From 1b567531dd89e5ae7214490be64b931ccfbb2d83 Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Wed, 18 Sep 2024 11:53:01 -0400 Subject: [PATCH 01/10] move preprocessing as part of pipeline --- .../toolkit/time_series_forecasting_pipeline.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/tsfm_public/toolkit/time_series_forecasting_pipeline.py b/tsfm_public/toolkit/time_series_forecasting_pipeline.py index fdba12dc..f22353fd 100644 --- a/tsfm_public/toolkit/time_series_forecasting_pipeline.py +++ b/tsfm_public/toolkit/time_series_forecasting_pipeline.py @@ -331,6 +331,7 @@ def preprocess(self, time_series, **kwargs) -> Dict[str, Union[GenericTensor, Li prediction_length = kwargs.get("prediction_length") timestamp_column = kwargs.get("timestamp_column") id_columns = kwargs.get("id_columns") + target_columns = kwargs.get("target_columns") # context_length = kwargs.get("context_length") # use the feature extractor here @@ -343,6 +344,9 @@ def preprocess(self, time_series, **kwargs) -> Dict[str, Union[GenericTensor, Li future_time_series = kwargs.pop("future_time_series", None) + if self.feature_extractor: + time_series = self.feature_extractor.preprocess(time_series) + if future_time_series is not None: if isinstance(future_time_series, str): future_time_series = pd.read_csv( @@ -370,6 +374,12 @@ def preprocess(self, time_series, **kwargs) -> Dict[str, Union[GenericTensor, Li f"If provided, `future_time_series` data should cover the prediction length for each of the time series in the test dataset. Received data of length {future_time_series.shape[0]} but expected {prediction_length * id_count}" ) + if self.feature_extractor: + # future data needs some values for targets, but they are unused + future_time_series[target_columns] = 0 + future_time_series = self.feature_extractor.preprocess(future_time_series) + future_time_series.drop(columns=target_columns) + time_series = pd.concat((time_series, future_time_series), axis=0) else: # no additional exogenous data provided, extend with empty periods From d687d86770910ed60dca34fbf04974cf8fac869c Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Wed, 18 Sep 2024 12:21:26 -0400 Subject: [PATCH 02/10] update test --- tests/toolkit/test_time_series_forecasting_pipeline.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/toolkit/test_time_series_forecasting_pipeline.py b/tests/toolkit/test_time_series_forecasting_pipeline.py index d2234f7d..fcc48c99 100644 --- a/tests/toolkit/test_time_series_forecasting_pipeline.py +++ b/tests/toolkit/test_time_series_forecasting_pipeline.py @@ -252,7 +252,7 @@ def test_forecasting_pipeline_forecasts_with_preprocessor(patchtst_model): inverse_scale_outputs=True, ) - forecasts = forecast_pipeline(tsp.preprocess(test_data)) + forecasts = forecast_pipeline(test_data) assert forecasts.shape == ( test_end_index - test_start_index - context_length + 1, From f971fc2b4714c0f93adc5ad384713ccddb10725e Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Wed, 18 Sep 2024 12:27:03 -0400 Subject: [PATCH 03/10] update notebook --- ...and_forecast_zeroshot_recipe_minimal.ipynb | 35 ++++++++----------- 1 file changed, 14 insertions(+), 21 deletions(-) diff --git a/notebooks/recipes/energy_demand_forecasting/demand_forecast_zeroshot_recipe_minimal.ipynb b/notebooks/recipes/energy_demand_forecasting/demand_forecast_zeroshot_recipe_minimal.ipynb index dd1bf605..7011a838 100644 --- a/notebooks/recipes/energy_demand_forecasting/demand_forecast_zeroshot_recipe_minimal.ipynb +++ b/notebooks/recipes/energy_demand_forecasting/demand_forecast_zeroshot_recipe_minimal.ipynb @@ -27,7 +27,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:p-26782:t-8661148224:config.py::PyTorch version 2.2.2 available.\n" + "INFO:p-75334:t-8214683904:config.py::PyTorch version 2.3.0 available.\n" ] } ], @@ -49,7 +49,7 @@ { "data": { "text/plain": [ - "'0.2.9.dev29+g6f55407.d20240830'" + "'0.2.10.dev14+gc889210.d20240918'" ] }, "execution_count": 2, @@ -76,12 +76,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "1563d66d-bf38-4fcf-bdd0-57a9187ef8e4", "metadata": {}, "outputs": [], "source": [ - "data_path = pathlib.Path(\"~/Dev/data\")" + "data_path = pathlib.Path(\"~/Downloads\")" ] }, { @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "984ca0d9", "metadata": {}, "outputs": [ @@ -342,7 +342,7 @@ "[5 rows x 29 columns]" ] }, - "execution_count": 4, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -382,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "eed3fe8b-b654-4fa4-9671-ce5ecd9e0b7b", "metadata": {}, "outputs": [ @@ -412,17 +412,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "d9aa0f26", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n" - ] - }, { "data": { "text/html": [ @@ -472,7 +465,7 @@ " \n", " 4\n", " 2019-01-01 04:00:00+01:00\n", - " 20697.185547\n", + " 20697.183594\n", " \n", " \n", "\n", @@ -484,10 +477,10 @@ "1 2019-01-01 01:00:00+01:00 22338.626953\n", "2 2019-01-01 02:00:00+01:00 21448.902344\n", "3 2019-01-01 03:00:00+01:00 20982.527344\n", - "4 2019-01-01 04:00:00+01:00 20697.185547" + "4 2019-01-01 04:00:00+01:00 20697.183594" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -515,13 +508,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "ba065a24", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -581,7 +574,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.4" + "version": "3.10.13" } }, "nbformat": 4, From 47c3e973bb0826afee0dc671111254be0c0718d6 Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Wed, 18 Sep 2024 12:28:41 -0400 Subject: [PATCH 04/10] adopt new pipeline behavior --- services/inference/tsfminference/inference.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/services/inference/tsfminference/inference.py b/services/inference/tsfminference/inference.py index 6de4554e..a79ac592 100644 --- a/services/inference/tsfminference/inference.py +++ b/services/inference/tsfminference/inference.py @@ -126,6 +126,7 @@ def _forecast_common(self, input_payload: ForecastingInferenceInput) -> PredictO explode_forecasts=True, feature_extractor=preprocessor, add_known_ground_truth=False, + feature_extractor=preprocessor ) # truncate data length when exploding @@ -135,14 +136,14 @@ def _forecast_common(self, input_payload: ForecastingInferenceInput) -> PredictO # data, id_columns=input.id_columns, start_index=-context_length # ) - test_data = preprocessor.preprocess(data) + # test_data = preprocessor.preprocess(data) - if future_data is not None: - # future data needs some values for targets, but they are unused - # Eventually this will be part of the forecast pipeline. - future_data[input_payload.target_columns] = 0 - future_data = preprocessor.preprocess(future_data) - future_data.drop(columns=input_payload.target_columns) + # if future_data is not None: + # # future data needs some values for targets, but they are unused + # # Eventually this will be part of the forecast pipeline. + # future_data[input_payload.target_columns] = 0 + # future_data = preprocessor.preprocess(future_data) + # future_data.drop(columns=input_payload.target_columns) forecasts = forecast_pipeline(test_data, future_time_series=future_data, inverse_scale_outputs=True) From b3ae7cfeb3492c562cddc223277ab7f3953971a3 Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Wed, 18 Sep 2024 12:43:07 -0400 Subject: [PATCH 05/10] fix typos --- services/inference/tsfminference/inference.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/services/inference/tsfminference/inference.py b/services/inference/tsfminference/inference.py index a79ac592..09e45fcd 100644 --- a/services/inference/tsfminference/inference.py +++ b/services/inference/tsfminference/inference.py @@ -126,7 +126,6 @@ def _forecast_common(self, input_payload: ForecastingInferenceInput) -> PredictO explode_forecasts=True, feature_extractor=preprocessor, add_known_ground_truth=False, - feature_extractor=preprocessor ) # truncate data length when exploding @@ -145,7 +144,7 @@ def _forecast_common(self, input_payload: ForecastingInferenceInput) -> PredictO # future_data = preprocessor.preprocess(future_data) # future_data.drop(columns=input_payload.target_columns) - forecasts = forecast_pipeline(test_data, future_time_series=future_data, inverse_scale_outputs=True) + forecasts = forecast_pipeline(data, future_time_series=future_data, inverse_scale_outputs=True) return PredictOutput( model_id=input_payload.model_id, From e94eaa4e7b623dfe9b56b7a87183fa35337a261f Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Wed, 18 Sep 2024 13:10:45 -0400 Subject: [PATCH 06/10] catch some edge cases --- tsfm_public/toolkit/visualization.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tsfm_public/toolkit/visualization.py b/tsfm_public/toolkit/visualization.py index db1dfe7b..2a42b643 100644 --- a/tsfm_public/toolkit/visualization.py +++ b/tsfm_public/toolkit/visualization.py @@ -282,6 +282,7 @@ def plot_predictions( if indices is None: l = len(predictions_df) + num_plots = min(num_plots, l) indices = np.random.choice(l, size=num_plots, replace=False) predictions_subset = [predictions_df.iloc[i] for i in indices] @@ -297,6 +298,7 @@ def plot_predictions( with torch.no_grad(): if indices is None: + num_plots = min(num_plots, len(dset)) indices = np.random.choice(len(dset), size=num_plots, replace=False) signature = inspect.signature(model.forward) From 0a9aa9b7bb62a2169d4cd57ec1ded0cb847d8a79 Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Wed, 18 Sep 2024 14:33:34 -0400 Subject: [PATCH 07/10] fix frequency inference --- services/inference/tsfminference/inference.py | 2 ++ tsfm_public/toolkit/time_series_forecasting_pipeline.py | 8 +------- 2 files changed, 3 insertions(+), 7 deletions(-) diff --git a/services/inference/tsfminference/inference.py b/services/inference/tsfminference/inference.py index 09e45fcd..fe534dd9 100644 --- a/services/inference/tsfminference/inference.py +++ b/services/inference/tsfminference/inference.py @@ -115,6 +115,8 @@ def _forecast_common(self, input_payload: ForecastingInferenceInput) -> PredictO # train to estimate freq if not available preprocessor.train(data) + LOGGER.info(f"Data frequency determined: {preprocessor.freq}") + # warn if future data is not provided, but is needed by the model if preprocessor.exogenous_channel_indices and future_data is None: raise ValueError( diff --git a/tsfm_public/toolkit/time_series_forecasting_pipeline.py b/tsfm_public/toolkit/time_series_forecasting_pipeline.py index f22353fd..bacad2de 100644 --- a/tsfm_public/toolkit/time_series_forecasting_pipeline.py +++ b/tsfm_public/toolkit/time_series_forecasting_pipeline.py @@ -3,7 +3,7 @@ """Hugging Face Pipeline for Time Series Tasks""" import inspect -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, List, Union import numpy as np import pandas as pd @@ -116,13 +116,11 @@ def __init__( self, model: Union["PreTrainedModel"], *args, - freq: Optional[str] = None, explode_forecasts: bool = False, inverse_scale_outputs: bool = True, add_known_ground_truth: bool = True, **kwargs, ): - kwargs["freq"] = freq kwargs["explode_forecasts"] = explode_forecasts kwargs["inverse_scale_outputs"] = inverse_scale_outputs kwargs["add_known_ground_truth"] = add_known_ground_truth @@ -142,10 +140,6 @@ def __init__( if p not in kwargs: kwargs[p] = getattr(kwargs["feature_extractor"], p) - # get freq from kwargs or the preprocessor - if "freq" not in kwargs: - kwargs["freq"] = kwargs["feature_extractor"].freq - if "context_length" not in kwargs: kwargs["context_length"] = model.config.context_length From 4668f55ffaaf3b4e27648f418cfb18ab40754b96 Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Wed, 18 Sep 2024 20:21:04 -0400 Subject: [PATCH 08/10] update version --- services/inference/pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/services/inference/pyproject.toml b/services/inference/pyproject.toml index c7d8fe18..b98472f2 100644 --- a/services/inference/pyproject.toml +++ b/services/inference/pyproject.toml @@ -30,7 +30,7 @@ __version_tuple__ = (0, 0, 0) # including 3.9 causes poetry lock to run forever python = ">=3.10,<3.13" numpy = { version = "<2" } -tsfm_public = { git = "https://github.com/IBM/tsfm.git", tag = "v0.2.6" } +tsfm_public = { git = "https://github.com/IBM-granite/granite-tsfm.git", tag = "v0.2.9" } # trying to pick up cpu version for tsfminference # to make image smaller From 762f280085b3dea7bdd87b32c66dd0c3af3bb179 Mon Sep 17 00:00:00 2001 From: Wesley Gifford <79663411+wgifford@users.noreply.github.com> Date: Thu, 19 Sep 2024 00:25:37 +0000 Subject: [PATCH 09/10] lock --- services/inference/poetry.lock | 1676 +++++++++++++++++--------------- 1 file changed, 866 insertions(+), 810 deletions(-) diff --git a/services/inference/poetry.lock b/services/inference/poetry.lock index 8ab4c3ed..3d7c2730 100644 --- a/services/inference/poetry.lock +++ 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forecasting pipeline --- services/inference/tsfminference/inference.py | 1 + 1 file changed, 1 insertion(+) diff --git a/services/inference/tsfminference/inference.py b/services/inference/tsfminference/inference.py index fe534dd9..94756d6b 100644 --- a/services/inference/tsfminference/inference.py +++ b/services/inference/tsfminference/inference.py @@ -128,6 +128,7 @@ def _forecast_common(self, input_payload: ForecastingInferenceInput) -> PredictO explode_forecasts=True, feature_extractor=preprocessor, add_known_ground_truth=False, + freq=preprocessor.freq, ) # truncate data length when exploding