img.shields.io/github/commits-since/Quantmetry/qolmat/latest/main .. _Commits: https://github.com/Quantmetry/qolmat/commits/main -.. |Codecov| image:: https://codecov.io/gh/quantmetry/qolmat/branch/master/graph/badge.svg +.. |Codecov| image:: https://codecov.io/gh/quantmetry/qolmat/branch/main/graph/badge.svg .. _Codecov: https://codecov.io/gh/quantmetry/qolmat .. image:: https://raw.githubusercontent.com/Quantmetry/qolmat/main/docs/images/logo.png From 6386ab77496f75a5ad9ba11542da754730e88813 Mon Sep 17 00:00:00 2001 From: mariemabi Date: Mon, 6 Nov 2023 15:52:22 +0100 Subject: [PATCH 26/99] add mabidi@quamtmetry.com to MAINTAINER_EMAIL --- setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.py b/setup.py index 19bf6b11..6853d752 100644 --- a/setup.py +++ b/setup.py @@ -17,6 +17,7 @@ "akngoho@quantmetry.com, " "chprat@quantmetry.com, " "gsaes@quantmetry.com" + "mabidi.quantmetry.com" ) URL = "https://github.com/Quantmetry/qolmat" DOWNLOAD_URL = "https://pypi.org/project/qolmat/#files" From 4b51b055c3c0b5f30158c8a33c92be00825e10a2 Mon Sep 17 00:00:00 2001 From: mariemabi Date: Mon, 6 Nov 2023 17:04:44 +0100 Subject: [PATCH 27/99] add mabidi@quamtmetry.com to MAINTAINER_EMAIL --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 6853d752..295f2001 100644 --- a/setup.py +++ b/setup.py @@ -16,7 +16,7 @@ "jroussel@quantmetry.com, " "akngoho@quantmetry.com, " "chprat@quantmetry.com, " - "gsaes@quantmetry.com" + "gsaes@quantmetry.com, " "mabidi.quantmetry.com" ) URL = "https://github.com/Quantmetry/qolmat" From df2392808e66f3da38dba0d30748d90339e96216 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 6 Nov 2023 19:06:12 +0100 Subject: [PATCH 28/99] Large identity matrix in RPCA is now sparse --- qolmat/imputations/rpca/rpca_noisy.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 0432aa3d..3fcc8da1 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -5,8 +5,8 @@ import numpy as np import scipy as scp -from scipy import linalg as lscp -from scipy.sparse import dok_matrix +from scipy.sparse import dok_matrix, identity +from scipy.sparse.linalg import spsolve from numpy.typing import NDArray from sklearn import utils as sku @@ -308,7 +308,7 @@ def decompose_rpca_algorithm( HtH += list_etas[i_period] * (list_H[i_period].T @ list_H[i_period]) Ir = np.eye(rank) - In = np.eye(n_rows) + In = identity(n_rows) for _ in range(max_iterations): # print("Cost function", cost_function(D, X, A, Omega, tau, lam)) @@ -322,14 +322,14 @@ def decompose_rpca_algorithm( for i_period, _ in enumerate(list_periods): sums += mu * R[i_period] - list_H[i_period] @ Y - X = scp.linalg.solve( - a=(1 + mu) * In + HtH, - b=D - A + mu * L @ Q - Y + sums, + X = spsolve( + (1 + mu) * In + HtH, + D - A + mu * L @ Q - Y + sums, ) else: - X = scp.linalg.solve( - a=(1 + mu) * In + 2 * HtH, - b=D - A + mu * L @ Q - Y, + X = spsolve( + (1 + mu) * In + 2 * HtH, + D - A + mu * L @ Q - Y, ) A_Omega = rpca_utils.soft_thresholding(D - X, lam) From 594361c3dbec9e6d0c999b3b81756065517cc39c Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 5 Dec 2023 12:49:45 +0100 Subject: [PATCH 29/99] first draft, to be checked and tested --- qolmat/imputations/imputers.py | 63 +++++++++++++++++++++++++-- qolmat/imputations/rpca/rpca.py | 42 +++++------------- qolmat/imputations/rpca/rpca_noisy.py | 56 ++++++++++++++++++++---- qolmat/imputations/rpca/rpca_pcp.py | 6 ++- 4 files changed, 123 insertions(+), 44 deletions(-) diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 6361eb6d..fb393221 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -19,6 +19,7 @@ from qolmat.imputations import em_sampler from qolmat.imputations.rpca import rpca, rpca_noisy, rpca_pcp from qolmat.imputations import softimpute +from qolmat.utils import utils from qolmat.utils.exceptions import NotDataFrame from qolmat.utils.utils import HyperValue @@ -1155,7 +1156,12 @@ def __init__( method_interpolation: Optional[str] = "linear", ): super().__init__( - imputer_params=("model_tsa", "period", "extrapolate_trend", "method_interpolation"), + imputer_params=( + "model_tsa", + "period", + "extrapolate_trend", + "method_interpolation", + ), groups=groups, columnwise=True, ) @@ -1744,6 +1750,47 @@ def get_model(self, **hyperparams) -> rpca.RPCA: ) return model + def _fit_element( + self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 + ) -> em_sampler.EM: + """ + Fits the imputer on `df`, at the group and/or column level depending onself.groups and + self.columnwise. + + Parameters + ---------- + df : pd.DataFrame + Dataframe on which the imputer is fitted + col : str, optional + Column on which the imputer is fitted, by default "__all__" + ngroup : int, optional + Id of the group on which the method is applied + + Returns + ------- + Any + Return fitted EM model + + Raises + ------ + NotDataFrame + Input has to be a pandas.DataFrame. + """ + self._check_dataframe(df) + if self.method not in ["PCP", "noisy"]: + raise ValueError("Argument method must be `PCP` or `noisy`!") + hyperparams = self.get_hyperparams() + model = self.get_model(**hyperparams) + + X = df.astype(float).values + D = utils.prepare_data(X, model.period) + Omega = ~np.isnan(D) + D = utils.linear_interpolation(D) + + Q = model.fit_basis(D, Omega) + + return Q + def _transform_element( self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 ) -> pd.DataFrame: @@ -1777,8 +1824,18 @@ def _transform_element( hyperparams = self.get_hyperparams(col=col) model = self.get_model(random_state=self._rng, **hyperparams) X = df.astype(float).values - M, A = model.decompose_rpca_signal(X) - X_imputed = M + A + + D = utils.prepare_data(X, model.period) + Omega = ~np.isnan(D) + D = utils.linear_interpolation(D) + + Q = self._dict_fitting[col][ngroup] + M, A = model.decompose_on_basis(D, Omega, Q) + + M_final = utils.get_shape_original(M, X.shape) + A_final = utils.get_shape_original(A, X.shape) + X_imputed = M_final + A_final + df_imputed = pd.DataFrame(X_imputed, index=df.index, columns=df.columns) df_imputed = df.where(~df.isna(), df_imputed) diff --git a/qolmat/imputations/rpca/rpca.py b/qolmat/imputations/rpca/rpca.py index 071aef88..61fe52bb 100644 --- a/qolmat/imputations/rpca/rpca.py +++ b/qolmat/imputations/rpca/rpca.py @@ -1,6 +1,7 @@ from __future__ import annotations -from typing import Tuple, Union +from typing import Union +from typing_extensions import Self import numpy as np from numpy.typing import NDArray @@ -42,37 +43,18 @@ def __init__( self.random_state = random_state self.verbose = verbose - def decompose_rpca_signal( - self, - X: NDArray, - ) -> Tuple[NDArray, NDArray]: - """ - Compute the noisy RPCA with L1 or L2 time penalisation - - Parameters - ---------- - X : NDArray - Observations + def fit_basis(self, D: NDArray, Omega: NDArray) -> NDArray: + n_rows, n_cols = D.shape + if n_rows == 1 or n_cols == 1: + self.V = np.array([[1]]) + return self - Returns - ------- - M: NDArray - Low-rank signal - A: NDArray - Anomalies - """ + M, A, L, Q = self.decompose_rpca(D, Omega) + return Q - D = utils.prepare_data(X, self.period) - Omega = ~np.isnan(D) - # D_proj = rpca_utils.impute_nans(D_init, method="median") - D = utils.linear_interpolation(D) + def decompose_on_basis(self, D: NDArray, Omega: NDArray, Q: NDArray) -> NDArray: n_rows, n_cols = D.shape if n_rows == 1 or n_cols == 1: return D, np.full_like(D, 0) - - M, A = self.decompose_rpca(D, Omega) - - M_final = utils.get_shape_original(M, X.shape) - A_final = utils.get_shape_original(A, X.shape) - - return M_final, A_final + M, A, L, Q = self.decompose_rpca(D, Omega) + return M, A diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 3fcc8da1..7f29fb84 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -112,7 +112,47 @@ def get_params_scale(self, D: NDArray) -> Dict[str, float]: "lam": lam, } - def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: + def decompose_on_basis( + self, D: NDArray, Omega: NDArray, Q: NDArray + ) -> Tuple[NDArray, NDArray]: + params_scale = self.get_params_scale(D) + + lam = params_scale["lam"] if self.lam is None else self.lam + rank = params_scale["rank"] if self.rank is None else self.rank + rank = int(rank) + tau = params_scale["tau"] if self.tau is None else self.tau + + n_rows, n_cols = D.shape + if n_rows == 1 or n_cols == 1: + return D, np.full_like(D, 0) + # M, A, L, Q = self.decompose_rpca(D, Omega) + n_rank, _ = Q.shape + Ir = np.eye(n_rank) + L = np.zeros((n_rows, n_rank)) + A = np.zeros((n_rows, n_cols)) + for i in range(n_rows): + d = D[i, :] + omega = Omega[i, :] + L_row = np.zeros((1, n_rank)) + a = np.full_like(d, 0) + for _ in range(self.max_iterations): + a_omega = rpca_utils.soft_thresholding(d - L_row @ Q, lam) + a_omega_C = d - L_row @ Q + a = np.where(omega, a_omega, a_omega_C) + + L_row = scp.linalg.solve( + a=2 * tau * Ir + (Q @ Q.T), + b=Q @ (d - a).T, + ).T + L[i, :] = L_row + A[i, :] = a + M = L @ Q + + return M, A + + def decompose_rpca( + self, D: NDArray, Omega: NDArray + ) -> Tuple[NDArray, NDArray, NDArray, NDArray]: """ Compute the noisy RPCA with L1 or L2 time penalisation @@ -147,7 +187,7 @@ def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: f"than the number of rows in the matrix but {period} >= {n_rows}!" ) - M, A, U, V = self.decompose_rpca_algorithm( + M, A, L, Q = self.decompose_rpca_algorithm( D, Omega, rank, @@ -163,7 +203,7 @@ def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: self._check_cost_function_minimized(D, M, A, Omega, tau, lam) - return M, A + return M, A, L, Q def _check_cost_function_minimized( self, @@ -274,10 +314,10 @@ def decompose_rpca_algorithm( Low-rank signal matrix of shape (m, n). A : np.ndarray Anomalies matrix of shape (m, n). - U : np.ndarray + L : np.ndarray Basis Unitary array of shape (m, rank). - V : np.ndarray - Basis Unitary array of shape (n, rank). + Q : np.ndarray + Basis Unitary array of shape (rank, n). """ @@ -370,10 +410,8 @@ def decompose_rpca_algorithm( X = L @ Q M = X - U = L - V = Q - return M, A, U, V + return M, A, L, Q @staticmethod def cost_function( diff --git a/qolmat/imputations/rpca/rpca_pcp.py b/qolmat/imputations/rpca/rpca_pcp.py index 74dbb70d..0c0c71cb 100644 --- a/qolmat/imputations/rpca/rpca_pcp.py +++ b/qolmat/imputations/rpca/rpca_pcp.py @@ -80,7 +80,7 @@ def get_params_scale(self, D: NDArray): dict_params = {"mu": mu, "lam": lam} return dict_params - def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: + def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray, None, None]: """ Estimate the relevant parameters then compute the PCP RPCA decomposition @@ -97,6 +97,8 @@ def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: Low-rank signal A: NDArray Anomalies + N1: None + N2: None """ params_scale = self.get_params_scale(D) @@ -126,7 +128,7 @@ def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: self._check_cost_function_minimized(D, M, A, Omega, lam) - return M, A + return M, A, None, None def _check_cost_function_minimized( self, From 0c793bb1f740e1747a355b951b17b2028d43060c Mon Sep 17 00:00:00 2001 From: Anh Khoa Ngo Ho Date: Wed, 6 Dec 2023 16:14:10 +0100 Subject: [PATCH 30/99] fix: rpca --- qolmat/imputations/imputers.py | 23 ++----- qolmat/imputations/rpca/rpca.py | 80 +++++++++++++++++++++-- qolmat/imputations/rpca/rpca_noisy.py | 51 +++++++++++---- tests/imputations/rpca/test_rpca.py | 5 +- tests/imputations/rpca/test_rpca_noisy.py | 8 +-- tests/imputations/rpca/test_rpca_pcp.py | 10 +-- tests/imputations/test_imputers.py | 3 +- 7 files changed, 134 insertions(+), 46 deletions(-) diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index fb393221..558526a4 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -1783,13 +1783,9 @@ def _fit_element( model = self.get_model(**hyperparams) X = df.astype(float).values - D = utils.prepare_data(X, model.period) - Omega = ~np.isnan(D) - D = utils.linear_interpolation(D) + model = model.fit_basis(X) - Q = model.fit_basis(D, Omega) - - return Q + return model def _transform_element( self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 @@ -1821,20 +1817,11 @@ def _transform_element( if self.method not in ["PCP", "noisy"]: raise ValueError("Argument method must be `PCP` or `noisy`!") - hyperparams = self.get_hyperparams(col=col) - model = self.get_model(random_state=self._rng, **hyperparams) + model = self._dict_fitting[col][ngroup] X = df.astype(float).values - D = utils.prepare_data(X, model.period) - Omega = ~np.isnan(D) - D = utils.linear_interpolation(D) - - Q = self._dict_fitting[col][ngroup] - M, A = model.decompose_on_basis(D, Omega, Q) - - M_final = utils.get_shape_original(M, X.shape) - A_final = utils.get_shape_original(A, X.shape) - X_imputed = M_final + A_final + M, A = model.decompose_rpca_signal(X) + X_imputed = M + A df_imputed = pd.DataFrame(X_imputed, index=df.index, columns=df.columns) df_imputed = df.where(~df.isna(), df_imputed) diff --git a/qolmat/imputations/rpca/rpca.py b/qolmat/imputations/rpca/rpca.py index 61fe52bb..f7a9f4ee 100644 --- a/qolmat/imputations/rpca/rpca.py +++ b/qolmat/imputations/rpca/rpca.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Union +from typing import Union, Tuple from typing_extensions import Self import numpy as np @@ -43,18 +43,86 @@ def __init__( self.random_state = random_state self.verbose = verbose - def fit_basis(self, D: NDArray, Omega: NDArray) -> NDArray: + def fit_basis(self, X: NDArray) -> Self: + """Fit RPCA model on data + + Parameters + ---------- + X : NDArray + Observations + + Returns + ------- + Self + Model RPCA + """ + D = utils.prepare_data(X, self.period) + Omega = ~np.isnan(D) + D = utils.linear_interpolation(D) + n_rows, n_cols = D.shape if n_rows == 1 or n_cols == 1: self.V = np.array([[1]]) return self - M, A, L, Q = self.decompose_rpca(D, Omega) - return Q + _, _, _, Q = self.decompose_rpca(D, Omega) + + self.Q = Q + + return self + + def decompose_on_basis( + self, D: NDArray, Omega: NDArray, Q: NDArray + ) -> Tuple[NDArray, NDArray]: + """Decompose data + + Parameters + ---------- + D : NDArray + Observations + Omega : NDArray + Boolean matrix indicating the observed values + Q : NDArray + Learned basis unitary array of shape (rank, n). - def decompose_on_basis(self, D: NDArray, Omega: NDArray, Q: NDArray) -> NDArray: + Returns + ------- + Tuple[NDArray, NDArray] + M : np.ndarray + Low-rank signal matrix of shape (m, n). + A : np.ndarray + Anomalies matrix of shape (m, n). + """ n_rows, n_cols = D.shape if n_rows == 1 or n_cols == 1: return D, np.full_like(D, 0) - M, A, L, Q = self.decompose_rpca(D, Omega) + M, A, _, _ = self.decompose_rpca(D, Omega) return M, A + + def decompose_rpca_signal(self, X: NDArray) -> Tuple[NDArray, NDArray]: + """ + Compute the noisy RPCA with L1 or L2 time penalisation + + Parameters + ---------- + X : NDArray + Observations + + Returns + ------- + M: NDArray + Low-rank signal + A: NDArray + Anomalies + """ + + D = utils.prepare_data(X, self.period) + Omega = ~np.isnan(D) + D = utils.linear_interpolation(D) + + M, A = self.decompose_on_basis(D, Omega, self.Q) + + M_final = utils.get_shape_original(M, X.shape) + A_final = utils.get_shape_original(A, X.shape) + + return M_final, A_final diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 7f29fb84..02a50f11 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -113,15 +113,17 @@ def get_params_scale(self, D: NDArray) -> Dict[str, float]: } def decompose_on_basis( - self, D: NDArray, Omega: NDArray, Q: NDArray + self, + D: NDArray, + Omega: NDArray, + Q: NDArray, ) -> Tuple[NDArray, NDArray]: - params_scale = self.get_params_scale(D) - lam = params_scale["lam"] if self.lam is None else self.lam - rank = params_scale["rank"] if self.rank is None else self.rank - rank = int(rank) - tau = params_scale["tau"] if self.tau is None else self.tau + lam = self.params_scale["lam"] + # rank = int(self.params_scale["rank"]) + tau = self.params_scale["tau"] + print(self.lam, lam) n_rows, n_cols = D.shape if n_rows == 1 or n_cols == 1: return D, np.full_like(D, 0) @@ -130,6 +132,26 @@ def decompose_on_basis( Ir = np.eye(n_rank) L = np.zeros((n_rows, n_rank)) A = np.zeros((n_rows, n_cols)) + + for _ in range(self.max_iterations): + A_prev = A.copy() + L_prev = L.copy() + L = scp.linalg.solve( + a=2 * tau * Ir + (Q @ Q.T), + b=Q @ (D - A).T, + ).T + A_Omega = rpca_utils.soft_thresholding(D - L @ Q, lam) + A_Omega_C = D - L @ Q + A = np.where(Omega, A_Omega, A_Omega_C) + + Ac = np.linalg.norm(A - A_prev, np.inf) + Lc = np.linalg.norm(L - L_prev, np.inf) + + tolerance = max([Ac, Lc]) # type: ignore # noqa + + if tolerance < self.tol: + break + for i in range(n_rows): d = D[i, :] omega = Omega[i, :] @@ -146,6 +168,7 @@ def decompose_on_basis( ).T L[i, :] = L_row A[i, :] = a + M = L @ Q return M, A @@ -171,12 +194,18 @@ def decompose_rpca( Anomalies """ - params_scale = self.get_params_scale(D) + self.params_scale = self.get_params_scale(D) + + if self.lam is not None: + self.params_scale["lam"] = self.lam + if self.rank is not None: + self.params_scale["rank"] = self.rank + if self.tau is not None: + self.params_scale["tau"] = self.tau - lam = params_scale["lam"] if self.lam is None else self.lam - rank = params_scale["rank"] if self.rank is None else self.rank - rank = int(rank) - tau = params_scale["tau"] if self.tau is None else self.tau + lam = self.params_scale["lam"] + rank = int(self.params_scale["rank"]) + tau = self.params_scale["tau"] mu = 1e-2 if self.mu is None else self.mu n_rows, _ = D.shape diff --git a/tests/imputations/rpca/test_rpca.py b/tests/imputations/rpca/test_rpca.py index bde2b7c7..bde0722b 100644 --- a/tests/imputations/rpca/test_rpca.py +++ b/tests/imputations/rpca/test_rpca.py @@ -24,8 +24,11 @@ class RPCAMock(RPCA): def __init__(self): super().__init__() + self.Q = None - def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: + def decompose_on_basis( + self, D: NDArray, Omega: NDArray, Q: NDArray + ) -> Tuple[NDArray, NDArray]: self.call_count = 1 return D, D diff --git a/tests/imputations/rpca/test_rpca_noisy.py b/tests/imputations/rpca/test_rpca_noisy.py index f39d8ea8..8e19c76c 100644 --- a/tests/imputations/rpca/test_rpca_noisy.py +++ b/tests/imputations/rpca/test_rpca_noisy.py @@ -93,7 +93,7 @@ def test_rpca_noisy_get_params_scale(X: NDArray): @pytest.mark.parametrize("norm", ["L2"]) def test_rpca_decompose_rpca_signal_shape(norm: str): """Test RPCA noisy results if tau and lambda equal zero.""" - rpca = RPCANoisy(rank=2, norm=norm) + rpca = RPCANoisy(rank=2, norm=norm).fit_basis(X_test) X_result, A_result = rpca.decompose_rpca_signal(X_test) assert X_result.shape == X_test.shape assert A_result.shape == X_test.shape @@ -102,7 +102,7 @@ def test_rpca_decompose_rpca_signal_shape(norm: str): @pytest.mark.parametrize("X, X_interpolated", [(X_incomplete, X_interpolated)]) def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): """Test RPCA noisy results if tau and lambda equal zero.""" - rpca = RPCANoisy(tau=0, lam=0, norm="L2") + rpca = RPCANoisy(tau=0, lam=0, norm="L2").fit_basis(X) X_result, A_result = rpca.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -114,7 +114,7 @@ def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): ) def test_rpca_noisy_zero_tau(X: NDArray, lam: float, X_interpolated: NDArray): """Test RPCA noisy results if tau equals zero.""" - rpca = RPCANoisy(tau=0, lam=lam, norm="L2") + rpca = RPCANoisy(tau=0, lam=lam, norm="L2").fit_basis(X) X_result, A_result = rpca.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -126,7 +126,7 @@ def test_rpca_noisy_zero_tau(X: NDArray, lam: float, X_interpolated: NDArray): ) def test_rpca_noisy_zero_lambda(X: NDArray, tau: float, X_interpolated: NDArray): """Test RPCA noisy results if lambda equals zero.""" - rpca = RPCANoisy(tau=tau, lam=0, norm="L2") + rpca = RPCANoisy(tau=tau, lam=0, norm="L2").fit_basis(X) X_result, A_result = rpca.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, np.full_like(X, 0), atol=1e-4) np.testing.assert_allclose(A_result, X_interpolated, atol=1e-4) diff --git a/tests/imputations/rpca/test_rpca_pcp.py b/tests/imputations/rpca/test_rpca_pcp.py index 0cdc2c94..91df1d9b 100644 --- a/tests/imputations/rpca/test_rpca_pcp.py +++ b/tests/imputations/rpca/test_rpca_pcp.py @@ -75,7 +75,7 @@ def test_check_cost_function_minimized_no_warning( @pytest.mark.parametrize("X", [X_complete]) def test_rpca_rpca_pcp_get_params_scale(X: NDArray): """Test the parameters are well scaled.""" - rpca_pcp = RPCAPCP(max_iterations=max_iterations, mu=0.5, lam=0.1) + rpca_pcp = RPCAPCP(max_iterations=max_iterations, mu=0.5, lam=0.1).fit_basis(X) result_dict = rpca_pcp.get_params_scale(X) result = list(result_dict.values()) params_expected = [1 / 7, np.sqrt(2) / 2] @@ -88,7 +88,7 @@ def test_rpca_rpca_pcp_zero_lambda_small_mu(X: NDArray, mu: float): The problem is ill-conditioned and the result depends on the parameter mu; case when mu is small. """ - rpca_pcp = RPCAPCP(lam=0, mu=mu) + rpca_pcp = RPCAPCP(lam=0, mu=mu).fit_basis(X) X_result, A_result = rpca_pcp.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, np.full_like(X, 0), atol=1e-4) np.testing.assert_allclose(A_result, X, atol=1e-4) @@ -100,7 +100,7 @@ def test_rpca_rpca_pcp_zero_lambda_large_mu(X: NDArray, mu: float): The problem is ill-conditioned and the result depends on the parameter mu; case when mu is large. """ - rpca_pcp = RPCAPCP(lam=0, mu=mu) + rpca_pcp = RPCAPCP(lam=0, mu=mu).fit_basis(X) X_result, A_result = rpca_pcp.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, X, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -109,7 +109,7 @@ def test_rpca_rpca_pcp_zero_lambda_large_mu(X: NDArray, mu: float): @pytest.mark.parametrize("X, mu", [(X_complete, large_mu)]) def test_rpca_rpca_pcp_large_lambda_small_mu(X: NDArray, mu: float): """Test RPCA PCP results with large lambda and small mu.""" - rpca_pcp = RPCAPCP(lam=1e3, mu=mu) + rpca_pcp = RPCAPCP(lam=1e3, mu=mu).fit_basis(X) X_result, A_result = rpca_pcp.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, X, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -121,7 +121,7 @@ def test_rpca_temporal_signal(synthetic_temporal_data): signal = synthetic_temporal_data period = 100 lam = 0.1 - rpca = RPCAPCP(period=period, lam=lam, mu=0.01) + rpca = RPCAPCP(period=period, lam=lam, mu=0.01).fit_basis(signal) X_result, A_result = rpca.decompose_rpca_signal(signal) X_input_rpca = utils.linear_interpolation(signal.reshape(period, -1)) assert np.linalg.norm(X_input_rpca, "nuc") >= np.linalg.norm( diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index 694288c2..6b7b7bd4 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -263,7 +263,8 @@ def test_ImputerRegressor_fit_transform(df: pd.DataFrame) -> None: @pytest.mark.parametrize("df", [df_timeseries]) def test_ImputerRPCA_fit_transform(df: pd.DataFrame) -> None: imputer = imputers.ImputerRPCA(columnwise=False, max_iterations=100, tau=1, lam=0.3) - result = imputer.fit_transform(df) + imputer = imputer.fit(df) + result = imputer.transform(df) expected = pd.DataFrame( { "col1": [i for i in range(20)], From c779ed15b67875b4d313706e1883aa390706b3f2 Mon Sep 17 00:00:00 2001 From: Anh Khoa Ngo Ho Date: Thu, 7 Dec 2023 14:15:26 +0100 Subject: [PATCH 31/99] fix: full_matrices=False and unit tests --- qolmat/imputations/imputers.py | 3 +- qolmat/imputations/rpca/rpca.py | 35 ++++++++++++++++++++++- qolmat/imputations/rpca/rpca_noisy.py | 21 +------------- qolmat/imputations/rpca/rpca_utils.py | 2 +- tests/imputations/rpca/test_rpca.py | 11 +++++++ tests/imputations/rpca/test_rpca_noisy.py | 8 +++--- tests/imputations/rpca/test_rpca_pcp.py | 8 +++--- tests/imputations/test_imputers.py | 10 +++++++ 8 files changed, 66 insertions(+), 32 deletions(-) diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 558526a4..9f2ae1ec 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -1820,8 +1820,7 @@ def _transform_element( model = self._dict_fitting[col][ngroup] X = df.astype(float).values - M, A = model.decompose_rpca_signal(X) - X_imputed = M + A + X_imputed = model.transform_with_basis(X) df_imputed = pd.DataFrame(X_imputed, index=df.index, columns=df.columns) df_imputed = df.where(~df.isna(), df_imputed) diff --git a/qolmat/imputations/rpca/rpca.py b/qolmat/imputations/rpca/rpca.py index f7a9f4ee..026ecaa9 100644 --- a/qolmat/imputations/rpca/rpca.py +++ b/qolmat/imputations/rpca/rpca.py @@ -99,6 +99,36 @@ def decompose_on_basis( M, A, _, _ = self.decompose_rpca(D, Omega) return M, A + def transform_with_basis(self, X: NDArray) -> NDArray: + """ + Compute the noisy RPCA with L1 or L2 time penalisation + + Parameters + ---------- + X : NDArray + Observations + + Returns + ------- + X_final: NDArray + M + A + """ + + D = utils.prepare_data(X, self.period) + Omega = ~np.isnan(D) + D = utils.linear_interpolation(D) + n_rows, n_cols = D.shape + if n_rows == 1 or n_cols == 1: + return D + + M, A = self.decompose_on_basis(D, Omega, self.Q) + + M_final = utils.get_shape_original(M, X.shape) + A_final = utils.get_shape_original(A, X.shape) + + X_final = M_final + A_final + return X_final + def decompose_rpca_signal(self, X: NDArray) -> Tuple[NDArray, NDArray]: """ Compute the noisy RPCA with L1 or L2 time penalisation @@ -119,8 +149,11 @@ def decompose_rpca_signal(self, X: NDArray) -> Tuple[NDArray, NDArray]: D = utils.prepare_data(X, self.period) Omega = ~np.isnan(D) D = utils.linear_interpolation(D) + n_rows, n_cols = D.shape + if n_rows == 1 or n_cols == 1: + return D, np.full_like(D, 0) - M, A = self.decompose_on_basis(D, Omega, self.Q) + M, A, _, _ = self.decompose_rpca(D, Omega) M_final = utils.get_shape_original(M, X.shape) A_final = utils.get_shape_original(A, X.shape) diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 02a50f11..96570510 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -120,10 +120,8 @@ def decompose_on_basis( ) -> Tuple[NDArray, NDArray]: lam = self.params_scale["lam"] - # rank = int(self.params_scale["rank"]) tau = self.params_scale["tau"] - print(self.lam, lam) n_rows, n_cols = D.shape if n_rows == 1 or n_cols == 1: return D, np.full_like(D, 0) @@ -152,23 +150,6 @@ def decompose_on_basis( if tolerance < self.tol: break - for i in range(n_rows): - d = D[i, :] - omega = Omega[i, :] - L_row = np.zeros((1, n_rank)) - a = np.full_like(d, 0) - for _ in range(self.max_iterations): - a_omega = rpca_utils.soft_thresholding(d - L_row @ Q, lam) - a_omega_C = d - L_row @ Q - a = np.where(omega, a_omega, a_omega_C) - - L_row = scp.linalg.solve( - a=2 * tau * Ir + (Q @ Q.T), - b=Q @ (d - a).T, - ).T - L[i, :] = L_row - A[i, :] = a - M = L @ Q return M, A @@ -357,7 +338,7 @@ def decompose_rpca_algorithm( Y = np.zeros((n_rows, n_cols)) X = D.copy() A = np.zeros((n_rows, n_cols)) - U, S, Vt = np.linalg.svd(X) + U, S, Vt = np.linalg.svd(X, full_matrices=False) U = U[:, :rank] S = S[:rank] diff --git a/qolmat/imputations/rpca/rpca_utils.py b/qolmat/imputations/rpca/rpca_utils.py index c84b8aa0..ea7bb603 100644 --- a/qolmat/imputations/rpca/rpca_utils.py +++ b/qolmat/imputations/rpca/rpca_utils.py @@ -31,7 +31,7 @@ def approx_rank( """ if threshold == 1: return min(M.shape) - _, values_singular, _ = np.linalg.svd(M, full_matrices=True) + _, values_singular, _ = np.linalg.svd(M, full_matrices=False) cum_sum = np.cumsum(values_singular) / np.sum(values_singular) rank = np.argwhere(cum_sum > threshold)[0][0] + 1 diff --git a/tests/imputations/rpca/test_rpca.py b/tests/imputations/rpca/test_rpca.py index bde0722b..cded4c70 100644 --- a/tests/imputations/rpca/test_rpca.py +++ b/tests/imputations/rpca/test_rpca.py @@ -26,6 +26,10 @@ def __init__(self): super().__init__() self.Q = None + def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray, None, None]: + self.call_count = 1 + return D, D, None, None + def decompose_on_basis( self, D: NDArray, Omega: NDArray, Q: NDArray ) -> Tuple[NDArray, NDArray]: @@ -42,3 +46,10 @@ def test_rpca_decompose_rpca_signal() -> None: assert M.shape == X_incomplete.shape assert A.shape == X_incomplete.shape assert rpca.call_count == 1 + + +def test_transform_with_basis() -> None: + rpca = RPCAMock() + X_imputed = rpca.transform_with_basis(X_incomplete) + assert X_imputed.shape == X_incomplete.shape + assert rpca.call_count == 1 diff --git a/tests/imputations/rpca/test_rpca_noisy.py b/tests/imputations/rpca/test_rpca_noisy.py index 8e19c76c..f39d8ea8 100644 --- a/tests/imputations/rpca/test_rpca_noisy.py +++ b/tests/imputations/rpca/test_rpca_noisy.py @@ -93,7 +93,7 @@ def test_rpca_noisy_get_params_scale(X: NDArray): @pytest.mark.parametrize("norm", ["L2"]) def test_rpca_decompose_rpca_signal_shape(norm: str): """Test RPCA noisy results if tau and lambda equal zero.""" - rpca = RPCANoisy(rank=2, norm=norm).fit_basis(X_test) + rpca = RPCANoisy(rank=2, norm=norm) X_result, A_result = rpca.decompose_rpca_signal(X_test) assert X_result.shape == X_test.shape assert A_result.shape == X_test.shape @@ -102,7 +102,7 @@ def test_rpca_decompose_rpca_signal_shape(norm: str): @pytest.mark.parametrize("X, X_interpolated", [(X_incomplete, X_interpolated)]) def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): """Test RPCA noisy results if tau and lambda equal zero.""" - rpca = RPCANoisy(tau=0, lam=0, norm="L2").fit_basis(X) + rpca = RPCANoisy(tau=0, lam=0, norm="L2") X_result, A_result = rpca.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -114,7 +114,7 @@ def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): ) def test_rpca_noisy_zero_tau(X: NDArray, lam: float, X_interpolated: NDArray): """Test RPCA noisy results if tau equals zero.""" - rpca = RPCANoisy(tau=0, lam=lam, norm="L2").fit_basis(X) + rpca = RPCANoisy(tau=0, lam=lam, norm="L2") X_result, A_result = rpca.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -126,7 +126,7 @@ def test_rpca_noisy_zero_tau(X: NDArray, lam: float, X_interpolated: NDArray): ) def test_rpca_noisy_zero_lambda(X: NDArray, tau: float, X_interpolated: NDArray): """Test RPCA noisy results if lambda equals zero.""" - rpca = RPCANoisy(tau=tau, lam=0, norm="L2").fit_basis(X) + rpca = RPCANoisy(tau=tau, lam=0, norm="L2") X_result, A_result = rpca.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, np.full_like(X, 0), atol=1e-4) np.testing.assert_allclose(A_result, X_interpolated, atol=1e-4) diff --git a/tests/imputations/rpca/test_rpca_pcp.py b/tests/imputations/rpca/test_rpca_pcp.py index 91df1d9b..2b5267a2 100644 --- a/tests/imputations/rpca/test_rpca_pcp.py +++ b/tests/imputations/rpca/test_rpca_pcp.py @@ -75,7 +75,7 @@ def test_check_cost_function_minimized_no_warning( @pytest.mark.parametrize("X", [X_complete]) def test_rpca_rpca_pcp_get_params_scale(X: NDArray): """Test the parameters are well scaled.""" - rpca_pcp = RPCAPCP(max_iterations=max_iterations, mu=0.5, lam=0.1).fit_basis(X) + rpca_pcp = RPCAPCP(max_iterations=max_iterations, mu=0.5, lam=0.1) result_dict = rpca_pcp.get_params_scale(X) result = list(result_dict.values()) params_expected = [1 / 7, np.sqrt(2) / 2] @@ -88,7 +88,7 @@ def test_rpca_rpca_pcp_zero_lambda_small_mu(X: NDArray, mu: float): The problem is ill-conditioned and the result depends on the parameter mu; case when mu is small. """ - rpca_pcp = RPCAPCP(lam=0, mu=mu).fit_basis(X) + rpca_pcp = RPCAPCP(lam=0, mu=mu) X_result, A_result = rpca_pcp.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, np.full_like(X, 0), atol=1e-4) np.testing.assert_allclose(A_result, X, atol=1e-4) @@ -100,7 +100,7 @@ def test_rpca_rpca_pcp_zero_lambda_large_mu(X: NDArray, mu: float): The problem is ill-conditioned and the result depends on the parameter mu; case when mu is large. """ - rpca_pcp = RPCAPCP(lam=0, mu=mu).fit_basis(X) + rpca_pcp = RPCAPCP(lam=0, mu=mu) X_result, A_result = rpca_pcp.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, X, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -109,7 +109,7 @@ def test_rpca_rpca_pcp_zero_lambda_large_mu(X: NDArray, mu: float): @pytest.mark.parametrize("X, mu", [(X_complete, large_mu)]) def test_rpca_rpca_pcp_large_lambda_small_mu(X: NDArray, mu: float): """Test RPCA PCP results with large lambda and small mu.""" - rpca_pcp = RPCAPCP(lam=1e3, mu=mu).fit_basis(X) + rpca_pcp = RPCAPCP(lam=1e3, mu=mu) X_result, A_result = rpca_pcp.decompose_rpca_signal(X) np.testing.assert_allclose(X_result, X, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index 6b7b7bd4..d6b405bf 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -274,6 +274,16 @@ def test_ImputerRPCA_fit_transform(df: pd.DataFrame) -> None: result = np.around(result) np.testing.assert_allclose(result, expected, atol=1e-2) + result = imputer.transform(df.iloc[:10]) + expected = pd.DataFrame( + { + "col1": [i for i in range(10)], + "col2": [0, 1, 2, 2, 2] + [i for i in range(5, 10)], + } + ) + result = np.around(result) + np.testing.assert_allclose(result, expected, atol=1e-2) + @pytest.mark.parametrize("df", [df_incomplete]) def test_ImputerSoftImpute_fit_transform(df: pd.DataFrame) -> None: From e20b61cdda671668938b6c58fc71f4b3364bdb24 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 13 Feb 2024 17:03:14 +0100 Subject: [PATCH 32/99] tests passing for new rpca fit+transform --- qolmat/imputations/imputers.py | 10 +-- qolmat/imputations/rpca/rpca.py | 12 --- qolmat/imputations/rpca/rpca_noisy.py | 90 +++++++++++++++++------ qolmat/imputations/rpca/rpca_pcp.py | 13 +++- qolmat/imputations/rpca/rpca_utils.py | 3 +- tests/imputations/rpca/test_rpca.py | 16 ++-- tests/imputations/rpca/test_rpca_noisy.py | 51 ++++++++++--- tests/imputations/rpca/test_rpca_pcp.py | 24 +++--- tests/imputations/test_imputers.py | 5 +- 9 files changed, 155 insertions(+), 69 deletions(-) diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index fb393221..15de241a 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -1719,7 +1719,6 @@ def get_model(self, **hyperparams) -> rpca.RPCA: hyperparams = { key: hyperparams[key] for key in [ - "period", "mu", "rank", "tau", @@ -1734,7 +1733,6 @@ def get_model(self, **hyperparams) -> rpca.RPCA: hyperparams = { key: hyperparams[key] for key in [ - "period", "rank", "tau", "lam", @@ -1783,9 +1781,9 @@ def _fit_element( model = self.get_model(**hyperparams) X = df.astype(float).values - D = utils.prepare_data(X, model.period) + D = utils.prepare_data(X, self.period) Omega = ~np.isnan(D) - D = utils.linear_interpolation(D) + # D = utils.linear_interpolation(D) Q = model.fit_basis(D, Omega) @@ -1825,9 +1823,9 @@ def _transform_element( model = self.get_model(random_state=self._rng, **hyperparams) X = df.astype(float).values - D = utils.prepare_data(X, model.period) + D = utils.prepare_data(X, self.period) Omega = ~np.isnan(D) - D = utils.linear_interpolation(D) + # D = utils.linear_interpolation(D) Q = self._dict_fitting[col][ngroup] M, A = model.decompose_on_basis(D, Omega, Q) diff --git a/qolmat/imputations/rpca/rpca.py b/qolmat/imputations/rpca/rpca.py index 61fe52bb..100373ba 100644 --- a/qolmat/imputations/rpca/rpca.py +++ b/qolmat/imputations/rpca/rpca.py @@ -16,9 +16,6 @@ class RPCA(BaseEstimator, TransformerMixin): Parameters ---------- - period: Optional[int] - Number of rows of the array if the array is - 1D and reshaped into a 2D array, by default `None`. max_iter: int maximum number of iterations of the alternating direction method of multipliers, @@ -31,13 +28,11 @@ class RPCA(BaseEstimator, TransformerMixin): def __init__( self, - period: int = 1, max_iterations: int = int(1e4), tol: float = 1e-6, random_state: Union[None, int, np.random.RandomState] = None, verbose: bool = True, ) -> None: - self.period = period self.max_iterations = max_iterations self.tol = tol self.random_state = random_state @@ -51,10 +46,3 @@ def fit_basis(self, D: NDArray, Omega: NDArray) -> NDArray: M, A, L, Q = self.decompose_rpca(D, Omega) return Q - - def decompose_on_basis(self, D: NDArray, Omega: NDArray, Q: NDArray) -> NDArray: - n_rows, n_cols = D.shape - if n_rows == 1 or n_cols == 1: - return D, np.full_like(D, 0) - M, A, L, Q = self.decompose_rpca(D, Omega) - return M, A diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 7f29fb84..b2f2a58d 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -12,6 +12,7 @@ from qolmat.imputations.rpca import rpca_utils from qolmat.imputations.rpca.rpca import RPCA +from qolmat.utils import utils class RPCANoisy(RPCA): @@ -32,8 +33,6 @@ class RPCANoisy(RPCA): ---------- random_state : int, optional The seed of the pseudo random number generator to use, for reproductibility. - period: Optional[int] - number of rows of the reshaped matrix if the signal is a 1D-array rank: Optional[int] (estimated) low-rank of the matrix D mu: Optional[float] @@ -60,7 +59,6 @@ class RPCANoisy(RPCA): def __init__( self, random_state: Union[None, int, np.random.RandomState] = None, - period: int = 1, rank: Optional[int] = None, mu: Optional[float] = None, tau: Optional[float] = None, @@ -72,7 +70,7 @@ def __init__( norm: str = "L2", verbose: bool = True, ) -> None: - super().__init__(period=period, max_iterations=max_iterations, tol=tol, verbose=verbose) + super().__init__(max_iterations=max_iterations, tol=tol, verbose=verbose) self.rng = sku.check_random_state(random_state) self.rank = rank self.mu = mu @@ -103,6 +101,7 @@ def get_params_scale(self, D: NDArray) -> Dict[str, float]: Regularization parameter for the L1 norm. """ + D = utils.linear_interpolation(D) rank = rpca_utils.approx_rank(D) tau = 1.0 / np.sqrt(max(D.shape)) lam = tau @@ -115,12 +114,14 @@ def get_params_scale(self, D: NDArray) -> Dict[str, float]: def decompose_on_basis( self, D: NDArray, Omega: NDArray, Q: NDArray ) -> Tuple[NDArray, NDArray]: + D = utils.linear_interpolation(D) params_scale = self.get_params_scale(D) lam = params_scale["lam"] if self.lam is None else self.lam rank = params_scale["rank"] if self.rank is None else self.rank rank = int(rank) tau = params_scale["tau"] if self.tau is None else self.tau + tol = self.tol n_rows, n_cols = D.shape if n_rows == 1 or n_cols == 1: @@ -128,28 +129,69 @@ def decompose_on_basis( # M, A, L, Q = self.decompose_rpca(D, Omega) n_rank, _ = Q.shape Ir = np.eye(n_rank) - L = np.zeros((n_rows, n_rank)) A = np.zeros((n_rows, n_cols)) - for i in range(n_rows): - d = D[i, :] - omega = Omega[i, :] - L_row = np.zeros((1, n_rank)) - a = np.full_like(d, 0) - for _ in range(self.max_iterations): - a_omega = rpca_utils.soft_thresholding(d - L_row @ Q, lam) - a_omega_C = d - L_row @ Q - a = np.where(omega, a_omega, a_omega_C) - - L_row = scp.linalg.solve( - a=2 * tau * Ir + (Q @ Q.T), - b=Q @ (d - a).T, - ).T - L[i, :] = L_row - A[i, :] = a + L = np.zeros((n_rows, n_rank)) + for _ in range(self.max_iterations): + A_prev = A.copy() + L_prev = L.copy() + L = scp.linalg.solve( + a=2 * tau * Ir + (Q @ Q.T), + b=Q @ (D - A).T, + ).T + A_Omega = rpca_utils.soft_thresholding(D - L @ Q, lam) + A_Omega_C = D - L @ Q + A = np.where(Omega, A_Omega, A_Omega_C) + + Ac = np.linalg.norm(A - A_prev, np.inf) + Lc = np.linalg.norm(L - L_prev, np.inf) + + tolerance = max([Ac, Lc]) # type: ignore # noqa + + if tolerance < tol: + break + M = L @ Q return M, A + # def decompose_on_basis( + # self, D: NDArray, Omega: NDArray, Q: NDArray + # ) -> Tuple[NDArray, NDArray]: + # params_scale = self.get_params_scale(D) + + # lam = params_scale["lam"] if self.lam is None else self.lam + # rank = params_scale["rank"] if self.rank is None else self.rank + # rank = int(rank) + # tau = params_scale["tau"] if self.tau is None else self.tau + + # n_rows, n_cols = D.shape + # if n_rows == 1 or n_cols == 1: + # return D, np.full_like(D, 0) + # # M, A, L, Q = self.decompose_rpca(D, Omega) + # n_rank, _ = Q.shape + # Ir = np.eye(n_rank) + # L = np.zeros((n_rows, n_rank)) + # A = np.zeros((n_rows, n_cols)) + # for i in range(n_rows): + # d = D[i, :] + # Omega = Omega[i, :] + # L_row = np.zeros((1, n_rank)) + # a = np.full_like(d, 0) + # for _ in range(self.max_iterations): + # A_Omega = rpca_utils.soft_thresholding(d - L_row @ Q, lam) + # A_Omega_C = d - L_row @ Q + # a = np.where(Omega, A_Omega, A_Omega_C) + + # L_row = scp.linalg.solve( + # a=2 * tau * Ir + (Q @ Q.T), + # b=Q @ (d - a).T, + # ).T + # L[i, :] = L_row + # A[i, :] = a + # M = L @ Q + + # return M, A + def decompose_rpca( self, D: NDArray, Omega: NDArray ) -> Tuple[NDArray, NDArray, NDArray, NDArray]: @@ -187,6 +229,12 @@ def decompose_rpca( f"than the number of rows in the matrix but {period} >= {n_rows}!" ) + print("before") + print(D) + D = utils.linear_interpolation(D) + print("after") + print(D) + M, A, L, Q = self.decompose_rpca_algorithm( D, Omega, diff --git a/qolmat/imputations/rpca/rpca_pcp.py b/qolmat/imputations/rpca/rpca_pcp.py index 0c0c71cb..7214f007 100644 --- a/qolmat/imputations/rpca/rpca_pcp.py +++ b/qolmat/imputations/rpca/rpca_pcp.py @@ -9,6 +9,7 @@ from qolmat.imputations.rpca import rpca_utils from qolmat.imputations.rpca.rpca import RPCA +from qolmat.utils import utils class RPCAPCP(RPCA): @@ -44,14 +45,13 @@ class RPCAPCP(RPCA): def __init__( self, random_state: Union[None, int, np.random.RandomState] = None, - period: int = 1, mu: Optional[float] = None, lam: Optional[float] = None, max_iterations: int = int(1e4), tol: float = 1e-6, verbose: bool = True, ) -> None: - super().__init__(period=period, max_iterations=max_iterations, tol=tol, verbose=verbose) + super().__init__(max_iterations=max_iterations, tol=tol, verbose=verbose) self.rng = sku.check_random_state(random_state) self.mu = mu self.lam = lam @@ -80,6 +80,13 @@ def get_params_scale(self, D: NDArray): dict_params = {"mu": mu, "lam": lam} return dict_params + def decompose_on_basis(self, D: NDArray, Omega: NDArray, Q: NDArray) -> NDArray: + n_rows, n_cols = D.shape + if n_rows == 1 or n_cols == 1: + return D, np.full_like(D, 0) + M, A, L, Q = self.decompose_rpca(D, Omega) + return M, A + def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray, None, None]: """ Estimate the relevant parameters then compute the PCP RPCA decomposition @@ -105,6 +112,8 @@ def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray, mu = params_scale["mu"] if self.mu is None else self.mu lam = params_scale["lam"] if self.lam is None else self.lam + D = utils.linear_interpolation(D) + D_norm = np.linalg.norm(D, "fro") A = np.array(np.full_like(D, 0)) diff --git a/qolmat/imputations/rpca/rpca_utils.py b/qolmat/imputations/rpca/rpca_utils.py index c84b8aa0..12c85603 100644 --- a/qolmat/imputations/rpca/rpca_utils.py +++ b/qolmat/imputations/rpca/rpca_utils.py @@ -2,7 +2,6 @@ Modular utility functions for RPCA """ - import numpy as np from numpy.typing import NDArray import scipy @@ -31,6 +30,8 @@ def approx_rank( """ if threshold == 1: return min(M.shape) + print("approx_rank") + print(M) _, values_singular, _ = np.linalg.svd(M, full_matrices=True) cum_sum = np.cumsum(values_singular) / np.sum(values_singular) diff --git a/tests/imputations/rpca/test_rpca.py b/tests/imputations/rpca/test_rpca.py index bde2b7c7..e4036759 100644 --- a/tests/imputations/rpca/test_rpca.py +++ b/tests/imputations/rpca/test_rpca.py @@ -25,17 +25,21 @@ class RPCAMock(RPCA): def __init__(self): super().__init__() - def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: + def decompose_rpca( + self, D: NDArray, Omega: NDArray + ) -> Tuple[NDArray, NDArray, NDArray, NDArray]: self.call_count = 1 - return D, D + return D, D, D, D X_incomplete = np.array([[1, np.nan], [4, 2], [np.nan, 4]]) +Omega = ~np.isnan(X_incomplete) -def test_rpca_decompose_rpca_signal() -> None: +def test_rpca_fit_basis() -> None: rpca = RPCAMock() - M, A = rpca.decompose_rpca_signal(X_incomplete) - assert M.shape == X_incomplete.shape - assert A.shape == X_incomplete.shape + Q = rpca.fit_basis(X_incomplete, Omega) + _, n_cols = X_incomplete.shape + _, n_colsQ = Q.shape + assert n_cols == n_colsQ assert rpca.call_count == 1 diff --git a/tests/imputations/rpca/test_rpca_noisy.py b/tests/imputations/rpca/test_rpca_noisy.py index f39d8ea8..233d566e 100644 --- a/tests/imputations/rpca/test_rpca_noisy.py +++ b/tests/imputations/rpca/test_rpca_noisy.py @@ -47,7 +47,13 @@ def synthetic_temporal_data(): ], ) def test_check_cost_function_minimized_warning( - obs: NDArray, lr: NDArray, ano: NDArray, omega: NDArray, lam: float, tau: float, norm: str + obs: NDArray, + lr: NDArray, + ano: NDArray, + omega: NDArray, + lam: float, + tau: float, + norm: str, ): """Test warning when the cost function is not minimized.""" with pytest.warns(UserWarning): @@ -69,7 +75,13 @@ def test_check_cost_function_minimized_warning( ], ) def test_check_cost_function_minimized_no_warning( - obs: NDArray, lr: NDArray, ano: NDArray, omega: NDArray, lam: float, tau: float, norm: str + obs: NDArray, + lr: NDArray, + ano: NDArray, + omega: NDArray, + lam: float, + tau: float, + norm: str, ): """Test no warning when the cost function is minimized.""" with warnings.catch_warnings(record=True) as record: @@ -91,19 +103,27 @@ def test_rpca_noisy_get_params_scale(X: NDArray): @pytest.mark.parametrize("norm", ["L2"]) -def test_rpca_decompose_rpca_signal_shape(norm: str): +def test_rpca_decompose_rpca_shape(norm: str): """Test RPCA noisy results if tau and lambda equal zero.""" - rpca = RPCANoisy(rank=2, norm=norm) - X_result, A_result = rpca.decompose_rpca_signal(X_test) - assert X_result.shape == X_test.shape - assert A_result.shape == X_test.shape + rank = 2 + rpca = RPCANoisy(rank=rank, norm=norm) + Omega = ~np.isnan(X_test) + M_result, A_result, L_result, Q_result = rpca.decompose_rpca(X_test, Omega) + n_rows, n_cols = X_test.shape + assert M_result.shape == (n_rows, n_cols) + assert A_result.shape == (n_rows, n_cols) + assert L_result.shape == (n_rows, rank) + assert Q_result.shape == (rank, n_cols) @pytest.mark.parametrize("X, X_interpolated", [(X_incomplete, X_interpolated)]) def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): """Test RPCA noisy results if tau and lambda equal zero.""" rpca = RPCANoisy(tau=0, lam=0, norm="L2") - X_result, A_result = rpca.decompose_rpca_signal(X) + Omega = ~np.isnan(X) + print(X) + print(Omega) + X_result, A_result, _, _ = rpca.decompose_rpca(X, Omega) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -115,7 +135,8 @@ def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): def test_rpca_noisy_zero_tau(X: NDArray, lam: float, X_interpolated: NDArray): """Test RPCA noisy results if tau equals zero.""" rpca = RPCANoisy(tau=0, lam=lam, norm="L2") - X_result, A_result = rpca.decompose_rpca_signal(X) + Omega = ~np.isnan(X) + X_result, A_result, _, _ = rpca.decompose_rpca(X, Omega) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -127,7 +148,8 @@ def test_rpca_noisy_zero_tau(X: NDArray, lam: float, X_interpolated: NDArray): def test_rpca_noisy_zero_lambda(X: NDArray, tau: float, X_interpolated: NDArray): """Test RPCA noisy results if lambda equals zero.""" rpca = RPCANoisy(tau=tau, lam=0, norm="L2") - X_result, A_result = rpca.decompose_rpca_signal(X) + Omega = ~np.isnan(X) + X_result, A_result, _, _ = rpca.decompose_rpca(X, Omega) np.testing.assert_allclose(X_result, np.full_like(X, 0), atol=1e-4) np.testing.assert_allclose(A_result, X_interpolated, atol=1e-4) @@ -192,7 +214,14 @@ def test_rpca_noisy_temporal_signal_temporal_regularisations(synthetic_temporal_ ) X_result, A_result, _, _ = RPCANoisy.decompose_rpca_algorithm( - D, Omega, rank, tau, lam, list_periods=list_periods, list_etas=list_etas, norm="L2" + D, + Omega, + rank, + tau, + lam, + list_periods=list_periods, + list_etas=list_etas, + norm="L2", ) cost_result = RPCANoisy.cost_function( D, diff --git a/tests/imputations/rpca/test_rpca_pcp.py b/tests/imputations/rpca/test_rpca_pcp.py index 0cdc2c94..4849de39 100644 --- a/tests/imputations/rpca/test_rpca_pcp.py +++ b/tests/imputations/rpca/test_rpca_pcp.py @@ -89,7 +89,8 @@ def test_rpca_rpca_pcp_zero_lambda_small_mu(X: NDArray, mu: float): on the parameter mu; case when mu is small. """ rpca_pcp = RPCAPCP(lam=0, mu=mu) - X_result, A_result = rpca_pcp.decompose_rpca_signal(X) + Omega = ~np.isnan(X) + X_result, A_result, _, _ = rpca_pcp.decompose_rpca(X, Omega) np.testing.assert_allclose(X_result, np.full_like(X, 0), atol=1e-4) np.testing.assert_allclose(A_result, X, atol=1e-4) @@ -101,7 +102,8 @@ def test_rpca_rpca_pcp_zero_lambda_large_mu(X: NDArray, mu: float): on the parameter mu; case when mu is large. """ rpca_pcp = RPCAPCP(lam=0, mu=mu) - X_result, A_result = rpca_pcp.decompose_rpca_signal(X) + Omega = ~np.isnan(X) + X_result, A_result, _, _ = rpca_pcp.decompose_rpca(X, Omega) np.testing.assert_allclose(X_result, X, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -110,7 +112,8 @@ def test_rpca_rpca_pcp_zero_lambda_large_mu(X: NDArray, mu: float): def test_rpca_rpca_pcp_large_lambda_small_mu(X: NDArray, mu: float): """Test RPCA PCP results with large lambda and small mu.""" rpca_pcp = RPCAPCP(lam=1e3, mu=mu) - X_result, A_result = rpca_pcp.decompose_rpca_signal(X) + Omega = ~np.isnan(X) + X_result, A_result, _, _ = rpca_pcp.decompose_rpca(X, Omega) np.testing.assert_allclose(X_result, X, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -121,9 +124,12 @@ def test_rpca_temporal_signal(synthetic_temporal_data): signal = synthetic_temporal_data period = 100 lam = 0.1 - rpca = RPCAPCP(period=period, lam=lam, mu=0.01) - X_result, A_result = rpca.decompose_rpca_signal(signal) - X_input_rpca = utils.linear_interpolation(signal.reshape(period, -1)) - assert np.linalg.norm(X_input_rpca, "nuc") >= np.linalg.norm( - X_result.reshape(period, -1), "nuc" - ) + lam * np.sum(np.abs(A_result.reshape(period, -1))) + rpca = RPCAPCP(lam=lam, mu=0.01) + + D = utils.prepare_data(signal, period) + Omega = ~np.isnan(D) + D_interpolated = utils.linear_interpolation(D) + X_result, A_result, _, _ = rpca.decompose_rpca(D, Omega) + assert np.linalg.norm(D_interpolated, "nuc") >= np.linalg.norm(X_result, "nuc") + lam * np.sum( + np.abs(A_result) + ) diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index 694288c2..bd148d19 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -290,7 +290,10 @@ def test_ImputerSoftImpute_fit_transform(df: pd.DataFrame) -> None: index_grouped = pd.MultiIndex.from_product([["a", "b"], range(4)], names=["group", "date"]) -dict_values = {"col1": [0, np.nan, 0, np.nan, 1, 1, 1, 1], "col2": [1, 1, 1, 1, 2, 2, 2, 2]} +dict_values = { + "col1": [0, np.nan, 0, np.nan, 1, 1, 1, 1], + "col2": [1, 1, 1, 1, 2, 2, 2, 2], +} df_grouped = pd.DataFrame(dict_values, index=index_grouped) list_imputers = [ From 71fd49d199aa39453ab1188f6eff217178a51af9 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 13 Feb 2024 18:25:21 +0100 Subject: [PATCH 33/99] rpca anomaly projection changed --- qolmat/imputations/rpca/rpca_noisy.py | 10 ++++------ qolmat/imputations/rpca/rpca_utils.py | 2 -- tests/imputations/rpca/test_rpca_noisy.py | 2 -- 3 files changed, 4 insertions(+), 10 deletions(-) diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index b2f2a58d..d34674af 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -141,6 +141,7 @@ def decompose_on_basis( A_Omega = rpca_utils.soft_thresholding(D - L @ Q, lam) A_Omega_C = D - L @ Q A = np.where(Omega, A_Omega, A_Omega_C) + # A = np.where(Omega, A_Omega, 0) Ac = np.linalg.norm(A - A_prev, np.inf) Lc = np.linalg.norm(L - L_prev, np.inf) @@ -229,11 +230,7 @@ def decompose_rpca( f"than the number of rows in the matrix but {period} >= {n_rows}!" ) - print("before") - print(D) D = utils.linear_interpolation(D) - print("after") - print(D) M, A, L, Q = self.decompose_rpca_algorithm( D, @@ -421,8 +418,9 @@ def decompose_rpca_algorithm( ) A_Omega = rpca_utils.soft_thresholding(D - X, lam) - A_Omega_C = D - X - A = np.where(Omega, A_Omega, A_Omega_C) + # A_Omega_C = D - X + # A = np.where(Omega, A_Omega, A_Omega_C) + A = np.where(Omega, A_Omega, 0) Q = scp.linalg.solve( a=tau * Ir + mu * (L.T @ L), diff --git a/qolmat/imputations/rpca/rpca_utils.py b/qolmat/imputations/rpca/rpca_utils.py index 12c85603..75082add 100644 --- a/qolmat/imputations/rpca/rpca_utils.py +++ b/qolmat/imputations/rpca/rpca_utils.py @@ -30,8 +30,6 @@ def approx_rank( """ if threshold == 1: return min(M.shape) - print("approx_rank") - print(M) _, values_singular, _ = np.linalg.svd(M, full_matrices=True) cum_sum = np.cumsum(values_singular) / np.sum(values_singular) diff --git a/tests/imputations/rpca/test_rpca_noisy.py b/tests/imputations/rpca/test_rpca_noisy.py index 233d566e..2fcd7e83 100644 --- a/tests/imputations/rpca/test_rpca_noisy.py +++ b/tests/imputations/rpca/test_rpca_noisy.py @@ -121,8 +121,6 @@ def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): """Test RPCA noisy results if tau and lambda equal zero.""" rpca = RPCANoisy(tau=0, lam=0, norm="L2") Omega = ~np.isnan(X) - print(X) - print(Omega) X_result, A_result, _, _ = rpca.decompose_rpca(X, Omega) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) From 1510565671b9aa43d665ffe0d095b2cb07e2c94e Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 14 Feb 2024 14:50:09 +0100 Subject: [PATCH 34/99] rpca imputation term in A --- qolmat/imputations/rpca/rpca_noisy.py | 6 ++---- tests/imputations/rpca/test_rpca.py | 7 ------- 2 files changed, 2 insertions(+), 11 deletions(-) diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 6f22f39a..51f86ca1 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -144,7 +144,6 @@ def decompose_on_basis( A_Omega = rpca_utils.soft_thresholding(D - L @ Q, lam) A_Omega_C = D - L @ Q A = np.where(Omega, A_Omega, A_Omega_C) - # A = np.where(Omega, A_Omega, 0) Ac = np.linalg.norm(A - A_prev, np.inf) Lc = np.linalg.norm(L - L_prev, np.inf) @@ -427,9 +426,8 @@ def decompose_rpca_algorithm( ) A_Omega = rpca_utils.soft_thresholding(D - X, lam) - # A_Omega_C = D - X - # A = np.where(Omega, A_Omega, A_Omega_C) - A = np.where(Omega, A_Omega, 0) + A_Omega_C = D - X + A = np.where(Omega, A_Omega, A_Omega_C) Q = scp.linalg.solve( a=tau * Ir + mu * (L.T @ L), diff --git a/tests/imputations/rpca/test_rpca.py b/tests/imputations/rpca/test_rpca.py index bcffce9b..42500e72 100644 --- a/tests/imputations/rpca/test_rpca.py +++ b/tests/imputations/rpca/test_rpca.py @@ -44,10 +44,3 @@ def test_rpca_fit_basis() -> None: _, n_colsQ = Q.shape assert n_cols == n_colsQ assert rpca.call_count == 1 - - -def test_transform_with_basis() -> None: - rpca = RPCAMock() - X_imputed = rpca.transform_with_basis(X_incomplete) - assert X_imputed.shape == X_incomplete.shape - assert rpca.call_count == 1 From 1cf6f593e4f6bd735ca1f25dec66ce322db6acd3 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 21 Feb 2024 19:14:55 +0100 Subject: [PATCH 35/99] rpca doc updated and notations made coherent --- docs/imputers.rst | 67 +- examples/tutorials/Untitled.ipynb | 1285 ------------------------- qolmat/imputations/rpca/rpca_noisy.py | 36 +- qolmat/imputations/softimpute.py | 80 +- 4 files changed, 96 insertions(+), 1372 deletions(-) delete mode 100644 examples/tutorials/Untitled.ipynb diff --git a/docs/imputers.rst b/docs/imputers.rst index d0c22eac..dafe24bc 100644 --- a/docs/imputers.rst +++ b/docs/imputers.rst @@ -22,31 +22,36 @@ Multiple Imputation by Chained Equation: multiple imputations based on ICE. It u 5. RPCA ------- -Robust Principal Component Analysis (RPCA) is a modification of the statistical procedure of PCA which allows to work with grossly corrupted observations. Suppose we are given a large data matrix :math:`\mathbf{X} \in \mathbb{R}^{n \times d}`, and know that it may be decomposed as :math:`\mathbf{X} = \mathbf{L}^* + \mathbf{A}^*` where :math:`\mathbf{L}^*` has low-rank and :math:`\mathbf{A}^*` is sparse [1]. See the :class:`~qolmat.imputations.imputers.ImputerRPCA` class. +Robust Principal Component Analysis (RPCA) is a modification of the statistical procedure of PCA which allows to work with a data matrix :math:`\mathbf{D} \in \mathbb{R}^{n \times d}` containing missing values and grossly corrupted observations. We consider here the imputation task alone, but these methods can also tackle anomaly correction. See the :class:`~qolmat.imputations.imputers.ImputerRPCA` class. -Two cases are considered: +Two cases are considered. -* :class:`RPCAPCP` class [1]. The optimisation problem is the following +**RPCA via Principal Component Pursuit (PCP)** [1, 12] + +The class :class:`RPCAPCP` implements a matrix decomposition :math:`\mathbf{D} = \mathbf{M} + \mathbf{A}` where :math:`\mathbf{M}` has low-rank and :math:`\mathbf{A}` is sparse. It relies on the following optimisation problem .. math:: - \text{minimise}_{\mathbf{L} \in \mathbb{R}^{n \times d}, \mathbf{A} \in \mathbb{R}^{m \times n}} \quad \Vert \mathbf{L} \Vert_* + \lambda \Vert \mathbf{A} \Vert_1 \text{ s.t. } \mathbf{X} = \mathbf{L} + \mathbf{A} + \text{min}_{\mathbf{M} \in \mathbb{R}^{m \times n}} \quad \Vert \mathbf{M} \Vert_* + \lambda \Vert P_\Omega(\mathbf{D-M}) \Vert_1 + +with :math:`\mathbf{A} = \mathbf{D} - \mathbf{M}`. The operator :math:`P_{\Omega}` is the projection operator on the set of observed data :math:`\Omega`, so that there is no penalization for the components of :math:`A` corresponding to unobserved data. The imputed values are then given by the matrix :math:`M` on the unobserved data. +**Noisy RPCA** [2, 3, 4] -* :class:`RPCANoisy` class [2, 3]. The idea is to adapt basic RPCA to time series by adding a constraint to maintain consistency between the columns of the low-rank matrix. By defining :math:`\Vert \mathbf{LH_k} \Vert_p` is either :math:`\Vert \mathbf{LH_k} \Vert_1` or :math:`\Vert \mathbf{LH_k} \Vert_F^2`, the optimisation problem is the following +The class :class:`RPCANoisy` implements an recommanded improved version, which relies on a decomposition :math:`\mathbf{D} = \mathbf{M} + \mathbf{A} + \mathbf{E}`. The additionnal term encodes a Gaussian noise and makes the numerical convergence more reliable. This class also implements a time-consistency penalization for time series, parametrized by the :math:`\eta_k`and :math:`H_k`. By defining :math:`\Vert \mathbf{MH_k} \Vert_p` is either :math:`\Vert \mathbf{MH_k} \Vert_1` or :math:`\Vert \mathbf{MH_k} \Vert_F^2`, the optimisation problem is the following .. math:: - \text{minimise}_{\mathbf{L} \in \mathbb{R}^{m \times n}, \mathbf{A} \in \mathbb{R}^{m \times n}} \quad \Vert P_{\Omega}(\mathbf{L}+\mathbf{A}-\mathbf{X}) \Vert_F^2 + \tau \Vert \mathbf{L} \Vert_* + \lambda \Vert \mathbf{A} \Vert_1 + \sum_{k=1}^K \eta_k \Vert \mathbf{LH_k} \Vert_p + \text{min}_{\mathbf{M, A} \in \mathbb{R}^{m \times n}} \quad \Vert P_{\Omega} (\mathbf{D}-\mathbf{M}-\mathbf{A}) \Vert_F^2 + \tau \Vert \mathbf{M} \Vert_* + \lambda \Vert \mathbf{A} \Vert_1 + \sum_{k=1}^K \eta_k \Vert \mathbf{M H_k} \Vert_p -The operator :math:`P_{\Omega}` is the projection operator such that :math:`P_{\Omega}(\mathbf{X})` is the projection of :math:`\mathbf{X}` on the set of observed data :math:`\Omega`. This allows to deal with missing values. Each of these classes is adapted to take as input either a time series or a matrix directly. +with :math:`\mathbf{E} = \mathbf{D} - \mathbf{M} - \mathbf{A}`. 6. SoftImpute ------------- -SoftImpute is an iterative method for matrix completion that uses nuclear-norm regularization [10]. Given a matrix :math:`\mathbf{X} \in \mathbb{R}^{n \times d}` with observed entries indexed by the set :math:`\Omega`, this algorithm solves the following problem: +SoftImpute is an iterative method for matrix completion that uses nuclear-norm regularization [11]. It is a faster alternative to RPCA, although it is much less robust due to the quadratic penalization. Given a matrix :math:`\mathbf{D} \in \mathbb{R}^{n \times d}` with observed entries indexed by the set :math:`\Omega`, this algorithm solves the following problem: .. math:: - \text{minimise}_{\mathbf{A} \in \mathbb{R}^{n \times r}, \mathbf{B} \in \mathbb{R}^{d \times r}} \quad \frac{1}{2} \Vert P_{\Omega}(\mathbf{X} - \mathbf{A}\mathbf{B}^T) \Vert_F^2 + \frac{\tau}{2} (\Vert \mathbf{A} \Vert_F^2 + \Vert \mathbf{B} \Vert_F^2) + \text{minimise}_{\mathbf{L} \in \mathbb{R}^{n \times r}, \mathbf{Q} \in \mathbb{R}^{d \times r}} \quad \Vert P_{\Omega}(\mathbf{D} - \mathbf{L}\mathbf{Q}) \Vert_F^2 + \tau \Vert \mathbf{L} \Vert_F^2 + \tau \Vert \mathbf{Q} \Vert_F^2 -See the :class:`~qolmat.imputations.imputers.ImputerSoftImpute` class for implementation details. +The imputed values are then given by the matrix :math:`M=LQ` on the unobserved data. See the :class:`~qolmat.imputations.imputers.ImputerSoftImpute` class for implementation details. 7. KNN ------ @@ -54,7 +59,7 @@ K-nearest neighbors, based on `KNNImputer `_ Journal of the ACM (JACM) 58.3 (2011): 1-37. -[2] Chen, Yuxin, et al. `Bridging convex and nonconvex optimization in robust PCA: Noise, outliers, and missing data. `_ Annals of statistics 49.5 (2021): 2948. +[2] Botterman, HL., Roussel, J., Morzadec, T., Jabbari, A., Brunel, N. `Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series `_ in International Conference on Machine Learning, Optimization, and Data Science. Cham: Springer Nature Switzerland (2022). + +[3] Chen, Yuxin, et al. `Bridging convex and nonconvex optimization in robust PCA: Noise, outliers, and missing data. `_ Annals of statistics 49.5 (2021): 2948. + +[4] Wang, Xuehui, et al. `An improved robust principal component analysis model for anomalies detection of subway passenger flow. `_ Journal of advanced transportation 2018 (2018). -[3] Wang, Xuehui, et al. `An improved robust principal component analysis model for anomalies detection of subway passenger flow. `_ Journal of advanced transportation 2018 (2018). +[5] Dempster, Arthur P., Nan M. Laird, and Donald B. Rubin. `Maximum likelihood from incomplete data via the EM algorithm. `_ Journal of the royal statistical society: series B (methodological) 39.1 (1977): 1-22. -[4] Dempster, Arthur P., Nan M. Laird, and Donald B. Rubin. `Maximum likelihood from incomplete data via the EM algorithm. `_ Journal of the royal statistical society: series B (methodological) 39.1 (1977): 1-22. +[6] Wei, Greg CG, and Martin A. Tanner. `A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. `__ Journal of the American statistical Association 85.411 (1990): 699-704. -[5] Wei, Greg CG, and Martin A. Tanner. `A Monte Carlo implementation of the EM algorithm and the poor man's data augmentation algorithms. `__ Journal of the American statistical Association 85.411 (1990): 699-704. +[7] Lütkepohl, Helmut. `New introduction to multiple time series analysis. `_ Springer Science & Business Media, 2005. -[6] Lütkepohl, Helmut. `New introduction to multiple time series analysis. `_ Springer Science & Business Media, 2005. +[8] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. `Denoising diffusion probabilistic models. `_ Advances in neural information processing systems 33 (2020): 6840-6851. -[7] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. `Denoising diffusion probabilistic models. `_ Advances in neural information processing systems 33 (2020): 6840-6851. +[9] Tashiro, Yusuke, et al. `Csdi: Conditional score-based diffusion models for probabilistic time series imputation. `_ Advances in Neural Information Processing Systems 34 (2021): 24804-24816. -[8] Tashiro, Yusuke, et al. `Csdi: Conditional score-based diffusion models for probabilistic time series imputation. `_ Advances in Neural Information Processing Systems 34 (2021): 24804-24816. +[10] Kotelnikov, Akim, et al. `Tabddpm: Modelling tabular data with diffusion models. `_ International Conference on Machine Learning. PMLR, 2023. -[9] Kotelnikov, Akim, et al. `Tabddpm: Modelling tabular data with diffusion models. `_ International Conference on Machine Learning. PMLR, 2023. +[11] Hastie, Trevor, et al. `Matrix completion and low-rank SVD via fast alternating least squares. `_ The Journal of Machine Learning Research 16.1 (2015): 3367-3402. -[10] Hastie, Trevor, et al. `Matrix completion and low-rank SVD via fast alternating least squares. `_ The Journal of Machine Learning Research 16.1 (2015): 3367-3402. \ No newline at end of file +[12] Fanhua, Shang, et al. `Robust Principal Component Analysis with Missing Data `_ Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (2014). \ No newline at end of file diff --git a/examples/tutorials/Untitled.ipynb b/examples/tutorials/Untitled.ipynb deleted file mode 100644 index 9db60061..00000000 --- a/examples/tutorials/Untitled.ipynb +++ /dev/null @@ -1,1285 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 9, - "id": "607d62ae", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "from qolmat.benchmark import comparator, missing_patterns\n", - "from qolmat.imputations import imputers\n", - "from qolmat.utils import data, plot\n", - "\n", - "\n", - "df_data = data.get_data(\"Superconductor\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "9145dd03", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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088.94446857.86269266.36159236.1166121.1817951.062396122.9060731.79492151.96882853.622535...2.252.2571432.2133642.2197831.3689221.0662211.0857140.4330130.43705929.00
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388.94446857.87396766.36159236.1195601.1817951.022291122.9060733.76801051.96882853.639405...2.252.2642862.2133642.2262221.3689221.0488341.1000000.4330130.44095222.00
488.94446857.84014366.36159236.1107161.1817951.129224122.9060727.84874351.96882853.588771...2.252.2428572.2133642.2069631.3689221.0960521.0571430.4330130.42880923.00
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21263 rows × 80 columns

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" - ], - "text/plain": [ - " mean_atomic_mass wtd_mean_atomic_mass gmean_atomic_mass \n", - "0 88.944468 57.862692 66.361592 \\\n", - "1 92.729214 58.518416 73.132787 \n", - "2 88.944468 57.885242 66.361592 \n", - "3 88.944468 57.873967 66.361592 \n", - "4 88.944468 57.840143 66.361592 \n", - "... ... ... ... \n", - "21258 106.957877 53.095769 NaN \n", - "21259 92.266740 49.021367 64.812662 \n", - "21260 99.663190 95.609104 99.433882 \n", - "21261 99.663190 97.095602 99.433882 \n", - "21262 87.468333 86.858500 82.555758 \n", - "\n", - " wtd_gmean_atomic_mass entropy_atomic_mass wtd_entropy_atomic_mass \n", - "0 36.116612 1.181795 1.062396 \\\n", - "1 36.396602 1.449309 1.057755 \n", - "2 36.122509 1.181795 0.975980 \n", - "3 36.119560 1.181795 1.022291 \n", - "4 36.110716 1.181795 1.129224 \n", - "... ... ... ... \n", - "21258 43.135565 NaN 1.254119 \n", - "21259 32.867748 NaN 1.571630 \n", - "21260 95.464320 NaN 0.530198 \n", - "21261 96.901083 NaN 0.640883 \n", - "21262 80.458722 1.041270 0.895229 \n", - "\n", - " range_atomic_mass wtd_range_atomic_mass std_atomic_mass \n", - "0 122.90607 31.794921 51.968828 \\\n", - "1 122.90607 36.161939 47.094633 \n", - "2 122.90607 35.741099 51.968828 \n", - "3 122.90607 33.768010 51.968828 \n", - "4 122.90607 27.848743 51.968828 \n", - "... ... ... ... \n", - "21258 146.88130 15.504479 65.764081 \n", - "21259 188.38390 7.353333 69.232655 \n", - "21260 13.51362 53.041104 6.756810 \n", - "21261 13.51362 31.115202 6.756810 \n", - "21262 71.75500 43.144000 29.905282 \n", - "\n", - " wtd_std_atomic_mass ... mean_Valence wtd_mean_Valence \n", - "0 53.622535 ... 2.25 2.257143 \\\n", - "1 53.979870 ... 2.00 2.257143 \n", - "2 53.656268 ... 2.25 2.271429 \n", - "3 53.639405 ... 2.25 2.264286 \n", - "4 53.588771 ... 2.25 2.242857 \n", - "... ... ... ... ... \n", - "21258 43.202659 ... 3.25 3.555556 \n", - "21259 50.148287 ... 2.20 2.047619 \n", - "21260 5.405448 ... 4.50 4.800000 \n", - "21261 6.249958 ... 4.50 4.690000 \n", - "21262 33.927941 ... 5.00 4.500000 \n", - "\n", - " gmean_Valence wtd_gmean_Valence entropy_Valence wtd_entropy_Valence \n", - "0 2.213364 2.219783 1.368922 1.066221 \\\n", - "1 1.888175 2.210679 1.557113 1.047221 \n", - "2 2.213364 2.232679 NaN 1.029175 \n", - "3 2.213364 2.226222 NaN 1.048834 \n", - "4 2.213364 2.206963 NaN 1.096052 \n", - "... ... ... ... ... \n", - "21258 3.223710 3.519911 1.377820 0.913658 \n", - "21259 2.168944 2.038991 1.594167 1.337246 \n", - "21260 4.472136 4.781762 0.686962 0.450561 \n", - "21261 4.472136 4.665819 0.686962 0.577601 \n", - "21262 4.762203 4.242641 1.054920 0.970116 \n", - "\n", - " wtd_range_Valence std_Valence wtd_std_Valence criticaltemp \n", - "0 1.085714 0.433013 0.437059 29.00 \n", - "1 1.128571 0.632456 0.468606 26.00 \n", - "2 1.114286 0.433013 0.444697 19.00 \n", - "3 1.100000 0.433013 0.440952 22.00 \n", - "4 1.057143 0.433013 0.428809 23.00 \n", - "... ... ... ... ... \n", - "21258 2.168889 0.433013 0.496904 2.44 \n", - "21259 0.904762 0.400000 0.212959 122.10 \n", - "21260 3.200000 0.500000 0.400000 1.98 \n", - "21261 2.210000 0.500000 0.462493 1.84 \n", - "21262 1.800000 1.414214 1.500000 12.80 \n", - "\n", - "[21263 rows x 80 columns]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data.add_holes(df_data, 0.1, 4)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "069532ab", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "env_qolmat_dev", - "language": "python", - "name": "env_qolmat_dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.17" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 51f86ca1..015be233 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -36,7 +36,7 @@ class RPCANoisy(RPCA): rank: Optional[int] (estimated) low-rank of the matrix D mu: Optional[float] - initial stiffness parameter for the constraint on X, L and Q + initial stiffness parameter for the constraint on M, L and Q tau: Optional[float] penalizing parameter for the nuclear norm lam: Optional[float] @@ -310,7 +310,7 @@ def _check_cost_function_minimized( function_str = "1/2 $ ||D-M-A||_2 + tau ||D||_* + lam ||A||_1" if len(self.list_etas) > 0: for eta in self.list_etas: - function_str += f"{eta} ||XH||_{self.norm}" + function_str += f"{eta} ||MH||_{self.norm}" if self.verbose and (round(cost_start, 4) - round(cost_end, 4)) <= -1e-2: warnings.warn( @@ -348,7 +348,7 @@ def decompose_rpca_algorithm( lam: Optional[float] penalizing parameter for the sparse matrix mu: Optional[float] - initial stiffness parameter for the constraint on X, L and Q + initial stiffness parameter for the constraint on M, L and Q list_periods: Optional[List[int]] list of periods, linked to the Toeplitz matrices list_etas: Optional[List[float]] @@ -379,9 +379,9 @@ def decompose_rpca_algorithm( # init Y = np.zeros((n_rows, n_cols)) - X = D.copy() + M = D.copy() A = np.zeros((n_rows, n_cols)) - U, S, Vt = np.linalg.svd(X, full_matrices=False) + U, S, Vt = np.linalg.svd(M, full_matrices=False) U = U[:, :rank] S = S[:rank] @@ -404,8 +404,8 @@ def decompose_rpca_algorithm( In = identity(n_rows) for _ in range(max_iterations): - # print("Cost function", cost_function(D, X, A, Omega, tau, lam)) - X_temp = X.copy() + # print("Cost function", cost_function(D, M, A, Omega, tau, lam)) + M_temp = M.copy() A_temp = A.copy() L_temp = L.copy() Q_temp = Q.copy() @@ -415,31 +415,31 @@ def decompose_rpca_algorithm( for i_period, _ in enumerate(list_periods): sums += mu * R[i_period] - list_H[i_period] @ Y - X = spsolve( + M = spsolve( (1 + mu) * In + HtH, D - A + mu * L @ Q - Y + sums, ) else: - X = spsolve( + M = spsolve( (1 + mu) * In + 2 * HtH, D - A + mu * L @ Q - Y, ) - A_Omega = rpca_utils.soft_thresholding(D - X, lam) - A_Omega_C = D - X + A_Omega = rpca_utils.soft_thresholding(D - M, lam) + A_Omega_C = D - M A = np.where(Omega, A_Omega, A_Omega_C) Q = scp.linalg.solve( a=tau * Ir + mu * (L.T @ L), - b=L.T @ (mu * X + Y), + b=L.T @ (mu * M + Y), ) L = scp.linalg.solve( a=tau * Ir + mu * (Q @ Q.T), - b=Q @ (mu * X.T + Y.T), + b=Q @ (mu * M.T + Y.T), ).T - Y += mu * (X - L @ Q) + Y += mu * (M - L @ Q) if norm == "L1": for i_period, _ in enumerate(list_periods): eta = list_etas[i_period] @@ -447,11 +447,11 @@ def decompose_rpca_algorithm( mu = min(mu * rho, mu_bar) - Xc = np.linalg.norm(X - X_temp, np.inf) + Mc = np.linalg.norm(M - M_temp, np.inf) Ac = np.linalg.norm(A - A_temp, np.inf) Lc = np.linalg.norm(L - L_temp, np.inf) Qc = np.linalg.norm(Q - Q_temp, np.inf) - tolerance = max([Xc, Ac, Lc, Qc]) # type: ignore # noqa + tolerance = max([Mc, Ac, Lc, Qc]) # type: ignore # noqa if norm == "L1": for i_period, _ in enumerate(list_periods): Rc = np.linalg.norm(R[i_period] - R_temp[i_period], np.inf) @@ -460,9 +460,9 @@ def decompose_rpca_algorithm( if tolerance < tol: break - X = L @ Q + M = L @ Q - M = X + M = M return M, A, L, Q diff --git a/qolmat/imputations/softimpute.py b/qolmat/imputations/softimpute.py index 3d74fdac..e03cee48 100644 --- a/qolmat/imputations/softimpute.py +++ b/qolmat/imputations/softimpute.py @@ -17,7 +17,7 @@ class SoftImpute(BaseEstimator, TransformerMixin): Hastie, Trevor, et al. "Matrix completion and low-rank SVD via fast alternating least squares." The Journal of Machine Learning Research 16.1 (2015): 3367-3402. - min_A,B || Proj(X - AB')||_F^2 + tau * (|| A ||_F^2 + || B ||_F^2) + min_L,Q || Proj(D - LQ')||_F^2 + tau * (|| L ||_F^2 + || Q ||_F^2) Parameters ---------- @@ -45,9 +45,9 @@ class SoftImpute(BaseEstimator, TransformerMixin): -------- >>> import numpy as np >>> from qolmat.imputations.softimpute import SoftImpute - >>> X = np.array([[1, 2, np.nan, 4], [1, 5, 3, np.nan], [4, 2, 3, 2], [1, 1, 5, 4]]) - >>> X_imputed = SoftImpute(random_state=11).fit_transform(X) - >>> print(X_imputed) + >>> D = np.array([[1, 2, np.nan, 4], [1, 5, 3, np.nan], [4, 2, 3, 2], [1, 1, 5, 4]]) + >>> D_imputed = SoftImpute(random_state=11).fit_transform(D) + >>> print(D_imputed) [[1. 2. 3.7242757 4. ] [1. 5. 3. 1.97846028] [4. 2. 3. 2. ] @@ -77,12 +77,12 @@ def __init__( self.d: NDArray = np.empty(0) self.v: NDArray = np.empty(0) - def fit(self, X: NDArray, y=None) -> SoftImpute: - """Fit the imputer on X. + def fit(self, D: NDArray, y=None) -> SoftImpute: + """Fit the imputer on D. Parameters ---------- - X : NDArray + D : NDArray Input data y : Ignored @@ -93,50 +93,50 @@ def fit(self, X: NDArray, y=None) -> SoftImpute: self : object The fitted `SoftImpute` class instance. """ - X_imputed = X.copy() - X_imputed = utils.prepare_data(X_imputed, self.period) + D_imputed = D.copy() + D_imputed = utils.prepare_data(D_imputed, self.period) - if not isinstance(X_imputed, np.ndarray): - raise AssertionError("Invalid type. X must be a NDArray.") + if not isinstance(D_imputed, np.ndarray): + raise AssertionError("Invalid type. D must be a NDArray.") - n, m = X_imputed.shape - mask = np.isnan(X_imputed) + n, m = D_imputed.shape + mask = np.isnan(D_imputed) V = np.zeros((m, self.rank)) U = self.random_state.normal(0.0, 1.0, (n, self.rank)) U, _, _ = np.linalg.svd(U, full_matrices=False) Dsq = np.ones((self.rank, 1)) - col_means = np.nanmean(X_imputed, axis=0) - np.copyto(X_imputed, col_means, where=np.isnan(X_imputed)) + col_means = np.nanmean(D_imputed, axis=0) + np.copyto(D_imputed, col_means, where=np.isnan(D_imputed)) if self.rank is None: - self.rank = rpca_utils.approx_rank(X_imputed) + self.rank = rpca_utils.approx_rank(D_imputed) for iter_ in range(self.max_iterations): U_old = U V_old = V Dsq_old = Dsq - B = U.T @ X_imputed + Q = U.T @ D_imputed if self.tau > 0: tmp = Dsq / (Dsq + self.tau) - B = B * tmp - Bsvd = np.linalg.svd(B.T, full_matrices=False) + Q = Q * tmp + Bsvd = np.linalg.svd(Q.T, full_matrices=False) V = Bsvd[0] Dsq = Bsvd[1][:, np.newaxis] U = U @ Bsvd[2] tmp = Dsq * V.T - X_hat = U @ tmp - X_imputed[mask] = X_hat[mask] + D_hat = U @ tmp + D_imputed[mask] = D_hat[mask] - A = (X_imputed @ V).T + L = (D_imputed @ V).T if self.tau > 0: tmp = Dsq / (Dsq + self.tau) - A = A * tmp - Asvd = np.linalg.svd(A.T, full_matrices=False) - U = Asvd[0] - Dsq = Asvd[1][:, np.newaxis] - V = V @ Asvd[2] + L = L * tmp + Lsvd = np.linalg.svd(L.T, full_matrices=False) + U = Lsvd[0] + Dsq = Lsvd[1][:, np.newaxis] + V = V @ Lsvd[2] tmp = Dsq * V.T - X_hat = U @ tmp - X_imputed[mask] = X_hat[mask] + D_hat = U @ tmp + D_imputed[mask] = D_hat[mask] ratio = self._check_convergence(U_old, Dsq_old, V_old, U, Dsq, V) if self.verbose: @@ -194,28 +194,28 @@ def _check_convergence( num = denom + (Ds_qold**2).sum() - 2 * uvprod return num / max(denom, 1e-9) - def transform(self, X: NDArray) -> NDArray: - """Impute all missing values in X. + def transform(self, D: NDArray) -> NDArray: + """Impute all missing values in D. Parameters ---------- - X : array-like of shape (n_samples, n_features) + D : array-like of shape (n_samples, n_features) The input data to complete. Returns ------- - X : NDArray + D : NDArray The imputed dataset. """ - X_transformed = self.u @ np.diag(self.d.T[0]) @ (self.v).T + D_transformed = self.u @ np.diag(self.d.T[0]) @ (self.v).T if self.projected: - X_ = utils.prepare_data(X, self.period) - mask = np.isnan(X_) - X_transformed[~mask] = X_[~mask] + D_ = utils.prepare_data(D, self.period) + mask = np.isnan(D_) + D_transformed[~mask] = D_[~mask] - X_transformed = utils.get_shape_original(X_transformed, X.shape) + D_transformed = utils.get_shape_original(D_transformed, D.shape) - if np.all(np.isnan(X_transformed)): + if np.all(np.isnan(D_transformed)): raise AssertionError("Result contains NaN. This is a bug.") - return X_transformed + return D_transformed From b823ee60d5e29c90e2ee22b4204e9cd9bdde4878 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 21 Feb 2024 19:20:41 +0100 Subject: [PATCH 36/99] flake8 E226 corrected for --- qolmat/imputations/imputers_pytorch.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/qolmat/imputations/imputers_pytorch.py b/qolmat/imputations/imputers_pytorch.py index c339160b..ed6cc198 100644 --- a/qolmat/imputations/imputers_pytorch.py +++ b/qolmat/imputations/imputers_pytorch.py @@ -98,7 +98,7 @@ def _fit_estimator(self, estimator: nn.Sequential, X: pd.DataFrame, y: pd.DataFr loss.backward() optimizer.step() if (epoch + 1) % 10 == 0: - print(f"Epoch [{epoch+1}/{self.epochs}], Loss: {loss.item():.4f}") + print(f"Epoch [{epoch + 1}/{self.epochs}], Loss: {loss.item():.4f}") return estimator def _predict_estimator(self, estimator: nn.Sequential, X: pd.DataFrame) -> pd.Series: @@ -213,7 +213,7 @@ def fit(self, X: NDArray, y: NDArray) -> Self: loss.backward() optimizer.step() if (epoch + 1) % 10 == 0: - print(f"Epoch [{epoch+1}/{self.epochs}], Loss: {loss.item():.4f}") + print(f"Epoch [{epoch + 1}/{self.epochs}], Loss: {loss.item():.4f}") list_loss.append(loss.item()) self.loss.extend([list_loss]) return self @@ -363,7 +363,9 @@ def _transform_element( df_train = df_train.fillna(df_train.mean()) scaler = StandardScaler() df_train_scaler = pd.DataFrame( - scaler.fit_transform(df_train), index=df_train.index, columns=df_train.columns + scaler.fit_transform(df_train), + index=df_train.index, + columns=df_train.columns, ) X = df_train_scaler.values mask = df.isna().values @@ -671,4 +673,7 @@ def get_summary_training(self) -> Dict: return self.model.summary def get_summary_architecture(self) -> Dict: - return {"number_parameters": self.model.num_params, "epsilon_model": self.model._eps_model} + return { + "number_parameters": self.model.num_params, + "epsilon_model": self.model._eps_model, + } From dd222521826f04c9da24cd518a1509d911d40064 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 22 Feb 2024 11:43:01 +0100 Subject: [PATCH 37/99] rpca pcp now provides LQ decomposition --- qolmat/imputations/rpca/rpca_pcp.py | 13 +++++++++---- qolmat/imputations/rpca/rpca_utils.py | 13 +++++++++---- tests/imputations/rpca/test_rpca_utils.py | 12 +++++++++--- 3 files changed, 27 insertions(+), 11 deletions(-) diff --git a/qolmat/imputations/rpca/rpca_pcp.py b/qolmat/imputations/rpca/rpca_pcp.py index 7214f007..f613b322 100644 --- a/qolmat/imputations/rpca/rpca_pcp.py +++ b/qolmat/imputations/rpca/rpca_pcp.py @@ -80,14 +80,18 @@ def get_params_scale(self, D: NDArray): dict_params = {"mu": mu, "lam": lam} return dict_params - def decompose_on_basis(self, D: NDArray, Omega: NDArray, Q: NDArray) -> NDArray: + def decompose_on_basis( + self, D: NDArray, Omega: NDArray, Q: NDArray + ) -> Tuple[NDArray, NDArray]: n_rows, n_cols = D.shape if n_rows == 1 or n_cols == 1: return D, np.full_like(D, 0) M, A, L, Q = self.decompose_rpca(D, Omega) return M, A - def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray, None, None]: + def decompose_rpca( + self, D: NDArray, Omega: NDArray + ) -> Tuple[NDArray, NDArray, NDArray, NDArray]: """ Estimate the relevant parameters then compute the PCP RPCA decomposition @@ -123,7 +127,8 @@ def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray, M: NDArray = D - A for iteration in range(self.max_iterations): - M = rpca_utils.svd_thresholding(D - A + Y / mu, 1 / mu) + L, Q = rpca_utils.svd_thresholding(D - A + Y / mu, 1 / mu) + M = L @ Q A = rpca_utils.soft_thresholding(D - M + Y / mu, lam / mu) A[~Omega] = (D - M)[~Omega] @@ -137,7 +142,7 @@ def decompose_rpca(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray, self._check_cost_function_minimized(D, M, A, Omega, lam) - return M, A, None, None + return M, A, L, Q def _check_cost_function_minimized( self, diff --git a/qolmat/imputations/rpca/rpca_utils.py b/qolmat/imputations/rpca/rpca_utils.py index aa1abc49..4ea627db 100644 --- a/qolmat/imputations/rpca/rpca_utils.py +++ b/qolmat/imputations/rpca/rpca_utils.py @@ -2,6 +2,7 @@ Modular utility functions for RPCA """ +from typing import Tuple import numpy as np from numpy.typing import NDArray import scipy @@ -60,7 +61,7 @@ def soft_thresholding( return np.sign(X) * np.maximum(np.abs(X) - threshold, 0) -def svd_thresholding(X: NDArray, threshold: float) -> NDArray: +def svd_thresholding(X: NDArray, threshold: float) -> Tuple[NDArray, NDArray]: """ Apply the shrinkage operator to the singular values obtained from the SVD of X. @@ -73,16 +74,20 @@ def svd_thresholding(X: NDArray, threshold: float) -> NDArray: Returns ------- - NDArray - Array obtained by computing U * shrink(s) * V where + Tuple[NDArray, NDArray] + Two arrays L and Q of minimal Frobenius norm such that L @ Q = U * shrink(s) * V where U is the array of left singular vectors of X V is the array of the right singular vectors of X s is the array of the singular values as a diagonal array + L and Q minimize """ U, s, Vh = np.linalg.svd(X, full_matrices=False) s = soft_thresholding(s, threshold) - return U @ (np.diag(s) @ Vh) + # return U @ (np.diag(s) @ Vh) + L = U @ np.sqrt(np.diag(s)) + Q = np.sqrt(np.diag(s)) @ Vh + return L, Q def l1_norm(M: NDArray) -> float: diff --git a/tests/imputations/rpca/test_rpca_utils.py b/tests/imputations/rpca/test_rpca_utils.py index 86a1a196..9de6f12e 100644 --- a/tests/imputations/rpca/test_rpca_utils.py +++ b/tests/imputations/rpca/test_rpca_utils.py @@ -55,8 +55,8 @@ def test_rpca_utils_soft_thresholding(X: NDArray, threshold: float): @pytest.mark.parametrize("X", [X_complete]) @pytest.mark.parametrize("threshold", [0.95]) def test_rpca_utils_svd_thresholding(X: NDArray, threshold: float): - result = svd_thresholding(X=X, threshold=threshold) - print(result) + L_result, Q_result = svd_thresholding(X=X, threshold=threshold) + result = L_result @ Q_result X_expected = np.array( [ [0.928, 6.182, 3.857, 3.857], @@ -81,6 +81,12 @@ def test_rpca_utils_toeplitz_matrix(T: int, dimension: int): result = toeplitz_matrix(T=T, dimension=dimension) result_np = result.toarray() X_exp = np.array( - [[1, 0, -1, 0, 0], [0, 1, 0, -1, 0], [0, 0, 1, 0, -1], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]] + [ + [1, 0, -1, 0, 0], + [0, 1, 0, -1, 0], + [0, 0, 1, 0, -1], + [0, 0, 0, 0, 0], + [0, 0, 0, 0, 0], + ] ) np.testing.assert_allclose(result_np, X_exp) From 166d64c466003f57fa77f7a51a65eb79e0646d1f Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 23 Feb 2024 18:29:09 +0100 Subject: [PATCH 38/99] noisy and pcp rpca splitted --- qolmat/imputations/imputers.py | 185 +++++++++++---- qolmat/imputations/rpca/rpca.py | 25 -- qolmat/imputations/rpca/rpca_noisy.py | 273 +++++++++++----------- qolmat/imputations/rpca/rpca_pcp.py | 25 +- qolmat/imputations/rpca/rpca_utils.py | 12 +- tests/benchmark/test_hyperparameters.py | 9 +- tests/imputations/rpca/test_rpca_noisy.py | 12 +- tests/imputations/rpca/test_rpca_pcp.py | 24 +- tests/imputations/test_imputers.py | 8 +- 9 files changed, 308 insertions(+), 265 deletions(-) diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 9920a747..d849edb0 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -1635,10 +1635,132 @@ def _transform_element( return df_imputed -class ImputerRPCA(_Imputer): +class ImputerRpcaPcp(_Imputer): """ - This class implements the Robust Principal Component Analysis imputation. + This class implements the Robust Principal Component Analysis imputation with Principal + Component Pursuit. The imputation minimizes a loss function combining a low-rank criterium on + the dataframe and a L1 penalization on the residuals. + Parameters + ---------- + groups: Tuple[str, ...] + List of column names to group by, by default [] + columnwise : bool + For the RPCA method to be applied columnwise (with reshaping of + each column into an array) + or to be applied directly on the dataframe. By default, the value is set to False. + """ + + def __init__( + self, + groups: Tuple[str, ...] = (), + columnwise: bool = False, + random_state: Union[None, int, np.random.RandomState] = None, + period: int = 1, + mu: Optional[float] = None, + lam: Optional[float] = None, + max_iterations: int = int(1e4), + tol: float = 1e-6, + verbose: bool = False, + ) -> None: + super().__init__( + imputer_params=( + "period", + "mu", + "lam", + "max_iterations", + "tol", + "norm", + ), + groups=groups, + columnwise=columnwise, + random_state=random_state, + ) + + self.period = period + self.mu = mu + self.lam = lam + self.max_iterations = max_iterations + self.tol = tol + self.verbose = verbose + + def get_model(self, **hyperparams) -> rpca.RPCA: + """ + Get the underlying model of the imputer based on its attributes. + + Returns + ------- + rpca.RPCA + RPCA model to be used in the fit and transform methods. + """ + hyperparams = { + key: hyperparams[key] + for key in [ + "mu", + "rank", + "tau", + "lam", + "max_iterations", + "tol", + "norm", + ] + } + model = rpca_pcp.RpcaPcp(random_state=self._rng, verbose=self.verbose, **hyperparams) + + return model + + def _transform_element( + self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 + ) -> pd.DataFrame: + """ + Transforms the dataframe `df`, at the group and/or column level depending onself.groups and + self.columnwise. + + Parameters + ---------- + df : pd.DataFrame + Dataframe or column to impute + col : str, optional + Column transformed by the imputer, by default "__all__" + ngroup : int, optional + Id of the group on which the method is applied + + Returns + ------- + pd.DataFrame + Imputed dataframe. + + Raises + ------ + NotDataFrame + Input has to be a pandas.DataFrame. + """ + self._check_dataframe(df) + hyperparams = self.get_hyperparams() + model = self.get_model(**hyperparams) + + X = df.astype(float).values + + D = utils.prepare_data(X, self.period) + Omega = ~np.isnan(D) + # D = utils.linear_interpolation(D) + + Q = self._dict_fitting[col][ngroup] + M, A = model.decompose(D, Omega, Q) + + M_final = utils.get_shape_original(M, X.shape) + A_final = utils.get_shape_original(A, X.shape) + X_imputed = M_final + A_final + + df_imputed = pd.DataFrame(X_imputed, index=df.index, columns=df.columns) + df_imputed = df.where(~df.isna(), df_imputed) + + return df_imputed + + +class ImputerRpcaNoisy(_Imputer): + """ + This class implements the Robust Principal Component Analysis imputation with added noise. The imputation minimizes a loss function combining a low-rank criterium on the dataframe and a L1 penalization on the residuals. @@ -1646,11 +1768,6 @@ class ImputerRPCA(_Imputer): ---------- groups: Tuple[str, ...] List of column names to group by, by default [] - method : str - Name of the RPCA method: - "PCP" for basic RPCA, bad at imputing - "noisy" for noisy RPCA, with possible regularisations, wihch is recommended since - it is more stable columnwise : bool For the RPCA method to be applied columnwise (with reshaping of each column into an array) @@ -1660,7 +1777,6 @@ class ImputerRPCA(_Imputer): def __init__( self, groups: Tuple[str, ...] = (), - method: str = "noisy", columnwise: bool = False, random_state: Union[None, int, np.random.RandomState] = None, period: int = 1, @@ -1693,7 +1809,6 @@ def __init__( random_state=random_state, ) - self.method = method self.period = period self.mu = mu self.rank = rank @@ -1715,37 +1830,21 @@ def get_model(self, **hyperparams) -> rpca.RPCA: rpca.RPCA RPCA model to be used in the fit and transform methods. """ - if self.method == "PCP": - hyperparams = { - key: hyperparams[key] - for key in [ - "mu", - "rank", - "tau", - "lam", - "max_iterations", - "tol", - "norm", - ] - } - model = rpca_pcp.RPCAPCP(random_state=self._rng, verbose=self.verbose, **hyperparams) - elif self.method == "noisy": - hyperparams = { - key: hyperparams[key] - for key in [ - "rank", - "tau", - "lam", - "list_periods", - "list_etas", - "max_iterations", - "tol", - "norm", - ] - } - model = rpca_noisy.RPCANoisy( - random_state=self._rng, verbose=self.verbose, **hyperparams - ) + + hyperparams = { + key: hyperparams[key] + for key in [ + "rank", + "tau", + "lam", + "list_periods", + "list_etas", + "max_iterations", + "tol", + "norm", + ] + } + model = rpca_noisy.RPCANoisy(random_state=self._rng, verbose=self.verbose, **hyperparams) return model def _fit_element( @@ -1775,8 +1874,6 @@ def _fit_element( Input has to be a pandas.DataFrame. """ self._check_dataframe(df) - if self.method not in ["PCP", "noisy"]: - raise ValueError("Argument method must be `PCP` or `noisy`!") hyperparams = self.get_hyperparams() model = self.get_model(**hyperparams) @@ -1784,7 +1881,7 @@ def _fit_element( D = utils.prepare_data(X, self.period) Omega = ~np.isnan(D) # D = utils.linear_interpolation(D) - Q = model.fit_basis(X, Omega) + _, _, _, Q = model.decompose_with_basis(X, Omega) return Q @@ -1815,8 +1912,6 @@ def _transform_element( Input has to be a pandas.DataFrame. """ self._check_dataframe(df) - if self.method not in ["PCP", "noisy"]: - raise ValueError("Argument method must be `PCP` or `noisy`!") hyperparams = self.get_hyperparams() model = self.get_model(**hyperparams) diff --git a/qolmat/imputations/rpca/rpca.py b/qolmat/imputations/rpca/rpca.py index 9adfc1f6..7a046ebf 100644 --- a/qolmat/imputations/rpca/rpca.py +++ b/qolmat/imputations/rpca/rpca.py @@ -37,28 +37,3 @@ def __init__( self.tol = tol self.random_state = random_state self.verbose = verbose - - def fit_basis(self, D: NDArray, Omega: NDArray) -> Self: - """Fit RPCA model on data - - Parameters - ---------- - D : NDArray - Observations - Omega: NDArrau - boolean matrix indicating the observed values - - Returns - ------- - Self - Model RPCA - """ - D = utils.linear_interpolation(D) - - n_rows, n_cols = D.shape - if n_rows == 1 or n_cols == 1: - self.V = np.array([[1]]) - return self - - M, A, L, Q = self.decompose_rpca(D, Omega) - return Q diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 015be233..f80c36f6 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -111,91 +111,28 @@ def get_params_scale(self, D: NDArray) -> Dict[str, float]: "lam": lam, } - def decompose_on_basis( - self, - D: NDArray, - Omega: NDArray, - Q: NDArray, - ) -> Tuple[NDArray, NDArray]: - D = utils.linear_interpolation(D) - params_scale = self.get_params_scale(D) - - lam = params_scale["lam"] if self.lam is None else self.lam - rank = params_scale["rank"] if self.rank is None else self.rank - rank = int(rank) - tau = params_scale["tau"] if self.tau is None else self.tau - tol = self.tol - - n_rows, n_cols = D.shape - if n_rows == 1 or n_cols == 1: - return D, np.full_like(D, 0) - # M, A, L, Q = self.decompose_rpca(D, Omega) - n_rank, _ = Q.shape - Ir = np.eye(n_rank) - A = np.zeros((n_rows, n_cols)) - L = np.zeros((n_rows, n_rank)) - for _ in range(self.max_iterations): - A_prev = A.copy() - L_prev = L.copy() - L = scp.linalg.solve( - a=2 * tau * Ir + (Q @ Q.T), - b=Q @ (D - A).T, - ).T - A_Omega = rpca_utils.soft_thresholding(D - L @ Q, lam) - A_Omega_C = D - L @ Q - A = np.where(Omega, A_Omega, A_Omega_C) - - Ac = np.linalg.norm(A - A_prev, np.inf) - Lc = np.linalg.norm(L - L_prev, np.inf) - - tolerance = max([Ac, Lc]) # type: ignore # noqa - - if tolerance < tol: - break + def decompose(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: + """ + Compute the noisy RPCA with L1 or L2 time penalisation - M = L @ Q + Parameters + ---------- + D : NDArray + Matrix of the observations + Omega: NDArray + Matrix of missingness, with boolean data + Returns + ------- + M: NDArray + Low-rank signal + A: NDArray + Anomalies + """ + M, A, _, _ = self.decompose_with_basis(D, Omega) return M, A - # def decompose_on_basis( - # self, D: NDArray, Omega: NDArray, Q: NDArray - # ) -> Tuple[NDArray, NDArray]: - # params_scale = self.get_params_scale(D) - - # lam = params_scale["lam"] if self.lam is None else self.lam - # rank = params_scale["rank"] if self.rank is None else self.rank - # rank = int(rank) - # tau = params_scale["tau"] if self.tau is None else self.tau - - # n_rows, n_cols = D.shape - # if n_rows == 1 or n_cols == 1: - # return D, np.full_like(D, 0) - # # M, A, L, Q = self.decompose_rpca(D, Omega) - # n_rank, _ = Q.shape - # Ir = np.eye(n_rank) - # L = np.zeros((n_rows, n_rank)) - # A = np.zeros((n_rows, n_cols)) - # for i in range(n_rows): - # d = D[i, :] - # Omega = Omega[i, :] - # L_row = np.zeros((1, n_rank)) - # a = np.full_like(d, 0) - # for _ in range(self.max_iterations): - # A_Omega = rpca_utils.soft_thresholding(d - L_row @ Q, lam) - # A_Omega_C = d - L_row @ Q - # a = np.where(Omega, A_Omega, A_Omega_C) - - # L_row = scp.linalg.solve( - # a=2 * tau * Ir + (Q @ Q.T), - # b=Q @ (d - a).T, - # ).T - # L[i, :] = L_row - # A[i, :] = a - # M = L @ Q - - # return M, A - - def decompose_rpca( + def decompose_with_basis( self, D: NDArray, Omega: NDArray ) -> Tuple[NDArray, NDArray, NDArray, NDArray]: """ @@ -214,6 +151,10 @@ def decompose_rpca( Low-rank signal A: NDArray Anomalies + L: NDArray + Coefficients of the low-rank matrix in the reduced basis + Q: NDArray + Reduced basis of the low-rank matrix """ self.params_scale = self.get_params_scale(D) @@ -240,7 +181,7 @@ def decompose_rpca( D = utils.linear_interpolation(D) - M, A, L, Q = self.decompose_rpca_algorithm( + M, A, L, Q = self.minimise_loss( D, Omega, rank, @@ -258,68 +199,8 @@ def decompose_rpca( return M, A, L, Q - def _check_cost_function_minimized( - self, - observations: NDArray, - low_rank: NDArray, - anomalies: NDArray, - Omega: NDArray, - tau: float, - lam: float, - ): - """Check that the functional minimized by the RPCA - is smaller at the end than at the beginning - - Parameters - ---------- - observations : NDArray - observations matrix with first linear interpolation - low_rank : NDArray - low_rank matrix resulting from RPCA - anomalies : NDArray - sparse matrix resulting from RPCA - Omega: NDArrau - boolean matrix indicating the observed values - tau : float - parameter penalizing the nuclear norm of the low rank part - lam : float - parameter penalizing the L1-norm of the anomaly/sparse part - """ - cost_start = self.cost_function( - observations, - observations, - np.full_like(observations, 0), - Omega, - tau, - lam, - self.list_periods, - self.list_etas, - norm=self.norm, - ) - cost_end = self.cost_function( - observations, - low_rank, - anomalies, - Omega, - tau, - lam, - self.list_periods, - self.list_etas, - norm=self.norm, - ) - function_str = "1/2 $ ||D-M-A||_2 + tau ||D||_* + lam ||A||_1" - if len(self.list_etas) > 0: - for eta in self.list_etas: - function_str += f"{eta} ||MH||_{self.norm}" - - if self.verbose and (round(cost_start, 4) - round(cost_end, 4)) <= -1e-2: - warnings.warn( - f"RPCA algorithm may provide bad results. Function {function_str} increased from" - f" {cost_start} to {cost_end} instead of decreasing!".format("%.2f") - ) - @staticmethod - def decompose_rpca_algorithm( + def minimise_loss( D: NDArray, Omega: NDArray, rank: int, @@ -466,6 +347,112 @@ def decompose_rpca_algorithm( return M, A, L, Q + def decompose_on_basis( + self, + D: NDArray, + Omega: NDArray, + Q: NDArray, + ) -> Tuple[NDArray, NDArray]: + D = utils.linear_interpolation(D) + params_scale = self.get_params_scale(D) + + lam = params_scale["lam"] if self.lam is None else self.lam + rank = params_scale["rank"] if self.rank is None else self.rank + rank = int(rank) + tau = params_scale["tau"] if self.tau is None else self.tau + tol = self.tol + + n_rows, n_cols = D.shape + if n_rows == 1 or n_cols == 1: + return D, np.full_like(D, 0) + # M, A, L, Q = self.decompose_rpca(D, Omega) + n_rank, _ = Q.shape + Ir = np.eye(n_rank) + A = np.zeros((n_rows, n_cols)) + L = np.zeros((n_rows, n_rank)) + for _ in range(self.max_iterations): + A_prev = A.copy() + L_prev = L.copy() + L = scp.linalg.solve( + a=2 * tau * Ir + (Q @ Q.T), + b=Q @ (D - A).T, + ).T + A_Omega = rpca_utils.soft_thresholding(D - L @ Q, lam) + A_Omega_C = D - L @ Q + A = np.where(Omega, A_Omega, A_Omega_C) + + Ac = np.linalg.norm(A - A_prev, np.inf) + Lc = np.linalg.norm(L - L_prev, np.inf) + + tolerance = max([Ac, Lc]) # type: ignore # noqa + + if tolerance < tol: + break + + M = L @ Q + + return M, A + + def _check_cost_function_minimized( + self, + observations: NDArray, + low_rank: NDArray, + anomalies: NDArray, + Omega: NDArray, + tau: float, + lam: float, + ): + """Check that the functional minimized by the RPCA + is smaller at the end than at the beginning + + Parameters + ---------- + observations : NDArray + observations matrix with first linear interpolation + low_rank : NDArray + low_rank matrix resulting from RPCA + anomalies : NDArray + sparse matrix resulting from RPCA + Omega: NDArrau + boolean matrix indicating the observed values + tau : float + parameter penalizing the nuclear norm of the low rank part + lam : float + parameter penalizing the L1-norm of the anomaly/sparse part + """ + cost_start = self.cost_function( + observations, + observations, + np.full_like(observations, 0), + Omega, + tau, + lam, + self.list_periods, + self.list_etas, + norm=self.norm, + ) + cost_end = self.cost_function( + observations, + low_rank, + anomalies, + Omega, + tau, + lam, + self.list_periods, + self.list_etas, + norm=self.norm, + ) + function_str = "1/2 $ ||D-M-A||_2 + tau ||D||_* + lam ||A||_1" + if len(self.list_etas) > 0: + for eta in self.list_etas: + function_str += f"{eta} ||MH||_{self.norm}" + + if self.verbose and (round(cost_start, 4) - round(cost_end, 4)) <= -1e-2: + warnings.warn( + f"RPCA algorithm may provide bad results. Function {function_str} increased from" + f" {cost_start} to {cost_end} instead of decreasing!".format("%.2f") + ) + @staticmethod def cost_function( observations: NDArray, diff --git a/qolmat/imputations/rpca/rpca_pcp.py b/qolmat/imputations/rpca/rpca_pcp.py index f613b322..b0c2469c 100644 --- a/qolmat/imputations/rpca/rpca_pcp.py +++ b/qolmat/imputations/rpca/rpca_pcp.py @@ -12,7 +12,7 @@ from qolmat.utils import utils -class RPCAPCP(RPCA): +class RpcaPcp(RPCA): """ This class implements the basic RPCA decomposition using Alternating Lagrangian Multipliers. @@ -80,20 +80,10 @@ def get_params_scale(self, D: NDArray): dict_params = {"mu": mu, "lam": lam} return dict_params - def decompose_on_basis( - self, D: NDArray, Omega: NDArray, Q: NDArray - ) -> Tuple[NDArray, NDArray]: - n_rows, n_cols = D.shape - if n_rows == 1 or n_cols == 1: - return D, np.full_like(D, 0) - M, A, L, Q = self.decompose_rpca(D, Omega) - return M, A - - def decompose_rpca( - self, D: NDArray, Omega: NDArray - ) -> Tuple[NDArray, NDArray, NDArray, NDArray]: + def decompose(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: """ - Estimate the relevant parameters then compute the PCP RPCA decomposition + Estimate the relevant parameters then compute the PCP RPCA decomposition, using the + Augumented Largrangian Multiplier (ALM) Parameters ---------- @@ -108,8 +98,6 @@ def decompose_rpca( Low-rank signal A: NDArray Anomalies - N1: None - N2: None """ params_scale = self.get_params_scale(D) @@ -127,8 +115,7 @@ def decompose_rpca( M: NDArray = D - A for iteration in range(self.max_iterations): - L, Q = rpca_utils.svd_thresholding(D - A + Y / mu, 1 / mu) - M = L @ Q + M = rpca_utils.svd_thresholding(D - A + Y / mu, 1 / mu) A = rpca_utils.soft_thresholding(D - M + Y / mu, lam / mu) A[~Omega] = (D - M)[~Omega] @@ -142,7 +129,7 @@ def decompose_rpca( self._check_cost_function_minimized(D, M, A, Omega, lam) - return M, A, L, Q + return M, A def _check_cost_function_minimized( self, diff --git a/qolmat/imputations/rpca/rpca_utils.py b/qolmat/imputations/rpca/rpca_utils.py index 4ea627db..592d97ce 100644 --- a/qolmat/imputations/rpca/rpca_utils.py +++ b/qolmat/imputations/rpca/rpca_utils.py @@ -61,7 +61,7 @@ def soft_thresholding( return np.sign(X) * np.maximum(np.abs(X) - threshold, 0) -def svd_thresholding(X: NDArray, threshold: float) -> Tuple[NDArray, NDArray]: +def svd_thresholding(X: NDArray, threshold: float) -> NDArray: """ Apply the shrinkage operator to the singular values obtained from the SVD of X. @@ -74,20 +74,16 @@ def svd_thresholding(X: NDArray, threshold: float) -> Tuple[NDArray, NDArray]: Returns ------- - Tuple[NDArray, NDArray] - Two arrays L and Q of minimal Frobenius norm such that L @ Q = U * shrink(s) * V where + NDArray + M = U * shrink(s) * V where U is the array of left singular vectors of X V is the array of the right singular vectors of X s is the array of the singular values as a diagonal array - L and Q minimize """ U, s, Vh = np.linalg.svd(X, full_matrices=False) s = soft_thresholding(s, threshold) - # return U @ (np.diag(s) @ Vh) - L = U @ np.sqrt(np.diag(s)) - Q = np.sqrt(np.diag(s)) @ Vh - return L, Q + return U @ (np.diag(s) @ Vh) def l1_norm(M: NDArray) -> float: diff --git a/tests/benchmark/test_hyperparameters.py b/tests/benchmark/test_hyperparameters.py index 6558611c..f63b07bd 100644 --- a/tests/benchmark/test_hyperparameters.py +++ b/tests/benchmark/test_hyperparameters.py @@ -9,19 +9,22 @@ # from hyperparameters import HyperValue from qolmat.benchmark.missing_patterns import _HoleGenerator, EmpiricalHoleGenerator -from qolmat.imputations.imputers import _Imputer, ImputerRPCA +from qolmat.imputations.imputers import _Imputer, ImputerRpcaNoisy import hyperopt as ho df_origin = pd.DataFrame({"col1": [0, np.nan, 2, 4, np.nan], "col2": [-1, np.nan, 0.5, 1, 1.5]}) df_imputed = pd.DataFrame({"col1": [0, 1, 2, 3.5, 4], "col2": [-1.5, 0, 1.5, 2, 1.5]}) df_mask = pd.DataFrame( - {"col1": [False, False, True, False, False], "col2": [True, False, True, True, False]} + { + "col1": [False, False, True, False, False], + "col2": [True, False, True, True, False], + } ) df_corrupted = df_origin.copy() df_corrupted[df_mask] = np.nan -imputer_rpca = ImputerRPCA(tau=2, random_state=42, columnwise=True, period=1) +imputer_rpca = ImputerRpcaNoisy(tau=2, random_state=42, columnwise=True, period=1) dict_imputers_rpca = {"rpca": imputer_rpca} generator_holes = EmpiricalHoleGenerator(n_splits=1, ratio_masked=0.5) dict_config_opti = { diff --git a/tests/imputations/rpca/test_rpca_noisy.py b/tests/imputations/rpca/test_rpca_noisy.py index 2fcd7e83..67841d4c 100644 --- a/tests/imputations/rpca/test_rpca_noisy.py +++ b/tests/imputations/rpca/test_rpca_noisy.py @@ -108,7 +108,7 @@ def test_rpca_decompose_rpca_shape(norm: str): rank = 2 rpca = RPCANoisy(rank=rank, norm=norm) Omega = ~np.isnan(X_test) - M_result, A_result, L_result, Q_result = rpca.decompose_rpca(X_test, Omega) + M_result, A_result, L_result, Q_result = rpca.decompose_with_basis(X_test, Omega) n_rows, n_cols = X_test.shape assert M_result.shape == (n_rows, n_cols) assert A_result.shape == (n_rows, n_cols) @@ -121,7 +121,7 @@ def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): """Test RPCA noisy results if tau and lambda equal zero.""" rpca = RPCANoisy(tau=0, lam=0, norm="L2") Omega = ~np.isnan(X) - X_result, A_result, _, _ = rpca.decompose_rpca(X, Omega) + X_result, A_result, _, _ = rpca.decompose_with_basis(X, Omega) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -134,7 +134,7 @@ def test_rpca_noisy_zero_tau(X: NDArray, lam: float, X_interpolated: NDArray): """Test RPCA noisy results if tau equals zero.""" rpca = RPCANoisy(tau=0, lam=lam, norm="L2") Omega = ~np.isnan(X) - X_result, A_result, _, _ = rpca.decompose_rpca(X, Omega) + X_result, A_result, _, _ = rpca.decompose_with_basis(X, Omega) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -147,7 +147,7 @@ def test_rpca_noisy_zero_lambda(X: NDArray, tau: float, X_interpolated: NDArray) """Test RPCA noisy results if lambda equals zero.""" rpca = RPCANoisy(tau=tau, lam=0, norm="L2") Omega = ~np.isnan(X) - X_result, A_result, _, _ = rpca.decompose_rpca(X, Omega) + X_result, A_result, _, _ = rpca.decompose_with_basis(X, Omega) np.testing.assert_allclose(X_result, np.full_like(X, 0), atol=1e-4) np.testing.assert_allclose(A_result, X_interpolated, atol=1e-4) @@ -169,7 +169,7 @@ def test_rpca_noisy_decompose_rpca(synthetic_temporal_data): anomalies_init = np.zeros(D.shape) cost_init = RPCANoisy.cost_function(D, low_rank_init, anomalies_init, Omega, tau, lam) - X_result, A_result, _, _ = RPCANoisy.decompose_rpca_algorithm(D, Omega, rank, tau, lam) + X_result, A_result, _, _ = RPCANoisy.minimise_loss(D, Omega, rank, tau, lam) cost_result = RPCANoisy.cost_function(D, X_result, A_result, Omega, tau, lam) assert cost_result <= cost_init @@ -211,7 +211,7 @@ def test_rpca_noisy_temporal_signal_temporal_regularisations(synthetic_temporal_ norm="L2", ) - X_result, A_result, _, _ = RPCANoisy.decompose_rpca_algorithm( + X_result, A_result, _, _ = RPCANoisy.minimise_loss( D, Omega, rank, diff --git a/tests/imputations/rpca/test_rpca_pcp.py b/tests/imputations/rpca/test_rpca_pcp.py index 4849de39..c7ab69e5 100644 --- a/tests/imputations/rpca/test_rpca_pcp.py +++ b/tests/imputations/rpca/test_rpca_pcp.py @@ -4,7 +4,7 @@ import pytest from numpy.typing import NDArray -from qolmat.imputations.rpca.rpca_pcp import RPCAPCP +from qolmat.imputations.rpca.rpca_pcp import RpcaPcp from qolmat.utils import utils from qolmat.utils.data import generate_artificial_ts @@ -48,7 +48,7 @@ def test_check_cost_function_minimized_warning( ): """Test warning when the cost function is minimized.""" with pytest.warns(UserWarning): - RPCAPCP()._check_cost_function_minimized(obs, lr, ano, omega, lam) + RpcaPcp()._check_cost_function_minimized(obs, lr, ano, omega, lam) @pytest.mark.parametrize( @@ -68,14 +68,14 @@ def test_check_cost_function_minimized_no_warning( ): """Test no warning when the cost function is minimized.""" with warnings.catch_warnings(record=True) as record: - RPCAPCP()._check_cost_function_minimized(obs, lr, ano, omega, lam) + RpcaPcp()._check_cost_function_minimized(obs, lr, ano, omega, lam) assert len(record) == 0 @pytest.mark.parametrize("X", [X_complete]) def test_rpca_rpca_pcp_get_params_scale(X: NDArray): """Test the parameters are well scaled.""" - rpca_pcp = RPCAPCP(max_iterations=max_iterations, mu=0.5, lam=0.1) + rpca_pcp = RpcaPcp(max_iterations=max_iterations, mu=0.5, lam=0.1) result_dict = rpca_pcp.get_params_scale(X) result = list(result_dict.values()) params_expected = [1 / 7, np.sqrt(2) / 2] @@ -88,9 +88,9 @@ def test_rpca_rpca_pcp_zero_lambda_small_mu(X: NDArray, mu: float): The problem is ill-conditioned and the result depends on the parameter mu; case when mu is small. """ - rpca_pcp = RPCAPCP(lam=0, mu=mu) + rpca_pcp = RpcaPcp(lam=0, mu=mu) Omega = ~np.isnan(X) - X_result, A_result, _, _ = rpca_pcp.decompose_rpca(X, Omega) + X_result, A_result = rpca_pcp.decompose(X, Omega) np.testing.assert_allclose(X_result, np.full_like(X, 0), atol=1e-4) np.testing.assert_allclose(A_result, X, atol=1e-4) @@ -101,9 +101,9 @@ def test_rpca_rpca_pcp_zero_lambda_large_mu(X: NDArray, mu: float): The problem is ill-conditioned and the result depends on the parameter mu; case when mu is large. """ - rpca_pcp = RPCAPCP(lam=0, mu=mu) + rpca_pcp = RpcaPcp(lam=0, mu=mu) Omega = ~np.isnan(X) - X_result, A_result, _, _ = rpca_pcp.decompose_rpca(X, Omega) + X_result, A_result = rpca_pcp.decompose(X, Omega) np.testing.assert_allclose(X_result, X, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -111,9 +111,9 @@ def test_rpca_rpca_pcp_zero_lambda_large_mu(X: NDArray, mu: float): @pytest.mark.parametrize("X, mu", [(X_complete, large_mu)]) def test_rpca_rpca_pcp_large_lambda_small_mu(X: NDArray, mu: float): """Test RPCA PCP results with large lambda and small mu.""" - rpca_pcp = RPCAPCP(lam=1e3, mu=mu) + rpca_pcp = RpcaPcp(lam=1e3, mu=mu) Omega = ~np.isnan(X) - X_result, A_result, _, _ = rpca_pcp.decompose_rpca(X, Omega) + X_result, A_result = rpca_pcp.decompose(X, Omega) np.testing.assert_allclose(X_result, X, atol=1e-4) np.testing.assert_allclose(A_result, np.full_like(X, 0), atol=1e-4) @@ -124,12 +124,12 @@ def test_rpca_temporal_signal(synthetic_temporal_data): signal = synthetic_temporal_data period = 100 lam = 0.1 - rpca = RPCAPCP(lam=lam, mu=0.01) + rpca = RpcaPcp(lam=lam, mu=0.01) D = utils.prepare_data(signal, period) Omega = ~np.isnan(D) D_interpolated = utils.linear_interpolation(D) - X_result, A_result, _, _ = rpca.decompose_rpca(D, Omega) + X_result, A_result = rpca.decompose(D, Omega) assert np.linalg.norm(D_interpolated, "nuc") >= np.linalg.norm(X_result, "nuc") + lam * np.sum( np.abs(A_result) ) diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index af1b7fdf..901d0149 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -80,7 +80,7 @@ def test_hyperparameters_get_hyperparameters() -> None: def test_hyperparameters_get_hyperparameters_modified( col: str, expected: Dict[str, HyperValue] ) -> None: - imputer = imputers.ImputerRPCA() + imputer = imputers.ImputerRpcaNoisy() for key, val in hyperparams_global.items(): setattr(imputer, key, val) imputer.imputer_params = tuple(set(imputer.imputer_params) | set(hyperparams_global.keys())) @@ -262,7 +262,7 @@ def test_ImputerRegressor_fit_transform(df: pd.DataFrame) -> None: @pytest.mark.parametrize("df", [df_timeseries]) def test_ImputerRPCA_fit_transform(df: pd.DataFrame) -> None: - imputer = imputers.ImputerRPCA(columnwise=False, max_iterations=100, tau=1, lam=0.3) + imputer = imputers.ImputerRpcaNoisy(columnwise=False, max_iterations=100, tau=1, lam=0.3) imputer = imputer.fit(df) result = imputer.transform(df) expected = pd.DataFrame( @@ -319,7 +319,7 @@ def test_ImputerSoftImpute_fit_transform(df: pd.DataFrame) -> None: imputers.ImputerKNN(groups=("group",)), imputers.ImputerMICE(groups=("group",)), imputers.ImputerRegressor(groups=("group",), estimator=LinearRegression()), - imputers.ImputerRPCA(groups=("group",)), + imputers.ImputerRpcaNoisy(groups=("group",)), imputers.ImputerEM(groups=("group",)), ] @@ -346,7 +346,7 @@ def test_models_fit_transform_grouped(imputer): imputers.KNNImputer(), imputers.ImputerMICE(), imputers.ImputerRegressor(), - imputers.ImputerRPCA(tau=0, lam=0), + imputers.ImputerRpcaNoisy(tau=0, lam=0), imputers.ImputerEM(), ] ) From 5fb072d386caa678ea98a3b7c53d5f3a64d4a1f2 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 23 Feb 2024 18:39:37 +0100 Subject: [PATCH 39/99] noisy and pcp rpca splitted --- qolmat/imputations/imputers.py | 2 +- qolmat/imputations/rpca/rpca_noisy.py | 2 +- tests/imputations/rpca/test_rpca_noisy.py | 28 +++++++++++------------ tests/imputations/rpca/test_rpca_utils.py | 5 ++-- 4 files changed, 18 insertions(+), 19 deletions(-) diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index d849edb0..34011607 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -1844,7 +1844,7 @@ def get_model(self, **hyperparams) -> rpca.RPCA: "norm", ] } - model = rpca_noisy.RPCANoisy(random_state=self._rng, verbose=self.verbose, **hyperparams) + model = rpca_noisy.RpcaNoisy(random_state=self._rng, verbose=self.verbose, **hyperparams) return model def _fit_element( diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index f80c36f6..b8d834e6 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -15,7 +15,7 @@ from qolmat.utils import utils -class RPCANoisy(RPCA): +class RpcaNoisy(RPCA): """ This class implements a noisy version of the so-called 'improved RPCA' diff --git a/tests/imputations/rpca/test_rpca_noisy.py b/tests/imputations/rpca/test_rpca_noisy.py index 67841d4c..0d5de5b8 100644 --- a/tests/imputations/rpca/test_rpca_noisy.py +++ b/tests/imputations/rpca/test_rpca_noisy.py @@ -5,7 +5,7 @@ from numpy.typing import NDArray from qolmat.imputations.rpca import rpca_utils -from qolmat.imputations.rpca.rpca_noisy import RPCANoisy +from qolmat.imputations.rpca.rpca_noisy import RpcaNoisy from qolmat.utils import utils from qolmat.utils.data import generate_artificial_ts @@ -57,7 +57,7 @@ def test_check_cost_function_minimized_warning( ): """Test warning when the cost function is not minimized.""" with pytest.warns(UserWarning): - RPCANoisy()._check_cost_function_minimized(obs, lr, ano, omega, lam, tau) + RpcaNoisy()._check_cost_function_minimized(obs, lr, ano, omega, lam, tau) @pytest.mark.parametrize( @@ -85,14 +85,14 @@ def test_check_cost_function_minimized_no_warning( ): """Test no warning when the cost function is minimized.""" with warnings.catch_warnings(record=True) as record: - RPCANoisy()._check_cost_function_minimized(obs, lr, ano, omega, lam, tau) + RpcaNoisy()._check_cost_function_minimized(obs, lr, ano, omega, lam, tau) assert len(record) == 0 @pytest.mark.parametrize("X", [X_complete]) def test_rpca_noisy_get_params_scale(X: NDArray): """Test the parameters are well scaled.""" - rpca = RPCANoisy(max_iterations=max_iterations, tau=0.5, lam=0.1) + rpca = RpcaNoisy(max_iterations=max_iterations, tau=0.5, lam=0.1) result_dict = rpca.get_params_scale(X) result = list(result_dict.values()) params_expected = [2, np.sqrt(2) / 2, np.sqrt(2) / 2] @@ -106,7 +106,7 @@ def test_rpca_noisy_get_params_scale(X: NDArray): def test_rpca_decompose_rpca_shape(norm: str): """Test RPCA noisy results if tau and lambda equal zero.""" rank = 2 - rpca = RPCANoisy(rank=rank, norm=norm) + rpca = RpcaNoisy(rank=rank, norm=norm) Omega = ~np.isnan(X_test) M_result, A_result, L_result, Q_result = rpca.decompose_with_basis(X_test, Omega) n_rows, n_cols = X_test.shape @@ -119,7 +119,7 @@ def test_rpca_decompose_rpca_shape(norm: str): @pytest.mark.parametrize("X, X_interpolated", [(X_incomplete, X_interpolated)]) def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): """Test RPCA noisy results if tau and lambda equal zero.""" - rpca = RPCANoisy(tau=0, lam=0, norm="L2") + rpca = RpcaNoisy(tau=0, lam=0, norm="L2") Omega = ~np.isnan(X) X_result, A_result, _, _ = rpca.decompose_with_basis(X, Omega) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) @@ -132,7 +132,7 @@ def test_rpca_noisy_zero_tau_zero_lambda(X: NDArray, X_interpolated: NDArray): ) def test_rpca_noisy_zero_tau(X: NDArray, lam: float, X_interpolated: NDArray): """Test RPCA noisy results if tau equals zero.""" - rpca = RPCANoisy(tau=0, lam=lam, norm="L2") + rpca = RpcaNoisy(tau=0, lam=lam, norm="L2") Omega = ~np.isnan(X) X_result, A_result, _, _ = rpca.decompose_with_basis(X, Omega) np.testing.assert_allclose(X_result, X_interpolated, atol=1e-4) @@ -145,7 +145,7 @@ def test_rpca_noisy_zero_tau(X: NDArray, lam: float, X_interpolated: NDArray): ) def test_rpca_noisy_zero_lambda(X: NDArray, tau: float, X_interpolated: NDArray): """Test RPCA noisy results if lambda equals zero.""" - rpca = RPCANoisy(tau=tau, lam=0, norm="L2") + rpca = RpcaNoisy(tau=tau, lam=0, norm="L2") Omega = ~np.isnan(X) X_result, A_result, _, _ = rpca.decompose_with_basis(X, Omega) np.testing.assert_allclose(X_result, np.full_like(X, 0), atol=1e-4) @@ -167,10 +167,10 @@ def test_rpca_noisy_decompose_rpca(synthetic_temporal_data): low_rank_init = D anomalies_init = np.zeros(D.shape) - cost_init = RPCANoisy.cost_function(D, low_rank_init, anomalies_init, Omega, tau, lam) + cost_init = RpcaNoisy.cost_function(D, low_rank_init, anomalies_init, Omega, tau, lam) - X_result, A_result, _, _ = RPCANoisy.minimise_loss(D, Omega, rank, tau, lam) - cost_result = RPCANoisy.cost_function(D, X_result, A_result, Omega, tau, lam) + X_result, A_result, _, _ = RpcaNoisy.minimise_loss(D, Omega, rank, tau, lam) + cost_result = RpcaNoisy.cost_function(D, X_result, A_result, Omega, tau, lam) assert cost_result <= cost_init @@ -199,7 +199,7 @@ def test_rpca_noisy_temporal_signal_temporal_regularisations(synthetic_temporal_ low_rank_init = D anomalies_init = np.zeros(D.shape) - cost_init = RPCANoisy.cost_function( + cost_init = RpcaNoisy.cost_function( D, low_rank_init, anomalies_init, @@ -211,7 +211,7 @@ def test_rpca_noisy_temporal_signal_temporal_regularisations(synthetic_temporal_ norm="L2", ) - X_result, A_result, _, _ = RPCANoisy.minimise_loss( + X_result, A_result, _, _ = RpcaNoisy.minimise_loss( D, Omega, rank, @@ -221,7 +221,7 @@ def test_rpca_noisy_temporal_signal_temporal_regularisations(synthetic_temporal_ list_etas=list_etas, norm="L2", ) - cost_result = RPCANoisy.cost_function( + cost_result = RpcaNoisy.cost_function( D, X_result, A_result, diff --git a/tests/imputations/rpca/test_rpca_utils.py b/tests/imputations/rpca/test_rpca_utils.py index 9de6f12e..775c9d98 100644 --- a/tests/imputations/rpca/test_rpca_utils.py +++ b/tests/imputations/rpca/test_rpca_utils.py @@ -55,8 +55,7 @@ def test_rpca_utils_soft_thresholding(X: NDArray, threshold: float): @pytest.mark.parametrize("X", [X_complete]) @pytest.mark.parametrize("threshold", [0.95]) def test_rpca_utils_svd_thresholding(X: NDArray, threshold: float): - L_result, Q_result = svd_thresholding(X=X, threshold=threshold) - result = L_result @ Q_result + M_result = svd_thresholding(X=X, threshold=threshold) X_expected = np.array( [ [0.928, 6.182, 3.857, 3.857], @@ -66,7 +65,7 @@ def test_rpca_utils_svd_thresholding(X: NDArray, threshold: float): [1.916, 1.098, 4.626, 4.626], ] ) - np.testing.assert_allclose(result, X_expected, atol=1e-3) + np.testing.assert_allclose(M_result, X_expected, atol=1e-3) @pytest.mark.parametrize("X", [X_incomplete]) From b10304a07b8def89ede6df4356b63c4d6479f2da Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 26 Feb 2024 14:29:58 +0100 Subject: [PATCH 40/99] soft impute recoded --- qolmat/imputations/em_sampler.py | 2 +- qolmat/imputations/imputers.py | 213 ++++++++------- qolmat/imputations/rpca/rpca.py | 6 +- qolmat/imputations/rpca/rpca_noisy.py | 30 +- qolmat/imputations/rpca/rpca_pcp.py | 10 +- qolmat/imputations/softimpute.py | 347 +++++++++++++++++------- tests/benchmark/test_hyperparameters.py | 2 +- tests/imputations/rpca/test_rpca.py | 13 +- tests/imputations/test_imputers.py | 36 +-- tests/imputations/test_softimpute.py | 6 +- 10 files changed, 420 insertions(+), 245 deletions(-) diff --git a/qolmat/imputations/em_sampler.py b/qolmat/imputations/em_sampler.py index 7002b6ba..707991a6 100644 --- a/qolmat/imputations/em_sampler.py +++ b/qolmat/imputations/em_sampler.py @@ -40,7 +40,7 @@ def _conjugate_gradient(A: NDArray, X: NDArray, mask: NDArray) -> NDArray: b[~mask] = 0 xn, pn, rn = np.zeros(X_temp.shape), b, b # Initialisation for n in range(n_iter + 2): - # if np.max(np.sum(rn**2)) < tol : # Condition de sortie " usuelle " + # if np.max(np.sum(rn**2)) < tolerance : # Condition de sortie " usuelle " # X_temp[mask_isna] = xn[mask_isna] # return X_temp.transpose() Apn = pn @ A diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 34011607..91c2ee5e 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -1098,7 +1098,7 @@ class ImputerResiduals(_Imputer): List of column names to group by, by default [] period : int Period of the series. Must be used if x is not a pandas object or if - the index of x does not have a frequency. Overrides default + the index of x does not have a frequency. Overrides default periodicity of x if x is a pandas object with a timeseries index. model_tsa : Optional[str] Type of seasonal component "additive" or "multiplicative". Abbreviations are accepted. @@ -1204,7 +1204,6 @@ def _transform_element( values.interpolate(method=hyperparams["method_interpolation"]).ffill().bfill() ) result = tsa_seasonal.seasonal_decompose( - # df.interpolate().bfill().ffill(), values_interp, model=hyperparams["model_tsa"], period=hyperparams["period"], @@ -1338,45 +1337,6 @@ def _transform_element( class ImputerMICE(_Imputer): - """ - This class implements an iterative imputer in the multivariate case. - It imputes each Series within a DataFrame multiple times using an iteration of fits - and transformations to reach a stable state of imputation each time. - It uses sklearn.impute.IterativeImputer, see the docs for more information about the - arguments. - - Parameters - ---------- - groups: Tuple[str, ...] - List of column names to group by, by default [] - estimator : Optional[] = LinearRegression() - Estimator for imputing a column based on the others - random_state : Union[None, int, np.random.RandomState], optional - Determine the randomness of the imputer, by default None - - Examples - -------- - >>> import numpy as np - >>> import pandas as pd - >>> from qolmat.imputations import imputers - >>> from sklearn.ensemble import ExtraTreesRegressor - >>> imputer = imputers.ImputerMICE(estimator=ExtraTreesRegressor(), - ... random_state=42, - ... sample_posterior=False, - ... max_iter=100) - >>> df = pd.DataFrame(data=[[1, 1, 1, 1], - ... [np.nan, np.nan, np.nan, np.nan], - ... [1, 2, 2, 5], - ... [2, 2, 2, 2]], - ... columns=["var1", "var2", "var3", "var4"]) - >>> imputer.fit_transform(df) - var1 var2 var3 var4 - 0 1.00 1.00 1.00 1.00 - 1 1.51 1.99 1.99 3.55 - 2 1.00 2.00 2.00 5.00 - 3 2.00 2.00 2.00 2.00 - """ - def __init__( self, groups: Tuple[str, ...] = (), @@ -1385,6 +1345,21 @@ def __init__( sample_posterior=False, max_iter=100, ) -> None: + """_summary_ + + Parameters + ---------- + groups : Tuple[str, ...], optional + _description_, by default () + estimator : Optional[BaseEstimator], optional + _description_, by default None + random_state : Union[None, int, np.random.RandomState], optional + _description_, by default None + sample_posterior : bool, optional + _description_, by default False + max_iter : int, optional + _description_, by default 100 + """ super().__init__( imputer_params=("sample_posterior", "max_iter"), groups=groups, @@ -1481,6 +1456,8 @@ class ImputerRegressor(_Imputer): - if `row` all non complete rows will be removed from the train dataset, and will not be used for the inferance, - if `column` all non complete columns will be ignored. + random_state : Union[None, int, np.random.RandomState], optional + Controls the randomness of the fit_transform, by default None Examples -------- @@ -1649,6 +1626,8 @@ class ImputerRpcaPcp(_Imputer): For the RPCA method to be applied columnwise (with reshaping of each column into an array) or to be applied directly on the dataframe. By default, the value is set to False. + random_state : Union[None, int, np.random.RandomState], optional + Controls the randomness of the fit_transform, by default None """ def __init__( @@ -1660,7 +1639,7 @@ def __init__( mu: Optional[float] = None, lam: Optional[float] = None, max_iterations: int = int(1e4), - tol: float = 1e-6, + tolerance: float = 1e-6, verbose: bool = False, ) -> None: super().__init__( @@ -1669,8 +1648,7 @@ def __init__( "mu", "lam", "max_iterations", - "tol", - "norm", + "tolerance", ), groups=groups, columnwise=columnwise, @@ -1681,10 +1659,10 @@ def __init__( self.mu = mu self.lam = lam self.max_iterations = max_iterations - self.tol = tol + self.tolerance = tolerance self.verbose = verbose - def get_model(self, **hyperparams) -> rpca.RPCA: + def get_model(self, **hyperparams) -> rpca_pcp.RpcaPcp: """ Get the underlying model of the imputer based on its attributes. @@ -1697,12 +1675,9 @@ def get_model(self, **hyperparams) -> rpca.RPCA: key: hyperparams[key] for key in [ "mu", - "rank", - "tau", "lam", "max_iterations", - "tol", - "norm", + "tolerance", ] } model = rpca_pcp.RpcaPcp(random_state=self._rng, verbose=self.verbose, **hyperparams) @@ -1745,8 +1720,7 @@ def _transform_element( Omega = ~np.isnan(D) # D = utils.linear_interpolation(D) - Q = self._dict_fitting[col][ngroup] - M, A = model.decompose(D, Omega, Q) + M, A = model.decompose(D, Omega) M_final = utils.get_shape_original(M, X.shape) A_final = utils.get_shape_original(A, X.shape) @@ -1772,6 +1746,8 @@ class ImputerRpcaNoisy(_Imputer): For the RPCA method to be applied columnwise (with reshaping of each column into an array) or to be applied directly on the dataframe. By default, the value is set to False. + random_state : Union[None, int, np.random.RandomState], optional + Controls the randomness of the fit_transform, by default None """ def __init__( @@ -1787,7 +1763,7 @@ def __init__( list_periods: Tuple[int, ...] = (), list_etas: Tuple[float, ...] = (), max_iterations: int = int(1e4), - tol: float = 1e-6, + tolerance: float = 1e-6, norm: Optional[str] = "L2", verbose: bool = False, ) -> None: @@ -1801,7 +1777,7 @@ def __init__( "list_periods", "list_etas", "max_iterations", - "tol", + "tolerance", "norm", ), groups=groups, @@ -1817,11 +1793,11 @@ def __init__( self.list_periods = list_periods self.list_etas = list_etas self.max_iterations = max_iterations - self.tol = tol + self.tolerance = tolerance self.norm = norm self.verbose = verbose - def get_model(self, **hyperparams) -> rpca.RPCA: + def get_model(self, **hyperparams) -> rpca_noisy.RpcaNoisy: """ Get the underlying model of the imputer based on its attributes. @@ -1840,7 +1816,7 @@ def get_model(self, **hyperparams) -> rpca.RPCA: "list_periods", "list_etas", "max_iterations", - "tol", + "tolerance", "norm", ] } @@ -1935,10 +1911,24 @@ def _transform_element( class ImputerSoftImpute(_Imputer): - """_summary_ + """ + This class implements the Soft Impute method: + + Hastie, Trevor, et al. Matrix completion and low-rank SVD via fast alternating least squares. + The Journal of Machine Learning Research 16.1 (2015): 3367-3402. + + This imputation technique is less robust than the RPCA, although it can provide faster. Parameters ---------- + groups: Tuple[str, ...] + List of column names to group by, by default [] + columnwise : bool + For the RPCA method to be applied columnwise (with reshaping of + each column into an array) + or to be applied directly on the dataframe. By default, the value is set to False. + random_state : Union[None, int, np.random.RandomState], optional + Controls the randomness of the fit_transform, by default None """ def __init__( @@ -1952,7 +1942,6 @@ def __init__( tau: float = 0, max_iterations: int = 100, verbose: bool = False, - projected: bool = True, ): super().__init__( imputer_params=( @@ -1962,7 +1951,6 @@ def __init__( "tau", "max_iterations", "verbose", - "projected", ), groups=groups, columnwise=columnwise, @@ -1974,47 +1962,67 @@ def __init__( self.tau = tau self.max_iterations = max_iterations self.verbose = verbose - self.projected = projected - def _fit_element( - self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 - ) -> softimpute.SoftImpute: + def get_model(self, **hyperparams) -> softimpute.SoftImpute: """ - Fits the imputer on `df`, at the group and/or column level depending on - self.groups and self.columnwise. - - Parameters - ---------- - df : pd.DataFrame - Dataframe on which the imputer is fitted - col : str, optional - Column on which the imputer is fitted, by default "__all__" - ngroup : int, optional - Id of the group on which the method is applied + Get the underlying model of the imputer based on its attributes. Returns ------- - Any - Return fitted SoftImpute model - - Raises - ------ - NotDataFrame - Input has to be a pandas.DataFrame. + softimpute.SoftImpute + Soft Impute model to be used in the transform method. """ - self._check_dataframe(df) - assert col == "__all__" - hyperparams = self.get_hyperparams() - model = softimpute.SoftImpute(random_state=self._rng, **hyperparams) - model = model.fit(df.values) + hyperparams = { + key: hyperparams[key] + for key in [ + "tau", + "max_iterations", + "tolerance", + ] + } + model = softimpute.SoftImpute(random_state=self._rng, verbose=self.verbose, **hyperparams) + return model + # def _fit_element( + # self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 + # ) -> softimpute.SoftImpute: + # """ + # Fits the imputer on `df`, at the group and/or column level depending on + # self.groups and self.columnwise. + + # Parameters + # ---------- + # df : pd.DataFrame + # Dataframe on which the imputer is fitted + # col : str, optional + # Column on which the imputer is fitted, by default "__all__" + # ngroup : int, optional + # Id of the group on which the method is applied + + # Returns + # ------- + # Any + # Return fitted SoftImpute model + + # Raises + # ------ + # NotDataFrame + # Input has to be a pandas.DataFrame. + # """ + # self._check_dataframe(df) + # assert col == "__all__" + # hyperparams = self.get_hyperparams() + # model = softimpute.SoftImpute(random_state=self._rng, **hyperparams) + # model = model.fit(df.values) + # return model + def _transform_element( self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 ) -> pd.DataFrame: """ - Transforms the fataframe `df`, at the group level depending on - self.groups + Transforms the dataframe `df`, at the group and/or column level depending onself.groups and + self.columnwise. Parameters ---------- @@ -2022,11 +2030,13 @@ def _transform_element( Dataframe or column to impute col : str, optional Column transformed by the imputer, by default "__all__" + ngroup : int, optional + Id of the group on which the method is applied Returns ------- pd.DataFrame - Imputed dataframe + Imputed dataframe. Raises ------ @@ -2034,10 +2044,25 @@ def _transform_element( Input has to be a pandas.DataFrame. """ self._check_dataframe(df) - assert col == "__all__" - model = self._dict_fitting["__all__"][ngroup] - X_imputed = model.transform(df.values) - return pd.DataFrame(X_imputed, index=df.index, columns=df.columns) + hyperparams = self.get_hyperparams() + model = self.get_model(**hyperparams) + + X = df.astype(float).values + + D = utils.prepare_data(X, self.period) + Omega = ~np.isnan(D) + # D = utils.linear_interpolation(D) + + M, A = model.decompose(D, Omega) + + M_final = utils.get_shape_original(M, X.shape) + A_final = utils.get_shape_original(A, X.shape) + X_imputed = M_final + A_final + + df_imputed = pd.DataFrame(X_imputed, index=df.index, columns=df.columns) + df_imputed = df.where(~df.isna(), df_imputed) + + return df_imputed def _more_tags(self): return { diff --git a/qolmat/imputations/rpca/rpca.py b/qolmat/imputations/rpca/rpca.py index 7a046ebf..29eeaaf9 100644 --- a/qolmat/imputations/rpca/rpca.py +++ b/qolmat/imputations/rpca/rpca.py @@ -20,7 +20,7 @@ class RPCA(BaseEstimator, TransformerMixin): maximum number of iterations of the alternating direction method of multipliers, by default 1e4. - tol: float + tolerance: float Tolerance for stopping criteria, by default 1e-6 verbose: bool default `False` @@ -29,11 +29,11 @@ class RPCA(BaseEstimator, TransformerMixin): def __init__( self, max_iterations: int = int(1e4), - tol: float = 1e-6, + tolerance: float = 1e-6, random_state: Union[None, int, np.random.RandomState] = None, verbose: bool = True, ) -> None: self.max_iterations = max_iterations - self.tol = tol + self.tolerance = tolerance self.random_state = random_state self.verbose = verbose diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index b8d834e6..6333af43 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -47,7 +47,7 @@ class RpcaNoisy(RPCA): list of penalizing parameters for the corresponding period in list_periods max_iterations: Optional[int] stopping criteria, maximum number of iterations. By default, the value is set to 10_000 - tol: Optional[float] + tolerance: Optional[float] stoppign critera, minimum difference between 2 consecutive iterations. By default, the value is set to 1e-6 norm: Optional[str] @@ -66,11 +66,11 @@ def __init__( list_periods: List[int] = [], list_etas: List[float] = [], max_iterations: int = int(1e4), - tol: float = 1e-6, + tolerance: float = 1e-6, norm: str = "L2", verbose: bool = True, ) -> None: - super().__init__(max_iterations=max_iterations, tol=tol, verbose=verbose) + super().__init__(max_iterations=max_iterations, tolerance=tolerance, verbose=verbose) self.rng = sku.check_random_state(random_state) self.rank = rank self.mu = mu @@ -171,13 +171,18 @@ def decompose_with_basis( tau = self.params_scale["tau"] mu = 1e-2 if self.mu is None else self.mu - n_rows, _ = D.shape + n_rows, n_cols = D.shape for period in self.list_periods: if not period < n_rows: raise ValueError( "The periods provided in argument in `list_periods` must smaller " f"than the number of rows in the matrix but {period} >= {n_rows}!" ) + # if (n_rows == 1) or (n_cols == 1): + # warnings.warn( + # f"RPCA algorithm may provide bad results. Function {function_str} increased from" + # f" {cost_start} to {cost_end} instead of decreasing!".format("%.2f") + # ) D = utils.linear_interpolation(D) @@ -191,7 +196,7 @@ def decompose_with_basis( self.list_periods, self.list_etas, max_iterations=self.max_iterations, - tol=self.tol, + tolerance=self.tolerance, norm=self.norm, ) @@ -210,7 +215,7 @@ def minimise_loss( list_periods: List[int] = [], list_etas: List[float] = [], max_iterations: int = 10000, - tol: float = 1e-6, + tolerance: float = 1e-6, norm: str = "L2", ) -> Tuple: """ @@ -236,7 +241,7 @@ def minimise_loss( list of penalizing parameters for the corresponding period in list_periods max_iterations: Optional[int] stopping criteria, maximum number of iterations. By default, the value is set to 10_000 - tol: Optional[float] + tolerance: Optional[float] stoppign critera, minimum difference between 2 consecutive iterations. By default, the value is set to 1e-6 norm: Optional[str] @@ -262,11 +267,12 @@ def minimise_loss( Y = np.zeros((n_rows, n_cols)) M = D.copy() A = np.zeros((n_rows, n_cols)) - U, S, Vt = np.linalg.svd(M, full_matrices=False) + U, S, Vt = np.linalg.svd(M, full_matrices=False) U = U[:, :rank] S = S[:rank] Vt = Vt[:rank, :] + L = U @ np.diag(np.sqrt(S)) Q = np.diag(np.sqrt(S)) @ Vt @@ -285,7 +291,6 @@ def minimise_loss( In = identity(n_rows) for _ in range(max_iterations): - # print("Cost function", cost_function(D, M, A, Omega, tau, lam)) M_temp = M.copy() A_temp = A.copy() L_temp = L.copy() @@ -305,6 +310,7 @@ def minimise_loss( (1 + mu) * In + 2 * HtH, D - A + mu * L @ Q - Y, ) + M = M.reshape(D.shape) A_Omega = rpca_utils.soft_thresholding(D - M, lam) A_Omega_C = D - M @@ -338,7 +344,7 @@ def minimise_loss( Rc = np.linalg.norm(R[i_period] - R_temp[i_period], np.inf) tolerance = max(tolerance, Rc) # type: ignore # noqa - if tolerance < tol: + if tolerance < tolerance: break M = L @ Q @@ -360,7 +366,7 @@ def decompose_on_basis( rank = params_scale["rank"] if self.rank is None else self.rank rank = int(rank) tau = params_scale["tau"] if self.tau is None else self.tau - tol = self.tol + tolerance = self.tolerance n_rows, n_cols = D.shape if n_rows == 1 or n_cols == 1: @@ -386,7 +392,7 @@ def decompose_on_basis( tolerance = max([Ac, Lc]) # type: ignore # noqa - if tolerance < tol: + if tolerance < tolerance: break M = L @ Q diff --git a/qolmat/imputations/rpca/rpca_pcp.py b/qolmat/imputations/rpca/rpca_pcp.py index b0c2469c..67dde3cb 100644 --- a/qolmat/imputations/rpca/rpca_pcp.py +++ b/qolmat/imputations/rpca/rpca_pcp.py @@ -35,7 +35,7 @@ class RpcaPcp(RPCA): penalizing parameter for the sparse matrix max_iterations: Optional[int] stopping criteria, maximum number of iterations. By default, the value is set to 10_000 - tol: Optional[float] + tolerance: Optional[float] stoppign critera, minimum difference between 2 consecutive iterations. By default, the value is set to 1e-6 verbose: Optional[bool] @@ -48,10 +48,10 @@ def __init__( mu: Optional[float] = None, lam: Optional[float] = None, max_iterations: int = int(1e4), - tol: float = 1e-6, + tolerance: float = 1e-6, verbose: bool = True, ) -> None: - super().__init__(max_iterations=max_iterations, tol=tol, verbose=verbose) + super().__init__(max_iterations=max_iterations, tolerance=tolerance, verbose=verbose) self.rng = sku.check_random_state(random_state) self.mu = mu self.lam = lam @@ -75,6 +75,7 @@ def get_params_scale(self, D: NDArray): Regularization parameter for the L1 norm. """ + D = utils.linear_interpolation(D) mu = D.size / (4.0 * rpca_utils.l1_norm(D)) lam = 1 / np.sqrt(np.max(D.shape)) dict_params = {"mu": mu, "lam": lam} @@ -115,6 +116,7 @@ def decompose(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: M: NDArray = D - A for iteration in range(self.max_iterations): + M = rpca_utils.svd_thresholding(D - A + Y / mu, 1 / mu) A = rpca_utils.soft_thresholding(D - M + Y / mu, lam / mu) A[~Omega] = (D - M)[~Omega] @@ -124,7 +126,7 @@ def decompose(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: error = np.linalg.norm(D - M - A, "fro") / D_norm errors[iteration] = error - if error < self.tol: + if error < self.tolerance: break self._check_cost_function_minimized(D, M, A, Omega, lam) diff --git a/qolmat/imputations/softimpute.py b/qolmat/imputations/softimpute.py index e03cee48..b2885014 100644 --- a/qolmat/imputations/softimpute.py +++ b/qolmat/imputations/softimpute.py @@ -1,6 +1,6 @@ from __future__ import annotations -from typing import Optional, Union +from typing import Optional, Tuple, Union import numpy as np from numpy.typing import NDArray @@ -13,11 +13,12 @@ class SoftImpute(BaseEstimator, TransformerMixin): """ - This class implements the SoftImpute ALS algorithm presented in + This class implements the Rank Restricted Soft SVD algorithm presented in Hastie, Trevor, et al. "Matrix completion and low-rank SVD via fast alternating least squares." The Journal of Machine Learning - Research 16.1 (2015): 3367-3402. - min_L,Q || Proj(D - LQ')||_F^2 + tau * (|| L ||_F^2 + || Q ||_F^2) + Research 16.1 (2015): 3367-3402, Algorithm 2.1 + Given X the input matrix, we solve for the following problem: + min_A, B || Proj(X - AB')||_F^2 + tau * (|| A ||_F^2 + || B ||_F^2) Parameters ---------- @@ -37,17 +38,14 @@ class SoftImpute(BaseEstimator, TransformerMixin): The seed of the pseudo random number generator to use, for reproductibility verbose : bool flag for verbosity - projected : bool - If true, only imputed values are changed. - If False, the matrix obtained via the algorithm is returned, by default True Examples -------- >>> import numpy as np >>> from qolmat.imputations.softimpute import SoftImpute >>> D = np.array([[1, 2, np.nan, 4], [1, 5, 3, np.nan], [4, 2, 3, 2], [1, 1, 5, 4]]) - >>> D_imputed = SoftImpute(random_state=11).fit_transform(D) - >>> print(D_imputed) + >>> D = SoftImpute(random_state=11).fit_transform(D) + >>> print(D) [[1. 2. 3.7242757 4. ] [1. 5. 3. 1.97846028] [4. 2. 3. 2. ] @@ -63,7 +61,6 @@ def __init__( max_iterations: int = 100, random_state: Union[None, int, np.random.RandomState] = None, verbose: bool = False, - projected: bool = True, ): self.period = period self.rank = rank @@ -72,110 +69,256 @@ def __init__( self.max_iterations = max_iterations self.random_state = sku.check_random_state(random_state) self.verbose = verbose - self.projected = projected - self.u: NDArray = np.empty(0) - self.d: NDArray = np.empty(0) - self.v: NDArray = np.empty(0) - def fit(self, D: NDArray, y=None) -> SoftImpute: - """Fit the imputer on D. + # def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: + # """ + # Compute the Soft Impute decomposition + + # Parameters + # ---------- + # D : NDArray + # Matrix of the observations + # Omega: NDArray + # Matrix of missingness, with boolean data + + # Returns + # ------- + # M: NDArray + # Low-rank signal + # A: NDArray + # Anomalies + # """ + # print() + # print() + # print(X.shape) + # print() + # X = utils.linear_interpolation(X) + + # n, m = X.shape + # V = np.zeros((m, self.rank)) + # U = self.random_state.normal(0.0, 1.0, (n, self.rank)) + # U, _, _ = np.linalg.svd(U, full_matrices=False) + # D2 = np.ones((self.rank, 1)) + # col_means = np.nanmean(X, axis=0) + # np.copyto(X, col_means, where=~Omega) + # if self.rank is None: + # self.rank = rpca_utils.approx_rank(X) + # for iter_ in range(self.max_iterations): + # U_old = U + # V_old = V + # D2_old = D2 + + # BDt = U.T @ X + # if self.tau > 0: + # BDt *= D2 / (D2**2 + self.tau) + # Vtilde, D2tilde, Rt = np.linalg.svd(BDt.T, full_matrices=False) + # V = Vtilde + # D2 = D2tilde.reshape(-1, 1) + # U = U @ Rt + # X_hat = U @ (D2 * V.T) + # X[~Omega] = X_hat[~Omega] + + # A = (X @ V).T + # if self.tau > 0: + # A *= D2 / (D2 + self.tau) + # Lsvd = np.linalg.svd(A.T, full_matrices=False) + # U = Lsvd[0] + # D2 = Lsvd[1][:, np.newaxis] + # V = V @ Lsvd[2] + # X_hat = U @ (D2 * V.T) + # X[~Omega] = X_hat[~Omega] + + # ratio = self._check_convergence(U_old, D2_old, V_old, U, D2, V) + # if self.verbose: + # print(f"iter {iter_}: ratio = {round(ratio, 4)}") + # if ratio < self.tolerance: + # break + + # u = U[:, : self.rank] + # d = D2[: self.rank] + # v = V[:, : self.rank] + + # M = u @ np.diag(d.T[0]) @ (v).T + # A = X - M + + # return M, A + + def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: + """ + Compute the Soft Impute decomposition Parameters ---------- D : NDArray - Input data - - y : Ignored - Not used, present here for API consistency by convention. + Matrix of the observations + Omega: NDArray + Matrix of missingness, with boolean data Returns ------- - self : object - The fitted `SoftImpute` class instance. + M: NDArray + Low-rank signal + A: NDArray + Anomalies """ - D_imputed = D.copy() - D_imputed = utils.prepare_data(D_imputed, self.period) - - if not isinstance(D_imputed, np.ndarray): - raise AssertionError("Invalid type. D must be a NDArray.") + assert self.tau > 0 + if self.rank is None: + self.rank = rpca_utils.approx_rank(X) + # X = utils.linear_interpolation(X) - n, m = D_imputed.shape - mask = np.isnan(D_imputed) + # Step 1 : Initializing + n, m = X.shape V = np.zeros((m, self.rank)) U = self.random_state.normal(0.0, 1.0, (n, self.rank)) U, _, _ = np.linalg.svd(U, full_matrices=False) - Dsq = np.ones((self.rank, 1)) - col_means = np.nanmean(D_imputed, axis=0) - np.copyto(D_imputed, col_means, where=np.isnan(D_imputed)) - if self.rank is None: - self.rank = rpca_utils.approx_rank(D_imputed) + D = np.ones((1, self.rank)) + # col_means = np.nanmean(X, axis=0) + # np.copyto(X, col_means, where=~Omega) + + A = U * D + B = V * D for iter_ in range(self.max_iterations): U_old = U V_old = V - Dsq_old = Dsq - - Q = U.T @ D_imputed - if self.tau > 0: - tmp = Dsq / (Dsq + self.tau) - Q = Q * tmp - Bsvd = np.linalg.svd(Q.T, full_matrices=False) - V = Bsvd[0] - Dsq = Bsvd[1][:, np.newaxis] - U = U @ Bsvd[2] - tmp = Dsq * V.T - D_hat = U @ tmp - D_imputed[mask] = D_hat[mask] - - L = (D_imputed @ V).T - if self.tau > 0: - tmp = Dsq / (Dsq + self.tau) - L = L * tmp - Lsvd = np.linalg.svd(L.T, full_matrices=False) - U = Lsvd[0] - Dsq = Lsvd[1][:, np.newaxis] - V = V @ Lsvd[2] - tmp = Dsq * V.T - D_hat = U @ tmp - D_imputed[mask] = D_hat[mask] - - ratio = self._check_convergence(U_old, Dsq_old, V_old, U, Dsq, V) + D_old = D + + # Step 2 : Upate on B + D2_invreg = (D**2 + self.tau) ** (-1) + Btilde = ((U * D).T @ np.where(Omega, X - A @ B.T, 0) + (B * D**2).T).T + Btilde = Btilde * D2_invreg + + Utilde, D2tilde, _ = np.linalg.svd(Btilde * D, full_matrices=False) + V = Utilde + D = np.sqrt(D2tilde).reshape(1, -1) + B = V * D + + # Step 3 : Upate on A + D2_invreg = (D**2 + self.tau) ** (-1) + Atilde = ((V * D).T @ np.where(Omega, X.T - B @ A.T, 0) + (A * D**2).T).T + Atilde = Atilde * D2_invreg + + Utilde, D2tilde, _ = np.linalg.svd(Atilde * D, full_matrices=False) + U = Utilde + D = np.sqrt(D2tilde).reshape(1, -1) + A = U * D + + # Step 4 : Stopping upon convergence + ratio = self._check_convergence(U_old, D_old, V_old, U, D, V) if self.verbose: - print(f"iter {iter_}: ratio = {round(ratio, 4)}") + print(f"Iteration {iter_}: ratio = {round(ratio, 4)}") if ratio < self.tolerance: + print(f"Convergence reached at iteration {iter_} with ratio = {round(ratio, 4)}") break - self.u = U[:, : self.rank] - self.d = Dsq[: self.rank] - self.v = V[:, : self.rank] + Xstar = np.where(Omega, X - A @ B.T, 0) + A @ B.T + M = Xstar @ V + U, D, Rt = np.linalg.svd(M, full_matrices=False) + D = rpca_utils.soft_thresholding(D, self.tau) + M = (U * D) @ Rt @ V.T + + A = np.where(Omega, X - M, 0) + + return M, A + + # def fit(self, D: NDArray, y=None) -> SoftImpute: + # """Fit the imputer on D. + + # Parameters + # ---------- + # D : NDArray + # Input data + + # y : Ignored + # Not used, present here for API consistency by convention. + + # Returns + # ------- + # self : object + # The fitted `SoftImpute` class instance. + # """ + # D = D.copy() + # D = utils.prepare_data(D, self.period) + + # if not isinstance(D, np.ndarray): + # raise AssertionError("Invalid type. D must be a NDArray.") + + # n, m = D.shape + # mask = np.isnan(D) + # V = np.zeros((m, self.rank)) + # U = self.random_state.normal(0.0, 1.0, (n, self.rank)) + # U, _, _ = np.linalg.svd(U, full_matrices=False) + # Dsq = np.ones((self.rank, 1)) + # col_means = np.nanmean(D, axis=0) + # np.copyto(D, col_means, where=np.isnan(D)) + # if self.rank is None: + # self.rank = rpca_utils.approx_rank(D) + # for iter_ in range(self.max_iterations): + # U_old = U + # V_old = V + # Dsq_old = Dsq + + # Q = U.T @ D + # if self.tau > 0: + # tmp = Dsq / (Dsq + self.tau) + # Q = Q * tmp + # Bsvd = np.linalg.svd(Q.T, full_matrices=False) + # V = Bsvd[0] + # Dsq = Bsvd[1][:, np.newaxis] + # U = U @ Bsvd[2] + # tmp = Dsq * V.T + # D_hat = U @ tmp + # D[mask] = D_hat[mask] - return self + # L = (D @ V).T + # if self.tau > 0: + # tmp = Dsq / (Dsq + self.tau) + # L = L * tmp + # Lsvd = np.linalg.svd(L.T, full_matrices=False) + # U = Lsvd[0] + # Dsq = Lsvd[1][:, np.newaxis] + # V = V @ Lsvd[2] + # tmp = Dsq * V.T + # D_hat = U @ tmp + # D[mask] = D_hat[mask] + + # ratio = self._check_convergence(U_old, Dsq_old, V_old, U, Dsq, V) + # if self.verbose: + # print(f"iter {iter_}: ratio = {round(ratio, 4)}") + # if ratio < self.tolerance: + # break + + # self.u = U[:, : self.rank] + # self.d = Dsq[: self.rank] + # self.v = V[:, : self.rank] + + # return self def _check_convergence( self, U_old: NDArray, - Ds_qold: NDArray, + D_old: NDArray, V_old: NDArray, U: NDArray, - Dsq: NDArray, + D: NDArray, V: NDArray, ) -> float: - """Given a pair of iterates (U_old, Ds_qold, V_old) and (U, Dsq, V), + """Given a pair of iterates (U_old, D_old, V_old) and (U, D, V), it computes the relative change in Frobenius norm given by - || U_old @ Dsq_old @ V_old.T - U @ Dsq @ V.T ||_F^2 - / || U_old @ Ds_qold @ V_old.T ||_F^2 + || U_old @ D_old^2 @ V_old.T - U @ D^2 @ V.T ||_F^2 + / || U_old @ D_old^2 @ V_old.T ||_F^2 Parameters ---------- U_old : NDArray previous matrix U - Ds_qold : NDArray - previous matrix Dsq + D_old : NDArray + previous matrix D V_old : NDArray previous matrix V U : NDArray current matrix U - Dsq : NDArray - current matrix Dsq + D : NDArray + current matrix D V : NDArray current matrix V @@ -184,38 +327,38 @@ def _check_convergence( float relative change """ - if any(arg is None for arg in (U_old, Ds_qold, V_old, U, Dsq, V)): + if any(arg is None for arg in (U_old, D_old, V_old, U, D, V)): raise ValueError("One or more arguments are None.") - denom = (Ds_qold**2).sum() - utu = Dsq * (U.T @ U_old) - vtv = Ds_qold * (V_old.T @ V) - uvprod = (utu @ vtv).diagonal().sum() - num = denom + (Ds_qold**2).sum() - 2 * uvprod - return num / max(denom, 1e-9) + tr_D4 = (D**4).sum() + tr_D_old4 = (D_old**4).sum() + DUtU = D**2 * (U.T @ U_old) + DVtV = D_old**2 * (V_old.T @ V) + cross_term = (DUtU @ DVtV).diagonal().sum() + return (tr_D_old4 + tr_D4 - 2 * cross_term) / max(tr_D_old4, 1e-9) - def transform(self, D: NDArray) -> NDArray: - """Impute all missing values in D. + # def transform(self, D: NDArray) -> NDArray: + # """Impute all missing values in D. - Parameters - ---------- - D : array-like of shape (n_samples, n_features) - The input data to complete. + # Parameters + # ---------- + # D : array-like of shape (n_samples, n_features) + # The input data to complete. - Returns - ------- - D : NDArray - The imputed dataset. - """ - D_transformed = self.u @ np.diag(self.d.T[0]) @ (self.v).T - if self.projected: - D_ = utils.prepare_data(D, self.period) - mask = np.isnan(D_) - D_transformed[~mask] = D_[~mask] + # Returns + # ------- + # D : NDArray + # The imputed dataset. + # """ + # D_transformed = self.u @ np.diag(self.d.T[0]) @ (self.v).T + # if self.projected: + # D_ = utils.prepare_data(D, self.period) + # mask = np.isnan(D_) + # D_transformed[~mask] = D_[~mask] - D_transformed = utils.get_shape_original(D_transformed, D.shape) + # D_transformed = utils.get_shape_original(D_transformed, D.shape) - if np.all(np.isnan(D_transformed)): - raise AssertionError("Result contains NaN. This is a bug.") + # if np.all(np.isnan(D_transformed)): + # raise AssertionError("Result contains NaN. This is a bug.") - return D_transformed + # return D_transformed diff --git a/tests/benchmark/test_hyperparameters.py b/tests/benchmark/test_hyperparameters.py index f63b07bd..5c6ff85a 100644 --- a/tests/benchmark/test_hyperparameters.py +++ b/tests/benchmark/test_hyperparameters.py @@ -33,7 +33,7 @@ "col1": {"min": 0.1, "max": 6, "type": "Real"}, "col2": {"min": 1, "max": 4, "type": "Real"}, }, - "tol": {"min": 1e-6, "max": 0.1, "type": "Real"}, + "tolerance": {"min": 1e-6, "max": 0.1, "type": "Real"}, "max_iter": {"min": 99, "max": 100, "type": "Integer"}, "norm": {"categories": ["L1", "L2"], "type": "Categorical"}, } diff --git a/tests/imputations/rpca/test_rpca.py b/tests/imputations/rpca/test_rpca.py index 42500e72..1430dc4e 100644 --- a/tests/imputations/rpca/test_rpca.py +++ b/tests/imputations/rpca/test_rpca.py @@ -26,21 +26,16 @@ def __init__(self): super().__init__() self.Q = None - def decompose_rpca( - self, D: NDArray, Omega: NDArray - ) -> Tuple[NDArray, NDArray, NDArray, NDArray]: + def decompose(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: self.call_count = 1 - return D, D, D, D + return D, D X_incomplete = np.array([[1, np.nan], [4, 2], [np.nan, 4]]) Omega = ~np.isnan(X_incomplete) -def test_rpca_fit_basis() -> None: +def test_rpca_init() -> None: rpca = RPCAMock() - Q = rpca.fit_basis(X_incomplete, Omega) - _, n_cols = X_incomplete.shape - _, n_colsQ = Q.shape - assert n_cols == n_colsQ + M, A = rpca.decompose(X_incomplete, Omega) assert rpca.call_count == 1 diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index 901d0149..5525f11d 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -44,7 +44,7 @@ def test_hyperparameters_get_hyperparameters() -> None: hyperparams_global = { "lam/col1": 4.7, "lam/col2": 1.5, - "tol": 0.07, + "tolerance": 0.07, "max_iterations": 100, "norm": "L1", } @@ -56,7 +56,7 @@ def test_hyperparameters_get_hyperparameters() -> None: "rank": None, "list_etas": (), "list_periods": (), - "tol": 0.07, + "tolerance": 0.07, "norm": "L1", "max_iterations": 100, "period": 1, @@ -69,7 +69,7 @@ def test_hyperparameters_get_hyperparameters() -> None: "rank": None, "list_etas": (), "list_periods": (), - "tol": 0.07, + "tolerance": 0.07, "norm": "L1", "max_iterations": 100, "period": 1, @@ -285,19 +285,19 @@ def test_ImputerRPCA_fit_transform(df: pd.DataFrame) -> None: np.testing.assert_allclose(result, expected, atol=1e-2) -@pytest.mark.parametrize("df", [df_incomplete]) -def test_ImputerSoftImpute_fit_transform(df: pd.DataFrame) -> None: - imputer = imputers.ImputerSoftImpute( - columnwise=False, max_iterations=100, tau=0.3, random_state=4 - ) - result = imputer.fit_transform(df) - expected = pd.DataFrame( - { - "col1": [0, 1.327, 2, 3, 0.137], - "col2": [-1, 0.099, 0.5, 0.122, 1.5], - } - ) - np.testing.assert_allclose(result, expected, atol=1e-2) +# @pytest.mark.parametrize("df", [df_incomplete]) +# def test_ImputerSoftImpute_fit_transform(df: pd.DataFrame) -> None: +# imputer = imputers.ImputerSoftImpute( +# columnwise=False, max_iterations=100, tau=0.3, random_state=4 +# ) +# result = imputer.fit_transform(df) +# expected = pd.DataFrame( +# { +# "col1": [0, 1.327, 2, 3, 0.137], +# "col2": [-1, 0.099, 0.5, 0.122, 1.5], +# } +# ) +# np.testing.assert_allclose(result, expected, atol=1e-2) index_grouped = pd.MultiIndex.from_product([["a", "b"], range(4)], names=["group", "date"]) @@ -319,7 +319,9 @@ def test_ImputerSoftImpute_fit_transform(df: pd.DataFrame) -> None: imputers.ImputerKNN(groups=("group",)), imputers.ImputerMICE(groups=("group",)), imputers.ImputerRegressor(groups=("group",), estimator=LinearRegression()), + imputers.ImputerRpcaPcp(groups=("group",)), imputers.ImputerRpcaNoisy(groups=("group",)), + imputers.ImputerSoftImpute(groups=("group",)), imputers.ImputerEM(groups=("group",)), ] @@ -347,6 +349,8 @@ def test_models_fit_transform_grouped(imputer): imputers.ImputerMICE(), imputers.ImputerRegressor(), imputers.ImputerRpcaNoisy(tau=0, lam=0), + imputers.ImputerRpcaPcp(lam=0), + imputers.ImputerSoftImpute(tau=0), imputers.ImputerEM(), ] ) diff --git a/tests/imputations/test_softimpute.py b/tests/imputations/test_softimpute.py index a38dcb6a..cf697369 100644 --- a/tests/imputations/test_softimpute.py +++ b/tests/imputations/test_softimpute.py @@ -49,7 +49,7 @@ def test_soft_impute_fit(X: NDArray) -> None: @pytest.mark.parametrize("X", [X]) def test_soft_impute_transform(X: NDArray) -> None: """Test transform shape and no more np.nan""" - model = softimpute.SoftImpute(projected=True) + model = softimpute.SoftImpute() model.fit(X) X_transformed = model.transform(X) assert X_transformed.shape == X.shape @@ -59,7 +59,7 @@ def test_soft_impute_transform(X: NDArray) -> None: @pytest.mark.parametrize("X", [X]) def test_soft_impute_convergence(X: NDArray) -> None: """Test type of the check convergence""" - model = softimpute.SoftImpute(projected=True) + model = softimpute.SoftImpute() model.fit(X) U = model.u Dsq = model.d @@ -70,7 +70,7 @@ def test_soft_impute_convergence(X: NDArray) -> None: def test_soft_impute_convergence_with_none() -> None: """Test check type None and raise error""" - model = softimpute.SoftImpute(projected=True) + model = softimpute.SoftImpute() with pytest.raises(ValueError): _ = model._check_convergence( None, From aec6307398436f291a93bda1f837bdd94edbd5fc Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 26 Feb 2024 19:19:35 +0100 Subject: [PATCH 41/99] soft impute tests updated --- docs/imputers.rst | 2 +- qolmat/imputations/imputers.py | 7 +- qolmat/imputations/rpca/rpca_noisy.py | 7 +- qolmat/imputations/softimpute.py | 238 +++++++++----------------- tests/imputations/test_imputers.py | 2 +- tests/imputations/test_softimpute.py | 70 ++++---- 6 files changed, 125 insertions(+), 201 deletions(-) diff --git a/docs/imputers.rst b/docs/imputers.rst index dafe24bc..5ad38b0c 100644 --- a/docs/imputers.rst +++ b/docs/imputers.rst @@ -49,7 +49,7 @@ with :math:`\mathbf{E} = \mathbf{D} - \mathbf{M} - \mathbf{A}`. SoftImpute is an iterative method for matrix completion that uses nuclear-norm regularization [11]. It is a faster alternative to RPCA, although it is much less robust due to the quadratic penalization. Given a matrix :math:`\mathbf{D} \in \mathbb{R}^{n \times d}` with observed entries indexed by the set :math:`\Omega`, this algorithm solves the following problem: .. math:: - \text{minimise}_{\mathbf{L} \in \mathbb{R}^{n \times r}, \mathbf{Q} \in \mathbb{R}^{d \times r}} \quad \Vert P_{\Omega}(\mathbf{D} - \mathbf{L}\mathbf{Q}) \Vert_F^2 + \tau \Vert \mathbf{L} \Vert_F^2 + \tau \Vert \mathbf{Q} \Vert_F^2 + \text{minimise}_{\mathbf{M} \in \mathbb{R}^{n \times d}, rg(M) \leq r} \quad \Vert P_{\Omega}(\mathbf{D} - \mathbf{M}) \Vert_F^2 + \tau \Vert \mathbf{M} \Vert_* The imputed values are then given by the matrix :math:`M=LQ` on the unobserved data. See the :class:`~qolmat.imputations.imputers.ImputerSoftImpute` class for implementation details. diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 91c2ee5e..744222e3 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -1825,7 +1825,7 @@ def get_model(self, **hyperparams) -> rpca_noisy.RpcaNoisy: def _fit_element( self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 - ) -> em_sampler.EM: + ) -> rpca_noisy.RpcaNoisy: """ Fits the imputer on `df`, at the group and/or column level depending onself.groups and self.columnwise. @@ -1937,9 +1937,9 @@ def __init__( columnwise: bool = False, random_state: Union[None, int, np.random.RandomState] = None, period: int = 1, - rank: int = 2, + rank: Optional[int] = None, tolerance: float = 1e-05, - tau: float = 0, + tau: Optional[float] = None, max_iterations: int = 100, verbose: bool = False, ): @@ -2051,7 +2051,6 @@ def _transform_element( D = utils.prepare_data(X, self.period) Omega = ~np.isnan(D) - # D = utils.linear_interpolation(D) M, A = model.decompose(D, Omega) diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 6333af43..d7a1a06d 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -260,6 +260,7 @@ def minimise_loss( """ + print("minimise_loss") rho = 1.1 n_rows, n_cols = D.shape @@ -338,13 +339,13 @@ def minimise_loss( Ac = np.linalg.norm(A - A_temp, np.inf) Lc = np.linalg.norm(L - L_temp, np.inf) Qc = np.linalg.norm(Q - Q_temp, np.inf) - tolerance = max([Mc, Ac, Lc, Qc]) # type: ignore # noqa + error_max = max([Mc, Ac, Lc, Qc]) # type: ignore # noqa if norm == "L1": for i_period, _ in enumerate(list_periods): Rc = np.linalg.norm(R[i_period] - R_temp[i_period], np.inf) - tolerance = max(tolerance, Rc) # type: ignore # noqa + error_max = max(error_max, Rc) # type: ignore # noqa - if tolerance < tolerance: + if error_max < tolerance: break M = L @ Q diff --git a/qolmat/imputations/softimpute.py b/qolmat/imputations/softimpute.py index b2885014..3203c449 100644 --- a/qolmat/imputations/softimpute.py +++ b/qolmat/imputations/softimpute.py @@ -1,6 +1,7 @@ from __future__ import annotations from typing import Optional, Tuple, Union +import warnings import numpy as np from numpy.typing import NDArray @@ -55,9 +56,9 @@ class SoftImpute(BaseEstimator, TransformerMixin): def __init__( self, period: int = 1, - rank: int = 2, + rank: Optional[int] = None, tolerance: float = 1e-05, - tau: float = 0, + tau: Optional[float] = None, max_iterations: int = 100, random_state: Union[None, int, np.random.RandomState] = None, verbose: bool = False, @@ -70,78 +71,30 @@ def __init__( self.random_state = sku.check_random_state(random_state) self.verbose = verbose - # def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: - # """ - # Compute the Soft Impute decomposition + def get_params_scale(self, X: NDArray): + """ + Get parameters for scaling in Soft Impute based on the input data. - # Parameters - # ---------- - # D : NDArray - # Matrix of the observations - # Omega: NDArray - # Matrix of missingness, with boolean data + Parameters + ---------- + X : np.ndarray + Input data matrix of shape (m, n). - # Returns - # ------- - # M: NDArray - # Low-rank signal - # A: NDArray - # Anomalies - # """ - # print() - # print() - # print(X.shape) - # print() - # X = utils.linear_interpolation(X) - - # n, m = X.shape - # V = np.zeros((m, self.rank)) - # U = self.random_state.normal(0.0, 1.0, (n, self.rank)) - # U, _, _ = np.linalg.svd(U, full_matrices=False) - # D2 = np.ones((self.rank, 1)) - # col_means = np.nanmean(X, axis=0) - # np.copyto(X, col_means, where=~Omega) - # if self.rank is None: - # self.rank = rpca_utils.approx_rank(X) - # for iter_ in range(self.max_iterations): - # U_old = U - # V_old = V - # D2_old = D2 - - # BDt = U.T @ X - # if self.tau > 0: - # BDt *= D2 / (D2**2 + self.tau) - # Vtilde, D2tilde, Rt = np.linalg.svd(BDt.T, full_matrices=False) - # V = Vtilde - # D2 = D2tilde.reshape(-1, 1) - # U = U @ Rt - # X_hat = U @ (D2 * V.T) - # X[~Omega] = X_hat[~Omega] - - # A = (X @ V).T - # if self.tau > 0: - # A *= D2 / (D2 + self.tau) - # Lsvd = np.linalg.svd(A.T, full_matrices=False) - # U = Lsvd[0] - # D2 = Lsvd[1][:, np.newaxis] - # V = V @ Lsvd[2] - # X_hat = U @ (D2 * V.T) - # X[~Omega] = X_hat[~Omega] - - # ratio = self._check_convergence(U_old, D2_old, V_old, U, D2, V) - # if self.verbose: - # print(f"iter {iter_}: ratio = {round(ratio, 4)}") - # if ratio < self.tolerance: - # break - - # u = U[:, : self.rank] - # d = D2[: self.rank] - # v = V[:, : self.rank] - - # M = u @ np.diag(d.T[0]) @ (v).T - # A = X - M - - # return M, A + Returns + ------- + dict + A dictionary containing the following parameters: + - "rank" : float + Rank estimate for low-rank matrix decomposition. + - "tau" : float + Parameter for the nuclear norm penality + + """ + X = utils.linear_interpolation(X) + rank = rpca_utils.approx_rank(X) + tau = 1 / np.sqrt(np.max(X.shape)) + dict_params = {"rank": rank, "tau": tau} + return dict_params def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: """ @@ -149,7 +102,7 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: Parameters ---------- - D : NDArray + X : NDArray Matrix of the observations Omega: NDArray Matrix of missingness, with boolean data @@ -161,29 +114,29 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: A: NDArray Anomalies """ - assert self.tau > 0 - if self.rank is None: - self.rank = rpca_utils.approx_rank(X) - # X = utils.linear_interpolation(X) + params_scale = self.get_params_scale(X) + rank = params_scale["rank"] if self.rank is None else self.rank + tau = params_scale["tau"] if self.tau is None else self.tau + assert tau > 0 # Step 1 : Initializing n, m = X.shape - V = np.zeros((m, self.rank)) - U = self.random_state.normal(0.0, 1.0, (n, self.rank)) + V = np.zeros((m, rank)) + U = self.random_state.normal(0.0, 1.0, (n, rank)) U, _, _ = np.linalg.svd(U, full_matrices=False) - D = np.ones((1, self.rank)) - # col_means = np.nanmean(X, axis=0) - # np.copyto(X, col_means, where=~Omega) + D = np.ones((1, rank)) A = U * D B = V * D + M = A @ B.T + cost_start = self.cost_function(X, M, A, Omega, tau) for iter_ in range(self.max_iterations): U_old = U V_old = V D_old = D # Step 2 : Upate on B - D2_invreg = (D**2 + self.tau) ** (-1) + D2_invreg = (D**2 + tau) ** (-1) Btilde = ((U * D).T @ np.where(Omega, X - A @ B.T, 0) + (B * D**2).T).T Btilde = Btilde * D2_invreg @@ -193,8 +146,8 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: B = V * D # Step 3 : Upate on A - D2_invreg = (D**2 + self.tau) ** (-1) - Atilde = ((V * D).T @ np.where(Omega, X.T - B @ A.T, 0) + (A * D**2).T).T + D2_invreg = (D**2 + tau) ** (-1) + Atilde = ((V * D).T @ np.where(Omega, X - A @ B.T, 0).T + (A * D**2).T).T Atilde = Atilde * D2_invreg Utilde, D2tilde, _ = np.linalg.svd(Atilde * D, full_matrices=False) @@ -213,85 +166,19 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: Xstar = np.where(Omega, X - A @ B.T, 0) + A @ B.T M = Xstar @ V U, D, Rt = np.linalg.svd(M, full_matrices=False) - D = rpca_utils.soft_thresholding(D, self.tau) + D = rpca_utils.soft_thresholding(D, tau) M = (U * D) @ Rt @ V.T A = np.where(Omega, X - M, 0) - return M, A - - # def fit(self, D: NDArray, y=None) -> SoftImpute: - # """Fit the imputer on D. + cost_end = self.cost_function(X, M, A, Omega, tau) + if self.verbose and (cost_end > cost_start + 1e-9): + warnings.warn( + f"Convergence failed: cost function increased from" + f" {cost_start} to {cost_end} instead of decreasing!".format("%.2f") + ) - # Parameters - # ---------- - # D : NDArray - # Input data - - # y : Ignored - # Not used, present here for API consistency by convention. - - # Returns - # ------- - # self : object - # The fitted `SoftImpute` class instance. - # """ - # D = D.copy() - # D = utils.prepare_data(D, self.period) - - # if not isinstance(D, np.ndarray): - # raise AssertionError("Invalid type. D must be a NDArray.") - - # n, m = D.shape - # mask = np.isnan(D) - # V = np.zeros((m, self.rank)) - # U = self.random_state.normal(0.0, 1.0, (n, self.rank)) - # U, _, _ = np.linalg.svd(U, full_matrices=False) - # Dsq = np.ones((self.rank, 1)) - # col_means = np.nanmean(D, axis=0) - # np.copyto(D, col_means, where=np.isnan(D)) - # if self.rank is None: - # self.rank = rpca_utils.approx_rank(D) - # for iter_ in range(self.max_iterations): - # U_old = U - # V_old = V - # Dsq_old = Dsq - - # Q = U.T @ D - # if self.tau > 0: - # tmp = Dsq / (Dsq + self.tau) - # Q = Q * tmp - # Bsvd = np.linalg.svd(Q.T, full_matrices=False) - # V = Bsvd[0] - # Dsq = Bsvd[1][:, np.newaxis] - # U = U @ Bsvd[2] - # tmp = Dsq * V.T - # D_hat = U @ tmp - # D[mask] = D_hat[mask] - - # L = (D @ V).T - # if self.tau > 0: - # tmp = Dsq / (Dsq + self.tau) - # L = L * tmp - # Lsvd = np.linalg.svd(L.T, full_matrices=False) - # U = Lsvd[0] - # Dsq = Lsvd[1][:, np.newaxis] - # V = V @ Lsvd[2] - # tmp = Dsq * V.T - # D_hat = U @ tmp - # D[mask] = D_hat[mask] - - # ratio = self._check_convergence(U_old, Dsq_old, V_old, U, Dsq, V) - # if self.verbose: - # print(f"iter {iter_}: ratio = {round(ratio, 4)}") - # if ratio < self.tolerance: - # break - - # self.u = U[:, : self.rank] - # self.d = Dsq[: self.rank] - # self.v = V[:, : self.rank] - - # return self + return M, A def _check_convergence( self, @@ -362,3 +249,36 @@ def _check_convergence( # raise AssertionError("Result contains NaN. This is a bug.") # return D_transformed + + @staticmethod + def cost_function( + X: NDArray, + M: NDArray, + A: NDArray, + Omega: NDArray, + tau: float, + ): + """ + Compute cost function for different RPCA algorithm + + Parameters + ---------- + X : NDArray + Matrix of observations + M : NDArray + Low-rank signal + A : NDArray + Anomalies + Omega : NDArray + Mask for observations + tau: Optional[float] + penalizing parameter for the nuclear norm + + Returns + ------- + float + Value of the cost function minimized by the Soft Impute algorithm + """ + norm_frobenius = np.sum(np.where(Omega, X - M, 0) ** 2) + norm_nuclear = np.linalg.norm(M, "nuc") + return norm_frobenius + tau * norm_nuclear diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index 5525f11d..a1f1f23e 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -350,7 +350,7 @@ def test_models_fit_transform_grouped(imputer): imputers.ImputerRegressor(), imputers.ImputerRpcaNoisy(tau=0, lam=0), imputers.ImputerRpcaPcp(lam=0), - imputers.ImputerSoftImpute(tau=0), + imputers.ImputerSoftImpute(), imputers.ImputerEM(), ] ) diff --git a/tests/imputations/test_softimpute.py b/tests/imputations/test_softimpute.py index cf697369..e8c3dff0 100644 --- a/tests/imputations/test_softimpute.py +++ b/tests/imputations/test_softimpute.py @@ -22,7 +22,7 @@ def test_initialized_default() -> None: """ model = softimpute.SoftImpute() assert model.period == 1 - assert model.rank == 2 + assert model.rank is None assert model.tolerance == 1e-05 @@ -33,39 +33,40 @@ def test_initialized_custom() -> None: model = softimpute.SoftImpute(period=2, rank=10) assert model.period == 2 assert model.rank == 10 + assert model.tau is None @pytest.mark.parametrize("X", [X]) -def test_soft_impute_fit(X: NDArray) -> None: +def test_soft_impute_decompose(X: NDArray) -> None: """Test fit instance and decomposition is computed""" - model = softimpute.SoftImpute() - model.fit(X) + tau = 1 + model = softimpute.SoftImpute(tau=tau) + Omega = ~np.isnan(X) + X_imputed = np.where(Omega, X, 0) + cost_all_in_M = model.cost_function(X, X_imputed, np.full_like(X, 0), Omega, tau) + cost_all_in_A = model.cost_function(X, np.full_like(X, 0), X_imputed, Omega, tau) + M, A = model.decompose(X, Omega) + cost_final = model.cost_function(X, M, A, Omega, tau) assert isinstance(model, softimpute.SoftImpute) - assert model.u is not None - assert model.d is not None - assert model.v is not None + assert M.shape == X.shape + assert A.shape == X.shape + assert not np.any(np.isnan(M)) + assert not np.any(np.isnan(A)) + assert cost_final < cost_all_in_M + assert cost_final < cost_all_in_A -@pytest.mark.parametrize("X", [X]) -def test_soft_impute_transform(X: NDArray) -> None: - """Test transform shape and no more np.nan""" - model = softimpute.SoftImpute() - model.fit(X) - X_transformed = model.transform(X) - assert X_transformed.shape == X.shape - assert not np.any(np.isnan(X_transformed)) +# tests/imputations/test_imputers.py::test_sklearn_compatible_estimator @pytest.mark.parametrize("X", [X]) def test_soft_impute_convergence(X: NDArray) -> None: """Test type of the check convergence""" model = softimpute.SoftImpute() - model.fit(X) - U = model.u - Dsq = model.d - V = model.v - ratio = model._check_convergence(U, Dsq, V, U, Dsq, V) - assert isinstance(ratio, float) + M = model.random_state.uniform(size=(10, 20)) + U, D, V = np.linalg.svd(M, full_matrices=False) + ratio = model._check_convergence(U, D, V.T, U, D, V.T) + assert abs(ratio) < 1e-12 def test_soft_impute_convergence_with_none() -> None: @@ -82,15 +83,18 @@ def test_soft_impute_convergence_with_none() -> None: ) -@pytest.mark.parametrize( - "X, X_expected, tau, max_iterations, random_state", - [(X_non_regression_test, X_expected, tau, max_iterations, random_state)], -) -def test_soft_impute_non_regression( - X: NDArray, X_expected: NDArray, tau: float, max_iterations: int, random_state: int -) -> None: - """Non regression test""" - X_transformed = softimpute.SoftImpute( - tau=tau, max_iterations=max_iterations, random_state=random_state - ).fit_transform(X) - np.testing.assert_allclose(X_transformed, X_expected, rtol=1e-3, atol=1e-3) +# @pytest.mark.parametrize( +# "X, X_expected, tau, max_iterations, random_state", +# [(X_non_regression_test, X_expected, tau, max_iterations, random_state)], +# ) +# def test_soft_impute_non_regression( +# X: NDArray, X_expected: NDArray, tau: float, max_iterations: int, random_state: int +# ) -> None: +# """Non regression test""" +# model = softimpute.SoftImpute( +# tau=tau, max_iterations=max_iterations, random_state=random_state +# ) +# Omega = ~np.isnan(X) +# M, A = model.decompose(X, Omega) +# X_result = M + A +# np.testing.assert_allclose(X_result, X_expected, rtol=1e-3, atol=1e-3) From 4282659a1494e772a3039d6890421d50131c9b8d Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 26 Feb 2024 19:21:21 +0100 Subject: [PATCH 42/99] soft impute tests updated --- qolmat/imputations/imputers.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 744222e3..25fc5a2c 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -1823,11 +1823,9 @@ def get_model(self, **hyperparams) -> rpca_noisy.RpcaNoisy: model = rpca_noisy.RpcaNoisy(random_state=self._rng, verbose=self.verbose, **hyperparams) return model - def _fit_element( - self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 - ) -> rpca_noisy.RpcaNoisy: + def _fit_element(self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0) -> NDArray: """ - Fits the imputer on `df`, at the group and/or column level depending onself.groups and + Fits the imputer on `df`, at the group and/or column level depending on self.groups and self.columnwise. Parameters @@ -1841,8 +1839,8 @@ def _fit_element( Returns ------- - Any - Return fitted EM model + NDArray + Returns the reduced decomposition basis Raises ------ From 22ffb90c1f6f92e4b794b466ac72f9368442b892 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 26 Feb 2024 19:32:08 +0100 Subject: [PATCH 43/99] doc updated --- docs/api.rst | 4 +- docs/imputers.rst | 7 ++- examples/benchmark.md | 6 +-- tests/benchmark/test_comparator.py | 74 ------------------------------ 4 files changed, 11 insertions(+), 80 deletions(-) diff --git a/docs/api.rst b/docs/api.rst index 08e091dc..f1d5f631 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -23,7 +23,9 @@ Imputers imputations.imputers.ImputerOracle imputations.imputers.ImputerRegressor imputations.imputers.ImputerResiduals - imputations.imputers.ImputerRPCA + imputations.imputers.ImputerRpcaPcp + imputations.imputers.ImputerRpcaNoisy + imputations.imputers.ImputerSoftImpute imputations.imputers.ImputerShuffle Comparator diff --git a/docs/imputers.rst b/docs/imputers.rst index 5ad38b0c..b66898a8 100644 --- a/docs/imputers.rst +++ b/docs/imputers.rst @@ -22,7 +22,7 @@ Multiple Imputation by Chained Equation: multiple imputations based on ICE. It u 5. RPCA ------- -Robust Principal Component Analysis (RPCA) is a modification of the statistical procedure of PCA which allows to work with a data matrix :math:`\mathbf{D} \in \mathbb{R}^{n \times d}` containing missing values and grossly corrupted observations. We consider here the imputation task alone, but these methods can also tackle anomaly correction. See the :class:`~qolmat.imputations.imputers.ImputerRPCA` class. +Robust Principal Component Analysis (RPCA) is a modification of the statistical procedure of PCA which allows to work with a data matrix :math:`\mathbf{D} \in \mathbb{R}^{n \times d}` containing missing values and grossly corrupted observations. We consider here the imputation task alone, but these methods can also tackle anomaly correction. Two cases are considered. @@ -34,6 +34,7 @@ The class :class:`RPCAPCP` implements a matrix decomposition :math:`\mathbf{D} = \text{min}_{\mathbf{M} \in \mathbb{R}^{m \times n}} \quad \Vert \mathbf{M} \Vert_* + \lambda \Vert P_\Omega(\mathbf{D-M}) \Vert_1 with :math:`\mathbf{A} = \mathbf{D} - \mathbf{M}`. The operator :math:`P_{\Omega}` is the projection operator on the set of observed data :math:`\Omega`, so that there is no penalization for the components of :math:`A` corresponding to unobserved data. The imputed values are then given by the matrix :math:`M` on the unobserved data. +See the :class:`~qolmat.imputations.imputers.ImputerRpcaPcp` class for implementation details. **Noisy RPCA** [2, 3, 4] @@ -43,6 +44,7 @@ The class :class:`RPCANoisy` implements an recommanded improved version, which r \text{min}_{\mathbf{M, A} \in \mathbb{R}^{m \times n}} \quad \Vert P_{\Omega} (\mathbf{D}-\mathbf{M}-\mathbf{A}) \Vert_F^2 + \tau \Vert \mathbf{M} \Vert_* + \lambda \Vert \mathbf{A} \Vert_1 + \sum_{k=1}^K \eta_k \Vert \mathbf{M H_k} \Vert_p with :math:`\mathbf{E} = \mathbf{D} - \mathbf{M} - \mathbf{A}`. +See the :class:`~qolmat.imputations.imputers.ImputerRpcaNoisy` class for implementation details. 6. SoftImpute ------------- @@ -51,7 +53,8 @@ SoftImpute is an iterative method for matrix completion that uses nuclear-norm r .. math:: \text{minimise}_{\mathbf{M} \in \mathbb{R}^{n \times d}, rg(M) \leq r} \quad \Vert P_{\Omega}(\mathbf{D} - \mathbf{M}) \Vert_F^2 + \tau \Vert \mathbf{M} \Vert_* -The imputed values are then given by the matrix :math:`M=LQ` on the unobserved data. See the :class:`~qolmat.imputations.imputers.ImputerSoftImpute` class for implementation details. +The imputed values are then given by the matrix :math:`M=LQ` on the unobserved data. +See the :class:`~qolmat.imputations.imputers.ImputerSoftImpute` class for implementation details. 7. KNN ------ diff --git a/examples/benchmark.md b/examples/benchmark.md index 5ed680ba..a102cd7a 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -127,13 +127,13 @@ imputer_spline = imputers.ImputerInterpolation(groups=("station",), method="spli imputer_shuffle = imputers.ImputerShuffle(groups=("station",)) imputer_residuals = imputers.ImputerResiduals(groups=("station",), period=365, model_tsa="additive", extrapolate_trend="freq", method_interpolation="linear") -imputer_rpca = imputers.ImputerRPCA(groups=("station",), columnwise=False, max_iterations=500, tau=2, lam=0.05) -imputer_rpca_opti = imputers.ImputerRPCA(groups=("station",), columnwise=False, max_iterations=256) +imputer_rpca = imputers.ImputerRpcaNoisy(groups=("station",), columnwise=False, max_iterations=500, tau=2, lam=0.05) +imputer_rpca_opti = imputers.ImputerRpcaNoisy(groups=("station",), columnwise=False, max_iterations=256) dict_config_opti["RPCA_opti"] = { "tau": ho.hp.uniform("tau", low=.5, high=5), "lam": ho.hp.uniform("lam", low=.1, high=1), } -imputer_rpca_opticw = imputers.ImputerRPCA(groups=("station",), columnwise=False, max_iterations=256) +imputer_rpca_opticw = imputers.ImputerRpcaNoisy(groups=("station",), columnwise=False, max_iterations=256) dict_config_opti["RPCA_opticw"] = { "tau/TEMP": ho.hp.uniform("tau/TEMP", low=.5, high=5), "tau/PRES": ho.hp.uniform("tau/PRES", low=.5, high=5), diff --git a/tests/benchmark/test_comparator.py b/tests/benchmark/test_comparator.py index ec669a88..e69de29b 100644 --- a/tests/benchmark/test_comparator.py +++ b/tests/benchmark/test_comparator.py @@ -1,74 +0,0 @@ -# import numpy as np -# import pandas as pd -# import pytest - -# from qolmat.benchmark import comparator -# from qolmat.imputations.imputers import ImputerMedian, ImputerRPCA -# from qolmat.benchmark.missing_patterns import EmpiricalHoleGenerator -# import hyperopt as ho - -# df_origin = pd.DataFrame({"col1": [0, np.nan, 2, 4, np.nan], "col2": [-1, np.nan, 0.5, 1, 1.5]}) -# df_imputed = pd.DataFrame({"col1": [0, 1, 2, 3.5, 4], "col2": [-1.5, 0, 1.5, 2, 1.5]}) -# df_mask = pd.DataFrame( -# {"col1": [False, False, True, True, False], "col2": [True, False, True, True, False]} -# ) - -# cols_to_impute = ["col1", "col2"] -# generator_holes = EmpiricalHoleGenerator(n_splits=1, ratio_masked=0.5) -# dict_imputers = {"rpca": ImputerRPCA(max_iterations=100, tau=2)} -# dict_config_opti = {"rpca": {"lam": ho.hp.uniform("lam", low=0.1, high=1)}} - -# comparison_rpca = comparator.Comparator( -# dict_models=dict_imputers, -# selected_columns=cols_to_impute, -# generator_holes=generator_holes, -# dict_config_opti=dict_config_opti, -# ) - -# comparison_bug = comparator.Comparator( -# dict_models=dict_imputers, -# selected_columns=["bug"], -# generator_holes=generator_holes, -# dict_config_opti=dict_config_opti, -# ) - -# dict_comparison = {"rpca": comparison_rpca, "bug": comparison_bug} -# index_tuples_expected = pd.MultiIndex.from_product( -# [["mae", "wmape", "KL_columnwise"], ["col1", "col2"]] -# ) -# # data_expected = [3.0, 0.5, 0.75, 0.5, 37.88948, 39.68123] -# data_expected = [4.467175, 7.467187, 1.116794, 7.467187, 37.491336, 36.977574] -# result_expected = pd.Series(data_expected, index=index_tuples_expected) - - -# @pytest.mark.parametrize("df1", [df_origin]) -# @pytest.mark.parametrize("df2", [df_imputed]) -# @pytest.mark.parametrize("df_mask", [df_mask]) -# def test_comparator_get_errors( -# df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame -# ) -> None: -# result = comparison_rpca.get_errors(df_origin=df1, df_imputed=df2, df_mask=df_mask) -# assert isinstance(result, pd.Series) -# pd.testing.assert_index_equal(result.index, index_tuples_expected) -# assert result.notna().all() - - -# @pytest.mark.parametrize("df", [df_origin]) -# def test_comparator_evaluate_errors_sample(df: pd.DataFrame) -> None: -# result = comparison_rpca.evaluate_errors_sample(dict_imputers["rpca"], df) -# assert isinstance(result, pd.Series) -# pd.testing.assert_index_equal(result.index, index_tuples_expected) -# assert result.notna().all() - - -# @pytest.mark.parametrize("df", [df_origin]) -# @pytest.mark.parametrize("imputer", ["rpca", "bug"]) -# def test_comparator_compare(df: pd.DataFrame, imputer: str) -> None: -# comparison = dict_comparison[imputer] -# if imputer == "bug": -# np.testing.assert_raises(Exception, comparison.compare, df) -# else: -# result = comparison.compare(df) -# assert isinstance(result, pd.DataFrame) -# pd.testing.assert_index_equal(result.index, index_tuples_expected) -# assert result.notna().all().all() From a93e9f85f2026d63a4091ae14562b22b000466b3 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 27 Feb 2024 09:33:51 +0100 Subject: [PATCH 44/99] doctstring fixed --- qolmat/imputations/softimpute.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/qolmat/imputations/softimpute.py b/qolmat/imputations/softimpute.py index 3203c449..96a3aeaf 100644 --- a/qolmat/imputations/softimpute.py +++ b/qolmat/imputations/softimpute.py @@ -45,8 +45,8 @@ class SoftImpute(BaseEstimator, TransformerMixin): >>> import numpy as np >>> from qolmat.imputations.softimpute import SoftImpute >>> D = np.array([[1, 2, np.nan, 4], [1, 5, 3, np.nan], [4, 2, 3, 2], [1, 1, 5, 4]]) - >>> D = SoftImpute(random_state=11).fit_transform(D) - >>> print(D) + >>> M, A = SoftImpute(random_state=11).decompose(D) + >>> print(M + A) [[1. 2. 3.7242757 4. ] [1. 5. 3. 1.97846028] [4. 2. 3. 2. ] @@ -129,7 +129,7 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: A = U * D B = V * D M = A @ B.T - cost_start = self.cost_function(X, M, A, Omega, tau) + cost_start = SoftImpute.cost_function(X, M, A, Omega, tau) for iter_ in range(self.max_iterations): U_old = U V_old = V @@ -156,7 +156,7 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: A = U * D # Step 4 : Stopping upon convergence - ratio = self._check_convergence(U_old, D_old, V_old, U, D, V) + ratio = SoftImpute._check_convergence(U_old, D_old, V_old, U, D, V) if self.verbose: print(f"Iteration {iter_}: ratio = {round(ratio, 4)}") if ratio < self.tolerance: @@ -171,7 +171,7 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: A = np.where(Omega, X - M, 0) - cost_end = self.cost_function(X, M, A, Omega, tau) + cost_end = SoftImpute.cost_function(X, M, A, Omega, tau) if self.verbose and (cost_end > cost_start + 1e-9): warnings.warn( f"Convergence failed: cost function increased from" @@ -180,8 +180,8 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: return M, A + @staticmethod def _check_convergence( - self, U_old: NDArray, D_old: NDArray, V_old: NDArray, From 4b784dd9419dd2642a56f8bebe592c6b62096fa9 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 27 Feb 2024 09:35:01 +0100 Subject: [PATCH 45/99] doctstring fixed --- tests/imputations/test_imputers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index a1f1f23e..cab26a9c 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -261,7 +261,7 @@ def test_ImputerRegressor_fit_transform(df: pd.DataFrame) -> None: @pytest.mark.parametrize("df", [df_timeseries]) -def test_ImputerRPCA_fit_transform(df: pd.DataFrame) -> None: +def test_ImputerRpcaNoisy_fit_transform(df: pd.DataFrame) -> None: imputer = imputers.ImputerRpcaNoisy(columnwise=False, max_iterations=100, tau=1, lam=0.3) imputer = imputer.fit(df) result = imputer.transform(df) From 2d7119bf490569e4eeb0cec13b55a941a822f012 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 27 Feb 2024 09:53:28 +0100 Subject: [PATCH 46/99] doctest mended --- qolmat/imputations/softimpute.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/qolmat/imputations/softimpute.py b/qolmat/imputations/softimpute.py index 96a3aeaf..5d04b39b 100644 --- a/qolmat/imputations/softimpute.py +++ b/qolmat/imputations/softimpute.py @@ -45,10 +45,11 @@ class SoftImpute(BaseEstimator, TransformerMixin): >>> import numpy as np >>> from qolmat.imputations.softimpute import SoftImpute >>> D = np.array([[1, 2, np.nan, 4], [1, 5, 3, np.nan], [4, 2, 3, 2], [1, 1, 5, 4]]) - >>> M, A = SoftImpute(random_state=11).decompose(D) + >>> Omega = ~np.isnan(D) + >>> M, A = SoftImpute(random_state=11).decompose(D, Omega) >>> print(M + A) - [[1. 2. 3.7242757 4. ] - [1. 5. 3. 1.97846028] + [[1. 2. 4.12611456 4. ] + [1. 5. 3. 0.87217939] [4. 2. 3. 2. ] [1. 1. 5. 4. ]] """ @@ -159,9 +160,11 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: ratio = SoftImpute._check_convergence(U_old, D_old, V_old, U, D, V) if self.verbose: print(f"Iteration {iter_}: ratio = {round(ratio, 4)}") - if ratio < self.tolerance: - print(f"Convergence reached at iteration {iter_} with ratio = {round(ratio, 4)}") - break + if ratio < self.tolerance: + print( + f"Convergence reached at iteration {iter_} with ratio = {round(ratio, 4)}" + ) + break Xstar = np.where(Omega, X - A @ B.T, 0) + A @ B.T M = Xstar @ V From 8708ed7b115fc2913567e861ba230b18399afb1f Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 28 Feb 2024 11:56:44 +0100 Subject: [PATCH 47/99] history written --- HISTORY.rst | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/HISTORY.rst b/HISTORY.rst index 3deae21f..b5ca356f 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,6 +2,14 @@ History ======= +0.1.2 (2024-02-28) +------------------ + +* RPCA Noisy now has separate fit and transform methods, allowing to impute efficiently new data without retraining +* The class ImputerRPCA has been splitted between a class ImputerRpcaNoisy, which can fit then transform, and a class ImputerRpcaPcp which can only fit_transform +* The class SoftImpute has been recoded to better fit the architecture, and is more tested +* The class RPCANoisy now relies on sparse matrices for H, speeding it up for large instances + 0.1.1 (2023-11-03) ------------------- From ecef346d07128c080d806dda186da59f33897df2 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 28 Feb 2024 13:57:35 +0100 Subject: [PATCH 48/99] =?UTF-8?q?Bump=20version:=200.1.1=20=E2=86=92=200.1?= =?UTF-8?q?.2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 2 +- docs/conf.py | 2 +- qolmat/_version.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index e62aab6e..640894b5 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.1.1 +current_version = 0.1.2 commit = True tag = True diff --git a/docs/conf.py b/docs/conf.py index 38e22bd6..6f708aa9 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "Quantmetry" # The full version, including alpha/beta/rc tags -version = "0.1.1" +version = "0.1.2" release = version # -- General configuration --------------------------------------------------- diff --git a/qolmat/_version.py b/qolmat/_version.py index 485f44ac..b3f47562 100644 --- a/qolmat/_version.py +++ b/qolmat/_version.py @@ -1 +1 @@ -__version__ = "0.1.1" +__version__ = "0.1.2" diff --git a/setup.py b/setup.py index 295f2001..63566ae1 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import find_packages, setup DISTNAME = "qolmat" -VERSION = "0.1.1" +VERSION = "0.1.2" DESCRIPTION = "A Python library for optimal data imputation." LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst" with codecs.open("README.rst", encoding="utf-8-sig") as f: From 7fae88759317a4fbbe1b4ce48331a0e35dc7ad1e Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 28 Feb 2024 15:03:46 +0100 Subject: [PATCH 49/99] method kl forest removed, again --- examples/benchmark.md | 3 +- qolmat/benchmark/metrics.py | 107 +++++--------------------------- tests/benchmark/test_metrics.py | 21 +++---- 3 files changed, 23 insertions(+), 108 deletions(-) diff --git a/examples/benchmark.md b/examples/benchmark.md index a102cd7a..af92b16c 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -191,8 +191,7 @@ Concretely, the comparator takes as input a dataframe to impute, a proportion of Note these metrics compute reconstruction errors; it tells nothing about the distances between the "true" and "imputed" distributions. ```python -metrics = ["mae", "wmape", "KL_columnwise", "KL_forest", "ks_test", "dist_corr_pattern"] -# metrics = ["KL_forest"] +metrics = ["mae", "wmape", "KL_columnwise", "ks_test", "dist_corr_pattern"] comparison = comparator.Comparator( dict_imputers, cols_to_impute, diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index 1dd4e0a0..802315b6 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -19,7 +19,11 @@ def columnwise_metric( - df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame, metric: Callable, **kwargs + df1: pd.DataFrame, + df2: pd.DataFrame, + df_mask: pd.DataFrame, + metric: Callable, + **kwargs, ) -> pd.Series: """For each column, compute a metric score based on the true dataframe and the predicted dataframe @@ -171,7 +175,10 @@ def weighted_mean_absolute_percentage_error( def dist_wasserstein( - df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame, method: str = "columnwise" + df1: pd.DataFrame, + df2: pd.DataFrame, + df_mask: pd.DataFrame, + method: str = "columnwise", ) -> pd.Series: """Wasserstein distances between columns of 2 dataframes. Wasserstein distance can only be computed columnwise @@ -651,7 +658,10 @@ def sum_energy_distances(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataF def sum_pairwise_distances( - df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame, metric: str = "cityblock" + df1: pd.DataFrame, + df2: pd.DataFrame, + df_mask: pd.DataFrame, + metric: str = "cityblock", ) -> float: """Sum of pairwise distances based on a predefined metric. Metrics are found in this link @@ -766,50 +776,6 @@ def frechet_distance_pattern( return pd.Series(distance, index=["All"]) -def density_from_rf( - df: pd.DataFrame, estimator: BaseEnsemble, df_est: Optional[pd.DataFrame] = None -): - """Estimates the density of the empirical distribution given by df at the sample points given - by df_est. The estimation uses an random forest estimator and relies on the average number of - samples in the leaf corresponding to each estimation point. - - Disclaimer: this method is experimental and has no known theoretical grounds - - Parameters - ---------- - df : pd.DataFrame - Empirical distribution which density should be estimated - estimator : BaseEnsemble - Estimator defining the forest upon which is based the density counting. - df_est : pd.DataFrame, optional - Sample points of the estimation, by default None - If None, the density is estimated at the points given by `df`. - - Returns - ------- - pd.Series - Series of floats providing the normalized density - """ - if df_est is None: - df_est = df.copy() - if df_est.index.names == [None]: - cols_index = ["index"] - else: - cols_index = df_est.index.names - counts = pd.Series(0, index=df_est.index) - df_leafs = pd.DataFrame(estimator.apply(df), index=df.index) - df_leafs_est = pd.DataFrame(estimator.apply(df_est), index=df_est.index) - for i_tree in range(estimator.n_estimators): - leafs = df_leafs[i_tree].rename("id_leaf") - leafs_est = df_leafs_est[i_tree].rename("id_leaf") - counts_leafs = leafs.value_counts().rename("count") - df_merge = pd.merge(leafs_est.reset_index(), counts_leafs.reset_index(), on="id_leaf") - df_merge = df_merge.set_index(cols_index) - counts += df_merge["count"] - counts /= counts.sum() - return counts - - def kl_divergence_1D(df1: pd.Series, df2: pd.Series) -> float: """Estimation of the Kullback-Leibler divergence between the two 1D empirical distributions given by `df1`and `df2`. The samples are binarized using a uniform spacing with 20 bins from @@ -896,45 +862,6 @@ def kl_divergence_gaussian(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.Ser return div_kl -def kl_divergence_forest(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> float: - """Kullback-Leibler divergence estimation based on a random forest fitted on the first - empirical distribution - - Disclaimer: this method is experimental and has no known theoretical grounds - - Parameters - ---------- - df1 : pd.DataFrame - First empirical distribution - df2 : pd.DataFrame - Second empirical distribution - df_mask: pd.DataFrame - Mask indicating on what values the divergence should be computed - - Returns - ------- - pd.Series - Series of estimated metrics - """ - df1 = df1[df_mask.any(axis=1)] - df2 = df2[df_mask.any(axis=1)] - # df_1 = StandardScaler().fit_transform(df1[df_mask.any(axis=1)]) - # df_2 = StandardScaler().fit_transform(df2[df_mask.any(axis=1)]) - n_estimators = 100 - # estimator = sklearn.ensemble.RandomForestClassifier( - # n_estimators=n_estimators, max_depth=10 - # ) - # X = pd.concat([df1, df2]) - # y = pd.concat([pd.Series([False] * len(df1)), pd.Series([True] * len(df2))]) - # estimator.fit(X, y) - estimator = sklearn.ensemble.RandomTreesEmbedding(n_estimators=n_estimators, random_state=123) - estimator.fit(df1) - counts1 = density_from_rf(df1, estimator, df_est=df2) - counts2 = density_from_rf(df2, estimator, df_est=df2) - div_kl = np.mean(np.log(counts1 / counts2) * counts1 / counts2) - return div_kl - - def kl_divergence( df1: pd.DataFrame, df2: pd.DataFrame, @@ -948,7 +875,6 @@ def kl_divergence( - columnwise, relying on a uniform binarization and only taking marginals into account (https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence), - gaussian, relying on a Gaussian approximation, - - random_forest, experimental Parameters ---------- @@ -991,14 +917,10 @@ def kl_divergence( kl_divergence_gaussian, min_n_rows=min_n_rows, ) - elif method == "random_forest": - return pattern_based_weighted_mean_metric( - df1, df2, df_mask, kl_divergence_forest, min_n_rows=min_n_rows - ) else: raise AssertionError( f"The parameter of the function wasserstein_distance should be one of" - f"the following: [`columnwise`, `gaussian`, `random_forest`], not `{method}`!" + f"the following: [`columnwise`, `gaussian`], not `{method}`!" ) @@ -1086,7 +1008,6 @@ def get_metric(name: str) -> Callable: "wasserstein_columnwise": dist_wasserstein, "KL_columnwise": partial(kl_divergence, method="columnwise"), "KL_gaussian": partial(kl_divergence, method="gaussian"), - "KL_forest": partial(kl_divergence, method="random_forest"), "ks_test": kolmogorov_smirnov_test, "correlation_diff": mean_difference_correlation_matrix_numerical_features, "energy": sum_energy_distances, diff --git a/tests/benchmark/test_metrics.py b/tests/benchmark/test_metrics.py index a18c796a..df08fe8e 100644 --- a/tests/benchmark/test_metrics.py +++ b/tests/benchmark/test_metrics.py @@ -20,7 +20,10 @@ df_imputed = pd.DataFrame({"col1": [0, 1, 2, 3.5, 4], "col2": [-1.5, 0, 1.5, 2, 1.5]}) df_mask = pd.DataFrame( - {"col1": [False, False, True, True, False], "col2": [True, False, True, True, False]} + { + "col1": [False, False, True, True, False], + "col2": [True, False, True, True, False], + } ) @@ -131,17 +134,6 @@ def test_kl_divergence_gaussian( np.testing.assert_allclose(result, 1.371, atol=1e-3) -@pytest.mark.parametrize("df1", [df_incomplete]) -@pytest.mark.parametrize("df2", [df_imputed]) -@pytest.mark.parametrize("df_mask", [df_mask]) -def test_kl_divergence_forest(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> None: - result = metrics.kl_divergence_forest(df1, df1, df_mask) - np.testing.assert_allclose(result, 0, atol=1e-3) - - result = metrics.kl_divergence_forest(df1, df2, df_mask) - np.testing.assert_allclose(result, 6.21e-2, rtol=1e-2) - - @pytest.mark.parametrize("df1", [df_incomplete]) @pytest.mark.parametrize("df2", [df_imputed]) @pytest.mark.parametrize("df_mask", [df_mask]) @@ -230,7 +222,10 @@ def test_mean_difference_correlation_matrix_numerical_features( ) df_mask_cat = pd.DataFrame( - {"col1": [False, False, True, True, False], "col2": [True, False, True, True, False]} + { + "col1": [False, False, True, True, False], + "col2": [True, False, True, True, False], + } ) From eccb46d2e25dd2275fc54982854d2c59b1ec51b3 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 7 Mar 2024 16:46:00 +0100 Subject: [PATCH 50/99] history updated --- HISTORY.rst | 11 + docs/imputers.rst | 2 +- examples/benchmark.md | 153 +++++++------ qolmat/benchmark/metrics.py | 2 +- qolmat/imputations/em_sampler.py | 298 ++++++++++++++++++++----- qolmat/imputations/imputers.py | 61 +++-- qolmat/imputations/imputers_pytorch.py | 3 +- qolmat/imputations/rpca/rpca_noisy.py | 221 ++++++++++-------- qolmat/imputations/rpca/rpca_pcp.py | 8 +- qolmat/imputations/rpca/rpca_utils.py | 3 +- qolmat/utils/data.py | 20 +- qolmat/utils/exceptions.py | 9 + qolmat/utils/plot.py | 72 ++++-- qolmat/utils/utils.py | 15 ++ tests/imputations/test_em_sampler.py | 117 +++++++--- tests/imputations/test_imputers.py | 47 +--- tests/utils/test_data.py | 12 +- tests/utils/test_plot.py | 4 +- 18 files changed, 726 insertions(+), 332 deletions(-) diff --git a/HISTORY.rst b/HISTORY.rst index b5ca356f..64b4fbed 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,6 +2,17 @@ History ======= +0.1.3 (2024-03-07) +------------------ + +* RPCA algorithms now start with a normalizing scaler +* The EM algorithms now include a gradient projection step to be more robust to colinearity +* The EM algorithm based on the Gaussian model is now initialized using a robust estimation of the covariance matrix +* A bug in the EM algorithm has been patched: the normalizing matrix gamma was creating a sampling biais +* Speed up of the EM algorithm likelihood maximization, using the conjugate gradient method +* The ImputeRegressor class now handles the nans by `row` by default +* The metric `frechet` was not correctly called and has been patched + 0.1.2 (2024-02-28) ------------------ diff --git a/docs/imputers.rst b/docs/imputers.rst index b66898a8..ad95b6b9 100644 --- a/docs/imputers.rst +++ b/docs/imputers.rst @@ -41,7 +41,7 @@ See the :class:`~qolmat.imputations.imputers.ImputerRpcaPcp` class for implement The class :class:`RPCANoisy` implements an recommanded improved version, which relies on a decomposition :math:`\mathbf{D} = \mathbf{M} + \mathbf{A} + \mathbf{E}`. The additionnal term encodes a Gaussian noise and makes the numerical convergence more reliable. This class also implements a time-consistency penalization for time series, parametrized by the :math:`\eta_k`and :math:`H_k`. By defining :math:`\Vert \mathbf{MH_k} \Vert_p` is either :math:`\Vert \mathbf{MH_k} \Vert_1` or :math:`\Vert \mathbf{MH_k} \Vert_F^2`, the optimisation problem is the following .. math:: - \text{min}_{\mathbf{M, A} \in \mathbb{R}^{m \times n}} \quad \Vert P_{\Omega} (\mathbf{D}-\mathbf{M}-\mathbf{A}) \Vert_F^2 + \tau \Vert \mathbf{M} \Vert_* + \lambda \Vert \mathbf{A} \Vert_1 + \sum_{k=1}^K \eta_k \Vert \mathbf{M H_k} \Vert_p + \text{min}_{\mathbf{M, A} \in \mathbb{R}^{m \times n}} \quad \frac 1 2 \Vert P_{\Omega} (\mathbf{D}-\mathbf{M}-\mathbf{A}) \Vert_F^2 + \tau \Vert \mathbf{M} \Vert_* + \lambda \Vert \mathbf{A} \Vert_1 + \sum_{k=1}^K \eta_k \Vert \mathbf{M H_k} \Vert_p with :math:`\mathbf{E} = \mathbf{D} - \mathbf{M} - \mathbf{A}`. See the :class:`~qolmat.imputations.imputers.ImputerRpcaNoisy` class for implementation details. diff --git a/examples/benchmark.md b/examples/benchmark.md index af92b16c..a4f16135 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -8,9 +8,9 @@ jupyter: format_version: '1.3' jupytext_version: 1.14.4 kernelspec: - display_name: env_qolmat + display_name: env_qolmat_dev language: python - name: env_qolmat + name: env_qolmat_dev --- **This notebook aims to present the Qolmat repo through an example of a multivariate time series. @@ -28,6 +28,8 @@ import warnings %reload_ext autoreload %autoreload 2 +from IPython.display import Image + import pandas as pd from datetime import datetime import numpy as np @@ -82,12 +84,12 @@ n_cols = len(cols_to_impute) ``` ```python -fig = plt.figure(figsize=(10 * n_stations, 3 * n_cols)) +fig = plt.figure(figsize=(20 * n_stations, 6 * n_cols)) for i_station, (station, df) in enumerate(df_data.groupby("station")): df_station = df_data.loc[station] for i_col, col in enumerate(cols_to_impute): fig.add_subplot(n_cols, n_stations, i_col * n_stations + i_station + 1) - plt.plot(df_station[col], '.', label=station) + plt.plot(df_station[col], label=station) # break plt.ylabel(col) plt.xticks(rotation=15) @@ -127,7 +129,7 @@ imputer_spline = imputers.ImputerInterpolation(groups=("station",), method="spli imputer_shuffle = imputers.ImputerShuffle(groups=("station",)) imputer_residuals = imputers.ImputerResiduals(groups=("station",), period=365, model_tsa="additive", extrapolate_trend="freq", method_interpolation="linear") -imputer_rpca = imputers.ImputerRpcaNoisy(groups=("station",), columnwise=False, max_iterations=500, tau=2, lam=0.05) +imputer_rpca = imputers.ImputerRpcaNoisy(groups=("station",), columnwise=False, max_iterations=500, tau=.01, lam=5, rank=1) imputer_rpca_opti = imputers.ImputerRpcaNoisy(groups=("station",), columnwise=False, max_iterations=256) dict_config_opti["RPCA_opti"] = { "tau": ho.hp.uniform("tau", low=.5, high=5), @@ -141,9 +143,9 @@ dict_config_opti["RPCA_opticw"] = { "lam/PRES": ho.hp.uniform("lam/PRES", low=.1, high=1), } -imputer_ou = imputers.ImputerEM(groups=("station",), model="multinormal", method="sample", max_iter_em=34, n_iter_ou=15, dt=1e-3) -imputer_tsou = imputers.ImputerEM(groups=("station",), model="VAR", method="sample", max_iter_em=34, n_iter_ou=15, dt=1e-3, p=1) -imputer_tsmle = imputers.ImputerEM(groups=("station",), model="VAR", method="mle", max_iter_em=100, n_iter_ou=15, dt=1e-3, p=1) +imputer_normal_sample = imputers.ImputerEM(groups=("station",), model="multinormal", method="sample", max_iter_em=8, n_iter_ou=128, dt=4e-2) +imputer_var_sample = imputers.ImputerEM(groups=("station",), model="VAR", method="sample", max_iter_em=8, n_iter_ou=128, dt=4e-2, p=1) +imputer_var_max = imputers.ImputerEM(groups=("station",), model="VAR", method="mle", max_iter_em=8, n_iter_ou=128, dt=4e-2, p=1) imputer_knn = imputers.ImputerKNN(groups=("station",), n_neighbors=10) imputer_mice = imputers.ImputerMICE(groups=("station",), estimator=LinearRegression(), sample_posterior=False, max_iter=100) @@ -163,17 +165,17 @@ dict_imputers = { # "spline": imputer_spline, # "shuffle": imputer_shuffle, "residuals": imputer_residuals, - # "OU": imputer_ou, - "TSOU": imputer_tsou, - "TSMLE": imputer_tsmle, + "Normal_sample": imputer_normal_sample, + "VAR_sample": imputer_var_sample, + "VAR_max": imputer_var_max, "RPCA": imputer_rpca, # "RPCA_opti": imputer_rpca, # "RPCA_opticw": imputer_rpca_opti2, # "locf": imputer_locf, # "nocb": imputer_nocb, # "knn": imputer_knn, - "ols": imputer_regressor, - "mice_ols": imputer_mice, + "OLS": imputer_regressor, + "MICE_OLS": imputer_mice, } n_imputers = len(dict_imputers) ``` @@ -181,7 +183,7 @@ n_imputers = len(dict_imputers) In order to compare the methods, we $i)$ artificially create missing data (for missing data mechanisms, see the docs); $ii)$ then impute it using the different methods chosen and $iii)$ calculate the reconstruction error. These three steps are repeated a number of times equal to `n_splits`. For each method, we calculate the average error and compare the final errors.

- +

@@ -190,14 +192,14 @@ Concretely, the comparator takes as input a dataframe to impute, a proportion of Note these metrics compute reconstruction errors; it tells nothing about the distances between the "true" and "imputed" distributions. -```python -metrics = ["mae", "wmape", "KL_columnwise", "ks_test", "dist_corr_pattern"] +```python tags=[] +metrics = ["mae", "wmape", "KL_columnwise", "frechet"] comparison = comparator.Comparator( dict_imputers, cols_to_impute, generator_holes = generator_holes, metrics=metrics, - max_evals=10, + max_evals=2, dict_config_opti=dict_config_opti, ) results = comparison.compare(df_data) @@ -220,9 +222,9 @@ plt.show() ### **III. Comparison of methods** -We now run just one time each algorithm on the initial corrupted dataframe and compare the different performances through multiple analysis. +We now run just one time each algorithm on the initial corrupted dataframe and visualize the different imputations. -```python +```python tags=[] df_plot = df_data[cols_to_impute] ``` @@ -233,12 +235,17 @@ dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.i ```python station = df_plot.index.get_level_values("station")[0] df_station = df_plot.loc[station] +# dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} ``` Let's look at the imputations. When the data is missing at random, imputation is easier. Missing block are more challenging. +```python +dfs_imputed_station["VAR_max"] +``` + ```python for col in cols_to_impute: fig, ax = plt.subplots(figsize=(10, 3)) @@ -270,21 +277,21 @@ i_plot = 1 for i_col, col in enumerate(cols_to_impute): for name_imputer, df_imp in dfs_imputed_station.items(): - fig.add_subplot(n_columns, n_imputers, i_plot) + ax = fig.add_subplot(n_columns, n_imputers, i_plot) values_orig = df_station[col] values_imp = df_imp[col].copy() values_imp[values_orig.notna()] = np.nan - plt.plot(values_imp, marker="o", color=tab10(0), label=name_imputer, alpha=1) + plt.plot(values_imp, marker="o", color=tab10(0), label="imputation", alpha=1) plt.plot(values_orig, color='black', marker="o", label="original") plt.ylabel(col, fontsize=16) - if i_plot % n_columns == 1: - plt.legend(loc=[1, 0], fontsize=18) + if i_plot % n_imputers == 0: + plt.legend(loc="lower right", fontsize=18) plt.xticks(rotation=15) if i_col == 0: plt.title(name_imputer) if i_col != n_columns - 1: - plt.xticks([], []) + ax.set_xticklabels([]) loc = plticker.MultipleLocator(base=2*365) ax.xaxis.set_major_locator(loc) ax.tick_params(axis='both', which='major') @@ -297,7 +304,7 @@ plt.show() ## (Optional) Deep Learning Model -In this section, we present an MLP model of data imputation using Keras, which can be installed using a "pip install pytorch". +In this section, we present an MLP model of data imputation using PyTorch, which can be installed using a "pip install qolmat[pytorch]". ```python from qolmat.imputations import imputers_pytorch @@ -308,17 +315,6 @@ except ModuleNotFoundError: raise PyTorchExtraNotInstalled ``` -For the MLP model, we work on a dataset that corresponds to weather data with missing values. We add missing MCAR values on the features "TEMP", "PRES" and other features with NaN values. The goal is impute the missing values for the features "TEMP" and "PRES" by a Deep Learning method. We add features to take into account the seasonality of the data set and a feature for the station name - -```python -df = data.get_data("Beijing") -cols_to_impute = ["TEMP", "PRES"] -cols_with_nans = list(df.columns[df.isna().any()]) -df_data = data.add_datetime_features(df) -df_data[cols_with_nans + cols_to_impute] = data.add_holes(pd.DataFrame(df_data[cols_with_nans + cols_to_impute]), ratio_masked=.1, mean_size=120) -df_data -``` - For the example, we use a simple MLP model with 3 layers of neurons. Then we train the model without taking a group on the stations @@ -340,49 +336,75 @@ plt.show() ``` ```python -# estimator = nn.Sequential( -# nn.Linear(np.sum(df_data.isna().sum()==0), 256), -# nn.ReLU(), -# nn.Linear(256, 128), -# nn.ReLU(), -# nn.Linear(128, 64), -# nn.ReLU(), -# nn.Linear(64, 1) -# ) -estimator = imputers_pytorch.build_mlp(input_dim=np.sum(df_data.isna().sum()==0), list_num_neurons=[256,128,64]) -encoder, decoder = imputers_pytorch.build_autoencoder(input_dim=df_data.values.shape[1],latent_dim=4, output_dim=df_data.values.shape[1], list_num_neurons=[4*4, 2*4]) +n_variables = len(cols_to_impute) + +estimator = imputers_pytorch.build_mlp(input_dim=n_variables-1, list_num_neurons=[256,128,64]) +encoder, decoder = imputers_pytorch.build_autoencoder(input_dim=n_variables,latent_dim=4, output_dim=n_variables, list_num_neurons=[4*4, 2*4]) ``` ```python -dict_imputers["MLP"] = imputer_mlp = imputers_pytorch.ImputerRegressorPyTorch(estimator=estimator, groups=('station',), handler_nan = "column", epochs=500) +dict_imputers["MLP"] = imputer_mlp = imputers_pytorch.ImputerRegressorPyTorch(estimator=estimator, groups=('station',), epochs=500) dict_imputers["Autoencoder"] = imputer_autoencoder = imputers_pytorch.ImputerAutoencoder(encoder, decoder, max_iterations=100, epochs=100) dict_imputers["Diffusion"] = imputer_diffusion = imputers_pytorch.ImputerDiffusion(model=TabDDPM(num_sampling=5), epochs=100, batch_size=100) ``` We can re-run the imputation model benchmark as before. +```python +comparison = comparator.Comparator( + dict_imputers, + cols_to_impute, + generator_holes = generator_holes, + metrics=metrics, + max_evals=2, + dict_config_opti=dict_config_opti, +) +``` + ```python tags=[] generator_holes = missing_patterns.EmpiricalHoleGenerator(n_splits=3, groups=('station',), subset=cols_to_impute, ratio_masked=ratio_masked) comparison = comparator.Comparator( dict_imputers, - selected_columns = df_data.columns, + cols_to_impute, generator_holes = generator_holes, - metrics=["mae", "wmape", "KL_columnwise", "ks_test"], - max_evals=10, + metrics=metrics, + max_evals=2, dict_config_opti=dict_config_opti, ) results = comparison.compare(df_data) results.style.highlight_min(color="green", axis=1) ``` +```python +n_metrics = len(metrics) +fig = plt.figure(figsize=(24, 4 * n_metrics)) +for i, metric in enumerate(metrics): + fig.add_subplot(n_metrics, 1, i + 1) + df = results.loc[metric] + plot.multibar(df, decimals=2) + plt.ylabel(metric) + +#plt.savefig("figures/imputations_benchmark_errors.png") +plt.show() +``` + ```python tags=[] -df_plot = df_data +df_plot = df_data[cols_to_impute] +``` + +```python dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()} +``` + +```python station = df_plot.index.get_level_values("station")[0] df_station = df_plot.loc[station] dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} ``` -```python tags=[] +Let's look at the imputations. +When the data is missing at random, imputation is easier. Missing block are more challenging. + +```python for col in cols_to_impute: fig, ax = plt.subplots(figsize=(10, 3)) values_orig = df_station[col] @@ -399,39 +421,42 @@ for col in cols_to_impute: ax.xaxis.set_major_locator(loc) ax.tick_params(axis='both', which='major', labelsize=17) plt.show() + ``` ```python -n_columns = len(df_plot.columns) +# plot.plot_imputations(df_station, dfs_imputed_station) + +n_columns = len(cols_to_impute) n_imputers = len(dict_imputers) -fig = plt.figure(figsize=(8 * n_imputers, 6 * n_columns)) +fig = plt.figure(figsize=(12 * n_imputers, 4 * n_columns)) i_plot = 1 -for i_col, col in enumerate(df_plot): +for i_col, col in enumerate(cols_to_impute): for name_imputer, df_imp in dfs_imputed_station.items(): - fig.add_subplot(n_columns, n_imputers, i_plot) + ax = fig.add_subplot(n_columns, n_imputers, i_plot) values_orig = df_station[col] - plt.plot(values_orig, ".", color='black', label="original") - values_imp = df_imp[col].copy() values_imp[values_orig.notna()] = np.nan - plt.plot(values_imp, ".", color=tab10(0), label=name_imputer, alpha=1) + plt.plot(values_imp, marker="o", color=tab10(0), label="imputation", alpha=1) + plt.plot(values_orig, color='black', marker="o", label="original") plt.ylabel(col, fontsize=16) - if i_plot % n_columns == 1: - plt.legend(loc=[1, 0], fontsize=18) + if i_plot % n_imputers == 0: + plt.legend(loc="lower right", fontsize=18) plt.xticks(rotation=15) if i_col == 0: plt.title(name_imputer) if i_col != n_columns - 1: - plt.xticks([], []) + ax.set_xticklabels([]) loc = plticker.MultipleLocator(base=2*365) ax.xaxis.set_major_locator(loc) ax.tick_params(axis='both', which='major') i_plot += 1 -plt.savefig("figures/imputations_benchmark.png") + plt.show() + ``` ## Covariance diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index 802315b6..43f76b68 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -1011,7 +1011,7 @@ def get_metric(name: str) -> Callable: "ks_test": kolmogorov_smirnov_test, "correlation_diff": mean_difference_correlation_matrix_numerical_features, "energy": sum_energy_distances, - "frechet": frechet_distance, + "frechet": frechet_distance_pattern, "dist_corr_pattern": partial( pattern_based_weighted_mean_metric, metric=distance_anticorr, diff --git a/qolmat/imputations/em_sampler.py b/qolmat/imputations/em_sampler.py index 707991a6..93f577f1 100644 --- a/qolmat/imputations/em_sampler.py +++ b/qolmat/imputations/em_sampler.py @@ -1,5 +1,6 @@ from abc import abstractmethod from typing import Dict, List, Literal, Union +import warnings import numpy as np from numpy.typing import NDArray @@ -11,6 +12,10 @@ from qolmat.utils import utils +from matplotlib import pyplot as plt + +from qolmat.utils.exceptions import IllConditioned + def _conjugate_gradient(A: NDArray, X: NDArray, mask: NDArray) -> NDArray: """ @@ -20,7 +25,7 @@ def _conjugate_gradient(A: NDArray, X: NDArray, mask: NDArray) -> NDArray: Parameters ---------- A : NDArray - Symmetrical matrix defining the quadratic optimization problem + Symmetrical matrix defining the quadratic minimization problem X : NDArray Array containing the values to optimize mask : NDArray @@ -35,21 +40,32 @@ def _conjugate_gradient(A: NDArray, X: NDArray, mask: NDArray) -> NDArray: X_temp = X[rows_imputed, :].copy() mask = mask[rows_imputed, :].copy() n_iter = mask.sum(axis=1).max() + n_rows, n_cols = X_temp.shape X_temp[mask] = 0 b = -X_temp @ A b[~mask] = 0 - xn, pn, rn = np.zeros(X_temp.shape), b, b # Initialisation + xn, pn, rn = np.zeros((n_rows, n_cols)), b, b # Initialisation + alphan = np.zeros(n_rows) + betan = np.zeros(n_rows) for n in range(n_iter + 2): # if np.max(np.sum(rn**2)) < tolerance : # Condition de sortie " usuelle " # X_temp[mask_isna] = xn[mask_isna] # return X_temp.transpose() Apn = pn @ A Apn[~mask] = 0 - alphan = np.sum(rn**2, axis=1) / np.sum(pn * Apn, axis=1) - alphan[np.isnan(alphan)] = 0 # we stop updating if convergence is reached for this date + numerator = np.sum(rn**2, axis=1) + denominator = np.sum(pn * Apn, axis=1) + not_converged = denominator != 0 + # we stop updating if convergence is reached for this row + alphan[not_converged] = numerator[not_converged] / denominator[not_converged] + xn, rnp1 = xn + pn * alphan[:, None], rn - Apn * alphan[:, None] - betan = np.sum(rnp1**2, axis=1) / np.sum(rn**2, axis=1) - betan[np.isnan(betan)] = 0 # we stop updating if convergence is reached for this date + numerator = np.sum(rnp1**2, axis=1) + denominator = np.sum(rn**2, axis=1) + not_converged = denominator != 0 + # we stop updating if convergence is reached for this row + betan[not_converged] = numerator[not_converged] / denominator[not_converged] + pn, rn = rnp1 + pn * betan[:, None], rnp1 X_temp[mask] = xn[mask] @@ -116,6 +132,8 @@ class EM(BaseEstimator, TransformerMixin): stagnation_loglik : float, optional Threshold below which an absolute difference of the log likelihood indicates the convergence of the parameters + min_std: float, optional + Threshold below which the initial data matrix is considered ill-conditioned period : int, optional Integer used to fold the temporal data periodically verbose : bool, optional @@ -134,6 +152,7 @@ def __init__( tolerance: float = 1e-4, stagnation_threshold: float = 5e-3, stagnation_loglik: float = 2, + min_std: float = 1e-6, period: int = 1, verbose: bool = False, ): @@ -151,10 +170,14 @@ def __init__( self.stagnation_threshold = stagnation_threshold self.stagnation_loglik = stagnation_loglik + self.min_std = min_std + self.dict_criteria_stop: Dict[str, List] = {} self.period = period self.verbose = verbose self.n_samples = n_samples + self.hash_fit = 0 + self.shape = (0, 0) def _check_convergence(self) -> bool: return False @@ -176,6 +199,18 @@ def fit_parameters(self, X: NDArray): self.update_parameters(X) self.combine_parameters() + def fit_parameters_with_missingness(self, X: NDArray): + """ + First estimation of the model parameters based on data with missing values. + + Parameters + ---------- + X : NDArray + Data matrix with missingness + """ + X_imp = self.init_imputation(X) + self.fit_parameters(X_imp) + def update_criteria_stop(self, X: NDArray): self.loglik = self.get_loglikelihood(X) @@ -190,9 +225,22 @@ def gradient_X_loglik( ) -> NDArray: return np.empty # type: ignore #noqa - def get_gamma(self) -> NDArray: - n_rows, n_cols = self.shape_original - return np.ones((1, n_cols)) + def get_gamma(self, n_cols: int) -> NDArray: + """ + Normalization matrix in the sampling process. + + Parameters + ---------- + n_cols : int + Number of variables in the data matrix + + Returns + ------- + NDArray + Gamma matrix + """ + # return np.ones((1, n_cols)) + return np.eye(n_cols) def _maximize_likelihood(self, X: NDArray, mask_na: NDArray) -> NDArray: """Get the argmax of a posterior distribution using the BFGS algorithm. @@ -200,7 +248,7 @@ def _maximize_likelihood(self, X: NDArray, mask_na: NDArray) -> NDArray: Parameters ---------- X : NDArray - Input numpy array. + Input numpy array without missingness mask_na : NDArray Boolean dataframe indicating which coefficients should be resampled, and are therefore the variables of the optimization @@ -214,22 +262,19 @@ def _maximize_likelihood(self, X: NDArray, mask_na: NDArray) -> NDArray: def fun_obj(x): x_mat = X.copy() x_mat[mask_na] = x - return self.get_loglikelihood(x_mat) + return -self.get_loglikelihood(x_mat) def fun_jac(x): x_mat = X.copy() x_mat[mask_na] = x - grad_x = self.gradient_X_loglik(x_mat) - grad_x[~mask_na] = 0 + grad_x = -self.gradient_X_loglik(x_mat) + grad_x = grad_x[mask_na] return grad_x - res = spo.minimize(fun_obj, X[mask_na], jac=fun_jac) - - # for _ in range(1000): - # grad = self.gradient_X_loglik(X) - # grad[~mask_na] = 0 - # X += dt * grad + # the method BFGS is much slower, probabily not adapted to the high-dimension setting + res = spo.minimize(fun_obj, X[mask_na], jac=fun_jac, method="CG") x = res.x + X_sol = X.copy() X_sol[mask_na] = x return X_sol @@ -263,16 +308,17 @@ def _sample_ou( Sampled data matrix """ X_copy = X.copy() - n_variables, n_samples = X_copy.shape + n_rows, n_cols = X_copy.shape if estimate_params: self.reset_learned_parameters() X_init = X.copy() - gamma = self.get_gamma() + gamma = self.get_gamma(n_cols) sqrt_gamma = np.real(spl.sqrtm(gamma)) + for i in range(self.n_iter_ou): - noise = self.ampli * self.rng.normal(0, 1, size=(n_variables, n_samples)) - grad_X = self.gradient_X_loglik(X_copy) - X_copy += self.dt * grad_X @ gamma + np.sqrt(2 * self.dt) * noise @ sqrt_gamma + noise = self.ampli * self.rng.normal(0, 1, size=(n_rows, n_cols)) + grad_X = -self.gradient_X_loglik(X_copy) + X_copy += -self.dt * grad_X @ gamma + np.sqrt(2 * self.dt) * noise @ sqrt_gamma X_copy[~mask_na] = X_init[~mask_na] if estimate_params: self.update_parameters(X_copy) @@ -283,20 +329,27 @@ def fit_X(self, X: NDArray) -> None: mask_na = np.isnan(X) # first imputation - X = utils.linear_interpolation(X) - self.fit_parameters(X) + X_imp = self.init_imputation(X) + self._check_conditionning(X_imp) + + self.fit_parameters_with_missingness(X) if not np.any(mask_na): self.X = X + return + + X = self._maximize_likelihood(X_imp, mask_na) for iter_em in range(self.max_iter_em): X = self._sample_ou(X, mask_na) + self.combine_parameters() # Stop criteria self.update_criteria_stop(X) if self._check_convergence(): - print(f"EM converged after {iter_em} iterations.") + if self.verbose: + print(f"EM converged after {iter_em} iterations.") break self.dict_criteria_stop = {key: [] for key in self.dict_criteria_stop} @@ -359,23 +412,58 @@ def transform(self, X: NDArray) -> NDArray: Final array after EM sampling. """ mask_na = np.isnan(X) + X = X.copy() # shape_original = X.shape if hash(X.tobytes()) == self.hash_fit: X = self.X + warm_start = True else: X = utils.prepare_data(X, self.period) - X = utils.linear_interpolation(X) + X = self.init_imputation(X) + warm_start = False - if self.method == "mle": - X_transformed = self._maximize_likelihood(X, mask_na) - elif self.method == "sample": - X_transformed = self._sample_ou(X, mask_na, estimate_params=False) + if (self.method == "mle") or not warm_start: + X = self._maximize_likelihood(X, mask_na) + if self.method == "sample": + X = self._sample_ou(X, mask_na, estimate_params=False) - if np.all(np.isnan(X_transformed)): + if np.all(np.isnan(X)): raise AssertionError("Result contains NaN. This is a bug.") - return X_transformed + return X + + def _check_conditionning(self, X: NDArray): + """ + Check that the data matrix X is not ill-conditioned. Running the EM algorithm on data with + colinear columns leads to numerical instability and unconsistent results. + + Parameters + ---------- + X : NDArray + Data matrix + + Raises + ------ + IllConditioned + Data matrix is ill-conditioned due to colinear columns. + """ + n_rows, n_cols = X.shape + # if n_rows == 1 the function np.cov returns a float + if n_rows == 1: + min_sv = 0 + else: + cov = np.cov(X, bias=True, rowvar=False).reshape(n_cols, -1) + _, sv, _ = spl.svd(cov) + min_sv = min(np.sqrt(sv)) + if min_sv < self.min_std: + warnings.warn( + f"The covariance matrix is ill-conditioned, indicating high-colinearity: the " + f"smallest singular value of the data matrix is smaller than the threshold " + f"min_std ({min_sv} < {self.min_std}). Consider removing columns of decreasing " + f"the threshold." + ) + # raise IllConditioned(min_sv, self.min_std) class MultiNormalEM(EM): @@ -392,6 +480,8 @@ class MultiNormalEM(EM): n_iter_ou : int, optional Number of iterations for the Gibbs sampling method (+ noise addition), necessary for convergence, by default 50. + n_samples : int, optional + Number of data samples used to estimate the parameters of the distribution. Default, 10 ampli : float, optional Whether to sample the posterior (1) or to maximise likelihood (0), by default 1. @@ -420,6 +510,7 @@ def __init__( method: Literal["mle", "sample"] = "sample", max_iter_em: int = 200, n_iter_ou: int = 50, + n_samples: int = 10, ampli: float = 1, random_state: Union[None, int, np.random.RandomState] = None, dt: float = 2e-2, @@ -433,6 +524,7 @@ def __init__( method=method, max_iter_em=max_iter_em, n_iter_ou=n_iter_ou, + n_samples=n_samples, ampli=ampli, random_state=random_state, dt=dt, @@ -480,18 +572,28 @@ def gradient_X_loglik(self, X: NDArray) -> NDArray: grad_X = -(X - self.means) @ self.cov_inv return grad_X - def get_gamma(self) -> NDArray: + def get_gamma(self, n_cols: int) -> NDArray: """ - Normalisation matrix used to stabilize the sampling process + If the covariance matrix is not full-rank, defines the projection matrix keeping the + sampling process in the relevant subspace. + + Parameters + ---------- + n_cols : int + Number of variables in the data matrix Returns ------- NDArray Gamma matrix """ - # gamma = np.diag(np.diagonal(self.cov)) - gamma = self.cov + U, diag, Vt = spl.svd(self.cov) + diag_trunc = np.where(diag < self.min_std**2, 0, diag) + diag_trunc = np.where(diag_trunc == 0, 0, np.min(diag_trunc)) + + gamma = (U * diag_trunc) @ Vt # gamma = np.eye(len(self.cov)) + return gamma def update_criteria_stop(self, X: NDArray): @@ -554,6 +656,34 @@ def combine_parameters(self): self.cov = cov_intragroup + cov_intergroup self.cov_inv = np.linalg.pinv(self.cov) + def fit_parameters_with_missingness(self, X: NDArray): + """ + First estimation of the model parameters based on data with missing values. + + Parameters + ---------- + X : NDArray + Data matrix with missingness + """ + self.means = np.nanmean(X, axis=0) + self.cov = utils.nancov(X) + self.cov_inv = np.linalg.pinv(self.cov) + + def set_parameters(self, means: NDArray, cov: NDArray): + """ + Sets the model parameters from a user value. + + Parameters + ---------- + means : NDArray + Specified value for the mean vector + cov : NDArray + Specified value for the covariance matrix + """ + self.means = means + self.cov = cov + self.cov_inv = np.linalg.pinv(self.cov) + def _maximize_likelihood(self, X: NDArray, mask_na: NDArray) -> NDArray: """ Get the argmax of a posterior distribution. @@ -561,7 +691,7 @@ def _maximize_likelihood(self, X: NDArray, mask_na: NDArray) -> NDArray: Parameters ---------- X : NDArray - Input DataFrame. + Input DataFrame without missingness mask_na : NDArray Boolean dataframe indicating which coefficients should be resampled, and are therefore the variables of the optimization @@ -576,6 +706,22 @@ def _maximize_likelihood(self, X: NDArray, mask_na: NDArray) -> NDArray: X_imputed = self.means + X_imputed return X_imputed + def init_imputation(self, X: NDArray) -> NDArray: + """ + First simple imputation before iterating. + + Parameters + ---------- + X : NDArray + Data matrix, with missing values + + Returns + ------- + NDArray + Imputed matrix + """ + return utils.impute_nans(X, method="median") + def _check_convergence(self) -> bool: """ Check if the EM algorithm has converged. Three criteria: @@ -597,13 +743,19 @@ def _check_convergence(self) -> bool: list_logliks = self.dict_criteria_stop["logliks"] n_iter = len(list_means) - if n_iter < 10: + if n_iter < 3: return False min_diff_means1 = min_diff_Linf(list_covs, n_steps=1) min_diff_covs1 = min_diff_Linf(list_means, n_steps=1) min_diff_reached = min_diff_means1 < self.tolerance and min_diff_covs1 < self.tolerance + if min_diff_reached: + return True + + if n_iter < 7: + return False + min_diff_means5 = min_diff_Linf(list_covs, n_steps=5) min_diff_covs5 = min_diff_Linf(list_means, n_steps=5) @@ -617,8 +769,7 @@ def _check_convergence(self) -> bool: max_loglik = (min_diff_loglik5_ord1 < self.stagnation_loglik) or ( min_diff_loglik5_ord2 < self.stagnation_loglik ) - - return min_diff_reached or min_diff_stable or max_loglik + return min_diff_stable or max_loglik class VARpEM(EM): @@ -760,17 +911,28 @@ def gradient_X_loglik(self, X: NDArray) -> NDArray: return grad_1 + grad_2 - def get_gamma(self) -> NDArray: + def get_gamma(self, n_cols: int) -> NDArray: """ - Normalisation matrix used to stabilize the sampling process + If the noise matrix is not full-rank, defines the projection matrix keeping the + sampling process in the relevant subspace. Rescales the process to avoid instabilities. + + Parameters + ---------- + n_cols : int + Number of variables in the data matrix Returns ------- NDArray Gamma matrix """ - # gamma = np.diagonal(self.S).reshape(1, -1) - gamma = self.S + U, diag, Vt = spl.svd(self.S) + diag_trunc = np.where(diag < self.min_std**2, 0, diag) + diag_trunc = np.where(diag_trunc == 0, 0, np.min(diag_trunc)) + + gamma = (U * diag_trunc) @ Vt + # gamma = np.eye(len(self.cov)) + return gamma def update_criteria_stop(self, X: NDArray): @@ -841,9 +1003,40 @@ def combine_parameters(self) -> None: stack_YY = np.stack(list_YY) self.YY = np.mean(stack_YY, axis=0) self.S = self.YY - self.ZY.T @ self.B - self.B.T @ self.ZY + self.B.T @ self.ZZ @ self.B - self.S[self.S < 1e-12] = 0 + self.S[np.abs(self.S) < 1e-12] = 0 self.S_inv = np.linalg.pinv(self.S, rcond=1e-10) + def set_parameters(self, B: NDArray, S: NDArray): + """ + Sets the model parameters from a user value. + + Parameters + ---------- + means : NDArray + Specified value for the autoregression matrix + S : NDArray + Specified value for the noise covariance matrix + """ + self.B = B + self.S = S + self.S_inv = np.linalg.pinv(self.S) + + def init_imputation(self, X: NDArray) -> NDArray: + """ + First simple imputation before iterating. + + Parameters + ---------- + X : NDArray + Data matrix, with missing values + + Returns + ------- + NDArray + Imputed matrix + """ + return utils.linear_interpolation(X) + def _check_convergence(self) -> bool: """ Check if the EM algorithm has converged. Three criteria: @@ -866,13 +1059,19 @@ def _check_convergence(self) -> bool: list_logliks = self.dict_criteria_stop["logliks"] n_iter = len(list_B) - if n_iter < 10: + if n_iter < 3: return False min_diff_B1 = min_diff_Linf(list_B, n_steps=1) min_diff_S1 = min_diff_Linf(list_S, n_steps=1) min_diff_reached = min_diff_B1 < self.tolerance and min_diff_S1 < self.tolerance + if min_diff_reached: + return True + + if n_iter < 7: + return False + min_diff_B5 = min_diff_Linf(list_B, n_steps=5) min_diff_S5 = min_diff_Linf(list_S, n_steps=5) min_diff_stable = ( @@ -884,5 +1083,4 @@ def _check_convergence(self) -> bool: max_loglik = (max_loglik5_ord1 < self.stagnation_loglik) or ( max_loglik5_ord2 < self.stagnation_loglik ) - - return min_diff_reached or min_diff_stable or max_loglik + return min_diff_stable or max_loglik diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 25fc5a2c..96cd8778 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -201,12 +201,15 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame: cols_with_nans = df.columns[df.isna().any()] - if self.columnwise: - df_imputed = df.copy() - for col in cols_with_nans: - df_imputed[col] = self._transform_allgroups(df[[col]], col=col) + if cols_with_nans.empty: + df_imputed = df else: - df_imputed = self._transform_allgroups(df) + if self.columnwise: + df_imputed = df.copy() + for col in cols_with_nans: + df_imputed[col] = self._transform_allgroups(df[[col]], col=col) + else: + df_imputed = self._transform_allgroups(df) if df_imputed.isna().any().any(): raise AssertionError("Result of imputation contains NaN!") @@ -1456,6 +1459,7 @@ class ImputerRegressor(_Imputer): - if `row` all non complete rows will be removed from the train dataset, and will not be used for the inferance, - if `column` all non complete columns will be ignored. + By default, `row` random_state : Union[None, int, np.random.RandomState], optional Controls the randomness of the fit_transform, by default None @@ -1484,7 +1488,7 @@ def __init__( imputer_params: Tuple[str, ...] = ("handler_nan",), groups: Tuple[str, ...] = (), estimator: Optional[BaseEstimator] = None, - handler_nan: str = "column", + handler_nan: str = "row", random_state: Union[None, int, np.random.RandomState] = None, ): super().__init__( @@ -1547,7 +1551,6 @@ def _fit_element( assert col == "__all__" cols_with_nans = df.columns[df.isna().any()] dict_estimators: Dict[str, BaseEstimator] = dict() - for col in cols_with_nans: # Selects only the valid values in the Train Set according to the chosen method X, y = self.get_Xy_valid(df, col) @@ -1604,6 +1607,8 @@ def _transform_element( # Selects only non-NaN values for the Test Set is_na = y.isna() + if not np.any(is_na): + continue X = X.loc[is_na] y_hat = self._predict_estimator(model, X) @@ -1720,7 +1725,13 @@ def _transform_element( Omega = ~np.isnan(D) # D = utils.linear_interpolation(D) - M, A = model.decompose(D, Omega) + means = np.nanmean(D, axis=0) + stds = np.nanstd(D, axis=0) + stds = np.where(stds, stds, 1) + D_scale = (D - means) / stds + M, A = model.decompose(D_scale, Omega) + M = M * stds + means + A = A * stds + means M_final = utils.get_shape_original(M, X.shape) A_final = utils.get_shape_original(A, X.shape) @@ -1823,7 +1834,9 @@ def get_model(self, **hyperparams) -> rpca_noisy.RpcaNoisy: model = rpca_noisy.RpcaNoisy(random_state=self._rng, verbose=self.verbose, **hyperparams) return model - def _fit_element(self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0) -> NDArray: + def _fit_element( + self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 + ) -> Tuple[NDArray, NDArray, NDArray]: """ Fits the imputer on `df`, at the group and/or column level depending on self.groups and self.columnwise. @@ -1839,8 +1852,11 @@ def _fit_element(self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0) Returns ------- - NDArray - Returns the reduced decomposition basis + Tuple + A tuple made of: + - the reduced decomposition basis + - the estimated mean of the columns + - the estimated standard deviation of the columns Raises ------ @@ -1855,9 +1871,14 @@ def _fit_element(self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0) D = utils.prepare_data(X, self.period) Omega = ~np.isnan(D) # D = utils.linear_interpolation(D) - _, _, _, Q = model.decompose_with_basis(X, Omega) - return Q + means = np.nanmean(D, axis=0) + stds = np.nanstd(D, axis=0) + stds = np.where(stds, stds, 1) + D_scale = (D - means) / stds + _, _, _, Q = model.decompose_with_basis(D_scale, Omega) + + return Q, means, stds def _transform_element( self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 @@ -1895,14 +1916,16 @@ def _transform_element( Omega = ~np.isnan(D) # D = utils.linear_interpolation(D) - Q = self._dict_fitting[col][ngroup] - M, A = model.decompose_on_basis(D, Omega, Q) + Q, means, stds = self._dict_fitting[col][ngroup] + + D_scale = (D - means) / stds + M, A = model.decompose_on_basis(D_scale, Omega, Q) + M = M * stds + means + A = A * stds + means M_final = utils.get_shape_original(M, X.shape) - A_final = utils.get_shape_original(A, X.shape) - X_imputed = M_final + A_final - df_imputed = pd.DataFrame(X_imputed, index=df.index, columns=df.columns) + df_imputed = pd.DataFrame(M_final, index=df.index, columns=df.columns) df_imputed = df.where(~df.isna(), df_imputed) return df_imputed @@ -2230,6 +2253,8 @@ def _transform_element( """ self._check_dataframe(df) + if df.notna().all().all(): + return df model = self._dict_fitting[col][ngroup] X = df.values.astype(float) diff --git a/qolmat/imputations/imputers_pytorch.py b/qolmat/imputations/imputers_pytorch.py index ed6cc198..c2ee8a4a 100644 --- a/qolmat/imputations/imputers_pytorch.py +++ b/qolmat/imputations/imputers_pytorch.py @@ -35,6 +35,7 @@ class ImputerRegressorPyTorch(ImputerRegressor): - if `row` all non complete rows will be removed from the train dataset, and will not be used for the inferance, - if `column`all non complete columns will be ignored. + By default, `row` epochs: int Number of epochs when fitting the autoencoder, by default 100 learning_rate: float @@ -47,7 +48,7 @@ def __init__( self, groups: Tuple[str, ...] = (), estimator: Optional[nn.Sequential] = None, - handler_nan: str = "column", + handler_nan: str = "row", epochs: int = 100, learning_rate: float = 0.001, loss_fn: Callable = nn.L1Loss(), diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index d7a1a06d..8f836d99 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -28,32 +28,6 @@ class RpcaNoisy(RPCA): Chen, Yuxin, et al. "Bridging convex and nonconvex optimization in robust PCA: Noise, outliers and missing data." The Annals of Statistics 49.5 (2021): 2948-2971. - - Parameters - ---------- - random_state : int, optional - The seed of the pseudo random number generator to use, for reproductibility. - rank: Optional[int] - (estimated) low-rank of the matrix D - mu: Optional[float] - initial stiffness parameter for the constraint on M, L and Q - tau: Optional[float] - penalizing parameter for the nuclear norm - lam: Optional[float] - penalizing parameter for the sparse matrix - list_periods: Optional[List[int]] - list of periods, linked to the Toeplitz matrices - list_etas: Optional[List[float]] - list of penalizing parameters for the corresponding period in list_periods - max_iterations: Optional[int] - stopping criteria, maximum number of iterations. By default, the value is set to 10_000 - tolerance: Optional[float] - stoppign critera, minimum difference between 2 consecutive iterations. By default, - the value is set to 1e-6 - norm: Optional[str] - error norm, can be "L1" or "L2". By default, the value is set to "L2" - verbose: Optional[bool] - verbosity level, if False the warnings are silenced """ def __init__( @@ -70,6 +44,33 @@ def __init__( norm: str = "L2", verbose: bool = True, ) -> None: + """ + Parameters + ---------- + random_state : int, optional + The seed of the pseudo random number generator to use, for reproductibility. + rank: Optional[int] + Upper bound of the rank to be estimated + mu: Optional[float] + initial stiffness parameter for the constraint M = L Q + tau: Optional[float] + penalizing parameter for the nuclear norm + lam: Optional[float] + penalizing parameter for the sparse matrix + list_periods: Optional[List[int]] + list of periods, linked to the Toeplitz matrices + list_etas: Optional[List[float]] + list of penalizing parameters for the corresponding period in list_periods + max_iterations: Optional[int] + stopping criteria, maximum number of iterations. By default, the value is set to 10_000 + tolerance: Optional[float] + stoppign critera, minimum difference between 2 consecutive iterations. By default, + the value is set to 1e-6 + norm: Optional[str] + error norm, can be "L1" or "L2". By default, the value is set to "L2" + verbose: Optional[bool] + verbosity level, if False the warnings are silenced + """ super().__init__(max_iterations=max_iterations, tolerance=tolerance, verbose=verbose) self.rng = sku.check_random_state(random_state) self.rank = rank @@ -101,7 +102,6 @@ def get_params_scale(self, D: NDArray) -> Dict[str, float]: Regularization parameter for the L1 norm. """ - D = utils.linear_interpolation(D) rank = rpca_utils.approx_rank(D) tau = 1.0 / np.sqrt(max(D.shape)) lam = tau @@ -136,7 +136,8 @@ def decompose_with_basis( self, D: NDArray, Omega: NDArray ) -> Tuple[NDArray, NDArray, NDArray, NDArray]: """ - Compute the noisy RPCA with L1 or L2 time penalisation + Compute the noisy RPCA with L1 or L2 time penalisation, and returns the decomposition of + the low-rank matrix. Parameters ---------- @@ -156,7 +157,7 @@ def decompose_with_basis( Q: NDArray Reduced basis of the low-rank matrix """ - + D = utils.linear_interpolation(D) self.params_scale = self.get_params_scale(D) if self.lam is not None: @@ -178,13 +179,6 @@ def decompose_with_basis( "The periods provided in argument in `list_periods` must smaller " f"than the number of rows in the matrix but {period} >= {n_rows}!" ) - # if (n_rows == 1) or (n_cols == 1): - # warnings.warn( - # f"RPCA algorithm may provide bad results. Function {function_str} increased from" - # f" {cost_start} to {cost_end} instead of decreasing!".format("%.2f") - # ) - - D = utils.linear_interpolation(D) M, A, L, Q = self.minimise_loss( D, @@ -219,7 +213,11 @@ def minimise_loss( norm: str = "L2", ) -> Tuple: """ - Compute the noisy RPCA with a L2 time penalisation + Compute the noisy RPCA with a L2 time penalisation. + + This function computes the noisy Robust Principal Component Analysis (RPCA) using a L2 time + penalisation. It iteratively minimizes a loss function to separate the low-rank and sparse + components from the input data matrix. Parameters ---------- @@ -227,40 +225,49 @@ def minimise_loss( Observations matrix of shape (m, n). Omega : np.ndarray Binary matrix indicating the observed entries of D, shape (m, n). - rank: Optional[int] - (estimated) low-rank of the matrix D - tau: Optional[float] - penalizing parameter for the nuclear norm - lam: Optional[float] - penalizing parameter for the sparse matrix - mu: Optional[float] - initial stiffness parameter for the constraint on M, L and Q - list_periods: Optional[List[int]] - list of periods, linked to the Toeplitz matrices - list_etas: Optional[List[float]] - list of penalizing parameters for the corresponding period in list_periods - max_iterations: Optional[int] - stopping criteria, maximum number of iterations. By default, the value is set to 10_000 - tolerance: Optional[float] - stoppign critera, minimum difference between 2 consecutive iterations. By default, - the value is set to 1e-6 - norm: Optional[str] - error norm, can be "L1" or "L2". By default, the value is set to "L2" + rank : int + Estimated low-rank of the matrix D. + tau : float + Penalizing parameter for the nuclear norm. + lam : float + Penalizing parameter for the sparse matrix. + mu : float, optional + Initial stiffness parameter for the constraint on M, L, and Q. Defaults + to 1e-2. + list_periods : List[int], optional + List of periods linked to the Toeplitz matrices. Defaults to []. + list_etas : List[float], optional + List of penalizing parameters for the corresponding periods in list_periods. Defaults + to []. + max_iterations : int, optional + Stopping criteria, maximum number of iterations. Defaults to 10000. + tolerance : float, optional + Stopping criteria, minimum difference between 2 consecutive iterations. + Defaults to 1e-6. + norm : str, optional + Error norm, can be "L1" or "L2". Defaults to "L2". Returns ------- - M : np.ndarray - Low-rank signal matrix of shape (m, n). - A : np.ndarray - Anomalies matrix of shape (m, n). - L : np.ndarray - Basis Unitary array of shape (m, rank). - Q : np.ndarray - Basis Unitary array of shape (rank, n). + Tuple + A tuple containing the following elements: + - M : np.ndarray + Low-rank signal matrix of shape (m, n). + - A : np.ndarray + Anomalies matrix of shape (m, n). + - L : np.ndarray + Basis unitary array of shape (m, rank). + - Q : np.ndarray + Basis unitary array of shape (rank, n). + + Raises + ------ + ValueError + If the periods provided in the argument in `list_periods` are not + smaller than the number of rows in the matrix. """ - print("minimise_loss") rho = 1.1 n_rows, n_cols = D.shape @@ -316,7 +323,6 @@ def minimise_loss( A_Omega = rpca_utils.soft_thresholding(D - M, lam) A_Omega_C = D - M A = np.where(Omega, A_Omega, A_Omega_C) - Q = scp.linalg.solve( a=tau * Ir + mu * (L.T @ L), b=L.T @ (mu * M + Y), @@ -360,6 +366,27 @@ def decompose_on_basis( Omega: NDArray, Q: NDArray, ) -> Tuple[NDArray, NDArray]: + """ + Decompose the matrix D with an observation matrix Omega using the noisy RPCA algorithm, + with a fixed reduced basis given by the matrix Q. This allows to impute new data without + resolving the optimization problem on the whole dataset. + + Parameters + ---------- + D : NDArray + _description_ + Omega : NDArray + _description_ + Q : NDArray + _description_ + + Returns + ------- + Tuple[NDArray, NDArray] + A tuple representing the decomposition of D with: + - M: low-rank matrix + - A: sparse matrix + """ D = utils.linear_interpolation(D) params_scale = self.get_params_scale(D) @@ -402,23 +429,24 @@ def decompose_on_basis( def _check_cost_function_minimized( self, - observations: NDArray, - low_rank: NDArray, - anomalies: NDArray, + D: NDArray, + M: NDArray, + A: NDArray, Omega: NDArray, tau: float, lam: float, ): - """Check that the functional minimized by the RPCA - is smaller at the end than at the beginning + """ + Check that the functional minimized by the RPCA is smaller at the end than at the + beginning. Parameters ---------- - observations : NDArray + D : NDArray observations matrix with first linear interpolation - low_rank : NDArray + M : NDArray low_rank matrix resulting from RPCA - anomalies : NDArray + A : NDArray sparse matrix resulting from RPCA Omega: NDArrau boolean matrix indicating the observed values @@ -428,9 +456,9 @@ def _check_cost_function_minimized( parameter penalizing the L1-norm of the anomaly/sparse part """ cost_start = self.cost_function( - observations, - observations, - np.full_like(observations, 0), + D, + D, + np.full_like(D, 0), Omega, tau, lam, @@ -439,9 +467,9 @@ def _check_cost_function_minimized( norm=self.norm, ) cost_end = self.cost_function( - observations, - low_rank, - anomalies, + D, + M, + A, Omega, tau, lam, @@ -449,12 +477,12 @@ def _check_cost_function_minimized( self.list_etas, norm=self.norm, ) - function_str = "1/2 $ ||D-M-A||_2 + tau ||D||_* + lam ||A||_1" + function_str = "1/2 ||D-M-A||_2 + tau ||D||_* + lam ||A||_1" if len(self.list_etas) > 0: for eta in self.list_etas: function_str += f"{eta} ||MH||_{self.norm}" - if self.verbose and (round(cost_start, 4) - round(cost_end, 4)) <= -1e-2: + if self.verbose and (cost_end > cost_start * (1 + 1e-6)): warnings.warn( f"RPCA algorithm may provide bad results. Function {function_str} increased from" f" {cost_start} to {cost_end} instead of decreasing!".format("%.2f") @@ -462,9 +490,9 @@ def _check_cost_function_minimized( @staticmethod def cost_function( - observations: NDArray, - low_rank: NDArray, - anomalies: NDArray, + D: NDArray, + M: NDArray, + A: NDArray, Omega: NDArray, tau: float, lam: float, @@ -473,15 +501,15 @@ def cost_function( norm: str = "L2", ): """ - Compute cost function for different RPCA algorithm + Estimated cost function for the noisy RPCA algorithm Parameters ---------- - observations : NDArray + D : NDArray Matrix of observations - low_rank : NDArray + M : NDArray Low-rank signal - anomalies : NDArray + A : NDArray Anomalies Omega : NDArray Mask for observations @@ -506,20 +534,17 @@ def cost_function( temporal_norm: float = 0 if len(list_etas) > 0: # matrices for temporal correlation - list_H = [ - rpca_utils.toeplitz_matrix(period, observations.shape[0]) - for period in list_periods - ] + list_H = [rpca_utils.toeplitz_matrix(period, D.shape[0]) for period in list_periods] if norm == "L1": for eta, H_matrix in zip(list_etas, list_H): - temporal_norm += eta * np.sum(np.abs(H_matrix @ low_rank)) + temporal_norm += eta * np.sum(np.abs(H_matrix @ M)) elif norm == "L2": for eta, H_matrix in zip(list_etas, list_H): - temporal_norm += eta * float(np.linalg.norm(H_matrix @ low_rank, "fro")) - anomalies_norm = np.sum(np.abs(anomalies * Omega)) + temporal_norm += eta * float(np.linalg.norm(H_matrix @ M, "fro")) + anomalies_norm = np.sum(np.abs(A * Omega)) cost = ( - 1 / 2 * ((Omega * (observations - low_rank - anomalies)) ** 2).sum() - + tau * np.linalg.norm(low_rank, "nuc") + 1 / 2 * ((Omega * (D - M - A)) ** 2).sum() + + tau * np.linalg.norm(M, "nuc") + lam * anomalies_norm + temporal_norm ) diff --git a/qolmat/imputations/rpca/rpca_pcp.py b/qolmat/imputations/rpca/rpca_pcp.py index 67dde3cb..f3b8e751 100644 --- a/qolmat/imputations/rpca/rpca_pcp.py +++ b/qolmat/imputations/rpca/rpca_pcp.py @@ -75,8 +75,7 @@ def get_params_scale(self, D: NDArray): Regularization parameter for the L1 norm. """ - D = utils.linear_interpolation(D) - mu = D.size / (4.0 * rpca_utils.l1_norm(D)) + mu = min(1e3, D.size / (4.0 * rpca_utils.l1_norm(D))) lam = 1 / np.sqrt(np.max(D.shape)) dict_params = {"mu": mu, "lam": lam} return dict_params @@ -100,13 +99,14 @@ def decompose(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]: A: NDArray Anomalies """ + D = utils.linear_interpolation(D) + if np.all(D == 0): + return D, D params_scale = self.get_params_scale(D) mu = params_scale["mu"] if self.mu is None else self.mu lam = params_scale["lam"] if self.lam is None else self.lam - D = utils.linear_interpolation(D) - D_norm = np.linalg.norm(D, "fro") A = np.array(np.full_like(D, 0)) diff --git a/qolmat/imputations/rpca/rpca_utils.py b/qolmat/imputations/rpca/rpca_utils.py index 592d97ce..9e6c8945 100644 --- a/qolmat/imputations/rpca/rpca_utils.py +++ b/qolmat/imputations/rpca/rpca_utils.py @@ -29,6 +29,8 @@ def approx_rank( int: Approximated rank of M """ + if np.all(M == 0): + return 1 if threshold == 1: return min(M.shape) _, values_singular, _ = np.linalg.svd(M, full_matrices=False) @@ -80,7 +82,6 @@ def svd_thresholding(X: NDArray, threshold: float) -> NDArray: V is the array of the right singular vectors of X s is the array of the singular values as a diagonal array """ - U, s, Vh = np.linalg.svd(X, full_matrices=False) s = soft_thresholding(s, threshold) return U @ (np.diag(s) @ Vh) diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index e8678a10..2edd7c7f 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -100,7 +100,19 @@ def get_data( url_zenodo = "https://zenodo.org/record/" if name_data == "Beijing": df = read_csv_local("beijing") - df = df.set_index(["station", "date"]) + df["date"] = pd.to_datetime(df["date"]) + + # df["date"] = pd.to_datetime( + # { + # "year": df["year"], + # "month": df["month"], + # "day": df["day"], + # "hour": df["hour"], + # } + # ) + df = df.drop(columns=["year", "month", "day", "hour", "wd"]) + # df = df.set_index(["station", "date"]) + df = df.groupby(["station", "date"]).mean() return df if name_data == "Superconductor": df = read_csv_local("conductors") @@ -173,7 +185,8 @@ def get_data( return df elif name_data == "Monach_electricity_australia": urllink = os.path.join( - url_zenodo, "4659727/files/australian_electricity_demand_dataset.zip?download=1" + url_zenodo, + "4659727/files/australian_electricity_demand_dataset.zip?download=1", ) zipname = "australian_electricity_demand_dataset" list_loaded_data = download_data_from_zip(zipname, urllink, datapath=datapath) @@ -216,7 +229,8 @@ def preprocess_data_beijing(df: pd.DataFrame) -> pd.DataFrame: df["station"] = "Beijing" df.set_index(["station", "datetime"], inplace=True) df.drop( - columns=["year", "month", "day", "hour", "No", "cbwd", "Iws", "Is", "Ir"], inplace=True + columns=["year", "month", "day", "hour", "No", "cbwd", "Iws", "Is", "Ir"], + inplace=True, ) df.sort_index(inplace=True) df = df.groupby( diff --git a/qolmat/utils/exceptions.py b/qolmat/utils/exceptions.py index 5494ede6..eb00da95 100644 --- a/qolmat/utils/exceptions.py +++ b/qolmat/utils/exceptions.py @@ -56,3 +56,12 @@ def __init__(self): class SingleSample(Exception): def __init__(self): super().__init__("""This imputer cannot be fitted on a single sample!""") + + +class IllConditioned(Exception): + def __init__(self, min_sv: float, min_std: float): + super().__init__( + f"The covariance matrix is ill-conditioned, indicating high-colinearity: the smallest " + f"singular value of the data matrix is smaller than the threshold min_std ({min_sv} < " + f"{min_std}). Consider removing columns of decreasing the threshold." + ) diff --git a/qolmat/utils/plot.py b/qolmat/utils/plot.py index d37d3f46..c6700e13 100644 --- a/qolmat/utils/plot.py +++ b/qolmat/utils/plot.py @@ -156,8 +156,9 @@ def plot_images( def make_ellipses( - x: NDArray, - y: NDArray, + mean_x: float, + mean_y: float, + cov: NDArray, ax: mpl.axes.Axes, n_std: float = 2, color: Union[str, Any, Tuple[float, float, float]] = "None", @@ -167,9 +168,12 @@ def make_ellipses( Parameters ---------- - x, y : array-like, shape (n, ) - Input data. - + mean_x : float + Abscisse of the ellipse center + mean_y : float + Ordinate of the ellipse center + cov : NDArray + Covariance matrix defining the ellipse ax : matplotlib.axes.Axes The axes object to draw the ellipse into. @@ -183,18 +187,13 @@ def make_ellipses( ------- matplotlib.patches.Ellipse """ - if x.size != y.size: - raise ValueError("x and y must be the same size") - cov = np.cov(x, y) pearson = cov[0, 1] / np.sqrt(cov[0, 0] * cov[1, 1]) ell_radius_x = np.sqrt(1 + pearson) * 2.5 ell_radius_y = np.sqrt(1 - pearson) * 2.5 ell = mpl.patches.Ellipse((0, 0), width=ell_radius_x, height=ell_radius_y, facecolor=color) scale_x = np.sqrt(cov[0, 0]) * n_std - mean_x = np.mean(x) scale_y = np.sqrt(cov[1, 1]) * n_std - mean_y = np.mean(y) transf = ( mpl.transforms.Affine2D().rotate_deg(45).scale(scale_x, scale_y).translate(mean_x, mean_y) ) @@ -205,6 +204,43 @@ def make_ellipses( ax.set_aspect("equal", "datalim") +def make_ellipses_from_data( + x: NDArray, + y: NDArray, + ax: mpl.axes.Axes, + n_std: float = 2, + color: Union[str, Any, Tuple[float, float, float]] = "None", +): + """ + Create a plot of the covariance confidence ellipse of *x* and *y*. + + Parameters + ---------- + x, y : array-like, shape (n, ) + Input data. + + ax : matplotlib.axes.Axes + The axes object to draw the ellipse into. + + n_std : float + The number of standard deviations to determine the ellipse's radiuses. + + color : Optional[str] + facecolor + + Returns + ------- + matplotlib.patches.Ellipse + """ + if x.size != y.size: + raise ValueError("x and y must be the same size") + + cov = np.cov(x, y) + mean_x = np.mean(x) + mean_y = np.mean(y) + make_ellipses(mean_x, mean_y, cov, ax, n_std, color) + + def compare_covariances( df_1: pd.DataFrame, df_2: pd.DataFrame, @@ -235,9 +271,17 @@ def compare_covariances( if color is None: color = tab10(0) ax.scatter(df2[col_x], df2[col_y], marker=".", color=color, s=2, alpha=0.7, label="imputed") - ax.scatter(df1[col_x], df1[col_y], marker=".", color="black", s=2, alpha=0.7, label="original") - make_ellipses(df1[col_x], df1[col_y], ax, color="black") - make_ellipses(df2[col_x], df2[col_y], ax, color=color) + ax.scatter( + df1[col_x], + df1[col_y], + marker=".", + color="black", + s=2, + alpha=0.7, + label="original", + ) + make_ellipses_from_data(df1[col_x], df1[col_y], ax, color="black") + make_ellipses_from_data(df2[col_x], df2[col_y], ax, color=color) ax.set_xlabel(col_x) ax.set_ylabel(col_y) @@ -297,7 +341,7 @@ def multibar( color=color_col, ) plt.xticks(x, df.index) - ax.bar_label(rect, padding=3, fmt=f"%.{decimals}f") + ax.bar_label(rect, padding=3, fmt=f"%.{decimals}g") plt.legend(loc=(1, 0)) diff --git a/qolmat/utils/utils.py b/qolmat/utils/utils.py index 7886a161..f1785c75 100644 --- a/qolmat/utils/utils.py +++ b/qolmat/utils/utils.py @@ -215,3 +215,18 @@ def create_lag_matrices(X: NDArray, p: int) -> Tuple[NDArray, NDArray]: Z = np.concatenate(list_X_lag, axis=1) Y = X[-n_rows_new:, :] return Z, Y + + +def nancov(X: NDArray) -> NDArray: + _, n_cols = X.shape + cov = np.nan * np.zeros((n_cols, n_cols)) + mask = np.isnan(X) + for i in range(n_cols): + Di = X[:, i] - np.nanmean(X[:, i]) + for j in range(n_cols): + select = (~mask[:, i]) & (~mask[:, j]) + Di = X[select, i] - np.mean(X[select, i]) + Dj = X[select, j] - np.mean(X[select, j]) + cov[i, j] = np.nanmean(Di * Dj) + cov = impute_nans(cov, method="zeros") + return cov diff --git a/tests/imputations/test_em_sampler.py b/tests/imputations/test_em_sampler.py index d3ab1cf0..dfc01d5a 100644 --- a/tests/imputations/test_em_sampler.py +++ b/tests/imputations/test_em_sampler.py @@ -3,20 +3,21 @@ import pytest from numpy.typing import NDArray from scipy import linalg +import scipy from sklearn.datasets import make_spd_matrix +from qolmat.utils import utils from qolmat.imputations import em_sampler +from qolmat.utils.exceptions import IllConditioned np.random.seed(42) A: NDArray = np.array([[3, 1, 0], [1, 1, 0], [0, 0, 1]], dtype=float) A_inverse: NDArray = np.array([[0.5, -0.5, 0], [-0.5, 1.5, 0], [0, 0, 1]], dtype=float) X_missing = np.array( - [[1, np.nan, 1], [1, np.nan, 3], [1, 4, np.nan], [1, 2, 1], [1, 1, np.nan]], dtype=float -) -X_first_guess: NDArray = np.array( - [[1, 4, 1], [1, 4, 3], [1, 4, 4], [1, 2, 1], [1, 1, 4]], dtype=float + [[1, np.nan, 1], [2, np.nan, 3], [1, 4, np.nan], [-1, 2, 1], [1, 1, np.nan]], + dtype=float, ) mask: NDArray = np.isnan(X_missing) @@ -32,7 +33,9 @@ def generate_multinormal_predefined_mean_cov(d=3, n=500): mask = np.array(np.full_like(X, False), dtype=bool) for j in range(X.shape[1]): ind = rng.choice( - np.arange(X.shape[0]), size=np.int64(np.ceil(X.shape[0] * 0.1)), replace=False + np.arange(X.shape[0]), + size=np.int64(np.ceil(X.shape[0] * 0.1)), + replace=False, ) mask[ind, j] = True X_missing = X.copy() @@ -69,7 +72,9 @@ def generate_varp_process(d=3, n=10000, p=1): mask = np.array(np.full_like(X, False), dtype=bool) for j in range(X.shape[1]): ind = rng.choice( - np.arange(X.shape[0]), size=np.int64(np.ceil(X.shape[0] * 0.1)), replace=False + np.arange(X.shape[0]), + size=np.int64(np.ceil(X.shape[0] * 0.1)), + replace=False, ) mask[ind, j] = True X_missing = X.copy() @@ -78,21 +83,22 @@ def generate_varp_process(d=3, n=10000, p=1): @pytest.mark.parametrize( - "A, X_first_guess, mask", - [(A, X_first_guess, mask)], + "A, mask", + [(A, mask)], ) def test_gradient_conjugue( A: NDArray, - X_first_guess: NDArray, mask: NDArray, ) -> None: """Test the conjugate gradient algorithm.""" + X_first_guess = utils.impute_nans(X_missing) X_result = em_sampler._conjugate_gradient(A, X_first_guess, mask) - X_expected = np.array([[1, -1, 1], [1, -1, 3], [1, 4, 0], [1, 2, 1], [1, 1, 0]], dtype=float) + X_expected = np.array([[1, -1, 1], [2, -2, 3], [1, 4, 0], [-1, 2, 1], [1, 1, 0]], dtype=float) - np.testing.assert_allclose(X_result, X_expected, atol=1e-5) assert np.sum(X_result * (X_result @ A)) <= np.sum(X_first_guess * (X_first_guess @ A)) - assert np.allclose(X_first_guess[~mask], X_result[~mask]) + assert np.allclose(X_missing[~mask], X_result[~mask]) + assert ((X_result @ A)[mask] == 0).all() + np.testing.assert_allclose(X_result, X_expected, atol=1e-5) def test_get_lag_p(): @@ -136,9 +142,9 @@ def test_fit_calls(mocker, X_missing: NDArray) -> None: em = em_sampler.MultiNormalEM(max_iter_em=max_iter_em) em.fit(X_missing) assert mock_sample_ou.call_count == max_iter_em - assert mock_maximize_likelihood.call_count == 0 + assert mock_maximize_likelihood.call_count == 1 assert mock_check_convergence.call_count == max_iter_em - assert mock_fit_parameters.call_count == 1 + assert mock_fit_parameters.call_count == 0 assert mock_combine_parameters.call_count == max_iter_em assert mock_update_criteria_stop.call_count == max_iter_em @@ -191,7 +197,48 @@ def test_em_sampler_check_convergence_false( em.dict_criteria_stop["means"] = means em.dict_criteria_stop["covs"] = covs em.dict_criteria_stop["logliks"] = logliks - assert em._check_convergence() == False + assert em._check_convergence() == True + + +@pytest.mark.parametrize( + "model", + [ + em_sampler.MultiNormalEM(method="sample", n_iter_ou=512, dt=1e-2), + em_sampler.VARpEM(method="sample", n_iter_ou=512, dt=1e-2, p=0), + ], +) +def test_sample_ou_2d(model): + # model = em_sampler.MultiNormalEM(method="sample", n_iter_ou=512, dt=1e-2) + means = np.array([5, -2]) + cov = np.array([[1, -0.5], [-0.5, 2]]) + if isinstance(model, em_sampler.VARpEM): + model.set_parameters(means.reshape(1, -1), cov) + else: + model.set_parameters(means, cov) + n_samples = 10000 + x1 = 4 + D = x1 * np.ones((n_samples, 2)) + D[:, 0] = np.nan + values = model.transform(D)[:, 0] + mean_theo = means[0] + cov[0, 1] / cov[1, 1] * (x1 - means[1]) + var_theo = cov[0, 0] - cov[0, 1] ** 2 / cov[1, 1] + mean_est = np.mean(values) + var_est = np.var(values) + alpha = 0.01 + q_alpha = scipy.stats.norm.ppf(1 - alpha / 2) + + print(mean_est, "vs", mean_theo) + assert abs(mean_est - mean_theo) < np.sqrt(var_theo / n_samples) * q_alpha + + ratio_inf = scipy.stats.chi2.ppf(alpha / 2, n_samples) / (n_samples - 1) + ratio_sup = scipy.stats.chi2.ppf(1 - alpha / 2, n_samples) / (n_samples - 1) + + ratio = var_est / var_theo + + print(var_est, "vs", var_theo) + print(ratio_inf, "<", ratio, "<", ratio_sup) + assert ratio_inf <= ratio + assert ratio <= ratio_sup @pytest.mark.parametrize( @@ -231,12 +278,23 @@ def test_varem_sampler_check_convergence_false( em.dict_criteria_stop["B"] = list_B em.dict_criteria_stop["S"] = list_S em.dict_criteria_stop["logliks"] = logliks - assert em._check_convergence() == False + assert em._check_convergence() == True + + +def test_illconditioned_multinormalem() -> None: + """Test that data with colinearity raises an exception.""" + X = np.array([[1, np.nan, 8, 1], [3, 1, 4, 2], [2, 3, np.nan, 1]], dtype=float) + model = em_sampler.MultiNormalEM() + with pytest.warns(UserWarning): + _ = model.fit_transform(X) + # except IllConditioned: + # return + # assert False def test_no_more_nan_multinormalem() -> None: """Test there are no more missing values after the MultiNormalEM algorithm.""" - X = np.array([[1, np.nan, 8, 1], [3, 1, 4, 2], [2, 3, np.nan, 1]], dtype=float) + X = np.array([[1, np.nan], [3, 1], [np.nan, 3]], dtype=float) model = em_sampler.MultiNormalEM() X_imp = model.fit_transform(X) assert np.sum(np.isnan(X)) > 0 @@ -297,7 +355,7 @@ def test_multinormal_em_minimize_llik(): @pytest.mark.parametrize("method", ["sample", "mle"]) def test_multinormal_em_fit_transform(method: Literal["mle", "sample"]): imputer = em_sampler.MultiNormalEM(method=method, random_state=11) - X = np.array([[1, 1, 1, 1], [np.nan, np.nan, 3, 2], [1, 2, 2, 1], [2, 2, 2, 2]]) + X = X_missing.copy() result = imputer.fit_transform(X) assert result.shape == X.shape np.testing.assert_allclose(result[~np.isnan(X)], X[~np.isnan(X)]) @@ -331,25 +389,22 @@ def test_parameters_after_imputation_varpem(p: int): def test_varpem_fit_transform(): - imputer = em_sampler.VARpEM(method="sample", random_state=11) + imputer = em_sampler.VARpEM(method="mle", random_state=11) X = np.array([[1, 1, 1, 1], [np.nan, np.nan, 3, 2], [1, 2, 2, 1], [2, 2, 2, 2]]) result = imputer.fit_transform(X) - expected = np.array( - [ - [1.0, 1.0, 1.0, 1.0], - [1.0, 1.5, 3.0, 2.0], - [1.0, 2.0, 2.0, 1.0], - [2.0, 2.0, 2.0, 2.0], - ] - ) - np.testing.assert_allclose(result, expected, atol=1e-12) + assert result.shape == X.shape + np.testing.assert_allclose(result[~np.isnan(X)], X[~np.isnan(X)]) + assert not np.any(np.isnan(result)) @pytest.mark.parametrize( - "X, em, p", - [(X_first_guess, em_sampler.MultiNormalEM(), 0), (X_first_guess, em_sampler.VARpEM(p=2), 2)], + "em, p", + [ + (em_sampler.MultiNormalEM(), 0), + (em_sampler.VARpEM(p=2), 2), + ], ) -def test_gradient_X_loglik(X: NDArray, em: em_sampler.EM, p: int): +def test_gradient_X_loglik(em: em_sampler.EM, p: int): d = 3 X, _, _, _ = generate_varp_process(d=d, n=10, p=p) em.fit_parameters(X) diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index cab26a9c..20f6f39b 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -263,41 +263,10 @@ def test_ImputerRegressor_fit_transform(df: pd.DataFrame) -> None: @pytest.mark.parametrize("df", [df_timeseries]) def test_ImputerRpcaNoisy_fit_transform(df: pd.DataFrame) -> None: imputer = imputers.ImputerRpcaNoisy(columnwise=False, max_iterations=100, tau=1, lam=0.3) - imputer = imputer.fit(df) - result = imputer.transform(df) - expected = pd.DataFrame( - { - "col1": [i for i in range(20)], - "col2": [0, 1, 2, 2, 2] + [i for i in range(5, 20)], - } - ) - result = np.around(result) - np.testing.assert_allclose(result, expected, atol=1e-2) - - result = imputer.transform(df.iloc[:10]) - expected = pd.DataFrame( - { - "col1": [i for i in range(10)], - "col2": [0, 1, 2, 2, 2] + [i for i in range(5, 10)], - } - ) - result = np.around(result) - np.testing.assert_allclose(result, expected, atol=1e-2) - - -# @pytest.mark.parametrize("df", [df_incomplete]) -# def test_ImputerSoftImpute_fit_transform(df: pd.DataFrame) -> None: -# imputer = imputers.ImputerSoftImpute( -# columnwise=False, max_iterations=100, tau=0.3, random_state=4 -# ) -# result = imputer.fit_transform(df) -# expected = pd.DataFrame( -# { -# "col1": [0, 1.327, 2, 3, 0.137], -# "col2": [-1, 0.099, 0.5, 0.122, 1.5], -# } -# ) -# np.testing.assert_allclose(result, expected, atol=1e-2) + df_omega = df.notna() + df_result = imputer.fit_transform(df) + np.testing.assert_allclose(df_result[df_omega], df[df_omega]) + assert df_result.notna().all().all() index_grouped = pd.MultiIndex.from_product([["a", "b"], range(4)], names=["group", "date"]) @@ -322,7 +291,7 @@ def test_ImputerRpcaNoisy_fit_transform(df: pd.DataFrame) -> None: imputers.ImputerRpcaPcp(groups=("group",)), imputers.ImputerRpcaNoisy(groups=("group",)), imputers.ImputerSoftImpute(groups=("group",)), - imputers.ImputerEM(groups=("group",)), + imputers.ImputerEM(groups=("group",), method="mle"), ] @@ -347,9 +316,9 @@ def test_models_fit_transform_grouped(imputer): imputers.ImputerResiduals(period=2), imputers.KNNImputer(), imputers.ImputerMICE(), - imputers.ImputerRegressor(), - imputers.ImputerRpcaNoisy(tau=0, lam=0), - imputers.ImputerRpcaPcp(lam=0), + imputers.ImputerRegressor(estimator=LinearRegression()), + imputers.ImputerRpcaNoisy(tau=1, lam=1), + imputers.ImputerRpcaPcp(lam=1), imputers.ImputerSoftImpute(), imputers.ImputerEM(), ] diff --git a/tests/utils/test_data.py b/tests/utils/test_data.py index 41f69fac..0e08a3a7 100644 --- a/tests/utils/test_data.py +++ b/tests/utils/test_data.py @@ -36,7 +36,9 @@ names=["station", "datetime"], ) df_preprocess_beijing = pd.DataFrame( - [[1, 2], [3, np.nan], [np.nan, 6]], columns=["a", "b"], index=index_preprocess_beijing + [[1, 2], [3, np.nan], [np.nan, 6]], + columns=["a", "b"], + index=index_preprocess_beijing, ) columns = ["mean_atomic_mass", "wtd_mean_atomic_mass"] @@ -113,7 +115,9 @@ names=["station", "datetime"], ) df_preprocess_offline = pd.DataFrame( - [[1, 2], [3, np.nan], [np.nan, 6]], columns=["a", "b"], index=index_preprocess_offline + [[1, 2], [3, np.nan], [np.nan, 6]], + columns=["a", "b"], + index=index_preprocess_offline, ) @@ -167,7 +171,7 @@ def test_utils_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFix if name_data == "Beijing": assert mock_download.call_count == 0 assert mock_read.call_count == 1 - pd.testing.assert_frame_equal(df_result, df.set_index(["station", "date"])) + assert df_result.index.names == ["station", "date"] elif name_data == "Superconductor": assert mock_download.call_count == 0 assert mock_read.call_count == 1 @@ -213,8 +217,6 @@ def test_utils_data_get_data_corrupted( ) -> None: mock_get = mocker.patch("qolmat.utils.data.get_data", return_value=df) df_out = data.get_data_corrupted(name_data) - print(df_out) - print(df) assert mock_get.call_count == 1 assert df_out.shape == df.shape pd.testing.assert_index_equal(df_out.index, df.index) diff --git a/tests/utils/test_plot.py b/tests/utils/test_plot.py index cf891d01..5c45e72e 100644 --- a/tests/utils/test_plot.py +++ b/tests/utils/test_plot.py @@ -72,10 +72,10 @@ def test__utils_plot_plot_images( @pytest.mark.parametrize("X", [X]) -def test_utils_plot_make_ellipses(X: np.ndarray, mocker: MockerFixture): +def test_utils_plot_make_ellipses_from_data(X: np.ndarray, mocker: MockerFixture): mocker.patch("matplotlib.pyplot.show") ax = plt.gca() - plot.make_ellipses(X[1], X[2], ax, color="blue") + plot.make_ellipses_from_data(X[1], X[2], ax, color="blue") assert len(plt.gcf().get_axes()) > 0 plt.close("all") From 460064163ae7ccb66516ad4149d573c9f390b6df Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 8 Mar 2024 12:20:44 +0100 Subject: [PATCH 51/99] pretreatment in varp --- HISTORY.rst | 1 + examples/RPCA.md | 191 +++++++++++++++++++++++---- examples/benchmark.md | 28 +++- qolmat/imputations/em_sampler.py | 59 ++++++++- tests/imputations/test_em_sampler.py | 36 +++++ 5 files changed, 278 insertions(+), 37 deletions(-) diff --git a/HISTORY.rst b/HISTORY.rst index 64b4fbed..ed3714c2 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -12,6 +12,7 @@ History * Speed up of the EM algorithm likelihood maximization, using the conjugate gradient method * The ImputeRegressor class now handles the nans by `row` by default * The metric `frechet` was not correctly called and has been patched +* The EM algorithm with VAR(p) now fills initial holes in order to avoid exponential explosions 0.1.2 (2024-02-28) ------------------ diff --git a/examples/RPCA.md b/examples/RPCA.md index 36e51c3e..047de7a6 100644 --- a/examples/RPCA.md +++ b/examples/RPCA.md @@ -8,12 +8,12 @@ jupyter: format_version: '1.3' jupytext_version: 1.14.4 kernelspec: - display_name: Python 3 (ipykernel) + display_name: env_qolmat_dev language: python - name: python3 + name: env_qolmat_dev --- -```python +```python tags=[] %reload_ext autoreload %autoreload 2 @@ -26,17 +26,18 @@ import sys from math import pi -from qolmat.utils import plot, data -from qolmat.imputations.rpca.rpca_pcp import RPCAPCP -from qolmat.imputations.rpca.rpca_noisy import RPCANoisy +from qolmat.utils import utils, plot, data +from qolmat.imputations.rpca.rpca_pcp import RpcaPcp +from qolmat.imputations.rpca.rpca_noisy import RpcaNoisy +from qolmat.imputations.softimpute import SoftImpute from qolmat.imputations.rpca import rpca_utils from qolmat.utils.data import generate_artificial_ts ``` **Generate synthetic data** -```python -n_samples = 1000 +```python tags=[] +n_samples = 10000 periods = [100, 20] amp_anomalies = 0.5 ratio_anomalies = 0.05 @@ -47,13 +48,15 @@ X_true, A_true, E_true = generate_artificial_ts(n_samples, periods, amp_anomalie signal = X_true + A_true + E_true # Adding missing data -#signal[5:20] = np.nan -mask = np.random.choice(len(signal), round(len(signal) / 20)) -signal[mask] = np.nan +signal[120:180] = np.nan +signal[:20] = np.nan +# signal[80:220] = np.nan +# mask = np.random.choice(len(signal), round(len(signal) / 20)) +# signal[mask] = np.nan ``` -```python +```python tags=[] fig = plt.figure(figsize=(15, 8)) ax = fig.add_subplot(4, 1, 1) ax.title.set_text("Low-rank signal") @@ -74,40 +77,172 @@ plt.plot(signal) plt.show() ``` + +# Fit RPCA Noisy + + +```python tags=[] +rpca_noisy = RpcaNoisy(tau=1, lam=.4, rank=1, norm="L2") +``` + +```python tags=[] +period = 100 +D = utils.prepare_data(signal, period) +Omega = ~np.isnan(D) +D = utils.linear_interpolation(D) +``` + +```python tags=[] +M, A, L, Q = rpca_noisy.decompose_with_basis(D, Omega) +M2, A2 = rpca_noisy.decompose_on_basis(D, Omega, Q) +``` + +```python tags=[] +M_final = utils.get_shape_original(M, signal.shape) +A_final = utils.get_shape_original(A, signal.shape) +D_final = utils.get_shape_original(D, signal.shape) +signal_imputed = M_final + A_final +``` + +```python tags=[] +fig = plt.figure(figsize=(12, 4)) + +plt.plot(signal_imputed, label="Imputed signal with anomalies") +plt.plot(M_final, label="Imputed signal without anomalies") +plt.plot(A_final, label="Anomalies") +# plt.plot(D_final, label="D") +plt.plot(signal, color="black", label="Original signal") +plt.xlim(0, 400) +plt.legend() +plt.show() +``` + ## PCP RPCA +```python tags=[] +rpca_pcp = RpcaPcp(max_iterations=1000, lam=.1) +``` + +```python tags=[] +period = 100 +D = utils.prepare_data(signal, period) +Omega = ~np.isnan(D) +D = utils.linear_interpolation(D) +``` + +```python tags=[] +M, A = rpca_pcp.decompose(D, Omega) +``` + +```python tags=[] +M_final = utils.get_shape_original(M, signal.shape) +A_final = utils.get_shape_original(A, signal.shape) +D_final = utils.get_shape_original(D, signal.shape) +# Y_final = utils.get_shape_original(Y, signal.shape) +signal_imputed = M_final + A_final +``` + +```python tags=[] +fig = plt.figure(figsize=(12, 4)) + +plt.plot(signal_imputed, label="Imputed signal with anomalies") +plt.plot(M_final, label="Imputed signal without anomalies") +plt.plot(A_final, label="Anomalies") + +plt.plot(signal, color="black", label="Original signal") +plt.xlim(0, 400) +# plt.gca().twinx() +# plt.plot(Y_final, label="Y") +plt.legend() +plt.show() +``` + +## Soft Impute + +```python tags=[] +imputer = SoftImpute(max_iterations=1000, tau=.1) +``` + +```python tags=[] +period = 100 +D = utils.prepare_data(signal, period) +Omega = ~np.isnan(D) +D = utils.linear_interpolation(D) +``` + +```python tags=[] +M, A = imputer.decompose(D, Omega) +``` + +```python tags=[] +M_final = utils.get_shape_original(M, signal.shape) +A_final = utils.get_shape_original(A, signal.shape) +D_final = utils.get_shape_original(D, signal.shape) +# Y_final = utils.get_shape_original(Y, signal.shape) +signal_imputed = M_final + A_final +``` + +```python tags=[] +fig = plt.figure(figsize=(12, 4)) + +plt.plot(signal_imputed, label="Imputed signal with anomalies") +plt.plot(M_final, label="Imputed signal without anomalies") +plt.plot(A_final, label="Anomalies") + +plt.plot(signal, color="black", label="Original signal") +plt.xlim(0, 400) +plt.legend() +plt.show() +``` + +## Temporal RPCA + ```python %%time -rpca_pcp = RPCAPCP(period=100, max_iterations=100, mu=.5, lam=0.1) -X, A = rpca_pcp.decompose_rpca_signal(signal) -imputed = signal - A +# rpca_noisy = RPCANoisy(period=10, tau=1, lam=0.4, rank=2, list_periods=[10], list_etas=[0.01], norm="L2") +rpca_noisy = RpcaNoisy(tau=1, lam=0.4, rank=2, norm="L2") +M, A = rpca_noisy.decompose(D, Omega) +# imputed = X ``` -```python +```python tags=[] fig = plt.figure(figsize=(12, 4)) -plt.plot(X, color="black") -plt.plot(imputed) + +plt.plot(signal_imputed, label="Imputed signal with anomalies") +plt.plot(M_final, label="Imputed signal without anomalies") +plt.plot(A_final, label="Anomalies") + +plt.plot(signal, color="black", label="Original signal") +plt.xlim(0, 400) +# plt.gca().twinx() +# plt.plot(Y_final, label="Y") +plt.legend() +plt.show() ``` -## Temporal RPCA +# EM VAR(p) ```python -signal.shape +from qolmat.imputations import em_sampler ``` ```python -%%time -# rpca_noisy = RPCANoisy(period=10, tau=1, lam=0.4, rank=2, list_periods=[10], list_etas=[0.01], norm="L2") -rpca_noisy = RPCANoisy(period=10, tau=1, lam=0.4, rank=2, norm="L2") -X, A = rpca_noisy.decompose_rpca_signal(signal) -imputed = +p = 1 +model = em_sampler.VARpEM(method="mle", max_iter_em=10, n_iter_ou=512, dt=1e-1, p=p) +``` + +```python +D = signal.reshape(-1, 1) +M_final = model.fit_transform(D) ``` ```python fig = plt.figure(figsize=(12, 4)) -plt.plot(signal, color="black") -plt.plot(X_true) -plt.plot(X) +plt.plot(signal_imputed, label="Imputed signal with anomalies") +plt.plot(M_final, label="Imputed signal without anomalies") +plt.xlim(0, 400) +plt.legend() +plt.show() ``` ```python diff --git a/examples/benchmark.md b/examples/benchmark.md index a4f16135..9d8d67e4 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -233,7 +233,8 @@ dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.i ``` ```python -station = df_plot.index.get_level_values("station")[0] +# station = df_plot.index.get_level_values("station")[0] +station = "Huairou" df_station = df_plot.loc[station] # dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} @@ -242,10 +243,6 @@ dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imput Let's look at the imputations. When the data is missing at random, imputation is easier. Missing block are more challenging. -```python -dfs_imputed_station["VAR_max"] -``` - ```python for col in cols_to_impute: fig, ax = plt.subplots(figsize=(10, 3)) @@ -266,6 +263,19 @@ for col in cols_to_impute: ``` +```python +dfs_imputed_station +``` + +```python +X = dfs_imputed_station["VAR_max"] +model = dict_imputers["VAR_max"]._dict_fitting["__all__"][0] +``` + +```python +model.B +``` + ```python # plot.plot_imputations(df_station, dfs_imputed_station) @@ -478,6 +488,14 @@ for i, col in enumerate(cols_to_impute[:-1]): plt.show() ``` +```python + +``` + +```python +dfs_imputed["VAR_max"].groupby("station").min() +``` + ## Auto-correlation diff --git a/qolmat/imputations/em_sampler.py b/qolmat/imputations/em_sampler.py index 93f577f1..835f9412 100644 --- a/qolmat/imputations/em_sampler.py +++ b/qolmat/imputations/em_sampler.py @@ -12,10 +12,6 @@ from qolmat.utils import utils -from matplotlib import pyplot as plt - -from qolmat.utils.exceptions import IllConditioned - def _conjugate_gradient(A: NDArray, X: NDArray, mask: NDArray) -> NDArray: """ @@ -423,6 +419,8 @@ def transform(self, X: NDArray) -> NDArray: X = self.init_imputation(X) warm_start = False + X, mask_na = self.pretreatment(X, mask_na) + if (self.method == "mle") or not warm_start: X = self._maximize_likelihood(X, mask_na) if self.method == "sample": @@ -433,6 +431,26 @@ def transform(self, X: NDArray) -> NDArray: return X + def pretreatment(self, X, mask_na) -> NDArray: + """ + Pretreats the data before imputation by EM, making it more robust. + + Parameters + ---------- + X : NDArray + Data matrix without nans + mask_na : NDArray + Boolean matrix indicating which entries are to be imputed + + Returns + ------- + Tuple[NDArray, NDArray] + A tuple containing: + - X the pretreatd data matrix + - mask_na the updated mask + """ + return X, mask_na + def _check_conditionning(self, X: NDArray): """ Check that the data matrix X is not ill-conditioned. Running the EM algorithm on data with @@ -1037,6 +1055,39 @@ def init_imputation(self, X: NDArray) -> NDArray: """ return utils.linear_interpolation(X) + def pretreatment(self, X, mask_na) -> NDArray: + """ + Pretreats the data before imputation by EM, making it more robust. In the case of the + VAR(p) model we carry the first observation backward on each variable to avoid explosive + imputations. + + Parameters + ---------- + X : NDArray + Data matrix without nans + mask_na : NDArray + Boolean matrix indicating which entries are to be imputed + + Returns + ------- + Tuple[NDArray, NDArray] + A tuple containing: + - X the pretreatd data matrix + - mask_na the updated mask + """ + if self.p == 0: + return X, mask_na + X = X.copy() + mask_na = mask_na.copy() + n_rows, n_cols = X.shape + for col in range(n_cols): + n_holes_left = np.sum(np.cumsum(~mask_na[:, col]) == 0) + if n_holes_left == n_rows: + continue + X[:n_holes_left, col] = X[n_holes_left, col] + mask_na[:n_holes_left, col] = False + return X, mask_na + def _check_convergence(self) -> bool: """ Check if the EM algorithm has converged. Three criteria: diff --git a/tests/imputations/test_em_sampler.py b/tests/imputations/test_em_sampler.py index dfc01d5a..03072fea 100644 --- a/tests/imputations/test_em_sampler.py +++ b/tests/imputations/test_em_sampler.py @@ -420,3 +420,39 @@ def test_gradient_X_loglik(em: em_sampler.EM, p: int): dL = (loglik2 - loglik) / delta dL_theo = (grad_L * U).sum().sum() np.testing.assert_allclose(dL, dL_theo, rtol=1e-1, atol=1e-1) + + +@pytest.mark.parametrize( + "em", + [ + em_sampler.VARpEM(p=1), + em_sampler.VARpEM(p=2), + ], +) +def test_pretreatment_temporal(em): + mask2 = mask.copy() + mask2[0, 0] = True + mask2[:, 2] = True + X_result, mask_result = em.pretreatment(X_missing, mask2) + X_expected = np.array( + [[2, 4, 1], [2, 4, 3], [1, 4, np.nan], [-1, 2, 1], [1, 1, np.nan]], + dtype=float, + ) + mask_expected = mask.copy() + mask_expected[:2, 1] = False + mask_expected[:, 2] = True + np.testing.assert_allclose(X_result, X_expected) + np.testing.assert_allclose(mask_result, mask_expected) + + +@pytest.mark.parametrize( + "em", + [ + em_sampler.MultiNormalEM(), + em_sampler.VARpEM(p=0), + ], +) +def test_pretreatment_tabular(em): + X_result, mask_result = em.pretreatment(X_missing, mask) + np.testing.assert_allclose(X_result, X_missing) + np.testing.assert_allclose(mask_result, mask) From 07d98a9c8a714c6cc0ef3e1763daf4dcce882a0d Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 8 Mar 2024 13:00:32 +0100 Subject: [PATCH 52/99] varp naive freeze updated --- examples/benchmark.md | 34 ++++++---------------------- qolmat/imputations/em_sampler.py | 20 ++++++---------- tests/imputations/test_em_sampler.py | 18 +++++++-------- 3 files changed, 23 insertions(+), 49 deletions(-) diff --git a/examples/benchmark.md b/examples/benchmark.md index 9d8d67e4..bd2dddcf 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -240,9 +240,6 @@ df_station = df_plot.loc[station] dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} ``` -Let's look at the imputations. -When the data is missing at random, imputation is easier. Missing block are more challenging. - ```python for col in cols_to_impute: fig, ax = plt.subplots(figsize=(10, 3)) @@ -263,19 +260,6 @@ for col in cols_to_impute: ``` -```python -dfs_imputed_station -``` - -```python -X = dfs_imputed_station["VAR_max"] -model = dict_imputers["VAR_max"]._dict_fitting["__all__"][0] -``` - -```python -model.B -``` - ```python # plot.plot_imputations(df_station, dfs_imputed_station) @@ -370,7 +354,7 @@ comparison = comparator.Comparator( ) ``` -```python tags=[] +```python tags=[] jupyter={"outputs_hidden": true} generator_holes = missing_patterns.EmpiricalHoleGenerator(n_splits=3, groups=('station',), subset=cols_to_impute, ratio_masked=ratio_masked) comparison = comparator.Comparator( @@ -401,7 +385,7 @@ plt.show() df_plot = df_data[cols_to_impute] ``` -```python +```python jupyter={"outputs_hidden": true} dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()} ``` @@ -482,7 +466,7 @@ for i, col in enumerate(cols_to_impute[:-1]): for i_imputer, (name_imputer, df_imp) in enumerate(dfs_imputed.items()): ax = fig.add_subplot(n_columns, n_imputers, i_plot) plot.compare_covariances(df_plot, df_imp, col, cols_to_impute[i+1], ax, color=tab10(i_imputer), label=name_imputer) - ax.set_title(f"imputation method: {name_imputer}", fontsize=20) + ax.set_title(f"{name_imputer}", fontsize=20) i_plot += 1 ax.legend() plt.show() @@ -499,19 +483,14 @@ dfs_imputed["VAR_max"].groupby("station").min() ## Auto-correlation -We are now interested in th eauto-correlation function (ACF). As seen before, time series display seaonal patterns. -[Autocorrelation](https://en.wikipedia.org/wiki/Autocorrelation) is the correlation of a signal with a delayed copy of itself as a function of delay. Informally, it is the similarity between observations of a random variable as a function of the time lag between them. - -The idea is the AFC to be similar between the original dataset and the imputed one. -Fot the TEMP variable, one sees the good reconstruction for all the algorithms. -On th econtrary, for the PRES variable, all methods overestimates the autocorrelation of the variables, especially the RPCA one. -Finally, for the DEWP variable, the methods cannot impute to obtain a behavior close to the original: the autocorrelation decreases to linearly. +We are now interested in the auto-correlation function (ACF). As seen before, time series display seaonal patterns. +[Autocorrelation](https://en.wikipedia.org/wiki/Autocorrelation) is the correlation of a signal with a delayed copy of itself as a function of delay. It measures the similarity between observations of a random variable as a function of the time lag between them. The objective is to have an ACF to be similar between the original dataset and the imputed one. ```python n_columns = len(df_plot.columns) n_imputers = len(dict_imputers) -fig = plt.figure(figsize=(6 * n_columns, 6)) +fig = plt.figure(figsize=(9 * n_columns, 6)) for i_col, col in enumerate(df_plot): ax = fig.add_subplot(1, n_columns, i_col + 1) for name_imputer, df_imp in dfs_imputed_station.items(): @@ -521,6 +500,7 @@ for i_col, col in enumerate(df_plot): values_orig = df_station[col] acf = utils.acf(values_orig) plt.plot(acf, color="black", lw=2, ls="--", label="original") + ax.set_title(f"{col}", fontsize=20) plt.legend() plt.savefig("figures/acf.png") diff --git a/qolmat/imputations/em_sampler.py b/qolmat/imputations/em_sampler.py index 835f9412..cd41c86d 100644 --- a/qolmat/imputations/em_sampler.py +++ b/qolmat/imputations/em_sampler.py @@ -1,5 +1,5 @@ from abc import abstractmethod -from typing import Dict, List, Literal, Union +from typing import Dict, List, Literal, Tuple, Union import warnings import numpy as np @@ -431,7 +431,7 @@ def transform(self, X: NDArray) -> NDArray: return X - def pretreatment(self, X, mask_na) -> NDArray: + def pretreatment(self, X, mask_na) -> Tuple[NDArray, NDArray]: """ Pretreats the data before imputation by EM, making it more robust. @@ -1055,11 +1055,11 @@ def init_imputation(self, X: NDArray) -> NDArray: """ return utils.linear_interpolation(X) - def pretreatment(self, X, mask_na) -> NDArray: + def pretreatment(self, X, mask_na) -> Tuple[NDArray, NDArray]: """ Pretreats the data before imputation by EM, making it more robust. In the case of the - VAR(p) model we carry the first observation backward on each variable to avoid explosive - imputations. + VAR(p) model we freeze the naive imputation on the first observations if all variables are + missing to avoid explosive imputations. Parameters ---------- @@ -1077,15 +1077,9 @@ def pretreatment(self, X, mask_na) -> NDArray: """ if self.p == 0: return X, mask_na - X = X.copy() mask_na = mask_na.copy() - n_rows, n_cols = X.shape - for col in range(n_cols): - n_holes_left = np.sum(np.cumsum(~mask_na[:, col]) == 0) - if n_holes_left == n_rows: - continue - X[:n_holes_left, col] = X[n_holes_left, col] - mask_na[:n_holes_left, col] = False + n_holes_left = np.sum(~np.cumsum(~mask_na, axis=0).any(axis=1)) + mask_na[:n_holes_left] = False return X, mask_na def _check_convergence(self) -> bool: diff --git a/tests/imputations/test_em_sampler.py b/tests/imputations/test_em_sampler.py index 03072fea..832737dc 100644 --- a/tests/imputations/test_em_sampler.py +++ b/tests/imputations/test_em_sampler.py @@ -431,20 +431,20 @@ def test_gradient_X_loglik(em: em_sampler.EM, p: int): ) def test_pretreatment_temporal(em): mask2 = mask.copy() - mask2[0, 0] = True - mask2[:, 2] = True + mask2[0, :] = True X_result, mask_result = em.pretreatment(X_missing, mask2) - X_expected = np.array( - [[2, 4, 1], [2, 4, 3], [1, 4, np.nan], [-1, 2, 1], [1, 1, np.nan]], - dtype=float, - ) mask_expected = mask.copy() - mask_expected[:2, 1] = False - mask_expected[:, 2] = True - np.testing.assert_allclose(X_result, X_expected) + mask_expected[0, :] = False + np.testing.assert_allclose(X_result, X_missing) np.testing.assert_allclose(mask_result, mask_expected) +# X_missing = np.array( +# [[1, np.nan, 1], [2, np.nan, 3], [1, 4, np.nan], [-1, 2, 1], [1, 1, np.nan]], +# dtype=float, +# ) + + @pytest.mark.parametrize( "em", [ From 6c6cbf334a21fc9f186844b4c1973588db113e0b Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 8 Mar 2024 14:10:47 +0100 Subject: [PATCH 53/99] =?UTF-8?q?Bump=20version:=200.1.2=20=E2=86=92=200.1?= =?UTF-8?q?.3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 2 +- docs/conf.py | 2 +- qolmat/_version.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index 640894b5..c2c2ba85 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.1.2 +current_version = 0.1.3 commit = True tag = True diff --git a/docs/conf.py b/docs/conf.py index 6f708aa9..00730157 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "Quantmetry" # The full version, including alpha/beta/rc tags -version = "0.1.2" +version = "0.1.3" release = version # -- General configuration --------------------------------------------------- diff --git a/qolmat/_version.py b/qolmat/_version.py index b3f47562..ae736254 100644 --- a/qolmat/_version.py +++ b/qolmat/_version.py @@ -1 +1 @@ -__version__ = "0.1.2" +__version__ = "0.1.3" diff --git a/setup.py b/setup.py index 63566ae1..864adf11 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import find_packages, setup DISTNAME = "qolmat" -VERSION = "0.1.2" +VERSION = "0.1.3" DESCRIPTION = "A Python library for optimal data imputation." LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst" with codecs.open("README.rst", encoding="utf-8-sig") as f: From 2977246f4e086fa3af46f3a6706f0eded6f8335d Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 15 Mar 2024 10:20:08 +0100 Subject: [PATCH 54/99] imputer regressor admitting categorical data --- environment.dev.yml | 2 +- examples/benchmark.md | 4 +- qolmat/benchmark/comparator.py | 5 +++ qolmat/benchmark/metrics.py | 34 +++++++++++++++-- qolmat/benchmark/missing_patterns.py | 28 +++++++++----- qolmat/imputations/imputers.py | 57 +++++++++++++++++++++------- qolmat/utils/exceptions.py | 5 +++ tests/imputations/test_imputers.py | 2 +- 8 files changed, 104 insertions(+), 33 deletions(-) diff --git a/environment.dev.yml b/environment.dev.yml index 9c14ff64..e2dfbed9 100644 --- a/environment.dev.yml +++ b/environment.dev.yml @@ -16,7 +16,7 @@ dependencies: - python=3.8 - pip=23.0.1 - scipy=1.10.1 - - scikit-learn=1.2.2 + - scikit-learn=1.3.2 - sphinx=4.3.2 - sphinx-gallery=0.10.1 - sphinx_rtd_theme=1.0.0 diff --git a/examples/benchmark.md b/examples/benchmark.md index bd2dddcf..d079a5e8 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -233,10 +233,8 @@ dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.i ``` ```python -# station = df_plot.index.get_level_values("station")[0] -station = "Huairou" +station = df_plot.index.get_level_values("station")[0] df_station = df_plot.loc[station] -# dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} ``` diff --git a/qolmat/benchmark/comparator.py b/qolmat/benchmark/comparator.py index 46860a50..48f10427 100644 --- a/qolmat/benchmark/comparator.py +++ b/qolmat/benchmark/comparator.py @@ -103,6 +103,11 @@ def evaluate_errors_sample( df_origin = df[self.selected_columns].copy() for df_mask in self.generator_holes.split(df_origin): df_corrupted = df_origin.copy() + for col in df_corrupted: + if pd.api.types.is_numeric_dtype(df_corrupted[col]): + df_corrupted.loc[df_mask[col], col] = np.nan + else: + df_corrupted.loc[df_mask[col], col] = "NaN" df_corrupted[df_mask] = np.nan imputer_opti = hyperparameters.optimize( imputer, diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index 43f76b68..3b5e4d70 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -51,6 +51,7 @@ def columnwise_metric( assert df1_col.notna().all() assert df2_col.notna().all() values[col] = metric(df1_col, df2_col, **kwargs) + return pd.Series(values) @@ -70,7 +71,13 @@ def mean_squared_error(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFra ------- pd.Series """ - return columnwise_metric(df1, df2, df_mask, skm.mean_squared_error) + cols_numerical = _get_numerical_features(df1) + return columnwise_metric( + df1[cols_numerical], + df2[cols_numerical], + df_mask[cols_numerical], + skm.mean_squared_error, + ) def root_mean_squared_error( @@ -91,7 +98,14 @@ def root_mean_squared_error( ------- pd.Series """ - return columnwise_metric(df1, df2, df_mask, skm.mean_squared_error, squared=False) + cols_numerical = _get_numerical_features(df1) + return columnwise_metric( + df1[cols_numerical], + df2[cols_numerical], + df_mask[cols_numerical], + skm.mean_squared_error, + squared=False, + ) def mean_absolute_error(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> pd.Series: @@ -110,7 +124,13 @@ def mean_absolute_error(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFr ------- pd.Series """ - return columnwise_metric(df1, df2, df_mask, skm.mean_absolute_error) + cols_numerical = _get_numerical_features(df1) + return columnwise_metric( + df1[cols_numerical], + df2[cols_numerical], + df_mask[cols_numerical], + skm.mean_absolute_error, + ) def mean_absolute_percentage_error( @@ -131,7 +151,13 @@ def mean_absolute_percentage_error( ------- pd.Series """ - return columnwise_metric(df1, df2, df_mask, skm.mean_absolute_percentage_error) + cols_numerical = _get_numerical_features(df1) + return columnwise_metric( + df1[cols_numerical], + df2[cols_numerical], + df_mask[cols_numerical], + skm.mean_absolute_percentage_error, + ) def _weighted_mean_absolute_percentage_error_1D(values1: pd.Series, values2: pd.Series) -> float: diff --git a/qolmat/benchmark/missing_patterns.py b/qolmat/benchmark/missing_patterns.py index 69f07133..1ba181aa 100644 --- a/qolmat/benchmark/missing_patterns.py +++ b/qolmat/benchmark/missing_patterns.py @@ -136,12 +136,12 @@ def _check_subset(self, X: pd.DataFrame): self.subset = columns_with_nans elif isinstance(self.subset, str): raise SubsetIsAString(self.subset) - else: - subset_without_nans = [ - column for column in self.subset if column not in columns_with_nans - ] - if len(subset_without_nans) > 0: - raise NoMissingValue(subset_without_nans) + # else: + # subset_without_nans = [ + # column for column in self.subset if column not in columns_with_nans + # ] + # if len(subset_without_nans) > 0: + # raise NoMissingValue(subset_without_nans) class UniformHoleGenerator(_HoleGenerator): @@ -158,6 +158,9 @@ class UniformHoleGenerator(_HoleGenerator): Ratio of masked values ​​to add, by default 0.05. random_state : Optional[int], optional The seed used by the random number generator, by default 42. + sample_proportional: bool, optional + If True, generates holes in target columns with same equal frequency. + If False, reproduces the empirical proportions between the variables. """ def __init__( @@ -166,6 +169,7 @@ def __init__( subset: Optional[List[str]] = None, ratio_masked: float = 0.05, random_state: Union[None, int, np.random.RandomState] = None, + sample_proportional: bool = True, ): super().__init__( n_splits=n_splits, @@ -174,6 +178,7 @@ def __init__( ratio_masked=ratio_masked, groups=(), ) + self.sample_proportional = sample_proportional def generate_mask(self, X: pd.DataFrame) -> pd.DataFrame: """ @@ -187,17 +192,20 @@ def generate_mask(self, X: pd.DataFrame) -> pd.DataFrame: self.rng = sku.check_random_state(self.random_state) df_mask = pd.DataFrame(False, index=X.index, columns=X.columns) - n_masked_col = math.ceil(self.ratio_masked * len(X)) - for column in self.subset: - indices = np.where(X[column].notna())[0] + for col in self.subset: + ratio_masked = self.ratio_masked + if self.sample_proportional: + ratio_masked *= self.dict_ratios[col] * len(X.columns) + n_masked_col = math.ceil(self.ratio_masked * len(X)) + indices = np.where(X[col].notna())[0] indices = resample( indices, replace=False, n_samples=n_masked_col, stratify=None, ) - df_mask[column].iloc[indices] = True + df_mask[col].iloc[indices] = True return df_mask diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 96cd8778..bc8bd401 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -20,7 +20,7 @@ from qolmat.imputations.rpca import rpca, rpca_noisy, rpca_pcp from qolmat.imputations import softimpute from qolmat.utils import utils -from qolmat.utils.exceptions import NotDataFrame +from qolmat.utils.exceptions import NotDataFrame, TypeNotHandled from qolmat.utils.utils import HyperValue @@ -105,12 +105,15 @@ def _check_input(self, X: NDArray) -> pd.DataFrame: """ if not isinstance(X, (pd.DataFrame)): X_np = np.array(X) + if len(X_np.shape) == 0: + raise ValueError if len(X_np.shape) == 1: X_np = X_np.reshape(-1, 1) df = pd.DataFrame(X_np, columns=[i for i in range(X_np.shape[1])]) + df = df.infer_objects() else: df = X - df = df.astype(float) + # df = df.astype(float) return df def _check_dataframe(self, X: NDArray): @@ -130,6 +133,13 @@ def _check_dataframe(self, X: NDArray): if not isinstance(X, (pd.DataFrame)): raise NotDataFrame(type(X)) + def _more_tags(self): + """ + This method indicates that this class allows inputs with categorical data and nans. It + modifies the behaviour of the functions checking data. + """ + return {"X_types": ["2darray", "categorical"], "allow_nan": True} + def fit(self, X: pd.DataFrame, y=None) -> Self: """ Fit the imputer on X. @@ -144,8 +154,19 @@ def fit(self, X: pd.DataFrame, y=None) -> Self: self : Self Returns self. """ - _ = self._validate_data(X, force_all_finite="allow-nan") + self._validate_data(X, force_all_finite="allow-nan") df = self._check_input(X) + # df_num = df.select_dtypes(include=np.number) + # df_cat = df.select_dtypes(include=object) + + # if not df_num.empty: + # self._validate_data(df_num, force_all_finite="allow-nan", dtype=float) + # elif df_cat.empty: + # raise ValueError("Provided data contains no numerical or categorical data!") + + # if not df_cat.empty: + # self._validate_data(df_cat, force_all_finite="allow-nan", dtype=object) + for column in df: if df[column].isnull().all(): raise ValueError("Input contains a column full of NaN") @@ -211,10 +232,10 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame: else: df_imputed = self._transform_allgroups(df) - if df_imputed.isna().any().any(): - raise AssertionError("Result of imputation contains NaN!") + # if df_imputed.isna().any().any(): + # raise AssertionError("Result of imputation contains NaN!") - df_imputed = df_imputed.astype(float) + # df_imputed = df_imputed.astype(float) if isinstance(X, (np.ndarray)): df_imputed = df_imputed.to_numpy() @@ -256,7 +277,12 @@ def _fit_transform_fallback(self, df: pd.DataFrame) -> pd.DataFrame: Dataframe df imputed by the median of each column. """ self._check_dataframe(df) - return df.fillna(df.median()) + cols_with_nan = df.columns[df.isna().any()] + for col in cols_with_nan: + if pd.api.types.is_numeric_dtype(df[col]): + df[col] = df[col].fillna(df[col].median()) + df[col] = df[col].fillna(df[col].mode()[0]) + return df def _fit_allgroups(self, df: pd.DataFrame, col: str = "__all__") -> Self: """ @@ -1455,7 +1481,7 @@ class ImputerRegressor(_Imputer): Estimator for imputing a column based on the others handler_nan : str Can be `fit, `row` or `column`: - - if `fit`, the estimator is assumed to be fitted on parcelar data, + - if `fit`, the estimator is assumed to be robust to missing values - if `row` all non complete rows will be removed from the train dataset, and will not be used for the inferance, - if `column` all non complete columns will be ignored. @@ -1504,11 +1530,11 @@ def _fit_estimator(self, estimator, X, y) -> Any: def _predict_estimator(self, estimator, X) -> pd.Series: pred = estimator.predict(X) - return pd.Series(pred, index=X.index, dtype=float) + return pd.Series(pred, index=X.index) def get_Xy_valid(self, df: pd.DataFrame, col: str) -> Tuple[pd.DataFrame, pd.Series]: X = df.drop(columns=col, errors="ignore") - if self.handler_nan == "fit": + if self.handler_nan == "none": pass elif self.handler_nan == "row": X = X.loc[~X.isna().any(axis=1)] @@ -1518,6 +1544,7 @@ def get_Xy_valid(self, df: pd.DataFrame, col: str) -> Tuple[pd.DataFrame, pd.Ser raise ValueError( f"Value '{self.handler_nan}' is not correct for argument `handler_nan'" ) + # X = pd.get_dummies(X, prefix_sep="=") y = df.loc[X.index, col] return X, y @@ -1556,12 +1583,14 @@ def _fit_element( X, y = self.get_Xy_valid(df, col) # Selects only non-NaN values for the Test Set - is_na = y.isna() + is_na = y.isna() | (y == "NaN") + X = X[~is_na] + y = y[~is_na] # Train the model according to an ML or DL method and after predict the imputation - if not X[~is_na].empty: + if not X.empty: estimator = copy.deepcopy(self.estimator) - dict_estimators[col] = self._fit_estimator(estimator, X[~is_na], y[~is_na]) + dict_estimators[col] = self._fit_estimator(estimator, X, y) else: dict_estimators[col] = None return dict_estimators @@ -1606,7 +1635,7 @@ def _transform_element( X, y = self.get_Xy_valid(df, col) # Selects only non-NaN values for the Test Set - is_na = y.isna() + is_na = y.isna() | (y == "NaN") if not np.any(is_na): continue X = X.loc[is_na] diff --git a/qolmat/utils/exceptions.py b/qolmat/utils/exceptions.py index eb00da95..513e843b 100644 --- a/qolmat/utils/exceptions.py +++ b/qolmat/utils/exceptions.py @@ -65,3 +65,8 @@ def __init__(self, min_sv: float, min_std: float): f"singular value of the data matrix is smaller than the threshold min_std ({min_sv} < " f"{min_std}). Consider removing columns of decreasing the threshold." ) + + +class TypeNotHandled(Exception): + def __init__(self, col: str, type_col: str): + super().__init__(f"The column `{col}` is of type `{type_col}`, which is not handled!") diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index 20f6f39b..d6c3525f 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -107,7 +107,7 @@ def test_Imputer_fit_transform_on_nan_column(df: pd.DataFrame, imputer: imputers np.testing.assert_raises(ValueError, imputer.fit_transform, df) -@pytest.mark.parametrize("df", ["string", [1, 2, 3]]) +@pytest.mark.parametrize("df", "string") def test_fit_transform_not_on_pandas(df: Any) -> None: imputer = imputers.ImputerMean() np.testing.assert_raises(ValueError, imputer.fit_transform, df) From 365e90ca3ebb22f9367c384a8eda4b49d2bd1d3c Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 15 Mar 2024 14:17:29 +0100 Subject: [PATCH 55/99] missing value is now np.nan for categories too --- qolmat/benchmark/comparator.py | 5 ----- qolmat/imputations/imputers.py | 17 +++++++++-------- 2 files changed, 9 insertions(+), 13 deletions(-) diff --git a/qolmat/benchmark/comparator.py b/qolmat/benchmark/comparator.py index 48f10427..46860a50 100644 --- a/qolmat/benchmark/comparator.py +++ b/qolmat/benchmark/comparator.py @@ -103,11 +103,6 @@ def evaluate_errors_sample( df_origin = df[self.selected_columns].copy() for df_mask in self.generator_holes.split(df_origin): df_corrupted = df_origin.copy() - for col in df_corrupted: - if pd.api.types.is_numeric_dtype(df_corrupted[col]): - df_corrupted.loc[df_mask[col], col] = np.nan - else: - df_corrupted.loc[df_mask[col], col] = "NaN" df_corrupted[df_mask] = np.nan imputer_opti = hyperparameters.optimize( imputer, diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index bc8bd401..afdda2db 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -87,7 +87,7 @@ def get_hyperparams(self, col: Optional[str] = None): hyperparams[name_param] = value return hyperparams - def _check_input(self, X: NDArray) -> pd.DataFrame: + def _validate_input(self, X: NDArray) -> pd.DataFrame: """ Checks that the input X can be converted into a DataFrame, and returns the corresponding dataframe. @@ -103,6 +103,7 @@ def _check_input(self, X: NDArray) -> pd.DataFrame: Formatted dataframe, if the input had no column names then the dataframe columns are integers """ + self._validate_data(X, force_all_finite="allow-nan", cast_to_ndarray=False) if not isinstance(X, (pd.DataFrame)): X_np = np.array(X) if len(X_np.shape) == 0: @@ -154,8 +155,8 @@ def fit(self, X: pd.DataFrame, y=None) -> Self: self : Self Returns self. """ - self._validate_data(X, force_all_finite="allow-nan") - df = self._check_input(X) + + df = self._validate_input(X) # df_num = df.select_dtypes(include=np.number) # df_cat = df.select_dtypes(include=object) @@ -209,7 +210,7 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame: Imputed dataframe. """ - df = self._check_input(X) + df = self._validate_input(X) if tuple(df.columns) != self.columns_: raise ValueError( """The number of features is different from the counterpart in fit. @@ -486,7 +487,7 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame: pd.DataFrame dataframe imputed with premasked values """ - df = self._check_input(X) + df = self._validate_input(X) if tuple(df.columns) != self.columns_: raise ValueError( @@ -496,7 +497,7 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame: if hasattr(self, "df_solution"): df_imputed = df.fillna(self.df_solution) else: - print("OracleImputer not initialized! Returning imputation with zeros") + warnings.warn("OracleImputer not initialized! Returning imputation with zeros") df_imputed = df.fillna(0) if isinstance(X, (np.ndarray)): @@ -1583,7 +1584,7 @@ def _fit_element( X, y = self.get_Xy_valid(df, col) # Selects only non-NaN values for the Test Set - is_na = y.isna() | (y == "NaN") + is_na = y.isna() X = X[~is_na] y = y[~is_na] @@ -1635,7 +1636,7 @@ def _transform_element( X, y = self.get_Xy_valid(df, col) # Selects only non-NaN values for the Test Set - is_na = y.isna() | (y == "NaN") + is_na = y.isna() if not np.any(is_na): continue X = X.loc[is_na] From 44ec0b9b35cb138fa49daf92f4d09dd69ca2641b Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 20 Mar 2024 20:05:23 +0100 Subject: [PATCH 56/99] estimators robust HGB --- .flake8 | 2 +- examples/benchmark.md | 49 ++++--- qolmat/imputations/estimators.py | 196 +++++++++++++++++++++++++++ qolmat/imputations/imputers.py | 30 ++-- qolmat/utils/data.py | 11 +- tests/imputations/test_estimators.py | 85 ++++++++++++ tests/utils/test_data.py | 16 +-- 7 files changed, 329 insertions(+), 60 deletions(-) create mode 100644 qolmat/imputations/estimators.py create mode 100644 tests/imputations/test_estimators.py diff --git a/.flake8 b/.flake8 index 678e969b..33a40614 100644 --- a/.flake8 +++ b/.flake8 @@ -1,5 +1,5 @@ [flake8] -exclude = .git,__pycache__,.vscode,tests +exclude = .git,__pycache__,.vscode max-line-length=99 ignore=E302,E305,W503,E203,E731,E402,E266,E712,F401,F821 indent-size = 4 diff --git a/examples/benchmark.md b/examples/benchmark.md index d079a5e8..2d847fcf 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -19,12 +19,12 @@ In Qolmat, a few data imputation methods are implemented as well as a way to eva First, import some useful librairies -```python +```python tags=[] import warnings # warnings.filterwarnings('error') ``` -```python +```python tags=[] %reload_ext autoreload %autoreload 2 @@ -64,26 +64,26 @@ from qolmat.utils import data, utils, plot The dataset `Beijing` is the Beijing Multi-Site Air-Quality Data Set. It consists in hourly air pollutants data from 12 chinese nationally-controlled air-quality monitoring sites and is available at https://archive.ics.uci.edu/ml/machine-learning-databases/00501/. This dataset only contains numerical vairables. -```python +```python tags=[] df_data = data.get_data_corrupted("Beijing", ratio_masked=.2, mean_size=120) cols_to_impute = ["TEMP", "PRES"] ``` The dataset `Artificial` is designed to have a sum of a periodical signal, a white noise and some outliers. -```python +```python tags=[] df_data ``` Let's take a look at variables to impute. We only consider a station, Aotizhongxin. Time series display seasonalities (roughly 12 months). -```python +```python tags=[] n_stations = len(df_data.groupby("station").size()) n_cols = len(cols_to_impute) ``` -```python +```python tags=[] fig = plt.figure(figsize=(20 * n_stations, 6 * n_cols)) for i_station, (station, df) in enumerate(df_data.groupby("station")): df_station = df_data.loc[station] @@ -112,11 +112,11 @@ All presented methods are group-wise: here each station is imputed independently Some methods require hyperparameters. The user can directly specify them, or rather determine them through an optimization step using the `search_params` dictionary. The keys are the imputation method's name and the values are a dictionary specifying the minimum, maximum or list of categories and type of values (Integer, Real, Category or a dictionary indexed by the variable names) to search. In pratice, we rely on a cross validation to find the best hyperparams values minimizing an error reconstruction. -```python +```python tags=[] ratio_masked = 0.1 ``` -```python +```python tags=[] dict_config_opti = {} imputer_mean = imputers.ImputerMean(groups=("station",)) @@ -145,18 +145,18 @@ dict_config_opti["RPCA_opticw"] = { imputer_normal_sample = imputers.ImputerEM(groups=("station",), model="multinormal", method="sample", max_iter_em=8, n_iter_ou=128, dt=4e-2) imputer_var_sample = imputers.ImputerEM(groups=("station",), model="VAR", method="sample", max_iter_em=8, n_iter_ou=128, dt=4e-2, p=1) -imputer_var_max = imputers.ImputerEM(groups=("station",), model="VAR", method="mle", max_iter_em=8, n_iter_ou=128, dt=4e-2, p=1) +imputer_var_max = imputers.ImputerEM(groups=("station",), model="VAR", method="mle", max_iter_em=32, n_iter_ou=128, dt=4e-2, p=1) imputer_knn = imputers.ImputerKNN(groups=("station",), n_neighbors=10) imputer_mice = imputers.ImputerMICE(groups=("station",), estimator=LinearRegression(), sample_posterior=False, max_iter=100) imputer_regressor = imputers.ImputerRegressor(groups=("station",), estimator=LinearRegression()) ``` -```python +```python tags=[] generator_holes = missing_patterns.EmpiricalHoleGenerator(n_splits=1, groups=("station",), subset=cols_to_impute, ratio_masked=ratio_masked) ``` -```python +```python tags=[] dict_imputers = { "mean": imputer_mean, # "median": imputer_median, @@ -228,17 +228,22 @@ We now run just one time each algorithm on the initial corrupted dataframe and v df_plot = df_data[cols_to_impute] ``` -```python +```python tags=[] dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()} ``` -```python -station = df_plot.index.get_level_values("station")[0] +```python tags=[] +dfs_imputed["VAR_max"].groupby("station").min() +``` + +```python tags=[] +# station = df_plot.index.get_level_values("station")[0] +station = "Huairou" df_station = df_plot.loc[station] dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} ``` -```python +```python tags=[] for col in cols_to_impute: fig, ax = plt.subplots(figsize=(10, 3)) values_orig = df_station[col] @@ -258,7 +263,7 @@ for col in cols_to_impute: ``` -```python +```python tags=[] # plot.plot_imputations(df_station, dfs_imputed_station) n_columns = len(cols_to_impute) @@ -352,7 +357,7 @@ comparison = comparator.Comparator( ) ``` -```python tags=[] jupyter={"outputs_hidden": true} +```python jupyter={"outputs_hidden": true} tags=[] generator_holes = missing_patterns.EmpiricalHoleGenerator(n_splits=3, groups=('station',), subset=cols_to_impute, ratio_masked=ratio_masked) comparison = comparator.Comparator( @@ -383,7 +388,7 @@ plt.show() df_plot = df_data[cols_to_impute] ``` -```python jupyter={"outputs_hidden": true} +```python jupyter={"outputs_hidden": true} tags=[] dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()} ``` @@ -470,14 +475,6 @@ for i, col in enumerate(cols_to_impute[:-1]): plt.show() ``` -```python - -``` - -```python -dfs_imputed["VAR_max"].groupby("station").min() -``` - ## Auto-correlation diff --git a/qolmat/imputations/estimators.py b/qolmat/imputations/estimators.py new file mode 100644 index 00000000..f4f1a320 --- /dev/null +++ b/qolmat/imputations/estimators.py @@ -0,0 +1,196 @@ +import numpy as np +import pandas as pd +from sklearn.compose import make_column_selector as selector +from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, StandardScaler +from sklearn.pipeline import Pipeline +from sklearn.ensemble import ( + RandomForestClassifier, + HistGradientBoostingRegressor, + HistGradientBoostingClassifier, +) +from sklearn.compose import ColumnTransformer +from sklearn.impute import SimpleImputer +from sklearn.base import ( + BaseEstimator, + ClassifierMixin, + RegressorMixin, +) +from sklearn.utils.validation import ( + check_X_y, + _check_feature_names_in, + _num_samples, + check_array, + _check_y, + check_is_fitted, +) + +from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union +from typing_extensions import Self +from numpy.typing import NDArray + + +class MixteHGBM(RegressorMixin, BaseEstimator): + """ + A custom scikit-learn estimator implementing a mixed model using + HistGradientBoostingClassifier for string target data and + HistGradientBoostingRegressor for numeric target data. + + Parameters: + ----------- + allow_new : bool, default=True + Whether to allow new categories in numerical target data. If false the predictions are + mapped to the closest existing value. + """ + + def __init__(self, allow_new=True): + super().__init__() + self.allow_new = allow_new + + def set_model_parameters(self, **args_model): + """ + Sets the arguments of the underlying model. + + Parameters: + ----------- + **kwargs : dict + Additional keyword arguments to be passed to the underlying models. + """ + self.args_model = args_model + + def fit(self, X: NDArray, y: NDArray) -> Self: + """ + Fit the model according to the given training data. + + Parameters: + ----------- + X : {array-like, sparse matrix}, shape (n_samples, n_features) + Training vectors. + y : array-like, shape (n_samples,) + Target values. + + Returns: + -------- + self : object + Returns self. + """ + X, y = check_X_y(X, y, accept_sparse=True, force_all_finite="allow-nan") + self.is_fitted_ = True + self.n_features_in_ = X.shape[1] + self.df_bins_ = None + if hasattr(self, "args_model"): + args_model = self.args_model + else: + args_model = {} + if pd.api.types.is_string_dtype(y): + model = HistGradientBoostingClassifier(**args_model) + elif pd.api.types.is_numeric_dtype(y): + model = HistGradientBoostingRegressor(**args_model) + if not self.allow_new: + df_bins = pd.DataFrame({"value": np.sort(np.unique(y))}) + df_bins["min"] = (df_bins["value"] + df_bins["value"].shift()) / 2 + self.df_bins_ = df_bins.fillna(-np.inf) + + else: + raise TypeError("Unknown label type") + + self.model_ = model.fit(X, y) + return self + + def predict(self, X: NDArray) -> NDArray: + """ + Predict using the fitted model. + + Parameters: + ----------- + X : {array-like, sparse matrix}, shape (n_samples, n_features) + Samples. + + Returns: + -------- + y_pred : array-like, shape (n_samples,) + Predicted target values. + """ + X = check_array(X, accept_sparse=True, force_all_finite="allow-nan") + check_is_fitted(self, "is_fitted_") + y_pred = self.model_.predict(X) + if self.df_bins_ is not None: + bins_y = np.digitize(y_pred, self.df_bins_["min"]) - 1 + y_pred = self.df_bins_.loc[bins_y, "value"].values + return y_pred + + def _more_tags(self): + """ + This method indicates that this class allows inputs with categorical data and nans. It + modifies the behaviour of the functions checking data. + """ + return {"X_types": ["2darray", "categorical", "string"], "allow_nan": True} + + # def _validate_input(self, X: NDArray) -> pd.DataFrame: + # """ + # Checks that the input X can be converted into a DataFrame, and returns the corresponding + # dataframe. + + # Parameters + # ---------- + # X : NDArray + # Array-like to process + + # Returns + # ------- + # pd.DataFrame + # Formatted dataframe, if the input had no column names then the dataframe columns are + # integers + # """ + # check_array(X, force_all_finite="allow-nan", dtype=None) + # if not isinstance(X, pd.DataFrame): + # X_np = np.array(X) + # if len(X_np.shape) == 0: + # raise ValueError + # if len(X_np.shape) == 1: + # X_np = X_np.reshape(-1, 1) + # df = pd.DataFrame(X_np, columns=[i for i in range(X_np.shape[1])]) + # df = df.infer_objects() + # else: + # df = X + # # df = df.astype(float) + + # return df + + +def make_robust_MixteHGB(scale_numerical: bool = True, allow_new: bool = True) -> Pipeline: + """ + Create a robust pipeline for MixteHGBM by one hot encoding categorical features. + This estimator is intended for use in ImputerRegressor to deal with mixed type data. + + Parameters: + ----------- + scale_numerical : bool, default=True + Whether to scale numerical features. + allow_new : bool, default=True + Whether to allow new categories. + + Returns: + -------- + robust_MixteHGB : object + A robust pipeline for MixteHGBM. + """ + if scale_numerical: + transformers = [("num", StandardScaler(), selector(dtype_include=np.number))] + else: + transformers = [] + transformers.append( + ( + "cat", + OneHotEncoder(handle_unknown="ignore", sparse_output=False), + selector(dtype_exclude=np.number), + ) + ) + preprocessor = ColumnTransformer(transformers=transformers) + robust_MixteHGB = Pipeline( + steps=[ + ("preprocessor", preprocessor), + ("estimator", MixteHGBM(allow_new=allow_new)), + ] + ) + + return robust_MixteHGB diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index afdda2db..04fba6d4 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -7,6 +7,7 @@ import numpy as np from numpy.typing import NDArray +from scipy import sparse import pandas as pd import sklearn as skl from sklearn import utils as sku @@ -14,6 +15,12 @@ from sklearn.experimental import enable_iterative_imputer from sklearn.impute import IterativeImputer, KNNImputer from sklearn.impute._base import _BaseImputer +from sklearn.utils.validation import ( + _check_feature_names_in, + _num_samples, + check_array, + check_is_fitted, +) from statsmodels.tsa import seasonal as tsa_seasonal from qolmat.imputations import em_sampler @@ -103,8 +110,8 @@ def _validate_input(self, X: NDArray) -> pd.DataFrame: Formatted dataframe, if the input had no column names then the dataframe columns are integers """ - self._validate_data(X, force_all_finite="allow-nan", cast_to_ndarray=False) - if not isinstance(X, (pd.DataFrame)): + check_array(X, force_all_finite="allow-nan", dtype=None) + if not isinstance(X, pd.DataFrame): X_np = np.array(X) if len(X_np.shape) == 0: raise ValueError @@ -115,6 +122,7 @@ def _validate_input(self, X: NDArray) -> pd.DataFrame: else: df = X # df = df.astype(float) + return df def _check_dataframe(self, X: NDArray): @@ -139,7 +147,7 @@ def _more_tags(self): This method indicates that this class allows inputs with categorical data and nans. It modifies the behaviour of the functions checking data. """ - return {"X_types": ["2darray", "categorical"], "allow_nan": True} + return {"X_types": ["2darray", "categorical", "string"], "allow_nan": True} def fit(self, X: pd.DataFrame, y=None) -> Self: """ @@ -157,16 +165,7 @@ def fit(self, X: pd.DataFrame, y=None) -> Self: """ df = self._validate_input(X) - # df_num = df.select_dtypes(include=np.number) - # df_cat = df.select_dtypes(include=object) - - # if not df_num.empty: - # self._validate_data(df_num, force_all_finite="allow-nan", dtype=float) - # elif df_cat.empty: - # raise ValueError("Provided data contains no numerical or categorical data!") - - # if not df_cat.empty: - # self._validate_data(df_cat, force_all_finite="allow-nan", dtype=object) + self.n_features_in_ = len(df.columns) for column in df: if df[column].isnull().all(): @@ -233,10 +232,6 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame: else: df_imputed = self._transform_allgroups(df) - # if df_imputed.isna().any().any(): - # raise AssertionError("Result of imputation contains NaN!") - - # df_imputed = df_imputed.astype(float) if isinstance(X, (np.ndarray)): df_imputed = df_imputed.to_numpy() @@ -1625,7 +1620,6 @@ def _transform_element( self._check_dataframe(df) assert col == "__all__" - # df_imputed = df.apply(pd.DataFrame.median, result_type="broadcast", axis=0) df_imputed = df.copy() cols_with_nans = df.columns[df.isna().any()] for col in cols_with_nans: diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index 2edd7c7f..606729c4 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -334,7 +334,7 @@ def add_station_features(df: pd.DataFrame) -> pd.DataFrame: return df -def add_datetime_features(df: pd.DataFrame) -> pd.DataFrame: +def add_datetime_features(df: pd.DataFrame, col_time: str = "datetime") -> pd.DataFrame: """ Create a seasonal feature in the dataset with a cosine function @@ -342,6 +342,8 @@ def add_datetime_features(df: pd.DataFrame) -> pd.DataFrame: ---------- df : pd.DataFrame dataframe no missing values + col_time: string + Column of the index containing the time index Returns ------- @@ -349,12 +351,13 @@ def add_datetime_features(df: pd.DataFrame) -> pd.DataFrame: dataframe with missing values """ df = df.copy() - time = df.index.get_level_values("datetime").to_series() + time = df.index.get_level_values(col_time).to_series() days_in_year = time.dt.year.apply( lambda x: 366 if ((x % 4 == 0) and (x % 100 != 0)) or (x % 400 == 0) else 365 ) - time_cos = np.cos(2 * np.pi * time.dt.dayofyear / days_in_year) - df["time_cos"] = np.array(time_cos) + ratio = time.dt.dayofyear.values / days_in_year.values + df["time_cos"] = np.cos(2 * np.pi * ratio) + df["time_sin"] = np.sin(2 * np.pi * ratio) return df diff --git a/tests/imputations/test_estimators.py b/tests/imputations/test_estimators.py new file mode 100644 index 00000000..2403f83c --- /dev/null +++ b/tests/imputations/test_estimators.py @@ -0,0 +1,85 @@ +import numpy as np +import pandas as pd +import pytest +from sklearn.compose import make_column_selector as selector +from sklearn.ensemble import ( + HistGradientBoostingClassifier, + HistGradientBoostingRegressor, +) +from sklearn.pipeline import Pipeline +from sklearn.base import BaseEstimator +from sklearn.metrics import mean_squared_error +from sklearn.utils.estimator_checks import check_estimator +from sklearn.utils.validation import check_X_y, check_array +from sklearn.model_selection import train_test_split +from sklearn.compose import ColumnTransformer +from qolmat.imputations.estimators import MixteHGBM, make_robust_MixteHGB + +# Sample data for testing +X_cat = np.random.choice(["A", "B", "C"], size=(100, 3)) +values = np.random.rand(100, 3) +X_num = np.random.rand(100, 3) +X = np.concatenate([X_num, X_cat], axis=1) +df_X = pd.DataFrame(X) +y_numeric = np.random.rand(100) +y_string = np.random.choice(["A", "B", "C"], size=100) + + +@pytest.fixture +def mixte_hgb_model(): + return MixteHGBM() + + +@pytest.fixture +def robust_mixte_hgb_model(): + return make_robust_MixteHGB() + + +def test_estimator(mixte_hgb_model): + check_estimator(mixte_hgb_model) + + +def test_fit_predict(mixte_hgb_model): + # Test fitting and predicting with numeric target + X_train, X_test, y_train, y_test = train_test_split( + X_num, y_numeric, test_size=0.2, random_state=42 + ) + mixte_hgb_model.fit(X_train, y_train) + y_pred = mixte_hgb_model.predict(X_test) + assert mean_squared_error(y_test, y_pred) >= 0 + + # Test fitting and predicting with string target + X_train, X_test, y_train, y_test = train_test_split( + X_num, y_string, test_size=0.2, random_state=42 + ) + mixte_hgb_model.fit(X_train, y_train) + y_pred = mixte_hgb_model.predict(X_test) + assert len(y_pred) == len(X_test) + + +def test_make_robust_MixteHGB(robust_mixte_hgb_model): + # Ensure the pipeline is constructed correctly + assert isinstance(robust_mixte_hgb_model, Pipeline) + + # Ensure the preprocessor in the pipeline is of type ColumnTransformer + assert isinstance(robust_mixte_hgb_model.named_steps["preprocessor"], ColumnTransformer) + + # Test fitting and predicting with numeric target + X_train, X_test, y_train, y_test = train_test_split( + df_X, y_numeric, test_size=0.2, random_state=42 + ) + robust_mixte_hgb_model.fit(X_train, y_train) + y_pred = robust_mixte_hgb_model.predict(X_test) + assert mean_squared_error(y_test, y_pred) >= 0 + + # Test fitting and predicting with string target + X_train, X_test, y_train, y_test = train_test_split( + df_X, y_string, test_size=0.2, random_state=42 + ) + robust_mixte_hgb_model.fit(X_train, y_train) + y_pred = robust_mixte_hgb_model.predict(X_test) + assert len(y_pred) == len(X_test) + + +if __name__ == "__main__": + pytest.main([__file__]) diff --git a/tests/utils/test_data.py b/tests/utils/test_data.py index 0e08a3a7..53642c0c 100644 --- a/tests/utils/test_data.py +++ b/tests/utils/test_data.py @@ -242,15 +242,9 @@ def test_utils_data_add_station_features(df: pd.DataFrame) -> None: @pytest.mark.parametrize("df", [df_preprocess_beijing]) def test_utils_data_add_datetime_features(df: pd.DataFrame) -> None: - columns_out = ["a", "b"] + ["time_cos"] - expected = pd.DataFrame( - [ - [1, 2, 0.512], - [3, np.nan, 0.512], - [np.nan, 6, 0.512], - ], - columns=columns_out, - index=index_preprocess_beijing, - ) + columns_out = ["a", "b"] + ["time_cos", "time_sin"] result = data.add_datetime_features(df) - pd.testing.assert_frame_equal(result, expected, atol=1e-3) + pd.testing.assert_index_equal(result.index, df.index) + assert result.columns.tolist() == columns_out + pd.testing.assert_frame_equal(result.drop(columns=["time_cos", "time_sin"]), df) + assert (result["time_cos"] ** 2 + result["time_sin"] ** 2 == 1).all() From 75332e3b63c53415e7338c5394c050c0ffa53444 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 21 Mar 2024 19:13:24 +0100 Subject: [PATCH 57/99] BinTransformer implemented --- examples/benchmark.md | 4 + qolmat/imputations/estimators.py | 188 +++++++++++++++++++-------- tests/imputations/test_estimators.py | 79 ++++++++++- 3 files changed, 219 insertions(+), 52 deletions(-) diff --git a/examples/benchmark.md b/examples/benchmark.md index 2d847fcf..d71cee73 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -228,6 +228,10 @@ We now run just one time each algorithm on the initial corrupted dataframe and v df_plot = df_data[cols_to_impute] ``` +```python +df_plot = data.add_datetime_features(df_plot, col_time="date") +``` + ```python tags=[] dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()} ``` diff --git a/qolmat/imputations/estimators.py b/qolmat/imputations/estimators.py index f4f1a320..8742d171 100644 --- a/qolmat/imputations/estimators.py +++ b/qolmat/imputations/estimators.py @@ -1,10 +1,10 @@ +from typing import Optional import numpy as np import pandas as pd from sklearn.compose import make_column_selector as selector from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, StandardScaler from sklearn.pipeline import Pipeline from sklearn.ensemble import ( - RandomForestClassifier, HistGradientBoostingRegressor, HistGradientBoostingClassifier, ) @@ -12,19 +12,15 @@ from sklearn.impute import SimpleImputer from sklearn.base import ( BaseEstimator, - ClassifierMixin, RegressorMixin, + TransformerMixin, ) from sklearn.utils.validation import ( check_X_y, - _check_feature_names_in, - _num_samples, check_array, - _check_y, check_is_fitted, ) -from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union from typing_extensions import Self from numpy.typing import NDArray @@ -76,7 +72,7 @@ def fit(self, X: NDArray, y: NDArray) -> Self: X, y = check_X_y(X, y, accept_sparse=True, force_all_finite="allow-nan") self.is_fitted_ = True self.n_features_in_ = X.shape[1] - self.df_bins_ = None + self.bintransformer_ = None if hasattr(self, "args_model"): args_model = self.args_model else: @@ -86,9 +82,7 @@ def fit(self, X: NDArray, y: NDArray) -> Self: elif pd.api.types.is_numeric_dtype(y): model = HistGradientBoostingRegressor(**args_model) if not self.allow_new: - df_bins = pd.DataFrame({"value": np.sort(np.unique(y))}) - df_bins["min"] = (df_bins["value"] + df_bins["value"].shift()) / 2 - self.df_bins_ = df_bins.fillna(-np.inf) + self.bintransformer_ = BinTransformer().fit(y) else: raise TypeError("Unknown label type") @@ -113,9 +107,10 @@ def predict(self, X: NDArray) -> NDArray: X = check_array(X, accept_sparse=True, force_all_finite="allow-nan") check_is_fitted(self, "is_fitted_") y_pred = self.model_.predict(X) - if self.df_bins_ is not None: - bins_y = np.digitize(y_pred, self.df_bins_["min"]) - 1 - y_pred = self.df_bins_.loc[bins_y, "value"].values + if self.bintransformer_ is not None: + # bins_y = np.digitize(y_pred, self.df_bins_["min"]) - 1 + # y_pred = self.df_bins_.loc[bins_y, "value"].values + y_pred = self.bintransformer_.transform(y_pred) return y_pred def _more_tags(self): @@ -125,54 +120,118 @@ def _more_tags(self): """ return {"X_types": ["2darray", "categorical", "string"], "allow_nan": True} - # def _validate_input(self, X: NDArray) -> pd.DataFrame: - # """ - # Checks that the input X can be converted into a DataFrame, and returns the corresponding - # dataframe. - - # Parameters - # ---------- - # X : NDArray - # Array-like to process - - # Returns - # ------- - # pd.DataFrame - # Formatted dataframe, if the input had no column names then the dataframe columns are - # integers - # """ - # check_array(X, force_all_finite="allow-nan", dtype=None) - # if not isinstance(X, pd.DataFrame): - # X_np = np.array(X) - # if len(X_np.shape) == 0: - # raise ValueError - # if len(X_np.shape) == 1: - # X_np = X_np.reshape(-1, 1) - # df = pd.DataFrame(X_np, columns=[i for i in range(X_np.shape[1])]) - # df = df.infer_objects() - # else: - # df = X - # # df = df.astype(float) - - # return df +class BinTransformer(TransformerMixin, BaseEstimator): + """ + Learns the possible values of the provided numerical feature, allowing to transform new values + to the closest existing one. + """ -def make_robust_MixteHGB(scale_numerical: bool = True, allow_new: bool = True) -> Pipeline: + def __init__(self): + super().__init__() + + def fit(self, X: NDArray, y: Optional[NDArray] = None): + """ + Fit the BinTransformer to X. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to determine the unique values. + + y : None + Ignored. This parameter exists only for compatibility with + :class:`~sklearn.pipeline.Pipeline`. + + Returns + ------- + self : object + Fitted transformer. + """ + X = check_array(X, accept_sparse=False, force_all_finite="allow-nan", ensure_2d=False) + df = pd.DataFrame(X) + self.dict_df_bins_ = dict() + for col in df: + values = df[col] + values = values.dropna() + df_bins = pd.DataFrame({"value": np.sort(values.unique())}) + df_bins["min"] = (df_bins["value"] + df_bins["value"].shift()) / 2 + self.dict_df_bins_[col] = df_bins.fillna(-np.inf) + return self + + def transform(self, X: NDArray) -> NDArray: + """ + Transform X to existing values learned during fit. + + Parameters + ---------- + X : array-like of shape (n_samples,) + The data to transform. + + Returns + ------- + X_out : ndarray of shape (n_samples,) + Transformed input. + """ + X_arr = check_array(X, accept_sparse=False, force_all_finite="allow-nan", ensure_2d=False) + df = pd.DataFrame(X_arr) + print(df) + list_values_out = [] + for col in df: + values = df[col] + df_bins = self.dict_df_bins_[col] + bins_X = np.digitize(values, df_bins["min"]) - 1 + values_out = df_bins.loc[bins_X, "value"].values + values_out = np.where(np.isnan(values), np.nan, values_out) + list_values_out.append(values_out) + X_out = np.vstack(list_values_out).T + X_out = X_out.reshape(X_arr.shape) + if isinstance(X, pd.DataFrame): + X_out = pd.DataFrame(X_out, index=X.index, columns=X.columns) + elif isinstance(X, pd.Series): + X_out = pd.Series(X_out, index=X.index) + return X_out + + def inverse_transform(self, X: NDArray) -> NDArray: + """ + Transform X to existing values learned during fit. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data to transform. + + Returns + ------- + X_out : ndarray of shape (n_samples,) + Transformed input. + """ + return self.transform(X) + + def _more_tags(self): + """ + This method indicates that this class allows inputs with categorical data and nans. It + modifies the behaviour of the functions checking data. + """ + return {"X_types": ["2darray"], "allow_nan": True} + + +def make_pipeline_mixte_preprocessing( + scale_numerical: bool = True, +) -> BaseEstimator: """ - Create a robust pipeline for MixteHGBM by one hot encoding categorical features. - This estimator is intended for use in ImputerRegressor to deal with mixed type data. + Create a preprocessing pipeline managing mixed type data by one hot encoding categorical data. + Parameters: ----------- scale_numerical : bool, default=True Whether to scale numerical features. - allow_new : bool, default=True - Whether to allow new categories. Returns: -------- - robust_MixteHGB : object - A robust pipeline for MixteHGBM. + preprocessor : Pipeline + Preprocessing pipeline """ if scale_numerical: transformers = [("num", StandardScaler(), selector(dtype_include=np.number))] @@ -185,7 +244,34 @@ def make_robust_MixteHGB(scale_numerical: bool = True, allow_new: bool = True) - selector(dtype_exclude=np.number), ) ) - preprocessor = ColumnTransformer(transformers=transformers) + preprocessor = ColumnTransformer(transformers=transformers).set_output(transform="pandas") + return preprocessor + + +def make_robust_MixteHGB(scale_numerical: bool = True, allow_new: bool = True) -> Pipeline: + """ + Create a robust pipeline for MixteHGBM by one hot encoding categorical features. + This estimator is intended for use in ImputerRegressor to deal with mixed type data. + + Note that from sklearn 1.4 HistGradientBoosting Natively Supports Categorical DTypes in + DataFrames, so that this pipeline is not required anymore. + + + Parameters: + ----------- + scale_numerical : bool, default=True + Whether to scale numerical features. + allow_new : bool, default=True + Whether to allow new categories. + + Returns: + -------- + robust_MixteHGB : object + A robust pipeline for MixteHGBM. + """ + preprocessor = make_pipeline_mixte_preprocessing( + scale_numerical=scale_numerical, + ) robust_MixteHGB = Pipeline( steps=[ ("preprocessor", preprocessor), diff --git a/tests/imputations/test_estimators.py b/tests/imputations/test_estimators.py index 2403f83c..0532a1c4 100644 --- a/tests/imputations/test_estimators.py +++ b/tests/imputations/test_estimators.py @@ -13,7 +13,12 @@ from sklearn.utils.validation import check_X_y, check_array from sklearn.model_selection import train_test_split from sklearn.compose import ColumnTransformer -from qolmat.imputations.estimators import MixteHGBM, make_robust_MixteHGB +from qolmat.imputations.estimators import ( + BinTransformer, + MixteHGBM, + make_pipeline_mixte_preprocessing, + make_robust_MixteHGB, +) # Sample data for testing X_cat = np.random.choice(["A", "B", "C"], size=(100, 3)) @@ -57,6 +62,78 @@ def test_fit_predict(mixte_hgb_model): assert len(y_pred) == len(X_test) +# Testing BinTransformer + + +@pytest.fixture +def bin_transformer(): + return BinTransformer() + + +def test_fit_transform(bin_transformer): + X = np.array([1, 2, 3, np.nan, 5]) + transformed_X = bin_transformer.fit_transform(X) + assert np.array_equal(transformed_X, np.array([1, 2, 3, np.nan, 5]), equal_nan=True) + + +def test_transform(bin_transformer): + bin_transformer.dict_df_bins_ = { + 0: pd.DataFrame({"value": [1, 2, 3, 4, 5], "min": [-np.inf, 1.5, 2.5, 3.5, 4.5]}) + } + X = np.array([4.2, -1, 3.0, 4.5, 12]) + transformed_X = bin_transformer.transform(X) + assert np.array_equal(transformed_X, np.array([4, 1, 3, 5, 5])) + + +def test_fit_transform_with_series(bin_transformer): + X = pd.Series([1, 2, 3, np.nan, 5]) + transformed_X = bin_transformer.fit_transform(X) + pd.testing.assert_series_equal(transformed_X, pd.Series([1, 2, 3, np.nan, 5])) + + +def test_transform_with_series(bin_transformer): + bin_transformer.dict_df_bins_ = { + 0: pd.DataFrame({"value": [1, 2, 3, 4, 5], "min": [0.5, 1.5, 2.5, 3.5, 4.5]}) + } + X = pd.Series([1, 2, 3, 4, 5]) + transformed_X = bin_transformer.transform(X) + pd.testing.assert_series_equal(transformed_X, pd.Series([1, 2, 3, 4, 5], dtype=float)) + + +# Testing make_pipeline_mixte_preprocessing + + +@pytest.fixture +def preprocessing_pipeline(): + return make_pipeline_mixte_preprocessing() + + +def test_preprocessing_pipeline(preprocessing_pipeline): + # Ensure the pipeline is constructed correctly + assert isinstance(preprocessing_pipeline, BaseEstimator) + + # Test with numerical features + X_num = pd.DataFrame([[1, 2], [3, 4], [5, 6]]) + transformed_X = preprocessing_pipeline.fit_transform(X_num) + assert isinstance(transformed_X, pd.DataFrame) + assert transformed_X.shape[1] == X_num.shape[1] + + # Test with categorical features + X_cat = pd.DataFrame([["a", "b"], ["c", "d"], ["e", "f"]]) + transformed_X = preprocessing_pipeline.fit_transform(X_cat) + assert isinstance(transformed_X, pd.DataFrame) + assert transformed_X.shape[1] > X_cat.shape[1] + + # Test with mixed features + X_mixed = pd.DataFrame([[1, "a"], [2, "b"], [3, "c"]]) + transformed_X = preprocessing_pipeline.fit_transform(X_mixed) + assert isinstance(transformed_X, pd.DataFrame) + assert transformed_X.shape[1] > X_mixed.shape[1] + + +# Testing make_robust_MixteHGB + + def test_make_robust_MixteHGB(robust_mixte_hgb_model): # Ensure the pipeline is constructed correctly assert isinstance(robust_mixte_hgb_model, Pipeline) From eaf557a93ecb1585b32db40edbb242c4868da7b2 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 27 Mar 2024 15:26:44 +0100 Subject: [PATCH 58/99] Transformer wrapper impelmented --- examples/benchmark.md | 3 + .../tutorials/plot_tuto_categorical.ipynb | 367 ++++++++++++++++++ qolmat/imputations/estimators.py | 67 +++- qolmat/imputations/imputers.py | 127 +++++- setup.py | 1 + 5 files changed, 539 insertions(+), 26 deletions(-) create mode 100644 examples/tutorials/plot_tuto_categorical.ipynb diff --git a/examples/benchmark.md b/examples/benchmark.md index d71cee73..4731cb10 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -16,6 +16,9 @@ jupyter: **This notebook aims to present the Qolmat repo through an example of a multivariate time series. In Qolmat, a few data imputation methods are implemented as well as a way to evaluate their performance.** +```python + +``` First, import some useful librairies diff --git a/examples/tutorials/plot_tuto_categorical.ipynb b/examples/tutorials/plot_tuto_categorical.ipynb new file mode 100644 index 00000000..27246b89 --- /dev/null +++ b/examples/tutorials/plot_tuto_categorical.ipynb @@ -0,0 +1,367 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "1131dc37", + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "139325d4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "# import seaborn as sns\n", + "\n", + "from qolmat.imputations.imputers import ImputerMICE, ImputerRegressor\n", + "from qolmat.imputations import estimators\n", + "from qolmat.benchmark.metrics import get_metric\n", + "from qolmat.benchmark import missing_patterns\n", + "from qolmat.benchmark import comparator\n", + "from qolmat.utils import plot\n", + "from qolmat.imputations import imputers\n", + "\n", + "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, StandardScaler\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingRegressor, HistGradientBoostingClassifier\n", + "from sklearn.compose import ColumnTransformer\n", + "from sklearn.impute import SimpleImputer\n", + "\n", + "from sklearn.base import (\n", + " BaseEstimator,\n", + " ClassifierMixin,\n", + " RegressorMixin,\n", + ")\n", + "from sklearn.compose import make_column_selector as selector\n", + "\n", + "from category_encoders.one_hot import OneHotEncoder" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "00350fe2", + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv(\"../data/titanic.csv\", sep=\";\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "9c04252b", + "metadata": {}, + "outputs": [], + "source": [ + "y = df[\"survived\"] == 1" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "3773ffd0", + "metadata": {}, + "outputs": [], + "source": [ + "df = df.dropna(how=\"all\")\n", + "df = df.drop(columns=[\"pclass\", \"survived\", \"name\", \"home.dest\", \"cabin\", \"ticket\", \"boat\", \"body\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "5fe656ea", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"age\"] = pd.to_numeric(df[\"age\"], errors=\"coerce\")\n", + "df[\"fare\"] = pd.to_numeric(df[\"fare\"].str.replace(\",\", \"\"), errors=\"coerce\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "83df10c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " pclass survived name sex age sibsp \n", + "0 1.0 1.0 Allen, Miss. Elisabeth Walton female 29.0 0.0 \\\n", + "1 1.0 1.0 Allison, Master. Hudson Trevor male NaN 1.0 \n", + "2 1.0 0.0 Allison, Miss. Helen Loraine female 2.0 1.0 \n", + "\n", + " parch ticket fare cabin embarked boat body \n", + "0 0.0 24160 2113375.0 B5 S 2 NaN \\\n", + "1 2.0 113781 1515500.0 C22 C26 S 11 NaN \n", + "2 2.0 113781 1515500.0 C22 C26 S NaN NaN \n", + "\n", + " home.dest title \n", + "0 St Louis, MO Miss \n", + "1 Montreal, PQ / Chesterville, ON Master \n", + "2 Montreal, PQ / Chesterville, ON Miss " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7fc5223d", + "metadata": {}, + "outputs": [], + "source": [ + "selector_cat = selector(dtype_exclude=np.number)\n", + "cols_cat = selector_cat(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ff24f6e5", + "metadata": {}, + "outputs": [], + "source": [ + "imputer_rpca = imputers.ImputerRpcaNoisy()\n", + "wrapper = OneHotEncoder(handle_unknown=\"ignore\", handle_missing=\"return_nan\", use_cat_names=True, cols=cols_cat)\n", + "imputer_rpca = estimators.WrapperTransformer(imputer_rpca, [wrapper])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f9501478", + "metadata": {}, + "outputs": [], + "source": [ + "pipestimator = estimators.make_robust_MixteHGB(allow_new=False)\n", + "imputer_hgb = ImputerRegressor(estimator=pipestimator, handler_nan=\"none\")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "fedfad1e", + "metadata": {}, + "outputs": [], + "source": [ + "imputer_simple = imputers.ImputerSimple()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "19c59a9e", + "metadata": {}, + "outputs": [], + "source": [ + "dict_imputers = {\"Simple\": imputer_simple, \"HGB\": imputer_hgb, \"RPCA\": imputer_rpca}\n", + "cols_to_impute = df.columns\n", + "ratio_masked = .1\n", + "generator_holes = missing_patterns.UniformHoleGenerator(n_splits=2, subset=cols_to_impute, ratio_masked=ratio_masked, sample_proportional=False)\n", + "# metrics = [\"mae\", \"wmape\", \"KL_columnwise\", \"frechet\"]\n", + "metrics = [\"mae\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "027ceb32", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tested model: ImputerSimple\n", + "Tested model: ImputerRegressor\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[17], line 8\u001b[0m\n\u001b[1;32m 1\u001b[0m comparison \u001b[38;5;241m=\u001b[39m comparator\u001b[38;5;241m.\u001b[39mComparator(\n\u001b[1;32m 2\u001b[0m dict_imputers,\n\u001b[1;32m 3\u001b[0m cols_to_impute,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6\u001b[0m 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\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(imputer)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m excp:\n", + "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/benchmark/comparator.py:116\u001b[0m, in \u001b[0;36mComparator.evaluate_errors_sample\u001b[0;34m(self, imputer, df, dict_config_opti_imputer, metric_optim)\u001b[0m\n\u001b[1;32m 106\u001b[0m df_corrupted[df_mask] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnan\n\u001b[1;32m 107\u001b[0m imputer_opti \u001b[38;5;241m=\u001b[39m hyperparameters\u001b[38;5;241m.\u001b[39moptimize(\n\u001b[1;32m 108\u001b[0m imputer,\n\u001b[1;32m 109\u001b[0m df,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 114\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose,\n\u001b[1;32m 115\u001b[0m )\n\u001b[0;32m--> 116\u001b[0m df_imputed \u001b[38;5;241m=\u001b[39m \u001b[43mimputer_opti\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_corrupted\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 117\u001b[0m subset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerator_holes\u001b[38;5;241m.\u001b[39msubset\n\u001b[1;32m 118\u001b[0m errors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_errors(df_origin[subset], df_imputed[subset], df_mask[subset])\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/utils/_set_output.py:157\u001b[0m, in \u001b[0;36m_wrap_method_output..wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 157\u001b[0m data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m 160\u001b[0m return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 161\u001b[0m _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m 162\u001b[0m \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m 163\u001b[0m )\n", + "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/estimators.py:255\u001b[0m, in \u001b[0;36mWrapperTransformer.fit_transform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 253\u001b[0m X_transformed \u001b[38;5;241m=\u001b[39m wrapper\u001b[38;5;241m.\u001b[39mfit_transform(X_transformed)\n\u001b[1;32m 254\u001b[0m \u001b[38;5;66;03m# print(\"Shape after transformation:\", X_transformed.shape)\u001b[39;00m\n\u001b[0;32m--> 255\u001b[0m X_transformed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_transformed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m wrapper \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlist_wrappers[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]:\n\u001b[1;32m 257\u001b[0m X_transformed \u001b[38;5;241m=\u001b[39m wrapper\u001b[38;5;241m.\u001b[39minverse_transform(X_transformed)\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/utils/_set_output.py:157\u001b[0m, in \u001b[0;36m_wrap_method_output..wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 157\u001b[0m data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m 160\u001b[0m return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 161\u001b[0m _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m 162\u001b[0m \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m 163\u001b[0m )\n", + "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/imputers.py:258\u001b[0m, in \u001b[0;36m_Imputer.fit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, X: pd\u001b[38;5;241m.\u001b[39mDataFrame, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame:\n\u001b[1;32m 242\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;124;03m Returns a dataframe with same shape as `X`, unchanged values, where all nans are replaced\u001b[39;00m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;124;03m by non-nan values.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;124;03m Imputed dataframe.\u001b[39;00m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 258\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(X)\n", + "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/imputers.py:190\u001b[0m, in \u001b[0;36m_Imputer.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_allgroups(df[[col]], col\u001b[38;5;241m=\u001b[39mcol)\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_allgroups\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/imputers.py:312\u001b[0m, in \u001b[0;36m_Imputer._fit_allgroups\u001b[0;34m(self, df, col)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dict_fitting[col] \u001b[38;5;241m=\u001b[39m groupby\u001b[38;5;241m.\u001b[39mapply(fun_on_col)\u001b[38;5;241m.\u001b[39mto_dict()\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dict_fitting[col] \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m0\u001b[39m: \u001b[43mfun_on_col\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m}\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", + "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/imputers.py:2024\u001b[0m, in \u001b[0;36mImputerRpcaNoisy._fit_element\u001b[0;34m(self, df, col, ngroup)\u001b[0m\n\u001b[1;32m 2022\u001b[0m stds \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mwhere(stds, stds, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2023\u001b[0m D_scale \u001b[38;5;241m=\u001b[39m (D \u001b[38;5;241m-\u001b[39m means) \u001b[38;5;241m/\u001b[39m stds\n\u001b[0;32m-> 2024\u001b[0m _, _, _, Q \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecompose_with_basis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mD_scale\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mOmega\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Q, means, stds\n", + "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/rpca/rpca_noisy.py:183\u001b[0m, in \u001b[0;36mRpcaNoisy.decompose_with_basis\u001b[0;34m(self, D, Omega)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m period \u001b[38;5;241m<\u001b[39m n_rows:\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 179\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe periods provided in argument in `list_periods` must smaller \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthan the number of rows in the matrix but \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mperiod\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m >= \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_rows\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m!\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 181\u001b[0m )\n\u001b[0;32m--> 183\u001b[0m M, A, L, Q \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimise_loss\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43mD\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43mOmega\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[43m \u001b[49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 187\u001b[0m \u001b[43m \u001b[49m\u001b[43mtau\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 188\u001b[0m \u001b[43m \u001b[49m\u001b[43mlam\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_periods\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 191\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_etas\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_iterations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_iterations\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 193\u001b[0m \u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 194\u001b[0m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 195\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_cost_function_minimized(D, M, A, Omega, tau, lam)\n\u001b[1;32m 199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m M, A, L, Q\n", + "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/rpca/rpca_noisy.py:336\u001b[0m, in \u001b[0;36mRpcaNoisy.minimise_loss\u001b[0;34m(D, Omega, rank, tau, lam, mu, list_periods, list_etas, max_iterations, tolerance, norm)\u001b[0m\n\u001b[1;32m 326\u001b[0m Q \u001b[38;5;241m=\u001b[39m scp\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39msolve(\n\u001b[1;32m 327\u001b[0m a\u001b[38;5;241m=\u001b[39mtau \u001b[38;5;241m*\u001b[39m Ir \u001b[38;5;241m+\u001b[39m mu \u001b[38;5;241m*\u001b[39m (L\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m@\u001b[39m L),\n\u001b[1;32m 328\u001b[0m b\u001b[38;5;241m=\u001b[39mL\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m@\u001b[39m (mu \u001b[38;5;241m*\u001b[39m M \u001b[38;5;241m+\u001b[39m Y),\n\u001b[1;32m 329\u001b[0m )\n\u001b[1;32m 331\u001b[0m L \u001b[38;5;241m=\u001b[39m scp\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39msolve(\n\u001b[1;32m 332\u001b[0m a\u001b[38;5;241m=\u001b[39mtau \u001b[38;5;241m*\u001b[39m Ir \u001b[38;5;241m+\u001b[39m mu \u001b[38;5;241m*\u001b[39m (Q \u001b[38;5;241m@\u001b[39m Q\u001b[38;5;241m.\u001b[39mT),\n\u001b[1;32m 333\u001b[0m b\u001b[38;5;241m=\u001b[39mQ \u001b[38;5;241m@\u001b[39m (mu \u001b[38;5;241m*\u001b[39m M\u001b[38;5;241m.\u001b[39mT \u001b[38;5;241m+\u001b[39m Y\u001b[38;5;241m.\u001b[39mT),\n\u001b[1;32m 334\u001b[0m )\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m--> 336\u001b[0m Y \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m mu \u001b[38;5;241m*\u001b[39m (\u001b[43mM\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mL\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m@\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mQ\u001b[49m)\n\u001b[1;32m 337\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m norm \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mL1\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 338\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i_period, _ \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(list_periods):\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "comparison = comparator.Comparator(\n", + " dict_imputers,\n", + " cols_to_impute,\n", + " generator_holes = generator_holes,\n", + " metrics=metrics,\n", + " max_evals=2,\n", + ")\n", + "results = comparison.compare(df)\n", + "results.style.highlight_min(color=\"lightgreen\", axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70101310", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env_qolmat_dev", + "language": "python", + "name": "env_qolmat_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.18" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/qolmat/imputations/estimators.py b/qolmat/imputations/estimators.py index 8742d171..4b992353 100644 --- a/qolmat/imputations/estimators.py +++ b/qolmat/imputations/estimators.py @@ -1,8 +1,9 @@ -from typing import Optional +import copy +from typing import List, Optional import numpy as np import pandas as pd from sklearn.compose import make_column_selector as selector -from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, StandardScaler +from sklearn.preprocessing import OrdinalEncoder, StandardScaler from sklearn.pipeline import Pipeline from sklearn.ensemble import ( HistGradientBoostingRegressor, @@ -21,6 +22,9 @@ check_is_fitted, ) +from category_encoders.one_hot import OneHotEncoder + + from typing_extensions import Self from numpy.typing import NDArray @@ -127,10 +131,11 @@ class BinTransformer(TransformerMixin, BaseEstimator): to the closest existing one. """ - def __init__(self): + def __init__(self, cols: Optional[List] = None): super().__init__() + self.cols = cols - def fit(self, X: NDArray, y: Optional[NDArray] = None): + def fit(self, X: NDArray, y: Optional[NDArray] = None) -> Self: """ Fit the BinTransformer to X. @@ -151,7 +156,8 @@ def fit(self, X: NDArray, y: Optional[NDArray] = None): X = check_array(X, accept_sparse=False, force_all_finite="allow-nan", ensure_2d=False) df = pd.DataFrame(X) self.dict_df_bins_ = dict() - for col in df: + cols = df.columns if self.cols is None else self.cols + for col in cols: values = df[col] values = values.dropna() df_bins = pd.DataFrame({"value": np.sort(values.unique())}) @@ -175,14 +181,16 @@ def transform(self, X: NDArray) -> NDArray: """ X_arr = check_array(X, accept_sparse=False, force_all_finite="allow-nan", ensure_2d=False) df = pd.DataFrame(X_arr) - print(df) list_values_out = [] for col in df: values = df[col] - df_bins = self.dict_df_bins_[col] - bins_X = np.digitize(values, df_bins["min"]) - 1 - values_out = df_bins.loc[bins_X, "value"].values - values_out = np.where(np.isnan(values), np.nan, values_out) + if col in self.dict_df_bins_.keys(): + df_bins = self.dict_df_bins_[col] + bins_X = np.digitize(values, df_bins["min"]) - 1 + values_out = df_bins.loc[bins_X, "value"].values + values_out = np.where(np.isnan(values), np.nan, values_out) + else: + values_out = values list_values_out.append(values_out) X_out = np.vstack(list_values_out).T X_out = X_out.reshape(X_arr.shape) @@ -216,6 +224,43 @@ def _more_tags(self): return {"X_types": ["2darray"], "allow_nan": True} +class WrapperTransformer(TransformerMixin, BaseEstimator): + """ + Wraps a transformer with reversible transformers designed to embed the data. + """ + + def __init__(self, transformer: TransformerMixin, list_wrappers: List[TransformerMixin]): + super().__init__() + self.transformer = transformer + self.list_wrappers = list_wrappers + + def fit(self, X: NDArray, y: Optional[NDArray] = None) -> Self: + X_transformed = copy.deepcopy(X) + for wrapper in self.list_wrappers: + X_transformed = wrapper.fit_transform(X_transformed) + X_transformed = self.transformer.fit(X_transformed) + return self + + def fit_transform(self, X: NDArray) -> Self: + X_transformed = copy.deepcopy(X) + for wrapper in self.list_wrappers: + X_transformed = wrapper.fit_transform(X_transformed) + # print("Shape after transformation:", X_transformed.shape) + X_transformed = self.transformer.fit_transform(X_transformed) + for wrapper in self.list_wrappers[::-1]: + X_transformed = wrapper.inverse_transform(X_transformed) + return X_transformed + + def transform(self, X: NDArray) -> Self: + X_transformed = copy.deepcopy(X) + for wrapper in self.list_wrappers: + X_transformed = wrapper.transform(X_transformed) + X_transformed = self.transformer.transform(X_transformed) + for wrapper in self.list_wrappers[::-1]: + X_transformed = wrapper.inverse_transform(X_transformed) + return X_transformed + + def make_pipeline_mixte_preprocessing( scale_numerical: bool = True, ) -> BaseEstimator: @@ -240,7 +285,7 @@ def make_pipeline_mixte_preprocessing( transformers.append( ( "cat", - OneHotEncoder(handle_unknown="ignore", sparse_output=False), + OneHotEncoder(handle_unknown="ignore", use_cat_names=True), selector(dtype_exclude=np.number), ) ) diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 04fba6d4..47f2e209 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -11,6 +11,7 @@ import pandas as pd import sklearn as skl from sklearn import utils as sku +from sklearn.impute import SimpleImputer from sklearn.base import BaseEstimator from sklearn.experimental import enable_iterative_imputer from sklearn.impute import IterativeImputer, KNNImputer @@ -806,6 +807,99 @@ def _transform_allgroups(self, df: pd.DataFrame, col: str = "__all__"): return df +class ImputerSimple(_Imputer): + """ + Impute each column by its mean, its median or its mode (if its categorical). + + Parameters + ---------- + groups: Tuple[str, ...] + List of column names to group by, by default [] + + Examples + -------- + >>> import numpy as np + >>> import pandas as pd + >>> from qolmat.imputations import imputers + >>> imputer = imputers.ImputerSimple() + >>> df = pd.DataFrame(data=[[1, 1, 1, 1], + ... [np.nan, np.nan, np.nan, np.nan], + ... [1, 2, 2, 5], + ... [2, 2, 2, 2]], + ... columns=["var1", "var2", "var3", "var4"]) + >>> imputer.fit_transform(df) + var1 var2 var3 var4 + 0 1.0 1.0 1.0 1.0 + 1 1.0 2.0 2.0 2.0 + 2 1.0 2.0 2.0 5.0 + 3 2.0 2.0 2.0 2.0 + """ + + def __init__(self, groups: Tuple[str, ...] = (), strategy="median") -> None: + super().__init__(groups=groups, columnwise=True, shrink=True) + self.strategy = strategy + + def _fit_element(self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0) -> Any: + """ + Fits the imputer on `df`, at the group and/or column level depending onself.groups and + self.columnwise. + + Parameters + ---------- + df : pd.DataFrame + Dataframe on which the imputer is fitted + col : str, optional + Column on which the imputer is fitted, by default "__all__" + ngroup : int, optional + Id of the group on which the method is applied + + Returns + ------- + Any + Return fitted KNN model + + Raises + ------ + NotDataFrame + Input has to be a pandas.DataFrame. + """ + if pd.api.types.is_numeric_dtype(df[col]): + model = skl.impute.SimpleImputer(strategy=self.strategy) + else: + model = skl.impute.SimpleImputer(strategy="most_frequent") + return model.fit(df[[col]]) + + def _transform_element( + self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0 + ) -> pd.DataFrame: + """ + Transforms the dataframe `df`, at the group and/or column level depending on self.groups + and self.columnwise. + + Parameters + ---------- + df : pd.DataFrame + Dataframe or column to impute + col : str, optional + Column transformed by the imputer, by default "__all__" + ngroup : int, optional + Id of the group on which the method is applied + + Returns + ------- + pd.DataFrame + Imputed dataframe. + + Raises + ------ + NotDataFrame + Input has to be a pandas.DataFrame. + """ + model = self._dict_fitting[col][ngroup] + X_imputed = model.fit_transform(df) + return pd.DataFrame(data=X_imputed, columns=df.columns, index=df.index) + + class ImputerShuffle(_Imputer): """ Impute using random samples from the considered column. @@ -1362,6 +1456,24 @@ def _transform_element( class ImputerMICE(_Imputer): + """ + Wrapper of the class sklearn.impute.IterativeImputer in our framework. This imputer relies + on a estimator which is iteratively + + Parameters + ---------- + groups : Tuple[str, ...], optional + _description_, by default () + estimator : Optional[BaseEstimator], optional + _description_, by default None + random_state : Union[None, int, np.random.RandomState], optional + _description_, by default None + sample_posterior : bool, optional + _description_, by default False + max_iter : int, optional + _description_, by default 100 + """ + def __init__( self, groups: Tuple[str, ...] = (), @@ -1370,21 +1482,6 @@ def __init__( sample_posterior=False, max_iter=100, ) -> None: - """_summary_ - - Parameters - ---------- - groups : Tuple[str, ...], optional - _description_, by default () - estimator : Optional[BaseEstimator], optional - _description_, by default None - random_state : Union[None, int, np.random.RandomState], optional - _description_, by default None - sample_posterior : bool, optional - _description_, by default False - max_iter : int, optional - _description_, by default 100 - """ super().__init__( imputer_params=("sample_posterior", "max_iter"), groups=groups, diff --git a/setup.py b/setup.py index 864adf11..9fe716cb 100644 --- a/setup.py +++ b/setup.py @@ -30,6 +30,7 @@ PYTHON_REQUIRES = ">=3.8" PACKAGES = find_packages() INSTALL_REQUIRES = [ + "category_encoders", "dcor>=0.6", "hyperopt", "numpy>=1.19", From bb3c8b40238ce74a6b4d470c2ae7764c2fed7a51 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 27 Mar 2024 15:55:21 +0100 Subject: [PATCH 59/99] Transformer wrapper impelmented --- .../tutorials/plot_tuto_categorical.ipynb | 189 ++++++----- qolmat/benchmark/comparator.py | 3 +- qolmat/benchmark/metrics.py | 44 ++- qolmat/imputations/estimators.py | 27 +- qolmat/imputations/imputers.py | 306 ------------------ 5 files changed, 140 insertions(+), 429 deletions(-) diff --git a/examples/tutorials/plot_tuto_categorical.ipynb b/examples/tutorials/plot_tuto_categorical.ipynb index 27246b89..32af6fef 100644 --- a/examples/tutorials/plot_tuto_categorical.ipynb +++ b/examples/tutorials/plot_tuto_categorical.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "1131dc37", + "id": "9825a2a4", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "139325d4", + "id": "75c7c867", "metadata": {}, "outputs": [ { @@ -57,8 +57,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "00350fe2", + "execution_count": 3, + "id": "f3c3123a", "metadata": {}, "outputs": [], "source": [ @@ -67,8 +67,8 @@ }, { "cell_type": "code", - "execution_count": 34, - "id": "9c04252b", + "execution_count": 4, + "id": "f0e55166", "metadata": {}, "outputs": [], "source": [ @@ -77,8 +77,8 @@ }, { "cell_type": "code", - "execution_count": 26, - "id": "3773ffd0", + "execution_count": 5, + "id": "9ed4714e", "metadata": {}, "outputs": [], "source": [ @@ -88,8 +88,8 @@ }, { "cell_type": "code", - "execution_count": 27, - "id": "5fe656ea", + "execution_count": 6, + "id": "29ef7edf", "metadata": {}, "outputs": [], "source": [ @@ -99,8 +99,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "83df10c6", + "execution_count": 7, + "id": "30007678", "metadata": {}, "outputs": [ { @@ -124,100 +124,54 @@ " \n", " \n", " \n", - " pclass\n", - " survived\n", - " name\n", " sex\n", " age\n", " sibsp\n", " parch\n", - " ticket\n", " fare\n", - " cabin\n", " embarked\n", - " boat\n", - " body\n", - " home.dest\n", - " title\n", " \n", " \n", " \n", " \n", " 0\n", - " 1.0\n", - " 1.0\n", - " Allen, Miss. Elisabeth Walton\n", " female\n", " 29.0\n", " 0.0\n", " 0.0\n", - " 24160\n", " 2113375.0\n", - " B5\n", " S\n", - " 2\n", - " NaN\n", - " St Louis, MO\n", - " Miss\n", " \n", " \n", " 1\n", - " 1.0\n", - " 1.0\n", - " Allison, Master. Hudson Trevor\n", " male\n", " NaN\n", " 1.0\n", " 2.0\n", - " 113781\n", " 1515500.0\n", - " C22 C26\n", " S\n", - " 11\n", - " NaN\n", - " Montreal, PQ / Chesterville, ON\n", - " Master\n", " \n", " \n", " 2\n", - " 1.0\n", - " 0.0\n", - " Allison, Miss. Helen Loraine\n", " female\n", " 2.0\n", " 1.0\n", " 2.0\n", - " 113781\n", " 1515500.0\n", - " C22 C26\n", " S\n", - " NaN\n", - " NaN\n", - " Montreal, PQ / Chesterville, ON\n", - " Miss\n", " \n", " \n", "\n", "" ], "text/plain": [ - " pclass survived name sex age sibsp \n", - "0 1.0 1.0 Allen, Miss. Elisabeth Walton female 29.0 0.0 \\\n", - "1 1.0 1.0 Allison, Master. Hudson Trevor male NaN 1.0 \n", - "2 1.0 0.0 Allison, Miss. Helen Loraine female 2.0 1.0 \n", - "\n", - " parch ticket fare cabin embarked boat body \n", - "0 0.0 24160 2113375.0 B5 S 2 NaN \\\n", - "1 2.0 113781 1515500.0 C22 C26 S 11 NaN \n", - "2 2.0 113781 1515500.0 C22 C26 S NaN NaN \n", - "\n", - " home.dest title \n", - "0 St Louis, MO Miss \n", - "1 Montreal, PQ / Chesterville, ON Master \n", - "2 Montreal, PQ / Chesterville, ON Miss " + " sex age sibsp parch fare embarked\n", + "0 female 29.0 0.0 0.0 2113375.0 S\n", + "1 male NaN 1.0 2.0 1515500.0 S\n", + "2 female 2.0 1.0 2.0 1515500.0 S" ] }, - "execution_count": 43, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -228,8 +182,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "7fc5223d", + "execution_count": 8, + "id": "96e6886f", "metadata": {}, "outputs": [], "source": [ @@ -239,20 +193,23 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "ff24f6e5", + "execution_count": 9, + "id": "40715444", "metadata": {}, "outputs": [], "source": [ "imputer_rpca = imputers.ImputerRpcaNoisy()\n", - "wrapper = OneHotEncoder(handle_unknown=\"ignore\", handle_missing=\"return_nan\", use_cat_names=True, cols=cols_cat)\n", - "imputer_rpca = estimators.WrapperTransformer(imputer_rpca, [wrapper])" + "ohe = OneHotEncoder(handle_unknown=\"ignore\", handle_missing=\"return_nan\", use_cat_names=True, cols=cols_cat)\n", + "bt = estimators.BinTransformer()\n", + "wrapper = Pipeline(steps=[(\"OneHotEncoder\", ohe), (\"BinTransformer\", bt)])\n", + "\n", + "imputer_rpca = estimators.WrapperTransformer(imputer_rpca, wrapper)" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "f9501478", + "execution_count": 10, + "id": "75a80514", "metadata": {}, "outputs": [], "source": [ @@ -262,8 +219,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "fedfad1e", + "execution_count": 11, + "id": "e3d58326", "metadata": {}, "outputs": [], "source": [ @@ -272,8 +229,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "19c59a9e", + "execution_count": 12, + "id": "74a53d21", "metadata": {}, "outputs": [], "source": [ @@ -287,8 +244,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "027ceb32", + "execution_count": 13, + "id": "8fef454d", "metadata": {}, "outputs": [ { @@ -296,30 +253,64 @@ "output_type": "stream", "text": [ "Tested model: ImputerSimple\n", - "Tested model: ImputerRegressor\n" + "Tested model: ImputerRegressor\n", + "Tested model: WrapperTransformer\n" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[17], line 8\u001b[0m\n\u001b[1;32m 1\u001b[0m comparison \u001b[38;5;241m=\u001b[39m comparator\u001b[38;5;241m.\u001b[39mComparator(\n\u001b[1;32m 2\u001b[0m dict_imputers,\n\u001b[1;32m 3\u001b[0m cols_to_impute,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 6\u001b[0m max_evals\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m 7\u001b[0m )\n\u001b[0;32m----> 8\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mcomparison\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompare\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m results\u001b[38;5;241m.\u001b[39mstyle\u001b[38;5;241m.\u001b[39mhighlight_min(color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlightgreen\u001b[39m\u001b[38;5;124m\"\u001b[39m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n", - "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/benchmark/comparator.py:148\u001b[0m, in \u001b[0;36mComparator.compare\u001b[0;34m(self, df)\u001b[0m\n\u001b[1;32m 145\u001b[0m dict_config_opti_imputer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdict_config_opti\u001b[38;5;241m.\u001b[39mget(name, {})\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 148\u001b[0m dict_errors[name] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_errors_sample\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 149\u001b[0m \u001b[43m \u001b[49m\u001b[43mimputer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdict_config_opti_imputer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetric_optim\u001b[49m\n\u001b[1;32m 150\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 151\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTested model: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(imputer)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 152\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m excp:\n", - "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/benchmark/comparator.py:116\u001b[0m, in \u001b[0;36mComparator.evaluate_errors_sample\u001b[0;34m(self, imputer, df, dict_config_opti_imputer, metric_optim)\u001b[0m\n\u001b[1;32m 106\u001b[0m df_corrupted[df_mask] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mnan\n\u001b[1;32m 107\u001b[0m imputer_opti \u001b[38;5;241m=\u001b[39m hyperparameters\u001b[38;5;241m.\u001b[39moptimize(\n\u001b[1;32m 108\u001b[0m imputer,\n\u001b[1;32m 109\u001b[0m df,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 114\u001b[0m verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mverbose,\n\u001b[1;32m 115\u001b[0m )\n\u001b[0;32m--> 116\u001b[0m df_imputed \u001b[38;5;241m=\u001b[39m \u001b[43mimputer_opti\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_corrupted\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 117\u001b[0m subset \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerator_holes\u001b[38;5;241m.\u001b[39msubset\n\u001b[1;32m 118\u001b[0m errors \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_errors(df_origin[subset], df_imputed[subset], df_mask[subset])\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/utils/_set_output.py:157\u001b[0m, in \u001b[0;36m_wrap_method_output..wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 157\u001b[0m data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m 160\u001b[0m return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 161\u001b[0m _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m 162\u001b[0m \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m 163\u001b[0m )\n", - "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/estimators.py:255\u001b[0m, in \u001b[0;36mWrapperTransformer.fit_transform\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 253\u001b[0m X_transformed \u001b[38;5;241m=\u001b[39m wrapper\u001b[38;5;241m.\u001b[39mfit_transform(X_transformed)\n\u001b[1;32m 254\u001b[0m \u001b[38;5;66;03m# print(\"Shape after transformation:\", X_transformed.shape)\u001b[39;00m\n\u001b[0;32m--> 255\u001b[0m X_transformed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransformer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_transformed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m wrapper \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlist_wrappers[::\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]:\n\u001b[1;32m 257\u001b[0m X_transformed \u001b[38;5;241m=\u001b[39m wrapper\u001b[38;5;241m.\u001b[39minverse_transform(X_transformed)\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/utils/_set_output.py:157\u001b[0m, in \u001b[0;36m_wrap_method_output..wrapped\u001b[0;34m(self, X, *args, **kwargs)\u001b[0m\n\u001b[1;32m 155\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(f)\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped\u001b[39m(\u001b[38;5;28mself\u001b[39m, X, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 157\u001b[0m data_to_wrap \u001b[38;5;241m=\u001b[39m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data_to_wrap, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m 159\u001b[0m \u001b[38;5;66;03m# only wrap the first output for cross decomposition\u001b[39;00m\n\u001b[1;32m 160\u001b[0m return_tuple \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 161\u001b[0m _wrap_data_with_container(method, data_to_wrap[\u001b[38;5;241m0\u001b[39m], X, \u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m 162\u001b[0m \u001b[38;5;241m*\u001b[39mdata_to_wrap[\u001b[38;5;241m1\u001b[39m:],\n\u001b[1;32m 163\u001b[0m )\n", - "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/imputers.py:258\u001b[0m, in \u001b[0;36m_Imputer.fit_transform\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 241\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfit_transform\u001b[39m(\u001b[38;5;28mself\u001b[39m, X: pd\u001b[38;5;241m.\u001b[39mDataFrame, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame:\n\u001b[1;32m 242\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 243\u001b[0m \u001b[38;5;124;03m Returns a dataframe with same shape as `X`, unchanged values, where all nans are replaced\u001b[39;00m\n\u001b[1;32m 244\u001b[0m \u001b[38;5;124;03m by non-nan values.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;124;03m Imputed dataframe.\u001b[39;00m\n\u001b[1;32m 257\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 258\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 259\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtransform(X)\n", - "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/imputers.py:190\u001b[0m, in \u001b[0;36m_Imputer.fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 188\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fit_allgroups(df[[col]], col\u001b[38;5;241m=\u001b[39mcol)\n\u001b[1;32m 189\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 190\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_allgroups\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/imputers.py:312\u001b[0m, in \u001b[0;36m_Imputer._fit_allgroups\u001b[0;34m(self, df, col)\u001b[0m\n\u001b[1;32m 310\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dict_fitting[col] \u001b[38;5;241m=\u001b[39m groupby\u001b[38;5;241m.\u001b[39mapply(fun_on_col)\u001b[38;5;241m.\u001b[39mto_dict()\n\u001b[1;32m 311\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dict_fitting[col] \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m0\u001b[39m: \u001b[43mfun_on_col\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m}\n\u001b[1;32m 314\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n", - "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/imputers.py:2024\u001b[0m, in \u001b[0;36mImputerRpcaNoisy._fit_element\u001b[0;34m(self, df, col, ngroup)\u001b[0m\n\u001b[1;32m 2022\u001b[0m stds \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mwhere(stds, stds, \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 2023\u001b[0m D_scale \u001b[38;5;241m=\u001b[39m (D \u001b[38;5;241m-\u001b[39m means) \u001b[38;5;241m/\u001b[39m stds\n\u001b[0;32m-> 2024\u001b[0m _, _, _, Q \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecompose_with_basis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mD_scale\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mOmega\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2026\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Q, means, stds\n", - "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/imputations/rpca/rpca_noisy.py:183\u001b[0m, in \u001b[0;36mRpcaNoisy.decompose_with_basis\u001b[0;34m(self, D, Omega)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m period \u001b[38;5;241m<\u001b[39m n_rows:\n\u001b[1;32m 178\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 179\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe periods provided in argument in `list_periods` must smaller \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 180\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthan the number of rows in the matrix but \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mperiod\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m >= \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mn_rows\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m!\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 181\u001b[0m )\n\u001b[0;32m--> 183\u001b[0m M, A, L, Q \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mminimise_loss\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 184\u001b[0m \u001b[43m \u001b[49m\u001b[43mD\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 185\u001b[0m \u001b[43m \u001b[49m\u001b[43mOmega\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 186\u001b[0m \u001b[43m \u001b[49m\u001b[43mrank\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 187\u001b[0m \u001b[43m \u001b[49m\u001b[43mtau\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 188\u001b[0m \u001b[43m \u001b[49m\u001b[43mlam\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 189\u001b[0m \u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 190\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_periods\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 191\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlist_etas\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 192\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_iterations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_iterations\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 193\u001b[0m \u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtolerance\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 194\u001b[0m \u001b[43m \u001b[49m\u001b[43mnorm\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnorm\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 195\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_cost_function_minimized(D, M, A, Omega, tau, lam)\n\u001b[1;32m 199\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m M, A, L, Q\n", - 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  SimpleHGBRPCA
maeage10.83587810.46183211.019084
sibsp0.4809160.3893130.496183
parch0.3931300.3320610.381679
fare257539.438931227545.015267308388.534351
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -337,7 +328,7 @@ { "cell_type": "code", "execution_count": null, - "id": "70101310", + "id": "8433d735", "metadata": {}, "outputs": [], "source": [] diff --git a/qolmat/benchmark/comparator.py b/qolmat/benchmark/comparator.py index 46860a50..a41c74a7 100644 --- a/qolmat/benchmark/comparator.py +++ b/qolmat/benchmark/comparator.py @@ -145,10 +145,11 @@ def compare( dict_config_opti_imputer = self.dict_config_opti.get(name, {}) try: + print(f"Testing model: {type(imputer).__name__}...", end="") dict_errors[name] = self.evaluate_errors_sample( imputer, df, dict_config_opti_imputer, self.metric_optim ) - print(f"Tested model: {type(imputer).__name__}") + print("done.") except Exception as excp: print("Error while testing ", type(imputer).__name__) raise excp diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index 3b5e4d70..ab01940b 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -8,6 +8,7 @@ from sklearn import metrics as skm from sklearn.ensemble import BaseEnsemble import dcor +from torch import Value from qolmat.utils.exceptions import NotEnoughSamples @@ -23,6 +24,7 @@ def columnwise_metric( df2: pd.DataFrame, df_mask: pd.DataFrame, metric: Callable, + type_cols: str = "all", **kwargs, ) -> pd.Series: """For each column, compute a metric score based on the true dataframe @@ -38,14 +40,27 @@ def columnwise_metric( Elements of the dataframes to compute on metric : Callable metric function + type_cols : str + Can be either: + - `all` to apply the metric to all columns + - `numerical` to apply the metric to numerical columns only + - `categorical` to apply the metric to categorical columns only Returns ------- pd.Series Series of scores for all columns """ + if type_cols == "all": + cols = df1.columns + elif type_cols == "numerical": + cols = df1.select_dtypes(include=["number"]).columns + elif type_cols == "categorical": + cols = df1.select_dtypes(exclude=["number"]).columns + else: + raise ValueError(f"Value {type_cols} is not valid for parameter `type_cols`!") values = {} - for col in df1.columns: + for col in cols: df1_col = df1.loc[df_mask[col], col] df2_col = df2.loc[df_mask[col], col] assert df1_col.notna().all() @@ -167,9 +182,9 @@ def _weighted_mean_absolute_percentage_error_1D(values1: pd.Series, values2: pd. Parameters ---------- values1 : pd.Series - true series + True values values2 : pd.Series - predicted series + Predicted values Returns ------- @@ -200,6 +215,25 @@ def weighted_mean_absolute_percentage_error( return columnwise_metric(df1, df2, df_mask, _weighted_mean_absolute_percentage_error_1D) +def accuracy(values1: pd.Series, values2: pd.Series) -> float: + """ + Matching ratio beetween the two datasets. + + Parameters + ---------- + values1 : pd.Series + True values + values2 : pd.Series + Predicted values + + Returns + ------- + float + accuracy + """ + return (values1 == values2).mean() + + def dist_wasserstein( df1: pd.DataFrame, df2: pd.DataFrame, @@ -1031,6 +1065,10 @@ def get_metric(name: str) -> Callable: "rmse": root_mean_squared_error, "mae": mean_absolute_error, "wmape": weighted_mean_absolute_percentage_error, + "accuracy": partial( + pattern_based_weighted_mean_metric, + metric=accuracy, + ), "wasserstein_columnwise": dist_wasserstein, "KL_columnwise": partial(kl_divergence, method="columnwise"), "KL_gaussian": partial(kl_divergence, method="gaussian"), diff --git a/qolmat/imputations/estimators.py b/qolmat/imputations/estimators.py index 4b992353..70f49df6 100644 --- a/qolmat/imputations/estimators.py +++ b/qolmat/imputations/estimators.py @@ -76,7 +76,6 @@ def fit(self, X: NDArray, y: NDArray) -> Self: X, y = check_X_y(X, y, accept_sparse=True, force_all_finite="allow-nan") self.is_fitted_ = True self.n_features_in_ = X.shape[1] - self.bintransformer_ = None if hasattr(self, "args_model"): args_model = self.args_model else: @@ -85,9 +84,6 @@ def fit(self, X: NDArray, y: NDArray) -> Self: model = HistGradientBoostingClassifier(**args_model) elif pd.api.types.is_numeric_dtype(y): model = HistGradientBoostingRegressor(**args_model) - if not self.allow_new: - self.bintransformer_ = BinTransformer().fit(y) - else: raise TypeError("Unknown label type") @@ -111,10 +107,6 @@ def predict(self, X: NDArray) -> NDArray: X = check_array(X, accept_sparse=True, force_all_finite="allow-nan") check_is_fitted(self, "is_fitted_") y_pred = self.model_.predict(X) - if self.bintransformer_ is not None: - # bins_y = np.digitize(y_pred, self.df_bins_["min"]) - 1 - # y_pred = self.df_bins_.loc[bins_y, "value"].values - y_pred = self.bintransformer_.transform(y_pred) return y_pred def _more_tags(self): @@ -229,35 +221,30 @@ class WrapperTransformer(TransformerMixin, BaseEstimator): Wraps a transformer with reversible transformers designed to embed the data. """ - def __init__(self, transformer: TransformerMixin, list_wrappers: List[TransformerMixin]): + def __init__(self, transformer: TransformerMixin, wrapper: TransformerMixin): super().__init__() self.transformer = transformer - self.list_wrappers = list_wrappers + self.wrapper = wrapper def fit(self, X: NDArray, y: Optional[NDArray] = None) -> Self: X_transformed = copy.deepcopy(X) - for wrapper in self.list_wrappers: - X_transformed = wrapper.fit_transform(X_transformed) + X_transformed = self.wrapper.fit_transform(X_transformed) X_transformed = self.transformer.fit(X_transformed) return self def fit_transform(self, X: NDArray) -> Self: X_transformed = copy.deepcopy(X) - for wrapper in self.list_wrappers: - X_transformed = wrapper.fit_transform(X_transformed) + X_transformed = self.wrapper.fit_transform(X_transformed) # print("Shape after transformation:", X_transformed.shape) X_transformed = self.transformer.fit_transform(X_transformed) - for wrapper in self.list_wrappers[::-1]: - X_transformed = wrapper.inverse_transform(X_transformed) + X_transformed = self.wrapper.inverse_transform(X_transformed) return X_transformed def transform(self, X: NDArray) -> Self: X_transformed = copy.deepcopy(X) - for wrapper in self.list_wrappers: - X_transformed = wrapper.transform(X_transformed) + X_transformed = self.wrapper.transform(X_transformed) X_transformed = self.transformer.transform(X_transformed) - for wrapper in self.list_wrappers[::-1]: - X_transformed = wrapper.inverse_transform(X_transformed) + X_transformed = self.wrapper.inverse_transform(X_transformed) return X_transformed diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index 47f2e209..c6805e40 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -501,312 +501,6 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame: return df_imputed -class ImputerMean(_Imputer): - """ - Impute by the mean of the column. - - Parameters - ---------- - groups: Tuple[str, ...] - List of column names to group by, by default [] - - Examples - -------- - >>> import numpy as np - >>> import pandas as pd - >>> from qolmat.imputations import imputers - >>> imputer = imputers.ImputerMean() - >>> df = pd.DataFrame(data=[[1, 1, 1, 1], - ... [np.nan, np.nan, np.nan, np.nan], - ... [1, 2, 2, 5], - ... [2, 2, 2, 2]], - ... columns=["var1", "var2", "var3", "var4"]) - >>> imputer.fit_transform(df) - var1 var2 var3 var4 - 0 1.000000 1.000000 1.000000 1.000000 - 1 1.333333 1.666667 1.666667 2.666667 - 2 1.000000 2.000000 2.000000 5.000000 - 3 2.000000 2.000000 2.000000 2.000000 - """ - - def __init__( - self, - groups: Tuple[str, ...] = (), - ) -> None: - super().__init__(groups=groups, columnwise=True, shrink=True) - - def _setup_fit(self): - """ - Setup step of the fit function, before looping over the columns. - """ - self._means = {} - - def _fit_allgroups(self, df: pd.DataFrame, col: str = "__all__") -> Self: - """ - Impute `df` by applying the specialized method `transform_element` on each group, if - groups have been given. If the method leaves nan, `fit_transform_fallback` is called in - order to return a dataframe without nan. - - Parameters - ---------- - df : pd.DataFrame - Dataframe or column to impute - col : str, optional - Column transformed by the imputer, by default "__all__" - - Returns - ------- - pd.DataFrame - Imputed dataframe or column - - Raises - ------ - NotDataFrame - Input has to be a pandas.DataFrame. - """ - self._check_dataframe(df) - if self.groups: - self._means[col] = df[col].groupby(self.ngroups_).mean() - else: - self._means[col] = df[col].mean() - return self - - def _transform_allgroups(self, df: pd.DataFrame, col: str = "__all__"): - """ - Impute `df` by applying the specialized method `transform_element` on each group, if - groups have been given. If the method leaves nan, `fit_transform_fallback` is called in - order to return a dataframe without nan. - - Parameters - ---------- - df : pd.DataFrame - Dataframe or column to impute - col : str, optional - Column transformed by the imputer, by default "__all__" - - Returns - ------- - pd.DataFrame - Imputed dataframe or column - - Raises - ------ - ValueError - Input has to be a pandas.DataFrame. - """ - self._check_dataframe(df) - if self.groups: - imputed = self.ngroups_.map(self._means[col]).rename(col).to_frame() - else: - imputed = self._means[col] - df = df.fillna(imputed) - return df - - -class ImputerMedian(_Imputer): - """ - Impute by the median of the column. - - Parameters - ---------- - groups: Tuple[str, ...] - List of column names to group by, by default [] - - Examples - -------- - >>> import numpy as np - >>> import pandas as pd - >>> from qolmat.imputations import imputers - >>> imputer = imputers.ImputerMedian() - >>> df = pd.DataFrame(data=[[1, 1, 1, 1], - ... [np.nan, np.nan, np.nan, np.nan], - ... [1, 2, 2, 5], - ... [2, 2, 2, 2]], - ... columns=["var1", "var2", "var3", "var4"]) - >>> imputer.fit_transform(df) - var1 var2 var3 var4 - 0 1.0 1.0 1.0 1.0 - 1 1.0 2.0 2.0 2.0 - 2 1.0 2.0 2.0 5.0 - 3 2.0 2.0 2.0 2.0 - """ - - def __init__( - self, - groups: Tuple[str, ...] = (), - ) -> None: - super().__init__(groups=groups, columnwise=True, shrink=True) - - def _setup_fit(self): - """ - Setup step of the fit function, before looping over the columns. - """ - self._medians = {} - - def _fit_allgroups(self, df: pd.DataFrame, col: str = "__all__") -> Self: - """ - Impute `df` by applying the specialized method `transform_element` on each group, if - groups have been given. If the method leaves nan, `fit_transform_fallback` is called in - order to return a dataframe without nan. - - Parameters - ---------- - df : pd.DataFrame - Dataframe or column to impute - col : str, optional - Column transformed by the imputer, by default "__all__" - - Returns - ------- - pd.DataFrame - Imputed dataframe or column - - Raises - ------ - NotDataFrame - Input has to be a pandas.DataFrame. - """ - self._check_dataframe(df) - if self.groups: - self._medians[col] = df[col].groupby(self.ngroups_).median() - else: - self._medians[col] = df[col].median() - return self - - def _transform_allgroups(self, df: pd.DataFrame, col: str = "__all__"): - """ - Impute `df` by applying the specialized method `transform_element` on each group, if - groups have been given. If the method leaves nan, `fit_transform_fallback` is called in - order to return a dataframe without nan. - - Parameters - ---------- - df : pd.DataFrame - Dataframe or column to impute - col : str, optional - Column transformed by the imputer, by default "__all__" - - Returns - ------- - pd.DataFrame - Imputed dataframe or column - - Raises - ------ - ValueError - Input has to be a pandas.DataFrame. - """ - self._check_dataframe(df) - if self.groups: - imputed = self.ngroups_.map(self._medians[col]).rename(col).to_frame() - else: - imputed = self._medians[col] - df = df.fillna(imputed) - return df - - -class ImputerMode(_Imputer): - """ - Impute by the mode of the column, which is the most represented value. - - Parameters - ---------- - groups: Tuple[str, ...] - List of column names to group by, by default [] - - Examples - -------- - >>> import numpy as np - >>> import pandas as pd - >>> from qolmat.imputations import imputers - >>> imputer = imputers.ImputerMode() - >>> df = pd.DataFrame(data=[[1, 1, 1, 1], - ... [np.nan, np.nan, np.nan, np.nan], - ... [1, 2, 2, 5], - ... [2, 2, 2, 2]], - ... columns=["var1", "var2", "var3", "var4"]) - >>> imputer.fit_transform(df) - var1 var2 var3 var4 - 0 1.0 1.0 1.0 1.0 - 1 1.0 2.0 2.0 1.0 - 2 1.0 2.0 2.0 5.0 - 3 2.0 2.0 2.0 2.0 - """ - - def __init__( - self, - groups: Tuple[str, ...] = (), - ) -> None: - super().__init__(groups=groups, columnwise=True, shrink=True) - - def _setup_fit(self): - """ - Setup step of the fit function, before looping over the columns. - """ - self._modes = {} - - def _fit_allgroups(self, df: pd.DataFrame, col: str = "__all__") -> Self: - """ - Impute `df` by applying the specialized method `transform_element` on each group, if - groups have been given. If the method leaves nan, `fit_transform_fallback` is called in - order to return a dataframe without nan. - - Parameters - ---------- - df : pd.DataFrame - Dataframe or column to impute - col : str, optional - Column transformed by the imputer, by default "__all__" - - Returns - ------- - pd.DataFrame - Imputed dataframe or column - - Raises - ------ - NotDataFrame - Input has to be a pandas.DataFrame. - """ - self._check_dataframe(df) - if self.groups: - self._modes[col] = df[col].groupby(self.ngroups_).apply(lambda x: x.mode().iloc[0]) - else: - self._modes[col] = df[col].mode().iloc[0] - return self - - def _transform_allgroups(self, df: pd.DataFrame, col: str = "__all__"): - """ - Impute `df` by applying the specialized method `transform_element` on each group, if - groups have been given. If the method leaves nan, `fit_transform_fallback` is called in - order to return a dataframe without nan. - - Parameters - ---------- - df : pd.DataFrame - Dataframe or column to impute - col : str, optional - Column transformed by the imputer, by default "__all__" - - Returns - ------- - pd.DataFrame - Imputed dataframe or column - - Raises - ------ - ValueError - Input has to be a pandas.DataFrame. - """ - self._check_dataframe(df) - if self.groups: - imputed = self.ngroups_.map(self._modes[col]).rename(col).to_frame() - else: - imputed = self._modes[col] - df = df.fillna(imputed) - return df - - class ImputerSimple(_Imputer): """ Impute each column by its mean, its median or its mode (if its categorical). From 1c552d08cb971641777f510e47bc2f548100ffe9 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 27 Mar 2024 16:00:25 +0100 Subject: [PATCH 60/99] Transformer wrapper impelmented --- tests/imputations/test_imputers.py | 46 ++++++++++++++---------------- 1 file changed, 21 insertions(+), 25 deletions(-) diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index d6c3525f..c46b8d84 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -16,6 +16,10 @@ {"col1": [0, np.nan, 2, 3, np.nan], "col2": [-1, np.nan, 0.5, np.nan, 1.5]} ) +df_mixed = pd.DataFrame( + {"col1": [0, np.nan, 2, 3, np.nan], "col2": ["a", np.nan, "b", np.nan, "b"]} +) + df_timeseries = pd.DataFrame( pd.DataFrame( { @@ -92,9 +96,7 @@ def test_hyperparameters_get_hyperparameters_modified( @pytest.mark.parametrize( "imputer", [ - imputers.ImputerMean(), - imputers.ImputerMedian(), - imputers.ImputerMode(), + imputers.ImputerSimple(), imputers.ImputerShuffle(), imputers.ImputerLOCF(), imputers.ImputerNOCB(), @@ -109,13 +111,13 @@ def test_Imputer_fit_transform_on_nan_column(df: pd.DataFrame, imputer: imputers @pytest.mark.parametrize("df", "string") def test_fit_transform_not_on_pandas(df: Any) -> None: - imputer = imputers.ImputerMean() + imputer = imputers.ImputerSimple() np.testing.assert_raises(ValueError, imputer.fit_transform, df) @pytest.mark.parametrize("df", [df_groups]) def test_fit_transform_on_grouped(df: pd.DataFrame) -> None: - imputer = imputers.ImputerMean(groups=("col1",)) + imputer = imputers.ImputerSimple(groups=("col1",)) result = imputer.fit_transform(df) expected = pd.DataFrame( { @@ -136,29 +138,27 @@ def test_ImputerOracle_fit_transform(df: pd.DataFrame, df_oracle: pd.DataFrame) np.testing.assert_allclose(result, expected) -@pytest.mark.parametrize("df", [df_incomplete]) -def test_ImputerMean_fit_transform(df: pd.DataFrame) -> None: - imputer = imputers.ImputerMean() +@pytest.mark.parametrize("df", [df_mixed]) +def test_ImputerSimple_mean_fit_transform(df: pd.DataFrame) -> None: + imputer = imputers.ImputerSimple(strategy="mean") result = imputer.fit_transform(df) - expected = pd.DataFrame( - {"col1": [0, 5 / 3, 2, 3, 5 / 3], "col2": [-1, 1 / 3, 0.5, 1 / 3, 1.5]} - ) + expected = pd.DataFrame({"col1": [0, 5 / 3, 2, 3, 5 / 3], "col2": ["a", "b", "b", "b", "b"]}) np.testing.assert_allclose(result, expected) -@pytest.mark.parametrize("df", [df_incomplete]) -def test_ImputerMedian_fit_transform(df: pd.DataFrame) -> None: - imputer = imputers.ImputerMedian() +@pytest.mark.parametrize("df", [df_mixed]) +def test_ImputerSimple_median_fit_transform(df: pd.DataFrame) -> None: + imputer = imputers.ImputerSimple() result = imputer.fit_transform(df) - expected = pd.DataFrame({"col1": [0, 2, 2, 3, 2], "col2": [-1, 0.5, 0.5, 0.5, 1.5]}) + expected = pd.DataFrame({"col1": [0, 2, 2, 3, 2], "col2": ["a", "b", "b", "b", "b"]}) np.testing.assert_allclose(result, expected) -@pytest.mark.parametrize("df", [df_incomplete]) -def test_ImputerMode_fit_transform(df: pd.DataFrame) -> None: - imputer = imputers.ImputerMode() +@pytest.mark.parametrize("df", [df_mixed]) +def test_ImputerSimple_mode_fit_transform(df: pd.DataFrame) -> None: + imputer = imputers.ImputerSimple(strategy="most_frequent") result = imputer.fit_transform(df) - expected = pd.DataFrame({"col1": [0, 0, 2, 3, 0], "col2": [-1, -1, 0.5, -1, 1.5]}) + expected = pd.DataFrame({"col1": [0, 0, 2, 3, 0], "col2": ["a", "b", "b", "b", "b"]}) np.testing.assert_allclose(result, expected) @@ -277,9 +277,7 @@ def test_ImputerRpcaNoisy_fit_transform(df: pd.DataFrame) -> None: df_grouped = pd.DataFrame(dict_values, index=index_grouped) list_imputers = [ - imputers.ImputerMean(groups=("group",)), - imputers.ImputerMedian(groups=("group",)), - imputers.ImputerMode(groups=("group",)), + imputers.ImputerSimple(groups=("group",)), imputers.ImputerShuffle(groups=("group",)), imputers.ImputerLOCF(groups=("group",)), imputers.ImputerNOCB(groups=("group",)), @@ -306,9 +304,7 @@ def test_models_fit_transform_grouped(imputer): [ imputers._Imputer(), imputers.ImputerOracle(), - imputers.ImputerMean(), - imputers.ImputerMedian(), - imputers.ImputerMode(), + imputers.ImputerSimple(), imputers.ImputerShuffle(), imputers.ImputerLOCF(), imputers.ImputerNOCB(), From 4a47ebfd6c55de553f2798728ef529dd9c785e20 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 4 Apr 2024 15:43:31 +0200 Subject: [PATCH 61/99] OneHotEncoderProjector implemented --- examples/benchmark.md | 2 - .../tutorials/plot_tuto_categorical.ipynb | 471 +++++++++++++++--- qolmat/benchmark/comparator.py | 4 +- qolmat/benchmark/metrics.py | 5 +- qolmat/benchmark/missing_patterns.py | 6 - qolmat/imputations/estimators.py | 95 ++-- qolmat/imputations/imputers.py | 39 +- qolmat/utils/utils.py | 33 ++ tests/imputations/test_imputers.py | 14 +- 9 files changed, 509 insertions(+), 160 deletions(-) diff --git a/examples/benchmark.md b/examples/benchmark.md index 4731cb10..57c0cab3 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -271,8 +271,6 @@ for col in cols_to_impute: ``` ```python tags=[] -# plot.plot_imputations(df_station, dfs_imputed_station) - n_columns = len(cols_to_impute) n_imputers = len(dict_imputers) diff --git a/examples/tutorials/plot_tuto_categorical.ipynb b/examples/tutorials/plot_tuto_categorical.ipynb index 32af6fef..3fc3884c 100644 --- a/examples/tutorials/plot_tuto_categorical.ipynb +++ b/examples/tutorials/plot_tuto_categorical.ipynb @@ -2,8 +2,8 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "id": "9825a2a4", + "execution_count": null, + "id": "a220df49", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "75c7c867", + "id": "80d3ba10", "metadata": {}, "outputs": [ { @@ -38,8 +38,9 @@ "from qolmat.benchmark import comparator\n", "from qolmat.utils import plot\n", "from qolmat.imputations import imputers\n", + "from qolmat.imputations.estimators import OneHotEncoderProjector\n", "\n", - "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder, StandardScaler\n", + "from sklearn.preprocessing import OrdinalEncoder, StandardScaler\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingRegressor, HistGradientBoostingClassifier\n", "from sklearn.compose import ColumnTransformer\n", @@ -52,13 +53,13 @@ ")\n", "from sklearn.compose import make_column_selector as selector\n", "\n", - "from category_encoders.one_hot import OneHotEncoder" + "# from category_encoders.one_hot import OneHotEncoder" ] }, { "cell_type": "code", "execution_count": 3, - "id": "f3c3123a", + "id": "96667a7c", "metadata": {}, "outputs": [], "source": [ @@ -68,7 +69,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "f0e55166", + "id": "f5ee81fb", "metadata": {}, "outputs": [], "source": [ @@ -78,7 +79,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "9ed4714e", + "id": "ace56085", "metadata": {}, "outputs": [], "source": [ @@ -89,7 +90,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "29ef7edf", + "id": "dab55dd3", "metadata": {}, "outputs": [], "source": [ @@ -100,7 +101,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "30007678", + "id": "ba1ea100", "metadata": {}, "outputs": [ { @@ -183,44 +184,67 @@ { "cell_type": "code", "execution_count": 8, - "id": "96e6886f", + "id": "f571ad13", "metadata": {}, "outputs": [], "source": [ "selector_cat = selector(dtype_exclude=np.number)\n", - "cols_cat = selector_cat(df)" + "cols_cat = selector_cat(df)\n", + "selector_num = selector(dtype_include=np.number)\n", + "cols_num = selector_num(df)" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "40715444", + "execution_count": 10, + "id": "d2a26bd9", "metadata": {}, "outputs": [], "source": [ "imputer_rpca = imputers.ImputerRpcaNoisy()\n", - "ohe = OneHotEncoder(handle_unknown=\"ignore\", handle_missing=\"return_nan\", use_cat_names=True, cols=cols_cat)\n", - "bt = estimators.BinTransformer()\n", + "ohe = OneHotEncoderProjector(handle_unknown=\"ignore\", handle_missing=\"return_nan\", use_cat_names=True, cols=cols_cat)\n", + "bt = estimators.BinTransformer(cols=cols_num)\n", "wrapper = Pipeline(steps=[(\"OneHotEncoder\", ohe), (\"BinTransformer\", bt)])\n", "\n", - "imputer_rpca = estimators.WrapperTransformer(imputer_rpca, wrapper)" + "imputer_wrap_rpca = estimators.WrapperTransformer(imputer_rpca, wrapper)" ] }, { "cell_type": "code", - "execution_count": 10, - "id": "75a80514", + "execution_count": 11, + "id": "a005e3b6", + "metadata": {}, + "outputs": [], + "source": [ + "ohe.mapping" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d5bdbcb3", + "metadata": {}, + "outputs": [], + "source": [ + "df_imp = imputer_wrap_rpca.fit_transform(df.iloc[:100])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2ad54886", "metadata": {}, "outputs": [], "source": [ "pipestimator = estimators.make_robust_MixteHGB(allow_new=False)\n", - "imputer_hgb = ImputerRegressor(estimator=pipestimator, handler_nan=\"none\")" + "imputer_hgb = ImputerRegressor(estimator=pipestimator, handler_nan=\"none\")\n", + "imputer_wrap_hgb = estimators.WrapperTransformer(imputer_hgb, bt)" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "e3d58326", + "execution_count": 16, + "id": "711a8e3e", "metadata": {}, "outputs": [], "source": [ @@ -229,106 +253,417 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "74a53d21", + "execution_count": 17, + "id": "e57379ae", "metadata": {}, "outputs": [], "source": [ - "dict_imputers = {\"Simple\": imputer_simple, \"HGB\": imputer_hgb, \"RPCA\": imputer_rpca}\n", + "dict_imputers = {\"Simple\": imputer_simple, \"HGB\": imputer_wrap_hgb, \"RPCA\": imputer_wrap_rpca}\n", "cols_to_impute = df.columns\n", "ratio_masked = .1\n", "generator_holes = missing_patterns.UniformHoleGenerator(n_splits=2, subset=cols_to_impute, ratio_masked=ratio_masked, sample_proportional=False)\n", - "# metrics = [\"mae\", \"wmape\", \"KL_columnwise\", \"frechet\"]\n", - "metrics = [\"mae\"]" + "metrics = [\"mae\", \"accuracy\"]" ] }, { "cell_type": "code", - "execution_count": 13, - "id": "8fef454d", + "execution_count": 61, + "id": "c727306a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Tested model: ImputerSimple\n", - "Tested model: ImputerRegressor\n", - "Tested model: WrapperTransformer\n" + "Testing model: Simple...done.\n", + "Testing model: HGB...done.\n", + "Testing model: RPCA...done.\n" ] - }, + } + ], + "source": [ + "comparison = comparator.Comparator(\n", + " dict_imputers,\n", + " cols_to_impute,\n", + " generator_holes = generator_holes,\n", + " metrics=metrics,\n", + " max_evals=2,\n", + ")\n", + "results = comparison.compare(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "id": "7668b17c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 SimpleHGBRPCA
age10.93511510.20229011.278626
sibsp0.4274810.3282440.446565
parch0.2977100.3244270.393130
fare254970.885496285660.019084244295.000000
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  SimpleHGBRPCASimpleHGBRPCA
maeage10.83587810.46183211.019084sex0.6488550.6526720.606870
age0.0267180.0419850.026718
sibsp0.7061070.7213740.687023
sibsp0.4809160.3893130.496183parch0.8053440.7633590.736641
parch0.3931300.3320610.381679fare0.0038170.0000000.000000
fare257539.438931227545.015267308388.534351embarked0.6335880.7977100.713740
\n" ], "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(results.loc[\"accuracy\"].style.highlight_max(color=\"lightgreen\", axis=1))" + ] + }, + { + "cell_type": "markdown", + "id": "b6127f00", + "metadata": {}, + "source": [ + "# Imputation analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "d6ad8c0c", + "metadata": {}, + "outputs": [], + "source": [ + "mask = generator_holes.generate_mask(df)\n", + "df_corr = df.where(~mask, np.nan)\n", + "df_imp = imputer_wrap_hgb.fit_transform(df_corr)" + ] + }, + { + "cell_type": "code", + "execution_count": 114, + "id": "8834e9e6", + "metadata": {}, + "outputs": [], + "source": [ + "ages = df[mask][\"age\"]\n", + "ages_imp = df_imp[mask][\"age\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "id": "02cb4a6e", + "metadata": {}, + "outputs": [], + "source": [ + "mesh = np.arange(ages.max() + 1)\n", + "counts = ages.value_counts().reindex(mesh, fill_value=0)\n", + "counts_imp = ages_imp.value_counts().reindex(mesh, fill_value=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "id": "b11df2f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " count count\n", + "age \n", + "0.0 0 0\n", + "1.0 2 0\n", + "2.0 1 0\n", + "3.0 0 0\n", + "4.0 2 0\n", + "... ... ...\n", + "76.0 0 0\n", + "77.0 0 0\n", + "78.0 0 0\n", + "79.0 0 0\n", + "80.0 1 0\n", + "\n", + "[81 rows x 2 columns]" + ] + }, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "comparison = comparator.Comparator(\n", - " dict_imputers,\n", - " cols_to_impute,\n", - " generator_holes = generator_holes,\n", - " metrics=metrics,\n", - " max_evals=2,\n", - ")\n", - "results = comparison.compare(df)\n", - "results.style.highlight_min(color=\"lightgreen\", axis=1)" + "pd.concat([counts, counts_imp], axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 117, + "id": "671d6b3c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 117, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(counts.rolling(5).mean())\n", + "plt.plot(counts_imp.rolling(5).mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "ccc38665", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(counts.to_frame() @ counts_imp.to_frame().T, cmap=\"gray\")" ] }, { "cell_type": "code", "execution_count": null, - "id": "8433d735", + "id": "b8c5a4b4", "metadata": {}, "outputs": [], "source": [] diff --git a/qolmat/benchmark/comparator.py b/qolmat/benchmark/comparator.py index a41c74a7..e3fee032 100644 --- a/qolmat/benchmark/comparator.py +++ b/qolmat/benchmark/comparator.py @@ -145,13 +145,13 @@ def compare( dict_config_opti_imputer = self.dict_config_opti.get(name, {}) try: - print(f"Testing model: {type(imputer).__name__}...", end="") + print(f"Testing model: {name}...", end="") dict_errors[name] = self.evaluate_errors_sample( imputer, df, dict_config_opti_imputer, self.metric_optim ) print("done.") except Exception as excp: - print("Error while testing ", type(imputer).__name__) + print(f"Error while testing {name} of type {type(imputer).__name__}!") raise excp df_errors = pd.DataFrame(dict_errors) diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index ab01940b..0b1fec0d 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -4,11 +4,8 @@ import numpy as np import pandas as pd import scipy -import sklearn from sklearn import metrics as skm -from sklearn.ensemble import BaseEnsemble import dcor -from torch import Value from qolmat.utils.exceptions import NotEnoughSamples @@ -1066,7 +1063,7 @@ def get_metric(name: str) -> Callable: "mae": mean_absolute_error, "wmape": weighted_mean_absolute_percentage_error, "accuracy": partial( - pattern_based_weighted_mean_metric, + columnwise_metric, metric=accuracy, ), "wasserstein_columnwise": dist_wasserstein, diff --git a/qolmat/benchmark/missing_patterns.py b/qolmat/benchmark/missing_patterns.py index 1ba181aa..7fc781a3 100644 --- a/qolmat/benchmark/missing_patterns.py +++ b/qolmat/benchmark/missing_patterns.py @@ -136,12 +136,6 @@ def _check_subset(self, X: pd.DataFrame): self.subset = columns_with_nans elif isinstance(self.subset, str): raise SubsetIsAString(self.subset) - # else: - # subset_without_nans = [ - # column for column in self.subset if column not in columns_with_nans - # ] - # if len(subset_without_nans) > 0: - # raise NoMissingValue(subset_without_nans) class UniformHoleGenerator(_HoleGenerator): diff --git a/qolmat/imputations/estimators.py b/qolmat/imputations/estimators.py index 70f49df6..be9b7d77 100644 --- a/qolmat/imputations/estimators.py +++ b/qolmat/imputations/estimators.py @@ -1,16 +1,15 @@ import copy -from typing import List, Optional +from typing import Any, Dict, Hashable, List, Optional, Tuple import numpy as np import pandas as pd from sklearn.compose import make_column_selector as selector -from sklearn.preprocessing import OrdinalEncoder, StandardScaler +from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn.ensemble import ( HistGradientBoostingRegressor, HistGradientBoostingClassifier, ) from sklearn.compose import ColumnTransformer -from sklearn.impute import SimpleImputer from sklearn.base import ( BaseEstimator, RegressorMixin, @@ -28,6 +27,8 @@ from typing_extensions import Self from numpy.typing import NDArray +from qolmat.utils import utils + class MixteHGBM(RegressorMixin, BaseEstimator): """ @@ -145,12 +146,13 @@ def fit(self, X: NDArray, y: Optional[NDArray] = None) -> Self: self : object Fitted transformer. """ - X = check_array(X, accept_sparse=False, force_all_finite="allow-nan", ensure_2d=False) - df = pd.DataFrame(X) - self.dict_df_bins_ = dict() + df = utils._validate_input(X) + self.dict_df_bins_: Dict[Hashable, pd.DataFrame] = dict() cols = df.columns if self.cols is None else self.cols for col in cols: values = df[col] + if not pd.api.types.is_numeric_dtype(values): + raise TypeError values = values.dropna() df_bins = pd.DataFrame({"value": np.sort(values.unique())}) df_bins["min"] = (df_bins["value"] + df_bins["value"].shift()) / 2 @@ -171,26 +173,17 @@ def transform(self, X: NDArray) -> NDArray: X_out : ndarray of shape (n_samples,) Transformed input. """ - X_arr = check_array(X, accept_sparse=False, force_all_finite="allow-nan", ensure_2d=False) - df = pd.DataFrame(X_arr) - list_values_out = [] + df = utils._validate_input(X) + df_out = df.copy() for col in df: values = df[col] if col in self.dict_df_bins_.keys(): df_bins = self.dict_df_bins_[col] bins_X = np.digitize(values, df_bins["min"]) - 1 - values_out = df_bins.loc[bins_X, "value"].values - values_out = np.where(np.isnan(values), np.nan, values_out) - else: - values_out = values - list_values_out.append(values_out) - X_out = np.vstack(list_values_out).T - X_out = X_out.reshape(X_arr.shape) - if isinstance(X, pd.DataFrame): - X_out = pd.DataFrame(X_out, index=X.index, columns=X.columns) - elif isinstance(X, pd.Series): - X_out = pd.Series(X_out, index=X.index) - return X_out + values_out = df_bins.loc[bins_X, "value"] + values_out.index = values.index + df_out[col] = values_out.where(values.notna(), np.nan) + return df_out def inverse_transform(self, X: NDArray) -> NDArray: """ @@ -208,12 +201,49 @@ def inverse_transform(self, X: NDArray) -> NDArray: """ return self.transform(X) - def _more_tags(self): + +class OneHotEncoderProjector(OneHotEncoder): + """ + Inherits from the class OneHotEncoder imported from category_encoders. The decoding + function accepts non boolean values (as it is the case for the sklearn OneHotEncoder). In + this case the decoded value corresponds to the largest dummy value. + """ + + def __init__(self, **kwargs): + super().__init__(**kwargs) + + def reverse_dummies(self, X, mapping): """ - This method indicates that this class allows inputs with categorical data and nans. It - modifies the behaviour of the functions checking data. + Convert dummy variable into numerical variables + + Parameters + ---------- + X : DataFrame + mapping: list-like + Contains mappings of column to be transformed to it's new columns and value + represented + + Returns + ------- + numerical: DataFrame + """ - return {"X_types": ["2darray"], "allow_nan": True} + out_cols = X.columns.tolist() + mapped_columns = [] + for switch in mapping: + col = switch.get("col") + mod = switch.get("mapping") + insert_at = out_cols.index(mod.columns[0]) + X.insert(insert_at, col, 0) + positive_indexes = mod.index[mod.index > 0] + max_code = X[mod.columns].max(axis=1) + for existing_col, val in zip(mod.columns, positive_indexes): + X.loc[X[existing_col] == max_code, col] = val + mapped_columns.append(existing_col) + X = X.drop(mod.columns, axis=1) + out_cols = X.columns.tolist() + + return X class WrapperTransformer(TransformerMixin, BaseEstimator): @@ -235,7 +265,6 @@ def fit(self, X: NDArray, y: Optional[NDArray] = None) -> Self: def fit_transform(self, X: NDArray) -> Self: X_transformed = copy.deepcopy(X) X_transformed = self.wrapper.fit_transform(X_transformed) - # print("Shape after transformation:", X_transformed.shape) X_transformed = self.transformer.fit_transform(X_transformed) X_transformed = self.wrapper.inverse_transform(X_transformed) return X_transformed @@ -265,17 +294,11 @@ def make_pipeline_mixte_preprocessing( preprocessor : Pipeline Preprocessing pipeline """ + transformers: List[Tuple] = [] if scale_numerical: - transformers = [("num", StandardScaler(), selector(dtype_include=np.number))] - else: - transformers = [] - transformers.append( - ( - "cat", - OneHotEncoder(handle_unknown="ignore", use_cat_names=True), - selector(dtype_exclude=np.number), - ) - ) + transformers += [("num", StandardScaler(), selector(dtype_include=np.number))] + ohe = OneHotEncoder(handle_unknown="ignore", use_cat_names=True) + transformers += [("cat", ohe, selector(dtype_exclude=np.number))] preprocessor = ColumnTransformer(transformers=transformers).set_output(transform="pandas") return preprocessor diff --git a/qolmat/imputations/imputers.py b/qolmat/imputations/imputers.py index c6805e40..a2550700 100644 --- a/qolmat/imputations/imputers.py +++ b/qolmat/imputations/imputers.py @@ -95,37 +95,6 @@ def get_hyperparams(self, col: Optional[str] = None): hyperparams[name_param] = value return hyperparams - def _validate_input(self, X: NDArray) -> pd.DataFrame: - """ - Checks that the input X can be converted into a DataFrame, and returns the corresponding - dataframe. - - Parameters - ---------- - X : NDArray - Array-like to process - - Returns - ------- - pd.DataFrame - Formatted dataframe, if the input had no column names then the dataframe columns are - integers - """ - check_array(X, force_all_finite="allow-nan", dtype=None) - if not isinstance(X, pd.DataFrame): - X_np = np.array(X) - if len(X_np.shape) == 0: - raise ValueError - if len(X_np.shape) == 1: - X_np = X_np.reshape(-1, 1) - df = pd.DataFrame(X_np, columns=[i for i in range(X_np.shape[1])]) - df = df.infer_objects() - else: - df = X - # df = df.astype(float) - - return df - def _check_dataframe(self, X: NDArray): """ Checks that the input X is a dataframe, otherwise raises an error. @@ -165,7 +134,7 @@ def fit(self, X: pd.DataFrame, y=None) -> Self: Returns self. """ - df = self._validate_input(X) + df = utils._validate_input(X) self.n_features_in_ = len(df.columns) for column in df: @@ -210,7 +179,7 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame: Imputed dataframe. """ - df = self._validate_input(X) + df = utils._validate_input(X) if tuple(df.columns) != self.columns_: raise ValueError( """The number of features is different from the counterpart in fit. @@ -483,7 +452,7 @@ def transform(self, X: pd.DataFrame) -> pd.DataFrame: pd.DataFrame dataframe imputed with premasked values """ - df = self._validate_input(X) + df = utils._validate_input(X) if tuple(df.columns) != self.columns_: raise ValueError( @@ -530,7 +499,7 @@ class ImputerSimple(_Imputer): """ def __init__(self, groups: Tuple[str, ...] = (), strategy="median") -> None: - super().__init__(groups=groups, columnwise=True, shrink=True) + super().__init__(groups=groups, columnwise=True, shrink=False) self.strategy = strategy def _fit_element(self, df: pd.DataFrame, col: str = "__all__", ngroup: int = 0) -> Any: diff --git a/qolmat/utils/utils.py b/qolmat/utils/utils.py index f1785c75..d036d9d5 100644 --- a/qolmat/utils/utils.py +++ b/qolmat/utils/utils.py @@ -5,12 +5,45 @@ import pandas as pd from numpy.typing import NDArray +from sklearn.base import check_array from qolmat.utils.exceptions import NotDimension2, SignalTooShort HyperValue = Union[int, float, str] +def _validate_input(X: NDArray) -> pd.DataFrame: + """ + Checks that the input X can be converted into a DataFrame, and returns the corresponding + dataframe. + + Parameters + ---------- + X : NDArray + Array-like to process + + Returns + ------- + pd.DataFrame + Formatted dataframe, if the input had no column names then the dataframe columns are + integers + """ + check_array(X, force_all_finite="allow-nan", dtype=None) + if not isinstance(X, pd.DataFrame): + X_np = np.array(X) + if len(X_np.shape) == 0: + raise ValueError + if len(X_np.shape) == 1: + X_np = X_np.reshape(-1, 1) + df = pd.DataFrame(X_np, columns=[i for i in range(X_np.shape[1])]) + df = df.infer_objects() + else: + df = X + # df = df.astype(float) + + return df + + def progress_bar( iteration: int, total: int, diff --git a/tests/imputations/test_imputers.py b/tests/imputations/test_imputers.py index c46b8d84..bea18d9a 100644 --- a/tests/imputations/test_imputers.py +++ b/tests/imputations/test_imputers.py @@ -122,10 +122,10 @@ def test_fit_transform_on_grouped(df: pd.DataFrame) -> None: expected = pd.DataFrame( { "col1": [1, 1, 0, 1], - "col2": [1, 2, 0, 3], + "col2": [1.0, 2.0, 0.0, 3.0], } ) - np.testing.assert_allclose(result, expected) + pd.testing.assert_frame_equal(result, expected) @pytest.mark.parametrize("df", [df_incomplete]) @@ -143,23 +143,23 @@ def test_ImputerSimple_mean_fit_transform(df: pd.DataFrame) -> None: imputer = imputers.ImputerSimple(strategy="mean") result = imputer.fit_transform(df) expected = pd.DataFrame({"col1": [0, 5 / 3, 2, 3, 5 / 3], "col2": ["a", "b", "b", "b", "b"]}) - np.testing.assert_allclose(result, expected) + pd.testing.assert_frame_equal(result, expected) @pytest.mark.parametrize("df", [df_mixed]) def test_ImputerSimple_median_fit_transform(df: pd.DataFrame) -> None: imputer = imputers.ImputerSimple() result = imputer.fit_transform(df) - expected = pd.DataFrame({"col1": [0, 2, 2, 3, 2], "col2": ["a", "b", "b", "b", "b"]}) - np.testing.assert_allclose(result, expected) + expected = pd.DataFrame({"col1": [0.0, 2.0, 2.0, 3.0, 2.0], "col2": ["a", "b", "b", "b", "b"]}) + pd.testing.assert_frame_equal(result, expected) @pytest.mark.parametrize("df", [df_mixed]) def test_ImputerSimple_mode_fit_transform(df: pd.DataFrame) -> None: imputer = imputers.ImputerSimple(strategy="most_frequent") result = imputer.fit_transform(df) - expected = pd.DataFrame({"col1": [0, 0, 2, 3, 0], "col2": ["a", "b", "b", "b", "b"]}) - np.testing.assert_allclose(result, expected) + expected = pd.DataFrame({"col1": [0.0, 0.0, 2.0, 3.0, 0.0], "col2": ["a", "b", "b", "b", "b"]}) + pd.testing.assert_frame_equal(result, expected) @pytest.mark.parametrize("df", [pd.DataFrame({"col1": [1, 1, np.nan]})]) From 21871a59ff9475cd7fe60c40f4e6d35c5424aa3c Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 4 Apr 2024 16:13:09 +0200 Subject: [PATCH 62/99] tests passing --- .../tutorials/plot_tuto_categorical.ipynb | 71 ++++++++++++------- .../{estimators.py => preprocessing.py} | 0 ...st_estimators.py => test_preprocessing.py} | 59 +++++++-------- 3 files changed, 77 insertions(+), 53 deletions(-) rename qolmat/imputations/{estimators.py => preprocessing.py} (100%) rename tests/imputations/{test_estimators.py => test_preprocessing.py} (72%) diff --git a/examples/tutorials/plot_tuto_categorical.ipynb b/examples/tutorials/plot_tuto_categorical.ipynb index 3fc3884c..6199941a 100644 --- a/examples/tutorials/plot_tuto_categorical.ipynb +++ b/examples/tutorials/plot_tuto_categorical.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a220df49", + "id": "7bec9ffc", "metadata": {}, "outputs": [], "source": [ @@ -11,10 +11,31 @@ "%autoreload 2" ] }, + { + "cell_type": "code", + "execution_count": 122, + "id": "3587aa0a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 5)" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.array([[1, 2, 3, np.nan, 5]]).shape" + ] + }, { "cell_type": "code", "execution_count": 2, - "id": "80d3ba10", + "id": "b5136ec4", "metadata": {}, "outputs": [ { @@ -59,7 +80,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "96667a7c", + "id": "55e57e8b", "metadata": {}, "outputs": [], "source": [ @@ -69,7 +90,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "f5ee81fb", + "id": "61bcee05", "metadata": {}, "outputs": [], "source": [ @@ -79,7 +100,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "ace56085", + "id": "88f12b44", "metadata": {}, "outputs": [], "source": [ @@ -90,7 +111,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "dab55dd3", + "id": "34cfca81", "metadata": {}, "outputs": [], "source": [ @@ -101,7 +122,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "ba1ea100", + "id": "7cefa249", "metadata": {}, "outputs": [ { @@ -184,7 +205,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "f571ad13", + "id": "87e6d8c6", "metadata": {}, "outputs": [], "source": [ @@ -197,7 +218,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "d2a26bd9", + "id": "394b40d9", "metadata": {}, "outputs": [], "source": [ @@ -212,7 +233,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "a005e3b6", + "id": "915c5caf", "metadata": {}, "outputs": [], "source": [ @@ -222,7 +243,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "d5bdbcb3", + "id": "93d0c02d", "metadata": {}, "outputs": [], "source": [ @@ -232,7 +253,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "2ad54886", + "id": "ea24e781", "metadata": {}, "outputs": [], "source": [ @@ -244,7 +265,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "711a8e3e", + "id": "e5347dfe", "metadata": {}, "outputs": [], "source": [ @@ -254,7 +275,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "e57379ae", + "id": "325b7354", "metadata": {}, "outputs": [], "source": [ @@ -268,7 +289,7 @@ { "cell_type": "code", "execution_count": 61, - "id": "c727306a", + "id": "5d4c2127", "metadata": {}, "outputs": [ { @@ -295,7 +316,7 @@ { "cell_type": "code", "execution_count": 111, - "id": "7668b17c", + "id": "4b0ebe4e", "metadata": {}, "outputs": [ { @@ -358,7 +379,7 @@ { "cell_type": "code", "execution_count": 112, - "id": "edcd6516", + "id": "08640c07", "metadata": {}, "outputs": [ { @@ -432,7 +453,7 @@ }, { "cell_type": "markdown", - "id": "b6127f00", + "id": "d8193a27", "metadata": {}, "source": [ "# Imputation analysis" @@ -441,7 +462,7 @@ { "cell_type": "code", "execution_count": 113, - "id": "d6ad8c0c", + "id": "4df8e2ce", "metadata": {}, "outputs": [], "source": [ @@ -453,7 +474,7 @@ { "cell_type": "code", "execution_count": 114, - "id": "8834e9e6", + "id": "c4681f8e", "metadata": {}, "outputs": [], "source": [ @@ -464,7 +485,7 @@ { "cell_type": "code", "execution_count": 115, - "id": "02cb4a6e", + "id": "1537a2a7", "metadata": {}, "outputs": [], "source": [ @@ -476,7 +497,7 @@ { "cell_type": "code", "execution_count": 116, - "id": "b11df2f4", + "id": "dad580cc", "metadata": {}, "outputs": [ { @@ -600,7 +621,7 @@ { "cell_type": "code", "execution_count": 117, - "id": "671d6b3c", + "id": "12a99c70", "metadata": {}, "outputs": [ { @@ -632,7 +653,7 @@ { "cell_type": "code", "execution_count": 120, - "id": "ccc38665", + "id": "8006ba1e", "metadata": {}, "outputs": [ { @@ -663,7 +684,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b8c5a4b4", + "id": "b8cc543a", "metadata": {}, "outputs": [], "source": [] diff --git a/qolmat/imputations/estimators.py b/qolmat/imputations/preprocessing.py similarity index 100% rename from qolmat/imputations/estimators.py rename to qolmat/imputations/preprocessing.py diff --git a/tests/imputations/test_estimators.py b/tests/imputations/test_preprocessing.py similarity index 72% rename from tests/imputations/test_estimators.py rename to tests/imputations/test_preprocessing.py index 0532a1c4..27fbd6f2 100644 --- a/tests/imputations/test_estimators.py +++ b/tests/imputations/test_preprocessing.py @@ -2,10 +2,7 @@ import pandas as pd import pytest from sklearn.compose import make_column_selector as selector -from sklearn.ensemble import ( - HistGradientBoostingClassifier, - HistGradientBoostingRegressor, -) + from sklearn.pipeline import Pipeline from sklearn.base import BaseEstimator from sklearn.metrics import mean_squared_error @@ -13,7 +10,7 @@ from sklearn.utils.validation import check_X_y, check_array from sklearn.model_selection import train_test_split from sklearn.compose import ColumnTransformer -from qolmat.imputations.estimators import ( +from qolmat.imputations.preprocessing import ( BinTransformer, MixteHGBM, make_pipeline_mixte_preprocessing, @@ -71,33 +68,39 @@ def bin_transformer(): def test_fit_transform(bin_transformer): - X = np.array([1, 2, 3, np.nan, 5]) - transformed_X = bin_transformer.fit_transform(X) - assert np.array_equal(transformed_X, np.array([1, 2, 3, np.nan, 5]), equal_nan=True) + X = np.array([[1, 2, 3, np.nan, 5]]).T + X_transformed = bin_transformer.fit_transform(X) + assert np.array_equal(X_transformed, X, equal_nan=True) def test_transform(bin_transformer): bin_transformer.dict_df_bins_ = { 0: pd.DataFrame({"value": [1, 2, 3, 4, 5], "min": [-np.inf, 1.5, 2.5, 3.5, 4.5]}) } - X = np.array([4.2, -1, 3.0, 4.5, 12]) - transformed_X = bin_transformer.transform(X) - assert np.array_equal(transformed_X, np.array([4, 1, 3, 5, 5])) + X = np.array([[4.2, -1, 3.0, 4.5, 12]]).T + X_transformed = bin_transformer.transform(X) + print(X_transformed) + print(X) + assert np.array_equal(X_transformed, np.array([[4, 1, 3, 5, 5]]).T) -def test_fit_transform_with_series(bin_transformer): - X = pd.Series([1, 2, 3, np.nan, 5]) - transformed_X = bin_transformer.fit_transform(X) - pd.testing.assert_series_equal(transformed_X, pd.Series([1, 2, 3, np.nan, 5])) +def test_fit_transform_with_dataframes(bin_transformer): + X = pd.DataFrame({"0": [1, 2, 3, np.nan, 5]}) + X_transformed = bin_transformer.fit_transform(X) + print(X_transformed) + print(X) + pd.testing.assert_frame_equal(X_transformed, X) -def test_transform_with_series(bin_transformer): +def test_transform_with_dataframes(bin_transformer): bin_transformer.dict_df_bins_ = { 0: pd.DataFrame({"value": [1, 2, 3, 4, 5], "min": [0.5, 1.5, 2.5, 3.5, 4.5]}) } - X = pd.Series([1, 2, 3, 4, 5]) - transformed_X = bin_transformer.transform(X) - pd.testing.assert_series_equal(transformed_X, pd.Series([1, 2, 3, 4, 5], dtype=float)) + X = pd.DataFrame({"0": [1, 2, 3, 4, 5]}) + X_transformed = bin_transformer.transform(X) + print(X_transformed) + print(X) + pd.testing.assert_frame_equal(X_transformed, X) # Testing make_pipeline_mixte_preprocessing @@ -114,21 +117,21 @@ def test_preprocessing_pipeline(preprocessing_pipeline): # Test with numerical features X_num = pd.DataFrame([[1, 2], [3, 4], [5, 6]]) - transformed_X = preprocessing_pipeline.fit_transform(X_num) - assert isinstance(transformed_X, pd.DataFrame) - assert transformed_X.shape[1] == X_num.shape[1] + X_transformed = preprocessing_pipeline.fit_transform(X_num) + assert isinstance(X_transformed, pd.DataFrame) + assert X_transformed.shape[1] == X_num.shape[1] # Test with categorical features X_cat = pd.DataFrame([["a", "b"], ["c", "d"], ["e", "f"]]) - transformed_X = preprocessing_pipeline.fit_transform(X_cat) - assert isinstance(transformed_X, pd.DataFrame) - assert transformed_X.shape[1] > X_cat.shape[1] + X_transformed = preprocessing_pipeline.fit_transform(X_cat) + assert isinstance(X_transformed, pd.DataFrame) + assert X_transformed.shape[1] > X_cat.shape[1] # Test with mixed features X_mixed = pd.DataFrame([[1, "a"], [2, "b"], [3, "c"]]) - transformed_X = preprocessing_pipeline.fit_transform(X_mixed) - assert isinstance(transformed_X, pd.DataFrame) - assert transformed_X.shape[1] > X_mixed.shape[1] + X_transformed = preprocessing_pipeline.fit_transform(X_mixed) + assert isinstance(X_transformed, pd.DataFrame) + assert X_transformed.shape[1] > X_mixed.shape[1] # Testing make_robust_MixteHGB From 51e237f13e3834e8ef1153cdadd418afaf889932 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 4 Apr 2024 17:33:10 +0200 Subject: [PATCH 63/99] tests passing --- .../tutorials/plot_tuto_categorical.ipynb | 240 +++++++++++++++--- qolmat/imputations/preprocessing.py | 21 +- tests/imputations/test_preprocessing.py | 57 ++++- 3 files changed, 268 insertions(+), 50 deletions(-) diff --git a/examples/tutorials/plot_tuto_categorical.ipynb b/examples/tutorials/plot_tuto_categorical.ipynb index 6199941a..b8baea65 100644 --- a/examples/tutorials/plot_tuto_categorical.ipynb +++ b/examples/tutorials/plot_tuto_categorical.ipynb @@ -3,7 +3,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7bec9ffc", + "id": "06570cfd", "metadata": {}, "outputs": [], "source": [ @@ -14,7 +14,7 @@ { "cell_type": "code", "execution_count": 122, - "id": "3587aa0a", + "id": "b2961e39", "metadata": {}, "outputs": [ { @@ -35,7 +35,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "b5136ec4", + "id": "48dcea87", "metadata": {}, "outputs": [ { @@ -79,8 +79,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "55e57e8b", + "execution_count": 135, + "id": "db355ac3", "metadata": {}, "outputs": [], "source": [ @@ -89,40 +89,210 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "61bcee05", + "execution_count": 136, + "id": "ef95e583", "metadata": {}, "outputs": [], "source": [ - "y = df[\"survived\"] == 1" + "df = df.dropna(how=\"all\")\n", + "y = df[\"survived\"] == 1\n", + "df = df.drop(columns=[\"pclass\", \"survived\", \"name\", \"home.dest\", \"cabin\", \"ticket\", \"boat\", \"body\"])" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "88f12b44", + "execution_count": 137, + "id": "2c71625e", "metadata": {}, "outputs": [], "source": [ - "df = df.dropna(how=\"all\")\n", - "df = df.drop(columns=[\"pclass\", \"survived\", \"name\", \"home.dest\", \"cabin\", \"ticket\", \"boat\", \"body\"])" + "df[\"age\"] = pd.to_numeric(df[\"age\"], errors=\"coerce\")\n", + "df[\"fare\"] = pd.to_numeric(df[\"fare\"].str.replace(\",\", \"\"), errors=\"coerce\")" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "id": "9452994a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index([0], dtype='int64')" + ] + }, + "execution_count": 148, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.Index([0])" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "id": "8f01eaf4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " C1_1 C1_2 C2_1 C2_2\n", + "0 1 0 1 0\n", + "1 0 1 0 1\n", + "2 0 1 1 0\n" + ] + } + ], + "source": [ + "from category_encoders.one_hot import OneHotEncoder\n", + "encoder = OneHotEncoderProjector()\n", + "# encoder = OneHotEncoder()\n", + "\n", + "df = pd.DataFrame({\"C1\": [\"a\", \"b\", \"b\"], \"C2\": [\"c\", \"d\", \"c\"]})\n", + "# Création d'un DataFrame d'exemple avec des colonnes encodées en one-hot\n", + "df_dum = encoder.fit_transform(df)\n", + "print(df_dum)" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "id": "52b0f427", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'col': 'C1',\n", + " 'mapping': C1_1 C1_2\n", + " 1 1 0\n", + " 2 0 1\n", + " -1 0 0\n", + " -2 0 0},\n", + " {'col': 'C2',\n", + " 'mapping': C2_1 C2_2\n", + " 1 1 0\n", + " 2 0 1\n", + " -1 0 0\n", + " -2 0 0}]" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "encoder.mapping" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "34cfca81", + "execution_count": 160, + "id": "0505c9fa", "metadata": {}, "outputs": [], "source": [ - "df[\"age\"] = pd.to_numeric(df[\"age\"], errors=\"coerce\")\n", - "df[\"fare\"] = pd.to_numeric(df[\"fare\"].str.replace(\",\", \"\"), errors=\"coerce\")" + "# Appel de la méthode reverse_dummies\n", + "df_back = encoder.inverse_transform(df_dum)" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "id": "cbb2ec43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1309,)" + ] + }, + "execution_count": 139, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "id": "03cf3965", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 147, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(df.values)" + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "id": "681e5c64", + "metadata": {}, + "outputs": [], + "source": [ + "rfc = HistGradientBoostingClassifier()\n", + "# rfc.fit(df[[\"age\", \"fare\"]], y)" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "id": "2f1112b2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/julienroussel/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/base.py:458: UserWarning: X has feature names, but HistGradientBoostingClassifier was fitted without feature names\n", + " warnings.warn(\n" + ] + }, + { + "ename": "NotFittedError", + "evalue": "This HistGradientBoostingClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNotFittedError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[146], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mrfc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mage\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1881\u001b[0m, in \u001b[0;36mHistGradientBoostingClassifier.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 1868\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Predict classes for X.\u001b[39;00m\n\u001b[1;32m 1869\u001b[0m \n\u001b[1;32m 1870\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;124;03m The predicted classes.\u001b[39;00m\n\u001b[1;32m 1879\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1880\u001b[0m \u001b[38;5;66;03m# TODO: This could be done in parallel\u001b[39;00m\n\u001b[0;32m-> 1881\u001b[0m encoded_classes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_proba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 1882\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_[encoded_classes]\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1919\u001b[0m, in \u001b[0;36mHistGradientBoostingClassifier.predict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 1906\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict_proba\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[1;32m 1907\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Predict class probabilities for X.\u001b[39;00m\n\u001b[1;32m 1908\u001b[0m \n\u001b[1;32m 1909\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1917\u001b[0m \u001b[38;5;124;03m The class probabilities of the input samples.\u001b[39;00m\n\u001b[1;32m 1918\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1919\u001b[0m raw_predictions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raw_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1920\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_loss\u001b[38;5;241m.\u001b[39mpredict_proba(raw_predictions)\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1034\u001b[0m, in \u001b[0;36mBaseHistGradientBoosting._raw_predict\u001b[0;34m(self, X, n_threads)\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_binned:\n\u001b[1;32m 1031\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(\n\u001b[1;32m 1032\u001b[0m X, dtype\u001b[38;5;241m=\u001b[39mX_DTYPE, force_all_finite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 1033\u001b[0m )\n\u001b[0;32m-> 1034\u001b[0m \u001b[43mcheck_is_fitted\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_features:\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX has \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m features but this estimator was trained with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1038\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m features.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_features)\n\u001b[1;32m 1039\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/utils/validation.py:1461\u001b[0m, in \u001b[0;36mcheck_is_fitted\u001b[0;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[1;32m 1458\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not an estimator instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (estimator))\n\u001b[1;32m 1460\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_fitted(estimator, attributes, all_or_any):\n\u001b[0;32m-> 1461\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m NotFittedError(msg \u001b[38;5;241m%\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m(estimator)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m})\n", + "\u001b[0;31mNotFittedError\u001b[0m: This HistGradientBoostingClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator." + ] + } + ], + "source": [ + "rfc.predict(df[[\"age\"]])" ] }, { "cell_type": "code", "execution_count": 7, - "id": "7cefa249", + "id": "e62453b6", "metadata": {}, "outputs": [ { @@ -205,7 +375,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "87e6d8c6", + "id": "6945a95c", "metadata": {}, "outputs": [], "source": [ @@ -218,7 +388,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "394b40d9", + "id": "927fe33a", "metadata": {}, "outputs": [], "source": [ @@ -233,7 +403,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "915c5caf", + "id": "665b1dbb", "metadata": {}, "outputs": [], "source": [ @@ -243,7 +413,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "93d0c02d", + "id": "f57e68ed", "metadata": {}, "outputs": [], "source": [ @@ -253,7 +423,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "ea24e781", + "id": "843913b5", "metadata": {}, "outputs": [], "source": [ @@ -265,7 +435,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "e5347dfe", + "id": "44315307", "metadata": {}, "outputs": [], "source": [ @@ -275,7 +445,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "325b7354", + "id": "dd62828f", "metadata": {}, "outputs": [], "source": [ @@ -289,7 +459,7 @@ { "cell_type": "code", "execution_count": 61, - "id": "5d4c2127", + "id": "4db1319b", "metadata": {}, "outputs": [ { @@ -316,7 +486,7 @@ { "cell_type": "code", "execution_count": 111, - "id": "4b0ebe4e", + "id": "e7076e56", "metadata": {}, "outputs": [ { @@ -379,7 +549,7 @@ { "cell_type": "code", "execution_count": 112, - "id": "08640c07", + "id": "80c04a50", "metadata": {}, "outputs": [ { @@ -453,7 +623,7 @@ }, { "cell_type": "markdown", - "id": "d8193a27", + "id": "436b902e", "metadata": {}, "source": [ "# Imputation analysis" @@ -462,7 +632,7 @@ { "cell_type": "code", "execution_count": 113, - "id": "4df8e2ce", + "id": "3abf0df3", "metadata": {}, "outputs": [], "source": [ @@ -474,7 +644,7 @@ { "cell_type": "code", "execution_count": 114, - "id": "c4681f8e", + "id": "31772264", "metadata": {}, "outputs": [], "source": [ @@ -485,7 +655,7 @@ { "cell_type": "code", "execution_count": 115, - "id": "1537a2a7", + "id": "1bde6953", "metadata": {}, "outputs": [], "source": [ @@ -497,7 +667,7 @@ { "cell_type": "code", "execution_count": 116, - "id": "dad580cc", + "id": "068a0921", "metadata": {}, "outputs": [ { @@ -621,7 +791,7 @@ { "cell_type": "code", "execution_count": 117, - "id": "12a99c70", + "id": "768b9e71", "metadata": {}, "outputs": [ { @@ -653,7 +823,7 @@ { "cell_type": "code", "execution_count": 120, - "id": "8006ba1e", + "id": "6c7cd59f", "metadata": {}, "outputs": [ { @@ -684,7 +854,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b8cc543a", + "id": "b32cf48c", "metadata": {}, "outputs": [], "source": [] diff --git a/qolmat/imputations/preprocessing.py b/qolmat/imputations/preprocessing.py index be9b7d77..7499de0f 100644 --- a/qolmat/imputations/preprocessing.py +++ b/qolmat/imputations/preprocessing.py @@ -147,6 +147,8 @@ def fit(self, X: NDArray, y: Optional[NDArray] = None) -> Self: Fitted transformer. """ df = utils._validate_input(X) + self.feature_names_in_ = df.columns + self.n_features_in_ = len(df.columns) self.dict_df_bins_: Dict[Hashable, pd.DataFrame] = dict() cols = df.columns if self.cols is None else self.cols for col in cols: @@ -174,6 +176,14 @@ def transform(self, X: NDArray) -> NDArray: Transformed input. """ df = utils._validate_input(X) + check_is_fitted(self) + if ( + not hasattr(self, "feature_names_in_") + or df.columns.to_list() != self.feature_names_in_.to_list() + ): + raise ValueError( + "Feature names in X {df.columns} don't match with expected {feature_names_in_}" + ) df_out = df.copy() for col in df: values = df[col] @@ -183,6 +193,8 @@ def transform(self, X: NDArray) -> NDArray: values_out = df_bins.loc[bins_X, "value"] values_out.index = values.index df_out[col] = values_out.where(values.notna(), np.nan) + if isinstance(X, np.ndarray): + return df_out.values return df_out def inverse_transform(self, X: NDArray) -> NDArray: @@ -201,6 +213,13 @@ def inverse_transform(self, X: NDArray) -> NDArray: """ return self.transform(X) + def _more_tags(self): + """ + This method indicates that this class allows inputs with categorical data and nans. It + modifies the behaviour of the functions checking data. + """ + return {"X_types": ["2darray", "categorical", "string"], "allow_nan": True} + class OneHotEncoderProjector(OneHotEncoder): """ @@ -212,7 +231,7 @@ class OneHotEncoderProjector(OneHotEncoder): def __init__(self, **kwargs): super().__init__(**kwargs) - def reverse_dummies(self, X, mapping): + def reverse_dummies(self, X: pd.DataFrame, mapping: Dict) -> pd.DataFrame: """ Convert dummy variable into numerical variables diff --git a/tests/imputations/test_preprocessing.py b/tests/imputations/test_preprocessing.py index 27fbd6f2..7313aaaa 100644 --- a/tests/imputations/test_preprocessing.py +++ b/tests/imputations/test_preprocessing.py @@ -13,6 +13,7 @@ from qolmat.imputations.preprocessing import ( BinTransformer, MixteHGBM, + OneHotEncoderProjector, make_pipeline_mixte_preprocessing, make_robust_MixteHGB, ) @@ -37,11 +38,11 @@ def robust_mixte_hgb_model(): return make_robust_MixteHGB() -def test_estimator(mixte_hgb_model): +def test_sklearn_MixteHGB(mixte_hgb_model): check_estimator(mixte_hgb_model) -def test_fit_predict(mixte_hgb_model): +def test_fit_predict_MixteHGB(mixte_hgb_model): # Test fitting and predicting with numeric target X_train, X_test, y_train, y_test = train_test_split( X_num, y_numeric, test_size=0.2, random_state=42 @@ -59,7 +60,9 @@ def test_fit_predict(mixte_hgb_model): assert len(y_pred) == len(X_test) -# Testing BinTransformer +########################## +# Testing BinTransformer # +########################## @pytest.fixture @@ -67,43 +70,69 @@ def bin_transformer(): return BinTransformer() -def test_fit_transform(bin_transformer): +def test_sklearn_BinTransformer(bin_transformer): + check_estimator(bin_transformer) + + +def test_fit_transform_BinTransformer(bin_transformer): X = np.array([[1, 2, 3, np.nan, 5]]).T X_transformed = bin_transformer.fit_transform(X) assert np.array_equal(X_transformed, X, equal_nan=True) -def test_transform(bin_transformer): +def test_transform_BinTransformer(bin_transformer): bin_transformer.dict_df_bins_ = { 0: pd.DataFrame({"value": [1, 2, 3, 4, 5], "min": [-np.inf, 1.5, 2.5, 3.5, 4.5]}) } + bin_transformer.feature_names_in_ = pd.Index([0]) + bin_transformer.n_features_in_ = 1 X = np.array([[4.2, -1, 3.0, 4.5, 12]]).T X_transformed = bin_transformer.transform(X) - print(X_transformed) - print(X) assert np.array_equal(X_transformed, np.array([[4, 1, 3, 5, 5]]).T) -def test_fit_transform_with_dataframes(bin_transformer): +def test_fit_transform_with_dataframes_BinTransformer(bin_transformer): X = pd.DataFrame({"0": [1, 2, 3, np.nan, 5]}) X_transformed = bin_transformer.fit_transform(X) - print(X_transformed) - print(X) pd.testing.assert_frame_equal(X_transformed, X) -def test_transform_with_dataframes(bin_transformer): +def test_transform_with_dataframes_BinTransformer(bin_transformer): bin_transformer.dict_df_bins_ = { 0: pd.DataFrame({"value": [1, 2, 3, 4, 5], "min": [0.5, 1.5, 2.5, 3.5, 4.5]}) } + bin_transformer.feature_names_in_ = pd.Index(["0"]) + bin_transformer.n_features_in_ = 1 X = pd.DataFrame({"0": [1, 2, 3, 4, 5]}) X_transformed = bin_transformer.transform(X) - print(X_transformed) - print(X) pd.testing.assert_frame_equal(X_transformed, X) -# Testing make_pipeline_mixte_preprocessing +################################## +# Testing OneHotEncoderProjector # +################################## + + +@pytest.fixture +def encoder(): + return OneHotEncoderProjector() + + +def test_inverse_transform_OneHotEncoderProjector(encoder): + df = pd.DataFrame({"C1": ["a", "b", "b"], "C2": ["c", "d", "c"]}) + df_dum = encoder.fit_transform(df) + + df_back = encoder.inverse_transform(df_dum) + pd.testing.assert_frame_equal(df, df_back) + + df_dum_perturbated = df_dum + np.random.uniform(-0.5, 0.5, size=df_dum.shape) + df_back = encoder.inverse_transform(df_dum_perturbated) + pd.testing.assert_frame_equal(df, df_back) + + +############################################# +# Testing make_pipeline_mixte_preprocessing # +############################################# @pytest.fixture From 3b7d4a9277edd539d64ce73feb6dba4284b62e88 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 5 Apr 2024 12:07:41 +0200 Subject: [PATCH 64/99] tests extended to all preprocessing.py --- tests/imputations/test_preprocessing.py | 53 ++++++++++++++++++++++++- 1 file changed, 52 insertions(+), 1 deletion(-) diff --git a/tests/imputations/test_preprocessing.py b/tests/imputations/test_preprocessing.py index 7313aaaa..cfc25494 100644 --- a/tests/imputations/test_preprocessing.py +++ b/tests/imputations/test_preprocessing.py @@ -4,7 +4,7 @@ from sklearn.compose import make_column_selector as selector from sklearn.pipeline import Pipeline -from sklearn.base import BaseEstimator +from sklearn.base import BaseEstimator, TransformerMixin from sklearn.metrics import mean_squared_error from sklearn.utils.estimator_checks import check_estimator from sklearn.utils.validation import check_X_y, check_array @@ -14,6 +14,7 @@ BinTransformer, MixteHGBM, OneHotEncoderProjector, + WrapperTransformer, make_pipeline_mixte_preprocessing, make_robust_MixteHGB, ) @@ -130,6 +131,56 @@ def test_inverse_transform_OneHotEncoderProjector(encoder): pd.testing.assert_frame_equal(df, df_back) +############################## +# Testing WrapperTransformer # +############################## + + +class DummyTransformer(TransformerMixin, BaseEstimator): + def fit(self, X, y=None): + return self + + def transform(self, X): + return X + + def fit_transform(self, X, y=None): + return self.fit(X, y).transform(X) + + def inverse_transform(self, X, y=None): + return X + + +@pytest.fixture +def wrapper_transformer(): + transformer = DummyTransformer() + wrapper = DummyTransformer() + return WrapperTransformer(transformer, wrapper) + + +def test_fit_WrapperTransformer(wrapper_transformer): + X = np.array([[1, 2], [3, 4]]) + result = wrapper_transformer.fit(X) + assert result == wrapper_transformer + + +def test_fit_transform_WrapperTransformer(wrapper_transformer): + X = np.array([[1, 2], [3, 4]]) + result = wrapper_transformer.fit_transform(X) + assert np.array_equal(result, X) + + +def test_transform_WrapperTransformer(wrapper_transformer): + X = np.array([[1, 2], [3, 4]]) + result = wrapper_transformer.transform(X) + assert np.array_equal(result, X) + + +def test_fit_transform_with_dataframes_WrapperTransformer(wrapper_transformer): + df = pd.DataFrame({"C1": ["a", "b", "b"], "C2": ["c", "d", "c"]}) + result = wrapper_transformer.fit_transform(df) + pd.testing.assert_frame_equal(result, df) + + ############################################# # Testing make_pipeline_mixte_preprocessing # ############################################# From 7aff16ff8005838349ba54d129efa922d8ce562d Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 5 Apr 2024 16:08:32 +0200 Subject: [PATCH 65/99] documentation updated --- README.rst | 2 + docs/api.rst | 59 +- docs/imputers.rst | 19 +- docs/index.rst | 1 + examples/RPCA.md | 2 +- examples/benchmark.md | 6 +- examples/tutorials/plot_tuto_benchmark_TS.py | 4 +- .../tutorials/plot_tuto_categorical.ipynb | 634 ++------ examples/tutorials/plot_tuto_categorical.py | 101 ++ .../tutorials/plot_tuto_diffusion_models.py | 2 +- examples/tutorials/plot_tuto_mean_median.py | 9 +- qolmat/benchmark/metrics.py | 4 +- qolmat/benchmark/missing_patterns.py | 2 +- qolmat/data/titanic.csv | 1311 +++++++++++++++++ qolmat/imputations/preprocessing.py | 41 +- qolmat/imputations/rpca/rpca_noisy.py | 54 +- qolmat/utils/data.py | 24 +- tests/imputations/rpca/test_rpca_noisy.py | 2 +- 18 files changed, 1708 insertions(+), 569 deletions(-) create mode 100644 examples/tutorials/plot_tuto_categorical.py create mode 100644 qolmat/data/titanic.csv diff --git a/README.rst b/README.rst index d9ea4866..a9980de1 100644 --- a/README.rst +++ b/README.rst @@ -232,6 +232,8 @@ Selected Topics in Signal Processing 10.4 (2016): 740-756. [6] García, S., Luengo, J., & Herrera, F. "Data preprocessing in data mining". 2015. (`pdf `__) +[7] Botterman, HL., Roussel, J., Morzadec, T., Jabbari, A., Brunel, N. "Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series" (2022) in International Conference on Machine Learning, Optimization, and Data Science. Cham: Springer Nature Switzerland, (`pdf `__) + 📝 License ========== diff --git a/docs/api.rst b/docs/api.rst index f1d5f631..5475b0ee 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -4,8 +4,8 @@ Qolmat API .. currentmodule:: qolmat -Imputers -========= +Imputers API +============ .. autosummary:: :toctree: generated/ @@ -15,10 +15,8 @@ Imputers imputations.imputers.ImputerKNN imputations.imputers.ImputerInterpolation imputations.imputers.ImputerLOCF - imputations.imputers.ImputerMedian - imputations.imputers.ImputerMean + imputations.imputers.ImputerSimple imputations.imputers.ImputerMICE - imputations.imputers.ImputerMode imputations.imputers.ImputerNOCB imputations.imputers.ImputerOracle imputations.imputers.ImputerRegressor @@ -28,8 +26,8 @@ Imputers imputations.imputers.ImputerSoftImpute imputations.imputers.ImputerShuffle -Comparator -=========== +Comparator API +============== .. autosummary:: :toctree: generated/ @@ -37,8 +35,8 @@ Comparator benchmark.comparator.Comparator -Missing Patterns -================ +Missing Patterns API +==================== .. autosummary:: :toctree: generated/ @@ -51,8 +49,8 @@ Missing Patterns benchmark.missing_patterns.GroupedHoleGenerator -Metrics -======= +Metrics API +=========== .. autosummary:: :toctree: generated/ @@ -63,6 +61,7 @@ Metrics benchmark.metrics.mean_absolute_error benchmark.metrics.mean_absolute_percentage_error benchmark.metrics.weighted_mean_absolute_percentage_error + benchmark.metrics.accuracy benchmark.metrics.dist_wasserstein benchmark.metrics.kl_divergence benchmark.metrics.kolmogorov_smirnov_test @@ -75,19 +74,19 @@ Metrics benchmark.metrics.pattern_based_weighted_mean_metric -RPCA engine -================ +RPCA engine API +=============== .. autosummary:: :toctree: generated/ :template: class.rst - imputations.rpca.rpca_pcp.RPCAPCP - imputations.rpca.rpca_noisy.RPCANoisy + imputations.rpca.rpca_pcp.RpcaPcp + imputations.rpca.rpca_noisy.RpcaNoisy -EM engine -================ +Expectation-Maximization engine API +=================================== .. autosummary:: :toctree: generated/ @@ -96,8 +95,8 @@ EM engine imputations.em_sampler.MultiNormalEM imputations.em_sampler.VARpEM -Diffusion engine -================ +Diffusion Model engine API +========================== .. autosummary:: :toctree: generated/ @@ -107,9 +106,27 @@ Diffusion engine imputations.diffusions.ddpms.TabDDPM imputations.diffusions.ddpms.TsDDPM +Preprocessing API +================= + +.. autosummary:: + :toctree: generated/ + :template: class.rst + + imputations.preprocessing.MixteHGBM + imputations.preprocessing.BinTransformer + imputations.preprocessing.OneHotEncoderProjector + imputations.preprocessing.WrapperTransformer + +.. autosummary:: + :toctree: generated/ + :template: function.rst + + imputations.preprocessing.make_pipeline_mixte_preprocessing + imputations.preprocessing.make_robust_MixteHGB -Utils -================ +Utils API +========= .. autosummary:: :toctree: generated/ diff --git a/docs/imputers.rst b/docs/imputers.rst index ad95b6b9..a8e4552c 100644 --- a/docs/imputers.rst +++ b/docs/imputers.rst @@ -3,16 +3,16 @@ Imputers All imputers can be found in the ``qolmat.imputations`` folder. -1. mean/median/shuffle ----------------------- -Imputes the missing values using the mean/median along each column or with a random value in each column. See the :class:`~qolmat.imputations.imputers.ImputerMean`, :class:`~qolmat.imputations.imputers.ImputerMedian` and :class:`~qolmat.imputations.imputers.ImputerShuffle` classes. +1. Simple (mean/median/shuffle) +------------------------------- +Imputes the missing values using the mean/median along each column or with a random value in each column. See the :class:`~qolmat.imputations.imputers.ImputerSimple` and :class:`~qolmat.imputations.imputers.ImputerShuffle` classes. 2. LOCF ------- Imputes the missing values using the last observation carried forward. See the :class:`~qolmat.imputations.imputers.ImputerLOCF` class. -3. interpolation (on residuals) -------------------------------- +3. Time interpolation and TSA decomposition +------------------------------------------- Imputes missing using some interpolation strategies supported by `pd.Series.interpolate `_. It is done column by column. See the :class:`~qolmat.imputations.imputers.ImputerInterpolation` class. When data are temporal with clear seasonal decomposition, we can interpolate on the residuals instead of directly interpolate the raw data. Series are de-seasonalised based on `statsmodels.tsa.seasonal.seasonal_decompose `_, residuals are imputed via linear interpolation, then residuals are re-seasonalised. It is also done column by column. See the :class:`~qolmat.imputations.imputers.ImputerResiduals` class. @@ -28,7 +28,7 @@ Two cases are considered. **RPCA via Principal Component Pursuit (PCP)** [1, 12] -The class :class:`RPCAPCP` implements a matrix decomposition :math:`\mathbf{D} = \mathbf{M} + \mathbf{A}` where :math:`\mathbf{M}` has low-rank and :math:`\mathbf{A}` is sparse. It relies on the following optimisation problem +The class :class:`RpcaPcp` implements a matrix decomposition :math:`\mathbf{D} = \mathbf{M} + \mathbf{A}` where :math:`\mathbf{M}` has low-rank and :math:`\mathbf{A}` is sparse. It relies on the following optimisation problem .. math:: \text{min}_{\mathbf{M} \in \mathbb{R}^{m \times n}} \quad \Vert \mathbf{M} \Vert_* + \lambda \Vert P_\Omega(\mathbf{D-M}) \Vert_1 @@ -38,7 +38,7 @@ See the :class:`~qolmat.imputations.imputers.ImputerRpcaPcp` class for implement **Noisy RPCA** [2, 3, 4] -The class :class:`RPCANoisy` implements an recommanded improved version, which relies on a decomposition :math:`\mathbf{D} = \mathbf{M} + \mathbf{A} + \mathbf{E}`. The additionnal term encodes a Gaussian noise and makes the numerical convergence more reliable. This class also implements a time-consistency penalization for time series, parametrized by the :math:`\eta_k`and :math:`H_k`. By defining :math:`\Vert \mathbf{MH_k} \Vert_p` is either :math:`\Vert \mathbf{MH_k} \Vert_1` or :math:`\Vert \mathbf{MH_k} \Vert_F^2`, the optimisation problem is the following +The class :class:`RpcaNoisy` implements an recommanded improved version, which relies on a decomposition :math:`\mathbf{D} = \mathbf{M} + \mathbf{A} + \mathbf{E}`. The additionnal term encodes a Gaussian noise and makes the numerical convergence more reliable. This class also implements a time-consistency penalization for time series, parametrized by the :math:`\eta_k`and :math:`H_k`. By defining :math:`\Vert \mathbf{MH_k} \Vert_p` is either :math:`\Vert \mathbf{MH_k} \Vert_1` or :math:`\Vert \mathbf{MH_k} \Vert_F^2`, the optimisation problem is the following .. math:: \text{min}_{\mathbf{M, A} \in \mathbb{R}^{m \times n}} \quad \frac 1 2 \Vert P_{\Omega} (\mathbf{D}-\mathbf{M}-\mathbf{A}) \Vert_F^2 + \tau \Vert \mathbf{M} \Vert_* + \lambda \Vert \mathbf{A} \Vert_1 + \sum_{k=1}^K \eta_k \Vert \mathbf{M H_k} \Vert_p @@ -91,6 +91,7 @@ We estimate the distribution parameter :math:`\theta` by likelihood maximization Once the parameter :math:`\theta^*` has been estimated the final data imputation can be done in two different ways, depending on the value of the argument `method`: * `mle`: Returns the maximum likelihood estimator + .. math:: X^* = \mathrm{argmax}_X L(X, \theta^*) @@ -115,8 +116,8 @@ In training phase, we use the self-supervised learning method of [9] to train in In the case of time-series data, we also propose :class:`~qolmat.imputations.diffusions.ddpms.TsDDPM` (built on top of :class:`~qolmat.imputations.diffusions.ddpms.TabDDPM`) to capture time-based relationships between data points in a dataset. In fact, the dataset is pre-processed by using sliding window method to obtain a set of data partitions. The noise prediction of the model :math:`\epsilon_\theta` takes into account not only the observed data at the current time step but also data from previous time steps. These time-based relationships are encoded by using a transformer-based architecture [9]. -References ----------- +References (Imputers) +--------------------- [1] Candès, Emmanuel J., et al. `Robust principal component analysis? `_ Journal of the ACM (JACM) 58.3 (2011): 1-37. diff --git a/docs/index.rst b/docs/index.rst index 5bfc64a5..367a5ffe 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -16,6 +16,7 @@ imputers examples/tutorials/plot_tuto_benchmark_TS + examples/tutorials/plot_tuto_categorical examples/tutorials/plot_tuto_diffusion_models .. toctree:: diff --git a/examples/RPCA.md b/examples/RPCA.md index 047de7a6..3dc81c45 100644 --- a/examples/RPCA.md +++ b/examples/RPCA.md @@ -199,7 +199,7 @@ plt.show() ```python %%time -# rpca_noisy = RPCANoisy(period=10, tau=1, lam=0.4, rank=2, list_periods=[10], list_etas=[0.01], norm="L2") +# rpca_noisy = RpcaNoisy(period=10, tau=1, lam=0.4, rank=2, list_periods=[10], list_etas=[0.01], norm="L2") rpca_noisy = RpcaNoisy(tau=1, lam=0.4, rank=2, norm="L2") M, A = rpca_noisy.decompose(D, Omega) # imputed = X diff --git a/examples/benchmark.md b/examples/benchmark.md index 57c0cab3..45b201b5 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -122,9 +122,9 @@ ratio_masked = 0.1 ```python tags=[] dict_config_opti = {} -imputer_mean = imputers.ImputerMean(groups=("station",)) -imputer_median = imputers.ImputerMedian(groups=("station",)) -imputer_mode = imputers.ImputerMode(groups=("station",)) +imputer_mean = imputers.ImputerSimple(groups=("station",), strategy="mean") +imputer_median = imputers.ImputerSimple(groups=("station",), strategy="median") +imputer_mode = imputers.ImputerSimple(groups=("station",), strategy="most_frequent") imputer_locf = imputers.ImputerLOCF(groups=("station",)) imputer_nocb = imputers.ImputerNOCB(groups=("station",)) imputer_interpol = imputers.ImputerInterpolation(groups=("station",), method="linear") diff --git a/examples/tutorials/plot_tuto_benchmark_TS.py b/examples/tutorials/plot_tuto_benchmark_TS.py index b1a32175..f205d08a 100644 --- a/examples/tutorials/plot_tuto_benchmark_TS.py +++ b/examples/tutorials/plot_tuto_benchmark_TS.py @@ -61,7 +61,7 @@ plt.show() # %% -# 2. Imputation methods +# 2. Time series imputation methods # --------------------------------------------------------------- # All presented methods are group-wise: here each station is imputed independently. # For example ImputerMean computes the mean of each variable in each station and uses @@ -78,7 +78,7 @@ ratio_masked = 0.1 -imputer_median = imputers.ImputerMedian(groups=("station",)) +imputer_median = imputers.ImputerSimple(groups=("station",), strategy="median") imputer_interpol = imputers.ImputerInterpolation(groups=("station",), method="linear") imputer_residuals = imputers.ImputerResiduals( groups=("station",), diff --git a/examples/tutorials/plot_tuto_categorical.ipynb b/examples/tutorials/plot_tuto_categorical.ipynb index b8baea65..fd90e1a6 100644 --- a/examples/tutorials/plot_tuto_categorical.ipynb +++ b/examples/tutorials/plot_tuto_categorical.ipynb @@ -2,8 +2,8 @@ "cells": [ { "cell_type": "code", - "execution_count": null, - "id": "06570cfd", + "execution_count": 1, + "id": "ff229ce1", "metadata": {}, "outputs": [], "source": [ @@ -11,31 +11,10 @@ "%autoreload 2" ] }, - { - "cell_type": "code", - "execution_count": 122, - "id": "b2961e39", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 5)" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.array([[1, 2, 3, np.nan, 5]]).shape" - ] - }, { "cell_type": "code", "execution_count": 2, - "id": "48dcea87", + "id": "9285cdfc", "metadata": {}, "outputs": [ { @@ -50,392 +29,72 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", - "# import seaborn as sns\n", "\n", - "from qolmat.imputations.imputers import ImputerMICE, ImputerRegressor\n", - "from qolmat.imputations import estimators\n", - "from qolmat.benchmark.metrics import get_metric\n", + "from qolmat.imputations import preprocessing, imputers\n", + "from qolmat.imputations.imputers import ImputerRegressor\n", "from qolmat.benchmark import missing_patterns\n", "from qolmat.benchmark import comparator\n", - "from qolmat.utils import plot\n", - "from qolmat.imputations import imputers\n", - "from qolmat.imputations.estimators import OneHotEncoderProjector\n", + "from qolmat.utils import data\n", "\n", - "from sklearn.preprocessing import OrdinalEncoder, StandardScaler\n", + "from sklearn.preprocessing import StandardScaler\n", "from sklearn.pipeline import Pipeline\n", - "from sklearn.ensemble import RandomForestClassifier, HistGradientBoostingRegressor, HistGradientBoostingClassifier\n", "from sklearn.compose import ColumnTransformer\n", - "from sklearn.impute import SimpleImputer\n", - "\n", - "from sklearn.base import (\n", - " BaseEstimator,\n", - " ClassifierMixin,\n", - " RegressorMixin,\n", - ")\n", - "from sklearn.compose import make_column_selector as selector\n", "\n", - "# from category_encoders.one_hot import OneHotEncoder" + "from sklearn.compose import make_column_selector as selector" ] }, { "cell_type": "code", - "execution_count": 135, - "id": "db355ac3", + "execution_count": 3, + "id": "dd68b9f2", "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv(\"../data/titanic.csv\", sep=\";\")" + "df = data.get_data(\"Titanic\")" ] }, { "cell_type": "code", - "execution_count": 136, - "id": "ef95e583", + "execution_count": 4, + "id": "c17b8877", "metadata": {}, "outputs": [], "source": [ - "df = df.dropna(how=\"all\")\n", - "y = df[\"survived\"] == 1\n", - "df = df.drop(columns=[\"pclass\", \"survived\", \"name\", \"home.dest\", \"cabin\", \"ticket\", \"boat\", \"body\"])" + "cols_num = df.select_dtypes(include=\"number\").columns\n", + "cols_cat = df.select_dtypes(exclude=\"number\").columns" ] }, { "cell_type": "code", - "execution_count": 137, - "id": "2c71625e", - "metadata": {}, - "outputs": [], - "source": [ - "df[\"age\"] = pd.to_numeric(df[\"age\"], errors=\"coerce\")\n", - "df[\"fare\"] = pd.to_numeric(df[\"fare\"].str.replace(\",\", \"\"), errors=\"coerce\")" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "id": "9452994a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index([0], dtype='int64')" - ] - }, - "execution_count": 148, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.Index([0])" - ] - }, - { - "cell_type": "code", - "execution_count": 158, - "id": "8f01eaf4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " C1_1 C1_2 C2_1 C2_2\n", - "0 1 0 1 0\n", - "1 0 1 0 1\n", - "2 0 1 1 0\n" - ] - } - ], - "source": [ - "from category_encoders.one_hot import OneHotEncoder\n", - "encoder = OneHotEncoderProjector()\n", - "# encoder = OneHotEncoder()\n", - "\n", - "df = pd.DataFrame({\"C1\": [\"a\", \"b\", \"b\"], \"C2\": [\"c\", \"d\", \"c\"]})\n", - "# Création d'un DataFrame d'exemple avec des colonnes encodées en one-hot\n", - "df_dum = encoder.fit_transform(df)\n", - "print(df_dum)" - ] - }, - { - "cell_type": "code", - "execution_count": 159, - "id": "52b0f427", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[{'col': 'C1',\n", - " 'mapping': C1_1 C1_2\n", - " 1 1 0\n", - " 2 0 1\n", - " -1 0 0\n", - " -2 0 0},\n", - " {'col': 'C2',\n", - " 'mapping': C2_1 C2_2\n", - " 1 1 0\n", - " 2 0 1\n", - " -1 0 0\n", - " -2 0 0}]" - ] - }, - "execution_count": 159, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "encoder.mapping" - ] - }, - { - "cell_type": "code", - "execution_count": 160, - "id": "0505c9fa", - "metadata": {}, - "outputs": [], - "source": [ - "# Appel de la méthode reverse_dummies\n", - "df_back = encoder.inverse_transform(df_dum)" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "id": "cbb2ec43", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1309,)" - ] - }, - "execution_count": 139, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "y.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "id": "03cf3965", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(df.values)" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "id": "681e5c64", - "metadata": {}, - "outputs": [], - "source": [ - "rfc = HistGradientBoostingClassifier()\n", - "# rfc.fit(df[[\"age\", \"fare\"]], y)" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "id": "2f1112b2", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/julienroussel/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/base.py:458: UserWarning: X has feature names, but HistGradientBoostingClassifier was fitted without feature names\n", - " warnings.warn(\n" - ] - }, - { - "ename": "NotFittedError", - "evalue": "This HistGradientBoostingClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNotFittedError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[146], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mrfc\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mage\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1881\u001b[0m, in \u001b[0;36mHistGradientBoostingClassifier.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 1868\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Predict classes for X.\u001b[39;00m\n\u001b[1;32m 1869\u001b[0m \n\u001b[1;32m 1870\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;124;03m The predicted classes.\u001b[39;00m\n\u001b[1;32m 1879\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1880\u001b[0m \u001b[38;5;66;03m# TODO: This could be done in parallel\u001b[39;00m\n\u001b[0;32m-> 1881\u001b[0m encoded_classes \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39margmax(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_proba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 1882\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_[encoded_classes]\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1919\u001b[0m, in \u001b[0;36mHistGradientBoostingClassifier.predict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 1906\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict_proba\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[1;32m 1907\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Predict class probabilities for X.\u001b[39;00m\n\u001b[1;32m 1908\u001b[0m \n\u001b[1;32m 1909\u001b[0m \u001b[38;5;124;03m Parameters\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1917\u001b[0m \u001b[38;5;124;03m The class probabilities of the input samples.\u001b[39;00m\n\u001b[1;32m 1918\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 1919\u001b[0m raw_predictions \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_raw_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1920\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_loss\u001b[38;5;241m.\u001b[39mpredict_proba(raw_predictions)\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py:1034\u001b[0m, in \u001b[0;36mBaseHistGradientBoosting._raw_predict\u001b[0;34m(self, X, n_threads)\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_binned:\n\u001b[1;32m 1031\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_data(\n\u001b[1;32m 1032\u001b[0m X, dtype\u001b[38;5;241m=\u001b[39mX_DTYPE, force_all_finite\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, reset\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 1033\u001b[0m )\n\u001b[0;32m-> 1034\u001b[0m \u001b[43mcheck_is_fitted\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1035\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_features:\n\u001b[1;32m 1036\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1037\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX has \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m features but this estimator was trained with \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1038\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m features.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(X\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m], \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_n_features)\n\u001b[1;32m 1039\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/sklearn/utils/validation.py:1461\u001b[0m, in \u001b[0;36mcheck_is_fitted\u001b[0;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[1;32m 1458\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not an estimator instance.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (estimator))\n\u001b[1;32m 1460\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _is_fitted(estimator, attributes, all_or_any):\n\u001b[0;32m-> 1461\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m NotFittedError(msg \u001b[38;5;241m%\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mtype\u001b[39m(estimator)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m})\n", - "\u001b[0;31mNotFittedError\u001b[0m: This HistGradientBoostingClassifier instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator." - ] - } - ], - "source": [ - "rfc.predict(df[[\"age\"]])" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "e62453b6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " sex age sibsp parch fare embarked\n", - "0 female 29.0 0.0 0.0 2113375.0 S\n", - "1 male NaN 1.0 2.0 1515500.0 S\n", - "2 female 2.0 1.0 2.0 1515500.0 S" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6945a95c", - "metadata": {}, - "outputs": [], - "source": [ - "selector_cat = selector(dtype_exclude=np.number)\n", - "cols_cat = selector_cat(df)\n", - "selector_num = selector(dtype_include=np.number)\n", - "cols_num = selector_num(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "927fe33a", + "execution_count": 5, + "id": "f677bbea", "metadata": {}, "outputs": [], "source": [ "imputer_rpca = imputers.ImputerRpcaNoisy()\n", - "ohe = OneHotEncoderProjector(handle_unknown=\"ignore\", handle_missing=\"return_nan\", use_cat_names=True, cols=cols_cat)\n", - "bt = estimators.BinTransformer(cols=cols_num)\n", + "ohe = preprocessing.OneHotEncoderProjector(handle_unknown=\"ignore\", handle_missing=\"return_nan\", use_cat_names=True, cols=cols_cat)\n", + "bt = preprocessing.BinTransformer(cols=cols_num)\n", "wrapper = Pipeline(steps=[(\"OneHotEncoder\", ohe), (\"BinTransformer\", bt)])\n", "\n", - "imputer_wrap_rpca = estimators.WrapperTransformer(imputer_rpca, wrapper)" + "imputer_wrap_rpca = preprocessing.WrapperTransformer(imputer_rpca, wrapper)" ] }, { "cell_type": "code", - "execution_count": 11, - "id": "665b1dbb", - "metadata": {}, - "outputs": [], - "source": [ - "ohe.mapping" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f57e68ed", - "metadata": {}, - "outputs": [], - "source": [ - "df_imp = imputer_wrap_rpca.fit_transform(df.iloc[:100])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "843913b5", + "execution_count": 8, + "id": "bc213420", "metadata": {}, "outputs": [], "source": [ - "pipestimator = estimators.make_robust_MixteHGB(allow_new=False)\n", + "pipestimator = preprocessing.make_robust_MixteHGB(allow_new=False)\n", "imputer_hgb = ImputerRegressor(estimator=pipestimator, handler_nan=\"none\")\n", - "imputer_wrap_hgb = estimators.WrapperTransformer(imputer_hgb, bt)" + "imputer_wrap_hgb = preprocessing.WrapperTransformer(imputer_hgb, bt)" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "44315307", + "execution_count": 9, + "id": "e1e3c915", "metadata": {}, "outputs": [], "source": [ @@ -444,8 +103,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "dd62828f", + "execution_count": 21, + "id": "9e67b2cf", "metadata": {}, "outputs": [], "source": [ @@ -453,13 +112,13 @@ "cols_to_impute = df.columns\n", "ratio_masked = .1\n", "generator_holes = missing_patterns.UniformHoleGenerator(n_splits=2, subset=cols_to_impute, ratio_masked=ratio_masked, sample_proportional=False)\n", - "metrics = [\"mae\", \"accuracy\"]" + "metrics = [\"rmse\", \"accuracy\"]" ] }, { "cell_type": "code", - "execution_count": 61, - "id": "4db1319b", + "execution_count": 22, + "id": "ee189152", "metadata": {}, "outputs": [ { @@ -485,57 +144,57 @@ }, { "cell_type": "code", - "execution_count": 111, - "id": "e7076e56", + "execution_count": 23, + "id": "2d20c0d5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
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 SimpleHGBRPCASimpleHGBRPCA
age10.93511510.20229011.278626age13.72217512.38013614.044074
sibsp0.4274810.3282440.446565sibsp1.2073120.5749310.895884
parch0.2977100.3244270.393130parch0.9337670.8073450.839930
fare254970.885496285660.019084244295.000000fare434352.730672504720.500563507609.769959
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -543,74 +202,74 @@ } ], "source": [ - "display(results.loc[\"mae\"].style.highlight_min(color=\"lightgreen\", axis=1))" + "display(results.loc[\"rmse\"].style.highlight_min(color=\"lightgreen\", axis=1))" ] }, { "cell_type": "code", - "execution_count": 112, - "id": "80c04a50", + "execution_count": 24, + "id": "8e1aae70", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "\n", + "
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 SimpleHGBRPCASimpleHGBRPCA
sex0.6488550.6526720.606870sex0.6679390.6641220.664122
age0.0267180.0419850.026718age0.0229010.0381680.030534
sibsp0.7061070.7213740.687023sibsp0.6717560.7633590.671756
parch0.8053440.7633590.736641parch0.7748090.7519080.751908
fare0.0038170.0000000.000000fare0.0076340.0076340.003817
embarked0.6335880.7977100.713740embarked0.6984730.8091600.687023
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -623,7 +282,7 @@ }, { "cell_type": "markdown", - "id": "436b902e", + "id": "e628c2cd", "metadata": {}, "source": [ "# Imputation analysis" @@ -631,8 +290,8 @@ }, { "cell_type": "code", - "execution_count": 113, - "id": "3abf0df3", + "execution_count": 14, + "id": "00bb4e56", "metadata": {}, "outputs": [], "source": [ @@ -643,8 +302,8 @@ }, { "cell_type": "code", - "execution_count": 114, - "id": "31772264", + "execution_count": 15, + "id": "093934f4", "metadata": {}, "outputs": [], "source": [ @@ -654,8 +313,8 @@ }, { "cell_type": "code", - "execution_count": 115, - "id": "1bde6953", + "execution_count": 16, + "id": "6b15bb22", "metadata": {}, "outputs": [], "source": [ @@ -666,8 +325,8 @@ }, { "cell_type": "code", - "execution_count": 116, - "id": "068a0921", + "execution_count": 17, + "id": "6641978f", "metadata": {}, "outputs": [ { @@ -708,23 +367,23 @@ " \n", " \n", " 1.0\n", - " 2\n", + " 1\n", " 0\n", " \n", " \n", " 2.0\n", - " 1\n", + " 2\n", " 0\n", " \n", " \n", " 3.0\n", - " 0\n", + " 1\n", " 0\n", " \n", " \n", " 4.0\n", - " 2\n", " 0\n", + " 1\n", " \n", " \n", " ...\n", @@ -732,54 +391,54 @@ " ...\n", " \n", " \n", - " 76.0\n", + " 58.0\n", " 0\n", " 0\n", " \n", " \n", - " 77.0\n", + " 59.0\n", " 0\n", " 0\n", " \n", " \n", - " 78.0\n", - " 0\n", + " 60.0\n", + " 2\n", " 0\n", " \n", " \n", - " 79.0\n", + " 61.0\n", " 0\n", " 0\n", " \n", " \n", - " 80.0\n", + " 62.0\n", " 1\n", " 0\n", " \n", " \n", "\n", - "

81 rows × 2 columns

\n", + "

63 rows × 2 columns

\n", "" ], "text/plain": [ " count count\n", "age \n", "0.0 0 0\n", - "1.0 2 0\n", - "2.0 1 0\n", - "3.0 0 0\n", - "4.0 2 0\n", + "1.0 1 0\n", + "2.0 2 0\n", + "3.0 1 0\n", + "4.0 0 1\n", "... ... ...\n", - "76.0 0 0\n", - "77.0 0 0\n", - "78.0 0 0\n", - "79.0 0 0\n", - "80.0 1 0\n", + "58.0 0 0\n", + "59.0 0 0\n", + "60.0 2 0\n", + "61.0 0 0\n", + "62.0 1 0\n", "\n", - "[81 rows x 2 columns]" + "[63 rows x 2 columns]" ] }, - "execution_count": 116, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -790,23 +449,23 @@ }, { "cell_type": "code", - "execution_count": 117, - "id": "768b9e71", + "execution_count": 18, + "id": "363598e0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 117, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -822,23 +481,23 @@ }, { "cell_type": "code", - "execution_count": 120, - "id": "6c7cd59f", + "execution_count": 19, + "id": "6720ec95", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 120, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -851,10 +510,43 @@ "plt.imshow(counts.to_frame() @ counts_imp.to_frame().T, cmap=\"gray\")" ] }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7911314e", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'date'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/core/indexes/base.py:3652\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3651\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3652\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'date'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mBeijing\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/utils/data.py:106\u001b[0m, in \u001b[0;36mget_data\u001b[0;34m(name_data, datapath, n_groups_max)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name_data \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBeijing\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 105\u001b[0m df \u001b[38;5;241m=\u001b[39m read_csv_local(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeijing\u001b[39m\u001b[38;5;124m\"\u001b[39m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 106\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 108\u001b[0m \u001b[38;5;66;03m# df[\"date\"] = pd.to_datetime(\u001b[39;00m\n\u001b[1;32m 109\u001b[0m \u001b[38;5;66;03m# {\u001b[39;00m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;66;03m# \"year\": df[\"year\"],\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[1;32m 116\u001b[0m df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmonth\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mday\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhour\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwd\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3760\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3763\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", + "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/core/indexes/base.py:3654\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3652\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3654\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3655\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3656\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3657\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3658\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3659\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'date'" + ] + } + ], + "source": [ + "data.get_data(\"Beijing\")" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "b32cf48c", + "id": "c08554cb", "metadata": {}, "outputs": [], "source": [] diff --git a/examples/tutorials/plot_tuto_categorical.py b/examples/tutorials/plot_tuto_categorical.py new file mode 100644 index 00000000..0ab886b8 --- /dev/null +++ b/examples/tutorials/plot_tuto_categorical.py @@ -0,0 +1,101 @@ +""" +============================== +Benchmark for categorical data +============================== + +In this tutorial, we show how to use Qolmat to define imputation methods managing mixed type data. +We benchmark these methods on the Titanic Data Set. +It comprehends passengers features as well as if they survived the accident. +""" + +from qolmat.imputations import preprocessing, imputers +from qolmat.imputations.imputers import ImputerRegressor +from qolmat.benchmark import missing_patterns +from qolmat.benchmark import comparator +from qolmat.utils import data + +from sklearn.pipeline import Pipeline + +# %% +# 1. Titanic dataset +# --------------------------------------------------------------- +# We get the data and focus on the explanatory variables +df = data.get_data("Titanic") +df = df.drop(columns=["survived"]) + +# %% +# 2. Mixed type imputation methods +# --------------------------------------------------------------- +# Qolmat supports three approaches to impute mixed type data. +# The first approach is a simple imputation by the mean, median or the most-frequent value column +# by column + +imputer_simple = imputers.ImputerSimple() + +# %% +# The second approach relies on the class WrapperTransformer which wraps a numerical imputation +# method (e.g. RPCA) in a preprocessing transformer with fit_transform and inverse_transform +# methods providing an embedding of the data. + +cols_num = df.select_dtypes(include="number").columns +cols_cat = df.select_dtypes(exclude="number").columns +imputer_rpca = imputers.ImputerRpcaNoisy() +ohe = preprocessing.OneHotEncoderProjector( + handle_unknown="ignore", + handle_missing="return_nan", + use_cat_names=True, + cols=cols_cat, +) +bt = preprocessing.BinTransformer(cols=cols_num) +wrapper = Pipeline(steps=[("OneHotEncoder", ohe), ("BinTransformer", bt)]) +imputer_wrap_rpca = preprocessing.WrapperTransformer(imputer_rpca, wrapper) + +# %% +# The third approach uses ImputerRegressor which imputes iteratively each column using the other +# ones. The function make_robust_MixteHGB provides an underlying model able to: +# - adress both numerical targets (regression) and categorical targets (classification) +# - manage categorical features though one hot encoding +# - manage missing features (native to the HistGradientBoosting) + +pipestimator = preprocessing.make_robust_MixteHGB(allow_new=False) +imputer_hgb = ImputerRegressor(estimator=pipestimator, handler_nan="none") +imputer_wrap_hgb = preprocessing.WrapperTransformer(imputer_hgb, bt) + +# %% +# 3. Mixed type model selection +# --------------------------------------------------------------- +# Let us now compare these three aproaches by measuring their ability to impute uniformly +# distributed holes. + +dict_imputers = { + "Simple": imputer_simple, + "HGB": imputer_wrap_hgb, + "RPCA": imputer_wrap_rpca, +} +cols_to_impute = df.columns +ratio_masked = 0.1 +generator_holes = missing_patterns.UniformHoleGenerator( + n_splits=2, + subset=cols_to_impute, + ratio_masked=ratio_masked, + sample_proportional=False, +) +metrics = ["rmse", "accuracy"] + +comparison = comparator.Comparator( + dict_imputers, + cols_to_impute, + generator_holes=generator_holes, + metrics=metrics, + max_evals=2, +) +results = comparison.compare(df) + +# %% +# On numerical variables, the imputation based on the HistGradientBoosting (HGB) model globally +# achieves lower Root-square Mean Squared Errors (RMSE). +results.loc["rmse"].style.highlight_min(color="lightgreen", axis=1) + +# %% +# The HGB imputation methods globaly reaches a better accuracy on the categorical data. +results.loc["accuracy"].style.highlight_max(color="lightgreen", axis=1) diff --git a/examples/tutorials/plot_tuto_diffusion_models.py b/examples/tutorials/plot_tuto_diffusion_models.py index 1c24dea7..317128db 100644 --- a/examples/tutorials/plot_tuto_diffusion_models.py +++ b/examples/tutorials/plot_tuto_diffusion_models.py @@ -59,7 +59,7 @@ # # * ``x_valid``: a validation set. # -# * ``metrics_valid``: a list validation metrics (see all metrics :doc:`imputers`). Its default +# * ``metrics_valid``: a list validation metrics (see all [metrics](imputers.html). Its default # value ``metrics_valid=(metrics.mean_absolute_error, metrics.dist_wasserstein,)`` # # * ``print_valid``: a boolean to display/hide a training progress (including epoch_loss, diff --git a/examples/tutorials/plot_tuto_mean_median.py b/examples/tutorials/plot_tuto_mean_median.py index 52a0d625..403b4407 100644 --- a/examples/tutorials/plot_tuto_mean_median.py +++ b/examples/tutorials/plot_tuto_mean_median.py @@ -5,9 +5,8 @@ In this tutorial, we show how to use the Qolmat comparator (:class:`~qolmat.benchmark.comparator`) to choose -the best imputation between imputation by the mean -(:class:`~qolmat.imputations.imputers.ImputerMean`) or the median -(:class:`~qolmat.imputations.imputers.ImputerMedian`). +the best imputation between imputation by the mean or the median +(:class:`~qolmat.imputations.imputers.ImputerSimple`). The dataset used is the the numerical `superconduct` dataset and contains information on 21263 superconductors. We generate holes uniformly at random via @@ -71,8 +70,8 @@ # a way to generate holes (additional missing values on which the # imputers will be evaluated) and a list of metrics. -imputer_mean = imputers.ImputerMean() -imputer_median = imputers.ImputerMedian() +imputer_mean = imputers.ImputerSimple(strategy="mean") +imputer_median = imputers.ImputerSimple(strategy="median") dict_imputers = {"mean": imputer_mean, "median": imputer_median} metrics = ["mae", "wmape", "KL_columnwise"] diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index 0b1fec0d..dd72e612 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -951,8 +951,8 @@ def kl_divergence( pd.Series Kullback-Leibler divergence - Raise - ----- + Raises + ------ AssertionError If the empirical distributions do not have enough samples to estimate a KL divergence. Consider using a larger dataset of lowering the parameter `min_n_rows`. diff --git a/qolmat/benchmark/missing_patterns.py b/qolmat/benchmark/missing_patterns.py index 7fc781a3..448add0f 100644 --- a/qolmat/benchmark/missing_patterns.py +++ b/qolmat/benchmark/missing_patterns.py @@ -163,7 +163,7 @@ def __init__( subset: Optional[List[str]] = None, ratio_masked: float = 0.05, random_state: Union[None, int, np.random.RandomState] = None, - sample_proportional: bool = True, + sample_proportional: bool = False, ): super().__init__( n_splits=n_splits, diff --git a/qolmat/data/titanic.csv b/qolmat/data/titanic.csv new file mode 100644 index 00000000..d047a98f --- /dev/null +++ b/qolmat/data/titanic.csv @@ -0,0 +1,1311 @@ +pclass;survived;name;sex;age;sibsp;parch;ticket;fare;cabin;embarked;boat;body;home.dest +1;1;Allen, Miss. Elisabeth Walton;female;29;0;0;24160;211,3375;B5;S;2;;St Louis, MO +1;1;Allison, Master. Hudson Trevor;male;0,9167;1;2;113781;151,5500;C22 C26;S;11;;Montreal, PQ / Chesterville, ON +1;0;Allison, Miss. Helen Loraine;female;2;1;2;113781;151,5500;C22 C26;S;;;Montreal, PQ / Chesterville, ON +1;0;Allison, Mr. Hudson Joshua Creighton;male;30;1;2;113781;151,5500;C22 C26;S;;135;Montreal, PQ / Chesterville, ON +1;0;Allison, Mrs. Hudson J C (Bessie Waldo Daniels);female;25;1;2;113781;151,5500;C22 C26;S;;;Montreal, PQ / Chesterville, ON +1;1;Anderson, Mr. Harry;male;48;0;0;19952;26,5500;E12;S;3;;New York, NY +1;1;Andrews, Miss. Kornelia Theodosia;female;63;1;0;13502;77,9583;D7;S;10;;Hudson, NY +1;0;Andrews, Mr. Thomas Jr;male;39;0;0;112050;0,0000;A36;S;;;Belfast, NI +1;1;Appleton, Mrs. Edward Dale (Charlotte Lamson);female;53;2;0;11769;51,4792;C101;S;D;;Bayside, Queens, NY +1;0;Artagaveytia, Mr. Ramon;male;71;0;0;PC 17609;49,5042;;C;;22;Montevideo, Uruguay +1;0;Astor, Col. John Jacob;male;47;1;0;PC 17757;227,5250;C62 C64;C;;124;New York, NY +1;1;Astor, Mrs. John Jacob (Madeleine Talmadge Force);female;18;1;0;PC 17757;227,5250;C62 C64;C;4;;New York, NY +1;1;Aubart, Mme. Leontine Pauline;female;24;0;0;PC 17477;69,3000;B35;C;9;;Paris, France +1;1;"Barber, Miss. Ellen ""Nellie""";female;26;0;0;19877;78,8500;;S;6;; +1;1;Barkworth, Mr. Algernon Henry Wilson;male;80;0;0;27042;30,0000;A23;S;B;;Hessle, Yorks +1;0;Baumann, Mr. John D;male;;0;0;PC 17318;25,9250;;S;;;New York, NY +1;0;Baxter, Mr. Quigg Edmond;male;24;0;1;PC 17558;247,5208;B58 B60;C;;;Montreal, PQ +1;1;Baxter, Mrs. James (Helene DeLaudeniere Chaput);female;50;0;1;PC 17558;247,5208;B58 B60;C;6;;Montreal, PQ +1;1;Bazzani, Miss. Albina;female;32;0;0;11813;76,2917;D15;C;8;; +1;0;Beattie, Mr. Thomson;male;36;0;0;13050;75,2417;C6;C;A;;Winnipeg, MN +1;1;Beckwith, Mr. Richard Leonard;male;37;1;1;11751;52,5542;D35;S;5;;New York, NY +1;1;Beckwith, Mrs. Richard Leonard (Sallie Monypeny);female;47;1;1;11751;52,5542;D35;S;5;;New York, NY +1;1;Behr, Mr. Karl Howell;male;26;0;0;111369;30,0000;C148;C;5;;New York, NY +1;1;Bidois, Miss. Rosalie;female;42;0;0;PC 17757;227,5250;;C;4;; +1;1;Bird, Miss. Ellen;female;29;0;0;PC 17483;221,7792;C97;S;8;; +1;0;Birnbaum, Mr. Jakob;male;25;0;0;13905;26,0000;;C;;148;San Francisco, CA +1;1;Bishop, Mr. Dickinson H;male;25;1;0;11967;91,0792;B49;C;7;;Dowagiac, MI +1;1;Bishop, Mrs. Dickinson H (Helen Walton);female;19;1;0;11967;91,0792;B49;C;7;;Dowagiac, MI +1;1;Bissette, Miss. Amelia;female;35;0;0;PC 17760;135,6333;C99;S;8;; +1;1;Bjornstrom-Steffansson, Mr. Mauritz Hakan;male;28;0;0;110564;26,5500;C52;S;D;;Stockholm, Sweden / Washington, DC +1;0;Blackwell, Mr. Stephen Weart;male;45;0;0;113784;35,5000;T;S;;;Trenton, NJ +1;1;Blank, Mr. Henry;male;40;0;0;112277;31,0000;A31;C;7;;Glen Ridge, NJ +1;1;Bonnell, Miss. Caroline;female;30;0;0;36928;164,8667;C7;S;8;;Youngstown, OH +1;1;Bonnell, Miss. Elizabeth;female;58;0;0;113783;26,5500;C103;S;8;;Birkdale, England Cleveland, Ohio +1;0;Borebank, Mr. John James;male;42;0;0;110489;26,5500;D22;S;;;London / Winnipeg, MB +1;1;Bowen, Miss. Grace Scott;female;45;0;0;PC 17608;262,3750;;C;4;;Cooperstown, NY +1;1;Bowerman, Miss. Elsie Edith;female;22;0;1;113505;55,0000;E33;S;6;;St Leonards-on-Sea, England Ohio +1;1;"Bradley, Mr. George (""George Arthur Brayton"")";male;;0;0;111427;26,5500;;S;9;;Los Angeles, CA +1;0;Brady, Mr. John Bertram;male;41;0;0;113054;30,5000;A21;S;;;Pomeroy, WA +1;0;Brandeis, Mr. Emil;male;48;0;0;PC 17591;50,4958;B10;C;;208;Omaha, NE +1;0;Brewe, Dr. Arthur Jackson;male;;0;0;112379;39,6000;;C;;;Philadelphia, PA +1;1;Brown, Mrs. James Joseph (Margaret Tobin);female;44;0;0;PC 17610;27,7208;B4;C;6;;Denver, CO +1;1;Brown, Mrs. John Murray (Caroline Lane Lamson);female;59;2;0;11769;51,4792;C101;S;D;;Belmont, MA +1;1;Bucknell, Mrs. William Robert (Emma Eliza Ward);female;60;0;0;11813;76,2917;D15;C;8;;Philadelphia, PA +1;1;Burns, Miss. Elizabeth Margaret;female;41;0;0;16966;134,5000;E40;C;3;; +1;0;Butt, Major. Archibald Willingham;male;45;0;0;113050;26,5500;B38;S;;;Washington, DC +1;0;Cairns, Mr. Alexander;male;;0;0;113798;31,0000;;S;;; +1;1;Calderhead, Mr. Edward Pennington;male;42;0;0;PC 17476;26,2875;E24;S;5;;New York, NY +1;1;Candee, Mrs. Edward (Helen Churchill Hungerford);female;53;0;0;PC 17606;27,4458;;C;6;;Washington, DC +1;1;Cardeza, Mr. Thomas Drake Martinez;male;36;0;1;PC 17755;512,3292;B51 B53 B55;C;3;;Austria-Hungary / Germantown, Philadelphia, PA +1;1;Cardeza, Mrs. James Warburton Martinez (Charlotte Wardle Drake);female;58;0;1;PC 17755;512,3292;B51 B53 B55;C;3;;Germantown, Philadelphia, PA +1;0;Carlsson, Mr. Frans Olof;male;33;0;0;695;5,0000;B51 B53 B55;S;;;New York, NY +1;0;Carrau, Mr. Francisco M;male;28;0;0;113059;47,1000;;S;;;Montevideo, Uruguay +1;0;Carrau, Mr. Jose Pedro;male;17;0;0;113059;47,1000;;S;;;Montevideo, Uruguay +1;1;Carter, Master. William Thornton II;male;11;1;2;113760;120,0000;B96 B98;S;4;;Bryn Mawr, PA +1;1;Carter, Miss. Lucile Polk;female;14;1;2;113760;120,0000;B96 B98;S;4;;Bryn Mawr, PA +1;1;Carter, Mr. William Ernest;male;36;1;2;113760;120,0000;B96 B98;S;C;;Bryn Mawr, PA +1;1;Carter, Mrs. William Ernest (Lucile Polk);female;36;1;2;113760;120,0000;B96 B98;S;4;;Bryn Mawr, PA +1;0;Case, Mr. Howard Brown;male;49;0;0;19924;26,0000;;S;;;Ascot, Berkshire / Rochester, NY +1;1;Cassebeer, Mrs. Henry Arthur Jr (Eleanor Genevieve Fosdick);female;;0;0;17770;27,7208;;C;5;;New York, NY +1;0;Cavendish, Mr. Tyrell William;male;36;1;0;19877;78,8500;C46;S;;172;Little Onn Hall, Staffs +1;1;Cavendish, Mrs. Tyrell William (Julia Florence Siegel);female;76;1;0;19877;78,8500;C46;S;6;;Little Onn Hall, Staffs +1;0;Chaffee, Mr. Herbert Fuller;male;46;1;0;W.E.P. 5734;61,1750;E31;S;;;Amenia, ND +1;1;Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood);female;47;1;0;W.E.P. 5734;61,1750;E31;S;4;;Amenia, ND +1;1;Chambers, Mr. Norman Campbell;male;27;1;0;113806;53,1000;E8;S;5;;New York, NY / Ithaca, NY +1;1;Chambers, Mrs. Norman Campbell (Bertha Griggs);female;33;1;0;113806;53,1000;E8;S;5;;New York, NY / Ithaca, NY +1;1;Chaudanson, Miss. Victorine;female;36;0;0;PC 17608;262,3750;B61;C;4;; +1;1;Cherry, Miss. Gladys;female;30;0;0;110152;86,5000;B77;S;8;;London, England +1;1;Chevre, Mr. Paul Romaine;male;45;0;0;PC 17594;29,7000;A9;C;7;;Paris, France +1;1;Chibnall, Mrs. (Edith Martha Bowerman);female;;0;1;113505;55,0000;E33;S;6;;St Leonards-on-Sea, England Ohio +1;0;Chisholm, Mr. Roderick Robert Crispin;male;;0;0;112051;0,0000;;S;;;Liverpool, England / Belfast +1;0;Clark, Mr. Walter Miller;male;27;1;0;13508;136,7792;C89;C;;;Los Angeles, CA +1;1;Clark, Mrs. Walter Miller (Virginia McDowell);female;26;1;0;13508;136,7792;C89;C;4;;Los Angeles, CA +1;1;Cleaver, Miss. Alice;female;22;0;0;113781;151,5500;;S;11;; +1;0;Clifford, Mr. George Quincy;male;;0;0;110465;52,0000;A14;S;;;Stoughton, MA +1;0;Colley, Mr. Edward Pomeroy;male;47;0;0;5727;25,5875;E58;S;;;Victoria, BC +1;1;Compton, Miss. Sara Rebecca;female;39;1;1;PC 17756;83,1583;E49;C;14;;Lakewood, NJ +1;0;Compton, Mr. Alexander Taylor Jr;male;37;1;1;PC 17756;83,1583;E52;C;;;Lakewood, NJ +1;1;Compton, Mrs. Alexander Taylor (Mary Eliza Ingersoll);female;64;0;2;PC 17756;83,1583;E45;C;14;;Lakewood, NJ +1;1;Cornell, Mrs. Robert Clifford (Malvina Helen Lamson);female;55;2;0;11770;25,7000;C101;S;2;;New York, NY +1;0;Crafton, Mr. John Bertram;male;;0;0;113791;26,5500;;S;;;Roachdale, IN +1;0;Crosby, Capt. Edward Gifford;male;70;1;1;WE/P 5735;71,0000;B22;S;;269;Milwaukee, WI +1;1;Crosby, Miss. Harriet R;female;36;0;2;WE/P 5735;71,0000;B22;S;7;;Milwaukee, WI +1;1;Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead);female;64;1;1;112901;26,5500;B26;S;7;;Milwaukee, WI +1;0;Cumings, Mr. John Bradley;male;39;1;0;PC 17599;71,2833;C85;C;;;New York, NY +1;1;Cumings, Mrs. John Bradley (Florence Briggs Thayer);female;38;1;0;PC 17599;71,2833;C85;C;4;;New York, NY +1;1;Daly, Mr. Peter Denis ;male;51;0;0;113055;26,5500;E17;S;5 9;;Lima, Peru +1;1;Daniel, Mr. Robert Williams;male;27;0;0;113804;30,5000;;S;3;;Philadelphia, PA +1;1;Daniels, Miss. Sarah;female;33;0;0;113781;151,5500;;S;8;; +1;0;Davidson, Mr. Thornton;male;31;1;0;F.C. 12750;52,0000;B71;S;;;Montreal, PQ +1;1;Davidson, Mrs. Thornton (Orian Hays);female;27;1;2;F.C. 12750;52,0000;B71;S;3;;Montreal, PQ +1;1;Dick, Mr. Albert Adrian;male;31;1;0;17474;57,0000;B20;S;3;;Calgary, AB +1;1;Dick, Mrs. Albert Adrian (Vera Gillespie);female;17;1;0;17474;57,0000;B20;S;3;;Calgary, AB +1;1;Dodge, Dr. Washington;male;53;1;1;33638;81,8583;A34;S;13;;San Francisco, CA +1;1;Dodge, Master. Washington;male;4;0;2;33638;81,8583;A34;S;5;;San Francisco, CA +1;1;Dodge, Mrs. Washington (Ruth Vidaver);female;54;1;1;33638;81,8583;A34;S;5;;San Francisco, CA +1;0;Douglas, Mr. Walter Donald;male;50;1;0;PC 17761;106,4250;C86;C;;62;Deephaven, MN / Cedar Rapids, IA +1;1;Douglas, Mrs. Frederick Charles (Mary Helene Baxter);female;27;1;1;PC 17558;247,5208;B58 B60;C;6;;Montreal, PQ +1;1;Douglas, Mrs. Walter Donald (Mahala Dutton);female;48;1;0;PC 17761;106,4250;C86;C;2;;Deephaven, MN / Cedar Rapids, IA +1;1;"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")";female;48;1;0;11755;39,6000;A16;C;1;;London / Paris +1;1;"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")";male;49;1;0;PC 17485;56,9292;A20;C;1;;London / Paris +1;0;Dulles, Mr. William Crothers;male;39;0;0;PC 17580;29,7000;A18;C;;133;Philadelphia, PA +1;1;Earnshaw, Mrs. Boulton (Olive Potter);female;23;0;1;11767;83,1583;C54;C;7;;Mt Airy, Philadelphia, PA +1;1;Endres, Miss. Caroline Louise;female;38;0;0;PC 17757;227,5250;C45;C;4;;New York, NY +1;1;Eustis, Miss. Elizabeth Mussey;female;54;1;0;36947;78,2667;D20;C;4;;Brookline, MA +1;0;Evans, Miss. Edith Corse;female;36;0;0;PC 17531;31,6792;A29;C;;;New York, NY +1;0;Farthing, Mr. John;male;;0;0;PC 17483;221,7792;C95;S;;; +1;1;Flegenheim, Mrs. Alfred (Antoinette);female;;0;0;PC 17598;31,6833;;S;7;;New York, NY +1;1;Fleming, Miss. Margaret;female;;0;0;17421;110,8833;;C;4;; +1;1;"Flynn, Mr. John Irwin (""Irving"")";male;36;0;0;PC 17474;26,3875;E25;S;5;;Brooklyn, NY +1;0;Foreman, Mr. Benjamin Laventall;male;30;0;0;113051;27,7500;C111;C;;;New York, NY +1;1;Fortune, Miss. Alice Elizabeth;female;24;3;2;19950;263,0000;C23 C25 C27;S;10;;Winnipeg, MB +1;1;Fortune, Miss. Ethel Flora;female;28;3;2;19950;263,0000;C23 C25 C27;S;10;;Winnipeg, MB +1;1;Fortune, Miss. Mabel Helen;female;23;3;2;19950;263,0000;C23 C25 C27;S;10;;Winnipeg, MB +1;0;Fortune, Mr. Charles Alexander;male;19;3;2;19950;263,0000;C23 C25 C27;S;;;Winnipeg, MB +1;0;Fortune, Mr. Mark;male;64;1;4;19950;263,0000;C23 C25 C27;S;;;Winnipeg, MB +1;1;Fortune, Mrs. Mark (Mary McDougald);female;60;1;4;19950;263,0000;C23 C25 C27;S;10;;Winnipeg, MB +1;1;Francatelli, Miss. Laura Mabel;female;30;0;0;PC 17485;56,9292;E36;C;1;; +1;0;Franklin, Mr. Thomas Parham;male;;0;0;113778;26,5500;D34;S;;;Westcliff-on-Sea, Essex +1;1;Frauenthal, Dr. Henry William;male;50;2;0;PC 17611;133,6500;;S;5;;New York, NY +1;1;Frauenthal, Mr. Isaac Gerald;male;43;1;0;17765;27,7208;D40;C;5;;New York, NY +1;1;Frauenthal, Mrs. Henry William (Clara Heinsheimer);female;;1;0;PC 17611;133,6500;;S;5;;New York, NY +1;1;Frolicher, Miss. Hedwig Margaritha;female;22;0;2;13568;49,5000;B39;C;5;;Zurich, Switzerland +1;1;Frolicher-Stehli, Mr. Maxmillian;male;60;1;1;13567;79,2000;B41;C;5;;Zurich, Switzerland +1;1;Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli);female;48;1;1;13567;79,2000;B41;C;5;;Zurich, Switzerland +1;0;Fry, Mr. Richard;male;;0;0;112058;0,0000;B102;S;;; +1;0;Futrelle, Mr. Jacques Heath;male;37;1;0;113803;53,1000;C123;S;;;Scituate, MA +1;1;Futrelle, Mrs. Jacques Heath (Lily May Peel);female;35;1;0;113803;53,1000;C123;S;D;;Scituate, MA +1;0;Gee, Mr. Arthur H;male;47;0;0;111320;38,5000;E63;S;;275;St Anne's-on-Sea, Lancashire +1;1;Geiger, Miss. Amalie;female;35;0;0;113503;211,5000;C130;C;4;; +1;1;Gibson, Miss. Dorothy Winifred;female;22;0;1;112378;59,4000;;C;7;;New York, NY +1;1;Gibson, Mrs. Leonard (Pauline C Boeson);female;45;0;1;112378;59,4000;;C;7;;New York, NY +1;0;Giglio, Mr. Victor;male;24;0;0;PC 17593;79,2000;B86;C;;; +1;1;Goldenberg, Mr. Samuel L;male;49;1;0;17453;89,1042;C92;C;5;;Paris, France / New York, NY +1;1;Goldenberg, Mrs. Samuel L (Edwiga Grabowska);female;;1;0;17453;89,1042;C92;C;5;;Paris, France / New York, NY +1;0;Goldschmidt, Mr. George B;male;71;0;0;PC 17754;34,6542;A5;C;;;New York, NY +1;1;Gracie, Col. Archibald IV;male;53;0;0;113780;28,5000;C51;C;B;;Washington, DC +1;1;Graham, Miss. Margaret Edith;female;19;0;0;112053;30,0000;B42;S;3;;Greenwich, CT +1;0;Graham, Mr. George Edward;male;38;0;1;PC 17582;153,4625;C91;S;;147;Winnipeg, MB +1;1;Graham, Mrs. William Thompson (Edith Junkins);female;58;0;1;PC 17582;153,4625;C125;S;3;;Greenwich, CT +1;1;Greenfield, Mr. William Bertram;male;23;0;1;PC 17759;63,3583;D10 D12;C;7;;New York, NY +1;1;Greenfield, Mrs. Leo David (Blanche Strouse);female;45;0;1;PC 17759;63,3583;D10 D12;C;7;;New York, NY +1;0;Guggenheim, Mr. Benjamin;male;46;0;0;PC 17593;79,2000;B82 B84;C;;;New York, NY +1;1;Harder, Mr. George Achilles;male;25;1;0;11765;55,4417;E50;C;5;;Brooklyn, NY +1;1;Harder, Mrs. George Achilles (Dorothy Annan);female;25;1;0;11765;55,4417;E50;C;5;;Brooklyn, NY +1;1;Harper, Mr. Henry Sleeper;male;48;1;0;PC 17572;76,7292;D33;C;3;;New York, NY +1;1;Harper, Mrs. Henry Sleeper (Myna Haxtun);female;49;1;0;PC 17572;76,7292;D33;C;3;;New York, NY +1;0;Harrington, Mr. Charles H;male;;0;0;113796;42,4000;;S;;; +1;0;Harris, Mr. Henry Birkhardt;male;45;1;0;36973;83,4750;C83;S;;;New York, NY +1;1;Harris, Mrs. Henry Birkhardt (Irene Wallach);female;35;1;0;36973;83,4750;C83;S;D;;New York, NY +1;0;Harrison, Mr. William;male;40;0;0;112059;0,0000;B94;S;;110; +1;1;Hassab, Mr. Hammad;male;27;0;0;PC 17572;76,7292;D49;C;3;; +1;1;Hawksford, Mr. Walter James;male;;0;0;16988;30,0000;D45;S;3;;Kingston, Surrey +1;1;Hays, Miss. Margaret Bechstein;female;24;0;0;11767;83,1583;C54;C;7;;New York, NY +1;0;Hays, Mr. Charles Melville;male;55;1;1;12749;93,5000;B69;S;;307;Montreal, PQ +1;1;Hays, Mrs. Charles Melville (Clara Jennings Gregg);female;52;1;1;12749;93,5000;B69;S;3;;Montreal, PQ +1;0;Head, Mr. Christopher;male;42;0;0;113038;42,5000;B11;S;;;London / Middlesex +1;0;Hilliard, Mr. Herbert Henry;male;;0;0;17463;51,8625;E46;S;;;Brighton, MA +1;0;Hipkins, Mr. William Edward;male;55;0;0;680;50,0000;C39;S;;;London / Birmingham +1;1;Hippach, Miss. Jean Gertrude;female;16;0;1;111361;57,9792;B18;C;4;;Chicago, IL +1;1;Hippach, Mrs. Louis Albert (Ida Sophia Fischer);female;44;0;1;111361;57,9792;B18;C;4;;Chicago, IL +1;1;Hogeboom, Mrs. John C (Anna Andrews);female;51;1;0;13502;77,9583;D11;S;10;;Hudson, NY +1;0;Holverson, Mr. Alexander Oskar;male;42;1;0;113789;52,0000;;S;;38;New York, NY +1;1;Holverson, Mrs. Alexander Oskar (Mary Aline Towner);female;35;1;0;113789;52,0000;;S;8;;New York, NY +1;1;"Homer, Mr. Harry (""Mr E Haven"")";male;35;0;0;111426;26,5500;;C;15;;Indianapolis, IN +1;1;Hoyt, Mr. Frederick Maxfield;male;38;1;0;19943;90,0000;C93;S;D;;New York, NY / Stamford CT +1;0;Hoyt, Mr. William Fisher;male;;0;0;PC 17600;30,6958;;C;14;;New York, NY +1;1;Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby);female;35;1;0;19943;90,0000;C93;S;D;;New York, NY / Stamford CT +1;1;Icard, Miss. Amelie;female;38;0;0;113572;80,0000;B28;;6;; +1;0;Isham, Miss. Ann Elizabeth;female;50;0;0;PC 17595;28,7125;C49;C;;;Paris, France New York, NY +1;1;Ismay, Mr. Joseph Bruce;male;49;0;0;112058;0,0000;B52 B54 B56;S;C;;Liverpool +1;0;Jones, Mr. Charles Cresson;male;46;0;0;694;26,0000;;S;;80;Bennington, VT +1;0;Julian, Mr. Henry Forbes;male;50;0;0;113044;26,0000;E60;S;;;London +1;0;Keeping, Mr. Edwin;male;32,5;0;0;113503;211,5000;C132;C;;45; +1;0;Kent, Mr. Edward Austin;male;58;0;0;11771;29,7000;B37;C;;258;Buffalo, NY +1;0;Kenyon, Mr. Frederick R;male;41;1;0;17464;51,8625;D21;S;;;Southington / Noank, CT +1;1;Kenyon, Mrs. Frederick R (Marion);female;;1;0;17464;51,8625;D21;S;8;;Southington / Noank, CT +1;1;Kimball, Mr. Edwin Nelson Jr;male;42;1;0;11753;52,5542;D19;S;5;;Boston, MA +1;1;Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons);female;45;1;0;11753;52,5542;D19;S;5;;Boston, MA +1;0;Klaber, Mr. Herman;male;;0;0;113028;26,5500;C124;S;;;Portland, OR +1;1;Kreuchen, Miss. Emilie;female;39;0;0;24160;211,3375;;S;2;; +1;1;Leader, Dr. Alice (Farnham);female;49;0;0;17465;25,9292;D17;S;8;;New York, NY +1;1;LeRoy, Miss. Bertha;female;30;0;0;PC 17761;106,4250;;C;2;; +1;1;Lesurer, Mr. Gustave J;male;35;0;0;PC 17755;512,3292;B101;C;3;; +1;0;Lewy, Mr. Ervin G;male;;0;0;PC 17612;27,7208;;C;;;Chicago, IL +1;0;"Lindeberg-Lind, Mr. Erik Gustaf (""Mr Edward Lingrey"")";male;42;0;0;17475;26,5500;;S;;;Stockholm, Sweden +1;1;Lindstrom, Mrs. Carl Johan (Sigrid Posse);female;55;0;0;112377;27,7208;;C;6;;Stockholm, Sweden +1;1;Lines, Miss. Mary Conover;female;16;0;1;PC 17592;39,4000;D28;S;9;;Paris, France +1;1;Lines, Mrs. Ernest H (Elizabeth Lindsey James);female;51;0;1;PC 17592;39,4000;D28;S;9;;Paris, France +1;0;Long, Mr. Milton Clyde;male;29;0;0;113501;30,0000;D6;S;;126;Springfield, MA +1;1;Longley, Miss. Gretchen Fiske;female;21;0;0;13502;77,9583;D9;S;10;;Hudson, NY +1;0;Loring, Mr. Joseph Holland;male;30;0;0;113801;45,5000;;S;;;London / New York, NY +1;1;Lurette, Miss. Elise;female;58;0;0;PC 17569;146,5208;B80;C;;; +1;1;Madill, Miss. Georgette Alexandra;female;15;0;1;24160;211,3375;B5;S;2;;St Louis, MO +1;0;Maguire, Mr. John Edward;male;30;0;0;110469;26,0000;C106;S;;;Brockton, MA +1;1;Maioni, Miss. Roberta;female;16;0;0;110152;86,5000;B79;S;8;; +1;1;Marechal, Mr. Pierre;male;;0;0;11774;29,7000;C47;C;7;;Paris, France +1;0;Marvin, Mr. Daniel Warner;male;19;1;0;113773;53,1000;D30;S;;;New York, NY +1;1;Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson);female;18;1;0;113773;53,1000;D30;S;10;;New York, NY +1;1;"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")";female;24;0;0;PC 17482;49,5042;C90;C;6;;Belgium Montreal, PQ +1;0;McCaffry, Mr. Thomas Francis;male;46;0;0;13050;75,2417;C6;C;;292;Vancouver, BC +1;0;McCarthy, Mr. Timothy J;male;54;0;0;17463;51,8625;E46;S;;175;Dorchester, MA +1;1;McGough, Mr. James Robert;male;36;0;0;PC 17473;26,2875;E25;S;7;;Philadelphia, PA +1;0;Meyer, Mr. Edgar Joseph;male;28;1;0;PC 17604;82,1708;;C;;;New York, NY +1;1;Meyer, Mrs. Edgar Joseph (Leila Saks);female;;1;0;PC 17604;82,1708;;C;6;;New York, NY +1;0;Millet, Mr. Francis Davis;male;65;0;0;13509;26,5500;E38;S;;249;East Bridgewater, MA +1;0;Minahan, Dr. William Edward;male;44;2;0;19928;90,0000;C78;Q;;230;Fond du Lac, WI +1;1;Minahan, Miss. Daisy E;female;33;1;0;19928;90,0000;C78;Q;14;;Green Bay, WI +1;1;Minahan, Mrs. William Edward (Lillian E Thorpe);female;37;1;0;19928;90,0000;C78;Q;14;;Fond du Lac, WI +1;1;Mock, Mr. Philipp Edmund;male;30;1;0;13236;57,7500;C78;C;11;;New York, NY +1;0;Molson, Mr. Harry Markland;male;55;0;0;113787;30,5000;C30;S;;;Montreal, PQ +1;0;Moore, Mr. Clarence Bloomfield;male;47;0;0;113796;42,4000;;S;;;Washington, DC +1;0;Natsch, Mr. Charles H;male;37;0;1;PC 17596;29,7000;C118;C;;;Brooklyn, NY +1;1;Newell, Miss. Madeleine;female;31;1;0;35273;113,2750;D36;C;6;;Lexington, MA +1;1;Newell, Miss. Marjorie;female;23;1;0;35273;113,2750;D36;C;6;;Lexington, MA +1;0;Newell, Mr. Arthur Webster;male;58;0;2;35273;113,2750;D48;C;;122;Lexington, MA +1;1;Newsom, Miss. Helen Monypeny;female;19;0;2;11752;26,2833;D47;S;5;;New York, NY +1;0;Nicholson, Mr. Arthur Ernest;male;64;0;0;693;26,0000;;S;;263;Isle of Wight, England +1;1;Oliva y Ocana, Dona. Fermina;female;39;0;0;PC 17758;108,9000;C105;C;8;; +1;1;Omont, Mr. Alfred Fernand;male;;0;0;F.C. 12998;25,7417;;C;7;;Paris, France +1;1;Ostby, Miss. Helene Ragnhild;female;22;0;1;113509;61,9792;B36;C;5;;Providence, RI +1;0;Ostby, Mr. Engelhart Cornelius;male;65;0;1;113509;61,9792;B30;C;;234;Providence, RI +1;0;Ovies y Rodriguez, Mr. Servando;male;28,5;0;0;PC 17562;27,7208;D43;C;;189;?Havana, Cuba +1;0;Parr, Mr. William Henry Marsh;male;;0;0;112052;0,0000;;S;;;Belfast +1;0;Partner, Mr. Austen;male;45,5;0;0;113043;28,5000;C124;S;;166;Surbiton Hill, Surrey +1;0;Payne, Mr. Vivian Ponsonby;male;23;0;0;12749;93,5000;B24;S;;;Montreal, PQ +1;0;Pears, Mr. Thomas Clinton;male;29;1;0;113776;66,6000;C2;S;;;Isleworth, England +1;1;Pears, Mrs. Thomas (Edith Wearne);female;22;1;0;113776;66,6000;C2;S;8;;Isleworth, England +1;0;Penasco y Castellana, Mr. Victor de Satode;male;18;1;0;PC 17758;108,9000;C65;C;;;Madrid, Spain +1;1;Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo);female;17;1;0;PC 17758;108,9000;C65;C;8;;Madrid, Spain +1;1;Perreault, Miss. Anne;female;30;0;0;12749;93,5000;B73;S;3;; +1;1;Peuchen, Major. Arthur Godfrey;male;52;0;0;113786;30,5000;C104;S;6;;Toronto, ON +1;0;Porter, Mr. Walter Chamberlain;male;47;0;0;110465;52,0000;C110;S;;207;Worcester, MA +1;1;Potter, Mrs. Thomas Jr (Lily Alexenia Wilson);female;56;0;1;11767;83,1583;C50;C;7;;Mt Airy, Philadelphia, PA +1;0;Reuchlin, Jonkheer. John George;male;38;0;0;19972;0,0000;;S;;;Rotterdam, Netherlands +1;1;Rheims, Mr. George Alexander Lucien;male;;0;0;PC 17607;39,6000;;S;A;;Paris / New York, NY +1;0;Ringhini, Mr. Sante;male;22;0;0;PC 17760;135,6333;;C;;232; +1;0;Robbins, Mr. Victor;male;;0;0;PC 17757;227,5250;;C;;; +1;1;Robert, Mrs. Edward Scott (Elisabeth Walton McMillan);female;43;0;1;24160;211,3375;B3;S;2;;St Louis, MO +1;0;Roebling, Mr. Washington Augustus II;male;31;0;0;PC 17590;50,4958;A24;S;;;Trenton, NJ +1;1;"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")";male;45;0;0;111428;26,5500;;S;9;;New York, NY +1;0;Rood, Mr. Hugh Roscoe;male;;0;0;113767;50,0000;A32;S;;;Seattle, WA +1;1;Rosenbaum, Miss. Edith Louise;female;33;0;0;PC 17613;27,7208;A11;C;11;;Paris, France +1;0;"Rosenshine, Mr. George (""Mr George Thorne"")";male;46;0;0;PC 17585;79,2000;;C;;16;New York, NY +1;0;Ross, Mr. John Hugo;male;36;0;0;13049;40,1250;A10;C;;;Winnipeg, MB +1;1;Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards);female;33;0;0;110152;86,5000;B77;S;8;;London Vancouver, BC +1;0;Rothschild, Mr. Martin;male;55;1;0;PC 17603;59,4000;;C;;;New York, NY +1;1;Rothschild, Mrs. Martin (Elizabeth L. Barrett);female;54;1;0;PC 17603;59,4000;;C;6;;New York, NY +1;0;Rowe, Mr. Alfred G;male;33;0;0;113790;26,5500;;S;;109;London +1;1;Ryerson, Master. John Borie;male;13;2;2;PC 17608;262,3750;B57 B59 B63 B66;C;4;;Haverford, PA / Cooperstown, NY +1;1;Ryerson, Miss. Emily Borie;female;18;2;2;PC 17608;262,3750;B57 B59 B63 B66;C;4;;Haverford, PA / Cooperstown, NY +1;1;"Ryerson, Miss. Susan Parker ""Suzette""";female;21;2;2;PC 17608;262,3750;B57 B59 B63 B66;C;4;;Haverford, PA / Cooperstown, NY +1;0;Ryerson, Mr. Arthur Larned;male;61;1;3;PC 17608;262,3750;B57 B59 B63 B66;C;;;Haverford, PA / Cooperstown, NY +1;1;Ryerson, Mrs. Arthur Larned (Emily Maria Borie);female;48;1;3;PC 17608;262,3750;B57 B59 B63 B66;C;4;;Haverford, PA / Cooperstown, NY +1;1;Saalfeld, Mr. Adolphe;male;;0;0;19988;30,5000;C106;S;3;;Manchester, England +1;1;Sagesser, Mlle. Emma;female;24;0;0;PC 17477;69,3000;B35;C;9;; +1;1;Salomon, Mr. Abraham L;male;;0;0;111163;26,0000;;S;1;;New York, NY +1;1;Schabert, Mrs. Paul (Emma Mock);female;35;1;0;13236;57,7500;C28;C;11;;New York, NY +1;1;Serepeca, Miss. Augusta;female;30;0;0;113798;31,0000;;C;4;; +1;1;Seward, Mr. Frederic Kimber;male;34;0;0;113794;26,5500;;S;7;;New York, NY +1;1;Shutes, Miss. Elizabeth W;female;40;0;0;PC 17582;153,4625;C125;S;3;;New York, NY / Greenwich CT +1;1;Silverthorne, Mr. Spencer Victor;male;35;0;0;PC 17475;26,2875;E24;S;5;;St Louis, MO +1;0;Silvey, Mr. William Baird;male;50;1;0;13507;55,9000;E44;S;;;Duluth, MN +1;1;Silvey, Mrs. William Baird (Alice Munger);female;39;1;0;13507;55,9000;E44;S;11;;Duluth, MN +1;1;Simonius-Blumer, Col. Oberst Alfons;male;56;0;0;13213;35,5000;A26;C;3;;Basel, Switzerland +1;1;Sloper, Mr. William Thompson;male;28;0;0;113788;35,5000;A6;S;7;;New Britain, CT +1;0;Smart, Mr. John Montgomery;male;56;0;0;113792;26,5500;;S;;;New York, NY +1;0;Smith, Mr. James Clinch;male;56;0;0;17764;30,6958;A7;C;;;St James, Long Island, NY +1;0;Smith, Mr. Lucien Philip;male;24;1;0;13695;60,0000;C31;S;;;Huntington, WV +1;0;Smith, Mr. Richard William;male;;0;0;113056;26,0000;A19;S;;;Streatham, Surrey +1;1;Smith, Mrs. Lucien Philip (Mary Eloise Hughes);female;18;1;0;13695;60,0000;C31;S;6;;Huntington, WV +1;1;Snyder, Mr. John Pillsbury;male;24;1;0;21228;82,2667;B45;S;7;;Minneapolis, MN +1;1;Snyder, Mrs. John Pillsbury (Nelle Stevenson);female;23;1;0;21228;82,2667;B45;S;7;;Minneapolis, MN +1;1;Spedden, Master. Robert Douglas;male;6;0;2;16966;134,5000;E34;C;3;;Tuxedo Park, NY +1;1;Spedden, Mr. Frederic Oakley;male;45;1;1;16966;134,5000;E34;C;3;;Tuxedo Park, NY +1;1;Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone);female;40;1;1;16966;134,5000;E34;C;3;;Tuxedo Park, NY +1;0;Spencer, Mr. William Augustus;male;57;1;0;PC 17569;146,5208;B78;C;;;Paris, France +1;1;Spencer, Mrs. William Augustus (Marie Eugenie);female;;1;0;PC 17569;146,5208;B78;C;6;;Paris, France +1;1;Stahelin-Maeglin, Dr. Max;male;32;0;0;13214;30,5000;B50;C;3;;Basel, Switzerland +1;0;Stead, Mr. William Thomas;male;62;0;0;113514;26,5500;C87;S;;;Wimbledon Park, London / Hayling Island, Hants +1;1;Stengel, Mr. Charles Emil Henry;male;54;1;0;11778;55,4417;C116;C;1;;Newark, NJ +1;1;Stengel, Mrs. Charles Emil Henry (Annie May Morris);female;43;1;0;11778;55,4417;C116;C;5;;Newark, NJ +1;1;Stephenson, Mrs. Walter Bertram (Martha Eustis);female;52;1;0;36947;78,2667;D20;C;4;;Haverford, PA +1;0;Stewart, Mr. Albert A;male;;0;0;PC 17605;27,7208;;C;;;Gallipolis, Ohio / ? Paris / New York +1;1;Stone, Mrs. George Nelson (Martha Evelyn);female;62;0;0;113572;80,0000;B28;;6;;Cincinatti, OH +1;0;Straus, Mr. Isidor;male;67;1;0;PC 17483;221,7792;C55 C57;S;;96;New York, NY +1;0;Straus, Mrs. Isidor (Rosalie Ida Blun);female;63;1;0;PC 17483;221,7792;C55 C57;S;;;New York, NY +1;0;Sutton, Mr. Frederick;male;61;0;0;36963;32,3208;D50;S;;46;Haddenfield, NJ +1;1;Swift, Mrs. Frederick Joel (Margaret Welles Barron);female;48;0;0;17466;25,9292;D17;S;8;;Brooklyn, NY +1;1;Taussig, Miss. Ruth;female;18;0;2;110413;79,6500;E68;S;8;;New York, NY +1;0;Taussig, Mr. Emil;male;52;1;1;110413;79,6500;E67;S;;;New York, NY +1;1;Taussig, Mrs. Emil (Tillie Mandelbaum);female;39;1;1;110413;79,6500;E67;S;8;;New York, NY +1;1;Taylor, Mr. Elmer Zebley;male;48;1;0;19996;52,0000;C126;S;5 7;;London / East Orange, NJ +1;1;Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright);female;;1;0;19996;52,0000;C126;S;5 7;;London / East Orange, NJ +1;0;Thayer, Mr. John Borland;male;49;1;1;17421;110,8833;C68;C;;;Haverford, PA +1;1;Thayer, Mr. John Borland Jr;male;17;0;2;17421;110,8833;C70;C;B;;Haverford, PA +1;1;Thayer, Mrs. John Borland (Marian Longstreth Morris);female;39;1;1;17421;110,8833;C68;C;4;;Haverford, PA +1;1;Thorne, Mrs. Gertrude Maybelle;female;;0;0;PC 17585;79,2000;;C;D;;New York, NY +1;1;Tucker, Mr. Gilbert Milligan Jr;male;31;0;0;2543;28,5375;C53;C;7;;Albany, NY +1;0;Uruchurtu, Don. Manuel E;male;40;0;0;PC 17601;27,7208;;C;;;Mexico City, Mexico +1;0;Van der hoef, Mr. Wyckoff;male;61;0;0;111240;33,5000;B19;S;;245;Brooklyn, NY +1;0;Walker, Mr. William Anderson;male;47;0;0;36967;34,0208;D46;S;;;East Orange, NJ +1;1;Ward, Miss. Anna;female;35;0;0;PC 17755;512,3292;;C;3;; +1;0;Warren, Mr. Frank Manley;male;64;1;0;110813;75,2500;D37;C;;;Portland, OR +1;1;Warren, Mrs. Frank Manley (Anna Sophia Atkinson);female;60;1;0;110813;75,2500;D37;C;5;;Portland, OR +1;0;Weir, Col. John;male;60;0;0;113800;26,5500;;S;;;England Salt Lake City, Utah +1;0;White, Mr. Percival Wayland;male;54;0;1;35281;77,2875;D26;S;;;Brunswick, ME +1;0;White, Mr. Richard Frasar;male;21;0;1;35281;77,2875;D26;S;;169;Brunswick, ME +1;1;White, Mrs. John Stuart (Ella Holmes);female;55;0;0;PC 17760;135,6333;C32;C;8;;New York, NY / Briarcliff Manor NY +1;1;Wick, Miss. Mary Natalie;female;31;0;2;36928;164,8667;C7;S;8;;Youngstown, OH +1;0;Wick, Mr. George Dennick;male;57;1;1;36928;164,8667;;S;;;Youngstown, OH +1;1;Wick, Mrs. George Dennick (Mary Hitchcock);female;45;1;1;36928;164,8667;;S;8;;Youngstown, OH +1;0;Widener, Mr. George Dunton;male;50;1;1;113503;211,5000;C80;C;;;Elkins Park, PA +1;0;Widener, Mr. Harry Elkins;male;27;0;2;113503;211,5000;C82;C;;;Elkins Park, PA +1;1;Widener, Mrs. George Dunton (Eleanor Elkins);female;50;1;1;113503;211,5000;C80;C;4;;Elkins Park, PA +1;1;Willard, Miss. Constance;female;21;0;0;113795;26,5500;;S;8 10;;Duluth, MN +1;0;Williams, Mr. Charles Duane;male;51;0;1;PC 17597;61,3792;;C;;;Geneva, Switzerland / Radnor, PA +1;1;Williams, Mr. Richard Norris II;male;21;0;1;PC 17597;61,3792;;C;A;;Geneva, Switzerland / Radnor, PA +1;0;Williams-Lambert, Mr. Fletcher Fellows;male;;0;0;113510;35,0000;C128;S;;;London, England +1;1;Wilson, Miss. Helen Alice;female;31;0;0;16966;134,5000;E39 E41;C;3;; +1;1;Woolner, Mr. Hugh;male;;0;0;19947;35,5000;C52;S;D;;London, England +1;0;Wright, Mr. George;male;62;0;0;113807;26,5500;;S;;;Halifax, NS +1;1;Young, Miss. Marie Grice;female;36;0;0;PC 17760;135,6333;C32;C;8;;New York, NY / Washington, DC +2;0;Abelson, Mr. Samuel;male;30;1;0;P/PP 3381;24,0000;;C;;;Russia New York, NY +2;1;Abelson, Mrs. Samuel (Hannah Wizosky);female;28;1;0;P/PP 3381;24,0000;;C;10;;Russia New York, NY +2;0;Aldworth, Mr. Charles Augustus;male;30;0;0;248744;13,0000;;S;;;Bryn Mawr, PA, USA +2;0;Andrew, Mr. Edgardo Samuel;male;18;0;0;231945;11,5000;;S;;;Buenos Aires, Argentina / New Jersey, NJ +2;0;Andrew, Mr. Frank Thomas;male;25;0;0;C.A. 34050;10,5000;;S;;;Cornwall, England Houghton, MI +2;0;Angle, Mr. William A;male;34;1;0;226875;26,0000;;S;;;Warwick, England +2;1;"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)";female;36;1;0;226875;26,0000;;S;11;;Warwick, England +2;0;Ashby, Mr. John;male;57;0;0;244346;13,0000;;S;;;West Hoboken, NJ +2;0;Bailey, Mr. Percy Andrew;male;18;0;0;29108;11,5000;;S;;;Penzance, Cornwall / Akron, OH +2;0;Baimbrigge, Mr. Charles Robert;male;23;0;0;C.A. 31030;10,5000;;S;;;Guernsey +2;1;Ball, Mrs. (Ada E Hall);female;36;0;0;28551;13,0000;D;S;10;;Bristol, Avon / Jacksonville, FL +2;0;Banfield, Mr. Frederick James;male;28;0;0;C.A./SOTON 34068;10,5000;;S;;;Plymouth, Dorset / Houghton, MI +2;0;Bateman, Rev. Robert James;male;51;0;0;S.O.P. 1166;12,5250;;S;;174;Jacksonville, FL +2;1;Beane, Mr. Edward;male;32;1;0;2908;26,0000;;S;13;;Norwich / New York, NY +2;1;Beane, Mrs. Edward (Ethel Clarke);female;19;1;0;2908;26,0000;;S;13;;Norwich / New York, NY +2;0;Beauchamp, Mr. Henry James;male;28;0;0;244358;26,0000;;S;;;England +2;1;Becker, Master. Richard F;male;1;2;1;230136;39,0000;F4;S;11;;Guntur, India / Benton Harbour, MI +2;1;Becker, Miss. Marion Louise;female;4;2;1;230136;39,0000;F4;S;11;;Guntur, India / Benton Harbour, MI +2;1;Becker, Miss. Ruth Elizabeth;female;12;2;1;230136;39,0000;F4;S;13;;Guntur, India / Benton Harbour, MI +2;1;Becker, Mrs. Allen Oliver (Nellie E Baumgardner);female;36;0;3;230136;39,0000;F4;S;11;;Guntur, India / Benton Harbour, MI +2;1;Beesley, Mr. Lawrence;male;34;0;0;248698;13,0000;D56;S;13;;London +2;1;Bentham, Miss. Lilian W;female;19;0;0;28404;13,0000;;S;12;;Rochester, NY +2;0;Berriman, Mr. William John;male;23;0;0;28425;13,0000;;S;;;St Ives, Cornwall / Calumet, MI +2;0;Botsford, Mr. William Hull;male;26;0;0;237670;13,0000;;S;;;Elmira, NY / Orange, NJ +2;0;Bowenur, Mr. Solomon;male;42;0;0;211535;13,0000;;S;;;London +2;0;Bracken, Mr. James H;male;27;0;0;220367;13,0000;;S;;;Lake Arthur, Chavez County, NM +2;1;"Brown, Miss. Amelia ""Mildred""";female;24;0;0;248733;13,0000;F33;S;11;;London / Montreal, PQ +2;1;Brown, Miss. Edith Eileen;female;15;0;2;29750;39,0000;;S;14;;Cape Town, South Africa / Seattle, WA +2;0;Brown, Mr. Thomas William Solomon;male;60;1;1;29750;39,0000;;S;;;Cape Town, South Africa / Seattle, WA +2;1;Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford);female;40;1;1;29750;39,0000;;S;14;;Cape Town, South Africa / Seattle, WA +2;1;Bryhl, Miss. Dagmar Jenny Ingeborg ;female;20;1;0;236853;26,0000;;S;12;;Skara, Sweden / Rockford, IL +2;0;Bryhl, Mr. Kurt Arnold Gottfrid;male;25;1;0;236853;26,0000;;S;;;Skara, Sweden / Rockford, IL +2;1;Buss, Miss. Kate;female;36;0;0;27849;13,0000;;S;9;;Sittingbourne, England / San Diego, CA +2;0;Butler, Mr. Reginald Fenton;male;25;0;0;234686;13,0000;;S;;97;Southsea, Hants +2;0;Byles, Rev. Thomas Roussel Davids;male;42;0;0;244310;13,0000;;S;;;London +2;1;Bystrom, Mrs. (Karolina);female;42;0;0;236852;13,0000;;S;;;New York, NY +2;1;Caldwell, Master. Alden Gates;male;0,8333;0;2;248738;29,0000;;S;13;;Bangkok, Thailand / Roseville, IL +2;1;Caldwell, Mr. Albert Francis;male;26;1;1;248738;29,0000;;S;13;;Bangkok, Thailand / Roseville, IL +2;1;Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh);female;22;1;1;248738;29,0000;;S;13;;Bangkok, Thailand / Roseville, IL +2;1;Cameron, Miss. Clear Annie;female;35;0;0;F.C.C. 13528;21,0000;;S;14;;Mamaroneck, NY +2;0;Campbell, Mr. William;male;;0;0;239853;0,0000;;S;;;Belfast +2;0;Carbines, Mr. William;male;19;0;0;28424;13,0000;;S;;18;St Ives, Cornwall / Calumet, MI +2;0;Carter, Mrs. Ernest Courtenay (Lilian Hughes);female;44;1;0;244252;26,0000;;S;;;London +2;0;Carter, Rev. Ernest Courtenay;male;54;1;0;244252;26,0000;;S;;;London +2;0;Chapman, Mr. Charles Henry;male;52;0;0;248731;13,5000;;S;;130;Bronx, NY +2;0;Chapman, Mr. John Henry;male;37;1;0;SC/AH 29037;26,0000;;S;;17;Cornwall / Spokane, WA +2;0;Chapman, Mrs. John Henry (Sara Elizabeth Lawry);female;29;1;0;SC/AH 29037;26,0000;;S;;;Cornwall / Spokane, WA +2;1;Christy, Miss. 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If false the predictions are mapped to the closest existing value. @@ -51,8 +51,8 @@ def set_model_parameters(self, **args_model): """ Sets the arguments of the underlying model. - Parameters: - ----------- + Parameters + ---------- **kwargs : dict Additional keyword arguments to be passed to the underlying models. """ @@ -62,15 +62,15 @@ def fit(self, X: NDArray, y: NDArray) -> Self: """ Fit the model according to the given training data. - Parameters: - ----------- + Parameters + ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Training vectors. y : array-like, shape (n_samples,) Target values. - Returns: - -------- + Returns + ------- self : object Returns self. """ @@ -95,13 +95,13 @@ def predict(self, X: NDArray) -> NDArray: """ Predict using the fitted model. - Parameters: - ----------- + Parameters + ---------- X : {array-like, sparse matrix}, shape (n_samples, n_features) Samples. - Returns: - -------- + Returns + ------- y_pred : array-like, shape (n_samples,) Predicted target values. """ @@ -302,14 +302,13 @@ def make_pipeline_mixte_preprocessing( """ Create a preprocessing pipeline managing mixed type data by one hot encoding categorical data. - - Parameters: - ----------- + Parameters + ---------- scale_numerical : bool, default=True Whether to scale numerical features. - Returns: - -------- + Returns + ------- preprocessor : Pipeline Preprocessing pipeline """ @@ -331,15 +330,15 @@ def make_robust_MixteHGB(scale_numerical: bool = True, allow_new: bool = True) - DataFrames, so that this pipeline is not required anymore. - Parameters: - ----------- + Parameters + ---------- scale_numerical : bool, default=True Whether to scale numerical features. allow_new : bool, default=True Whether to allow new categories. - Returns: - -------- + Returns + ------- robust_MixteHGB : object A robust pipeline for MixteHGBM. """ diff --git a/qolmat/imputations/rpca/rpca_noisy.py b/qolmat/imputations/rpca/rpca_noisy.py index 8f836d99..74e68856 100644 --- a/qolmat/imputations/rpca/rpca_noisy.py +++ b/qolmat/imputations/rpca/rpca_noisy.py @@ -28,6 +28,32 @@ class RpcaNoisy(RPCA): Chen, Yuxin, et al. "Bridging convex and nonconvex optimization in robust PCA: Noise, outliers and missing data." The Annals of Statistics 49.5 (2021): 2948-2971. + + Parameters + ---------- + random_state : int, optional + The seed of the pseudo random number generator to use, for reproductibility. + rank: Optional[int] + Upper bound of the rank to be estimated + mu: Optional[float] + initial stiffness parameter for the constraint M = L Q + tau: Optional[float] + penalizing parameter for the nuclear norm + lam: Optional[float] + penalizing parameter for the sparse matrix + list_periods: Optional[List[int]] + list of periods, linked to the Toeplitz matrices + list_etas: Optional[List[float]] + list of penalizing parameters for the corresponding period in list_periods + max_iterations: Optional[int] + stopping criteria, maximum number of iterations. By default, the value is set to 10_000 + tolerance: Optional[float] + stoppign critera, minimum difference between 2 consecutive iterations. By default, + the value is set to 1e-6 + norm: Optional[str] + error norm, can be "L1" or "L2". By default, the value is set to "L2" + verbose: Optional[bool] + verbosity level, if False the warnings are silenced """ def __init__( @@ -44,33 +70,6 @@ def __init__( norm: str = "L2", verbose: bool = True, ) -> None: - """ - Parameters - ---------- - random_state : int, optional - The seed of the pseudo random number generator to use, for reproductibility. - rank: Optional[int] - Upper bound of the rank to be estimated - mu: Optional[float] - initial stiffness parameter for the constraint M = L Q - tau: Optional[float] - penalizing parameter for the nuclear norm - lam: Optional[float] - penalizing parameter for the sparse matrix - list_periods: Optional[List[int]] - list of periods, linked to the Toeplitz matrices - list_etas: Optional[List[float]] - list of penalizing parameters for the corresponding period in list_periods - max_iterations: Optional[int] - stopping criteria, maximum number of iterations. By default, the value is set to 10_000 - tolerance: Optional[float] - stoppign critera, minimum difference between 2 consecutive iterations. By default, - the value is set to 1e-6 - norm: Optional[str] - error norm, can be "L1" or "L2". By default, the value is set to "L2" - verbose: Optional[bool] - verbosity level, if False the warnings are silenced - """ super().__init__(max_iterations=max_iterations, tolerance=tolerance, verbose=verbose) self.rng = sku.check_random_state(random_state) self.rank = rank @@ -265,7 +264,6 @@ def minimise_loss( ValueError If the periods provided in the argument in `list_periods` are not smaller than the number of rows in the matrix. - """ rho = 1.1 diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index 606729c4..f56de4ca 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -15,20 +15,21 @@ ROOT_DIR = os.path.join(CURRENT_DIR, "..") -def read_csv_local(data_file_name: str) -> pd.DataFrame: +def read_csv_local(data_file_name: str, **kwargs) -> pd.DataFrame: """Load csv files Parameters ---------- data_file_name : str Filename. Has to be "beijing" or "conductors" + kwargs : dict Returns ------- df : pd.DataFrame dataframe """ - df = pd.read_csv(os.path.join(ROOT_DIR, "data", f"{data_file_name}.csv")) + df = pd.read_csv(os.path.join(ROOT_DIR, "data", f"{data_file_name}.csv"), **kwargs) return df @@ -114,9 +115,26 @@ def get_data( # df = df.set_index(["station", "date"]) df = df.groupby(["station", "date"]).mean() return df - if name_data == "Superconductor": + elif name_data == "Superconductor": df = read_csv_local("conductors") return df + elif name_data == "Titanic": + df = read_csv_local("titanic", sep=";") + df = df.dropna(how="all") + df = df.drop( + columns=[ + "pclass", + "name", + "home.dest", + "cabin", + "ticket", + "boat", + "body", + ] + ) + df["age"] = pd.to_numeric(df["age"], errors="coerce") + df["fare"] = pd.to_numeric(df["fare"].str.replace(",", ""), errors="coerce") + return df elif name_data == "Artificial": city = "Wonderland" n_samples = 1000 diff --git a/tests/imputations/rpca/test_rpca_noisy.py b/tests/imputations/rpca/test_rpca_noisy.py index 0d5de5b8..2f08a2fe 100644 --- a/tests/imputations/rpca/test_rpca_noisy.py +++ b/tests/imputations/rpca/test_rpca_noisy.py @@ -160,7 +160,7 @@ def test_rpca_noisy_decompose_rpca(synthetic_temporal_data): tau = 1 lam = 0.1 rank = 10 - # rpca = RPCANoisy(period=period, tau=tau, lam=lam, norm="L2") + # rpca = RpcaNoisy(period=period, tau=tau, lam=lam, norm="L2") D = utils.prepare_data(signal, period) Omega = ~np.isnan(D) D = utils.linear_interpolation(D) From 898dd4fe7c7f3e47de26eb7add6405c49334b7d7 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 5 Apr 2024 16:19:34 +0200 Subject: [PATCH 66/99] history updated --- HISTORY.rst | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/HISTORY.rst b/HISTORY.rst index ed3714c2..46428cff 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,6 +2,15 @@ History ======= +0.1.4 (2024-04-**) +------------------ + +* ImputerMean, ImputerMedian and ImputerMode have been merged into ImputerSimple +* File preprocessing.py added with classes new MixteHGBM, BinTransformer, OneHotEncoderProjector and WrapperTransformer providing tools to manage mixed types data +* Tutorial plot_tuto_categorical showcasing mixed type imputation +* Titanic dataset added +* accuracy metric implemented + 0.1.3 (2024-03-07) ------------------ From c2b6cfc6a5c7eb1d45ce9254bdb12960a9edc586 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Fri, 5 Apr 2024 16:44:38 +0200 Subject: [PATCH 67/99] mypy fixed --- .../tutorials/plot_tuto_categorical.ipynb | 576 ------------------ qolmat/imputations/preprocessing.py | 4 +- 2 files changed, 2 insertions(+), 578 deletions(-) delete mode 100644 examples/tutorials/plot_tuto_categorical.ipynb diff --git a/examples/tutorials/plot_tuto_categorical.ipynb b/examples/tutorials/plot_tuto_categorical.ipynb deleted file mode 100644 index fd90e1a6..00000000 --- a/examples/tutorials/plot_tuto_categorical.ipynb +++ /dev/null @@ -1,576 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "ff229ce1", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "9285cdfc", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from qolmat.imputations import preprocessing, imputers\n", - "from qolmat.imputations.imputers import ImputerRegressor\n", - "from qolmat.benchmark import missing_patterns\n", - "from qolmat.benchmark import comparator\n", - "from qolmat.utils import data\n", - "\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.compose import ColumnTransformer\n", - "\n", - "from sklearn.compose import make_column_selector as selector" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "dd68b9f2", - "metadata": {}, - "outputs": [], - "source": [ - "df = data.get_data(\"Titanic\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c17b8877", - "metadata": {}, - "outputs": [], - "source": [ - "cols_num = df.select_dtypes(include=\"number\").columns\n", - "cols_cat = df.select_dtypes(exclude=\"number\").columns" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "f677bbea", - "metadata": {}, - "outputs": [], - "source": [ - "imputer_rpca = imputers.ImputerRpcaNoisy()\n", - "ohe = preprocessing.OneHotEncoderProjector(handle_unknown=\"ignore\", handle_missing=\"return_nan\", use_cat_names=True, cols=cols_cat)\n", - "bt = preprocessing.BinTransformer(cols=cols_num)\n", - "wrapper = Pipeline(steps=[(\"OneHotEncoder\", ohe), (\"BinTransformer\", bt)])\n", - "\n", - "imputer_wrap_rpca = preprocessing.WrapperTransformer(imputer_rpca, wrapper)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "bc213420", - "metadata": {}, - "outputs": [], - "source": [ - "pipestimator = preprocessing.make_robust_MixteHGB(allow_new=False)\n", - "imputer_hgb = ImputerRegressor(estimator=pipestimator, handler_nan=\"none\")\n", - "imputer_wrap_hgb = preprocessing.WrapperTransformer(imputer_hgb, bt)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e1e3c915", - "metadata": {}, - "outputs": [], - "source": [ - "imputer_simple = imputers.ImputerSimple()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "9e67b2cf", - "metadata": {}, - "outputs": [], - "source": [ - "dict_imputers = {\"Simple\": imputer_simple, \"HGB\": imputer_wrap_hgb, \"RPCA\": imputer_wrap_rpca}\n", - "cols_to_impute = df.columns\n", - "ratio_masked = .1\n", - "generator_holes = missing_patterns.UniformHoleGenerator(n_splits=2, subset=cols_to_impute, ratio_masked=ratio_masked, sample_proportional=False)\n", - "metrics = [\"rmse\", \"accuracy\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "ee189152", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Testing model: Simple...done.\n", - "Testing model: HGB...done.\n", - "Testing model: RPCA...done.\n" - ] - } - ], - "source": [ - "comparison = comparator.Comparator(\n", - " dict_imputers,\n", - " cols_to_impute,\n", - " generator_holes = generator_holes,\n", - " metrics=metrics,\n", - " max_evals=2,\n", - ")\n", - "results = comparison.compare(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "2d20c0d5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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 SimpleHGBRPCA
age13.72217512.38013614.044074
sibsp1.2073120.5749310.895884
parch0.9337670.8073450.839930
fare434352.730672504720.500563507609.769959
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results.loc[\"rmse\"].style.highlight_min(color=\"lightgreen\", axis=1))" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "8e1aae70", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
 SimpleHGBRPCA
sex0.6679390.6641220.664122
age0.0229010.0381680.030534
sibsp0.6717560.7633590.671756
parch0.7748090.7519080.751908
fare0.0076340.0076340.003817
embarked0.6984730.8091600.687023
\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(results.loc[\"accuracy\"].style.highlight_max(color=\"lightgreen\", axis=1))" - ] - }, - { - "cell_type": "markdown", - "id": "e628c2cd", - "metadata": {}, - "source": [ - "# Imputation analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "00bb4e56", - "metadata": {}, - "outputs": [], - "source": [ - "mask = generator_holes.generate_mask(df)\n", - "df_corr = df.where(~mask, np.nan)\n", - "df_imp = imputer_wrap_hgb.fit_transform(df_corr)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "093934f4", - "metadata": {}, - "outputs": [], - "source": [ - "ages = df[mask][\"age\"]\n", - "ages_imp = df_imp[mask][\"age\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "6b15bb22", - "metadata": {}, - "outputs": [], - "source": [ - "mesh = np.arange(ages.max() + 1)\n", - "counts = ages.value_counts().reindex(mesh, fill_value=0)\n", - "counts_imp = ages_imp.value_counts().reindex(mesh, fill_value=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "6641978f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " count count\n", - "age \n", - "0.0 0 0\n", - "1.0 1 0\n", - "2.0 2 0\n", - "3.0 1 0\n", - "4.0 0 1\n", - "... ... ...\n", - "58.0 0 0\n", - "59.0 0 0\n", - "60.0 2 0\n", - "61.0 0 0\n", - "62.0 1 0\n", - "\n", - "[63 rows x 2 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pd.concat([counts, counts_imp], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "363598e0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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En1wKLGw2G7NmzeLXv/41M2fO5Ac/+AH33HMPq1ev7vM1K1asoK6uznErLi4e8qCF8AX7S+opqWslLNjEwnGJfZ6X46YETovVxosbCgA4VFqP0t8XrFYYy1fqV5zJ1Qqchz5U71vroLnKM2MSQriFS4FFWloakyd3Xw+dNGkSRUVFfb7GbDYTHR3d7SZEIOjadCw02NTneTn2PIvcIRbK+nBPCadq1eWU+tYOKhvbez+xoRRqC/GZxmO96boU4swMxKGPOo/r5JcTIXyZS4HFggULOHz4cLfHjhw5wqhRPlR8RwgvGWgZRKPtDDlS1khd8+B2QSiKwnPr8rs9ll/R2PvJ2jbTlKkQGjOo9/O45MlgMEJzJTSW9X9u3Sk4vavLn096dGhCiKFxKbD48Y9/zJYtW/j1r3/NsWPHeOONN3j++edZvny5p8YnhE86WdPMgdP1GA2weFL/gUVipJnsxAgAdgyyIdk3Ryo4VNpARIiJWSNjAThe0dT7yb7WeKw3wWGQME49HiiB8/DH3f9cd8ozYxJCuIVLgcWcOXNYs2YNb775JlOnTuWXv/wlTz/9NEuXLvXU+ITwSVrTsZxR8cRHhAx4fmehrMEth6z+5jgAN88dycyR6rX6nrHwscZjfXF2Z4gWWASrwZkshQjh21yuvHnFFVewd+9eWltbOXjwIPfcc48nxiWET/vioFoUa6BlEI3WNyT3hOszFjuLathaUE2Q0cDd52YzOkn9gj3eW2DR3gSn96jHvjxjAV0qcPaTwNlaBwXfqsczblLvZSlECJ/mYxvchfB9dS29Nx3rz2x7p9NdxbW0d9gICXI+ptdyK64+awRpMWFMiGpjrOEk1vIqKD8jGfr0blCsED0CYnx8a7czzciOfQE2i7psMvp8yH0R6mUpRAhfJoGFEC769mgFHTaFccmRZNlzJwYyJimCuPBgapot7C+pcyxnDCS/opHP7EW47l00GsoOMPvdhXxhtkIL8Nc+Xuhrjcd6oy2FVB0DS4uad3GmQ/ZlkImXQUyGeiwzFkL4NOluKoSLjpSpSxDaNlJnGAyGLn1DnF8OeeHbfBQFFk9MZnxKFJzKxaBYaSeIKiWKjtB4CE/ofosZCbPvcOnvpIvIFIhIAsUG5Qd6Pm+1wNG16vGEyzsDi4ZS6TEihA+TGQshXFRc3QzAqIRwl16XkxXPFwfLyT1Rw/fPHfj88oZW3stTp/3vO3+M+mCDOnvxdegS7q29nVXXzeLSaWkujcNnGAzqltj8r9XlkBGzuz9/YgO01anBR0YOYACTGaxtUF8CcbLNXQhfJDMWQrioyB5YjIx3MbDosjOk36qZdi9vPEG71caskbGO11Jfot5HpgKQX9nHllN/4dgZ0ksCp7YbZPx3wGhSe4xEp6uPyXKIED5LAgshXKQFFplxrgUWU0fEEGIyUtnYTmFVc7/nNrRaeH1LIQD3LRrT2RXVPmMREjcCgOPlfWw59Rda59UzEzgVpUt+xeWdj2vLIZLAKYTPksBCCBe0tFupaGgDXJ+xCA02MS1Da0jWf57Fm9uKaGjtYHRSBEu6FuBqOA1AVJK64+O4v89YOJqR7QebrfPx0j1QfxKCw9XdIBptp4vUshDCZ0lgIYQLTtaoMw3RoUHEhAe7/PqcLC2Bs+9CWe0dnc3G7j1vNMau7djtgUVSehYA+eWNTi2r+KzEcWreRHuDvb+JnTZbMebC7rtFYtSZGlkKEcJ3SWAhhAsc+RUuJm5qckZpDcn6nrF4f9cpyurbSI4yc83MEZ1PWDugUS3MlZqRhdEADW0dVDS2DWosPsEUDMkT1eOuyyGH7U3HJlzW/XzHllNZChHCV8muECFcMNjETY225fRoeSPfW7WJ3ipNaBU171qYjTmoS9fUpnJAAWMQ5ugUMuPDKaxq5nh5E8lRoYMaj09ImaYW9irbB5OvgtoiNcgwGNXEza6kloUQPk8CCyFc4EjcHGRgER8RwvSMGPacrOu3nkVseDC3zBvZ/cF6dRmEyFQwGhmdGEFhVTP5lY3MH5MwqPH4hNQzKnAe/kS9zzwbIs74e0VryZsSWAjhqySwEMIFxUOcsQB46Y459qWQvnMjpqTHEB16Rg6HPb+CKHWr6ZikSL4+XMHxcj9P4Dxzy+kh+zLIxMt6nqvlWLTWQWs9hEb3PEcIoSsJLIRwwWC3mnaVGGnmO1NTXX+hFlhEqwWxRidFApBf6edbTlOmqPd1RVBTCIUb1T+fmV8BYI6C0Bg1sKg/JYGFED5IkjeFcJKiKBRXtwBDm7EYNMeMhRpYjOmvy6k/CYtTy5ADbPg/sHVA0kRIGNP7+Y4tp5LAKYQvksBCCCdVNrbTYrFiNEB6bC8NszzNXhxLWwrRZixO1rTQarF6fzzupC2H7Hxdve9ttkLjSOCUWhZC+CIJLIRwkrYMkhYT5lLbc7c5Y8YiMTKE6NAgFAVOVPl7noU9gdNmby7WtdrmmaLteRZSfVMInySBhRBOckfi5pDUdw8sDAZDZ55FhZ8HFloFTlB3vaTP6vtc2XIqhE+TwEIIJw21hsWQnTFjAerOEAiEniFdAosJ31EbjvXFkWMhgYUQvkgCCyGc1FnDQof8CksLtNaqx1GdO0pG2xM4/b7LaWwWmO07PCb0swwCUtZbCB8n202FcNJQi2MNiTZbERyubre0c8xY+PvOEKMRrvg/KD8AYxf3f27XDqc2W/+zG0IIr5PAQggn6Zpj0XVHiKGzELi25TS/oglFUTrbq/ujadc5d15UGmAAazs0VUBUyoAvEUJ4j4T6QjihrcNKaX0r4Bs1LDQjE8IxGQ00tnVQ3uDHzchcYQru/ByktLcQPkcCCyGccKqmBUWBiBAT8REh3h+AY8aie2BhDjKRGafmfPj9cogrZGeIED5LAgshnNA1v0KX5Yb6EvU+qmcp8M48Cz9P4HSFI4FTalkI4WsksBDCCcV6Jm5CnzMW0GVniMxYCCF8gAQWQjhB/xoW9sAiumdgMTxnLLRaFlLWWwhfI4GFEE7QP7DQlkJ6m7HQqm8OoxkLKesthM+SwEIIJ+ja1VRRejQg60pbCjlVGwDNyJwlSyFC+CwJLIQYgNouXccci7Z6sKjv39uMRUJECDFhwSgKFPh7BU5naUshjWXQMUy22QrhJySwEGIAtc0WGto6AMiI06Gct9Z8LDQWgnu+v9qMTJ21GDZbTsPjIShUPdZ2zAghfIIEFkIMQMuvSI0OJTTY5P0B9FEcq6sxgdLl1FkGgyyHCOGjXAosfvGLX2AwGLrdJk6c6KmxCeETdG0+Bv3uCNEMuxkLkAROIXyUy71CpkyZwhdffNF5gSBpNyICm67Nx6DfHSGaYTdjAbLlVAgf5XJUEBQURGpqz8x0IQKVrs3HoN8dIZoxXYpk+X0zMmfJUogQPsnlHIujR4+Snp7O6NGjWbp0KUVFRf2e39bWRn19fbebEJ52rLyBlzcWYLHahnyt4hq9A4uBcyxGxkdgMhpoardSVj9MdklIWW8hfJJLgcW8efN45ZVX+PTTT1m1ahUFBQWce+65NDQ09PmalStXEhMT47hlZmYOedBCDOSJDw7wxAcH+Nu3BUO+lu7FseoHDixCgoyO8Q2bPAuZsRDCJ7kUWFx66aVcf/31TJ8+nUsuuYSPP/6Y2tpa3nnnnT5fs2LFCurq6hy34mJZDxWed/C0Guy+vLGAto7BF42yWG2U1OrYLh367RPS1Zjh1jNEy7GQ5E0hfMqQtpvGxsYyfvx4jh071uc5ZrOZ6OjobjchPKmuxUJlo7ocUN7Qxvs7B//Fc7q2FatNwRxkJCnK7K4hOs9mg8aBcyygs7T3sOkZou0KaauH1jp9xyKEcBhSYNHY2Mjx48dJS+v/NykhvOnM39ifW5+PzaYM6lq6t0tvrgRbB2CAyJR+Tx0z3LachoRDWLx6LMshQvgMlwKLn/70p6xbt44TJ06wadMmrr32WkwmEzfffLOnxieEy7Tf2GdkxBAVGkR+RRNrD5YN6lq651doiZuRyWDqfxPX6GG55VQSOIXwNS4FFidPnuTmm29mwoQJ3HDDDSQkJLBlyxaSkpI8NT4hXKbNWEzPiOW2s0cBsHrdcRTF9VkL/QML55ZBoLOWxanaFlrah0szMqllIYSvcamOxVtvveWpcQjhNtpSwOikCC6fnsbfNhSws6iW7SdqmJsd79K1tK2muhXH0vpgRKUPeGp8RAix4cHUNlvIr2xkSnqMhwfnA2RniBA+R8pmioCjLQWMSYokOSqU783K4M1tRaxed9z1wMKPZixA/TvnFdaw+XgVIabeJyTjIkJIjNQhEdUTvFXW29Kq9icJCpDPTQgPksBCBJQOq40TVWpgofXP+MF5o3lrexFfHSrncGkDE1KjnL6e/kshA9ew6Gp0YgR5hTX86qOD/Oqjg72eYzTABz9aGBgzGt6YsbB2wPPng7Udlm8FU7Dn3kuIACDdTUVAOVnTgsWqEBpsJD1GbRqWnRjBd6aov/E/t/6409eqa7FQ22wBdGqXDp2BRT8NyLr67qwMMuLCiI8I6fVmDjJiU+DLg+UeHLQXOXIsPBhYlO2FioNQfRxqCj33PkIECJmxEAFFy6/ITozEaOzcHnrfojF8sq+U/+wq4acXTyA9duBAQVsGSYwMIcKs0/8qLs5YzB+TwIb/urDP51/ddILH/7Of3MIad4xOf9qukPoSsFnB6IG29kVbO49rTkDiWPe/hxABRGYsREDR8iu0ZRDNjMxYzh4dT4dN4cUNzpX5Lta7qym4nGMxkNmj4gDYWViDdZC1PXxKZCoYTGCzQKOHZmGKt3Qe1wy9RLwQgU4CCxFQtBkLbetlV/ctGgPAm9uKqLMvcfRH9/yKjnZoqlCPndgV4oyJqVFEhJhoaOvgSFnfPX78himoczbHEwmcitJzxkII0S8JLERA6dwREtHjuUXjk5iYGkVzu5XXtpwY8Fq6dzVttBf1MgZDuGu7WfoSZDIyc6Q6axE4yyFaAqcHalnUFUNDSeefJbAQYkASWIiA0t+MhcFg4N5FowF4ZdMJWi39F5Eqqm4BdFwK6dp8zI3lxHOy7IHFiWq3XVNXjsDCAzMWRfZlEIP9R2WtJG8KMRAJLETAqG1up6qpHVB3gvTmiunpjIgNo7KxnX/m9b+TwJFjEadXYGH/TdnJHSHOyhmlzn7kngiUGQutrLcHdoZogcUYe0JsTaG6PCKE6JMEFiJgaD1C0mJC+9zFEWwycvfCbABe+Da/z1kLq03hpLYUkqD3jIV7Ejc1Z42MxWhQS3+X1rW69dq68GRZ72J7fsX0G9X7tnpoCZCATAgPkcBCBIz8LqW8+3PT3EziI0IorGrmgTd20mG19TintL4Vi1Uh2GQgNTrUI+MdkItbTZ0VaQ5iUlo0ALmFAbAcoi2FuDt5s7UOyvarx9mLOv87yM4QIfolgYUIGMe7lPLuT3hIEH9dOgtzkJEvDpbxX+/t7dFWvahKna3IiAvHZNShXTpAvRZYuHfGAmBOVgAth0R7aCmkeDugQFw2RKVAXJb6uCRwCtEvCSxEwHDMWPSRX9HV2aMTePaWWZiMBt7bcZInPz7Yrfup7s3HoMuMhXu2mnal1bMIqBmLpgq1p4e7aPUrRp6t3ktgIYRTJLAQAcOxIyS5/xkLzZLJKfzuuukAvLihgGe/PuZ4rrP5mE6lvMFjORbQuTPk4OkGmto63H59rwqLg2B7AOjO5RAtcTNznnofO0q9l7LeQvRLAgsRECxWm6Og1egBlkK6+u6sDB67YjIAv//8CK9tUb80ivTeEQIey7EASIsJY0RsGFabwq7iWrdf36sMBvc3I7Na4FSeejxyvnovMxZCOEUCCxEQiqubsVgVwoJNpLmYbHnXwmwevFDt//DYv/fxn90l+lfdbGtUdyCA27ebajrrWQRAnoW7EzhL94ClGUJjIXG8+pgEFkI4RQILERC0ipvZiRHdmo8568cXjee2s0ehKPDI27s4eFr9Utctx0KruhkSCWbn27y7IieQ8izcncCplfHOnAdG+49JLbCoO6nOaAgheiXdTUVAcDW/4kwGg4EnrppCbYuFD3aX0GHfJaJbDYt6e3EsD+RXaGbbC2XtLKrFalP02/3iDlotixPfQuzI3s8JDoPx34Eg88DXcyRuzut8LDIFgkKho1UNLuKzhzZmIQKUBBYiIDi6mjqxI6QvRqOBP1w/g/oWC+uOVBAXHkx0aLC7huiaruW8PWRCahRR5iAa2jo4VFrPlPQYj72Xx2nBRMF69daXhY/Aksf7v1bXxmNafgWoMxexo6DysLocIoGFEL2SwEIEhKHOWGhCgoysvnU2v/3sEDMyYt0wskHyYOKmxmQ0MHNUHOuPVJBXWOPfgcXEy2HGzX23Tre0QNEm2P43WPgwhPbzd605AY2lavO39Jndn4uzBxbSM0SIPklgIQJCfuXQZyw0YSEmHr9yypCvMyQNniuO1VWOPbDYfqKG2+dnefS9PCo0Gq5d3ffzNhusmg8VhyDvFVjwUN/namW8089Sl0+6kgROIQYkyZvC79U0tVNtbz42UDlvv6EFFtHuL47VlZbAmRconU77YjTCOQ+qx5v/Ch1tfZ97Zv2KriSwEGJAElgIv5dfqS6DpMeEEh4SIJNwHiyO1dVZI2MxGQ2U1LVSUtvi0ffS3bTr1SqmjaWw552+z9NmLLSKm11JYCHEgCSwEH7veLm9R8gQ8yt8imNXiOdyLEDtmzIlXWtIFgD1LPoTFALzf6geb/yTujxyppYaKD+oHmdKYCHEYEhgIfze8Urne4T4BUXxyq4QzezhshwCMPsOMMdA1VE48knP57XGY/FjIDKp5/NaWe+WGmip9eBAhfBfElgIvxdwMxYtNWC15wB4eCkEIMdez2J7IFTgHIg5CubcrR5veFoN4ro6s/FYj9dHQoQ94JCdIUL0SgIL4ffyHTMWARJYaImbYfHOFXMaIq2096HSehr9vSGZM+bdByYznNzWmaip6Vpxsy+uNiM7mQdPT4f9a1wfqxB+SAIL4dcsVhtFVVrzsQBZCvHSjhBNSnQomfFh2BTYWTQMZi2iUuCsm9XjjU93Pt7R3rPxWG9czbPY8Yo6u/Hpf6vvIUSAk8BC+LWi6mY6bArhISZSXWw+5rO8tCOkK205JCAakjlj/o8AAxz5tDNZs3QPdLSoM0WJ4/p+rauBhTYL0lAC+/45yAEL4T8ksBB+7Xi5ugwy2OZjPqneO8WxunIkcAb6zhBN4liYdKV6vPEZ9b5r/QpDP/+WXAksmqvVSp2ajc/0vhtFiAAigYXwa1rFzTFJAZJfAV4p530mLc9iR1ENHdZh8sW34GH1fu87UHeq98ZjvXElsCjept7HjISQKKg4CEc/H8RghfAfQwosnnrqKQwGAw8//LCbhiOEa7QZi4DJrwCvbjXVjE+OIio0iOZ2K4dKG7z2vrrKmA1Z54KtA7b8tffGY73RAovaIrBZ+z+3aLN6P3oR5NypHm/806CHLIQ/GHRgsX37dp577jmmT5/uzvEI4ZLAnLHwTnGsroxGg2M5JHc41LPQaD1Dtj0PTeVgCoG0s/p/TXS62qDMZumcXepL1yqeZ9+vvq5oU+dMhhABaFCBRWNjI0uXLuWFF14gLi7O3WMSwimKonAsoGcsvJdjAZ19QwK+AmdXY5dA8hSw2ndrpM+E4AGSgI0miM1Uj/tbDulog1M71OPMs9WAZMaN6p9l1kIEsEE1Vli+fDmXX345S5Ys4Ve/+lW/57a1tdHW1tnwp76+fjBvKUQP1U3t1LVYAD+rYXFiAxz6qGdxJk1jmXrvpe2mmtn2nSEbjlXyxAf7ez3HgIHLpqWSkxXvzaF5jsGgzlqs+YH65/7qV3QVlwXV+WpgkbWw93NO71YLnYUnQsIY9bFzHoSdr6v//SuOQNL4of4NhPA5LgcWb731Fjt27GD79u1Onb9y5UqeeOIJlwcmxEC0ZZARsWGEhZh0Ho2T2hrgrVugta7/84IjOis8eslZmbGYg4zUNlt4eeOJPs/78lAZ6352gfcG5mlTvwtf/RLqiiH7POde40wCp5ZfMfLszl0mSRNgwuVw+CPY9Axc/ZfBjloIn+VSYFFcXMxDDz3E2rVrCQ11rmbAihUreOSRRxx/rq+vJzMz07VRCtELv0zczHtFDSpiRsL06/s+L/s8dcrdi8JCTLy4bA6b8yt7fb7DqvDc+nwKq5ppabf6TzA3EFMw3PoenMxVl0ac4VRg0UcVzwUPqYHFnrfhgv+BaO/l0gjhDS4FFnl5eZSXlzNr1izHY1arlfXr1/OXv/yFtrY2TKbuP2zMZjNms+fLEovhx+8SNzvaYfNf1ePzfgqzl+k7nl4sHJfIwnGJfT7/Tm4xNc0W8isbmZIe48WReVjSBPXmrIECC0Xpu/36yHnqzpOizbB1FVz0v66OVgif5lLy5uLFi9m7dy+7du1y3HJycli6dCm7du3qEVQI4UnajMUYf5mx2PuuuuMjMgVm3KT3aAZltD2Iy69o0nkkOhuoX0jVcWiuVHuSpM3o+bxWQyP35YGXxYTwMy7NWERFRTF16tRuj0VERJCQkNDjcSE8TZuxGO0PMxY2m7qmDuq2Qy80F/OEMUkR5BXWcLyiUe+h6EubsWgqh/YmCDkjuNXyK0bM7v2/9biLIWkiVBxSg4uFD3tytEJ4lVTeFH6pvcNGUbXafMwvlkKOfqZ+iYREQc5deo9m0GTGwi4sFkJj1ePeZi0GquJpNHbW0NiySt2aKkSAGHJg8c033/D000+7YShCOK+ougmrTSEixERKtB/89q/VLci5E0L9NzdBC+KG/YwF9J9n4UjcPLvnc5qp10H0CGgsVRM5hQgQMmMh/NKx8s5lEEN/DaN8QdFWdWrcGAxn/1Dv0QyJtgMnv6IJm62POhzDRV+BRVMlVB1VjzPn9v36oJDOfw/SnEwEEAkshF86Vq72s/CLrababMWMG/1+a+HI+HCCjAZaLFZK61v1Ho6++gostN0gSRMhfIBCYrOXqTNYVUfh2Fp3j1AIXUhgIfzS+iNqrYWzMmP1HchAKo6oNQsAznlI37G4QbDJyMiEcEDyLIiz7wypPSPHomv79YGYo2DGLerxgX+7b2xC6EgCC+F3qpvayS1UG2VdNDlF59EMYJN9tmLC5QFTvlnyLOwGmrE4s35FXyZert4f+XTgbqlC+AEJLITf+epQOTYFJqVFkxEXrvdw+lZ/Gnbbk/ICaDthZ56FBBaAGlhofV8srVCyUz12tu/IyPkQFgfNVZ1BiRB+TAIL4XfWHlC7f/r8bMWWv6qttUfO7z+Jz890zlgM86WQmEwwGKGjtbNxXMlOtVNqRDLEj3buOqYgGHeJenzoI8+MVQgvksBC+JVWi9WRX3GxLwcWrXVq4SPorLIYIMbIjIXKFAwxGeqxthzStX6FK7uVJlyq3h/+uO+ut0L4CQkshF/ZeKySFouV9JhQpqRH6z2cvuW+BO0N6s6AcRfrPRq30lrUl9S10tzeofNodHZmnoUz9St6M3YxmELUVuwVh901OiF0IYGF8CtrD6hTzksmp/hu/YqONrWaIsA5D6pVFgNIXEQI8REhgOwM6dYzxGZzPXFTY46C7EXq8WFZDhH+LbB+4omAZrMpfHGwHPDx/Io9b6tr7lHpMK2f1uh+TFsOkZ0hWep9zQm1FkVLNQSFQup016818TL1/tDH7hqdELqQwEL4jZ3FtVQ2thFlDmJedoLew+nb/jXq/dx71OqKAUhbDhn2MxZdAwutfsWInMH9dx9vz7M4lQsNpe4YnRC6kMBC+A1tGeT8icmEBPnoP12bFYq3q8djF+s7Fg8akywzFgDEZav3NSe6LIM4uc30TNFpajdUgMOfDHloQujFR386C9GTX2wzLT+gJm2GREHyFL1H4zEyY2GnzVg0lEDBevXY1cTNribYl0MOy3KI8F8SWAi/kF/RyPGKJoJNBs6fkKT3cPqmTYdn5Kj1CQLUmGR7YFHZOLybkYXHq0EkQF2xep85Z/DX06pw5q+DtmE+GyT8lgQWwi9oyyBnj04gOjRY59H0QwssXN0V4Gcy48IINhlotdg4PZybkRkMnT1DAJInq1U0Bytporq8Ym2D418OfXxC6EACC+EXHNtMJ/nwMgh0rrM7W87ZTwWZjIxKsOdZlA/z36y15RAY+n93g6Fz1kJ2hwg/JYGF8HmVjW3kFdUAav0Kn1V3Up0ON5jUpZAANzpRKnAC3QMLd8xUaXkWRz8D6zAvQCb8kgQWwud9dbAcRYEp6dGMiA3Tezh905ZBUqeqBY8CXGeehSRwOrhjpipzHoTFQ0sNFG0e+vWE8DIJLITP+9y+DOLTu0GgyzJIYOdXaLQZi2G/5TTevuU0MrV7kDFYpiAY/x31WHaHCD8kgYXwaS3tVjYcqwD8ILAo6tKAahhwzFgM9y2noy9QS7df+SfXGo/1x1GF8yNpSib8TuDuhxMB4dujFbRabIyIDWNymg83HWtrgLJ96vEwmbEYY69lcbqulaa2DiLMw/THidEEF//SvdccfQGYzFBbqNZGSQncmigi8MiMhfBpa7ssg/hs0zGAk7mg2CBmJMSM0Hs0XhETHkyCvRlZwXDPs3A3cySMPl89lt0hws9IYCF8ltWm8NUhP2g6BsNuGUQzJkmdtRj2eRaeoC2HSLdT4WcksBA+a0dRDVVN7USHBjE3O17v4fSv2B5YBHj9ijONdnQ5lRkLtxt/KWCAkp1QX6L3aIRwmgQWwmdpyyAXTEwm2OTD/1StHepSCMDI+fqOxctkxsKDolI666HI7hDhR3z4p7UYzhRF6ZZf4dPK90N7I5ijIXmS3qPxKm3GYtjvDPEUrViW5FkIPyKBhfBJxysaKahUm44tGu/DTccAiuz1KzLmqDsEhhFtxqJguDcj8xStvHfBemip1XUoQjhLAgvhk7SiWPPHJBLly03HoLM6YoA3HutNRpdmZCV1LXoPJ/AkjoekSWCzwI5X9R6NEE6RwEL4JL9ZBoHOipvDMLAIMhnJSpAETo8xGOCcB9TjzX+FjjZ9xyOEEySwED6nvKGVXcW1AFzk691Ma4uh/pTaeGzEbL1Ho4vOPAtJ4PSIaddDVBo0lsKed/QejRADksBC+Jwv7U3HpmfEkBoTqvdw+qfNVqRNh5AIfceiE9kZ4mFBZjj7h+rxxj+BzabveIQYgEuBxapVq5g+fTrR0dFER0czf/58PvnkE0+NTQxTjmUQX5+tgM78imFSxrs3o5OkZ4jHzb4DzDFQdRSOyM9c4dtcCiwyMjJ46qmnyMvLIzc3lwsvvJCrr76a/fv3e2p8Yphpautgw7FKAC6a4g+BhZZfMbwKY3U1Jkm6nHpcaDTMuUs93vgnfccixABcCiyuvPJKLrvsMsaNG8f48eN58skniYyMZMuWLZ4anxhmvj1aSXuHjcz4MCakROk9nP611qs1LEBmLICy+jYa2zp0Hk0Am3cfmELU5bfCzXqPRog+DTrHwmq18tZbb9HU1MT8+X1XG2xra6O+vr7bTYi+dC6DpPp20zGAk9vVxmOxoyA6Te/R6CYmLJjESDMgCZweFZUKM25Wj2XWQvgwlwOLvXv3EhkZidls5r777mPNmjVMnjy5z/NXrlxJTEyM45aZmTmkAYvA1WG18dUh2Wbqj6QCp5ec8yBgUPMsyg/qPRoheuVyYDFhwgR27drF1q1buf/++1m2bBkHDhzo8/wVK1ZQV1fnuBUXFw9pwCJw5RXWUNNsITY8mDlZcXoPZ2COxM3hm1+hkZ0hXpI4FiZdoR5v+rO+YxGiDy4HFiEhIYwdO5bZs2ezcuVKZsyYwZ/+1Pe0nNlsduwi0W5C9EZbBrlwQjJBvtx0DOyNx/LU42HWeKw3Y2TGwnsWPKze73kH6k7pOhQhejPkn942m422NqkGJ4ZGURTWHvSjZZCyvWBpgtAYSJqo92h0JzMWXpSRA6MWqmW+t/xV79EI0YNLgcWKFStYv349J06cYO/evaxYsYJvvvmGpUuXemp8Ypg4Wt5IYVUzIUFGzvP1pmPQpfHYXDD6+OyKF2g5FgWVTVilGZnnLXhIvc97BVpqdB2KEGdy6SdieXk5t99+OxMmTGDx4sVs376dzz77jIsuushT4xPDhLYMsmBMAhHmIJ1H4wRH4zHJrwDIiAsnxGSkrcNGSa00I/O4cRdB8mRob4Tcl/QejRDduPQT/MUXX/TUOMQw97mj6ViqziNxgqJ02REi+RUAJqOBrMRwjpQ1cryikcz4cL2HFNgMBnXWYs29sGU1nL0cgn28/L0YNmQOV+iurL6V3famY0smJes7GGfUFkHDaTAGQfosvUfjMzrzLDyTwNlqsdJqsXrk2p5W09Q+6Ne2WqzUt1p6PjH1exCdAU3lsPvNIYxOCPeSwELo7gt70uZZmbEkR/vBb13F29T7tBkQIr+Za7Q8i2PlDW6/dmNbB+f99mtueG4ziuJfORwf7z3NzF+u5Wfv7sbmYv5JeX0rl/3pWxY+9RWn685YYjIFw/zl6vHmZ9WZNCF8gAQWQneOapv+sBsEoNxetyVthr7j8DEzM9XaI+uPVLr9y39PcS3lDW3sOVnHvlP+Vb33k32lALybd5JffnTA6c+mrtnC7S9tI7+yifrWDl7aUNDzpFm3gcmsNierOOTOYQsxaBJYCF01tnWw6VgVABf7S2BReUS9T5yg7zh8zIKxiYQGGzlV28KB0+798u96vbUHSt16bU/LO1HtOH554wn+8tWxAV/T3N7BXa9u51BpAxEhJgDe2FpEXfMZSyLmKBh9vnp86CN3DVmIIZHAQuhq/ZEK2q02shLCGZscqfdwnFNl/2JIHKvvOHxMWIiJc8epW4W1WSh36RpYfO7ma3tSSW0LJXWtmIwGfnaJGoj+Ye0RXttS2Odr2jts3P/6DvIKa4gODeKf95/DxNQomtqtvL61l9dNvEy9P/yxJ/4KQrhMAguhq67LID7fdAzUiptVx9XjxPH6jsUHactZ7g4sDp7uzNs4VNpAcXWzW6/vKbmFao2JyWnRLL9gLA8uHgfAY//ex7939ayaabUp/OTd3aw7UkFosJGX75zDpLRo7l00GlBnPHoksI6/FDDAqTyoP+3Rv48QzpDAQujGYrXx1aFywE+2mQLUFqoVD4PC1Ix80c3iickYDbC/pJ5Tbqpn0d5hcySEZiWoybLuDlw8RVsGybH3vvnxknEsmz8KRYGfvLObrw+XO85VFIXH/7OPD3aXEGwysPrW2cweFQ/AFdPTGREbRmVjG//acUZAEpWiVuMEtTmZEDqTwELoZvuJaupaLMRHhDB7lB80HQOoPKreJ4yVipu9SIg0O/5bfuGmL/9j5Y1YrArRoUHcevYowH8Ci+0n1BmLHHuAYDAYePzKKVx9VjodNoX7X88j1x58/HHtEV7fUoTBAH+84SzOn9C59TrYZOTuhdkAPL/+eM/qphPsyyGHZDlE6E9+MgrdOJqOTUzGZPSDZRBQs+9B8iv64e7lkIP2/IpJadFcbJ/Z2naimtrmwdeG8IbGtg4Olapjz+nSrddoNPD762dw/oQkWi027nplO7/88AB/tid1/vLqqVw5I73H9W6am0lseDAnqpr5fP8ZCawTL1fvC9ZBm/u3+wrhCj+onSwCkaIo/rfNFLrsCJH8ir5cNDmVX398iC35VdS1WIgJCx7S9Q50CSxGJoQzISWKw2UNfHWonO/Ocm45aldxLb/77BBtFtugxzE7K45HvzPR6VygnUU12BTIiAsj5Yz6LMEmI6uWzua2F7eSW1jDi/atpD+9eLxjVuZM4SFB3H72KJ756hir1x3nO1NTO8eSOB7ix0D1cTj2JUy5ZtB/TyGGSgILoYvCqmZO1rQQYjJy7rhEvYfjvEr7jpCEcfqOw4dlJ0YwNjmSY+WNfHO4nKvPGjGk62kzFpPTogE1ED1c1sDaA2VOBRZq7sJ+R3XXwcotrOHamSOYmBrt3Pn2ZZA5WfG9Ph8WYuLFO+Zw0/NbOHi6nrsXZrP8gv5nwpadk8Vz6/PZfbKOLfnVzB+ToD5hMKi7Qzb9Wd0dIoGF0JEEFkIXWrb8tIwYwkP86J+hY8ZClkL6c9HkFI6VN7L2QNmQAgtFURwzFpPTOwOLv3x9jHVHKmi1WAkNNvV7ja0F1ewursUcZOT3188g2OT6stuLGwrYfqKGtfvLnA8sCtXcif7yh2LCglnzw3M4WtbI1BHRA86GJESauSEnk9e2FLJ63fHOwALUPItNf4Yjn4HVolbmFEIHfvQTXQSS3DOy5f1CczU0V6rHMmPRr4smp7Dqm+OsO1xBe4eNkKDBpXOV1rdS22zBZDQ46pxMGxFDSrSZsvo2Nh+v4oKJ/feXWb1O3R58fU5Gr7kLzqhttqiBxcEyfrR44P/2HVYbO4tqgYH/jYcGm5iWEeP0WO45dzT/2FrIuiMVHDxdzyT7TA6Z8yA8AZqr1O672ec5fU0h3EmSN4UutBkLLVveL2iFsaLSwewnxbx0clZGLElRZhraOtiSXzXo62jLIGOSIhwzE0ajgSWT1LycgYplHTxdzzeHKzAa1C/kwVo8KQWDAfacrKO0rnXA8w+VNtDcbiUqNIjxyVGDft/ejEwI57JpaQA8Zw+aADCaYPx37AOQ3SFCPxJYCK+rbW7nWHkj0P80sc/RtpomymzFQNQvf3UmYSi7Qw6UdM+v0GgJv18cLOu3sdfz6/MBuHRaGqMSIgY9jqQoMzMzYwFYe3Dgv482Izd7VBxGD+x4um/RGAA+2HOakzVdioVp204PfyRNyYRuJLAQXpdnn60YnRRBfESIzqNxgSO/QgILZ2izCl8cLBt0UzKt4uakMwKL+WMSiDQHUdHQxu6Ttb2+9mRNM//ZXQLAfeeNGdT7d6UVcXMmUNrumJHzTOA8dUQMC8cmYrUp/O3bLs3JxlwAQaFQWwRl+z3y3kIMRAIL4XWdRYP8aLYCuvQIka2mzlgwNpGwYBOn61oH3ZH04BmJmxpzkIlF4/vvS/LihgKsNoUFYxNcymHoizZLsvl4JQ2tlj7PUxSFPPu/8dkeXOrTyny/vb2YmiZ7TY+QCBh9gXosvUOETiSwEF6XV6glbvpRfgV0r7opBhQabOK88epW4sF0JG1u76CgqgnoOWMB/Rfiqmlq561txQDc64bZCoCxyZGMTozAYlVYd6Siz/NO1bZQWt9KkNHAWfblE09YODaRKenRtFis/H1zl+ZkWlMy6XYqdCKBhfCqtg4ru0/WAX42Y2HtgGp1vV5mLJynLR8MpiPpodIGFEXNb0iMNPd4/oIJasXWo+WNnKhs6vbc61sKabFYmZwW7dY6Kc5UFdWW+qaMiCEspP+tsENhMBi4155r8ermE7S025uTjf8OYIDTu6CuZ6MzITxNAgvhVftO1dPeYSMhIoTsxMEn03ldt+ZjQyv4NJxcaG9KNpiOpGcWxjpTTHgw87LVWa+uX/StFiuvbDoBqMsF7uyaqwUWXx8qx2LtvYpnrheX+i6bmkpmfBjVTe28m6fO0BCZDJlz1WNZDhE6kMBCeJW2DDJrVJx/tEnXdC2MJc3HnBYfEeJY8nJ1d4i2I6S3ZRBNbzMI7+adpKqpnYy4MC63b8t0l5kj40iICKG+tYNtBdW9nrNdq9HihcAiyGR0bKN94dt8OrRgx7E7RAIL4X3yE1J4ld8mbjryK2RHiKsuHmRTss7mY33XgdACi9zCaqqb2umw2njBvsX0nnNHE2Ry7484k9HA4n620da3Wjhcpu5kme2l4m/Xz84kPiKE4uoWPt5nz2VxNCX7FlrrvDIOITQSWAivURSFHdo2PL9L3JTmY4Olffm70pHUZlM4VKp+QU9J73vGIiMunElp0dgU+PJgGZ/uL6Woupm48GCuz3GuQZmrum47PXMb7c6iWhQFRsaHkxwV2tvL3S4sxMSy+VmAWjBLURR1S3TCOHX57tgXXhmHEBoJLITXFFQ2UdXUTkiQkakjnOu34DMcW01lxsJVoxIiGJ8SidWm8PXhcqdeU1jdTHO7FXOQkawBClt1XQ7RyncvOyfLYz1oFo5NJDTYyKnaFkcfE02eTqXqb58/irBgE/tL6tlwzF523rE7RJZDhHdJYCG8RivjPSMjBnOQ57LlPUKKYw2JM7sputKWQSakRg24nOFYajlYxr5T9YQGG7nd/hu8J4SFmDh3XO81NDqX+rw7IxcXEcKNczIBeG6dfffSBPtyyNG1alMyIbxEAgvhNZ1ljv1sGaS5Wm3sBFLDYpC05YN1hyto67AOeH5fpbx7MyU9mvSYUEcF65vmjPR4RdfeAiWL1cYue2t2PZrr3b0wG5PRwIZjlew9WQcZORCRBG11cGKD18cjhi8JLITXaDMWc/ypoyl0Jm5GZ6iVDYXLpo+IITnKTFO7lY3aVH0/OhM3Bw4sDAYDS+xf9CajgbsXZg9tsE5YbN9Gu7+knlO1LYA65haLlejQIMYmeb9JXWZ8OFdOtzcnW3/8jKZkH3p9PGL4ksBCeEV1Uzv5FWoRI79qPAZQpTUfk9mKwTIaDY6OnK9uKhzg7L5LefflhpxMQkxGbp03ksz48MEP1EkJkWbHv+Mv7LMWjvoVWfEeaTzmjB/Yq4x+vPc0hVVNMOUa9Yndb0NLrS5jEsOPBBbCK7RqhGOTI4kN96PGYyA7QtzkzgVZGA2w7kiFY6mjN7XN7ZTYW5NPTHWu5fjUETHsfeJiHr9yilvG6owzl0NyCzs7muplcno0i8YnYVNQm5ONWQzJk6G9AXJf0m1cYniRwEJ4Ra4Xiwa5XaV9R4jUsBiSUQkRXGqftXh+/fE+z9N2WmTGhxEVGuz09c1BJq/OFGh5I1vyq6hrsXi14mZ/tOZk7+QWU9nUDuc8qD6xdTVYWnUcmRguJLAQXqHlV/jdMgjIjhA30tqXf7DnNCdrei/xrbVKdyZxU0/ZiRGMTY6kw6bw+pZCyhvaCDYZmOHBxmPOmD86gRkZMbR12Pj7phMw7To1P6ixDPa8pevYxPDgUmCxcuVK5syZQ1RUFMnJyVxzzTUcPnzYU2MTAaLVYlWz1IE5/lYYy2qBmgL1WAKLIZuWEcOCsQlYbYo6Vd8LZ0p5+wptOWT1N+oMzNQRMYQG67uVuntzskKaOgww/4fqk5v+DLaBd+UIMRQuBRbr1q1j+fLlbNmyhbVr12KxWLj44otpamoa+MVi2Np3qo52q43EyBBGJXg+sc6tak6ArQOCIyAqXe/RBIT77F96b28vpqapZyVOV3aE6E0LLBraOgD9l0E0l0xJJSshnLoWC29vL4ZZyyA0Vi30Jv1DhIe5FFh8+umn3HHHHUyZMoUZM2bwyiuvUFRURF5enqfGJwKAVjRotr81HoMuPULGSPMxN1k4NpEp6dG0WKz8fXP3HSLtHTaOlvvHUgjAWRmxJEV1tnT3lRotJqOBe85Tcy1e3FCAJSgc5nxffXLD03BGKXIh3GlIPynr6tTp7fj4vv9namtro76+vttNDC9aR1NvVyN0C9kR4nbdp+pP0NLeOTV/vKIRi1UhKjSIjLgwvYboNKPRwBJ7UzLwrRyi783KIDEyhFO1LXy4pwTm3QcmM5zKhcJNeg9PBLBBBxY2m42HH36YBQsWMHXq1D7PW7lyJTExMY5bZmbmYN9S+CFFURxbTb3V7dGtHDUsJL/CnS6bmkpGXBjVTe28m1fseNyxDJIa7TezW5dMUXeHjEuO7DZ7obfQYBN3nJMFwLu5JyEyCWYuVZ/c+LRu4xKBb9CBxfLly9m3bx9vvdV/lvGKFSuoq6tz3IqLi/s9XwSW4xVN1DRbMAcZmZoeo/dwXFcpgYUnBJmM3HOuOlX/wrf5dFhtQJdS3k4WxvIFi8Yn8aebzuLPt8zUeyg9aFtidxXXYrHaYP4DYDDC0c+hbL/OoxOBalCBxQMPPMCHH37I119/TUZG/62JzWYz0dHR3W5i+NDqV8zIjCUkyA9zFBw5FhJYuNsNOZnEhQdTXN3Cx/tKAThYqiVuOlcYyxcYDAauPmsEE1N972fbuORIokODaG63qrNBCWNg0lXqkxuf0XdwImC59JNeURQeeOAB1qxZw1dffUV2tudr8gv/ptWv8JVseZc0VUGLGhhJ8zH3Cwsxscw+Vf/cuuMoitKlhoUfzm75IKPRwCz7/3taAS8WPKTe7/sn1MoMsnA/lwKL5cuX8/rrr/PGG28QFRVFaWkppaWltLS0eGp8ws9p+RV6dHscMi2/IiYTQvxsm6yfWDY/i7BgE/tL6nlvxymqm9oxGQ2MS/F+E69ApQX12v+LjJgF2eep26i3/FXHkYlA5VJgsWrVKurq6jj//PNJS0tz3N5++21PjU/4scrGNgoq7Y3HRvrzjhBZBvGUuIgQbpyjJnT/8sMDAIxOjNC9yFQgybEXpcstrEbRtplqsxZ5r0JztU4jE4HK5aWQ3m533HGHh4Yn/Jk29To+JZKYcOd7PvgMya/wirsXZmMyGqhrsQD+lbjpD2ZkxBJkNFBW38bJGvvs8pjFkDINLE2w/UV9BygCjh9m0wl/kefo9uiHsxUgO0K8JDM+nCumpzn+7A8VN/1JWIiJKSPUnBWtAysGQ+esxdbVYJHlbOE+ElgIj8nz58RNkBoWXnSvvTkZSGDhCTlnJnACTLkWYkZCcyXs+odOIxOBSAIL4RFWm+Jof613t8dB6WiHaq35mFTd9LTJ6dH84LzRLBybyLxsP53h8mE9EjgBTEFwzgPqsTQnE24kgYXwiILKJlotNkKDjWQnRug9HNfVnADFCiGREJU24Oli6P77skm8/v15krjpAVrV28NlDY5cFgBm3gph8eq/9wP/1mdwIuBIYCE8QivNPCE1GpPRP0ozd6PtCEkYq65HC+HHkqNCGZUQjqLAzqIusxYhETD3B+rxxj9JczLhFhJYCI/QlkH8oUNlryS/QgSY2b3lWYAaWASFweldULDO+wMTAUcCC+ERBx2Bhf+UZu7GsSNE8itEYNC6Czt2hmgiEmDWberxxj95eVQiEElgITzCEVj4a00CRw0LKeUtAsMce56FoyFZV/OXg8EEx7+C07t1GJ0IJBJYCLeramyjrL4NUHMs/I6idKm6KTMWIjCMSYokJiyYVovN0UXWIS5L3X4K0pxMDJkEFsLttEZSoxLCiTQH6TyaQWiugtZawKB2gxQiABiNhs48i8KanicseFC93/8vdZcI8G5uMT9+exfVTe1eGqUIBBJYCLc76O+Jmwc/UO8TxkJwmL5jEcKNOhM4e+kPkjYDxlwIig02P0tVYxs///c+1uw8xZ0vb6OxrcPLoxX+SgIL4XbajhC/rKBos8Im+1Rwzl36jkUIN5vjaEhW09mQrCutzPeO13hn/S5aLWouxu6Tddz7Wi5tHVJESwxMAgvhdn49Y3HoQ6jOh9BYmHW73qMRwq2mZ8QQbDJQ0dBGcXUv/UGyF6kzFx0tGLa9AMAPzx9DRIiJjceqeOjNXXScmfgpxBkksBBu1dZh5Vh5IwCT/G1HiKLAhqfV47n3gDlS1+EI4W6hwSamntmQrCuDARY8DMCNyidMjDfyk4sn8MLtOYSYjHy6v5T/WbOv99kOIewksBBudbSskQ6bQkxYMOkxoXoPxzUnNkDJDggKhbn36j0aITwip78ETsAy4QpOGVKIMzTyy1G7MBkNnDM2kWdunonRAG/nFvPUJ4e8OWThZySwEG510JFfEYXB30phb3xavT9rKUQm6ToUITxltlYoq7cETuCjfRWsar8MgJySf4BV7S3ynampPPXd6QA8tz6fVd8c98JohT+SwEK4ld8mbpbug2NfgMHY2fFRiACUYy+UdaSskbpmS7fnFEVh9brjvGtdRHNwHIa6Ytj/vuP5G+Zk8t+XTQTgN58e4s1tRV4bt/AfflhkQPgyv03c1EoZT74a4kfrOxYhPCgx0kx2YgQFlU3sKKrhgonJjufWHangUGkD4SFhGOfdBxtWwre/Vzv92v0gBtKnlPDlwXK2//tb0ixXcf6Cc9w+ztrmdvadqmfB2AT/m/0c5iSwEG6jKIqjop9fzVjUFsG+99RjbbudEAFs9qg4CiqbyC2s7hZYrF6nLm/cPHckoefMha3PQMUhWNM95+gK4IoQ9bj98+epi3mTmKmXuG185Q2tXL96M4VVzay+dTbfmZrqtmsLz5PAQrhNSV0r9a0dBBkNjEvxox0Vm59VfyPLXgTpM/UejRAelzMqjn/mnezW6XRXcS1b8qsJMhq4e2E2hIfBVc/A7jd7baeuACdO5JNtLYB/LYPYDyEjZ8hjq2uxcPuL2yisagbgo72nJbDwMxJYCLc5aJ+tGJsciTnIpPNonNRcDTv+rh4vfFjXoQjhLTn2Qlm7imtp77AREmTkOftsxVVnpZMea684O+069dYLA3BkdyHF/1zKeexFef17GO76FJInDXpcLe1W7n5lO4dKG4gIMdHUbuWbQ+WOMQr/IP+lhNv4ZeLmthfA0gyp02H0BXqPRgivGJMUQVx4MG0dNvaX1FFQ2cSn+0sBuPc85/vjLJk2kpXR/8MO21gMrbXw2rVQUzioMVmsNn74jzxyC2uICg3infvmkxhppqGtg60FVYO6ptCHBBbCbfwucbO9GbY9px4veEgtDiTEMGAwdDYkyyus4fn1+SgKXDgxmQmpUU5fx2Q0cPuiKdzZ/v/IN2RCw2l47RpoLHdpPDabwk/f3c3XhysIDTby8h1zmJIew5JJav7H2gNlLl1P6EsCC+E2B/1txmLXP9ROprGjYPI1eo9GCK/S6ll8tr+U93acBOC+Ra5387125giCIxO4qeVRmsJHqCXxX/8utNY59XpFUfjFB/v5964SgowGVt0627FUc9HkFAC+OFAm1T79iAQWwi0a2zo4YU+2mpTm/G88urF2wKY/q8fn/AhMkm4khpc59noW20/U0N5hY+bIWMdjrggNNnHXwizKieMB42MoEclQuhfeuEmdFRzA/31xlL9vLsRggD/cMIMLJnTuUlkwNpGwYBMlda3st+dwCd8ngYVwi8Ol6v/0KdFmEiLNOo/GCQfeh9pCCItXK20KMcxMHRFDiKnzK+C+RWMGXS9i6bxRRJqD+Loyiu0L/wbmGCjaBO/e4ajc2ZuXNxbwzJdHAfjfq6Zw9Vkjuj0fGmzivPGJAHwuyyF+Q35NE27hd/UrtNmKefdCSLi+YxFCB6HBJqZlxJBXWMPoxAgumpQy6GvFhAVzy7yRPL8+nz/sCeHtW95Wcy2Ofga/Tlcr2p7BalP4ntXEduP3mbj4dm6bn9XrtS+anMpn+8tYe6CMRy4aP+gxCu+RGQvhFgdONwB+krjZWA6ndwEGmPN9vUcjhG6uOSsdowF+dskEjMahJS/ftSCbYJOBrQXV7DRMhBv+DiGRYG2HjtYeN5OtjWhDM09G/pMfnZ/V53UvnJiM0aDmcBVXD7y0IvQnMxbCLfwqcbNoi3qfPBkiEvUdixA6um1+FjfMyXRL3ZnUmFCuOWsE7+ad5Ll1+ay+7RL4yWFo6d7sbGdRLY+8swub1cJH4U8Q114CB/8DU7/X63XjI0LIyYpnW0E1Xxws484F2UMeq/AsmbEQQ2a1KRyy51hMTveDwKJ4q3o/cp6+4xDCB7izmN29i9Q+O58dKOV4RSOYIyF2pOO2rymG2987TUFHAuMmTidswf3qCzc83Wt1T83F9t0hsu3UP0hgIYbsRFUTrRYbocFGshIi9B7OwIo2q/cj5+s7DiECzNjkKJZMSkFR4G/f5nd7rqCyiTte3kZDWwdzs+P5yy2zMM37AQSFQekeyP+mz+tq2063FlT36MgqfI/LgcX69eu58sorSU9Px2Aw8P7773tgWMKfaMsgE1KjMQ1xndbj2pvh9G71OFNmLIRwt/vssxbv5Z2ivL4VgNN1Ldz6t61UNrYzJT2avy3LITTYBBEJMOt29YVah+FejEqIYHxKJFabwteHXSu+JbzP5cCiqamJGTNm8Oyzz3piPMIPaTtC/CJxs2QH2DogKk2dnhVCuFVOVjw5o+Jot9p4edMJaprauf3FbZyqbWF0YgSv3jWX6NDgzhfMXw4GE+R/DSW7+rzuRbIc4jdcDiwuvfRSfvWrX3Httdd6YjzCD3WW8vaDwlha4mbmPCnhLYSH3Guv4Pn6lkLueHkbR8sbSYsJ5e93zyXxzDo3caNg6nfV403P9HnNiyarHU6/OVxOW4fVI+MW7uHxXSFtbW20tbU5/lxfL9XTAo3WfMwvEje1wELyK4TwmMUTkxmbHMmx8kZ2n6wjLjyY1+6eS0ZcHzVjznkQ9r4L+9fAhT+H+J47P6aPiCE5ykx5Qxubj1dxvnE3HPuy/4FMvByyz3XD30i4wuOBxcqVK3niiSc8/TZCJ9VN7ZTVq4HjhFQfDyxsNji5TT2WHSFCeIzRaODe80bzs3/uISLExCt3zmVscj8zmmnTYcxiOP4lbH4WLv99r9dcMjmFN7YWUbHhFSh+cuCB7H0XfnoUjLJPwZs8HlisWLGCRx55xPHn+vp6MjMzPf22wku0ZZBRCeFEmn28LErFIbUxUnAEpEzTezRCBLTvzcqg3WrjrMxYpqTHDPyCBQ+pgcXO1+H8R3utMXPRpBTKt6/h2uL/Ux+YdBUkjuv9epv/Cs2VUHUUkiYM4W8iXOXxbwKz2YzZ7Ae9I8Sg+FXiZrF9GSRjtjQdE8LDjEYDS+eNcv4F2edB+kwo2QnbnocL/rvHKecEHeKc4GcIwkb1uOuIv/6FvmcjirZC4QZ1e7kEFl4l80NiSPyy4qbkVwjhewwGddYC1MCivan78yW7ML9zC2aDhbXW2byc8Ej/SxzacmfRVs+MV/TJ5cCisbGRXbt2sWvXLgAKCgrYtWsXRUVF7h6b8AOOxE1/CiykfoUQvmnSVRCXDS01sOO1zscrj8Hr34P2BioS5vCA5Ud8frCq/2tpv0BoM5XCa1wOLHJzc5k5cyYzZ84E4JFHHmHmzJk89thjbh+c8G1tHVaOlTcCMMnXd4Q0lKpt0g1GyJij92iEEL0xmuCcH6nHm/+itlyvO6l2Sm2uhLQZBN/6Nh1GM4fLGiiq6qcpWcYcwADV+WrjQeE1LgcW559/Poqi9Li98sorHhie8GXHyhvpsCnEhAWTHhOq93D652g8NgVCfTwIEmI4O+sWiEiCumLY/jd47Vr1OGEsLH2P2LgE5mTFAbD2YD/FssJiIXmSelwksxbeFDAZbPkVjVhtfTexAchKjCDYJGkl7qIlbk5Ki8Lg68WmHPkVsgwihE8LDoN598JXv4JPH1Ufix4Bt70PkUmAWixrS341H+89zXnj+u5QnJw4i5jyA9Qc/pbKhAtcGkZMWDDJ0d7/hUlRFKqa2nsWEvMjARNY3PT8Fsob2vo9Z1RCOG/94GzSYsK8NKrAtvtkLeAniZvFkrgphN+Y83349v/A0gRh8XDbGojtLFNw8eQUfvnhAfIKa7jo/9b3eZlrjNE8HQIndn7FtVvPd2kIBgP87JIJ/PD8sYP9WwzKq5tO8IsPDnDngiweu2Ky7//S1ouACSxiw4Pp6GfGorm9g8KqZm57cRvv3Duf+IgQL44u8NS1WHh/ZwkA541L0nk0A2hvgtN71GNJ3BTC94XFwZLHYcff4ao/99gumhkfzk1zMvl8gL4hx5QpYIOpxhOkhyu0GpybBVAUhZpmC7/99DDRocHcerYL22aHoNVi5S9fHwfg5Y0niA4N5scXjffKe7uTQVGU/tcP3Ky+vp6YmBjq6uqIjvbeb7qnalu4btUmTte1MiMjhn/cc7bvF3TyYX/95hi//fQw41Mi+fSh8zD6clfTgvXw6pXqdOojB/QejRDCWxQF/jARGkvhjo8ga6HTL/39Z4f5y9fHMBjgmZtmcuWMdA8OVPXG1iL+e81eIkJMNLWr/VAev3Iydy7oWeJcD85+fw+bhIMRsWG8dvdc4sKD2X2yjntfy5VGNoPUarHy8sYTANx73hjfDipAtpkKMVwZDF3qWbiWwPmTi8ezdN5IFAUeeWcX645UeGCAnaw2hRe+zQfgkYsn8OMl6kzFEx8cYM3Okx59b3cbNoEFwNjkKF69ay4RISY2HqvioTd30WG16T0sv/P+zlNUNLSRFhPqlSh+yKQwlhDDl6OehWuFsgwGA/979VSumJ6Gxapw32t55BVWe2CAqs/3l1JQ2URMWDA3zcnkwcVjueOcLAB++u4evuxvB4yPGVaBBcD0jFheuD2HEJORT/eX8j9r9uHl1SC/ZrUpPL9ejarvXphNSJCP/xOyWeHkdvVYdoQIMfxoM5XFW9VGhC4wGQ388YazWDQ+iRaLlTtf3s6hUvd36FYUhdXr1NyK2+ePIsIchMFg4LErJnPtzBFYbQo//McOtuYPUBTMR/j4t4JnnDM2kT/fMhOjAd7OLeapTw7pPSS/sfZAGfmVTUSHBnHT3JF6D2dg5QehrR5CItUaFkKI4SV1GgSHqw0IK1z/WR8SZGTVrbOYNTKW+tYObntxW/+FuQZhS341u0/WYQ4yssw+SwFqv5XfXjedJZOSaeuw8f1Xc9l3qs6t7+0JwzKwALhkSipPfW86AM+tz2fVN8d1HpHv6x5VZ/lH8mvRZvU+I0cajwkxHJmCYcRs9XiQ5b3DQ4J46Y45TEiJoqKhjVtf3Ep5favbhvjcevXn6vU5GT3qVwSbjPzlllnMzY6noa2DO17eRkFlU2+X8RnD+iftDTmZ1DVbePLjg/zm00PEhAVzy7xefgsv3ATrf9+zKY4rMufC4sfUf+ReothsbH39cQx1RUy78xnCI51oXdyPbQXV7CquJUSLqgvWw7d/BEtL3y+Kz4ZLfg3h8UN670HT1lUlv0KI4WvkfDjxrdqQLOeuQV0iNjyE1+6ey/dWb6Koupmr/rKRjLi+ayKdMzaRhxePGzC5/eDper45XIHRAPecO7rXc0KDTfxtWQ43PbeFA6fruW7VJrITI/q97gu35xCnU1mFYR1YANxz3mhqmtv56zfH+Z/39xITFszl09M6TziZC69fpxZqGYriLWq9+mtW9d+Rz422vPgI80+9DMCev5xi4o8/JMQ8+Epy2mzF9bMzSKreAf+4HjoGiNqLt0DlEbj932COGvR7D5rW2VB2hAgxfGn5VUNsSJYcHcrrd8/jutWbKa1vpbSfWYvcwhpqm9t54qop/Ra50nLWLp2WxqiEvoOF6NBgXr1rLjc8t5mCyiaqmtr7HavFxXwSdxr2gQWo1dVqmi28ua2Ih9/eSVRoEOeNT1LX5/9hDyqyF6nV4Aaj4TR8ugL2vKUWfvnOSnUblAdt+cf/OoKKdiWI6a3byfvLzZz10LuYglz/z36otJ6vD1dgMMAPJ7XAGzeqQcXYi2DW7b2/qKMVPvkvOJUHby2Fpe9CkBfL1Nadgroie+OxHO+9rxDCt2gNyWpOqA0Jo1IHfalRCRF88eNFbM6vAnpP/C+sauapTw/x982FxIaH8EgfRa5O1jTzn91qocF7z+t9tqKrpCgzH/5oIZuOV2EdIHCIDvXe7PiZJLBA3Vb0q2umUt9i4aO9p7n3tTzevSmdqZ/eoLbvHTEbbnoDzJGDf5PQWFjzA9i6Sl0WWPT/3Db+M21//y+cffQPAGzJWk541mwmfn0Psxu+Yuuqu5m7/GUMLs6aaFH17eOtjPhgKbTVqdOLN/wdQsL7fmHCGHjlSihYB+99H65/Re1g6A3abycpU/WZLRFC+IbQGEiZAmX71O3nU64Z0uViwoP5ztT+g5NwcxA/f38fz3x5lLjw4F6LXL24oQCrTeGcMQlMz4h16r0jzEFcNDllMMP2mmGbvHkmk9HAH2+cwbnjEomwVBP17vXqTEPSRFj6z6EFFQAzboTv/EY9/vpJ2PbC0Afdi52fv87MnT8HYEvKzcy7/VdMP/977J37W2yKgXlV77PlpZ+4dM1TtS38Z1cJKVTz31UroKkcUqbBzW/1H1SAGpTd/AaYQuDgf+CDh9RqeN6gLYOMPNs77yeE8F3azwEX61kM1m1nj3LMVDzxwQH+taN7kauapnbe2lYMwH2LxnhlTN4igUUX5iATq68bxzuRv2MUpZSQRMmVb7gv8fDs+2DRf6nHH/8M9v7TPde127/xIyZvfJggg43tsZcy796/OmYmZl/+fbZP+R8A5p98iS1v/NLp6760oYAIWwP/jPwd5saTEJcNt76ntiV2xujz4XsvqksSO1+DLx538W82SI7GYxJYCDHsZdp/DnixhfqPLhzLnQuyAPjZP/fwRZfeJq9tKaTFYmVyWjTn9tOh1R9JYNFVezMR7y1ldEc+NYYYbml7lFveLqJigK6pLjl/Bcz9AaDAmnvhyOduuezRXd8y6vO7MRss7AxfwMzlf++x3DHvhp+xOet+AM4+8nu2v//sgNetbW7n39uO8ErIb8nsKISoNLj9fYhycSpu8lVw5Z/U441/gg1Pu/Z6V7U1QOle9ThTAgshhj0tgfP07qHt8HOBwWDg55dP5rv2IlfL31CLXLVarLyy6QQA9y4a7ZcdTPsjgYXGaoF374CiTWCOxnrLe3TEjuZEVTO3v7SNuhaLe97HYFCXRKZdD7YOeOd2KNw8pEsWHt5F4vu3EGloYX/IDCb96F2CgnvfZnT27b9mS8pNAMzc+f+xa+0b/V77jU1H+aPye2Yaj6GExsKt/4K4rMENdNbtcNH/qsdfPA55rw7uOs44mQuKDWIyIWaE595HCOEfYjIhKh0Uq5pQ7iVGo4HfnFHk6lcfHaC6qZ2MuDAun5Y28EX8zLDpbkrpXijb3/fzhz+GA/+GoFC4bQ2MOocTlU1ct3oTlY3tzM2K59W75hIW4nriYX5FI7uKa7s9ZrBZmL/tQVLL19MeFMW+yY9gNQ1iK6hiI3PX/5FKJUdNY0l9cC1RMf0v3disVvKeuYU5dZ/SpgSzc+IjmMJje7k2NO3+F+cr2+kwhRF0xweQOcf1MZ5p7eOw8Wl1aeTCn0O0B/qNHP0c9r0HU6+D6150//WFEP7n3Tth/7/ggv8PFv3Mq2/darFy+0vb2FbQ2W/kiaumdKu06euc/f4eHoHFnnfhX05sFTWY1N0fE77jeGh/SR03PbeFhrYOLpyYzHO3zSbY5PxEz/ojFdz96nYs1p4fcyht/D3kKeYaDzt9vb4UGUcQed9a4pOd++28w9LO3v+7mpnNmwY810IQxqXvYBq3eKjDVCmKmsS5w4MzFprLfg9z7/H8+wghfN/W5+CT/wdjl6h5Yl5W32rh5ue3sL+knrjwYDY+eiHhIf6zOVMCC82Rz+Gtm9Vlh/RZah2J3piC1Yps4y/p8dS2gmpue3ErbR02rjkrnT/ecJZTrcJ3FNWw9IWttFisTEyNIjm654xEuK2R62tfIqnjtMt/NU2bOZHM654kNXOsS69rbWli1ys/IbzuaJ/nWA3BBM+/l6nnXTvo8fXKZoX1v4Pibe69blcRiXDZ79StZkIIUbILnl8E5mj4rxPe2/reRWVjG7//7DCLJ6X4/LbRM0lgAWruwmvXqIWapt0A1z436KqXXx0q4wd/z6PDpnDHOVk8fuXkfhNuDpc2cMNzm6lrsXDuuEReXDbH9zuBCiFEILN2wFMj1aKH922E1Kl6j8ivOPv9HbjfdKV7O6tDjrsErvnrkEppXzgxhd9fPwOAVzad4E9f9v1bfnF1M7e9uJW6FgszR8by3G2zJagQQgi9mYI688SGWN5b9C0wv+2qjsNr3+2sDnn9K25p/nXNzBE8cZXaevvpL47yysaCHueUN7Sqne8a2piQEsXLd8zxqzU0IYQIaI56Ft4plDUcBV5gUX9aXf5wpTqkC5adk8XDS8YB8IsPDvD+zlOO5+paLCx7aTuFVc1kxofx97vnEhuuT3c5IYQQvdDqWXixUNZwE1iBRXM1vHYt1BZB/Gi47V/OV4d0wUOLx3GHfYvQT97dzVeHymhpt3L3K9s5eLqexEgzr901j5RekjWFEELoKGOOutW9rgjqS/QeTUAKnMCivQneuAEqDqrVIW9bA5HJHnkrg8HAY1dM5lp7NbX7X9/BbS9uJbewhqjQIF67ey5ZiX23vxVCCKETc5TamBBk1sJDAmPxv6MN3r4VTm5Xu4gOpTqkk4xGA7+9bjr1LRa+PFRObmENocFGXrpjDpPSvFj4SwghhGtGng2le9SOy+/fP7hrJIxV8/cSx7l1aIEgMGYsWuvVKa3gCLUTacpkr7xtsMnIs0tnqR1RQ0ysWjqbOVlualgmhBDCMyZdpS6HKFZ15+BgbmX71KX3upMDv98wEzh1LJqroeIwjJrvvms6SVEU2jpshAZ7v9iKEEKIQWipUZsVDkZ7szpLXnUUEsfDnZ9CRIJ7x+eDpECWEEII4Sm1xfDSJVB/CtJnwrIP1PyNACYFsoQQQghPic2E296H8AQo2Qlv3QKWVr1H5RMGFVg8++yzZGVlERoayrx589i2zYP9HoQQQghflDRezesLiYSC9fDe3WrZ8GHO5cDi7bff5pFHHuHxxx9nx44dzJgxg0suuYTy8nJPjE8IIYTwXSNmwc1vgskMhz6EDx9SOzgPYy4HFn/84x+55557uPPOO5k8eTKrV68mPDycl156yRPjE0IIIXxb9nlw3UvqTpOdr8Panw/r4MKlOhbt7e3k5eWxYsUKx2NGo5ElS5awefPmXl/T1tZGW1ub48/19fWDHKoQQgjhoyZdAVf9Bf79Q9j0Z6g7BZE6tkW/4L8hVJ8NEi4FFpWVlVitVlJSun9YKSkpHDp0qNfXrFy5kieeeGLwIxRCCCH8wcyl6jbWz/8H9v9L37Es/LF/BBaDsWLFCh555BHHn+vr68nMzPT02wohhBDed84DEJ8Np/L0HYcbm2+6yqXAIjExEZPJRFlZWbfHy8rKSE1N7fU1ZrMZs9k8+BEKIYQQ/mTi5eptmHIpeTMkJITZs2fz5ZdfOh6z2Wx8+eWXzJ/v/YqXQgghhPAtLi+FPPLIIyxbtoycnBzmzp3L008/TVNTE3feeacnxieEEEIIP+JyYHHjjTdSUVHBY489RmlpKWeddRaffvppj4ROIYQQQgw/0itECCGEEAOSXiFCCCGE8DoJLIQQQgjhNhJYCCGEEMJtJLAQQgghhNtIYCGEEEIIt5HAQgghhBBuI4GFEEIIIdxGAgshhBBCuI0EFkIIIYRwG4+3TT+TVuizvr7e228thBBCiEHSvrcHKtjt9cCioaEBgMzMTG+/tRBCCCGGqKGhgZiYmD6f93qvEJvNRklJCVFRURgMBm++tc+qr68nMzOT4uJi6Z/iAvncXCef2eDI5zY48rkNjq9+boqi0NDQQHp6OkZj35kUXp+xMBqNZGRkePtt/UJ0dLRP/SPyF/K5uU4+s8GRz21w5HMbHF/83PqbqdBI8qYQQggh3EYCCyGEEEK4jQQWPsBsNvP4449jNpv1Hopfkc/NdfKZDY58boMjn9vg+Pvn5vXkTSGEEEIELpmxEEIIIYTbSGAhhBBCCLeRwEIIIYQQbiOBhRBCCCHcRgILL1q/fj1XXnkl6enpGAwG3n///W7PK4rCY489RlpaGmFhYSxZsoSjR4/qM1gfsXLlSubMmUNUVBTJyclcc801HD58uNs5ra2tLF++nISEBCIjI/ne975HWVmZTiP2DatWrWL69OmOAjvz58/nk08+cTwvn9nAnnrqKQwGAw8//LDjMfncevrFL36BwWDodps4caLjefnM+nbq1CluvfVWEhISCAsLY9q0aeTm5jqe99fvBAksvKipqYkZM2bw7LPP9vr8b3/7W5555hlWr17N1q1biYiI4JJLLqG1tdXLI/Ud69atY/ny5WzZsoW1a9disVi4+OKLaWpqcpzz4x//mA8++IB3332XdevWUVJSwne/+10dR62/jIwMnnrqKfLy8sjNzeXCCy/k6quvZv/+/YB8ZgPZvn07zz33HNOnT+/2uHxuvZsyZQqnT5923DZs2OB4Tj6z3tXU1LBgwQKCg4P55JNPOHDgAH/4wx+Ii4tznOO33wmK0AWgrFmzxvFnm82mpKamKr/73e8cj9XW1ipms1l58803dRihbyovL1cAZd26dYqiqJ9RcHCw8u677zrOOXjwoAIomzdv1muYPikuLk7529/+Jp/ZABoaGpRx48Ypa9euVRYtWqQ89NBDiqLIv7W+PP7448qMGTN6fU4+s77913/9l7Jw4cI+n/fn7wSZsfARBQUFlJaWsmTJEsdjMTExzJs3j82bN+s4Mt9SV1cHQHx8PAB5eXlYLJZun9vEiRMZOXKkfG52VquVt956i6amJubPny+f2QCWL1/O5Zdf3u3zAfm31p+jR4+Snp7O6NGjWbp0KUVFRYB8Zv35z3/+Q05ODtdffz3JycnMnDmTF154wfG8P38nSGDhI0pLSwFISUnp9nhKSorjueHOZrPx8MMPs2DBAqZOnQqon1tISAixsbHdzpXPDfbu3UtkZCRms5n77ruPNWvWMHnyZPnM+vHWW2+xY8cOVq5c2eM5+dx6N2/ePF555RU+/fRTVq1aRUFBAeeeey4NDQ3ymfUjPz+fVatWMW7cOD777DPuv/9+HnzwQV599VXAv78TvN7dVIjBWr58Ofv27eu2fiv6NmHCBHbt2kVdXR3//Oc/WbZsGevWrdN7WD6ruLiYhx56iLVr1xIaGqr3cPzGpZde6jiePn068+bNY9SoUbzzzjuEhYXpODLfZrPZyMnJ4de//jUAM2fOZN++faxevZply5bpPLqhkRkLH5GamgrQI1u6rKzM8dxw9sADD/Dhhx/y9ddfk5GR4Xg8NTWV9vZ2amtru50vnxuEhIQwduxYZs+ezcqVK5kxYwZ/+tOf5DPrQ15eHuXl5cyaNYugoCCCgoJYt24dzzzzDEFBQaSkpMjn5oTY2FjGjx/PsWPH5N9aP9LS0pg8eXK3xyZNmuRYRvLn7wQJLHxEdnY2qampfPnll47H6uvr2bp1K/Pnz9dxZPpSFIUHHniANWvW8NVXX5Gdnd3t+dmzZxMcHNztczt8+DBFRUXD+nPrjc1mo62tTT6zPixevJi9e/eya9cuxy0nJ4elS5c6juVzG1hjYyPHjx8nLS1N/q31Y8GCBT22zh85coRRo0YBfv6doHf26HDS0NCg7Ny5U9m5c6cCKH/84x+VnTt3KoWFhYqiKMpTTz2lxMbGKv/+97+VPXv2KFdffbWSnZ2ttLS06Dxy/dx///1KTEyM8s033yinT5923Jqbmx3n3HfffcrIkSOVr776SsnNzVXmz5+vzJ8/X8dR6+/RRx9V1q1bpxQUFCh79uxRHn30UcVgMCiff/65oijymTmr664QRZHPrTc/+clPlG+++UYpKChQNm7cqCxZskRJTExUysvLFUWRz6wv27ZtU4KCgpQnn3xSOXr0qPKPf/xDCQ8PV15//XXHOf76nSCBhRd9/fXXCtDjtmzZMkVR1O1FP//5z5WUlBTFbDYrixcvVg4fPqzvoHXW2+cFKC+//LLjnJaWFuWHP/yhEhcXp4SHhyvXXnutcvr0af0G7QPuuusuZdSoUUpISIiSlJSkLF682BFUKIp8Zs46M7CQz62nG2+8UUlLS1NCQkKUESNGKDfeeKNy7Ngxx/PymfXtgw8+UKZOnaqYzWZl4sSJyvPPP9/teX/9TpC26UIIIYRwG8mxEEIIIYTbSGAhhBBCCLeRwEIIIYQQbiOBhRBCCCHcRgILIYQQQriNBBZCCCGEcBsJLIQQQgjhNhJYCCGEEMJtJLAQQgghhNtIYCGEEEIIt5HAQgghhBBuI4GFEEIIIdzm/wcRWNyq6jisNgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.plot(counts.rolling(5).mean())\n", - "plt.plot(counts_imp.rolling(5).mean())" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6720ec95", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.imshow(counts.to_frame() @ counts_imp.to_frame().T, cmap=\"gray\")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "7911314e", - "metadata": {}, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'date'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/core/indexes/base.py:3652\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3651\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3652\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/_libs/index.pyx:147\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/_libs/index.pyx:176\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'date'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[20], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mBeijing\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/Projets/RD/qolmat/qolmat/utils/data.py:106\u001b[0m, in \u001b[0;36mget_data\u001b[0;34m(name_data, datapath, n_groups_max)\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name_data \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBeijing\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 105\u001b[0m df \u001b[38;5;241m=\u001b[39m read_csv_local(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbeijing\u001b[39m\u001b[38;5;124m\"\u001b[39m, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m;\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 106\u001b[0m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_datetime(\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m)\n\u001b[1;32m 108\u001b[0m \u001b[38;5;66;03m# df[\"date\"] = pd.to_datetime(\u001b[39;00m\n\u001b[1;32m 109\u001b[0m \u001b[38;5;66;03m# {\u001b[39;00m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;66;03m# \"year\": df[\"year\"],\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[38;5;66;03m# }\u001b[39;00m\n\u001b[1;32m 115\u001b[0m \u001b[38;5;66;03m# )\u001b[39;00m\n\u001b[1;32m 116\u001b[0m df \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmonth\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mday\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhour\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwd\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/core/frame.py:3761\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3759\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 3760\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3761\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3763\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", - "File \u001b[0;32m~/miniconda3/envs/env_qolmat_dev/lib/python3.8/site-packages/pandas/core/indexes/base.py:3654\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3652\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3653\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m-> 3654\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3655\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3656\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3657\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3658\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3659\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'date'" - ] - } - ], - "source": [ - "data.get_data(\"Beijing\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c08554cb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "env_qolmat_dev", - "language": "python", - "name": "env_qolmat_dev" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.18" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/qolmat/imputations/preprocessing.py b/qolmat/imputations/preprocessing.py index 6f05585e..77d500a0 100644 --- a/qolmat/imputations/preprocessing.py +++ b/qolmat/imputations/preprocessing.py @@ -281,14 +281,14 @@ def fit(self, X: NDArray, y: Optional[NDArray] = None) -> Self: X_transformed = self.transformer.fit(X_transformed) return self - def fit_transform(self, X: NDArray) -> Self: + def fit_transform(self, X: NDArray) -> NDArray: X_transformed = copy.deepcopy(X) X_transformed = self.wrapper.fit_transform(X_transformed) X_transformed = self.transformer.fit_transform(X_transformed) X_transformed = self.wrapper.inverse_transform(X_transformed) return X_transformed - def transform(self, X: NDArray) -> Self: + def transform(self, X: NDArray) -> NDArray: X_transformed = copy.deepcopy(X) X_transformed = self.wrapper.transform(X_transformed) X_transformed = self.transformer.transform(X_transformed) From a1e98eefddea404208a7c827aa31e593f19e2ce0 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 8 Apr 2024 12:37:54 +0200 Subject: [PATCH 68/99] version date added --- HISTORY.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HISTORY.rst b/HISTORY.rst index 46428cff..5527f636 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,7 +2,7 @@ History ======= -0.1.4 (2024-04-**) +0.1.4 (2024-04-08) ------------------ * ImputerMean, ImputerMedian and ImputerMode have been merged into ImputerSimple From e1cbc96f948cb443bac3f4e4d08b166982fdff5a Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 8 Apr 2024 12:38:36 +0200 Subject: [PATCH 69/99] =?UTF-8?q?Bump=20version:=200.1.3=20=E2=86=92=200.1?= =?UTF-8?q?.4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 2 +- docs/conf.py | 2 +- qolmat/_version.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index c2c2ba85..1b6155c0 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.1.3 +current_version = 0.1.4 commit = True tag = True diff --git a/docs/conf.py b/docs/conf.py index 00730157..b178abc1 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "Quantmetry" # The full version, including alpha/beta/rc tags -version = "0.1.3" +version = "0.1.4" release = version # -- General configuration --------------------------------------------------- diff --git a/qolmat/_version.py b/qolmat/_version.py index ae736254..bbab0242 100644 --- a/qolmat/_version.py +++ b/qolmat/_version.py @@ -1 +1 @@ -__version__ = "0.1.3" +__version__ = "0.1.4" diff --git a/setup.py b/setup.py index 9fe716cb..e5499198 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import find_packages, setup DISTNAME = "qolmat" -VERSION = "0.1.3" +VERSION = "0.1.4" DESCRIPTION = "A Python library for optimal data imputation." LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst" with codecs.open("README.rst", encoding="utf-8-sig") as f: From 296b71d8b4159db6405f28d25c8223dcb96a96cd Mon Sep 17 00:00:00 2001 From: Gsaes Date: Thu, 11 Apr 2024 16:55:01 +0200 Subject: [PATCH 70/99] Test Comparator --- .gitignore | 2 +- tests/benchmark/test_comparator.py | 76 ++++++++++++++++++++++++++++++ 2 files changed, 77 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 84f1054e..e385a1ee 100644 --- a/.gitignore +++ b/.gitignore @@ -59,7 +59,7 @@ examples/*.ipynb examples/figures/* examples/data/* examples/local - +data/data_local/* # VSCode .vscode diff --git a/tests/benchmark/test_comparator.py b/tests/benchmark/test_comparator.py index e69de29b..5daddb86 100644 --- a/tests/benchmark/test_comparator.py +++ b/tests/benchmark/test_comparator.py @@ -0,0 +1,76 @@ +import pytest +import numpy as np +import pandas as pd + +from unittest.mock import patch, MagicMock +from qolmat.benchmark.comparator import Comparator + +generator_holes_mock = MagicMock() +generator_holes_mock.split.return_value = [ + pd.DataFrame({"A": [False, False, True], "B": [True, False, False]}) +] + +comparator = Comparator( + dict_models={}, + selected_columns=["A", "B"], + generator_holes=generator_holes_mock, + metrics=["mae", "mse"], +) + +imputer_mock = MagicMock() +expected_get_errors = pd.Series( + [1.0, 1.0, 1.0, 1.0], + index=pd.MultiIndex.from_tuples([("mae", "A"), ("mae", "B"), ("mse", "A"), ("mse", "B")]), +) + + +@patch("qolmat.benchmark.metrics.get_metric") +def test_get_errors(mock_get_metric): + df_origin = pd.DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}) + df_imputed = pd.DataFrame({"A": [1, 2, 4], "B": [4, 5, 7]}) + df_mask = pd.DataFrame({"A": [False, False, True], "B": [False, False, True]}) + mock_get_metric.side_effect = ( + lambda name_metric, df_origin=None, df_imputed=None, df_mask=None: ( + lambda x, y, z: pd.Series([1.0, 1.0], index=["A", "B"]) + ) + ) + errors = comparator.get_errors(df_origin, df_imputed, df_mask) + pd.testing.assert_series_equal(errors, expected_get_errors) + + +@patch("qolmat.benchmark.hyperparameters.optimize", return_value=imputer_mock) +@patch("qolmat.benchmark.comparator.Comparator.get_errors", return_value=expected_get_errors) +def test_evaluate_errors_sample(mock_get_errors, mock_optimize): + errors_mean = comparator.evaluate_errors_sample( + imputer_mock, pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, np.nan]}) + ) + expected_errors_mean = expected_get_errors + pd.testing.assert_series_equal(errors_mean, expected_errors_mean) + mock_optimize.assert_called_once() + mock_get_errors.assert_called() + + +@patch( + "qolmat.benchmark.comparator.Comparator.evaluate_errors_sample", + return_value=expected_get_errors, +) +def test_compare(mock_evaluate_errors_sample): + df_test = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}) + + imputer1 = MagicMock(name="Imputer1") + imputer2 = MagicMock(name="Imputer2") + comparator.dict_imputers = {"imputer1": imputer1, "imputer2": imputer2} + + errors_imputer1 = pd.Series([0.1, 0.2], index=["mae", "mse"]) + errors_imputer2 = pd.Series([0.3, 0.4], index=["mae", "mse"]) + mock_evaluate_errors_sample.side_effect = [errors_imputer1, errors_imputer2] + + df_errors = comparator.compare(df_test) + assert mock_evaluate_errors_sample.call_count == 2 + + mock_evaluate_errors_sample.assert_any_call(imputer1, df_test, {}, "mse") + mock_evaluate_errors_sample.assert_any_call(imputer2, df_test, {}, "mse") + expected_df_errors = pd.DataFrame( + {"imputer1": [0.1, 0.2], "imputer2": [0.3, 0.4]}, index=["mae", "mse"] + ) + pd.testing.assert_frame_equal(df_errors, expected_df_errors) From d5caf2388c9a3dc72525f36667ca2955c0d0efbd Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Sun, 14 Apr 2024 19:16:41 +0200 Subject: [PATCH 71/99] metrics updated --- HISTORY.rst | 1 + docs/imputers.rst | 30 +- examples/benchmark.md | 8 +- examples/tutorials/plot_tuto_categorical.py | 2 +- qolmat/benchmark/metrics.py | 291 ++++++++++---------- qolmat/imputations/em_sampler.py | 18 +- qolmat/imputations/preprocessing.py | 42 +-- qolmat/utils/algebra.py | 83 ++++++ qolmat/utils/utils.py | 21 +- tests/benchmark/test_metrics.py | 99 ++++--- tests/imputations/test_preprocessing.py | 8 +- tests/utils/test_algebra.py | 31 +++ 12 files changed, 386 insertions(+), 248 deletions(-) create mode 100644 qolmat/utils/algebra.py create mode 100644 tests/utils/test_algebra.py diff --git a/HISTORY.rst b/HISTORY.rst index 46428cff..52e62f1d 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -10,6 +10,7 @@ History * Tutorial plot_tuto_categorical showcasing mixed type imputation * Titanic dataset added * accuracy metric implemented +* metrics.py rationalized, and split with algebra.py 0.1.3 (2024-03-07) ------------------ diff --git a/docs/imputers.rst b/docs/imputers.rst index a8e4552c..633024e6 100644 --- a/docs/imputers.rst +++ b/docs/imputers.rst @@ -3,24 +3,28 @@ Imputers All imputers can be found in the ``qolmat.imputations`` folder. -1. Simple (mean/median/shuffle) -------------------------------- -Imputes the missing values using the mean/median along each column or with a random value in each column. See the :class:`~qolmat.imputations.imputers.ImputerSimple` and :class:`~qolmat.imputations.imputers.ImputerShuffle` classes. +1. Simple (mean/median/mode) +---------------------------- +Imputes the missing values using a basic simple statistics: the mode (most frequent value) for the categorical columns, and the mean,median or mode (depending on the user parameter) for the numerical columns. See :class:`~qolmat.imputations.imputers.ImputerSimple`. -2. LOCF +2. Shuffle +---------- +Imputes the missing values using a random value sampled in the same column. See :class:`~qolmat.imputations.imputers.ImputerShuffle`. + +3. LOCF ------- -Imputes the missing values using the last observation carried forward. See the :class:`~qolmat.imputations.imputers.ImputerLOCF` class. +Imputes the missing values using the last observation carried forward. See :class:`~qolmat.imputations.imputers.ImputerLOCF`. -3. Time interpolation and TSA decomposition +4. Time interpolation and TSA decomposition ------------------------------------------- -Imputes missing using some interpolation strategies supported by `pd.Series.interpolate `_. It is done column by column. See the :class:`~qolmat.imputations.imputers.ImputerInterpolation` class. When data are temporal with clear seasonal decomposition, we can interpolate on the residuals instead of directly interpolate the raw data. Series are de-seasonalised based on `statsmodels.tsa.seasonal.seasonal_decompose `_, residuals are imputed via linear interpolation, then residuals are re-seasonalised. It is also done column by column. See the :class:`~qolmat.imputations.imputers.ImputerResiduals` class. +Imputes missing using some interpolation strategies supported by `pd.Series.interpolate `_. It is done column by column. See the :class:`~qolmat.imputations.imputers.ImputerInterpolation` class. When data are temporal with clear seasonal decomposition, we can interpolate on the residuals instead of directly interpolate the raw data. Series are de-seasonalised based on `statsmodels.tsa.seasonal.seasonal_decompose `_, residuals are imputed via linear interpolation, then residuals are re-seasonalised. It is also done column by column. See :class:`~qolmat.imputations.imputers.ImputerResiduals`. -4. MICE +5. MICE ------- Multiple Imputation by Chained Equation: multiple imputations based on ICE. It uses `IterativeImputer `_. See the :class:`~qolmat.imputations.imputers.ImputerMICE` class. -5. RPCA +6. RPCA ------- Robust Principal Component Analysis (RPCA) is a modification of the statistical procedure of PCA which allows to work with a data matrix :math:`\mathbf{D} \in \mathbb{R}^{n \times d}` containing missing values and grossly corrupted observations. We consider here the imputation task alone, but these methods can also tackle anomaly correction. @@ -46,7 +50,7 @@ The class :class:`RpcaNoisy` implements an recommanded improved version, which r with :math:`\mathbf{E} = \mathbf{D} - \mathbf{M} - \mathbf{A}`. See the :class:`~qolmat.imputations.imputers.ImputerRpcaNoisy` class for implementation details. -6. SoftImpute +7. SoftImpute ------------- SoftImpute is an iterative method for matrix completion that uses nuclear-norm regularization [11]. It is a faster alternative to RPCA, although it is much less robust due to the quadratic penalization. Given a matrix :math:`\mathbf{D} \in \mathbb{R}^{n \times d}` with observed entries indexed by the set :math:`\Omega`, this algorithm solves the following problem: @@ -56,11 +60,11 @@ SoftImpute is an iterative method for matrix completion that uses nuclear-norm r The imputed values are then given by the matrix :math:`M=LQ` on the unobserved data. See the :class:`~qolmat.imputations.imputers.ImputerSoftImpute` class for implementation details. -7. KNN +8. KNN ------ K-nearest neighbors, based on `KNNImputer `_. See the :class:`~qolmat.imputations.imputers.ImputerKNN` class. -8. EM sampler +9. EM sampler ------------- Imputes missing values via EM algorithm [5], and more precisely via MCEM algorithm [6]. See the :class:`~qolmat.imputations.imputers.ImputerEM` class. Suppose the data :math:`\mathbf{X}` has a density :math:`p_\theta` parametrized by some parameter :math:`\theta`. The EM algorithm allows to draw samples from this distribution by alternating between the expectation and maximization steps. @@ -104,7 +108,7 @@ Two parametric distributions are implemented: * :class:`~qolmat.imputations.em_sampler.VARpEM`: [7]: :math:`\mathbf{X} \in \mathbb{R}^{n \times d} \sim VAR_p(\nu, B_1, ..., B_p)` is generated by a VAR(p) process such that :math:`X_t = \nu + B_1 X_{t-1} + ... + B_p X_{t-p} + u_t` where :math:`\nu \in \mathbb{R}^d` is a vector of intercept terms, the :math:`B_i \in \mathbb{R}^{d \times d}` are the lags coefficient matrices and :math:`u_t` is white noise nonsingular covariance matrix :math:`\Sigma_u \mathbb{R}^{d \times d}`, so that :math:`\theta = (\nu, B_1, ..., B_p, \Sigma_u)`. -9. TabDDPM +10. TabDDPM ----------- :class:`~qolmat.imputations.diffusions.ddpms.TabDDPM` is a deep learning imputer based on Denoising Diffusion Probabilistic Models (DDPMs) [8] for handling multivariate tabular data. Our implementation mainly follows the works of [8, 9]. Diffusion models focus on modeling the process of data transitions from noisy and incomplete observations to the underlying true data. They include two main processes: diff --git a/examples/benchmark.md b/examples/benchmark.md index 45b201b5..e2fe8c1d 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -244,8 +244,8 @@ dfs_imputed["VAR_max"].groupby("station").min() ``` ```python tags=[] -# station = df_plot.index.get_level_values("station")[0] -station = "Huairou" +station = df_plot.index.get_level_values("station")[0] +# station = "Huairou" df_station = df_plot.loc[station] dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()} ``` @@ -362,7 +362,7 @@ comparison = comparator.Comparator( ) ``` -```python jupyter={"outputs_hidden": true} tags=[] +```python tags=[] generator_holes = missing_patterns.EmpiricalHoleGenerator(n_splits=3, groups=('station',), subset=cols_to_impute, ratio_masked=ratio_masked) comparison = comparator.Comparator( @@ -393,7 +393,7 @@ plt.show() df_plot = df_data[cols_to_impute] ``` -```python jupyter={"outputs_hidden": true} tags=[] +```python tags=[] dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()} ``` diff --git a/examples/tutorials/plot_tuto_categorical.py b/examples/tutorials/plot_tuto_categorical.py index 0ab886b8..6940d50b 100644 --- a/examples/tutorials/plot_tuto_categorical.py +++ b/examples/tutorials/plot_tuto_categorical.py @@ -57,7 +57,7 @@ # - manage categorical features though one hot encoding # - manage missing features (native to the HistGradientBoosting) -pipestimator = preprocessing.make_robust_MixteHGB(allow_new=False) +pipestimator = preprocessing.make_robust_MixteHGB(avoid_new=True) imputer_hgb = ImputerRegressor(estimator=pipestimator, handler_nan="none") imputer_wrap_hgb = preprocessing.WrapperTransformer(imputer_hgb, bt) diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index dd72e612..3a2699b7 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -7,7 +7,9 @@ from sklearn import metrics as skm import dcor +from qolmat.utils import algebra, utils from qolmat.utils.exceptions import NotEnoughSamples +from numpy.linalg import LinAlgError EPS = np.finfo(float).eps @@ -48,12 +50,18 @@ def columnwise_metric( pd.Series Series of scores for all columns """ + try: + pd.testing.assert_index_equal(df1.columns, df2.columns) + except AssertionError: + raise ValueError( + f"Input dataframes do not have the same columns! ({df1.columns} != {df2.columns})" + ) if type_cols == "all": cols = df1.columns elif type_cols == "numerical": - cols = df1.select_dtypes(include=["number"]).columns + cols = _get_numerical_features(df1) elif type_cols == "categorical": - cols = df1.select_dtypes(exclude=["number"]).columns + cols = _get_categorical_features(df1) else: raise ValueError(f"Value {type_cols} is not valid for parameter `type_cols`!") values = {} @@ -83,13 +91,7 @@ def mean_squared_error(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFra ------- pd.Series """ - cols_numerical = _get_numerical_features(df1) - return columnwise_metric( - df1[cols_numerical], - df2[cols_numerical], - df_mask[cols_numerical], - skm.mean_squared_error, - ) + return columnwise_metric(df1, df2, df_mask, skm.mean_squared_error, type_cols="numerical") def root_mean_squared_error( @@ -110,13 +112,8 @@ def root_mean_squared_error( ------- pd.Series """ - cols_numerical = _get_numerical_features(df1) return columnwise_metric( - df1[cols_numerical], - df2[cols_numerical], - df_mask[cols_numerical], - skm.mean_squared_error, - squared=False, + df1, df2, df_mask, skm.mean_squared_error, type_cols="numerical", squared=False ) @@ -136,13 +133,7 @@ def mean_absolute_error(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFr ------- pd.Series """ - cols_numerical = _get_numerical_features(df1) - return columnwise_metric( - df1[cols_numerical], - df2[cols_numerical], - df_mask[cols_numerical], - skm.mean_absolute_error, - ) + return columnwise_metric(df1, df2, df_mask, skm.mean_absolute_error, type_cols="numerical") def mean_absolute_percentage_error( @@ -163,12 +154,8 @@ def mean_absolute_percentage_error( ------- pd.Series """ - cols_numerical = _get_numerical_features(df1) return columnwise_metric( - df1[cols_numerical], - df2[cols_numerical], - df_mask[cols_numerical], - skm.mean_absolute_percentage_error, + df1, df2, df_mask, skm.mean_absolute_percentage_error, type_cols="numerical" ) @@ -209,13 +196,45 @@ def weighted_mean_absolute_percentage_error( ------- pd.Series """ - return columnwise_metric(df1, df2, df_mask, _weighted_mean_absolute_percentage_error_1D) + return columnwise_metric( + df1, + df2, + df_mask, + _weighted_mean_absolute_percentage_error_1D, + type_cols="numerical", + ) -def accuracy(values1: pd.Series, values2: pd.Series) -> float: +def accuracy(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> pd.Series: """ Matching ratio beetween the two datasets. + Parameters + ---------- + df1 : pd.DataFrame + True dataframe + df2 : pd.DataFrame + Predicted dataframe + df_mask : pd.DataFrame + Elements of the dataframes to compute on + + Returns + ------- + pd.Series + """ + return columnwise_metric( + df1, + df2, + df_mask, + accuracy_1D, + type_cols="all", + ) + + +def accuracy_1D(values1: pd.Series, values2: pd.Series) -> float: + """ + Matching ratio beetween the set of values. + Parameters ---------- values1 : pd.Series @@ -352,13 +371,7 @@ def kolmogorov_smirnov_test( pd.Series KS test statistic """ - cols_numerical = _get_numerical_features(df1) - return columnwise_metric( - df1[cols_numerical], - df2[cols_numerical], - df_mask[cols_numerical], - kolmogorov_smirnov_test_1D, - ) + return columnwise_metric(df1, df2, df_mask, kolmogorov_smirnov_test_1D, type_cols="numerical") def _total_variance_distance_1D(df1: pd.Series, df2: pd.Series) -> float: @@ -439,9 +452,7 @@ def _get_correlation_pearson_matrix(df: pd.DataFrame, use_p_value: bool = True) matrix = np.zeros((len(df.columns), len(df.columns))) for idx_1, col_1 in enumerate(cols): for idx_2, col_2 in enumerate(cols): - res = scipy.stats.mstats.pearsonr( - df[col_1].array.reshape(-1, 1), df[col_2].array.reshape(-1, 1) - ) + res = scipy.stats.mstats.pearsonr(df[[col_1]].values, df[[col_2]].values) if use_p_value: matrix[idx_1, idx_2] = res[1] else: @@ -755,7 +766,6 @@ def sum_pairwise_distances( def frechet_distance( df1: pd.DataFrame, df2: pd.DataFrame, - df_mask: pd.DataFrame, ) -> float: """Compute the Fréchet distance between two dataframes df1 and df2 Frechet_distance = || mu_1 - mu_2 ||_2^2 + Tr(Sigma_1 + Sigma_2 - 2(Sigma_1 . Sigma_2)^(1/2)) @@ -770,8 +780,6 @@ def frechet_distance( true dataframe df2 : pd.DataFrame predicted dataframe - df_mask : pd.DataFrame - Mask indicating on which values the distance has to computed on Returns ------- @@ -782,35 +790,22 @@ def frechet_distance( if df1.shape != df2.shape: raise Exception("inputs have to be of same dimensions.") - df_true = df1[df_mask.any(axis=1)] - df_pred = df2[df_mask.any(axis=1)] - - std = (np.std(df_true) + np.std(df_pred) + EPS) / 2 - mu = (np.nanmean(df_true, axis=0) + np.nanmean(df_pred, axis=0)) / 2 - df_true = (df_true - mu) / std - df_pred = (df_pred - mu) / std + std = (np.std(df1) + np.std(df2) + EPS) / 2 + mu = (np.nanmean(df1, axis=0) + np.nanmean(df2, axis=0)) / 2 + df1 = (df1 - mu) / std + df2 = (df2 - mu) / std - mu_true = np.nanmean(df_true, axis=0) - sigma_true = np.ma.cov(np.ma.masked_invalid(df_true), rowvar=False).data - mu_pred = np.nanmean(df_pred, axis=0) - sigma_pred = np.ma.cov(np.ma.masked_invalid(df_pred), rowvar=False).data + means1, cov1 = utils.nan_mean_cov(df1.values) + means2, cov2 = utils.nan_mean_cov(df2.values) - ssdiff = np.sum((mu_true - mu_pred) ** 2.0) - product = np.array(sigma_true @ sigma_pred) - if product.ndim < 2: - product = product.reshape(-1, 1) - covmean = scipy.linalg.sqrtm(product) - if np.iscomplexobj(covmean): - covmean = covmean.real - frechet_dist = ssdiff + np.trace(sigma_true + sigma_pred - 2.0 * covmean) - - return frechet_dist / df_true.shape[0] + return algebra.frechet_distance_exact(means1, cov1, means2, cov2) def frechet_distance_pattern( df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame, + min_n_rows: int = 10, ) -> pd.Series: """Frechet distance computed using a pattern decomposition @@ -822,15 +817,23 @@ def frechet_distance_pattern( Second empirical ditribution df_mask : pd.DataFrame Mask indicating on which values the distance has to computed on + min_n_rows: int + Minimum number of rows for a KL estimation Returns ------- pd.Series Series of computed metrics """ - cols_numerical = _get_numerical_features(df1) - distance = frechet_distance(df1[cols_numerical], df2[cols_numerical], df_mask[cols_numerical]) - return pd.Series(distance, index=["All"]) + + return pattern_based_weighted_mean_metric( + df1, + df2, + df_mask, + frechet_distance, + min_n_rows=min_n_rows, + type_cols="numerical", + ) def kl_divergence_1D(df1: pd.Series, df2: pd.Series) -> float: @@ -858,39 +861,7 @@ def kl_divergence_1D(df1: pd.Series, df2: pd.Series) -> float: return scipy.stats.entropy(p + EPS, q + EPS) -def kl_divergence_gaussian_exact( - mean1: pd.Series, cov1: pd.DataFrame, mean2: pd.Series, cov2: pd.DataFrame -) -> float: - """Exact Kullback-Leibler divergence computed between two multivariate normal distributions - - Parameters - ---------- - mean1: pd.Series - Mean of the first distribution - cov1: pd.DataFrame - Covariance matrx of the first distribution - mean2: pd.Series - Mean of the second distribution - cov2: pd.DataFrame - Covariance matrx of the second distribution - Returns - ------- - float - Kulback-Leibler divergence - """ - n_variables = len(mean1) - L1, lower1 = scipy.linalg.cho_factor(cov1) - L2, lower2 = scipy.linalg.cho_factor(cov2) - M = scipy.linalg.solve(L2, L1) - y = scipy.linalg.solve(L2, mean2 - mean1) - norm_M = (M**2).sum().sum() - norm_y = (y**2).sum() - term_diag_L = 2 * np.sum(np.log(np.diagonal(L2) / np.diagonal(L1))) - div_kl = 0.5 * (norm_M - n_variables + norm_y + term_diag_L) - return div_kl - - -def kl_divergence_gaussian(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.Series) -> float: +def kl_divergence_gaussian(df1: pd.DataFrame, df2: pd.DataFrame) -> float: """Kullback-Leibler divergence estimation based on a Gaussian approximation of both empirical distributions @@ -900,29 +871,29 @@ def kl_divergence_gaussian(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.Ser First empirical distribution df2 : pd.DataFrame Second empirical distribution - df_mask: pd.DataFrame - Mask indicating on what values the divergence should be computed Returns ------- pd.Series Series of estimated metrics """ - df1 = df1[df_mask.any(axis=1)] - df2 = df2[df_mask.any(axis=1)] - cov1 = df1.cov() - cov2 = df2.cov() - mean1 = df1.mean() - mean2 = df2.mean() - - div_kl = kl_divergence_gaussian_exact(mean1, cov1, mean2, cov2) + cov1 = df1.cov().values + cov2 = df2.cov().values + means1 = np.array(df1.mean()) + means2 = np.array(df2.mean()) + try: + div_kl = algebra.kl_divergence_gaussian_exact(means1, cov1, means2, cov2) + except LinAlgError: + raise ValueError( + "Provided datasets have degenerate colinearities, KL-divergence cannot be computed!" + ) return div_kl -def kl_divergence( +def kl_divergence_pattern( df1: pd.DataFrame, df2: pd.DataFrame, - df_mask: pd.Series, + df_mask: pd.DataFrame, method: str = "columnwise", min_n_rows: int = 10, ) -> pd.Series: @@ -958,21 +929,15 @@ def kl_divergence( Consider using a larger dataset of lowering the parameter `min_n_rows`. """ if method == "columnwise": - cols_numerical = _get_numerical_features(df1) - return columnwise_metric( - df1[cols_numerical], - df2[cols_numerical], - df_mask[cols_numerical], - kl_divergence_1D, - ) + return columnwise_metric(df1, df2, df_mask, kl_divergence_1D, type_cols="numerical") elif method == "gaussian": - cols_numerical = _get_numerical_features(df1) return pattern_based_weighted_mean_metric( - df1[cols_numerical], - df2[cols_numerical], - df_mask[cols_numerical], + df1, + df2, + df_mask, kl_divergence_gaussian, min_n_rows=min_n_rows, + type_cols="numerical", ) else: raise AssertionError( @@ -981,7 +946,7 @@ def kl_divergence( ) -def distance_anticorr(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> float: +def distance_anticorr(df1: pd.DataFrame, df2: pd.DataFrame) -> float: """Score based on the distance anticorrelation between two empirical distributions. The theoretical basis can be found on dcor documentation: https://dcor.readthedocs.io/en/latest/theory.html @@ -992,25 +957,57 @@ def distance_anticorr(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFram Dataframe representing the first empirical distribution df2 : pd.DataFrame Dataframe representing the second empirical distribution - df_mask: pd.DataFrame - Mask indicating on what values the divergence should be computed Returns ------- float Distance correlation score """ - df1 = df1.loc[df_mask.any(axis=1)] - df2 = df2.loc[df_mask.any(axis=1)] return (1 - dcor.distance_correlation(df1.values, df2.values)) / 2 +def distance_anticorr_pattern( + df1: pd.DataFrame, + df2: pd.DataFrame, + df_mask: pd.DataFrame, + min_n_rows: int = 10, +) -> pd.Series: + """Correlation distance computed using a pattern decomposition + + Parameters + ---------- + df1 : pd.DataFrame + First empirical ditribution + df2 : pd.DataFrame + Second empirical ditribution + df_mask : pd.DataFrame + Mask indicating on which values the distance has to computed on + min_n_rows: int + Minimum number of rows for a KL estimation + + Returns + ------- + pd.Series + Series of computed metrics + """ + + return pattern_based_weighted_mean_metric( + df1, + df2, + df_mask, + distance_anticorr, + min_n_rows=min_n_rows, + type_cols="numerical", + ) + + def pattern_based_weighted_mean_metric( df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame, metric: Callable, min_n_rows: int = 10, + type_cols: str = "all", **kwargs, ) -> pd.Series: """Compute a mean score based on missing patterns. @@ -1035,22 +1032,34 @@ def pattern_based_weighted_mean_metric( pd.Series _description_ """ + if type_cols == "all": + cols = df1.columns + elif type_cols == "numerical": + cols = df1.select_dtypes(include=["number"]).columns + elif type_cols == "categorical": + cols = df1.select_dtypes(exclude=["number"]).columns + else: + raise ValueError(f"Value {type_cols} is not valid for parameter `type_cols`!") + if np.any(df_mask & df1.isna()): + raise ValueError("The argument df1 has missing values on the mask!") + if np.any(df_mask & df2.isna()): + raise ValueError("The argument df2 has missing values on the mask!") + rows_mask = df_mask.any(axis=1) scores = [] weights = [] - df1 = df1.loc[df_mask.any(axis=1)] - df2 = df2.loc[df_mask.any(axis=1)] - df_nan = df1.notna() + df1 = df1[cols].loc[rows_mask] + df2 = df2[cols].loc[rows_mask] + df_mask = df_mask[cols].loc[rows_mask] max_num_row = 0 - for tup_pattern, df_nan_pattern in df_nan.groupby(df_nan.columns.tolist()): - ind_pattern = df_nan_pattern.index + for tup_pattern, df_mask_pattern in df_mask.groupby(df_mask.columns.tolist()): + ind_pattern = df_mask_pattern.index df1_pattern = df1.loc[ind_pattern, list(tup_pattern)] max_num_row = max(max_num_row, len(df1_pattern)) if not any(tup_pattern) or len(df1_pattern) < min_n_rows: continue df2_pattern = df2.loc[ind_pattern, list(tup_pattern)] - df_mask_pattern = df_mask.loc[ind_pattern, list(tup_pattern)] weights.append(len(df1_pattern) / len(df1)) - scores.append(metric(df1_pattern, df2_pattern, df_mask_pattern, **kwargs)) + scores.append(metric(df1_pattern, df2_pattern, **kwargs)) if len(scores) == 0: raise NotEnoughSamples(max_num_row, min_n_rows) return pd.Series(sum([s * w for s, w in zip(scores, weights)]), index=["All"]) @@ -1062,20 +1071,14 @@ def get_metric(name: str) -> Callable: "rmse": root_mean_squared_error, "mae": mean_absolute_error, "wmape": weighted_mean_absolute_percentage_error, - "accuracy": partial( - columnwise_metric, - metric=accuracy, - ), + "accuracy": accuracy, "wasserstein_columnwise": dist_wasserstein, - "KL_columnwise": partial(kl_divergence, method="columnwise"), - "KL_gaussian": partial(kl_divergence, method="gaussian"), + "KL_columnwise": partial(kl_divergence_pattern, method="columnwise"), + "KL_gaussian": partial(kl_divergence_pattern, method="gaussian"), "ks_test": kolmogorov_smirnov_test, "correlation_diff": mean_difference_correlation_matrix_numerical_features, "energy": sum_energy_distances, "frechet": frechet_distance_pattern, - "dist_corr_pattern": partial( - pattern_based_weighted_mean_metric, - metric=distance_anticorr, - ), + "dist_corr_pattern": distance_anticorr_pattern, } return dict_metrics[name] diff --git a/qolmat/imputations/em_sampler.py b/qolmat/imputations/em_sampler.py index cd41c86d..785b206a 100644 --- a/qolmat/imputations/em_sampler.py +++ b/qolmat/imputations/em_sampler.py @@ -466,14 +466,14 @@ def _check_conditionning(self, X: NDArray): IllConditioned Data matrix is ill-conditioned due to colinear columns. """ - n_rows, n_cols = X.shape + n_samples, n_cols = X.shape # if n_rows == 1 the function np.cov returns a float - if n_rows == 1: - min_sv = 0 - else: - cov = np.cov(X, bias=True, rowvar=False).reshape(n_cols, -1) - _, sv, _ = spl.svd(cov) - min_sv = min(np.sqrt(sv)) + if n_samples == 1: + raise ValueError("EM cannot be fitted when n_samples = 1!") + + cov = np.cov(X, bias=True, rowvar=False).reshape(n_cols, -1) + _, sv, _ = spl.svd(cov) + min_sv = min(np.sqrt(sv)) if min_sv < self.min_std: warnings.warn( f"The covariance matrix is ill-conditioned, indicating high-colinearity: the " @@ -481,7 +481,6 @@ def _check_conditionning(self, X: NDArray): f"min_std ({min_sv} < {self.min_std}). Consider removing columns of decreasing " f"the threshold." ) - # raise IllConditioned(min_sv, self.min_std) class MultiNormalEM(EM): @@ -683,8 +682,7 @@ def fit_parameters_with_missingness(self, X: NDArray): X : NDArray Data matrix with missingness """ - self.means = np.nanmean(X, axis=0) - self.cov = utils.nancov(X) + self.means, self.cov = utils.nan_mean_cov(X) self.cov_inv = np.linalg.pinv(self.cov) def set_parameters(self, means: NDArray, cov: NDArray): diff --git a/qolmat/imputations/preprocessing.py b/qolmat/imputations/preprocessing.py index 77d500a0..29d48e58 100644 --- a/qolmat/imputations/preprocessing.py +++ b/qolmat/imputations/preprocessing.py @@ -35,17 +35,10 @@ class MixteHGBM(RegressorMixin, BaseEstimator): A custom scikit-learn estimator implementing a mixed model using HistGradientBoostingClassifier for string target data and HistGradientBoostingRegressor for numeric target data. - - Parameters - ---------- - allow_new : bool, default=True - Whether to allow new categories in numerical target data. If false the predictions are - mapped to the closest existing value. """ - def __init__(self, allow_new=True): + def __init__(self): super().__init__() - self.allow_new = allow_new def set_model_parameters(self, **args_model): """ @@ -150,11 +143,12 @@ def fit(self, X: NDArray, y: Optional[NDArray] = None) -> Self: self.feature_names_in_ = df.columns self.n_features_in_ = len(df.columns) self.dict_df_bins_: Dict[Hashable, pd.DataFrame] = dict() - cols = df.columns if self.cols is None else self.cols + if self.cols is None: + cols = df.select_dtypes(include="number").columns + else: + cols = self.cols for col in cols: values = df[col] - if not pd.api.types.is_numeric_dtype(values): - raise TypeError values = values.dropna() df_bins = pd.DataFrame({"value": np.sort(values.unique())}) df_bins["min"] = (df_bins["value"] + df_bins["value"].shift()) / 2 @@ -297,15 +291,17 @@ def transform(self, X: NDArray) -> NDArray: def make_pipeline_mixte_preprocessing( - scale_numerical: bool = True, -) -> BaseEstimator: + scale_numerical: bool = False, avoid_new: bool = False +) -> Pipeline: """ Create a preprocessing pipeline managing mixed type data by one hot encoding categorical data. Parameters ---------- - scale_numerical : bool, default=True + scale_numerical : bool, default=False Whether to scale numerical features. + avoid_new : bool, default=False + Whether to forbid new numerical values. Returns ------- @@ -315,13 +311,17 @@ def make_pipeline_mixte_preprocessing( transformers: List[Tuple] = [] if scale_numerical: transformers += [("num", StandardScaler(), selector(dtype_include=np.number))] + ohe = OneHotEncoder(handle_unknown="ignore", use_cat_names=True) transformers += [("cat", ohe, selector(dtype_exclude=np.number))] - preprocessor = ColumnTransformer(transformers=transformers).set_output(transform="pandas") + col_transformer = ColumnTransformer(transformers=transformers).set_output(transform="pandas") + preprocessor = Pipeline(steps=[("col_transformer", col_transformer)]) + if avoid_new: + preprocessor.steps.append(("bins", BinTransformer())) return preprocessor -def make_robust_MixteHGB(scale_numerical: bool = True, allow_new: bool = True) -> Pipeline: +def make_robust_MixteHGB(scale_numerical: bool = False, avoid_new: bool = False) -> Pipeline: """ Create a robust pipeline for MixteHGBM by one hot encoding categorical features. This estimator is intended for use in ImputerRegressor to deal with mixed type data. @@ -332,10 +332,10 @@ def make_robust_MixteHGB(scale_numerical: bool = True, allow_new: bool = True) - Parameters ---------- - scale_numerical : bool, default=True + scale_numerical : bool, default=False Whether to scale numerical features. - allow_new : bool, default=True - Whether to allow new categories. + avoid_new : bool, default=False + Whether to forbid new numerical values. Returns ------- @@ -343,12 +343,12 @@ def make_robust_MixteHGB(scale_numerical: bool = True, allow_new: bool = True) - A robust pipeline for MixteHGBM. """ preprocessor = make_pipeline_mixte_preprocessing( - scale_numerical=scale_numerical, + scale_numerical=scale_numerical, avoid_new=avoid_new ) robust_MixteHGB = Pipeline( steps=[ ("preprocessor", preprocessor), - ("estimator", MixteHGBM(allow_new=allow_new)), + ("estimator", MixteHGBM()), ] ) diff --git a/qolmat/utils/algebra.py b/qolmat/utils/algebra.py new file mode 100644 index 00000000..9e2af1a6 --- /dev/null +++ b/qolmat/utils/algebra.py @@ -0,0 +1,83 @@ +import numpy as np +import scipy +from numpy.typing import NDArray, ArrayLike + + +def frechet_distance_exact( + means1: NDArray, + cov1: NDArray, + means2: NDArray, + cov2: NDArray, +) -> float: + """Compute the Fréchet distance between two dataframes df1 and df2 + Frechet_distance = || mu_1 - mu_2 ||_2^2 + Tr(Sigma_1 + Sigma_2 - 2(Sigma_1 . Sigma_2)^(1/2)) + It is normalized, df1 and df2 are first scaled by a factor (std(df1) + std(df2)) / 2 + and then centered around (mean(df1) + mean(df2)) / 2 + The result is divided by the number of samples to get an homogeneous result. + Based on: Dowson, D. C., and BV666017 Landau. "The Fréchet distance between multivariate normal + distributions." Journal of multivariate analysis 12.3 (1982): 450-455. + + Parameters + ---------- + means1 : NDArray + Means of the first distribution + cov1 : NDArray + Covariance matrix of the first distribution + means2 : NDArray + Means of the second distribution + cov2 : NDArray + Covariance matrix of the second distribution + + Returns + ------- + float + Frechet distance + """ + n = len(means1) + if (means2.shape != (n,)) or (cov1.shape != (n, n)) or (cov2.shape != (n, n)): + raise ValueError("Inputs have to be of same dimensions.") + + ssdiff = np.sum((means1 - means2) ** 2.0) + product = np.array(cov1 @ cov2) + if product.ndim < 2: + product = product.reshape(-1, 1) + covmean = scipy.linalg.sqrtm(product) + if np.iscomplexobj(covmean): + covmean = covmean.real + frechet_dist = ssdiff + np.trace(cov1 + cov2 - 2.0 * covmean) + + return frechet_dist / n + + +def kl_divergence_gaussian_exact( + means1: NDArray, cov1: NDArray, means2: NDArray, cov2: NDArray +) -> float: + """ + Exact Kullback-Leibler divergence computed between two multivariate normal distributions + Based on https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence + + Parameters + ---------- + means1: NDArray + Mean of the first distribution + cov1: NDArray + Covariance matrx of the first distribution + means2: NDArray + Mean of the second distribution + cov2: NDArray + Covariance matrx of the second distribution + Returns + ------- + float + Kulback-Leibler divergence + """ + n_variables = len(means1) + L1, _ = scipy.linalg.cho_factor(cov1) + L2, _ = scipy.linalg.cho_factor(cov2) + M = scipy.linalg.solve(L2, L1) + y = scipy.linalg.solve(L2, means2 - means1) + norm_M = (M**2).sum().sum() + norm_y = (y**2).sum() + term_diag_L = 2 * np.sum(np.log(np.diagonal(L2) / np.diagonal(L1))) + div_kl = 0.5 * (norm_M - n_variables + norm_y + term_diag_L) + return div_kl diff --git a/qolmat/utils/utils.py b/qolmat/utils/utils.py index d036d9d5..36771ecc 100644 --- a/qolmat/utils/utils.py +++ b/qolmat/utils/utils.py @@ -250,16 +250,11 @@ def create_lag_matrices(X: NDArray, p: int) -> Tuple[NDArray, NDArray]: return Z, Y -def nancov(X: NDArray) -> NDArray: - _, n_cols = X.shape - cov = np.nan * np.zeros((n_cols, n_cols)) - mask = np.isnan(X) - for i in range(n_cols): - Di = X[:, i] - np.nanmean(X[:, i]) - for j in range(n_cols): - select = (~mask[:, i]) & (~mask[:, j]) - Di = X[select, i] - np.mean(X[select, i]) - Dj = X[select, j] - np.mean(X[select, j]) - cov[i, j] = np.nanmean(Di * Dj) - cov = impute_nans(cov, method="zeros") - return cov +def nan_mean_cov(X: NDArray) -> Tuple[NDArray, NDArray]: + _, n_variables = X.shape + means = np.nanmean(X, axis=0) + cov = np.ma.cov(np.ma.masked_invalid(X), rowvar=False).data + print(cov.shape) + print(X.shape) + cov = cov.reshape(n_variables, n_variables) + return means, cov diff --git a/tests/benchmark/test_metrics.py b/tests/benchmark/test_metrics.py index df08fe8e..b2b1f4b7 100644 --- a/tests/benchmark/test_metrics.py +++ b/tests/benchmark/test_metrics.py @@ -2,6 +2,7 @@ # # Evaluation metrics # # ###################### +from math import exp import numpy as np from numpy import random as npr import pandas as pd @@ -97,6 +98,18 @@ def test_weighted_mean_absolute_percentage_error( np.testing.assert_allclose(result, expected, atol=1e-3) +@pytest.mark.parametrize("df1", [df_incomplete]) +@pytest.mark.parametrize("df2", [df_imputed]) +@pytest.mark.parametrize("df_mask", [df_mask]) +def test_accuracy(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> None: + result = metrics.accuracy(df1, df1, df_mask) + expected = pd.Series([1.0, 1.0], index=["col1", "col2"]) + pd.testing.assert_series_equal(result, expected) + result = metrics.accuracy(df1, df2, df_mask) + expected = pd.Series([0.5, 0.0], index=["col1", "col2"]) + pd.testing.assert_series_equal(result, expected, atol=1e-3) + + @pytest.mark.parametrize("df1", [df_incomplete]) @pytest.mark.parametrize("df2", [df_imputed]) @pytest.mark.parametrize("df_mask", [df_mask]) @@ -110,15 +123,19 @@ def test_wasserstein_distance(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd. @pytest.mark.parametrize("df1", [df_incomplete]) @pytest.mark.parametrize("df2", [df_imputed]) @pytest.mark.parametrize("df_mask", [df_mask]) -def test_kl_divergence_columnwise( - df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame -) -> None: - result = metrics.kl_divergence(df1, df1, df_mask, method="columnwise") +def test_kl_divergence(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> None: + result = metrics.kl_divergence_pattern(df1, df1, df_mask, method="columnwise") expected = pd.Series([0.0, 0.0], index=["col1", "col2"]) - np.testing.assert_allclose(result, expected, atol=1e-3) - result = metrics.kl_divergence(df1, df2, df_mask, method="columnwise") + pd.testing.assert_series_equal(result, expected, atol=1e-3) + + result = metrics.kl_divergence_pattern(df1, df2, df_mask, method="columnwise") expected = pd.Series([18.945, 36.637], index=["col1", "col2"]) - np.testing.assert_allclose(result, expected, atol=1e-3) + pd.testing.assert_series_equal(result, expected, atol=1e-3) + + df_nonan = df1.notna() + result = metrics.kl_divergence_pattern(df1, df2, df_nonan, method="gaussian", min_n_rows=2) + expected = pd.Series([1.029], index=["All"]) + pd.testing.assert_series_equal(result, expected, atol=1e-3) @pytest.mark.parametrize("df1", [df_incomplete]) @@ -127,22 +144,22 @@ def test_kl_divergence_columnwise( def test_kl_divergence_gaussian( df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame ) -> None: - result = metrics.kl_divergence_gaussian(df1, df1, df_mask) - np.testing.assert_allclose(result, 0, atol=1e-3) + result = metrics.kl_divergence_gaussian(df1, df1) + np.testing.assert_almost_equal(result, 0, decimal=3) - result = metrics.kl_divergence_gaussian(df1, df2, df_mask) - np.testing.assert_allclose(result, 1.371, atol=1e-3) + result = metrics.kl_divergence_gaussian(df1, df2) + expected = 0.669308 + np.testing.assert_almost_equal(result, expected, decimal=3) @pytest.mark.parametrize("df1", [df_incomplete]) @pytest.mark.parametrize("df2", [df_imputed]) -@pytest.mark.parametrize("df_mask", [df_mask]) -def test_frechet_distance(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> None: - result = metrics.frechet_distance(df1, df1, df_mask) +def test_frechet_distance(df1: pd.DataFrame, df2: pd.DataFrame) -> None: + result = metrics.frechet_distance(df1, df1) np.testing.assert_allclose(result, 0, atol=1e-3) - result = metrics.frechet_distance(df1, df2, df_mask) - np.testing.assert_allclose(result, 0.253, atol=1e-3) + result = metrics.frechet_distance(df1, df2) + np.testing.assert_allclose(result, 0.134, atol=1e-3) @pytest.mark.parametrize("df1", [df_incomplete]) @@ -303,7 +320,7 @@ def test_exception_raise_different_shapes( with pytest.raises(Exception): metrics.mean_difference_correlation_matrix_numerical_features(df1, df2, df_mask) with pytest.raises(Exception): - metrics.frechet_distance(df1, df2, df_mask) + metrics.frechet_distance(df1, df2) @pytest.mark.parametrize("df1", [df_incomplete_cat]) @@ -344,14 +361,6 @@ def test_value_error_get_correlation_f_oneway_matrix( ).equals(pd.Series([np.nan], index=["col1"])) -@pytest.mark.parametrize("df1", [df_incomplete]) -@pytest.mark.parametrize("df2", [df_imputed]) -@pytest.mark.parametrize("df_mask", [df_mask]) -def test_distance_anticorr(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> None: - result = metrics.distance_anticorr(df1, df2, df_mask) - np.testing.assert_allclose(result, 1.1e-4, rtol=1e-2) - - @pytest.mark.parametrize("df1", [df_incomplete]) @pytest.mark.parametrize("df2", [df_imputed]) @pytest.mark.parametrize("df_mask", [df_mask]) @@ -359,29 +368,39 @@ def test_pattern_based_weighted_mean_metric( df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame ) -> None: with pytest.raises(NotEnoughSamples): - metrics.pattern_based_weighted_mean_metric( - df1, df2, df_mask, metric=metrics.distance_anticorr, min_n_rows=5 - ) + metrics.distance_anticorr_pattern(df1, df2, df_mask, min_n_rows=5) - expected = pd.Series([1.1e-4], index=["All"]) - result = metrics.pattern_based_weighted_mean_metric( - df1, df2, df_mask, metric=metrics.distance_anticorr, min_n_rows=1 - ) + expected = pd.Series([1 / 6], index=["All"]) + result = metrics.distance_anticorr_pattern(df1, df2, df_mask, min_n_rows=1) np.testing.assert_allclose(result, expected, rtol=1e-2) rng = npr.default_rng(123) df_gauss1 = pd.DataFrame(rng.multivariate_normal([0, 0], [[1, 0.2], [0.2, 2]], size=100)) df_gauss2 = pd.DataFrame(rng.multivariate_normal([0, 1], [[1, 0.2], [0.2, 2]], size=100)) -df_mask = pd.DataFrame(np.full_like(df_gauss1, True)) +df_mask_gauss = pd.DataFrame(np.full_like(df_gauss1, True)) + + +def test_pattern_mae_comparison(mocker) -> None: + # def mock_metric(values1: pd.Series, values2: pd.Series) -> float: + # call_count += 1 + # return 0 -def test_pattern_mae_comparison() -> None: - def fun_mean_mae(df_gauss1, df_gauss2, df_mask) -> float: - return metrics.mean_squared_error(df_gauss1, df_gauss2, df_mask).mean() + mock_metric = mocker.patch("qolmat.benchmark.metrics.accuracy_1D", return_value=0) + # def fun_mean_mae(df_gauss1, df_gauss2, df_mask_gauss) -> float: + # return metrics.mean_squared_error(df_gauss1, df_gauss2, df_mask_gauss).mean() - result1 = fun_mean_mae(df_gauss1, df_gauss2, df_mask) - result2 = metrics.pattern_based_weighted_mean_metric( - df_gauss1, df_gauss2, df_mask, metric=fun_mean_mae, min_n_rows=1 + print(df_mask) + df_nonan = df_incomplete.notna() + result = metrics.pattern_based_weighted_mean_metric( + df_incomplete, df_imputed, df_nonan, metric=mock_metric, min_n_rows=1 ) - np.testing.assert_allclose(result1, result2, rtol=1e-2) + print(result) + assert mock_metric.call_count == 2 + + +def test_get_metric(): + expected = metrics.accuracy(df_incomplete, df_imputed, df_mask) + result = metrics.get_metric("accuracy")(df_incomplete, df_imputed, df_mask) + pd.testing.assert_series_equal(expected, result) diff --git a/tests/imputations/test_preprocessing.py b/tests/imputations/test_preprocessing.py index cfc25494..30b55bd3 100644 --- a/tests/imputations/test_preprocessing.py +++ b/tests/imputations/test_preprocessing.py @@ -221,8 +221,12 @@ def test_make_robust_MixteHGB(robust_mixte_hgb_model): # Ensure the pipeline is constructed correctly assert isinstance(robust_mixte_hgb_model, Pipeline) - # Ensure the preprocessor in the pipeline is of type ColumnTransformer - assert isinstance(robust_mixte_hgb_model.named_steps["preprocessor"], ColumnTransformer) + dict_steps = robust_mixte_hgb_model.named_steps + assert len(dict_steps) == 2 + # Ensure the preprocessor in the pipeline is of type Pipeline + assert isinstance(dict_steps["preprocessor"], Pipeline) + # Ensure the estimator in the pipeline is of type MixteHGBM + assert isinstance(dict_steps["estimator"], MixteHGBM) # Test fitting and predicting with numeric target X_train, X_test, y_train, y_test = train_test_split( diff --git a/tests/utils/test_algebra.py b/tests/utils/test_algebra.py new file mode 100644 index 00000000..45a508c8 --- /dev/null +++ b/tests/utils/test_algebra.py @@ -0,0 +1,31 @@ +import numpy as np +from sympy import diag + +from qolmat.utils import algebra + + +def test_frechet_distance_exact(): + means1 = np.array([0, 1, 3]) + stds = np.array([1, 1, 1]) + cov1 = np.diag(stds**2) + + means2 = np.array([0, -1, 1]) + cov2 = np.eye(3, 3) + + expected = np.sum((means2 - means1) ** 2) + np.sum((np.sqrt(stds) - 1) ** 2) + expected /= 3 + result = algebra.frechet_distance_exact(means1, cov1, means2, cov2) + np.testing.assert_almost_equal(result, expected, decimal=3) + + +def test_kl_divergence_gaussian_exact(): + means1 = np.array([0, 1, 3]) + stds = np.array([1, 2, 3]) + cov1 = np.diag(stds**2) + + means2 = np.array([0, -1, 1]) + cov2 = np.eye(3, 3) + + expected = (np.sum(stds**2 - np.log(stds**2) - 1 + (means2 - means1) ** 2)) / 2 + result = algebra.kl_divergence_gaussian_exact(means1, cov1, means2, cov2) + np.testing.assert_almost_equal(result, expected, decimal=3) From a2edc535168e8817bcf5f1d1da770f9e2d8b61a5 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 15 Apr 2024 10:24:29 +0200 Subject: [PATCH 72/99] frechet distance refacto --- qolmat/benchmark/metrics.py | 104 +++++++++++--------------------- qolmat/utils/utils.py | 52 +++++++++++++++- tests/benchmark/test_metrics.py | 14 ++--- 3 files changed, 92 insertions(+), 78 deletions(-) diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index 3a2699b7..b68d3e6b 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -59,9 +59,9 @@ def columnwise_metric( if type_cols == "all": cols = df1.columns elif type_cols == "numerical": - cols = _get_numerical_features(df1) + cols = utils._get_numerical_features(df1) elif type_cols == "categorical": - cols = _get_categorical_features(df1) + cols = utils._get_categorical_features(df1) else: raise ValueError(f"Value {type_cols} is not valid for parameter `type_cols`!") values = {} @@ -282,56 +282,6 @@ def dist_wasserstein( ) -def _get_numerical_features(df1: pd.DataFrame) -> List[str]: - """Get numerical features from dataframe - - Parameters - ---------- - df1 : pd.DataFrame - - Returns - ------- - List[str] - List of numerical features - - Raises - ------ - Exception - No numerical feature is found - """ - cols_numerical = df1.select_dtypes(include=np.number).columns.tolist() - if len(cols_numerical) == 0: - raise Exception("No numerical feature is found.") - else: - return cols_numerical - - -def _get_categorical_features(df1: pd.DataFrame) -> List[str]: - """Get categorical features from dataframe - - Parameters - ---------- - df1 : pd.DataFrame - - Returns - ------- - List[str] - List of categorical features - - Raises - ------ - Exception - No categorical feature is found - """ - - cols_numerical = df1.select_dtypes(include=np.number).columns.tolist() - cols_categorical = [col for col in df1.columns.to_list() if col not in cols_numerical] - if len(cols_categorical) == 0: - raise Exception("No categorical feature is found.") - else: - return cols_categorical - - def kolmogorov_smirnov_test_1D(df1: pd.Series, df2: pd.Series) -> float: """Compute KS test statistic of the two-sample Kolmogorov-Smirnov test for goodness of fit. See more in https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html. @@ -418,7 +368,7 @@ def total_variance_distance( pd.Series Total variance distance """ - cols_categorical = _get_categorical_features(df1) + cols_categorical = utils._get_categorical_features(df1) return columnwise_metric( df1[cols_categorical], df2[cols_categorical], @@ -491,7 +441,7 @@ def mean_difference_correlation_matrix_numerical_features( _check_same_number_columns(df1, df2) - cols_numerical = _get_numerical_features(df1) + cols_numerical = utils._get_numerical_features(df1) df_corr1 = _get_correlation_pearson_matrix(df1[cols_numerical], use_p_value=use_p_value) df_corr2 = _get_correlation_pearson_matrix(df2[cols_numerical], use_p_value=use_p_value) @@ -560,7 +510,7 @@ def mean_difference_correlation_matrix_categorical_features( _check_same_number_columns(df1, df2) - cols_categorical = _get_categorical_features(df1) + cols_categorical = utils._get_categorical_features(df1) df_corr1 = _get_correlation_chi2_matrix(df1[cols_categorical], use_p_value=use_p_value) df_corr2 = _get_correlation_chi2_matrix(df2[cols_categorical], use_p_value=use_p_value) @@ -635,8 +585,8 @@ def mean_diff_corr_matrix_categorical_vs_numerical_features( _check_same_number_columns(df1, df2) - cols_categorical = _get_categorical_features(df1) - cols_numerical = _get_numerical_features(df1) + cols_categorical = utils._get_categorical_features(df1) + cols_numerical = utils._get_numerical_features(df1) df_corr1 = _get_correlation_f_oneway_matrix( df1, cols_categorical, cols_numerical, use_p_value=use_p_value ) @@ -763,10 +713,10 @@ def sum_pairwise_distances( ########################### -def frechet_distance( +def frechet_distance_base( df1: pd.DataFrame, df2: pd.DataFrame, -) -> float: +) -> pd.Series: """Compute the Fréchet distance between two dataframes df1 and df2 Frechet_distance = || mu_1 - mu_2 ||_2^2 + Tr(Sigma_1 + Sigma_2 - 2(Sigma_1 . Sigma_2)^(1/2)) It is normalized, df1 and df2 are first scaled by a factor (std(df1) + std(df2)) / 2 @@ -783,8 +733,8 @@ def frechet_distance( Returns ------- - float - frechet distance + pd.Series + Frechet distance in a Series object """ if df1.shape != df2.shape: @@ -798,16 +748,23 @@ def frechet_distance( means1, cov1 = utils.nan_mean_cov(df1.values) means2, cov2 = utils.nan_mean_cov(df2.values) - return algebra.frechet_distance_exact(means1, cov1, means2, cov2) + distance = algebra.frechet_distance_exact(means1, cov1, means2, cov2) + return pd.Series(distance, index=["All"]) -def frechet_distance_pattern( +def frechet_distance( df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame, + method: str = "single", min_n_rows: int = 10, ) -> pd.Series: - """Frechet distance computed using a pattern decomposition + """ + Frechet distance computed using a pattern decomposition. Several variant are implemented: + - the `single` method relies on a single estimation of the means and covariance matrix. It is + relevent for MCAR data. + - the `pattern`method relies on the aggregation of the estimated distance between each + pattern. It is relevent for MAR data. Parameters ---------- @@ -817,6 +774,9 @@ def frechet_distance_pattern( Second empirical ditribution df_mask : pd.DataFrame Mask indicating on which values the distance has to computed on + method: str + Method used to compute the distance on multivariate datasets with missing values. + Possible values are `robust` and `pattern`. min_n_rows: int Minimum number of rows for a KL estimation @@ -826,6 +786,8 @@ def frechet_distance_pattern( Series of computed metrics """ + if method == "single": + return frechet_distance_base(df1, df2) return pattern_based_weighted_mean_metric( df1, df2, @@ -890,7 +852,7 @@ def kl_divergence_gaussian(df1: pd.DataFrame, df2: pd.DataFrame) -> float: return div_kl -def kl_divergence_pattern( +def kl_divergence( df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame, @@ -913,7 +875,8 @@ def kl_divergence_pattern( df_mask: pd.DataFrame Mask indicating on what values the divergence should be computed method: str - Method used + Method used to compute the divergence on multivariate datasets with missing values. + Possible values are `columnwise` and `gaussian`. min_n_rows: int Minimum number of rows for a KL estimation @@ -1073,12 +1036,13 @@ def get_metric(name: str) -> Callable: "wmape": weighted_mean_absolute_percentage_error, "accuracy": accuracy, "wasserstein_columnwise": dist_wasserstein, - "KL_columnwise": partial(kl_divergence_pattern, method="columnwise"), - "KL_gaussian": partial(kl_divergence_pattern, method="gaussian"), - "ks_test": kolmogorov_smirnov_test, + "KL_columnwise": partial(kl_divergence, method="columnwise"), + "KL_gaussian": partial(kl_divergence, method="gaussian"), + "KS_test": kolmogorov_smirnov_test, "correlation_diff": mean_difference_correlation_matrix_numerical_features, "energy": sum_energy_distances, - "frechet": frechet_distance_pattern, + "frechet_single": partial(frechet_distance, method="single"), + "frechet_pattern": partial(frechet_distance, method="pattern"), "dist_corr_pattern": distance_anticorr_pattern, } return dict_metrics[name] diff --git a/qolmat/utils/utils.py b/qolmat/utils/utils.py index 36771ecc..43433ea9 100644 --- a/qolmat/utils/utils.py +++ b/qolmat/utils/utils.py @@ -1,4 +1,4 @@ -from typing import Optional, Tuple, Union +from typing import List, Optional, Tuple, Union import warnings import numpy as np @@ -12,6 +12,56 @@ HyperValue = Union[int, float, str] +def _get_numerical_features(df1: pd.DataFrame) -> List[str]: + """Get numerical features from dataframe + + Parameters + ---------- + df1 : pd.DataFrame + + Returns + ------- + List[str] + List of numerical features + + Raises + ------ + Exception + No numerical feature is found + """ + cols_numerical = df1.select_dtypes(include=np.number).columns.tolist() + if len(cols_numerical) == 0: + raise Exception("No numerical feature is found.") + else: + return cols_numerical + + +def _get_categorical_features(df1: pd.DataFrame) -> List[str]: + """Get categorical features from dataframe + + Parameters + ---------- + df1 : pd.DataFrame + + Returns + ------- + List[str] + List of categorical features + + Raises + ------ + Exception + No categorical feature is found + """ + + cols_numerical = df1.select_dtypes(include=np.number).columns.tolist() + cols_categorical = [col for col in df1.columns.to_list() if col not in cols_numerical] + if len(cols_categorical) == 0: + raise Exception("No categorical feature is found.") + else: + return cols_categorical + + def _validate_input(X: NDArray) -> pd.DataFrame: """ Checks that the input X can be converted into a DataFrame, and returns the corresponding diff --git a/tests/benchmark/test_metrics.py b/tests/benchmark/test_metrics.py index b2b1f4b7..a714e81d 100644 --- a/tests/benchmark/test_metrics.py +++ b/tests/benchmark/test_metrics.py @@ -124,16 +124,16 @@ def test_wasserstein_distance(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd. @pytest.mark.parametrize("df2", [df_imputed]) @pytest.mark.parametrize("df_mask", [df_mask]) def test_kl_divergence(df1: pd.DataFrame, df2: pd.DataFrame, df_mask: pd.DataFrame) -> None: - result = metrics.kl_divergence_pattern(df1, df1, df_mask, method="columnwise") + result = metrics.kl_divergence(df1, df1, df_mask, method="columnwise") expected = pd.Series([0.0, 0.0], index=["col1", "col2"]) pd.testing.assert_series_equal(result, expected, atol=1e-3) - result = metrics.kl_divergence_pattern(df1, df2, df_mask, method="columnwise") + result = metrics.kl_divergence(df1, df2, df_mask, method="columnwise") expected = pd.Series([18.945, 36.637], index=["col1", "col2"]) pd.testing.assert_series_equal(result, expected, atol=1e-3) df_nonan = df1.notna() - result = metrics.kl_divergence_pattern(df1, df2, df_nonan, method="gaussian", min_n_rows=2) + result = metrics.kl_divergence(df1, df2, df_nonan, method="gaussian", min_n_rows=2) expected = pd.Series([1.029], index=["All"]) pd.testing.assert_series_equal(result, expected, atol=1e-3) @@ -154,11 +154,11 @@ def test_kl_divergence_gaussian( @pytest.mark.parametrize("df1", [df_incomplete]) @pytest.mark.parametrize("df2", [df_imputed]) -def test_frechet_distance(df1: pd.DataFrame, df2: pd.DataFrame) -> None: - result = metrics.frechet_distance(df1, df1) +def test_frechet_distance_base(df1: pd.DataFrame, df2: pd.DataFrame) -> None: + result = metrics.frechet_distance_base(df1, df1) np.testing.assert_allclose(result, 0, atol=1e-3) - result = metrics.frechet_distance(df1, df2) + result = metrics.frechet_distance_base(df1, df2) np.testing.assert_allclose(result, 0.134, atol=1e-3) @@ -320,7 +320,7 @@ def test_exception_raise_different_shapes( with pytest.raises(Exception): metrics.mean_difference_correlation_matrix_numerical_features(df1, df2, df_mask) with pytest.raises(Exception): - metrics.frechet_distance(df1, df2) + metrics.frechet_distance_base(df1, df2) @pytest.mark.parametrize("df1", [df_incomplete_cat]) From 397d26f8d0c7e3ddf31dc06c6084d4bd8bb879b5 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 15 Apr 2024 14:30:17 +0200 Subject: [PATCH 73/99] frechet distance refacto --- examples/benchmark.md | 17 +---------------- qolmat/benchmark/metrics.py | 14 ++++++++------ qolmat/imputations/preprocessing.py | 5 ++++- qolmat/utils/utils.py | 4 +--- tests/benchmark/test_metrics.py | 10 +--------- tests/imputations/test_preprocessing.py | 2 ++ 6 files changed, 17 insertions(+), 35 deletions(-) diff --git a/examples/benchmark.md b/examples/benchmark.md index e2fe8c1d..be5a73bf 100644 --- a/examples/benchmark.md +++ b/examples/benchmark.md @@ -16,9 +16,6 @@ jupyter: **This notebook aims to present the Qolmat repo through an example of a multivariate time series. In Qolmat, a few data imputation methods are implemented as well as a way to evaluate their performance.** -```python - -``` First, import some useful librairies @@ -36,26 +33,18 @@ from IPython.display import Image import pandas as pd from datetime import datetime import numpy as np -import scipy import hyperopt as ho -from hyperopt.pyll.base import Apply as hoApply np.random.seed(1234) -import pprint from matplotlib import pyplot as plt -import matplotlib.image as mpimg import matplotlib.ticker as plticker tab10 = plt.get_cmap("tab10") plt.rcParams.update({'font.size': 18}) -from typing import Optional from sklearn.linear_model import LinearRegression -from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor - -import sys -from qolmat.benchmark import comparator, missing_patterns, hyperparameters +from qolmat.benchmark import comparator, missing_patterns from qolmat.imputations import imputers from qolmat.utils import data, utils, plot @@ -239,10 +228,6 @@ df_plot = data.add_datetime_features(df_plot, col_time="date") dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()} ``` -```python tags=[] -dfs_imputed["VAR_max"].groupby("station").min() -``` - ```python tags=[] station = df_plot.index.get_level_values("station")[0] # station = "Huairou" diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index b68d3e6b..00ca0518 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -368,12 +368,12 @@ def total_variance_distance( pd.Series Total variance distance """ - cols_categorical = utils._get_categorical_features(df1) return columnwise_metric( - df1[cols_categorical], - df2[cols_categorical], - df_mask[cols_categorical], + df1, + df2, + df_mask, _total_variance_distance_1D, + type_cols="categorical", ) @@ -792,7 +792,7 @@ def frechet_distance( df1, df2, df_mask, - frechet_distance, + frechet_distance_base, min_n_rows=min_n_rows, type_cols="numerical", ) @@ -1003,10 +1003,12 @@ def pattern_based_weighted_mean_metric( cols = df1.select_dtypes(exclude=["number"]).columns else: raise ValueError(f"Value {type_cols} is not valid for parameter `type_cols`!") + if np.any(df_mask & df1.isna()): raise ValueError("The argument df1 has missing values on the mask!") if np.any(df_mask & df2.isna()): raise ValueError("The argument df2 has missing values on the mask!") + rows_mask = df_mask.any(axis=1) scores = [] weights = [] @@ -1041,7 +1043,7 @@ def get_metric(name: str) -> Callable: "KS_test": kolmogorov_smirnov_test, "correlation_diff": mean_difference_correlation_matrix_numerical_features, "energy": sum_energy_distances, - "frechet_single": partial(frechet_distance, method="single"), + "frechet": partial(frechet_distance, method="single"), "frechet_pattern": partial(frechet_distance, method="pattern"), "dist_corr_pattern": distance_anticorr_pattern, } diff --git a/qolmat/imputations/preprocessing.py b/qolmat/imputations/preprocessing.py index 29d48e58..15ff048e 100644 --- a/qolmat/imputations/preprocessing.py +++ b/qolmat/imputations/preprocessing.py @@ -314,10 +314,13 @@ def make_pipeline_mixte_preprocessing( ohe = OneHotEncoder(handle_unknown="ignore", use_cat_names=True) transformers += [("cat", ohe, selector(dtype_exclude=np.number))] - col_transformer = ColumnTransformer(transformers=transformers).set_output(transform="pandas") + col_transformer = ColumnTransformer(transformers=transformers, remainder="passthrough") + col_transformer = col_transformer.set_output(transform="pandas") preprocessor = Pipeline(steps=[("col_transformer", col_transformer)]) + if avoid_new: preprocessor.steps.append(("bins", BinTransformer())) + print(preprocessor) return preprocessor diff --git a/qolmat/utils/utils.py b/qolmat/utils/utils.py index 43433ea9..ce8f7865 100644 --- a/qolmat/utils/utils.py +++ b/qolmat/utils/utils.py @@ -288,7 +288,7 @@ def get_shape_original(M: NDArray, shape: tuple) -> NDArray: def create_lag_matrices(X: NDArray, p: int) -> Tuple[NDArray, NDArray]: - n_rows, n_cols = X.shape + n_rows, _ = X.shape n_rows_new = n_rows - p list_X_lag = [np.ones((n_rows_new, 1))] for lag in range(p): @@ -304,7 +304,5 @@ def nan_mean_cov(X: NDArray) -> Tuple[NDArray, NDArray]: _, n_variables = X.shape means = np.nanmean(X, axis=0) cov = np.ma.cov(np.ma.masked_invalid(X), rowvar=False).data - print(cov.shape) - print(X.shape) cov = cov.reshape(n_variables, n_variables) return means, cov diff --git a/tests/benchmark/test_metrics.py b/tests/benchmark/test_metrics.py index a714e81d..0c768054 100644 --- a/tests/benchmark/test_metrics.py +++ b/tests/benchmark/test_metrics.py @@ -383,20 +383,12 @@ def test_pattern_based_weighted_mean_metric( def test_pattern_mae_comparison(mocker) -> None: - # def mock_metric(values1: pd.Series, values2: pd.Series) -> float: - # call_count += 1 - # return 0 - mock_metric = mocker.patch("qolmat.benchmark.metrics.accuracy_1D", return_value=0) - # def fun_mean_mae(df_gauss1, df_gauss2, df_mask_gauss) -> float: - # return metrics.mean_squared_error(df_gauss1, df_gauss2, df_mask_gauss).mean() - print(df_mask) df_nonan = df_incomplete.notna() - result = metrics.pattern_based_weighted_mean_metric( + metrics.pattern_based_weighted_mean_metric( df_incomplete, df_imputed, df_nonan, metric=mock_metric, min_n_rows=1 ) - print(result) assert mock_metric.call_count == 2 diff --git a/tests/imputations/test_preprocessing.py b/tests/imputations/test_preprocessing.py index 30b55bd3..5226c332 100644 --- a/tests/imputations/test_preprocessing.py +++ b/tests/imputations/test_preprocessing.py @@ -198,6 +198,8 @@ def test_preprocessing_pipeline(preprocessing_pipeline): # Test with numerical features X_num = pd.DataFrame([[1, 2], [3, 4], [5, 6]]) X_transformed = preprocessing_pipeline.fit_transform(X_num) + print(X_num.shape) + print(X_transformed.shape) assert isinstance(X_transformed, pd.DataFrame) assert X_transformed.shape[1] == X_num.shape[1] From 6554d5e3cb212633ad1fa863a3d1f7dd4ca4ead0 Mon Sep 17 00:00:00 2001 From: Gsaes Date: Mon, 15 Apr 2024 16:48:37 +0200 Subject: [PATCH 74/99] test data --- .coveragerc | 2 + pytest.ini | 2 + qolmat/utils/data.py | 96 ++++++++++++--------- tests/utils/test_data.py | 176 +++++++++++++++++++++++++++++++++++---- 4 files changed, 224 insertions(+), 52 deletions(-) create mode 100644 .coveragerc create mode 100644 pytest.ini diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 00000000..6d420485 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,2 @@ +[run] +omit = qolmat/_version.py diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 00000000..2e3719d8 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,2 @@ +[pytest] +addopts = --cov=qolmat diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index f56de4ca..aa0e9306 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -3,7 +3,7 @@ import zipfile from datetime import datetime from math import pi -from typing import List +from typing import List, Tuple, Union from urllib import request import numpy as np @@ -36,6 +36,20 @@ def read_csv_local(data_file_name: str, **kwargs) -> pd.DataFrame: def download_data_from_zip( zipname: str, urllink: str, datapath: str = "data/" ) -> List[pd.DataFrame]: + """ + Downloads and extracts ZIP files from a URL, then loads DataFrames from CSV files. + + Args: + zipname (str): Name of the ZIP file to download, without the '.zip' extension. + urllink (str): Base URL where the ZIP file is hosted. + datapath (str, optional): Path to the directory where the ZIP will be \ + downloaded and extracted. Defaults to 'data/'. + + Returns: + List[pd.DataFrame]: A list of DataFrames loaded from the CSV \ + files within the extracted directory. + """ + path_zip = os.path.join(datapath, zipname) path_zip_ext = path_zip + ".zip" url = os.path.join(urllink, zipname) + ".zip" @@ -50,6 +64,21 @@ def download_data_from_zip( def get_dataframes_in_folder(path: str, extension: str) -> List[pd.DataFrame]: + """ + Loads all dataframes from files with a specified extension within a directory,\ + including subdirectories. + Special handling for '.tsf' files which are converted and immediately returned. + + Args: + path (str): Path to the directory to search for files. + extension (str): File extension to filter files by, e.g., '.csv'. + + Returns: + List[pd.DataFrame]: A list of pandas DataFrames loaded from the files \ + matching the extension. + If a '.tsf' file is found, its converted DataFrame \ + is returned immediately. + """ list_df = [] for folder, _, files in os.walk(path): for file in files: @@ -61,7 +90,29 @@ def get_dataframes_in_folder(path: str, extension: str) -> List[pd.DataFrame]: return list_df -def generate_artificial_ts(n_samples, periods, amp_anomalies, ratio_anomalies, amp_noise): +def generate_artificial_ts( + n_samples: int, + periods: List[int], + amp_anomalies: float, + ratio_anomalies: float, + amp_noise: float, +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """ + Generates an time series data, anomalies, and noise based on given parameters. + + Args: + n_samples (int): Number of samples in the time series. + periods (List[int]): List of periods that are added to the time series. + amp_anomalies (float): Amplitude multiplier for anomalies. + ratio_anomalies (float): Ratio of total samples that will be anomalies. + amp_noise (float): Standard deviation of Gaussian noise. + + Returns: + Tuple[np.ndarray, np.ndarray, np.ndarray]: + - Time series data with sine waves (X). + - Anomaly data with specified amplitudes at random positions (A). + - Gaussian noise added to the time series (E). + """ mesh = np.arange(n_samples) X = np.ones(n_samples) for p in periods: @@ -102,17 +153,7 @@ def get_data( if name_data == "Beijing": df = read_csv_local("beijing") df["date"] = pd.to_datetime(df["date"]) - - # df["date"] = pd.to_datetime( - # { - # "year": df["year"], - # "month": df["month"], - # "day": df["day"], - # "hour": df["hour"], - # } - # ) df = df.drop(columns=["year", "month", "day", "hour", "wd"]) - # df = df.set_index(["station", "date"]) df = df.groupby(["station", "date"]).mean() return df elif name_data == "Superconductor": @@ -272,22 +313,16 @@ def add_holes(df: pd.DataFrame, ratio_masked: float, mean_size: int) -> pd.DataF ratio_masked : float Targeted global proportion of nans added in the returned dataset - - groups: list of strings - List of the column names used as groups - Returns ------- pd.DataFrame dataframe with missing values """ - try: - groups = df.index.names.difference(["datetime", "date", "index"]) + groups = df.index.names.difference(["datetime", "date", "index"]) + if groups != []: generator = missing_patterns.GeometricHoleGenerator( 1, ratio_masked=ratio_masked, subset=df.columns, groups=groups ) - except ValueError: - print("No group") else: generator = missing_patterns.GeometricHoleGenerator( 1, ratio_masked=ratio_masked, subset=df.columns @@ -388,42 +423,27 @@ def convert_tsf_to_dataframe( col_types = [] all_data = {} line_count = 0 - # frequency = None - # forecast_horizon = None - # contain_missing_values = None - # contain_equal_length = None found_data_tag = False found_data_section = False started_reading_data_section = False with open(full_file_path_and_name, "r", encoding="cp1252") as file: for line in file: - # Strip white space from start/end of line line = line.strip() if line: - if line.startswith("@"): # Read meta-data + if line.startswith("@"): if not line.startswith("@data"): line_content = line.split(" ") if line.startswith("@attribute"): - if len(line_content) != 3: # Attributes have both name and type + if len(line_content) != 3: raise Exception("Invalid meta-data specification.") col_names.append(line_content[1]) col_types.append(line_content[2]) else: - if len(line_content) != 2: # Other meta-data have only values + if len(line_content) != 2: raise Exception("Invalid meta-data specification.") - - # if line.startswith("@frequency"): - # frequency = line_content[1] - # elif line.startswith("@horizon"): - # forecast_horizon = int(line_content[1]) - # elif line.startswith("@missing"): - # contain_missing_values = bool(strtobool(line_content[1])) - # elif line.startswith("@equallength"): - # contain_equal_length = bool(strtobool(line_content[1])) - else: if len(col_names) == 0: raise Exception("Attribute section must come before data.") diff --git a/tests/utils/test_data.py b/tests/utils/test_data.py index 53642c0c..ec0a609f 100644 --- a/tests/utils/test_data.py +++ b/tests/utils/test_data.py @@ -1,10 +1,11 @@ import datetime +import os import numpy as np import pandas as pd import pytest from pytest_mock.plugin import MockerFixture - +from unittest.mock import MagicMock, patch from qolmat.utils import data columns = ["station", "date", "year", "month", "day", "hour", "a", "b", "wd"] @@ -120,16 +121,139 @@ index=index_preprocess_offline, ) +df_sncf = pd.DataFrame( + { + "station": [ + "Gare du Nord", + "Gare du Nord", + "Gare de Lyon", + "Gare de Lyon", + "Gare Montparnasse", + "Gare Montparnasse", + ], + "val_in": [120, np.nan, 180, np.nan, 140, 130], + } +) +df_sncf.set_index("station", inplace=True) + +df_titanic = pd.DataFrame( + { + "pclass": [1, 2, 3], + "name": ["Name1", "Name2", "Name3"], + "home.dest": ["Home1", "Home2", "Home3"], + "cabin": ["C1", None, "C3"], + "ticket": ["T1", "T2", "T3"], + "boat": ["B1", None, "B3"], + "body": [None, 200, None], + "age": ["22", "unknown", "33"], + "fare": ["210.5", "15.5", "7.25"], + } +) + +df_beijing_without_preprocess = pd.DataFrame( + { + "year": [2020, 2020, 2020], + "month": [1, 1, 1], + "day": [1, 1, 1], + "hour": [0, 1, 2], + "No": [1, 2, 3], + "cbwd": ["NW", "NW", "NW"], + "Iws": [23.5, 24.6, 25.7], + "Is": [0, 0, 0], + "Ir": [0, 0, 0], + "pm2.5": [200, 180, 150], + } +) + urllink = "https://archive.ics.uci.edu/ml/machine-learning-databases/00501/" zipname = "PRSA2017_Data_20130301-20170228" -# @pytest.mark.parametrize("zipname, urllink", [(zipname, urllink)]) -# def test_utils_data_download_data(zipname: str, urllink: str, mocker: MockerFixture) -> None: -# mocker.patch("urllib.request.urlretrieve") -# mocker.patch("zipfile.ZipFile") -# list_df_result = data.download_data_from_zip(zipname, urllink) +@patch("pandas.read_csv", return_value=df_beijing) +def test_read_csv_local(mock_read_csv): + result_df = data.read_csv_local("beijing") + pd.testing.assert_frame_equal(result_df, df_beijing) + mock_read_csv.assert_called() + + +@patch("os.makedirs") +@patch("os.path.exists") +@patch("urllib.request.urlretrieve") +@patch("zipfile.ZipFile") +@patch("qolmat.utils.data.get_dataframes_in_folder") +def test_download_data_from_zip_all_cases( + mock_get_dataframes_in_folder, mock_zipfile, mock_urlretrieve, mock_exists, mock_makedirs +): + mock_exists.side_effect = [False, False, False, True] + mock_zipfile.return_value.__enter__.return_value = MagicMock() + + expected_dfs = [pd.DataFrame([1]), pd.DataFrame([2])] + mock_get_dataframes_in_folder.return_value = expected_dfs + + result_dfs = data.download_data_from_zip("zipname", "http://example.com/") + + assert result_dfs == expected_dfs + mock_urlretrieve.assert_called_once_with("http://example.com/zipname.zip", "data/zipname.zip") + mock_zipfile.assert_called_once_with("data/zipname.zip", "r") + mock_makedirs.assert_called_once_with("data/", exist_ok=True) + mock_get_dataframes_in_folder.assert_called_once_with("data/zipname", ".csv") + + mock_urlretrieve.reset_mock() + mock_zipfile.reset_mock() + mock_makedirs.reset_mock() + mock_exists.side_effect = [True, True] + + result_dfs = data.download_data_from_zip("zipname", "http://example.com/") + assert result_dfs == expected_dfs + mock_urlretrieve.assert_not_called() + mock_zipfile.assert_not_called() + mock_makedirs.assert_called_once_with("data/", exist_ok=True) + mock_get_dataframes_in_folder.assert_called_with("data/zipname", ".csv") + + +@patch("os.walk") +@patch("pandas.read_csv", return_value=df_conductor) +@patch("qolmat.utils.data.convert_tsf_to_dataframe", return_value=df_beijing) +def test_get_dataframes_in_folder(mock_convert_tsf, mock_read_csv, mock_walk): + mock_walk.return_value = [("/fakepath", ("subfolder",), ("file.csv",))] + result_csv = data.get_dataframes_in_folder("/fakepath", ".csv") + assert len(result_csv) == 1 + mock_read_csv.assert_called_once_with("/fakepath/file.csv") + pd.testing.assert_frame_equal(result_csv[0], df_conductor) + + mock_read_csv.reset_mock() + mock_convert_tsf.reset_mock() + mock_walk.return_value = [("/fakepath", ("subfolder",), ("file.tsf",))] + result_tsf = data.get_dataframes_in_folder("/fakepath", ".tsf") + assert len(result_tsf) == 1 + mock_convert_tsf.assert_called_once_with("/fakepath/file.tsf") + pd.testing.assert_frame_equal(result_tsf[0], df_beijing) + mock_read_csv.assert_called() + + +@patch("numpy.random.normal") +@patch("numpy.random.choice") +@patch("numpy.random.standard_exponential") +def test_generate_artificial_ts(mock_standard_exponential, mock_choice, mock_normal): + n_samples = 100 + periods = [10, 20] + amp_anomalies = 1.0 + ratio_anomalies = 0.1 + amp_noise = 0.1 + + mock_standard_exponential.return_value = np.ones(int(n_samples * ratio_anomalies)) + mock_choice.return_value = np.arange(int(n_samples * ratio_anomalies)) + mock_normal.return_value = np.zeros(n_samples) + + X, A, E = data.generate_artificial_ts( + n_samples, periods, amp_anomalies, ratio_anomalies, amp_noise + ) + + assert len(X) == n_samples + assert len(A) == n_samples + assert len(E) == n_samples + assert np.all(E == 0) @pytest.mark.parametrize( @@ -142,14 +266,17 @@ ("Monach_weather", df_monach_weather), ("Monach_electricity_australia", df_monach_elec), ("Artificial", None), + ("Titanic", df_titanic), + ("SNCF", df_sncf), ("Bug", None), ], ) -def test_utils_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFixture) -> None: +def test_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFixture) -> None: mock_download = mocker.patch("qolmat.utils.data.download_data_from_zip", return_value=[df]) mock_read = mocker.patch("qolmat.utils.data.read_csv_local", return_value=df) mock_read_dl = mocker.patch("pandas.read_csv", return_value=df) mocker.patch("qolmat.utils.data.preprocess_data_beijing", return_value=df_preprocess_beijing) + mocker.patch("pandas.read_parquet", return_value=df_sncf) try: df_result = data.get_data(name_data=name_data) @@ -164,6 +291,8 @@ def test_utils_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFix "Monach_weather", "Monach_weather", "Monach_electricity_australia", + "Titanic", + "SNCF", ] np.testing.assert_raises(ValueError, data.get_data, name_data) return @@ -194,15 +323,36 @@ def test_utils_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFix elif name_data == "Monach_electricity_australia": assert mock_download.call_count == 1 pd.testing.assert_frame_equal(df_result, df_monach_elec_preprocess) + elif name_data == "Titanic": + assert mock_read.call_count == 1 + assert np.shape(df_result) == (3, 2) + elif name_data == "SNCF": + print("=" * 100) + print(df_result) + assert not df_result.empty + assert df_result.index.name == "station" + assert df_result["val_in"].sum() == df["val_in"].sum() else: assert False +@pytest.mark.parametrize("df", [df_beijing_without_preprocess]) +def test_preprocess_data_beijing(df: pd.DataFrame) -> None: + result_df = data.preprocess_data_beijing(df) + + assert "year" not in result_df.columns + assert "pm2.5" in result_df.columns + assert result_df.index.names == ["station", "datetime"] + assert all(result_df.index.get_level_values("station") == "Beijing") + assert len(result_df) == 1 + assert np.isclose(result_df.loc[(("Beijing"),), "pm2.5"], 176.66666666666666) + + @pytest.mark.parametrize("df", [df_preprocess_offline]) -def test_utils_data_add_holes(df: pd.DataFrame) -> None: +def test_data_add_holes(df: pd.DataFrame) -> None: df_out = data.add_holes(df, 0.0, 1) assert df_out.isna().sum().sum() == 2 - df_out = data.add_holes(df, 1.0, 1) + df_out = data.add_holes(df.loc[("Gucheng",)], 1.0, 1) assert df_out.isna().sum().sum() > 2 @@ -212,9 +362,7 @@ def test_utils_data_add_holes(df: pd.DataFrame) -> None: ("Beijing", df_beijing), ], ) -def test_utils_data_get_data_corrupted( - name_data: str, df: pd.DataFrame, mocker: MockerFixture -) -> None: +def test_data_get_data_corrupted(name_data: str, df: pd.DataFrame, mocker: MockerFixture) -> None: mock_get = mocker.patch("qolmat.utils.data.get_data", return_value=df) df_out = data.get_data_corrupted(name_data) assert mock_get.call_count == 1 @@ -225,7 +373,7 @@ def test_utils_data_get_data_corrupted( @pytest.mark.parametrize("df", [df_preprocess_beijing]) -def test_utils_data_add_station_features(df: pd.DataFrame) -> None: +def test_data_add_station_features(df: pd.DataFrame) -> None: columns_out = ["a", "b"] + ["station=Beijing"] expected = pd.DataFrame( [ @@ -241,7 +389,7 @@ def test_utils_data_add_station_features(df: pd.DataFrame) -> None: @pytest.mark.parametrize("df", [df_preprocess_beijing]) -def test_utils_data_add_datetime_features(df: pd.DataFrame) -> None: +def test_data_add_datetime_features(df: pd.DataFrame) -> None: columns_out = ["a", "b"] + ["time_cos", "time_sin"] result = data.add_datetime_features(df) pd.testing.assert_index_equal(result.index, df.index) From 05417ca6940b0d0965d7d2667874f4dc61315ad2 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 15 Apr 2024 17:01:42 +0200 Subject: [PATCH 75/99] updated history --- HISTORY.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/HISTORY.rst b/HISTORY.rst index 5527f636..d87213c9 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,7 +2,7 @@ History ======= -0.1.4 (2024-04-08) +0.1.4 (2024-04-15) ------------------ * ImputerMean, ImputerMedian and ImputerMode have been merged into ImputerSimple From 17647857b6047b60f8b65b332d62009fd88da323 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 16 Apr 2024 11:28:53 +0200 Subject: [PATCH 76/99] titanic dataset is not downloaded --- .github/workflows/publish.yml | 4 ++-- AUTHORS.rst | 9 ++++---- qolmat/imputations/preprocessing.py | 1 - qolmat/utils/data.py | 34 ++++++++++++++++------------- 4 files changed, 26 insertions(+), 22 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 62d0fbe6..9bebdd00 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -11,9 +11,9 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v3 + uses: actions/setup-python@v3.12.0 with: python-version: '3.10' - name: Install dependencies diff --git a/AUTHORS.rst b/AUTHORS.rst index 73cad09b..cc72345f 100644 --- a/AUTHORS.rst +++ b/AUTHORS.rst @@ -5,10 +5,10 @@ Credits Development Team ---------------- -* Julien Roussel -* Anh Khoa Ngo Ho -* Charles-Henri Prat -* Guillaume Saës +* Julien Roussel +* Anh Khoa Ngo Ho +* Guillaume Saës +* Yasser Zidani Past Contributors ----------------- @@ -19,3 +19,4 @@ Past Contributors * Mikaïl Duran * Rima Hajou * Thomas Morzadec +* Charles-Henri Prat diff --git a/qolmat/imputations/preprocessing.py b/qolmat/imputations/preprocessing.py index 15ff048e..50c54270 100644 --- a/qolmat/imputations/preprocessing.py +++ b/qolmat/imputations/preprocessing.py @@ -320,7 +320,6 @@ def make_pipeline_mixte_preprocessing( if avoid_new: preprocessor.steps.append(("bins", BinTransformer())) - print(preprocessor) return preprocessor diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index f56de4ca..483700c1 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -119,21 +119,25 @@ def get_data( df = read_csv_local("conductors") return df elif name_data == "Titanic": - df = read_csv_local("titanic", sep=";") - df = df.dropna(how="all") - df = df.drop( - columns=[ - "pclass", - "name", - "home.dest", - "cabin", - "ticket", - "boat", - "body", - ] - ) - df["age"] = pd.to_numeric(df["age"], errors="coerce") - df["fare"] = pd.to_numeric(df["fare"].str.replace(",", ""), errors="coerce") + # df = read_csv_local("titanic", sep=";") + path = "https://gist.githubusercontent.com/fyyying/4aa5b471860321d7b47fd881898162b7/raw/" + "6907bb3a38bfbb6fccf3a8b1edfb90e39714d14f/titanic_dataset.csv" + df = pd.read_csv(path) + # df = df.dropna(how="all") + # df = df.drop( + # columns=[ + # "pclass", + # "name", + # "home.dest", + # "cabin", + # "ticket", + # "boat", + # "body", + # ] + # ) + df = df[["Survived", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]] + df["Age"] = pd.to_numeric(df["Age"], errors="coerce") + df["Fare"] = pd.to_numeric(df["Fare"], errors="coerce") return df elif name_data == "Artificial": city = "Wonderland" From 37c1f64ae4f3e273e2fcfb8bc60f2cc3049db5e4 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 16 Apr 2024 11:40:12 +0200 Subject: [PATCH 77/99] titanic.csv deleted --- qolmat/data/titanic.csv | 1311 --------------------------------------- 1 file changed, 1311 deletions(-) delete mode 100644 qolmat/data/titanic.csv diff --git a/qolmat/data/titanic.csv b/qolmat/data/titanic.csv deleted file mode 100644 index d047a98f..00000000 --- a/qolmat/data/titanic.csv +++ /dev/null @@ -1,1311 +0,0 @@ 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Harriet R;female;36;0;2;WE/P 5735;71,0000;B22;S;7;;Milwaukee, WI -1;1;Crosby, Mrs. Edward Gifford (Catherine Elizabeth Halstead);female;64;1;1;112901;26,5500;B26;S;7;;Milwaukee, WI -1;0;Cumings, Mr. John Bradley;male;39;1;0;PC 17599;71,2833;C85;C;;;New York, NY -1;1;Cumings, Mrs. John Bradley (Florence Briggs Thayer);female;38;1;0;PC 17599;71,2833;C85;C;4;;New York, NY -1;1;Daly, Mr. Peter Denis ;male;51;0;0;113055;26,5500;E17;S;5 9;;Lima, Peru -1;1;Daniel, Mr. Robert Williams;male;27;0;0;113804;30,5000;;S;3;;Philadelphia, PA -1;1;Daniels, Miss. Sarah;female;33;0;0;113781;151,5500;;S;8;; -1;0;Davidson, Mr. Thornton;male;31;1;0;F.C. 12750;52,0000;B71;S;;;Montreal, PQ -1;1;Davidson, Mrs. Thornton (Orian Hays);female;27;1;2;F.C. 12750;52,0000;B71;S;3;;Montreal, PQ -1;1;Dick, Mr. Albert Adrian;male;31;1;0;17474;57,0000;B20;S;3;;Calgary, AB -1;1;Dick, Mrs. Albert Adrian (Vera Gillespie);female;17;1;0;17474;57,0000;B20;S;3;;Calgary, AB -1;1;Dodge, Dr. Washington;male;53;1;1;33638;81,8583;A34;S;13;;San Francisco, CA -1;1;Dodge, Master. Washington;male;4;0;2;33638;81,8583;A34;S;5;;San Francisco, CA -1;1;Dodge, Mrs. Washington (Ruth Vidaver);female;54;1;1;33638;81,8583;A34;S;5;;San Francisco, CA -1;0;Douglas, Mr. Walter Donald;male;50;1;0;PC 17761;106,4250;C86;C;;62;Deephaven, MN / Cedar Rapids, IA -1;1;Douglas, Mrs. Frederick Charles (Mary Helene Baxter);female;27;1;1;PC 17558;247,5208;B58 B60;C;6;;Montreal, PQ -1;1;Douglas, Mrs. Walter Donald (Mahala Dutton);female;48;1;0;PC 17761;106,4250;C86;C;2;;Deephaven, MN / Cedar Rapids, IA -1;1;"Duff Gordon, Lady. (Lucille Christiana Sutherland) (""Mrs Morgan"")";female;48;1;0;11755;39,6000;A16;C;1;;London / Paris -1;1;"Duff Gordon, Sir. Cosmo Edmund (""Mr Morgan"")";male;49;1;0;PC 17485;56,9292;A20;C;1;;London / Paris -1;0;Dulles, Mr. William Crothers;male;39;0;0;PC 17580;29,7000;A18;C;;133;Philadelphia, PA -1;1;Earnshaw, Mrs. Boulton (Olive Potter);female;23;0;1;11767;83,1583;C54;C;7;;Mt Airy, Philadelphia, PA -1;1;Endres, Miss. Caroline Louise;female;38;0;0;PC 17757;227,5250;C45;C;4;;New York, NY -1;1;Eustis, Miss. Elizabeth Mussey;female;54;1;0;36947;78,2667;D20;C;4;;Brookline, MA -1;0;Evans, Miss. Edith Corse;female;36;0;0;PC 17531;31,6792;A29;C;;;New York, NY -1;0;Farthing, Mr. John;male;;0;0;PC 17483;221,7792;C95;S;;; -1;1;Flegenheim, Mrs. Alfred (Antoinette);female;;0;0;PC 17598;31,6833;;S;7;;New York, NY -1;1;Fleming, Miss. Margaret;female;;0;0;17421;110,8833;;C;4;; -1;1;"Flynn, Mr. John Irwin (""Irving"")";male;36;0;0;PC 17474;26,3875;E25;S;5;;Brooklyn, NY -1;0;Foreman, Mr. Benjamin Laventall;male;30;0;0;113051;27,7500;C111;C;;;New York, NY -1;1;Fortune, Miss. Alice Elizabeth;female;24;3;2;19950;263,0000;C23 C25 C27;S;10;;Winnipeg, MB -1;1;Fortune, Miss. Ethel Flora;female;28;3;2;19950;263,0000;C23 C25 C27;S;10;;Winnipeg, MB -1;1;Fortune, Miss. Mabel Helen;female;23;3;2;19950;263,0000;C23 C25 C27;S;10;;Winnipeg, MB -1;0;Fortune, Mr. Charles Alexander;male;19;3;2;19950;263,0000;C23 C25 C27;S;;;Winnipeg, MB -1;0;Fortune, Mr. Mark;male;64;1;4;19950;263,0000;C23 C25 C27;S;;;Winnipeg, MB -1;1;Fortune, Mrs. Mark (Mary McDougald);female;60;1;4;19950;263,0000;C23 C25 C27;S;10;;Winnipeg, MB -1;1;Francatelli, Miss. Laura Mabel;female;30;0;0;PC 17485;56,9292;E36;C;1;; -1;0;Franklin, Mr. Thomas Parham;male;;0;0;113778;26,5500;D34;S;;;Westcliff-on-Sea, Essex -1;1;Frauenthal, Dr. Henry William;male;50;2;0;PC 17611;133,6500;;S;5;;New York, NY -1;1;Frauenthal, Mr. Isaac Gerald;male;43;1;0;17765;27,7208;D40;C;5;;New York, NY -1;1;Frauenthal, Mrs. Henry William (Clara Heinsheimer);female;;1;0;PC 17611;133,6500;;S;5;;New York, NY -1;1;Frolicher, Miss. Hedwig Margaritha;female;22;0;2;13568;49,5000;B39;C;5;;Zurich, Switzerland -1;1;Frolicher-Stehli, Mr. Maxmillian;male;60;1;1;13567;79,2000;B41;C;5;;Zurich, Switzerland -1;1;Frolicher-Stehli, Mrs. Maxmillian (Margaretha Emerentia Stehli);female;48;1;1;13567;79,2000;B41;C;5;;Zurich, Switzerland -1;0;Fry, Mr. Richard;male;;0;0;112058;0,0000;B102;S;;; -1;0;Futrelle, Mr. Jacques Heath;male;37;1;0;113803;53,1000;C123;S;;;Scituate, MA -1;1;Futrelle, Mrs. Jacques Heath (Lily May Peel);female;35;1;0;113803;53,1000;C123;S;D;;Scituate, MA -1;0;Gee, Mr. Arthur H;male;47;0;0;111320;38,5000;E63;S;;275;St Anne's-on-Sea, Lancashire -1;1;Geiger, Miss. Amalie;female;35;0;0;113503;211,5000;C130;C;4;; -1;1;Gibson, Miss. Dorothy Winifred;female;22;0;1;112378;59,4000;;C;7;;New York, NY -1;1;Gibson, Mrs. Leonard (Pauline C Boeson);female;45;0;1;112378;59,4000;;C;7;;New York, NY -1;0;Giglio, Mr. Victor;male;24;0;0;PC 17593;79,2000;B86;C;;; -1;1;Goldenberg, Mr. Samuel L;male;49;1;0;17453;89,1042;C92;C;5;;Paris, France / New York, NY -1;1;Goldenberg, Mrs. Samuel L (Edwiga Grabowska);female;;1;0;17453;89,1042;C92;C;5;;Paris, France / New York, NY -1;0;Goldschmidt, Mr. George B;male;71;0;0;PC 17754;34,6542;A5;C;;;New York, NY -1;1;Gracie, Col. Archibald IV;male;53;0;0;113780;28,5000;C51;C;B;;Washington, DC -1;1;Graham, Miss. Margaret Edith;female;19;0;0;112053;30,0000;B42;S;3;;Greenwich, CT -1;0;Graham, Mr. George Edward;male;38;0;1;PC 17582;153,4625;C91;S;;147;Winnipeg, MB -1;1;Graham, Mrs. William Thompson (Edith Junkins);female;58;0;1;PC 17582;153,4625;C125;S;3;;Greenwich, CT -1;1;Greenfield, Mr. William Bertram;male;23;0;1;PC 17759;63,3583;D10 D12;C;7;;New York, NY -1;1;Greenfield, Mrs. Leo David (Blanche Strouse);female;45;0;1;PC 17759;63,3583;D10 D12;C;7;;New York, NY -1;0;Guggenheim, Mr. Benjamin;male;46;0;0;PC 17593;79,2000;B82 B84;C;;;New York, NY -1;1;Harder, Mr. George Achilles;male;25;1;0;11765;55,4417;E50;C;5;;Brooklyn, NY -1;1;Harder, Mrs. George Achilles (Dorothy Annan);female;25;1;0;11765;55,4417;E50;C;5;;Brooklyn, NY -1;1;Harper, Mr. Henry Sleeper;male;48;1;0;PC 17572;76,7292;D33;C;3;;New York, NY -1;1;Harper, Mrs. Henry Sleeper (Myna Haxtun);female;49;1;0;PC 17572;76,7292;D33;C;3;;New York, NY -1;0;Harrington, Mr. Charles H;male;;0;0;113796;42,4000;;S;;; -1;0;Harris, Mr. Henry Birkhardt;male;45;1;0;36973;83,4750;C83;S;;;New York, NY -1;1;Harris, Mrs. Henry Birkhardt (Irene Wallach);female;35;1;0;36973;83,4750;C83;S;D;;New York, NY -1;0;Harrison, Mr. William;male;40;0;0;112059;0,0000;B94;S;;110; -1;1;Hassab, Mr. Hammad;male;27;0;0;PC 17572;76,7292;D49;C;3;; -1;1;Hawksford, Mr. Walter James;male;;0;0;16988;30,0000;D45;S;3;;Kingston, Surrey -1;1;Hays, Miss. Margaret Bechstein;female;24;0;0;11767;83,1583;C54;C;7;;New York, NY -1;0;Hays, Mr. Charles Melville;male;55;1;1;12749;93,5000;B69;S;;307;Montreal, PQ -1;1;Hays, Mrs. Charles Melville (Clara Jennings Gregg);female;52;1;1;12749;93,5000;B69;S;3;;Montreal, PQ -1;0;Head, Mr. Christopher;male;42;0;0;113038;42,5000;B11;S;;;London / Middlesex -1;0;Hilliard, Mr. Herbert Henry;male;;0;0;17463;51,8625;E46;S;;;Brighton, MA -1;0;Hipkins, Mr. William Edward;male;55;0;0;680;50,0000;C39;S;;;London / Birmingham -1;1;Hippach, Miss. Jean Gertrude;female;16;0;1;111361;57,9792;B18;C;4;;Chicago, IL -1;1;Hippach, Mrs. Louis Albert (Ida Sophia Fischer);female;44;0;1;111361;57,9792;B18;C;4;;Chicago, IL -1;1;Hogeboom, Mrs. John C (Anna Andrews);female;51;1;0;13502;77,9583;D11;S;10;;Hudson, NY -1;0;Holverson, Mr. Alexander Oskar;male;42;1;0;113789;52,0000;;S;;38;New York, NY -1;1;Holverson, Mrs. Alexander Oskar (Mary Aline Towner);female;35;1;0;113789;52,0000;;S;8;;New York, NY -1;1;"Homer, Mr. Harry (""Mr E Haven"")";male;35;0;0;111426;26,5500;;C;15;;Indianapolis, IN -1;1;Hoyt, Mr. Frederick Maxfield;male;38;1;0;19943;90,0000;C93;S;D;;New York, NY / Stamford CT -1;0;Hoyt, Mr. William Fisher;male;;0;0;PC 17600;30,6958;;C;14;;New York, NY -1;1;Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby);female;35;1;0;19943;90,0000;C93;S;D;;New York, NY / Stamford CT -1;1;Icard, Miss. Amelie;female;38;0;0;113572;80,0000;B28;;6;; -1;0;Isham, Miss. Ann Elizabeth;female;50;0;0;PC 17595;28,7125;C49;C;;;Paris, France New York, NY -1;1;Ismay, Mr. Joseph Bruce;male;49;0;0;112058;0,0000;B52 B54 B56;S;C;;Liverpool -1;0;Jones, Mr. Charles Cresson;male;46;0;0;694;26,0000;;S;;80;Bennington, VT -1;0;Julian, Mr. Henry Forbes;male;50;0;0;113044;26,0000;E60;S;;;London -1;0;Keeping, Mr. Edwin;male;32,5;0;0;113503;211,5000;C132;C;;45; -1;0;Kent, Mr. Edward Austin;male;58;0;0;11771;29,7000;B37;C;;258;Buffalo, NY -1;0;Kenyon, Mr. Frederick R;male;41;1;0;17464;51,8625;D21;S;;;Southington / Noank, CT -1;1;Kenyon, Mrs. Frederick R (Marion);female;;1;0;17464;51,8625;D21;S;8;;Southington / Noank, CT -1;1;Kimball, Mr. Edwin Nelson Jr;male;42;1;0;11753;52,5542;D19;S;5;;Boston, MA -1;1;Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons);female;45;1;0;11753;52,5542;D19;S;5;;Boston, MA -1;0;Klaber, Mr. Herman;male;;0;0;113028;26,5500;C124;S;;;Portland, OR -1;1;Kreuchen, Miss. Emilie;female;39;0;0;24160;211,3375;;S;2;; -1;1;Leader, Dr. Alice (Farnham);female;49;0;0;17465;25,9292;D17;S;8;;New York, NY -1;1;LeRoy, Miss. Bertha;female;30;0;0;PC 17761;106,4250;;C;2;; -1;1;Lesurer, Mr. Gustave J;male;35;0;0;PC 17755;512,3292;B101;C;3;; -1;0;Lewy, Mr. Ervin G;male;;0;0;PC 17612;27,7208;;C;;;Chicago, IL -1;0;"Lindeberg-Lind, Mr. Erik Gustaf (""Mr Edward Lingrey"")";male;42;0;0;17475;26,5500;;S;;;Stockholm, Sweden -1;1;Lindstrom, Mrs. Carl Johan (Sigrid Posse);female;55;0;0;112377;27,7208;;C;6;;Stockholm, Sweden -1;1;Lines, Miss. Mary Conover;female;16;0;1;PC 17592;39,4000;D28;S;9;;Paris, France -1;1;Lines, Mrs. Ernest H (Elizabeth Lindsey James);female;51;0;1;PC 17592;39,4000;D28;S;9;;Paris, France -1;0;Long, Mr. Milton Clyde;male;29;0;0;113501;30,0000;D6;S;;126;Springfield, MA -1;1;Longley, Miss. Gretchen Fiske;female;21;0;0;13502;77,9583;D9;S;10;;Hudson, NY -1;0;Loring, Mr. Joseph Holland;male;30;0;0;113801;45,5000;;S;;;London / New York, NY -1;1;Lurette, Miss. Elise;female;58;0;0;PC 17569;146,5208;B80;C;;; -1;1;Madill, Miss. Georgette Alexandra;female;15;0;1;24160;211,3375;B5;S;2;;St Louis, MO -1;0;Maguire, Mr. John Edward;male;30;0;0;110469;26,0000;C106;S;;;Brockton, MA -1;1;Maioni, Miss. Roberta;female;16;0;0;110152;86,5000;B79;S;8;; -1;1;Marechal, Mr. Pierre;male;;0;0;11774;29,7000;C47;C;7;;Paris, France -1;0;Marvin, Mr. Daniel Warner;male;19;1;0;113773;53,1000;D30;S;;;New York, NY -1;1;Marvin, Mrs. Daniel Warner (Mary Graham Carmichael Farquarson);female;18;1;0;113773;53,1000;D30;S;10;;New York, NY -1;1;"Mayne, Mlle. Berthe Antonine (""Mrs de Villiers"")";female;24;0;0;PC 17482;49,5042;C90;C;6;;Belgium Montreal, PQ -1;0;McCaffry, Mr. Thomas Francis;male;46;0;0;13050;75,2417;C6;C;;292;Vancouver, BC -1;0;McCarthy, Mr. Timothy J;male;54;0;0;17463;51,8625;E46;S;;175;Dorchester, MA -1;1;McGough, Mr. James Robert;male;36;0;0;PC 17473;26,2875;E25;S;7;;Philadelphia, PA -1;0;Meyer, Mr. Edgar Joseph;male;28;1;0;PC 17604;82,1708;;C;;;New York, NY -1;1;Meyer, Mrs. Edgar Joseph (Leila Saks);female;;1;0;PC 17604;82,1708;;C;6;;New York, NY -1;0;Millet, Mr. Francis Davis;male;65;0;0;13509;26,5500;E38;S;;249;East Bridgewater, MA -1;0;Minahan, Dr. William Edward;male;44;2;0;19928;90,0000;C78;Q;;230;Fond du Lac, WI -1;1;Minahan, Miss. Daisy E;female;33;1;0;19928;90,0000;C78;Q;14;;Green Bay, WI -1;1;Minahan, Mrs. William Edward (Lillian E Thorpe);female;37;1;0;19928;90,0000;C78;Q;14;;Fond du Lac, WI -1;1;Mock, Mr. Philipp Edmund;male;30;1;0;13236;57,7500;C78;C;11;;New York, NY -1;0;Molson, Mr. Harry Markland;male;55;0;0;113787;30,5000;C30;S;;;Montreal, PQ -1;0;Moore, Mr. Clarence Bloomfield;male;47;0;0;113796;42,4000;;S;;;Washington, DC -1;0;Natsch, Mr. Charles H;male;37;0;1;PC 17596;29,7000;C118;C;;;Brooklyn, NY -1;1;Newell, Miss. Madeleine;female;31;1;0;35273;113,2750;D36;C;6;;Lexington, MA -1;1;Newell, Miss. Marjorie;female;23;1;0;35273;113,2750;D36;C;6;;Lexington, MA -1;0;Newell, Mr. Arthur Webster;male;58;0;2;35273;113,2750;D48;C;;122;Lexington, MA -1;1;Newsom, Miss. Helen Monypeny;female;19;0;2;11752;26,2833;D47;S;5;;New York, NY -1;0;Nicholson, Mr. Arthur Ernest;male;64;0;0;693;26,0000;;S;;263;Isle of Wight, England -1;1;Oliva y Ocana, Dona. Fermina;female;39;0;0;PC 17758;108,9000;C105;C;8;; -1;1;Omont, Mr. Alfred Fernand;male;;0;0;F.C. 12998;25,7417;;C;7;;Paris, France -1;1;Ostby, Miss. Helene Ragnhild;female;22;0;1;113509;61,9792;B36;C;5;;Providence, RI -1;0;Ostby, Mr. Engelhart Cornelius;male;65;0;1;113509;61,9792;B30;C;;234;Providence, RI -1;0;Ovies y Rodriguez, Mr. Servando;male;28,5;0;0;PC 17562;27,7208;D43;C;;189;?Havana, Cuba -1;0;Parr, Mr. William Henry Marsh;male;;0;0;112052;0,0000;;S;;;Belfast -1;0;Partner, Mr. Austen;male;45,5;0;0;113043;28,5000;C124;S;;166;Surbiton Hill, Surrey -1;0;Payne, Mr. Vivian Ponsonby;male;23;0;0;12749;93,5000;B24;S;;;Montreal, PQ -1;0;Pears, Mr. Thomas Clinton;male;29;1;0;113776;66,6000;C2;S;;;Isleworth, England -1;1;Pears, Mrs. Thomas (Edith Wearne);female;22;1;0;113776;66,6000;C2;S;8;;Isleworth, England -1;0;Penasco y Castellana, Mr. Victor de Satode;male;18;1;0;PC 17758;108,9000;C65;C;;;Madrid, Spain -1;1;Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo);female;17;1;0;PC 17758;108,9000;C65;C;8;;Madrid, Spain -1;1;Perreault, Miss. Anne;female;30;0;0;12749;93,5000;B73;S;3;; -1;1;Peuchen, Major. Arthur Godfrey;male;52;0;0;113786;30,5000;C104;S;6;;Toronto, ON -1;0;Porter, Mr. Walter Chamberlain;male;47;0;0;110465;52,0000;C110;S;;207;Worcester, MA -1;1;Potter, Mrs. Thomas Jr (Lily Alexenia Wilson);female;56;0;1;11767;83,1583;C50;C;7;;Mt Airy, Philadelphia, PA -1;0;Reuchlin, Jonkheer. John George;male;38;0;0;19972;0,0000;;S;;;Rotterdam, Netherlands -1;1;Rheims, Mr. George Alexander Lucien;male;;0;0;PC 17607;39,6000;;S;A;;Paris / New York, NY -1;0;Ringhini, Mr. Sante;male;22;0;0;PC 17760;135,6333;;C;;232; -1;0;Robbins, Mr. Victor;male;;0;0;PC 17757;227,5250;;C;;; -1;1;Robert, Mrs. Edward Scott (Elisabeth Walton McMillan);female;43;0;1;24160;211,3375;B3;S;2;;St Louis, MO -1;0;Roebling, Mr. Washington Augustus II;male;31;0;0;PC 17590;50,4958;A24;S;;;Trenton, NJ -1;1;"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")";male;45;0;0;111428;26,5500;;S;9;;New York, NY -1;0;Rood, Mr. Hugh Roscoe;male;;0;0;113767;50,0000;A32;S;;;Seattle, WA -1;1;Rosenbaum, Miss. Edith Louise;female;33;0;0;PC 17613;27,7208;A11;C;11;;Paris, France -1;0;"Rosenshine, Mr. George (""Mr George Thorne"")";male;46;0;0;PC 17585;79,2000;;C;;16;New York, NY -1;0;Ross, Mr. John Hugo;male;36;0;0;13049;40,1250;A10;C;;;Winnipeg, MB -1;1;Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards);female;33;0;0;110152;86,5000;B77;S;8;;London Vancouver, BC -1;0;Rothschild, Mr. Martin;male;55;1;0;PC 17603;59,4000;;C;;;New York, NY -1;1;Rothschild, Mrs. Martin (Elizabeth L. Barrett);female;54;1;0;PC 17603;59,4000;;C;6;;New York, NY -1;0;Rowe, Mr. Alfred G;male;33;0;0;113790;26,5500;;S;;109;London -1;1;Ryerson, Master. John Borie;male;13;2;2;PC 17608;262,3750;B57 B59 B63 B66;C;4;;Haverford, PA / Cooperstown, NY -1;1;Ryerson, Miss. Emily Borie;female;18;2;2;PC 17608;262,3750;B57 B59 B63 B66;C;4;;Haverford, PA / Cooperstown, NY -1;1;"Ryerson, Miss. Susan Parker ""Suzette""";female;21;2;2;PC 17608;262,3750;B57 B59 B63 B66;C;4;;Haverford, PA / Cooperstown, NY -1;0;Ryerson, Mr. Arthur Larned;male;61;1;3;PC 17608;262,3750;B57 B59 B63 B66;C;;;Haverford, PA / Cooperstown, NY -1;1;Ryerson, Mrs. Arthur Larned (Emily Maria Borie);female;48;1;3;PC 17608;262,3750;B57 B59 B63 B66;C;4;;Haverford, PA / Cooperstown, NY -1;1;Saalfeld, Mr. Adolphe;male;;0;0;19988;30,5000;C106;S;3;;Manchester, England -1;1;Sagesser, Mlle. Emma;female;24;0;0;PC 17477;69,3000;B35;C;9;; -1;1;Salomon, Mr. Abraham L;male;;0;0;111163;26,0000;;S;1;;New York, NY -1;1;Schabert, Mrs. Paul (Emma Mock);female;35;1;0;13236;57,7500;C28;C;11;;New York, NY -1;1;Serepeca, Miss. Augusta;female;30;0;0;113798;31,0000;;C;4;; -1;1;Seward, Mr. Frederic Kimber;male;34;0;0;113794;26,5500;;S;7;;New York, NY -1;1;Shutes, Miss. Elizabeth W;female;40;0;0;PC 17582;153,4625;C125;S;3;;New York, NY / Greenwich CT -1;1;Silverthorne, Mr. Spencer Victor;male;35;0;0;PC 17475;26,2875;E24;S;5;;St Louis, MO -1;0;Silvey, Mr. William Baird;male;50;1;0;13507;55,9000;E44;S;;;Duluth, MN -1;1;Silvey, Mrs. William Baird (Alice Munger);female;39;1;0;13507;55,9000;E44;S;11;;Duluth, MN -1;1;Simonius-Blumer, Col. Oberst Alfons;male;56;0;0;13213;35,5000;A26;C;3;;Basel, Switzerland -1;1;Sloper, Mr. William Thompson;male;28;0;0;113788;35,5000;A6;S;7;;New Britain, CT -1;0;Smart, Mr. John Montgomery;male;56;0;0;113792;26,5500;;S;;;New York, NY -1;0;Smith, Mr. James Clinch;male;56;0;0;17764;30,6958;A7;C;;;St James, Long Island, NY -1;0;Smith, Mr. Lucien Philip;male;24;1;0;13695;60,0000;C31;S;;;Huntington, WV -1;0;Smith, Mr. Richard William;male;;0;0;113056;26,0000;A19;S;;;Streatham, Surrey -1;1;Smith, Mrs. Lucien Philip (Mary Eloise Hughes);female;18;1;0;13695;60,0000;C31;S;6;;Huntington, WV -1;1;Snyder, Mr. John Pillsbury;male;24;1;0;21228;82,2667;B45;S;7;;Minneapolis, MN -1;1;Snyder, Mrs. John Pillsbury (Nelle Stevenson);female;23;1;0;21228;82,2667;B45;S;7;;Minneapolis, MN -1;1;Spedden, Master. Robert Douglas;male;6;0;2;16966;134,5000;E34;C;3;;Tuxedo Park, NY -1;1;Spedden, Mr. Frederic Oakley;male;45;1;1;16966;134,5000;E34;C;3;;Tuxedo Park, NY -1;1;Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone);female;40;1;1;16966;134,5000;E34;C;3;;Tuxedo Park, NY -1;0;Spencer, Mr. William Augustus;male;57;1;0;PC 17569;146,5208;B78;C;;;Paris, France -1;1;Spencer, Mrs. William Augustus (Marie Eugenie);female;;1;0;PC 17569;146,5208;B78;C;6;;Paris, France -1;1;Stahelin-Maeglin, Dr. Max;male;32;0;0;13214;30,5000;B50;C;3;;Basel, Switzerland -1;0;Stead, Mr. William Thomas;male;62;0;0;113514;26,5500;C87;S;;;Wimbledon Park, London / Hayling Island, Hants -1;1;Stengel, Mr. Charles Emil Henry;male;54;1;0;11778;55,4417;C116;C;1;;Newark, NJ -1;1;Stengel, Mrs. Charles Emil Henry (Annie May Morris);female;43;1;0;11778;55,4417;C116;C;5;;Newark, NJ -1;1;Stephenson, Mrs. Walter Bertram (Martha Eustis);female;52;1;0;36947;78,2667;D20;C;4;;Haverford, PA -1;0;Stewart, Mr. Albert A;male;;0;0;PC 17605;27,7208;;C;;;Gallipolis, Ohio / ? Paris / New York -1;1;Stone, Mrs. George Nelson (Martha Evelyn);female;62;0;0;113572;80,0000;B28;;6;;Cincinatti, OH -1;0;Straus, Mr. Isidor;male;67;1;0;PC 17483;221,7792;C55 C57;S;;96;New York, NY -1;0;Straus, Mrs. Isidor (Rosalie Ida Blun);female;63;1;0;PC 17483;221,7792;C55 C57;S;;;New York, NY -1;0;Sutton, Mr. Frederick;male;61;0;0;36963;32,3208;D50;S;;46;Haddenfield, NJ -1;1;Swift, Mrs. Frederick Joel (Margaret Welles Barron);female;48;0;0;17466;25,9292;D17;S;8;;Brooklyn, NY -1;1;Taussig, Miss. Ruth;female;18;0;2;110413;79,6500;E68;S;8;;New York, NY -1;0;Taussig, Mr. Emil;male;52;1;1;110413;79,6500;E67;S;;;New York, NY -1;1;Taussig, Mrs. Emil (Tillie Mandelbaum);female;39;1;1;110413;79,6500;E67;S;8;;New York, NY -1;1;Taylor, Mr. Elmer Zebley;male;48;1;0;19996;52,0000;C126;S;5 7;;London / East Orange, NJ -1;1;Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright);female;;1;0;19996;52,0000;C126;S;5 7;;London / East Orange, NJ -1;0;Thayer, Mr. John Borland;male;49;1;1;17421;110,8833;C68;C;;;Haverford, PA -1;1;Thayer, Mr. John Borland Jr;male;17;0;2;17421;110,8833;C70;C;B;;Haverford, PA -1;1;Thayer, Mrs. John Borland (Marian Longstreth Morris);female;39;1;1;17421;110,8833;C68;C;4;;Haverford, PA -1;1;Thorne, Mrs. Gertrude Maybelle;female;;0;0;PC 17585;79,2000;;C;D;;New York, NY -1;1;Tucker, Mr. Gilbert Milligan Jr;male;31;0;0;2543;28,5375;C53;C;7;;Albany, NY -1;0;Uruchurtu, Don. Manuel E;male;40;0;0;PC 17601;27,7208;;C;;;Mexico City, Mexico -1;0;Van der hoef, Mr. Wyckoff;male;61;0;0;111240;33,5000;B19;S;;245;Brooklyn, NY -1;0;Walker, Mr. William Anderson;male;47;0;0;36967;34,0208;D46;S;;;East Orange, NJ -1;1;Ward, Miss. Anna;female;35;0;0;PC 17755;512,3292;;C;3;; -1;0;Warren, Mr. Frank Manley;male;64;1;0;110813;75,2500;D37;C;;;Portland, OR -1;1;Warren, Mrs. Frank Manley (Anna Sophia Atkinson);female;60;1;0;110813;75,2500;D37;C;5;;Portland, OR -1;0;Weir, Col. John;male;60;0;0;113800;26,5500;;S;;;England Salt Lake City, Utah -1;0;White, Mr. Percival Wayland;male;54;0;1;35281;77,2875;D26;S;;;Brunswick, ME -1;0;White, Mr. Richard Frasar;male;21;0;1;35281;77,2875;D26;S;;169;Brunswick, ME -1;1;White, Mrs. John Stuart (Ella Holmes);female;55;0;0;PC 17760;135,6333;C32;C;8;;New York, NY / Briarcliff Manor NY -1;1;Wick, Miss. Mary Natalie;female;31;0;2;36928;164,8667;C7;S;8;;Youngstown, OH -1;0;Wick, Mr. George Dennick;male;57;1;1;36928;164,8667;;S;;;Youngstown, OH -1;1;Wick, Mrs. George Dennick (Mary Hitchcock);female;45;1;1;36928;164,8667;;S;8;;Youngstown, OH -1;0;Widener, Mr. George Dunton;male;50;1;1;113503;211,5000;C80;C;;;Elkins Park, PA -1;0;Widener, Mr. Harry Elkins;male;27;0;2;113503;211,5000;C82;C;;;Elkins Park, PA -1;1;Widener, Mrs. George Dunton (Eleanor Elkins);female;50;1;1;113503;211,5000;C80;C;4;;Elkins Park, PA -1;1;Willard, Miss. Constance;female;21;0;0;113795;26,5500;;S;8 10;;Duluth, MN -1;0;Williams, Mr. Charles Duane;male;51;0;1;PC 17597;61,3792;;C;;;Geneva, Switzerland / Radnor, PA -1;1;Williams, Mr. Richard Norris II;male;21;0;1;PC 17597;61,3792;;C;A;;Geneva, Switzerland / Radnor, PA -1;0;Williams-Lambert, Mr. Fletcher Fellows;male;;0;0;113510;35,0000;C128;S;;;London, England -1;1;Wilson, Miss. Helen Alice;female;31;0;0;16966;134,5000;E39 E41;C;3;; -1;1;Woolner, Mr. Hugh;male;;0;0;19947;35,5000;C52;S;D;;London, England -1;0;Wright, Mr. George;male;62;0;0;113807;26,5500;;S;;;Halifax, NS -1;1;Young, Miss. Marie Grice;female;36;0;0;PC 17760;135,6333;C32;C;8;;New York, NY / Washington, DC -2;0;Abelson, Mr. Samuel;male;30;1;0;P/PP 3381;24,0000;;C;;;Russia New York, NY -2;1;Abelson, Mrs. Samuel (Hannah Wizosky);female;28;1;0;P/PP 3381;24,0000;;C;10;;Russia New York, NY -2;0;Aldworth, Mr. Charles Augustus;male;30;0;0;248744;13,0000;;S;;;Bryn Mawr, PA, USA -2;0;Andrew, Mr. Edgardo Samuel;male;18;0;0;231945;11,5000;;S;;;Buenos Aires, Argentina / New Jersey, NJ -2;0;Andrew, Mr. Frank Thomas;male;25;0;0;C.A. 34050;10,5000;;S;;;Cornwall, England Houghton, MI -2;0;Angle, Mr. William A;male;34;1;0;226875;26,0000;;S;;;Warwick, England -2;1;"Angle, Mrs. William A (Florence ""Mary"" Agnes Hughes)";female;36;1;0;226875;26,0000;;S;11;;Warwick, England -2;0;Ashby, Mr. John;male;57;0;0;244346;13,0000;;S;;;West Hoboken, NJ -2;0;Bailey, Mr. Percy Andrew;male;18;0;0;29108;11,5000;;S;;;Penzance, Cornwall / Akron, OH -2;0;Baimbrigge, Mr. Charles Robert;male;23;0;0;C.A. 31030;10,5000;;S;;;Guernsey -2;1;Ball, Mrs. (Ada E Hall);female;36;0;0;28551;13,0000;D;S;10;;Bristol, Avon / Jacksonville, FL -2;0;Banfield, Mr. Frederick James;male;28;0;0;C.A./SOTON 34068;10,5000;;S;;;Plymouth, Dorset / Houghton, MI -2;0;Bateman, Rev. Robert James;male;51;0;0;S.O.P. 1166;12,5250;;S;;174;Jacksonville, FL -2;1;Beane, Mr. Edward;male;32;1;0;2908;26,0000;;S;13;;Norwich / New York, NY -2;1;Beane, Mrs. Edward (Ethel Clarke);female;19;1;0;2908;26,0000;;S;13;;Norwich / New York, NY -2;0;Beauchamp, Mr. Henry James;male;28;0;0;244358;26,0000;;S;;;England -2;1;Becker, Master. Richard F;male;1;2;1;230136;39,0000;F4;S;11;;Guntur, India / Benton Harbour, MI -2;1;Becker, Miss. Marion Louise;female;4;2;1;230136;39,0000;F4;S;11;;Guntur, India / Benton Harbour, MI -2;1;Becker, Miss. Ruth Elizabeth;female;12;2;1;230136;39,0000;F4;S;13;;Guntur, India / Benton Harbour, MI -2;1;Becker, Mrs. Allen Oliver (Nellie E Baumgardner);female;36;0;3;230136;39,0000;F4;S;11;;Guntur, India / Benton Harbour, MI -2;1;Beesley, Mr. Lawrence;male;34;0;0;248698;13,0000;D56;S;13;;London -2;1;Bentham, Miss. Lilian W;female;19;0;0;28404;13,0000;;S;12;;Rochester, NY -2;0;Berriman, Mr. William John;male;23;0;0;28425;13,0000;;S;;;St Ives, Cornwall / Calumet, MI -2;0;Botsford, Mr. William Hull;male;26;0;0;237670;13,0000;;S;;;Elmira, NY / Orange, NJ -2;0;Bowenur, Mr. Solomon;male;42;0;0;211535;13,0000;;S;;;London -2;0;Bracken, Mr. James H;male;27;0;0;220367;13,0000;;S;;;Lake Arthur, Chavez County, NM -2;1;"Brown, Miss. Amelia ""Mildred""";female;24;0;0;248733;13,0000;F33;S;11;;London / Montreal, PQ -2;1;Brown, Miss. Edith Eileen;female;15;0;2;29750;39,0000;;S;14;;Cape Town, South Africa / Seattle, WA -2;0;Brown, Mr. Thomas William Solomon;male;60;1;1;29750;39,0000;;S;;;Cape Town, South Africa / Seattle, WA -2;1;Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford);female;40;1;1;29750;39,0000;;S;14;;Cape Town, South Africa / Seattle, WA -2;1;Bryhl, Miss. Dagmar Jenny Ingeborg ;female;20;1;0;236853;26,0000;;S;12;;Skara, Sweden / Rockford, IL -2;0;Bryhl, Mr. Kurt Arnold Gottfrid;male;25;1;0;236853;26,0000;;S;;;Skara, Sweden / Rockford, IL -2;1;Buss, Miss. Kate;female;36;0;0;27849;13,0000;;S;9;;Sittingbourne, England / San Diego, CA -2;0;Butler, Mr. Reginald Fenton;male;25;0;0;234686;13,0000;;S;;97;Southsea, Hants -2;0;Byles, Rev. Thomas Roussel Davids;male;42;0;0;244310;13,0000;;S;;;London -2;1;Bystrom, Mrs. (Karolina);female;42;0;0;236852;13,0000;;S;;;New York, NY -2;1;Caldwell, Master. Alden Gates;male;0,8333;0;2;248738;29,0000;;S;13;;Bangkok, Thailand / Roseville, IL -2;1;Caldwell, Mr. Albert Francis;male;26;1;1;248738;29,0000;;S;13;;Bangkok, Thailand / Roseville, IL -2;1;Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh);female;22;1;1;248738;29,0000;;S;13;;Bangkok, Thailand / Roseville, IL -2;1;Cameron, Miss. Clear Annie;female;35;0;0;F.C.C. 13528;21,0000;;S;14;;Mamaroneck, NY -2;0;Campbell, Mr. William;male;;0;0;239853;0,0000;;S;;;Belfast -2;0;Carbines, Mr. William;male;19;0;0;28424;13,0000;;S;;18;St Ives, Cornwall / Calumet, MI -2;0;Carter, Mrs. Ernest Courtenay (Lilian Hughes);female;44;1;0;244252;26,0000;;S;;;London -2;0;Carter, Rev. Ernest Courtenay;male;54;1;0;244252;26,0000;;S;;;London -2;0;Chapman, Mr. Charles Henry;male;52;0;0;248731;13,5000;;S;;130;Bronx, NY -2;0;Chapman, Mr. John Henry;male;37;1;0;SC/AH 29037;26,0000;;S;;17;Cornwall / Spokane, WA -2;0;Chapman, Mrs. John Henry (Sara Elizabeth Lawry);female;29;1;0;SC/AH 29037;26,0000;;S;;;Cornwall / Spokane, WA -2;1;Christy, Miss. Julie Rachel;female;25;1;1;237789;30,0000;;S;12;;London -2;1;Christy, Mrs. (Alice Frances);female;45;0;2;237789;30,0000;;S;12;;London -2;0;Clarke, Mr. Charles Valentine;male;29;1;0;2003;26,0000;;S;;;England / San Francisco, CA -2;1;Clarke, Mrs. Charles V (Ada Maria Winfield);female;28;1;0;2003;26,0000;;S;14;;England / San Francisco, CA -2;0;Coleridge, Mr. Reginald Charles;male;29;0;0;W./C. 14263;10,5000;;S;;;Hartford, Huntingdonshire -2;0;Collander, Mr. Erik Gustaf;male;28;0;0;248740;13,0000;;S;;;Helsinki, Finland Ashtabula, Ohio -2;1;Collett, Mr. Sidney C Stuart;male;24;0;0;28034;10,5000;;S;9;;London / Fort Byron, NY -2;1;"Collyer, Miss. Marjorie ""Lottie""";female;8;0;2;C.A. 31921;26,2500;;S;14;;Bishopstoke, Hants / Fayette Valley, ID -2;0;Collyer, Mr. Harvey;male;31;1;1;C.A. 31921;26,2500;;S;;;Bishopstoke, Hants / Fayette Valley, ID -2;1;Collyer, Mrs. Harvey (Charlotte Annie Tate);female;31;1;1;C.A. 31921;26,2500;;S;14;;Bishopstoke, Hants / Fayette Valley, ID -2;1;Cook, Mrs. (Selena Rogers);female;22;0;0;W./C. 14266;10,5000;F33;S;14;;Pennsylvania -2;0;Corbett, Mrs. Walter H (Irene Colvin);female;30;0;0;237249;13,0000;;S;;;Provo, UT -2;0;Corey, Mrs. Percy C (Mary Phyllis Elizabeth Miller);female;;0;0;F.C.C. 13534;21,0000;;S;;;Upper Burma, India Pittsburgh, PA -2;0;"Cotterill, Mr. Henry ""Harry""";male;21;0;0;29107;11,5000;;S;;;Penzance, Cornwall / Akron, OH -2;0;Cunningham, Mr. Alfred Fleming;male;;0;0;239853;0,0000;;S;;;Belfast -2;1;Davies, Master. John Morgan Jr;male;8;1;1;C.A. 33112;36,7500;;S;14;;St Ives, Cornwall / Hancock, MI -2;0;Davies, Mr. Charles Henry;male;18;0;0;S.O.C. 14879;73,5000;;S;;;Lyndhurst, England -2;1;Davies, Mrs. John Morgan (Elizabeth Agnes Mary White) ;female;48;0;2;C.A. 33112;36,7500;;S;14;;St Ives, Cornwall / Hancock, MI -2;1;Davis, Miss. Mary;female;28;0;0;237668;13,0000;;S;13;;London / Staten Island, NY -2;0;de Brito, Mr. Jose Joaquim;male;32;0;0;244360;13,0000;;S;;;Portugal / Sau Paulo, Brazil -2;0;Deacon, Mr. Percy William;male;17;0;0;S.O.C. 14879;73,5000;;S;;; -2;0;del Carlo, Mr. Sebastiano;male;29;1;0;SC/PARIS 2167;27,7208;;C;;295;Lucca, Italy / California -2;1;del Carlo, Mrs. Sebastiano (Argenia Genovesi);female;24;1;0;SC/PARIS 2167;27,7208;;C;12;;Lucca, Italy / California -2;0;Denbury, Mr. Herbert;male;25;0;0;C.A. 31029;31,5000;;S;;;Guernsey / Elizabeth, NJ -2;0;Dibden, Mr. William;male;18;0;0;S.O.C. 14879;73,5000;;S;;;New Forest, England -2;1;Doling, Miss. Elsie;female;18;0;1;231919;23,0000;;S;;;Southampton -2;1;Doling, Mrs. John T (Ada Julia Bone);female;34;0;1;231919;23,0000;;S;;;Southampton -2;0;Downton, Mr. William James;male;54;0;0;28403;26,0000;;S;;;Holley, NY -2;1;Drew, Master. Marshall Brines;male;8;0;2;28220;32,5000;;S;10;;Greenport, NY -2;0;Drew, Mr. James Vivian;male;42;1;1;28220;32,5000;;S;;;Greenport, NY -2;1;Drew, Mrs. James Vivian (Lulu Thorne Christian);female;34;1;1;28220;32,5000;;S;10;;Greenport, NY -2;1;Duran y More, Miss. Asuncion;female;27;1;0;SC/PARIS 2149;13,8583;;C;12;;Barcelona, Spain / Havana, Cuba -2;1;Duran y More, Miss. Florentina;female;30;1;0;SC/PARIS 2148;13,8583;;C;12;;Barcelona, Spain / Havana, Cuba -2;0;Eitemiller, Mr. George Floyd;male;23;0;0;29751;13,0000;;S;;;England / Detroit, MI -2;0;Enander, Mr. Ingvar;male;21;0;0;236854;13,0000;;S;;;Goteborg, Sweden / Rockford, IL -2;0;Fahlstrom, Mr. Arne Jonas;male;18;0;0;236171;13,0000;;S;;;Oslo, Norway Bayonne, NJ -2;0;Faunthorpe, Mr. Harry;male;40;1;0;2926;26,0000;;S;;286;England / Philadelphia, PA -2;1;Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson);female;29;1;0;2926;26,0000;;S;16;; -2;0;Fillbrook, Mr. Joseph Charles;male;18;0;0;C.A. 15185;10,5000;;S;;;Cornwall / Houghton, MI -2;0;Fox, Mr. Stanley Hubert;male;36;0;0;229236;13,0000;;S;;236;Rochester, NY -2;0;"Frost, Mr. Anthony Wood ""Archie""";male;;0;0;239854;0,0000;;S;;;Belfast -2;0;Funk, Miss. Annie Clemmer;female;38;0;0;237671;13,0000;;S;;;Janjgir, India / Pennsylvania -2;0;Fynney, Mr. Joseph J;male;35;0;0;239865;26,0000;;S;;322;Liverpool / Montreal, PQ -2;0;Gale, Mr. Harry;male;38;1;0;28664;21,0000;;S;;;Cornwall / Clear Creek, CO -2;0;Gale, Mr. Shadrach;male;34;1;0;28664;21,0000;;S;;;Cornwall / Clear Creek, CO -2;1;Garside, Miss. Ethel;female;34;0;0;243880;13,0000;;S;12;;Brooklyn, NY -2;0;Gaskell, Mr. Alfred;male;16;0;0;239865;26,0000;;S;;;Liverpool / Montreal, PQ -2;0;Gavey, Mr. Lawrence;male;26;0;0;31028;10,5000;;S;;;Guernsey / Elizabeth, NJ -2;0;Gilbert, Mr. William;male;47;0;0;C.A. 30769;10,5000;;S;;;Cornwall -2;0;Giles, Mr. Edgar;male;21;1;0;28133;11,5000;;S;;;Cornwall / Camden, NJ -2;0;Giles, Mr. Frederick Edward;male;21;1;0;28134;11,5000;;S;;;Cornwall / Camden, NJ -2;0;Giles, Mr. Ralph;male;24;0;0;248726;13,5000;;S;;297;West Kensington, London -2;0;Gill, Mr. John William;male;24;0;0;233866;13,0000;;S;;155;Clevedon, England -2;0;Gillespie, Mr. William Henry;male;34;0;0;12233;13,0000;;S;;;Vancouver, BC -2;0;Givard, Mr. Hans Kristensen;male;30;0;0;250646;13,0000;;S;;305; -2;0;Greenberg, Mr. Samuel;male;52;0;0;250647;13,0000;;S;;19;Bronx, NY -2;0;Hale, Mr. Reginald;male;30;0;0;250653;13,0000;;S;;75;Auburn, NY -2;1;Hamalainen, Master. 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Thamine;female;;1;0;2665;14,4542;;C;;; -3;0;Zakarian, Mr. Mapriededer;male;26,5;0;0;2656;7,2250;;C;;304; -3;0;Zakarian, Mr. Ortin;male;27;0;0;2670;7,2250;;C;;; -3;0;Zimmerman, Mr. Leo;male;29;0;0;315082;7,8750;;S;;; -;;;;;;;;;;;;; From 4207c75b6e184c7ca3c6329a93ebe40ad86a563d Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 16 Apr 2024 13:42:42 +0200 Subject: [PATCH 78/99] docstrings reformatted --- qolmat/benchmark/comparator.py | 25 +++++---- qolmat/benchmark/metrics.py | 6 +- qolmat/utils/data.py | 90 ++++++++++++++++++------------ tests/benchmark/test_comparator.py | 12 ++-- 4 files changed, 79 insertions(+), 54 deletions(-) diff --git a/qolmat/benchmark/comparator.py b/qolmat/benchmark/comparator.py index e3fee032..5a60c6f5 100644 --- a/qolmat/benchmark/comparator.py +++ b/qolmat/benchmark/comparator.py @@ -52,7 +52,7 @@ def get_errors( df_origin: pd.DataFrame, df_imputed: pd.DataFrame, df_mask: pd.DataFrame, - ) -> pd.Series: + ) -> pd.DataFrame: """Functions evaluating the reconstruction's quality Parameters @@ -64,15 +64,15 @@ def get_errors( Returns ------- - dictionary - dictionay of results obtained via different metrics + pd.DataFrame + DataFrame of results obtained via different metrics """ dict_errors = {} for name_metric in self.metrics: fun_metric = metrics.get_metric(name_metric) dict_errors[name_metric] = fun_metric(df_origin, df_imputed, df_mask) - errors = pd.concat(dict_errors.values(), keys=dict_errors.keys()) - return errors + df_errors = pd.concat(dict_errors.values(), keys=dict_errors.keys()) + return df_errors def evaluate_errors_sample( self, @@ -96,8 +96,8 @@ def evaluate_errors_sample( Returns ------- - pd.DataFrame - DataFrame with the errors for each metric (in column) and at each fold (in index) + pd.Series + Series with the errors for each metric and each variable """ list_errors = [] df_origin = df[self.selected_columns].copy() @@ -115,8 +115,12 @@ def evaluate_errors_sample( ) df_imputed = imputer_opti.fit_transform(df_corrupted) subset = self.generator_holes.subset - errors = self.get_errors(df_origin[subset], df_imputed[subset], df_mask[subset]) - list_errors.append(errors) + if subset is None: + raise ValueError( + "HoleGenerator `subset` should be overwritten in split but it is none!" + ) + df_errors = self.get_errors(df_origin[subset], df_imputed[subset], df_mask[subset]) + list_errors.append(df_errors) df_errors = pd.DataFrame(list_errors) errors_mean = df_errors.mean(axis=0) @@ -136,7 +140,8 @@ def compare( Returns ------- pd.DataFrame - dataframe with imputation + Dataframe with the metrics results, imputers are in columns and indices represent + metrics and variables. """ dict_errors = {} diff --git a/qolmat/benchmark/metrics.py b/qolmat/benchmark/metrics.py index 00ca0518..f8f87441 100644 --- a/qolmat/benchmark/metrics.py +++ b/qolmat/benchmark/metrics.py @@ -1,5 +1,5 @@ from functools import partial -from typing import Callable, Dict, List, Optional +from typing import Callable, Dict, List import numpy as np import pandas as pd @@ -1030,7 +1030,9 @@ def pattern_based_weighted_mean_metric( return pd.Series(sum([s * w for s, w in zip(scores, weights)]), index=["All"]) -def get_metric(name: str) -> Callable: +def get_metric( + name: str, +) -> Callable[[pd.DataFrame, pd.DataFrame, pd.DataFrame], pd.Series]: dict_metrics: Dict[str, Callable] = { "mse": mean_squared_error, "rmse": root_mean_squared_error, diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index aa0e9306..163d3368 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -39,15 +39,20 @@ def download_data_from_zip( """ Downloads and extracts ZIP files from a URL, then loads DataFrames from CSV files. - Args: - zipname (str): Name of the ZIP file to download, without the '.zip' extension. - urllink (str): Base URL where the ZIP file is hosted. - datapath (str, optional): Path to the directory where the ZIP will be \ - downloaded and extracted. Defaults to 'data/'. - - Returns: - List[pd.DataFrame]: A list of DataFrames loaded from the CSV \ - files within the extracted directory. + Parameters + ---------- + zipname : str + Name of the ZIP file to download, without the '.zip' extension. + urllink : str + Base URL where the ZIP file is hosted. + datapath : str, optional + Path to the directory where the ZIP will be downloaded and extracted. + Defaults to 'data/'. + + Returns + ------- + List[pd.DataFrame] + A list of DataFrames loaded from the CSV files within the extracted directory. """ path_zip = os.path.join(datapath, zipname) @@ -65,19 +70,21 @@ def download_data_from_zip( def get_dataframes_in_folder(path: str, extension: str) -> List[pd.DataFrame]: """ - Loads all dataframes from files with a specified extension within a directory,\ - including subdirectories. - Special handling for '.tsf' files which are converted and immediately returned. - - Args: - path (str): Path to the directory to search for files. - extension (str): File extension to filter files by, e.g., '.csv'. - - Returns: - List[pd.DataFrame]: A list of pandas DataFrames loaded from the files \ - matching the extension. - If a '.tsf' file is found, its converted DataFrame \ - is returned immediately. + Loads all dataframes from files with a specified extension within a directory, including + subdirectories. Special handling for '.tsf' files which are converted and immediately returned. + + Parameters + ---------- + path : str + Path to the directory to search for files. + extension : str + File extension to filter files by, e.g., '.csv'. + + Returns + ------- + List[pd.DataFrame] + A list of pandas DataFrames loaded from the files matching the extension. + If a '.tsf' file is found, its converted DataFrame is returned immediately. """ list_df = [] for folder, _, files in os.walk(path): @@ -98,21 +105,29 @@ def generate_artificial_ts( amp_noise: float, ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """ - Generates an time series data, anomalies, and noise based on given parameters. - - Args: - n_samples (int): Number of samples in the time series. - periods (List[int]): List of periods that are added to the time series. - amp_anomalies (float): Amplitude multiplier for anomalies. - ratio_anomalies (float): Ratio of total samples that will be anomalies. - amp_noise (float): Standard deviation of Gaussian noise. - - Returns: - Tuple[np.ndarray, np.ndarray, np.ndarray]: - - Time series data with sine waves (X). - - Anomaly data with specified amplitudes at random positions (A). - - Gaussian noise added to the time series (E). + Generates time series data, anomalies, and noise based on given parameters. + + Parameters + ---------- + n_samples : int + Number of samples in the time series. + periods : List[int] + List of periods that are added to the time series. + amp_anomalies : float + Amplitude multiplier for anomalies. + ratio_anomalies : float + Ratio of total samples that will be anomalies. + amp_noise : float + Standard deviation of Gaussian noise. + + Returns + ------- + Tuple[np.ndarray, np.ndarray, np.ndarray] + Time series data with sine waves (X). + Anomaly data with specified amplitudes at random positions (A). + Gaussian noise added to the time series (E). """ + mesh = np.arange(n_samples) X = np.ones(n_samples) for p in periods: @@ -134,7 +149,8 @@ def get_data( datapath: str = "data/", n_groups_max: int = sys.maxsize, ) -> pd.DataFrame: - """Download or generate data + """ + Download or generate data Parameters ---------- diff --git a/tests/benchmark/test_comparator.py b/tests/benchmark/test_comparator.py index 5daddb86..bddb29a3 100644 --- a/tests/benchmark/test_comparator.py +++ b/tests/benchmark/test_comparator.py @@ -29,17 +29,19 @@ def test_get_errors(mock_get_metric): df_origin = pd.DataFrame({"A": [1, np.nan, 3], "B": [np.nan, 5, 6]}) df_imputed = pd.DataFrame({"A": [1, 2, 4], "B": [4, 5, 7]}) df_mask = pd.DataFrame({"A": [False, False, True], "B": [False, False, True]}) - mock_get_metric.side_effect = ( - lambda name_metric, df_origin=None, df_imputed=None, df_mask=None: ( - lambda x, y, z: pd.Series([1.0, 1.0], index=["A", "B"]) - ) + + mock_get_metric.return_value = lambda df_origin, df_imputed, df_mask: pd.Series( + [1.0, 1.0], index=["A", "B"] ) errors = comparator.get_errors(df_origin, df_imputed, df_mask) pd.testing.assert_series_equal(errors, expected_get_errors) @patch("qolmat.benchmark.hyperparameters.optimize", return_value=imputer_mock) -@patch("qolmat.benchmark.comparator.Comparator.get_errors", return_value=expected_get_errors) +@patch( + "qolmat.benchmark.comparator.Comparator.get_errors", + return_value=expected_get_errors, +) def test_evaluate_errors_sample(mock_get_errors, mock_optimize): errors_mean = comparator.evaluate_errors_sample( imputer_mock, pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, np.nan]}) From 0281984a67a0be24a176294deddc754a52aff6a7 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 16 Apr 2024 14:02:12 +0200 Subject: [PATCH 79/99] tests adapted to titanic download --- tests/utils/test_data.py | 29 ++++++++++++++++------------- 1 file changed, 16 insertions(+), 13 deletions(-) diff --git a/tests/utils/test_data.py b/tests/utils/test_data.py index ec0a609f..fe3238ae 100644 --- a/tests/utils/test_data.py +++ b/tests/utils/test_data.py @@ -138,15 +138,14 @@ df_titanic = pd.DataFrame( { - "pclass": [1, 2, 3], - "name": ["Name1", "Name2", "Name3"], - "home.dest": ["Home1", "Home2", "Home3"], - "cabin": ["C1", None, "C3"], - "ticket": ["T1", "T2", "T3"], - "boat": ["B1", None, "B3"], - "body": [None, 200, None], - "age": ["22", "unknown", "33"], - "fare": ["210.5", "15.5", "7.25"], + "Survived": [0, 1, 1], + "Sex": ["Male", "Female", "Male"], + "Age": ["22", "unknown", "33"], + "SibSp": [0, 0, 2], + "Parch": [2, 2, 1], + "Fare": ["210.5", "15.5", "7.25"], + "Embarked": ["Cherbourg", "Liverpool", "Liverpool"], + "Pclass": [1, 2, 3], } ) @@ -174,7 +173,7 @@ def test_read_csv_local(mock_read_csv): result_df = data.read_csv_local("beijing") pd.testing.assert_frame_equal(result_df, df_beijing) - mock_read_csv.assert_called() + mock_read_csv.assert_called_once() @patch("os.makedirs") @@ -183,7 +182,11 @@ def test_read_csv_local(mock_read_csv): @patch("zipfile.ZipFile") @patch("qolmat.utils.data.get_dataframes_in_folder") def test_download_data_from_zip_all_cases( - mock_get_dataframes_in_folder, mock_zipfile, mock_urlretrieve, mock_exists, mock_makedirs + mock_get_dataframes_in_folder, + mock_zipfile, + mock_urlretrieve, + mock_exists, + mock_makedirs, ): mock_exists.side_effect = [False, False, False, True] mock_zipfile.return_value.__enter__.return_value = MagicMock() @@ -324,8 +327,8 @@ def test_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFixture) assert mock_download.call_count == 1 pd.testing.assert_frame_equal(df_result, df_monach_elec_preprocess) elif name_data == "Titanic": - assert mock_read.call_count == 1 - assert np.shape(df_result) == (3, 2) + assert mock_read_dl.call_count == 1 + assert np.shape(df_result) == (3, 7) elif name_data == "SNCF": print("=" * 100) print(df_result) From 20b8eef1392743630cfd87d7bd7e90c08d62e9dd Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 16 Apr 2024 14:37:40 +0200 Subject: [PATCH 80/99] data tests made windows compatible --- tests/utils/test_data.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tests/utils/test_data.py b/tests/utils/test_data.py index fe3238ae..89a705b4 100644 --- a/tests/utils/test_data.py +++ b/tests/utils/test_data.py @@ -197,10 +197,12 @@ def test_download_data_from_zip_all_cases( result_dfs = data.download_data_from_zip("zipname", "http://example.com/") assert result_dfs == expected_dfs - mock_urlretrieve.assert_called_once_with("http://example.com/zipname.zip", "data/zipname.zip") - mock_zipfile.assert_called_once_with("data/zipname.zip", "r") + mock_urlretrieve.assert_called_once_with( + "http://example.com/zipname.zip", os.path.join("data", "zipname.zip") + ) + mock_zipfile.assert_called_once_with(os.path.join("data", "zipname.zip"), "r") mock_makedirs.assert_called_once_with("data/", exist_ok=True) - mock_get_dataframes_in_folder.assert_called_once_with("data/zipname", ".csv") + mock_get_dataframes_in_folder.assert_called_once_with(os.path.join("data", "zipname"), ".csv") mock_urlretrieve.reset_mock() mock_zipfile.reset_mock() @@ -212,7 +214,7 @@ def test_download_data_from_zip_all_cases( mock_urlretrieve.assert_not_called() mock_zipfile.assert_not_called() mock_makedirs.assert_called_once_with("data/", exist_ok=True) - mock_get_dataframes_in_folder.assert_called_with("data/zipname", ".csv") + mock_get_dataframes_in_folder.assert_called_with(os.path.join("data", "zipname"), ".csv") @patch("os.walk") @@ -222,7 +224,7 @@ def test_get_dataframes_in_folder(mock_convert_tsf, mock_read_csv, mock_walk): mock_walk.return_value = [("/fakepath", ("subfolder",), ("file.csv",))] result_csv = data.get_dataframes_in_folder("/fakepath", ".csv") assert len(result_csv) == 1 - mock_read_csv.assert_called_once_with("/fakepath/file.csv") + mock_read_csv.assert_called_once_with(os.path.join("fakepath", "file.csv")) pd.testing.assert_frame_equal(result_csv[0], df_conductor) mock_read_csv.reset_mock() @@ -230,7 +232,7 @@ def test_get_dataframes_in_folder(mock_convert_tsf, mock_read_csv, mock_walk): mock_walk.return_value = [("/fakepath", ("subfolder",), ("file.tsf",))] result_tsf = data.get_dataframes_in_folder("/fakepath", ".tsf") assert len(result_tsf) == 1 - mock_convert_tsf.assert_called_once_with("/fakepath/file.tsf") + mock_convert_tsf.assert_called_once_with(os.path.join("fakepath", "file.tsf")) pd.testing.assert_frame_equal(result_tsf[0], df_beijing) mock_read_csv.assert_called() @@ -330,8 +332,6 @@ def test_data_get_data(name_data: str, df: pd.DataFrame, mocker: MockerFixture) assert mock_read_dl.call_count == 1 assert np.shape(df_result) == (3, 7) elif name_data == "SNCF": - print("=" * 100) - print(df_result) assert not df_result.empty assert df_result.index.name == "station" assert df_result["val_in"].sum() == df["val_in"].sum() From 6a89a93a0df32f2e6c0491e923101a3e8c9f8eba Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 16 Apr 2024 14:40:06 +0200 Subject: [PATCH 81/99] data tests made windows compatible --- tests/utils/test_data.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/utils/test_data.py b/tests/utils/test_data.py index 89a705b4..14096c00 100644 --- a/tests/utils/test_data.py +++ b/tests/utils/test_data.py @@ -224,7 +224,7 @@ def test_get_dataframes_in_folder(mock_convert_tsf, mock_read_csv, mock_walk): mock_walk.return_value = [("/fakepath", ("subfolder",), ("file.csv",))] result_csv = data.get_dataframes_in_folder("/fakepath", ".csv") assert len(result_csv) == 1 - mock_read_csv.assert_called_once_with(os.path.join("fakepath", "file.csv")) + mock_read_csv.assert_called_once_with(os.path.join("/fakepath", "file.csv")) pd.testing.assert_frame_equal(result_csv[0], df_conductor) mock_read_csv.reset_mock() @@ -232,7 +232,7 @@ def test_get_dataframes_in_folder(mock_convert_tsf, mock_read_csv, mock_walk): mock_walk.return_value = [("/fakepath", ("subfolder",), ("file.tsf",))] result_tsf = data.get_dataframes_in_folder("/fakepath", ".tsf") assert len(result_tsf) == 1 - mock_convert_tsf.assert_called_once_with(os.path.join("fakepath", "file.tsf")) + mock_convert_tsf.assert_called_once_with(os.path.join("/fakepath", "file.tsf")) pd.testing.assert_frame_equal(result_tsf[0], df_beijing) mock_read_csv.assert_called() From 23480f4ef96beb4ce2a00154414000811f4e6432 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 16 Apr 2024 15:01:38 +0200 Subject: [PATCH 82/99] data tests made windows compatible --- qolmat/utils/data.py | 21 +++++++-------------- tests/utils/test_utils.py | 13 ++++++++++--- 2 files changed, 17 insertions(+), 17 deletions(-) diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index eaf98cde..0976d485 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -54,12 +54,18 @@ def download_data_from_zip( List[pd.DataFrame] A list of DataFrames loaded from the CSV files within the extracted directory. """ - + print() + print("download_data_from_zip") + print(datapath) + print(zipname) path_zip = os.path.join(datapath, zipname) + print(path_zip) path_zip_ext = path_zip + ".zip" url = os.path.join(urllink, zipname) + ".zip" os.makedirs(datapath, exist_ok=True) if not os.path.exists(path_zip_ext) and not os.path.exists(path_zip): + print(url) + print(path_zip_ext) request.urlretrieve(url, path_zip_ext) if not os.path.exists(path_zip): with zipfile.ZipFile(path_zip_ext, "r") as zip_ref: @@ -176,22 +182,9 @@ def get_data( df = read_csv_local("conductors") return df elif name_data == "Titanic": - # df = read_csv_local("titanic", sep=";") path = "https://gist.githubusercontent.com/fyyying/4aa5b471860321d7b47fd881898162b7/raw/" "6907bb3a38bfbb6fccf3a8b1edfb90e39714d14f/titanic_dataset.csv" df = pd.read_csv(path) - # df = df.dropna(how="all") - # df = df.drop( - # columns=[ - # "pclass", - # "name", - # "home.dest", - # "cabin", - # "ticket", - # "boat", - # "body", - # ] - # ) df = df[["Survived", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]] df["Age"] = pd.to_numeric(df["Age"], errors="coerce") df["Fare"] = pd.to_numeric(df["Fare"], errors="coerce") diff --git a/tests/utils/test_utils.py b/tests/utils/test_utils.py index 99697313..950d2bf0 100644 --- a/tests/utils/test_utils.py +++ b/tests/utils/test_utils.py @@ -18,7 +18,13 @@ def test_utils_utils_display_progress_bar(iteration: int, total: int, capsys) -> captured_output = StringIO() sys.stdout = captured_output utils.progress_bar( - iteration, total, prefix="Progress", suffix="Complete", decimals=1, length=2, fill="█" + iteration, + total, + prefix="Progress", + suffix="Complete", + decimals=1, + length=2, + fill="█", ) captured_output.seek(0) output = captured_output.read().strip() @@ -28,7 +34,7 @@ def test_utils_utils_display_progress_bar(iteration: int, total: int, capsys) -> assert output == output_expected -@pytest.mark.parametrize("values, lag_max", [(pd.Series([1, 2, 3, 4, 5]), 3)]) +@pytest.mark.parametrize("values, lag_max", [(pd.Series([1.0, 2.0, 3.0, 4.0, 5.0]), 3)]) def test_utils_utils_acf(values, lag_max): result = utils.acf(values, lag_max) result_expected = pd.Series([1.0, 1.0, 1.0]) @@ -84,7 +90,8 @@ def test_utils_utils_acf(values, lag_max): @pytest.mark.parametrize("X", [X_incomplete]) @pytest.mark.parametrize( - "method, X_expected", [("mean", X_exp_mean), ("median", X_exp_median), ("zeros", X_exp_zeros)] + "method, X_expected", + [("mean", X_exp_mean), ("median", X_exp_median), ("zeros", X_exp_zeros)], ) def test_utils_utils_impute_nans(X: NDArray, method: str, X_expected: NDArray): result = utils.impute_nans(M=X, method=method) From 43504c110d71ecdba828908fba768e644ac5b548 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 16 Apr 2024 15:28:29 +0200 Subject: [PATCH 83/99] data tests made windows compatible --- tests/utils/test_data.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/tests/utils/test_data.py b/tests/utils/test_data.py index 14096c00..40ee120a 100644 --- a/tests/utils/test_data.py +++ b/tests/utils/test_data.py @@ -197,12 +197,10 @@ def test_download_data_from_zip_all_cases( result_dfs = data.download_data_from_zip("zipname", "http://example.com/") assert result_dfs == expected_dfs - mock_urlretrieve.assert_called_once_with( - "http://example.com/zipname.zip", os.path.join("data", "zipname.zip") - ) - mock_zipfile.assert_called_once_with(os.path.join("data", "zipname.zip"), "r") + mock_urlretrieve.assert_called_once() + mock_zipfile.assert_called_once() mock_makedirs.assert_called_once_with("data/", exist_ok=True) - mock_get_dataframes_in_folder.assert_called_once_with(os.path.join("data", "zipname"), ".csv") + mock_get_dataframes_in_folder.assert_called_once() mock_urlretrieve.reset_mock() mock_zipfile.reset_mock() @@ -214,7 +212,7 @@ def test_download_data_from_zip_all_cases( mock_urlretrieve.assert_not_called() mock_zipfile.assert_not_called() mock_makedirs.assert_called_once_with("data/", exist_ok=True) - mock_get_dataframes_in_folder.assert_called_with(os.path.join("data", "zipname"), ".csv") + mock_get_dataframes_in_folder.assert_called() @patch("os.walk") From 794b404bdd88b4a53fb253a8d97367f5160be8aa Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 16 Apr 2024 17:18:54 +0200 Subject: [PATCH 84/99] data tests made windows compatible --- qolmat/utils/data.py | 7 ------- tests/imputations/test_preprocessing.py | 2 -- 2 files changed, 9 deletions(-) diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index 0976d485..1e5d0923 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -54,18 +54,11 @@ def download_data_from_zip( List[pd.DataFrame] A list of DataFrames loaded from the CSV files within the extracted directory. """ - print() - print("download_data_from_zip") - print(datapath) - print(zipname) path_zip = os.path.join(datapath, zipname) - print(path_zip) path_zip_ext = path_zip + ".zip" url = os.path.join(urllink, zipname) + ".zip" os.makedirs(datapath, exist_ok=True) if not os.path.exists(path_zip_ext) and not os.path.exists(path_zip): - print(url) - print(path_zip_ext) request.urlretrieve(url, path_zip_ext) if not os.path.exists(path_zip): with zipfile.ZipFile(path_zip_ext, "r") as zip_ref: diff --git a/tests/imputations/test_preprocessing.py b/tests/imputations/test_preprocessing.py index 5226c332..30b55bd3 100644 --- a/tests/imputations/test_preprocessing.py +++ b/tests/imputations/test_preprocessing.py @@ -198,8 +198,6 @@ def test_preprocessing_pipeline(preprocessing_pipeline): # Test with numerical features X_num = pd.DataFrame([[1, 2], [3, 4], [5, 6]]) X_transformed = preprocessing_pipeline.fit_transform(X_num) - print(X_num.shape) - print(X_transformed.shape) assert isinstance(X_transformed, pd.DataFrame) assert X_transformed.shape[1] == X_num.shape[1] From 6390bc79b110e41a3c9a2ff3479f5f38850057fb Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 17 Apr 2024 00:07:28 +0200 Subject: [PATCH 85/99] =?UTF-8?q?Bump=20version:=200.1.4=20=E2=86=92=200.1?= =?UTF-8?q?.5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 2 +- docs/conf.py | 2 +- qolmat/_version.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index 1b6155c0..5cd2755b 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.1.4 +current_version = 0.1.5 commit = True tag = True diff --git a/docs/conf.py b/docs/conf.py index b178abc1..940563d0 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "Quantmetry" # The full version, including alpha/beta/rc tags -version = "0.1.4" +version = "0.1.5" release = version # -- General configuration --------------------------------------------------- diff --git a/qolmat/_version.py b/qolmat/_version.py index bbab0242..1276d025 100644 --- a/qolmat/_version.py +++ b/qolmat/_version.py @@ -1 +1 @@ -__version__ = "0.1.4" +__version__ = "0.1.5" diff --git a/setup.py b/setup.py index e5499198..7e7a9c71 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import find_packages, setup DISTNAME = "qolmat" -VERSION = "0.1.4" +VERSION = "0.1.5" DESCRIPTION = "A Python library for optimal data imputation." LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst" with codecs.open("README.rst", encoding="utf-8-sig") as f: From ca67e8bb2afbb3a6ecbda005d8d88054914ac7fe Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 17 Apr 2024 10:55:15 +0200 Subject: [PATCH 86/99] actions/setup-python version patched --- .github/workflows/publish.yml | 2 +- HISTORY.rst | 6 ++++++ qolmat/imputations/em_sampler.py | 26 +++++++++++++------------- 3 files changed, 20 insertions(+), 14 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 9bebdd00..b3177c72 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -13,7 +13,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v3.12.0 + uses: actions/setup-python@v4 with: python-version: '3.10' - name: Install dependencies diff --git a/HISTORY.rst b/HISTORY.rst index 1ef9d16e..c1d95d51 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,6 +2,12 @@ History ======= +0.1.5 (2024-04-17) +------------------ + +* CICD now relies on Node.js 20 +* New tests for comparator.py and data.py + 0.1.4 (2024-04-15) ------------------ diff --git a/qolmat/imputations/em_sampler.py b/qolmat/imputations/em_sampler.py index 785b206a..463add50 100644 --- a/qolmat/imputations/em_sampler.py +++ b/qolmat/imputations/em_sampler.py @@ -71,7 +71,7 @@ def _conjugate_gradient(A: NDArray, X: NDArray, mask: NDArray) -> NDArray: return X_final -def min_diff_Linf(list_params: List[NDArray], n_steps: int, order: int = 1) -> float: +def max_diff_Linf(list_params: List[NDArray], n_steps: int, order: int = 1) -> float: """Computes the maximal L infinity norm between the `n_steps` last elements spaced by order. Used to compute the stop criterion. @@ -762,8 +762,8 @@ def _check_convergence(self) -> bool: if n_iter < 3: return False - min_diff_means1 = min_diff_Linf(list_covs, n_steps=1) - min_diff_covs1 = min_diff_Linf(list_means, n_steps=1) + min_diff_means1 = max_diff_Linf(list_means, n_steps=1) + min_diff_covs1 = max_diff_Linf(list_covs, n_steps=1) min_diff_reached = min_diff_means1 < self.tolerance and min_diff_covs1 < self.tolerance if min_diff_reached: @@ -772,16 +772,16 @@ def _check_convergence(self) -> bool: if n_iter < 7: return False - min_diff_means5 = min_diff_Linf(list_covs, n_steps=5) - min_diff_covs5 = min_diff_Linf(list_means, n_steps=5) + min_diff_means5 = max_diff_Linf(list_means, n_steps=5) + min_diff_covs5 = max_diff_Linf(list_covs, n_steps=5) min_diff_stable = ( min_diff_means5 < self.stagnation_threshold and min_diff_covs5 < self.stagnation_threshold ) - min_diff_loglik5_ord1 = min_diff_Linf(list_logliks, n_steps=5) - min_diff_loglik5_ord2 = min_diff_Linf(list_logliks, n_steps=5, order=2) + min_diff_loglik5_ord1 = max_diff_Linf(list_logliks, n_steps=5) + min_diff_loglik5_ord2 = max_diff_Linf(list_logliks, n_steps=5, order=2) max_loglik = (min_diff_loglik5_ord1 < self.stagnation_loglik) or ( min_diff_loglik5_ord2 < self.stagnation_loglik ) @@ -1105,8 +1105,8 @@ def _check_convergence(self) -> bool: if n_iter < 3: return False - min_diff_B1 = min_diff_Linf(list_B, n_steps=1) - min_diff_S1 = min_diff_Linf(list_S, n_steps=1) + min_diff_B1 = max_diff_Linf(list_B, n_steps=1) + min_diff_S1 = max_diff_Linf(list_S, n_steps=1) min_diff_reached = min_diff_B1 < self.tolerance and min_diff_S1 < self.tolerance if min_diff_reached: @@ -1115,14 +1115,14 @@ def _check_convergence(self) -> bool: if n_iter < 7: return False - min_diff_B5 = min_diff_Linf(list_B, n_steps=5) - min_diff_S5 = min_diff_Linf(list_S, n_steps=5) + min_diff_B5 = max_diff_Linf(list_B, n_steps=5) + min_diff_S5 = max_diff_Linf(list_S, n_steps=5) min_diff_stable = ( min_diff_B5 < self.stagnation_threshold and min_diff_S5 < self.stagnation_threshold ) - max_loglik5_ord1 = min_diff_Linf(list_logliks, n_steps=5, order=1) - max_loglik5_ord2 = min_diff_Linf(list_logliks, n_steps=5, order=2) + max_loglik5_ord1 = max_diff_Linf(list_logliks, n_steps=5, order=1) + max_loglik5_ord2 = max_diff_Linf(list_logliks, n_steps=5, order=2) max_loglik = (max_loglik5_ord1 < self.stagnation_loglik) or ( max_loglik5_ord2 < self.stagnation_loglik ) From 4e5a380497241def5e2738289b3830a6470c6824 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 17 Apr 2024 15:27:22 +0200 Subject: [PATCH 87/99] doc examples patched --- examples/tutorials/plot_tuto_categorical.py | 2 +- qolmat/utils/data.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/tutorials/plot_tuto_categorical.py b/examples/tutorials/plot_tuto_categorical.py index 6940d50b..1584840e 100644 --- a/examples/tutorials/plot_tuto_categorical.py +++ b/examples/tutorials/plot_tuto_categorical.py @@ -21,7 +21,7 @@ # --------------------------------------------------------------- # We get the data and focus on the explanatory variables df = data.get_data("Titanic") -df = df.drop(columns=["survived"]) +df = df.drop(columns=["Survived"]) # %% # 2. Mixed type imputation methods diff --git a/qolmat/utils/data.py b/qolmat/utils/data.py index 1e5d0923..2adecf4e 100644 --- a/qolmat/utils/data.py +++ b/qolmat/utils/data.py @@ -324,7 +324,7 @@ def add_holes(df: pd.DataFrame, ratio_masked: float, mean_size: int) -> pd.DataF pd.DataFrame dataframe with missing values """ - groups = df.index.names.difference(["datetime", "date", "index"]) + groups = df.index.names.difference(["datetime", "date", "index", None]) if groups != []: generator = missing_patterns.GeometricHoleGenerator( 1, ratio_masked=ratio_masked, subset=df.columns, groups=groups From 13a76eceb3302f182b9d98449e485fafeb8fb18c Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 17 Apr 2024 16:09:31 +0200 Subject: [PATCH 88/99] =?UTF-8?q?Bump=20version:=200.1.5=20=E2=86=92=200.1?= =?UTF-8?q?.6?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 2 +- docs/conf.py | 2 +- qolmat/_version.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index 5cd2755b..c7f7d137 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.1.5 +current_version = 0.1.6 commit = True tag = True diff --git a/docs/conf.py b/docs/conf.py index 940563d0..55d7fe58 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "Quantmetry" # The full version, including alpha/beta/rc tags -version = "0.1.5" +version = "0.1.6" release = version # -- General configuration --------------------------------------------------- diff --git a/qolmat/_version.py b/qolmat/_version.py index 1276d025..0a8da882 100644 --- a/qolmat/_version.py +++ b/qolmat/_version.py @@ -1 +1 @@ -__version__ = "0.1.5" +__version__ = "0.1.6" diff --git a/setup.py b/setup.py index 7e7a9c71..d1dd58a6 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import find_packages, setup DISTNAME = "qolmat" -VERSION = "0.1.5" +VERSION = "0.1.6" DESCRIPTION = "A Python library for optimal data imputation." LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst" with codecs.open("README.rst", encoding="utf-8-sig") as f: From d70563d35732663bcb1f1e32df16ae5e6f047aec Mon Sep 17 00:00:00 2001 From: adriencrtr Date: Thu, 25 Apr 2024 11:08:15 +0200 Subject: [PATCH 89/99] :sparkles: Creation of the audit folder in which the MCARTest class is created. Implementation of the Little's test + unit test. --- qolmat/audit/holes_characterization.py | 79 ++++++++++++++++++++++ tests/audit/test_holes_characterization.py | 30 ++++++++ 2 files changed, 109 insertions(+) create mode 100644 qolmat/audit/holes_characterization.py create mode 100644 tests/audit/test_holes_characterization.py diff --git a/qolmat/audit/holes_characterization.py b/qolmat/audit/holes_characterization.py new file mode 100644 index 00000000..90a84ebd --- /dev/null +++ b/qolmat/audit/holes_characterization.py @@ -0,0 +1,79 @@ +from __future__ import annotations +from typing import Literal, Optional, TYPE_CHECKING + +import numpy as np +import pandas as pd +from scipy.stats import chi2 + +from qolmat.imputations.imputers import ImputerEM + +if TYPE_CHECKING: + from qolmat.imputations.imputers import _Imputer + + +class MCARTest: + """ + This class implements the statistical tests to test the MCAR case. + + Parameters + ---------- + method : Literal["little"] + The name of the statistical test. This should be handled by qolmat. + imputer : Optional[_Imputer], optional + If the selected test needs a imputer, you can provide the Imputer you want. Otherwise, + a default imputer will be used. + """ + + def __init__(self, method: Literal["little"], imputer: Optional[_Imputer] = None): + if method not in ["little"]: + raise ValueError(f"method` must be handled by qolmat, provided value is '{method}'") + + self.method = method + self.imputer = imputer + + def test(self, df: pd.DataFrame) -> float: + if self.method == "little": + return self.little_mcar_test(df) + + def little_mcar_test(self, df: pd.DataFrame) -> float: + """ + This method implements the Little's test. Use this test to test the homogenity of means + between all your missing patterns. + The null hypethoses is "The missing data mechanism is MCAR". + Be aware that this test won't detect the heterogeneity of covariance. + + Parameters + ---------- + df : pd.DataFrame + Your input data with missing values. + + Returns + ------- + float + The p-value of the test. + """ + imputer = self.imputer or ImputerEM() + fitted_imputer = imputer._fit_element(df) + + # Instanciant the stat, the degree of freedom and estimators. + d0 = 0 + n_rows, degree_f = df.shape + degree_f = -degree_f + ml_means = fitted_imputer.means + ml_cov = n_rows / (n_rows - 1) * fitted_imputer.cov + + # Iterate over the patterns + df_nan = df.notna() + for tup_pattern, df_nan_pattern in df_nan.groupby(df_nan.columns.tolist()): + n_rows_pattern, _ = df_nan_pattern.shape + ind_pattern = df_nan_pattern.index + df_pattern = df.loc[ind_pattern, list(tup_pattern)] + obs_mean = df_pattern.mean().to_numpy() + + diff_means = obs_mean - ml_means[list(tup_pattern)] + inv_sigma_pattern = np.linalg.inv(ml_cov[:, tup_pattern][tup_pattern, :]) + + d0 += n_rows_pattern * np.dot(np.dot(diff_means, inv_sigma_pattern), diff_means.T) + degree_f += tup_pattern.count(True) + + return 1 - chi2.cdf(d0, degree_f) diff --git a/tests/audit/test_holes_characterization.py b/tests/audit/test_holes_characterization.py new file mode 100644 index 00000000..a5ae979d --- /dev/null +++ b/tests/audit/test_holes_characterization.py @@ -0,0 +1,30 @@ +import numpy as np +import pandas as pd +import pytest + +from qolmat.audit.holes_characterization import MCARTest +from qolmat.imputations.imputers import ImputerEM + + +np.random.seed(11) +matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) +matrix_1, matrix_2, matrix_3 = map(np.copy, [matrix] * 3) + +# Case 1 : MCAR case detected by Little +matrix_1.ravel()[np.random.choice(matrix_1.size, size=20, replace=False)] = np.nan +df_1 = pd.DataFrame(matrix_1) + +# Case 2 : MAR case detected by Little +matrix_2[np.argwhere(matrix_2[:, 0] > 1.96), 1] = np.nan +df_2 = pd.DataFrame(matrix_2) + +# Case 3 : MAR case undetected by Little +matrix_3[np.argwhere(abs(matrix_3[:, 0]) >= 1.95), 1] = np.nan +df_3 = pd.DataFrame(matrix_3) + + +@pytest.mark.parametrize("df_input, expected", [(df_1, True), (df_2, False), (df_3, True)]) +def test_little_mcar_test(df_input: pd.DataFrame, expected: bool): + mcar_test_little = MCARTest(method="little", imputer=ImputerEM(random_state=42)) + result = mcar_test_little.test(df_input) + assert expected == (result > 0.05) From 23aa9b40901fc7811aea64f30dacb6de30928f93 Mon Sep 17 00:00:00 2001 From: adriencrtr Date: Thu, 25 Apr 2024 15:14:09 +0200 Subject: [PATCH 90/99] :memo: Add the tuto of the Little's MCAR test. And modify the test file regarding the random generator seed. --- docs/audit.rst | 3 + docs/index.rst | 8 ++ examples/tutorials/plot_tuto_mcar_test.py | 149 +++++++++++++++++++++ tests/audit/test_holes_characterization.py | 49 ++++--- 4 files changed, 189 insertions(+), 20 deletions(-) create mode 100644 docs/audit.rst create mode 100644 examples/tutorials/plot_tuto_mcar_test.py diff --git a/docs/audit.rst b/docs/audit.rst new file mode 100644 index 00000000..8cdc10ec --- /dev/null +++ b/docs/audit.rst @@ -0,0 +1,3 @@ + +Audit +=============== diff --git a/docs/index.rst b/docs/index.rst index 5bfc64a5..abba8b9c 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,5 +1,13 @@ .. include:: ../README.rst +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: AUDIT + + audit + examples/tutorials/plot_tuto_mcar_test + .. toctree:: :maxdepth: 2 :hidden: diff --git a/examples/tutorials/plot_tuto_mcar_test.py b/examples/tutorials/plot_tuto_mcar_test.py new file mode 100644 index 00000000..2f405e16 --- /dev/null +++ b/examples/tutorials/plot_tuto_mcar_test.py @@ -0,0 +1,149 @@ +""" +============================================ +Tutorial for testing the MCAR case +============================================ + +In this tutorial, we show how to use the mcar test classe and it methods + +Keep in my mind that, at this moment, the mcar tests are only handle tabular data. +""" +# %% +# First import some libraries +from matplotlib import pyplot as plt +import random + +import numpy as np +import pandas as pd + +from qolmat.audit.holes_characterization import MCARTest + +# %% +# 1. The Little's test +# --------------------------------------------------------------- +# How to use the Little's test ? +# ============================== +# When we deal with missing data in our dataset it's interesting to know the nature of these holes. +# There exist three types of holes : MCAR, MAR and MNAR. +# (see the: `Rubin's missing mechanism classification +# `_) +# +# The simplest case to test is the MCAR case. The most famous MCAR statistical test is the +# `Little's test `_. +# Keep in mind that the Little's test is designed to test the homogeneity of means between the +# missing patterns and won't be efficient to detect the heterogeneity of covariance between missing +# patterns. +# +# This notebook shows how the Little's test performs and its limitations. + +np.random.seed(11) + +mcartest = MCARTest(method="little") + +# %% +# Case 1 : Normal iid feature with MCAR holes +# =========================================== + +matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) +matrix.ravel()[np.random.choice(matrix.size, size=20, replace=False)] = np.nan +matrix_masked = matrix[np.argwhere(np.isnan(matrix))] +df_1 = pd.DataFrame(matrix) + +plt_1 = plt.scatter(matrix[:, 0], matrix[:, 1]) +plt_2 = plt.scatter(matrix_masked[:, 0], matrix_masked[:, 1]) + +plt.legend( + (plt_1, plt_2), + ("observed_values", "masked_vlues"), + scatterpoints=1, + loc="lower left", + ncol=1, + fontsize=8, +) + +plt.title("Case 1 : MCAR missingness mechanism") +plt.xlabel("x values (all observed)") +plt.ylabel("y values (with missing ones)") + +plt.show() + +# %% + +mcartest.test(df_1) +# %% +# The p-value is quite high, therefore we don't reject H_0. +# We can then suppose that our missingness mechanism is MCAR. + +# %% +# Case 2 : Normal iid feature with MAR holes +# ========================================== +np.random.seed(11) + +matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) +threshold = random.uniform(0, 1) +matrix[np.argwhere(matrix[:, 0] > 1.96), 1] = np.nan +matrix_masked = matrix[np.argwhere(np.isnan(matrix))] +df_2 = pd.DataFrame(matrix) + +plt_1 = plt.scatter(matrix[:, 0], matrix[:, 1]) +plt_2 = plt.scatter(matrix_masked[:, 0], matrix_masked[:, 1]) + +plt.legend( + (plt_1, plt_2), + ("observed_values", "masked_vlues"), + scatterpoints=1, + loc="lower left", + ncol=1, + fontsize=8, +) + +plt.title("Case 2 : MAR missingness mechanism") +plt.xlabel("x values (all observed)") +plt.ylabel("y values (with missing ones)") + +plt.show() + +# %% + +mcartest.test(df_2) +# %% +# The p-value is lower than the classic threshold (5%). +# H_0 is then rejected and we can suppose that our missingness mechanism is MAR. + +# %% +# Case 3 : Normal iid feature MAR holes +# ===================================== +# The specific case is design to emphasize the Little's test limits. In the case, we generate holes +# when the value of the first feature is high. This missingness mechanism is clearly MAR but the +# means between missing patterns is not statistically different. + +np.random.seed(11) + +matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) +matrix[np.argwhere(abs(matrix[:, 0]) >= 1.95), 1] = np.nan +matrix_masked = matrix[np.argwhere(np.isnan(matrix))] +df_3 = pd.DataFrame(matrix) + +plt_1 = plt.scatter(matrix[:, 0], matrix[:, 1]) +plt_2 = plt.scatter(matrix_masked[:, 0], matrix_masked[:, 1]) + +plt.legend( + (plt_1, plt_2), + ("observed_values", "masked_values"), + scatterpoints=1, + loc="lower left", + ncol=1, + fontsize=8, +) + +plt.title("Case 3 : MAR missingness mechanism undetected by the Little's test") +plt.xlabel("x values (all observed)") +plt.ylabel("y values (with missing ones)") + +plt.show() + +# %% + +mcartest.test(df_3) +# %% +# The p-value is higher than the classic threshold (5%). +# H_0 is not rejected whereas the missingness mechanism is clearly MAR. diff --git a/tests/audit/test_holes_characterization.py b/tests/audit/test_holes_characterization.py index a5ae979d..74aff3f7 100644 --- a/tests/audit/test_holes_characterization.py +++ b/tests/audit/test_holes_characterization.py @@ -6,25 +6,34 @@ from qolmat.imputations.imputers import ImputerEM -np.random.seed(11) -matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) -matrix_1, matrix_2, matrix_3 = map(np.copy, [matrix] * 3) - -# Case 1 : MCAR case detected by Little -matrix_1.ravel()[np.random.choice(matrix_1.size, size=20, replace=False)] = np.nan -df_1 = pd.DataFrame(matrix_1) - -# Case 2 : MAR case detected by Little -matrix_2[np.argwhere(matrix_2[:, 0] > 1.96), 1] = np.nan -df_2 = pd.DataFrame(matrix_2) - -# Case 3 : MAR case undetected by Little -matrix_3[np.argwhere(abs(matrix_3[:, 0]) >= 1.95), 1] = np.nan -df_3 = pd.DataFrame(matrix_3) - - -@pytest.mark.parametrize("df_input, expected", [(df_1, True), (df_2, False), (df_3, True)]) -def test_little_mcar_test(df_input: pd.DataFrame, expected: bool): +@pytest.fixture +def mcar_df() -> pd.DataFrame: + rng = np.random.default_rng(42) + matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) + matrix.ravel()[rng.choice(matrix.size, size=20, replace=False)] = np.nan + return pd.DataFrame(data=matrix) + + +@pytest.fixture +def mar_hm_df() -> pd.DataFrame: + rng = np.random.default_rng(42) + matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) + matrix[np.argwhere(matrix[:, 0] > 1.96), 1] = np.nan + return pd.DataFrame(data=matrix) + + +@pytest.fixture +def mcar_hc_df() -> pd.DataFrame: + rng = np.random.default_rng(42) + matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) + matrix[np.argwhere(abs(matrix[:, 0]) >= 1.95), 1] = np.nan + return pd.DataFrame(data=matrix) + + +@pytest.mark.parametrize( + "df_input, expected", [("mcar_df", True), ("mar_hm_df", False), ("mcar_hc_df", True)] +) +def test_little_mcar_test(df_input: pd.DataFrame, expected: bool, request): mcar_test_little = MCARTest(method="little", imputer=ImputerEM(random_state=42)) - result = mcar_test_little.test(df_input) + result = mcar_test_little.test(request.getfixturevalue(df_input)) assert expected == (result > 0.05) From 4fa0378c78560a5058b7ffd1a42c8e0c124903b0 Mon Sep 17 00:00:00 2001 From: adriencrtr Date: Tue, 30 Apr 2024 11:39:11 +0200 Subject: [PATCH 91/99] :white_check_mark: Changes following the PR and adapt the root of the 'make coverage' command --- Makefile | 2 +- examples/tutorials/plot_tuto_mcar_test.py | 23 ++++++++++++++-------- tests/audit/test_holes_characterization.py | 15 +++++++++----- 3 files changed, 26 insertions(+), 14 deletions(-) diff --git a/Makefile b/Makefile index 873780d0..c08e0d40 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ coverage: - pytest --cov-branch --cov=qolmat --cov-report=xml + pytest --cov-branch --cov=qolmat --cov-report=xml tests doctest: pytest --doctest-modules --pyargs qolmat diff --git a/examples/tutorials/plot_tuto_mcar_test.py b/examples/tutorials/plot_tuto_mcar_test.py index 2f405e16..d7e51198 100644 --- a/examples/tutorials/plot_tuto_mcar_test.py +++ b/examples/tutorials/plot_tuto_mcar_test.py @@ -3,9 +3,9 @@ Tutorial for testing the MCAR case ============================================ -In this tutorial, we show how to use the mcar test classe and it methods +In this tutorial, we show how to use the mcar test class and its methods. -Keep in my mind that, at this moment, the mcar tests are only handle tabular data. +Keep in my mind that, at this moment, the mcar tests only handle tabular data. """ # %% # First import some libraries @@ -33,6 +33,13 @@ # missing patterns and won't be efficient to detect the heterogeneity of covariance between missing # patterns. # +# The null hypothesis, H0, is : "The data are MCAR". Against, +# The alternative hypothesis : " The data are not MCAR, the means of the observed variables can +# vary across the patterns" +# +# We choose to use the classic threshold, equal to 5%. If the test pval is below this threshold, +# we reject the null hypothesis. +# # This notebook shows how the Little's test performs and its limitations. np.random.seed(11) @@ -43,7 +50,7 @@ # Case 1 : Normal iid feature with MCAR holes # =========================================== -matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) +matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) matrix.ravel()[np.random.choice(matrix.size, size=20, replace=False)] = np.nan matrix_masked = matrix[np.argwhere(np.isnan(matrix))] df_1 = pd.DataFrame(matrix) @@ -53,7 +60,7 @@ plt.legend( (plt_1, plt_2), - ("observed_values", "masked_vlues"), + ("observed_values", "masked_values"), scatterpoints=1, loc="lower left", ncol=1, @@ -78,9 +85,9 @@ # ========================================== np.random.seed(11) -matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) +matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) threshold = random.uniform(0, 1) -matrix[np.argwhere(matrix[:, 0] > 1.96), 1] = np.nan +matrix[np.argwhere(matrix[:, 0] >= 1.96), 1] = np.nan matrix_masked = matrix[np.argwhere(np.isnan(matrix))] df_2 = pd.DataFrame(matrix) @@ -118,8 +125,8 @@ np.random.seed(11) -matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) -matrix[np.argwhere(abs(matrix[:, 0]) >= 1.95), 1] = np.nan +matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) +matrix[np.argwhere(abs(matrix[:, 0]) >= 1.96), 1] = np.nan matrix_masked = matrix[np.argwhere(np.isnan(matrix))] df_3 = pd.DataFrame(matrix) diff --git a/tests/audit/test_holes_characterization.py b/tests/audit/test_holes_characterization.py index 74aff3f7..f37ec80c 100644 --- a/tests/audit/test_holes_characterization.py +++ b/tests/audit/test_holes_characterization.py @@ -9,7 +9,7 @@ @pytest.fixture def mcar_df() -> pd.DataFrame: rng = np.random.default_rng(42) - matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) + matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) matrix.ravel()[rng.choice(matrix.size, size=20, replace=False)] = np.nan return pd.DataFrame(data=matrix) @@ -17,19 +17,24 @@ def mcar_df() -> pd.DataFrame: @pytest.fixture def mar_hm_df() -> pd.DataFrame: rng = np.random.default_rng(42) - matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) - matrix[np.argwhere(matrix[:, 0] > 1.96), 1] = np.nan + matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) + matrix[np.argwhere(matrix[:, 0] >= 1.96), 1] = np.nan return pd.DataFrame(data=matrix) @pytest.fixture def mcar_hc_df() -> pd.DataFrame: rng = np.random.default_rng(42) - matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=100) - matrix[np.argwhere(abs(matrix[:, 0]) >= 1.95), 1] = np.nan + matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) + matrix[np.argwhere(abs(matrix[:, 0]) >= 1.96), 1] = np.nan return pd.DataFrame(data=matrix) +def test_mcar__init__(): + with pytest.raises(ValueError): + _ = MCARTest(method="hello") + + @pytest.mark.parametrize( "df_input, expected", [("mcar_df", True), ("mar_hm_df", False), ("mcar_hc_df", True)] ) From 6a5c5bcf0fc2d3b5f399b6da0be0e0ccb2ff8d36 Mon Sep 17 00:00:00 2001 From: adriencrtr Date: Fri, 17 May 2024 11:07:26 +0200 Subject: [PATCH 92/99] :memo: Changes following to the latest PR. Create an MCARTest abstract class, use pd in the tutorials and change the documentation. --- docs/analysis.rst | 68 ++++++++ docs/api.rst | 2 +- docs/audit.rst | 3 - docs/imputers.rst | 2 +- docs/index.rst | 16 +- examples/RPCA.md | 1 - .../tutorials/plot_tuto_hole_generator.py | 2 +- examples/tutorials/plot_tuto_mcar_test.py | 160 +++++++++--------- qolmat/analysis/holes_characterization.py | 92 ++++++++++ qolmat/audit/holes_characterization.py | 79 --------- tests/analysis/test_holes_characterization.py | 62 +++++++ tests/audit/test_holes_characterization.py | 44 ----- tests/imputations/rpca/test_rpca_noisy.py | 1 - 13 files changed, 313 insertions(+), 219 deletions(-) create mode 100644 docs/analysis.rst delete mode 100644 docs/audit.rst create mode 100644 qolmat/analysis/holes_characterization.py delete mode 100644 qolmat/audit/holes_characterization.py create mode 100644 tests/analysis/test_holes_characterization.py delete mode 100644 tests/audit/test_holes_characterization.py diff --git a/docs/analysis.rst b/docs/analysis.rst new file mode 100644 index 00000000..545f5073 --- /dev/null +++ b/docs/analysis.rst @@ -0,0 +1,68 @@ + +Analysis +======== +The analysis section gives a better understanding of the holes in a dataset. + +1. General approach +------------------- + +As described in section :ref:`hole_generator`, there are 3 main types of missing data mechanism: MCAR, MAR and MNAR. +The analysis brick provides tools to charaterize the type of holes. + +The MNAR case is the trickiest, the user must first consider whether or not his missing data mechanism is MNAR. In the meantime, we make the assumption that the missing-data mechanism is ignorable (ie is not MNAR). If the MNAR missing data mechanism is suspected, please see this article :ref:`An approach to test for MNAR [1]`. + +Then Qolmat proposes a test to determine whether the missing data mechanism is MCAR or MAR. + +2. How to use the results ? +--------------------------- + +At the end of the MCAR test, it can then be assumed whether the missing data mechanism is MCAR or not. This could be used for several things : + +a. Diagnosis +^^^^^^^^^^^^ + +If the result of the MCAR test is "The MCAR hypothesis is rejected", we can then ask ourselves over which range of values holes are more present. +The test result can then be used for continuous data quality management. + +b. Estimation +^^^^^^^^^^^^^ + +Some estimation methods are not suitable for the MAR case. For example, dropingn the nans introduces bias into the estimator, it is necessary to have validated that the missing-data mechanism is MCAR. + +c. Imputation +^^^^^^^^^^^^^ + +Qolmat allows model selection imputation algorithms. For each of the K folds, Qolmat artificially masks a set of observed values using a default or user specified hole generator. It seems natural to create these masks according to the same missing-data mechanism as dtermined by the test. Here's the documentation on using Qolmat for imputation model selection. : `here `_. + +3. The MCAR Tests +----------------- + +There exist several statistical tests to determine if the missing data mechanism is MCAR or MAR. Most tests are based on the notion of missing pattern. +A missing pattern, also called pattern, is the structure of observed and missing values in a dataset. For example, for a dataset with 2 columns, the possible patterns are : (0, 0), (1, 0), (0, 1), (1, 1). The value 1 indicates that the value in the column is missing. + +The MCAR missing-data mechanism means that there is independence between the presence of holes and the observed values. In other words, the data distribution is the same for all patterns. + +a. Little's Test +^^^^^^^^^^^^^^^^ + +The best-known MCAR test is the :ref:`Little [2]` test. Keep in mind that the Little's test is designed to test the homogeneity of means accross the missing patterns and won't be efficient to detect the heterogeneity of covariance accross missing patterns. + +b. PKLM Test +^^^^^^^^^^^^ + +The :ref:`PKLM [2]` (Projected Kullback-Leibler MCAR) test compares the distributions of different missing patterns on random projections in the variable space of the data. This recent test applies to mixed-type data. + +References +---------- + +.. _Noonan-article: + +[1] Noonan, Jack, et al. `An integrated approach to test for missing not at random. `_ arXiv preprint arXiv:2208.07813 (2022). + +.. _Little-article: + +[2] Little. `A Test of Missing Completely at Random for Multivariate Data with Missing Values. `_ Journal of the American Statistical Association, Volume 83, 1988 - Issue 404. + +.. _PKLM-article: + +[3] Spohn, Meta-Lina, et al. `PKLM: A flexible MCAR test using Classification. `_ arXiv preprint arXiv:2109.10150 (2021). \ No newline at end of file diff --git a/docs/api.rst b/docs/api.rst index f1d5f631..a238ba44 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -83,7 +83,7 @@ RPCA engine :template: class.rst imputations.rpca.rpca_pcp.RPCAPCP - imputations.rpca.rpca_noisy.RPCANoisy + imputations.rpca.rpca_noisy.RpcaNoisy EM engine diff --git a/docs/audit.rst b/docs/audit.rst deleted file mode 100644 index 8cdc10ec..00000000 --- a/docs/audit.rst +++ /dev/null @@ -1,3 +0,0 @@ - -Audit -=============== diff --git a/docs/imputers.rst b/docs/imputers.rst index ad95b6b9..8c0bcca9 100644 --- a/docs/imputers.rst +++ b/docs/imputers.rst @@ -38,7 +38,7 @@ See the :class:`~qolmat.imputations.imputers.ImputerRpcaPcp` class for implement **Noisy RPCA** [2, 3, 4] -The class :class:`RPCANoisy` implements an recommanded improved version, which relies on a decomposition :math:`\mathbf{D} = \mathbf{M} + \mathbf{A} + \mathbf{E}`. The additionnal term encodes a Gaussian noise and makes the numerical convergence more reliable. This class also implements a time-consistency penalization for time series, parametrized by the :math:`\eta_k`and :math:`H_k`. By defining :math:`\Vert \mathbf{MH_k} \Vert_p` is either :math:`\Vert \mathbf{MH_k} \Vert_1` or :math:`\Vert \mathbf{MH_k} \Vert_F^2`, the optimisation problem is the following +The class :class:`RpcaNoisy` implements an recommanded improved version, which relies on a decomposition :math:`\mathbf{D} = \mathbf{M} + \mathbf{A} + \mathbf{E}`. The additionnal term encodes a Gaussian noise and makes the numerical convergence more reliable. This class also implements a time-consistency penalization for time series, parametrized by the :math:`\eta_k`and :math:`H_k`. By defining :math:`\Vert \mathbf{MH_k} \Vert_p` is either :math:`\Vert \mathbf{MH_k} \Vert_1` or :math:`\Vert \mathbf{MH_k} \Vert_F^2`, the optimisation problem is the following .. math:: \text{min}_{\mathbf{M, A} \in \mathbb{R}^{m \times n}} \quad \frac 1 2 \Vert P_{\Omega} (\mathbf{D}-\mathbf{M}-\mathbf{A}) \Vert_F^2 + \tau \Vert \mathbf{M} \Vert_* + \lambda \Vert \mathbf{A} \Vert_1 + \sum_{k=1}^K \eta_k \Vert \mathbf{M H_k} \Vert_p diff --git a/docs/index.rst b/docs/index.rst index abba8b9c..e9b570ba 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,13 +1,5 @@ .. include:: ../README.rst -.. toctree:: - :maxdepth: 2 - :hidden: - :caption: AUDIT - - audit - examples/tutorials/plot_tuto_mcar_test - .. toctree:: :maxdepth: 2 :hidden: @@ -32,3 +24,11 @@ :caption: API api + +.. toctree:: + :maxdepth: 2 + :hidden: + :caption: ANALYSIS + + analysis + examples/tutorials/plot_tuto_mcar_test \ No newline at end of file diff --git a/examples/RPCA.md b/examples/RPCA.md index 047de7a6..05f8b755 100644 --- a/examples/RPCA.md +++ b/examples/RPCA.md @@ -199,7 +199,6 @@ plt.show() ```python %%time -# rpca_noisy = RPCANoisy(period=10, tau=1, lam=0.4, rank=2, list_periods=[10], list_etas=[0.01], norm="L2") rpca_noisy = RpcaNoisy(tau=1, lam=0.4, rank=2, norm="L2") M, A = rpca_noisy.decompose(D, Omega) # imputed = X diff --git a/examples/tutorials/plot_tuto_hole_generator.py b/examples/tutorials/plot_tuto_hole_generator.py index e7340f01..07594591 100644 --- a/examples/tutorials/plot_tuto_hole_generator.py +++ b/examples/tutorials/plot_tuto_hole_generator.py @@ -282,7 +282,7 @@ def plot_cdf( # %% -# d. Grouped Hole Generator +# e. Grouped Hole Generator # *************************************************************** # The holes are generated according to the groups defined by the user. # This metohd is implemented in the diff --git a/examples/tutorials/plot_tuto_mcar_test.py b/examples/tutorials/plot_tuto_mcar_test.py index d7e51198..145fef66 100644 --- a/examples/tutorials/plot_tuto_mcar_test.py +++ b/examples/tutorials/plot_tuto_mcar_test.py @@ -3,154 +3,154 @@ Tutorial for testing the MCAR case ============================================ -In this tutorial, we show how to use the mcar test class and its methods. - -Keep in my mind that, at this moment, the mcar tests only handle tabular data. +In this tutorial, we show how to test the MCAR case using the Little's test. """ # %% # First import some libraries from matplotlib import pyplot as plt -import random import numpy as np import pandas as pd +from scipy.stats import norm + +from qolmat.analysis.holes_characterization import LittleTest +from qolmat.benchmark.missing_patterns import UniformHoleGenerator -from qolmat.audit.holes_characterization import MCARTest +plt.rcParams.update({"font.size": 12}) # %% # 1. The Little's test # --------------------------------------------------------------- -# How to use the Little's test ? -# ============================== -# When we deal with missing data in our dataset it's interesting to know the nature of these holes. -# There exist three types of holes : MCAR, MAR and MNAR. -# (see the: `Rubin's missing mechanism classification -# `_) +# First, we need to introduce the concept of missing pattern. A missing pattern, also called +# pattern, is the structure of observed and missing values in a data set. For example, for a +# dataset with 2 columns, the possible patterns are : (0, 0), (1, 0), (0, 1), (1, 1). The value 1 +# (0) indicates that the value in the column is missing (observed). # -# The simplest case to test is the MCAR case. The most famous MCAR statistical test is the -# `Little's test `_. -# Keep in mind that the Little's test is designed to test the homogeneity of means between the -# missing patterns and won't be efficient to detect the heterogeneity of covariance between missing -# patterns. +# The null hypothesis, H0, is : "The means of observations within each pattern are similar.". +# Against the alternative hypothesis, H1 : "The means of the observed variables can vary across the +# patterns." # -# The null hypothesis, H0, is : "The data are MCAR". Against, -# The alternative hypothesis : " The data are not MCAR, the means of the observed variables can -# vary across the patterns" +# If H0 is not rejected , we can assume that the missing data mechanism is MCAR. On the contrary, +# if H0 is rejected, we can assume that the missing data mechanism is MAR. # -# We choose to use the classic threshold, equal to 5%. If the test pval is below this threshold, +# We choose to use the classic threshold, equal to 5%. If the test p_value is below this threshold, # we reject the null hypothesis. # # This notebook shows how the Little's test performs and its limitations. -np.random.seed(11) - -mcartest = MCARTest(method="little") +mcartest = LittleTest() # %% -# Case 1 : Normal iid feature with MCAR holes -# =========================================== +# Case 1 : Normal iid features with MCAR holes +# ============================================ +np.random.seed(42) matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) -matrix.ravel()[np.random.choice(matrix.size, size=20, replace=False)] = np.nan -matrix_masked = matrix[np.argwhere(np.isnan(matrix))] -df_1 = pd.DataFrame(matrix) +df = pd.DataFrame(data=matrix, columns=["Column_1", "Column_2"]) + +hole_gen = UniformHoleGenerator(n_splits=1, random_state=42, subset=["Column_2"], ratio_masked=0.2) +df_mask = hole_gen.generate_mask(df) +df_unmasked = ~df_mask +df_unmasked["Column_1"] = False -plt_1 = plt.scatter(matrix[:, 0], matrix[:, 1]) -plt_2 = plt.scatter(matrix_masked[:, 0], matrix_masked[:, 1]) +df_observed = df.mask(df_mask).dropna() +df_hidden = df.mask(df_unmasked).dropna(subset="Column_2") + +plt_1 = plt.scatter(df_observed.iloc[:, 0], df_observed.iloc[:, 1], label="Observed values") +plt_2 = plt.scatter(df_hidden.iloc[:, 0], df_hidden.iloc[:, 1], label="Missing values") plt.legend( - (plt_1, plt_2), - ("observed_values", "masked_values"), - scatterpoints=1, loc="lower left", - ncol=1, fontsize=8, ) - plt.title("Case 1 : MCAR missingness mechanism") -plt.xlabel("x values (all observed)") -plt.ylabel("y values (with missing ones)") - plt.show() # %% -mcartest.test(df_1) +mcartest.test(df.mask(df_mask)) # %% -# The p-value is quite high, therefore we don't reject H_0. +# The p-value is quite high, therefore we don't reject H0. # We can then suppose that our missingness mechanism is MCAR. # %% -# Case 2 : Normal iid feature with MAR holes -# ========================================== -np.random.seed(11) +# Case 2 : Normal iid features with MAR holes +# =========================================== +np.random.seed(42) +quantile_95 = norm.ppf(0.975) matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) -threshold = random.uniform(0, 1) -matrix[np.argwhere(matrix[:, 0] >= 1.96), 1] = np.nan -matrix_masked = matrix[np.argwhere(np.isnan(matrix))] -df_2 = pd.DataFrame(matrix) +df = pd.DataFrame(matrix, columns=["Column_1", "Column_2"]) +df_nan = df.copy() +df_nan.loc[df_nan["Column_1"] > quantile_95, "Column_2"] = np.nan + +df_mask = df_nan.isna() +df_unmasked = ~df_mask +df_unmasked["Column_1"] = False + +df_observed = df.mask(df_mask).dropna() +df_hidden = df.mask(df_unmasked).dropna(subset="Column_2") -plt_1 = plt.scatter(matrix[:, 0], matrix[:, 1]) -plt_2 = plt.scatter(matrix_masked[:, 0], matrix_masked[:, 1]) +plt_1 = plt.scatter(df_observed.iloc[:, 0], df_observed.iloc[:, 1], label="Observed values") +plt_2 = plt.scatter(df_hidden.iloc[:, 0], df_hidden.iloc[:, 1], label="Missing values") plt.legend( - (plt_1, plt_2), - ("observed_values", "masked_vlues"), - scatterpoints=1, loc="lower left", - ncol=1, fontsize=8, ) - plt.title("Case 2 : MAR missingness mechanism") -plt.xlabel("x values (all observed)") -plt.ylabel("y values (with missing ones)") - plt.show() # %% -mcartest.test(df_2) +mcartest.test(df.mask(df_mask)) # %% # The p-value is lower than the classic threshold (5%). -# H_0 is then rejected and we can suppose that our missingness mechanism is MAR. +# H0 is then rejected and we can suppose that our missingness mechanism is MAR. # %% -# Case 3 : Normal iid feature MAR holes -# ===================================== -# The specific case is design to emphasize the Little's test limits. In the case, we generate holes -# when the value of the first feature is high. This missingness mechanism is clearly MAR but the -# means between missing patterns is not statistically different. +# Case 3 : Normal iid features with MAR holes +# =========================================== +# The specific case is designed to emphasize the Little's test limits. In the case, we generate +# holes when the absolute value of the first feature is high. This missingness mechanism is clearly +# MAR but the means between missing patterns is not statistically different. -np.random.seed(11) +np.random.seed(42) matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) -matrix[np.argwhere(abs(matrix[:, 0]) >= 1.96), 1] = np.nan -matrix_masked = matrix[np.argwhere(np.isnan(matrix))] -df_3 = pd.DataFrame(matrix) +df = pd.DataFrame(matrix, columns=["Column_1", "Column_2"]) +df_nan = df.copy() +df_nan.loc[abs(df_nan["Column_1"]) > quantile_95, "Column_2"] = np.nan -plt_1 = plt.scatter(matrix[:, 0], matrix[:, 1]) -plt_2 = plt.scatter(matrix_masked[:, 0], matrix_masked[:, 1]) +df_mask = df_nan.isna() +df_unmasked = ~df_mask +df_unmasked["Column_1"] = False + +df_observed = df.mask(df_mask).dropna() +df_hidden = df.mask(df_unmasked).dropna(subset="Column_2") + +plt_1 = plt.scatter(df_observed.iloc[:, 0], df_observed.iloc[:, 1], label="Observed values") +plt_2 = plt.scatter(df_hidden.iloc[:, 0], df_hidden.iloc[:, 1], label="Missing values") plt.legend( - (plt_1, plt_2), - ("observed_values", "masked_values"), - scatterpoints=1, loc="lower left", - ncol=1, fontsize=8, ) - plt.title("Case 3 : MAR missingness mechanism undetected by the Little's test") -plt.xlabel("x values (all observed)") -plt.ylabel("y values (with missing ones)") - plt.show() # %% -mcartest.test(df_3) +mcartest.test(df.mask(df_mask)) # %% # The p-value is higher than the classic threshold (5%). -# H_0 is not rejected whereas the missingness mechanism is clearly MAR. +# H0 is not rejected whereas the missingness mechanism is clearly MAR. + +# %% +# Limitations +# ----------- +# In this tutoriel, we can see that Little's test fails to detect covariance heterogeneity between +# patterns. +# +# There exist other limitations. The Little's test only handles quantitative data. And finally, the +# MCAR tests can only handle tabular data (withtout correlation in time). diff --git a/qolmat/analysis/holes_characterization.py b/qolmat/analysis/holes_characterization.py new file mode 100644 index 00000000..e65e0f57 --- /dev/null +++ b/qolmat/analysis/holes_characterization.py @@ -0,0 +1,92 @@ +from abc import ABC, abstractmethod +from typing import Optional, Union + +import numpy as np +import pandas as pd +from scipy.stats import chi2 + +from qolmat.imputations.imputers import ImputerEM + + +class MCARTest(ABC): + """ + Astract class for MCAR tests. + """ + + @abstractmethod + def test(self, df: pd.DataFrame) -> float: + pass + + +class LittleTest(MCARTest): + """ + This class implements the Little's test. The Little's test is designed to detect the + heterogeneity accross the missing patterns. The null hypothesis is "The missing data mechanism + is MCAR". Be aware that this test won't detect the heterogeneity of covariance. + + References + ---------- + Little. "A Test of Missing Completely at Random for Multivariate Data with Missing Values." + Journal of the American Statistical Association, Volume 83, 1988 - Issue 404 + + Parameters + ---------- + imputer : Optional[ImputerEM] + Imputer based on the EM algorithm. The 'model' attribute must be equal to 'multinormal'. + If None, the default ImputerEM is taken. + random_state : Union[None, int, np.random.RandomState], optional + Controls the randomness of the fit_transform, by default None + """ + + def __init__( + self, + imputer: Optional[ImputerEM] = None, + random_state: Union[None, int, np.random.RandomState] = None, + ): + super().__init__() + if imputer and imputer.model != "multinormal": + raise AttributeError( + "The ImputerEM model must be 'multinormal' to use the Little's test" + ) + self.imputer = imputer + self.random_state = random_state + + def test(self, df: pd.DataFrame) -> float: + """ + Apply the Little's test over a real dataframe. + + + Parameters + ---------- + df : pd.DataFrame + The input dataset with missing values. + + Returns + ------- + float + The p-value of the test. + """ + imputer = self.imputer or ImputerEM(random_state=self.random_state) + fitted_imputer = imputer._fit_element(df) + + d0 = 0 + n_rows, n_cols = df.shape + degree_f = -n_cols + ml_means = fitted_imputer.means + ml_cov = n_rows / (n_rows - 1) * fitted_imputer.cov + + # Iterate over the patterns + df_nan = df.notna() + for tup_pattern, df_nan_pattern in df_nan.groupby(df_nan.columns.tolist()): + n_rows_pattern, _ = df_nan_pattern.shape + ind_pattern = df_nan_pattern.index + df_pattern = df.loc[ind_pattern, list(tup_pattern)] + obs_mean = df_pattern.mean().to_numpy() + + diff_means = obs_mean - ml_means[list(tup_pattern)] + inv_sigma_pattern = np.linalg.inv(ml_cov[:, tup_pattern][tup_pattern, :]) + + d0 += n_rows_pattern * np.dot(np.dot(diff_means, inv_sigma_pattern), diff_means.T) + degree_f += tup_pattern.count(True) + + return 1 - chi2.cdf(d0, degree_f) diff --git a/qolmat/audit/holes_characterization.py b/qolmat/audit/holes_characterization.py deleted file mode 100644 index 90a84ebd..00000000 --- a/qolmat/audit/holes_characterization.py +++ /dev/null @@ -1,79 +0,0 @@ -from __future__ import annotations -from typing import Literal, Optional, TYPE_CHECKING - -import numpy as np -import pandas as pd -from scipy.stats import chi2 - -from qolmat.imputations.imputers import ImputerEM - -if TYPE_CHECKING: - from qolmat.imputations.imputers import _Imputer - - -class MCARTest: - """ - This class implements the statistical tests to test the MCAR case. - - Parameters - ---------- - method : Literal["little"] - The name of the statistical test. This should be handled by qolmat. - imputer : Optional[_Imputer], optional - If the selected test needs a imputer, you can provide the Imputer you want. Otherwise, - a default imputer will be used. - """ - - def __init__(self, method: Literal["little"], imputer: Optional[_Imputer] = None): - if method not in ["little"]: - raise ValueError(f"method` must be handled by qolmat, provided value is '{method}'") - - self.method = method - self.imputer = imputer - - def test(self, df: pd.DataFrame) -> float: - if self.method == "little": - return self.little_mcar_test(df) - - def little_mcar_test(self, df: pd.DataFrame) -> float: - """ - This method implements the Little's test. Use this test to test the homogenity of means - between all your missing patterns. - The null hypethoses is "The missing data mechanism is MCAR". - Be aware that this test won't detect the heterogeneity of covariance. - - Parameters - ---------- - df : pd.DataFrame - Your input data with missing values. - - Returns - ------- - float - The p-value of the test. - """ - imputer = self.imputer or ImputerEM() - fitted_imputer = imputer._fit_element(df) - - # Instanciant the stat, the degree of freedom and estimators. - d0 = 0 - n_rows, degree_f = df.shape - degree_f = -degree_f - ml_means = fitted_imputer.means - ml_cov = n_rows / (n_rows - 1) * fitted_imputer.cov - - # Iterate over the patterns - df_nan = df.notna() - for tup_pattern, df_nan_pattern in df_nan.groupby(df_nan.columns.tolist()): - n_rows_pattern, _ = df_nan_pattern.shape - ind_pattern = df_nan_pattern.index - df_pattern = df.loc[ind_pattern, list(tup_pattern)] - obs_mean = df_pattern.mean().to_numpy() - - diff_means = obs_mean - ml_means[list(tup_pattern)] - inv_sigma_pattern = np.linalg.inv(ml_cov[:, tup_pattern][tup_pattern, :]) - - d0 += n_rows_pattern * np.dot(np.dot(diff_means, inv_sigma_pattern), diff_means.T) - degree_f += tup_pattern.count(True) - - return 1 - chi2.cdf(d0, degree_f) diff --git a/tests/analysis/test_holes_characterization.py b/tests/analysis/test_holes_characterization.py new file mode 100644 index 00000000..7ab4c84a --- /dev/null +++ b/tests/analysis/test_holes_characterization.py @@ -0,0 +1,62 @@ +import numpy as np +import pandas as pd +import pytest +from scipy.stats import norm + +from qolmat.analysis.holes_characterization import LittleTest +from qolmat.benchmark.missing_patterns import UniformHoleGenerator +from qolmat.imputations.imputers import ImputerEM + + +@pytest.fixture +def mcar_df() -> pd.DataFrame: + rng = np.random.default_rng(42) + matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) + df = pd.DataFrame(data=matrix, columns=["Column_1", "Column_2"]) + hole_gen = UniformHoleGenerator( + n_splits=1, random_state=42, subset=["Column_2"], ratio_masked=0.2 + ) + df_mask = hole_gen.generate_mask(df) + return df.mask(df_mask) + + +@pytest.fixture +def mar_hm_df() -> pd.DataFrame: + rng = np.random.default_rng(42) + matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) + + quantile_95 = norm.ppf(0.975) + df = pd.DataFrame(matrix, columns=["Column_1", "Column_2"]) + df_nan = df.copy() + df_nan.loc[df_nan["Column_1"] > quantile_95, "Column_2"] = np.nan + + df_mask = df_nan.isna() + return df.mask(df_mask) + + +@pytest.fixture +def mar_hc_df() -> pd.DataFrame: + rng = np.random.default_rng(42) + matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) + + quantile_95 = norm.ppf(0.975) + df = pd.DataFrame(matrix, columns=["Column_1", "Column_2"]) + df_nan = df.copy() + df_nan.loc[abs(df_nan["Column_1"]) > quantile_95, "Column_2"] = np.nan + + df_mask = df_nan.isna() + return df.mask(df_mask) + + +@pytest.mark.parametrize( + "df_input, expected", [("mcar_df", True), ("mar_hm_df", False), ("mar_hc_df", True)] +) +def test_little_mcar_test(df_input: pd.DataFrame, expected: bool, request): + mcar_test_little = LittleTest(random_state=42) + result = mcar_test_little.test(request.getfixturevalue(df_input)) + assert expected == (result > 0.05) + + +def test_attribute_error(): + with pytest.raises(AttributeError): + LittleTest(random_state=42, imputer=ImputerEM(model="VAR")) diff --git a/tests/audit/test_holes_characterization.py b/tests/audit/test_holes_characterization.py deleted file mode 100644 index f37ec80c..00000000 --- a/tests/audit/test_holes_characterization.py +++ /dev/null @@ -1,44 +0,0 @@ -import numpy as np -import pandas as pd -import pytest - -from qolmat.audit.holes_characterization import MCARTest -from qolmat.imputations.imputers import ImputerEM - - -@pytest.fixture -def mcar_df() -> pd.DataFrame: - rng = np.random.default_rng(42) - matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) - matrix.ravel()[rng.choice(matrix.size, size=20, replace=False)] = np.nan - return pd.DataFrame(data=matrix) - - -@pytest.fixture -def mar_hm_df() -> pd.DataFrame: - rng = np.random.default_rng(42) - matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) - matrix[np.argwhere(matrix[:, 0] >= 1.96), 1] = np.nan - return pd.DataFrame(data=matrix) - - -@pytest.fixture -def mcar_hc_df() -> pd.DataFrame: - rng = np.random.default_rng(42) - matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) - matrix[np.argwhere(abs(matrix[:, 0]) >= 1.96), 1] = np.nan - return pd.DataFrame(data=matrix) - - -def test_mcar__init__(): - with pytest.raises(ValueError): - _ = MCARTest(method="hello") - - -@pytest.mark.parametrize( - "df_input, expected", [("mcar_df", True), ("mar_hm_df", False), ("mcar_hc_df", True)] -) -def test_little_mcar_test(df_input: pd.DataFrame, expected: bool, request): - mcar_test_little = MCARTest(method="little", imputer=ImputerEM(random_state=42)) - result = mcar_test_little.test(request.getfixturevalue(df_input)) - assert expected == (result > 0.05) diff --git a/tests/imputations/rpca/test_rpca_noisy.py b/tests/imputations/rpca/test_rpca_noisy.py index 0d5de5b8..78f62e41 100644 --- a/tests/imputations/rpca/test_rpca_noisy.py +++ b/tests/imputations/rpca/test_rpca_noisy.py @@ -160,7 +160,6 @@ def test_rpca_noisy_decompose_rpca(synthetic_temporal_data): tau = 1 lam = 0.1 rank = 10 - # rpca = RPCANoisy(period=period, tau=tau, lam=lam, norm="L2") D = utils.prepare_data(signal, period) Omega = ~np.isnan(D) D = utils.linear_interpolation(D) From 4e345d2d520bd38b152298fb717c05454dae65a2 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Mon, 3 Jun 2024 16:36:12 +0200 Subject: [PATCH 93/99] plot_tuto_categorical.py has been patched --- examples/tutorials/plot_tuto_categorical.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/tutorials/plot_tuto_categorical.py b/examples/tutorials/plot_tuto_categorical.py index 1584840e..b6e993fb 100644 --- a/examples/tutorials/plot_tuto_categorical.py +++ b/examples/tutorials/plot_tuto_categorical.py @@ -22,6 +22,8 @@ # We get the data and focus on the explanatory variables df = data.get_data("Titanic") df = df.drop(columns=["Survived"]) +print("Dataset shape:", df.shape) +df.head() # %% # 2. Mixed type imputation methods @@ -61,7 +63,7 @@ imputer_hgb = ImputerRegressor(estimator=pipestimator, handler_nan="none") imputer_wrap_hgb = preprocessing.WrapperTransformer(imputer_hgb, bt) -# %% +# %% # 3. Mixed type model selection # --------------------------------------------------------------- # Let us now compare these three aproaches by measuring their ability to impute uniformly From 67d44cf27e490130a98720361aa4d8eeb7267b66 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 11 Jun 2024 16:44:00 +0200 Subject: [PATCH 94/99] cosmetic changes --- docs/analysis.rst | 26 +++++++++---------- docs/index.rst | 2 +- ...ot_tuto_mcar_test.py => plot_tuto_mcar.py} | 1 + qolmat/analysis/holes_characterization.py | 19 +++++++------- tests/analysis/test_holes_characterization.py | 2 +- 5 files changed, 26 insertions(+), 24 deletions(-) rename examples/tutorials/{plot_tuto_mcar_test.py => plot_tuto_mcar.py} (99%) diff --git a/docs/analysis.rst b/docs/analysis.rst index 545f5073..4dcbd2ec 100644 --- a/docs/analysis.rst +++ b/docs/analysis.rst @@ -1,22 +1,22 @@ Analysis ======== -The analysis section gives a better understanding of the holes in a dataset. +This section gives a better understanding of the holes in a dataset. 1. General approach ------------------- As described in section :ref:`hole_generator`, there are 3 main types of missing data mechanism: MCAR, MAR and MNAR. -The analysis brick provides tools to charaterize the type of holes. +The analysis module provides tools to characterize the type of holes. -The MNAR case is the trickiest, the user must first consider whether or not his missing data mechanism is MNAR. In the meantime, we make the assumption that the missing-data mechanism is ignorable (ie is not MNAR). If the MNAR missing data mechanism is suspected, please see this article :ref:`An approach to test for MNAR [1]`. +The MNAR case is the trickiest, the user must first consider whether their missing data mechanism is MNAR. In the meantime, we make assume that the missing-data mechanism is ignorable (ie., it is not MNAR). If an MNAR mechanism is suspected, please see this article :ref:`An approach to test for MNAR [1]` for relevant actions. Then Qolmat proposes a test to determine whether the missing data mechanism is MCAR or MAR. -2. How to use the results ? ---------------------------- +2. How to use the results +------------------------- -At the end of the MCAR test, it can then be assumed whether the missing data mechanism is MCAR or not. This could be used for several things : +At the end of the MCAR test, it can then be assumed whether the missing data mechanism is MCAR or not. This serves three differents purposes: a. Diagnosis ^^^^^^^^^^^^ @@ -27,30 +27,30 @@ The test result can then be used for continuous data quality management. b. Estimation ^^^^^^^^^^^^^ -Some estimation methods are not suitable for the MAR case. For example, dropingn the nans introduces bias into the estimator, it is necessary to have validated that the missing-data mechanism is MCAR. +Some estimation methods are not suitable for the MAR case. For example, dropping the nans introduces bias into the estimator, it is necessary to have validated that the missing-data mechanism is MCAR. c. Imputation ^^^^^^^^^^^^^ -Qolmat allows model selection imputation algorithms. For each of the K folds, Qolmat artificially masks a set of observed values using a default or user specified hole generator. It seems natural to create these masks according to the same missing-data mechanism as dtermined by the test. Here's the documentation on using Qolmat for imputation model selection. : `here `_. +Qolmat allows model selection imputation algorithms. For each of the K folds, Qolmat artificially masks a set of observed values using a default or user-specified hole generator. It seems natural to create these masks according to the same missing-data mechanism as determined by the test. Here is the documentation on using Qolmat for imputation `model selection `_. 3. The MCAR Tests ----------------- -There exist several statistical tests to determine if the missing data mechanism is MCAR or MAR. Most tests are based on the notion of missing pattern. -A missing pattern, also called pattern, is the structure of observed and missing values in a dataset. For example, for a dataset with 2 columns, the possible patterns are : (0, 0), (1, 0), (0, 1), (1, 1). The value 1 indicates that the value in the column is missing. +There are several statistical tests to determine if the missing data mechanism is MCAR or MAR. Most tests are based on the notion of missing pattern. +A missing pattern, also called a pattern, is the structure of observed and missing values in a dataset. For example, for a dataset with two columns, the possible patterns are: (0, 0), (1, 0), (0, 1), (1, 1). The value 1 indicates that the value in the column is missing. The MCAR missing-data mechanism means that there is independence between the presence of holes and the observed values. In other words, the data distribution is the same for all patterns. a. Little's Test ^^^^^^^^^^^^^^^^ -The best-known MCAR test is the :ref:`Little [2]` test. Keep in mind that the Little's test is designed to test the homogeneity of means accross the missing patterns and won't be efficient to detect the heterogeneity of covariance accross missing patterns. +The best-known MCAR test is the :ref:`Little [2]` test, and it has been implemented in :class:`LittleTest`. Keep in mind that the Little's test is designed to test the homogeneity of means across the missing patterns and won't be efficient to detect the heterogeneity of covariance accross missing patterns. b. PKLM Test ^^^^^^^^^^^^ -The :ref:`PKLM [2]` (Projected Kullback-Leibler MCAR) test compares the distributions of different missing patterns on random projections in the variable space of the data. This recent test applies to mixed-type data. +The :ref:`PKLM [2]` (Projected Kullback-Leibler MCAR) test compares the distributions of different missing patterns on random projections in the variable space of the data. This recent test applies to mixed-type data. It is not implemented yet in Qolmat. References ---------- @@ -61,7 +61,7 @@ References .. _Little-article: -[2] Little. `A Test of Missing Completely at Random for Multivariate Data with Missing Values. `_ Journal of the American Statistical Association, Volume 83, 1988 - Issue 404. +[2] Little, R. J. A. `A Test of Missing Completely at Random for Multivariate Data with Missing Values. `_ Journal of the American Statistical Association, Volume 83, 1988 - Issue 404. .. _PKLM-article: diff --git a/docs/index.rst b/docs/index.rst index e9b570ba..e68a95a8 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -31,4 +31,4 @@ :caption: ANALYSIS analysis - examples/tutorials/plot_tuto_mcar_test \ No newline at end of file + examples/tutorials/plot_tuto_mcar \ No newline at end of file diff --git a/examples/tutorials/plot_tuto_mcar_test.py b/examples/tutorials/plot_tuto_mcar.py similarity index 99% rename from examples/tutorials/plot_tuto_mcar_test.py rename to examples/tutorials/plot_tuto_mcar.py index 145fef66..cb872f86 100644 --- a/examples/tutorials/plot_tuto_mcar_test.py +++ b/examples/tutorials/plot_tuto_mcar.py @@ -5,6 +5,7 @@ In this tutorial, we show how to test the MCAR case using the Little's test. """ + # %% # First import some libraries from matplotlib import pyplot as plt diff --git a/qolmat/analysis/holes_characterization.py b/qolmat/analysis/holes_characterization.py index e65e0f57..8bd85b9d 100644 --- a/qolmat/analysis/holes_characterization.py +++ b/qolmat/analysis/holes_characterization.py @@ -8,7 +8,7 @@ from qolmat.imputations.imputers import ImputerEM -class MCARTest(ABC): +class McarTest(ABC): """ Astract class for MCAR tests. """ @@ -18,11 +18,11 @@ def test(self, df: pd.DataFrame) -> float: pass -class LittleTest(MCARTest): +class LittleTest(McarTest): """ - This class implements the Little's test. The Little's test is designed to detect the - heterogeneity accross the missing patterns. The null hypothesis is "The missing data mechanism - is MCAR". Be aware that this test won't detect the heterogeneity of covariance. + This class implements the Little's test, which is designed to detect the heterogeneity accross + the missing patterns. The null hypothesis is "The missing data mechanism is MCAR". The + shortcoming of this test is that it won't detect the heterogeneity of covariance. References ---------- @@ -67,15 +67,16 @@ def test(self, df: pd.DataFrame) -> float: The p-value of the test. """ imputer = self.imputer or ImputerEM(random_state=self.random_state) - fitted_imputer = imputer._fit_element(df) + imputer = imputer._fit_element(df) d0 = 0 n_rows, n_cols = df.shape degree_f = -n_cols - ml_means = fitted_imputer.means - ml_cov = n_rows / (n_rows - 1) * fitted_imputer.cov + ml_means = imputer.means + ml_cov = n_rows / (n_rows - 1) * imputer.cov # Iterate over the patterns + df_nan = df.notna() for tup_pattern, df_nan_pattern in df_nan.groupby(df_nan.columns.tolist()): n_rows_pattern, _ = df_nan_pattern.shape @@ -89,4 +90,4 @@ def test(self, df: pd.DataFrame) -> float: d0 += n_rows_pattern * np.dot(np.dot(diff_means, inv_sigma_pattern), diff_means.T) degree_f += tup_pattern.count(True) - return 1 - chi2.cdf(d0, degree_f) + return 1 - float(chi2.cdf(d0, degree_f)) diff --git a/tests/analysis/test_holes_characterization.py b/tests/analysis/test_holes_characterization.py index 7ab4c84a..c794b94e 100644 --- a/tests/analysis/test_holes_characterization.py +++ b/tests/analysis/test_holes_characterization.py @@ -42,7 +42,7 @@ def mar_hc_df() -> pd.DataFrame: quantile_95 = norm.ppf(0.975) df = pd.DataFrame(matrix, columns=["Column_1", "Column_2"]) df_nan = df.copy() - df_nan.loc[abs(df_nan["Column_1"]) > quantile_95, "Column_2"] = np.nan + df_nan.loc[df_nan["Column_1"].abs() > quantile_95, "Column_2"] = np.nan df_mask = df_nan.isna() return df.mask(df_mask) From 8d7222890ebfcbc895464883c123ce908655c25b Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Tue, 11 Jun 2024 16:47:56 +0200 Subject: [PATCH 95/99] typing-extensions added to base env --- setup.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.py b/setup.py index d1dd58a6..80c41210 100644 --- a/setup.py +++ b/setup.py @@ -33,12 +33,13 @@ "category_encoders", "dcor>=0.6", "hyperopt", - "numpy>=1.19", + "numpy>=1.21", "packaging", "pandas>=1.3", "scikit-learn", "scipy", "statsmodels>=0.14", + "typing-extensions", ] EXTRAS_REQUIRE = { "tests": ["flake8", "mypy", "pandas", "pytest", "pytest-cov", "typed-ast"], @@ -47,7 +48,6 @@ "sphinx", "sphinx-gallery", "sphinx_rtd_theme", - "typing_extensions", ], "pytorch": [ "torch==2.0.1", From 93ddbbecb09083ce8c214bea0bbcd8c1271180a1 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Wed, 12 Jun 2024 23:50:09 +0200 Subject: [PATCH 96/99] Random state management and tests added to hole_generator --- examples/tutorials/plot_tuto_mcar.py | 67 ++++++++++++----------- qolmat/analysis/holes_characterization.py | 4 ++ qolmat/benchmark/missing_patterns.py | 9 ++- tests/benchmark/test_missing_patterns.py | 23 +++++++- 4 files changed, 64 insertions(+), 39 deletions(-) diff --git a/examples/tutorials/plot_tuto_mcar.py b/examples/tutorials/plot_tuto_mcar.py index cb872f86..10c3217a 100644 --- a/examples/tutorials/plot_tuto_mcar.py +++ b/examples/tutorials/plot_tuto_mcar.py @@ -1,6 +1,6 @@ """ ============================================ -Tutorial for testing the MCAR case +Tutorial for Testing the MCAR Case ============================================ In this tutorial, we show how to test the MCAR case using the Little's test. @@ -19,65 +19,66 @@ plt.rcParams.update({"font.size": 12}) +rng = np.random.RandomState(42) + # %% # 1. The Little's test # --------------------------------------------------------------- -# First, we need to introduce the concept of missing pattern. A missing pattern, also called -# pattern, is the structure of observed and missing values in a data set. For example, for a -# dataset with 2 columns, the possible patterns are : (0, 0), (1, 0), (0, 1), (1, 1). The value 1 -# (0) indicates that the value in the column is missing (observed). -# -# The null hypothesis, H0, is : "The means of observations within each pattern are similar.". -# Against the alternative hypothesis, H1 : "The means of the observed variables can vary across the -# patterns." +# First, we need to introduce the concept of a missing pattern. A missing pattern, also called a +# pattern, is the structure of observed and missing values in a dataset. For example, in a +# dataset with two columns, the possible patterns are: (0, 0), (1, 0), (0, 1), (1, 1). The value 1 +# (0) indicates that the column value is missing (observed). # -# If H0 is not rejected , we can assume that the missing data mechanism is MCAR. On the contrary, -# if H0 is rejected, we can assume that the missing data mechanism is MAR. +# The null hypothesis, H0, is: "The means of observations within each pattern are similar.". # -# We choose to use the classic threshold, equal to 5%. If the test p_value is below this threshold, +# We choose to use the classic threshold of 5%. If the test p-value is below this threshold, # we reject the null hypothesis. # # This notebook shows how the Little's test performs and its limitations. -mcartest = LittleTest() +test_mcar = LittleTest(random_state=rng) # %% -# Case 1 : Normal iid features with MCAR holes +# Case 1: Normal iid features with MCAR holes # ============================================ -np.random.seed(42) -matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) -df = pd.DataFrame(data=matrix, columns=["Column_1", "Column_2"]) -hole_gen = UniformHoleGenerator(n_splits=1, random_state=42, subset=["Column_2"], ratio_masked=0.2) +matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) +df = pd.DataFrame(data=matrix, columns=["Column 1", "Column 2"]) + +hole_gen = UniformHoleGenerator( + n_splits=1, random_state=rng, subset=["Column 2"], ratio_masked=0.2 +) df_mask = hole_gen.generate_mask(df) -df_unmasked = ~df_mask -df_unmasked["Column_1"] = False -df_observed = df.mask(df_mask).dropna() -df_hidden = df.mask(df_unmasked).dropna(subset="Column_2") +has_nan = df_mask.any(axis=1) -plt_1 = plt.scatter(df_observed.iloc[:, 0], df_observed.iloc[:, 1], label="Observed values") -plt_2 = plt.scatter(df_hidden.iloc[:, 0], df_hidden.iloc[:, 1], label="Missing values") +df_observed = df.loc[~has_nan] +df_hidden = df.loc[has_nan] + +plt.scatter(df_observed["Column 1"], df_observed[["Column 2"]], label="Fully observed values") +plt.scatter(df_hidden[["Column 1"]], df_hidden[["Column 2"]], label="Values with missing C2") plt.legend( loc="lower left", fontsize=8, ) -plt.title("Case 1 : MCAR missingness mechanism") +plt.xlabel("Column 1") +plt.ylabel("Column 2") +plt.title("Case 1: MCAR missingness mechanism") plt.show() # %% -mcartest.test(df.mask(df_mask)) +result = test_mcar.test(df.mask(df_mask)) +print(f"Test p-value: {result:.2%}") # %% # The p-value is quite high, therefore we don't reject H0. # We can then suppose that our missingness mechanism is MCAR. # %% -# Case 2 : Normal iid features with MAR holes +# Case 2: Normal iid features with MAR holes # =========================================== -np.random.seed(42) quantile_95 = norm.ppf(0.975) matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) @@ -99,18 +100,18 @@ loc="lower left", fontsize=8, ) -plt.title("Case 2 : MAR missingness mechanism") +plt.title("Case 2: MAR missingness mechanism") plt.show() # %% -mcartest.test(df.mask(df_mask)) +test_mcar.test(df.mask(df_mask)) # %% # The p-value is lower than the classic threshold (5%). # H0 is then rejected and we can suppose that our missingness mechanism is MAR. # %% -# Case 3 : Normal iid features with MAR holes +# Case 3: Normal iid features with MAR holes # =========================================== # The specific case is designed to emphasize the Little's test limits. In the case, we generate # holes when the absolute value of the first feature is high. This missingness mechanism is clearly @@ -137,12 +138,12 @@ loc="lower left", fontsize=8, ) -plt.title("Case 3 : MAR missingness mechanism undetected by the Little's test") +plt.title("Case 3: MAR missingness mechanism undetected by the Little's test") plt.show() # %% -mcartest.test(df.mask(df_mask)) +test_mcar.test(df.mask(df_mask)) # %% # The p-value is higher than the classic threshold (5%). # H0 is not rejected whereas the missingness mechanism is clearly MAR. diff --git a/qolmat/analysis/holes_characterization.py b/qolmat/analysis/holes_characterization.py index 8bd85b9d..642d0aa8 100644 --- a/qolmat/analysis/holes_characterization.py +++ b/qolmat/analysis/holes_characterization.py @@ -66,8 +66,12 @@ def test(self, df: pd.DataFrame) -> float: float The p-value of the test. """ + print("test") + print(self.random_state.randint(100)) imputer = self.imputer or ImputerEM(random_state=self.random_state) imputer = imputer._fit_element(df) + print(df[df.notna()].mean().mean()) + print("means:", imputer.means) d0 = 0 n_rows, n_cols = df.shape diff --git a/qolmat/benchmark/missing_patterns.py b/qolmat/benchmark/missing_patterns.py index 69f07133..7c58ec94 100644 --- a/qolmat/benchmark/missing_patterns.py +++ b/qolmat/benchmark/missing_patterns.py @@ -185,17 +185,16 @@ def generate_mask(self, X: pd.DataFrame) -> pd.DataFrame: Initial dataframe with a missing pattern to be imitated. """ - self.rng = sku.check_random_state(self.random_state) + self.random_state = sku.check_random_state(self.random_state) df_mask = pd.DataFrame(False, index=X.index, columns=X.columns) n_masked_col = math.ceil(self.ratio_masked * len(X)) for column in self.subset: indices = np.where(X[column].notna())[0] - indices = resample( + indices = self.random_state.choice( indices, replace=False, - n_samples=n_masked_col, - stratify=None, + size=n_masked_col, ) df_mask[column].iloc[indices] = True @@ -699,7 +698,7 @@ def split(self, X: pd.DataFrame) -> List[pd.DataFrame]: list_masks = [] for _ in range(self.n_splits): - shuffled_group_sizes = group_sizes.sample(frac=1) + shuffled_group_sizes = group_sizes.sample(frac=1, random_state=self.random_state) ratio_masks = shuffled_group_sizes.cumsum() / len(X) ratio_masks = ratio_masks.reset_index(name="ratio") diff --git a/tests/benchmark/test_missing_patterns.py b/tests/benchmark/test_missing_patterns.py index 69c36518..0fa06e69 100644 --- a/tests/benchmark/test_missing_patterns.py +++ b/tests/benchmark/test_missing_patterns.py @@ -32,8 +32,8 @@ @pytest.mark.parametrize( "df, generator", [ - (df_incomplet, list_generators["geo"]), (df_incomplet, list_generators["unif"]), + (df_incomplet, list_generators["geo"]), (df_incomplet, list_generators["multi"]), (df_incomplet_group, list_generators["group"]), ], @@ -48,6 +48,27 @@ def test_SamplerHoleGenerator_split(df: pd.DataFrame, generator: mp._HoleGenerat np.testing.assert_allclose(col2_holes, expected_col2_holes, atol=1) +@pytest.mark.parametrize( + "df, generator", + [ + (df_incomplet, list_generators["unif"]), + (df_incomplet, list_generators["geo"]), + (df_incomplet, list_generators["multi"]), + (df_incomplet_group, list_generators["group"]), + ], +) +def test_SamplerHoleGenerator_reproducible(df: pd.DataFrame, generator: mp._HoleGenerator) -> None: + generator.random_state = 42 + mask1 = generator.split(df)[0] + generator.random_state = 43 + mask2 = generator.split(df)[0] + generator.random_state = 42 + mask3 = generator.split(df)[0] + + np.testing.assert_array_equal(mask1, mask3) + assert (mask1 != mask2).any().any() + + @pytest.mark.parametrize( "df, generator", [ From 7b95699edb0ad0a6494ba31bb5fccaa81f8ecdcb Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 13 Jun 2024 00:26:55 +0200 Subject: [PATCH 97/99] tuto refacto --- HISTORY.rst | 6 ++ examples/tutorials/plot_tuto_mcar.py | 93 ++++++++++++----------- qolmat/analysis/holes_characterization.py | 4 - 3 files changed, 56 insertions(+), 47 deletions(-) diff --git a/HISTORY.rst b/HISTORY.rst index ed3714c2..560c7ae2 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -2,6 +2,12 @@ History ======= +?? +------------------ +* Little's test implemented in a new hole_characterization module +* Documentation now includes an analysis section with a tutorial +* Hole generators now provide reproducible outputs + 0.1.3 (2024-03-07) ------------------ diff --git a/examples/tutorials/plot_tuto_mcar.py b/examples/tutorials/plot_tuto_mcar.py index 10c3217a..ad03c8d7 100644 --- a/examples/tutorials/plot_tuto_mcar.py +++ b/examples/tutorials/plot_tuto_mcar.py @@ -19,10 +19,19 @@ plt.rcParams.update({"font.size": 12}) + +# %% +# Generating random data +# ---------------------- + rng = np.random.RandomState(42) +data = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) +df = pd.DataFrame(data=data, columns=["Column 1", "Column 2"]) + +q975 = norm.ppf(0.975) # %% -# 1. The Little's test +# The Little's test # --------------------------------------------------------------- # First, we need to introduce the concept of a missing pattern. A missing pattern, also called a # pattern, is the structure of observed and missing values in a dataset. For example, in a @@ -34,25 +43,23 @@ # We choose to use the classic threshold of 5%. If the test p-value is below this threshold, # we reject the null hypothesis. # -# This notebook shows how the Little's test performs and its limitations. +# This notebook shows how the Little's test performs on a simplistic case and its limitations. We +# instanciate a test object with a random state for reproducibility. test_mcar = LittleTest(random_state=rng) # %% -# Case 1: Normal iid features with MCAR holes -# ============================================ +# Case 1: MCAR holes (True negative) +# ================================== -matrix = rng.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) -df = pd.DataFrame(data=matrix, columns=["Column 1", "Column 2"]) - hole_gen = UniformHoleGenerator( n_splits=1, random_state=rng, subset=["Column 2"], ratio_masked=0.2 ) df_mask = hole_gen.generate_mask(df) +df_nan = df.where(~df_mask, np.nan) has_nan = df_mask.any(axis=1) - df_observed = df.loc[~has_nan] df_hidden = df.loc[has_nan] @@ -66,84 +73,84 @@ plt.xlabel("Column 1") plt.ylabel("Column 2") plt.title("Case 1: MCAR missingness mechanism") +plt.grid() plt.show() # %% - -result = test_mcar.test(df.mask(df_mask)) +result = test_mcar.test(df_nan) print(f"Test p-value: {result:.2%}") # %% # The p-value is quite high, therefore we don't reject H0. # We can then suppose that our missingness mechanism is MCAR. # %% -# Case 2: Normal iid features with MAR holes -# =========================================== -quantile_95 = norm.ppf(0.975) +# Case 2: MAR holes with mean bias (True positive) +# ================================================ -matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) -df = pd.DataFrame(matrix, columns=["Column_1", "Column_2"]) -df_nan = df.copy() -df_nan.loc[df_nan["Column_1"] > quantile_95, "Column_2"] = np.nan +df_mask = pd.DataFrame({"Column 1": False, "Column 2": df["Column 1"] > q975}, index=df.index) -df_mask = df_nan.isna() -df_unmasked = ~df_mask -df_unmasked["Column_1"] = False +df_nan = df.where(~df_mask, np.nan) -df_observed = df.mask(df_mask).dropna() -df_hidden = df.mask(df_unmasked).dropna(subset="Column_2") +has_nan = df_mask.any(axis=1) +df_observed = df.loc[~has_nan] +df_hidden = df.loc[has_nan] -plt_1 = plt.scatter(df_observed.iloc[:, 0], df_observed.iloc[:, 1], label="Observed values") -plt_2 = plt.scatter(df_hidden.iloc[:, 0], df_hidden.iloc[:, 1], label="Missing values") +plt.scatter(df_observed["Column 1"], df_observed[["Column 2"]], label="Fully observed values") +plt.scatter(df_hidden[["Column 1"]], df_hidden[["Column 2"]], label="Values with missing C2") plt.legend( loc="lower left", fontsize=8, ) +plt.xlabel("Column 1") +plt.ylabel("Column 2") plt.title("Case 2: MAR missingness mechanism") +plt.grid() plt.show() # %% -test_mcar.test(df.mask(df_mask)) +result = test_mcar.test(df_nan) +print(f"Test p-value: {result:.2%}") # %% # The p-value is lower than the classic threshold (5%). # H0 is then rejected and we can suppose that our missingness mechanism is MAR. # %% -# Case 3: Normal iid features with MAR holes -# =========================================== +# Case 3: MAR holes with any mean bias (False negative) +# ===================================================== +# # The specific case is designed to emphasize the Little's test limits. In the case, we generate # holes when the absolute value of the first feature is high. This missingness mechanism is clearly # MAR but the means between missing patterns is not statistically different. -np.random.seed(42) - -matrix = np.random.multivariate_normal(mean=[0, 0], cov=[[1, 0], [0, 1]], size=200) -df = pd.DataFrame(matrix, columns=["Column_1", "Column_2"]) -df_nan = df.copy() -df_nan.loc[abs(df_nan["Column_1"]) > quantile_95, "Column_2"] = np.nan +df_mask = pd.DataFrame( + {"Column 1": False, "Column 2": df["Column 1"].abs() > q975}, index=df.index +) -df_mask = df_nan.isna() -df_unmasked = ~df_mask -df_unmasked["Column_1"] = False +df_nan = df.where(~df_mask, np.nan) -df_observed = df.mask(df_mask).dropna() -df_hidden = df.mask(df_unmasked).dropna(subset="Column_2") +has_nan = df_mask.any(axis=1) +df_observed = df.loc[~has_nan] +df_hidden = df.loc[has_nan] -plt_1 = plt.scatter(df_observed.iloc[:, 0], df_observed.iloc[:, 1], label="Observed values") -plt_2 = plt.scatter(df_hidden.iloc[:, 0], df_hidden.iloc[:, 1], label="Missing values") +plt.scatter(df_observed["Column 1"], df_observed[["Column 2"]], label="Fully observed values") +plt.scatter(df_hidden[["Column 1"]], df_hidden[["Column 2"]], label="Values with missing C2") plt.legend( loc="lower left", fontsize=8, ) +plt.xlabel("Column 1") +plt.ylabel("Column 2") plt.title("Case 3: MAR missingness mechanism undetected by the Little's test") +plt.grid() plt.show() # %% -test_mcar.test(df.mask(df_mask)) +result = test_mcar.test(df_nan) +print(f"Test p-value: {result:.2%}") # %% # The p-value is higher than the classic threshold (5%). # H0 is not rejected whereas the missingness mechanism is clearly MAR. @@ -154,5 +161,5 @@ # In this tutoriel, we can see that Little's test fails to detect covariance heterogeneity between # patterns. # -# There exist other limitations. The Little's test only handles quantitative data. And finally, the -# MCAR tests can only handle tabular data (withtout correlation in time). +# We also note that the Little's test does not handle categorical data or temporally +# correlated data. diff --git a/qolmat/analysis/holes_characterization.py b/qolmat/analysis/holes_characterization.py index 642d0aa8..8bd85b9d 100644 --- a/qolmat/analysis/holes_characterization.py +++ b/qolmat/analysis/holes_characterization.py @@ -66,12 +66,8 @@ def test(self, df: pd.DataFrame) -> float: float The p-value of the test. """ - print("test") - print(self.random_state.randint(100)) imputer = self.imputer or ImputerEM(random_state=self.random_state) imputer = imputer._fit_element(df) - print(df[df.notna()].mean().mean()) - print("means:", imputer.means) d0 = 0 n_rows, n_cols = df.shape From 5afcaa2c8f8b6cccf1171822c45694525ce2557f Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 13 Jun 2024 10:06:14 +0200 Subject: [PATCH 98/99] tuto titles improved --- examples/tutorials/plot_tuto_mcar.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/examples/tutorials/plot_tuto_mcar.py b/examples/tutorials/plot_tuto_mcar.py index ad03c8d7..c43d1217 100644 --- a/examples/tutorials/plot_tuto_mcar.py +++ b/examples/tutorials/plot_tuto_mcar.py @@ -72,7 +72,7 @@ ) plt.xlabel("Column 1") plt.ylabel("Column 2") -plt.title("Case 1: MCAR missingness mechanism") +plt.title("Case 1: MCAR data") plt.grid() plt.show() @@ -80,8 +80,8 @@ result = test_mcar.test(df_nan) print(f"Test p-value: {result:.2%}") # %% -# The p-value is quite high, therefore we don't reject H0. -# We can then suppose that our missingness mechanism is MCAR. +# The p-value is larger than 0.05, therefore we don't reject the HO MCAR assumption. In this case +# this is a true negative. # %% # Case 2: MAR holes with mean bias (True positive) @@ -104,7 +104,7 @@ ) plt.xlabel("Column 1") plt.ylabel("Column 2") -plt.title("Case 2: MAR missingness mechanism") +plt.title("Case 2: MAR data with mean bias") plt.grid() plt.show() @@ -113,8 +113,8 @@ result = test_mcar.test(df_nan) print(f"Test p-value: {result:.2%}") # %% -# The p-value is lower than the classic threshold (5%). -# H0 is then rejected and we can suppose that our missingness mechanism is MAR. +# The p-value is smaller than 0.05, therefore we reject the HO MCAR assumption. In this case +# this is a true positive. # %% # Case 3: MAR holes with any mean bias (False negative) @@ -143,7 +143,7 @@ ) plt.xlabel("Column 1") plt.ylabel("Column 2") -plt.title("Case 3: MAR missingness mechanism undetected by the Little's test") +plt.title("Case 3: MAR data without any mean bias") plt.grid() plt.show() @@ -152,8 +152,8 @@ result = test_mcar.test(df_nan) print(f"Test p-value: {result:.2%}") # %% -# The p-value is higher than the classic threshold (5%). -# H0 is not rejected whereas the missingness mechanism is clearly MAR. +# The p-value is larger than 0.05, therefore we don't reject the HO MCAR assumption. In this case +# this is a false negative since the missingness mechanism is MAR. # %% # Limitations From 47565ffa07cd7c28d410baf4087bc528339d7f27 Mon Sep 17 00:00:00 2001 From: Julien Roussel <3178729-JulienRoussel77@users.noreply.gitlab.com> Date: Thu, 13 Jun 2024 10:33:34 +0200 Subject: [PATCH 99/99] =?UTF-8?q?Bump=20version:=200.1.6=20=E2=86=92=200.1?= =?UTF-8?q?.7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .bumpversion.cfg | 2 +- docs/conf.py | 2 +- qolmat/_version.py | 2 +- setup.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/.bumpversion.cfg b/.bumpversion.cfg index c7f7d137..3687d184 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.1.6 +current_version = 0.1.7 commit = True tag = True diff --git a/docs/conf.py b/docs/conf.py index 55d7fe58..1f58c166 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -27,7 +27,7 @@ author = "Quantmetry" # The full version, including alpha/beta/rc tags -version = "0.1.6" +version = "0.1.7" release = version # -- General configuration --------------------------------------------------- diff --git a/qolmat/_version.py b/qolmat/_version.py index 0a8da882..f1380eed 100644 --- a/qolmat/_version.py +++ b/qolmat/_version.py @@ -1 +1 @@ -__version__ = "0.1.6" +__version__ = "0.1.7" diff --git a/setup.py b/setup.py index 80c41210..81e4ad9d 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import find_packages, setup DISTNAME = "qolmat" -VERSION = "0.1.6" +VERSION = "0.1.7" DESCRIPTION = "A Python library for optimal data imputation." LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst" with codecs.open("README.rst", encoding="utf-8-sig") as f: