From c28bb049c4dd9cdabb963963ea83fa3717b031d6 Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Wed, 31 Jul 2024 13:14:17 -0400 Subject: [PATCH 1/5] formatting, address https://www.mend.io/vulnerability-database/CVE-2024-37891 --- pyproject.toml | 50 ++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 36 insertions(+), 14 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index dd482e08..1ffbafea 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,10 @@ authors = [{ name = "IBM" }] description = "Public notebooks and utilities for TSFM" readme = "README.md" license = { file = "LICENSE" } -classifiers = ["Development Status :: 4 - Beta", "License :: OSI Approved :: Apache Software License"] +classifiers = [ + "Development Status :: 4 - Beta", + "License :: OSI Approved :: Apache Software License", +] dependencies = [ "pandas>=2.2.0", @@ -18,22 +21,41 @@ dependencies = [ "transformers[torch]>=4.38.0", "datasets", "deprecated", - "urllib3>=1.26.19,<2", - "numpy<2" + "urllib3>=2.2.2", + "numpy<2", ] [tool.setuptools] -packages = ["tsfm_public", "tsfm_public.toolkit", "tsfm_public.models", "tsfm_public.models.tinytimemixer", "tsfm_public.models.tinytimemixer.utils", "tsfmhfdemos", "tsfmhfdemos.neurips", "tsfmhfdemos.neurips.backends", "tsfmhfdemos.neurips.backends.v1", "tsfmhfdemos.neurips.backends.v1.figures"] +packages = [ + "tsfm_public", + "tsfm_public.toolkit", + "tsfm_public.models", + "tsfm_public.models.tinytimemixer", + "tsfm_public.models.tinytimemixer.utils", + "tsfmhfdemos", + "tsfmhfdemos.neurips", + "tsfmhfdemos.neurips.backends", + "tsfmhfdemos.neurips.backends.v1", + "tsfmhfdemos.neurips.backends.v1.figures", +] [project.optional-dependencies] -notebooks = ["jupyter", "matplotlib", "datasets", "ipywidgets", "plotly", "kaleido", "tensorboard"] + +all = ["tsfm_public[notebooks,testing,dev]"] + +notebooks = [ + "jupyter", + "matplotlib", + "datasets", + "ipywidgets", + "plotly", + "kaleido", + "tensorboard", +] testing = ["pytest", "tsfm_public[notebooks]", "parameterized"] dev = ["pre-commit", "tsfm_public[testing]", "ruff==0.4.4"] -evaluation = [ - "tsevaluate @ git+ssh://git@github.ibm.com/srom/tsevaluate.git", -] -all = ["tsfm_public[notebooks,testing,dev]"] +evaluation = ["tsevaluate @ git+ssh://git@github.ibm.com/srom/tsevaluate.git"] # ogv deployments will already have jupyter # and we don't want to mess with RH's tested version ogv = ["datasets", "plotly", "kaleido"] @@ -44,7 +66,7 @@ demos = [ "plotly", "streamlit-aggrid", "kaleido", - "toml" + "toml", ] # ################################################### @@ -53,10 +75,10 @@ demos = [ version_file = "tsfm_public/_version.py" [tool.ruff] -# Never enforce `E501` (line length violations). -lint.ignore = ["C901", "E501", "E741", "F402", "F823" ] -lint.select = ["C", "E", "F", "I", "W"] + line-length = 119 +lint.ignore = ["C901", "E501", "E741", "F402", "F823"] +lint.select = ["C", "E", "F", "I", "W"] extend-exclude = ["tsfm_public/_version.py"] # Ignore import violations in all `__init__.py` files. @@ -78,4 +100,4 @@ indent-style = "space" skip-magic-trailing-comma = false # Like Black, automatically detect the appropriate line ending. -line-ending = "auto" \ No newline at end of file +line-ending = "auto" From 59d36ca65e0cd9e92c85022cdcd1c017459acfec Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Wed, 31 Jul 2024 13:14:44 -0400 Subject: [PATCH 2/5] remove optional evaluation --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 1ffbafea..69942659 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,7 +55,7 @@ notebooks = [ ] testing = ["pytest", "tsfm_public[notebooks]", "parameterized"] dev = ["pre-commit", "tsfm_public[testing]", "ruff==0.4.4"] -evaluation = ["tsevaluate @ git+ssh://git@github.ibm.com/srom/tsevaluate.git"] + # ogv deployments will already have jupyter # and we don't want to mess with RH's tested version ogv = ["datasets", "plotly", "kaleido"] From b9978d7e097da8cd5ce832ae4fd34d1b0b6ab08c Mon Sep 17 00:00:00 2001 From: Stuart Siegel Date: Wed, 31 Jul 2024 13:16:40 -0400 Subject: [PATCH 3/5] remove neurips demo from main --- tsfmhfdemos/neurips/README.md | 50 --- tsfmhfdemos/neurips/__init__.py | 2 - tsfmhfdemos/neurips/app.py | 298 ------------------ tsfmhfdemos/neurips/backends/__init__.py | 2 - tsfmhfdemos/neurips/backends/v1/__init__.py | 10 - tsfmhfdemos/neurips/backends/v1/config.toml | 200 ------------ .../backends/v1/figures/patchTSMixer.png | Bin 1544033 -> 0 bytes .../neurips/backends/v1/figures/patchTST.png | Bin 338464 -> 0 bytes tsfmhfdemos/neurips/backends/v1/model_util.py | 229 -------------- 9 files changed, 791 deletions(-) delete mode 100644 tsfmhfdemos/neurips/README.md delete mode 100644 tsfmhfdemos/neurips/__init__.py delete mode 100644 tsfmhfdemos/neurips/app.py delete mode 100644 tsfmhfdemos/neurips/backends/__init__.py delete mode 100644 tsfmhfdemos/neurips/backends/v1/__init__.py delete mode 100644 tsfmhfdemos/neurips/backends/v1/config.toml delete mode 100644 tsfmhfdemos/neurips/backends/v1/figures/patchTSMixer.png delete mode 100644 tsfmhfdemos/neurips/backends/v1/figures/patchTST.png delete mode 100644 tsfmhfdemos/neurips/backends/v1/model_util.py diff --git a/tsfmhfdemos/neurips/README.md b/tsfmhfdemos/neurips/README.md deleted file mode 100644 index 7dcb7fb5..00000000 --- a/tsfmhfdemos/neurips/README.md +++ /dev/null @@ -1,50 +0,0 @@ -# NeurIPS Expo 2023 Demo - - -## Installation -Running the demo requires first installing `tsfm_public` and then creating an appropriate set of models. At a later date we may release the pre-trained and finetuned models. To install the requirements use `pip` and then run the app using streamlit: - - -```bash -pip install ".[demos]" -cd tsfmhfdemos/neurips -streamlit run app.py -``` - -## Model structure - -The models should be placed in the `neurips` folder in a folder named `models`. The folder is expected to have the folowing structure: -``` -models -├── patchtsmixer -│   └── electricity -│   └── model -│   ├── pretrain -│   └── transfer -│   ├── ETTh1 -│   │   ├── model -│   │   │   ├── fine_tuning -│   │   │   └── linear_probe -│   │   └── preprocessor -│   └── ETTh2 -│   ├── model -│   │   ├── fine_tuning -│   │   └── linear_probe -│   └── preprocessor -└── patchtst - └── electricity - └── model - ├── pretrain - └── transfer - ├── ETTh1 - │   ├── model - │   │   ├── fine_tuning - │   │   └── linear_probe - │   └── preprocessor - └── ETTh2 - ├── model - │   ├── fine_tuning - │   └── linear_probe - └── preprocessor -``` -The demo uses these pre-created models to perform inference and then plots and evaluates the forecasting results. The `pretrain`, `fine_tuning`, and `linear_probe` subfolders contain the serialized output from the appropriate Hugging Face model (using `Trainer.save_model()` or `TimeSeriesPreprocessor.save_pretrained()`). \ No newline at end of file diff --git a/tsfmhfdemos/neurips/__init__.py b/tsfmhfdemos/neurips/__init__.py deleted file mode 100644 index 4f85bd0b..00000000 --- a/tsfmhfdemos/neurips/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Copyright contributors to the TSFM project -# diff --git a/tsfmhfdemos/neurips/app.py b/tsfmhfdemos/neurips/app.py deleted file mode 100644 index b60b1781..00000000 --- a/tsfmhfdemos/neurips/app.py +++ /dev/null @@ -1,298 +0,0 @@ -# Copyright contributors to the TSFM project -# -"""Core code for the demo app""" - -import logging -import re - -import numpy as np -import pandas as pd -import streamlit as st - -from tsfmhfdemos.neurips.backends.v1 import GLOBAL_CONFIG, model_util - - -logging.basicConfig(level=logging.INFO) - -# ********************************************************** -# There should be no need to edit anything below -# unless you are altering the content of the -# UI itself -# ********************************************************** - -logging.info(GLOBAL_CONFIG) - -MODEL_DOCS = GLOBAL_CONFIG["MODEL_DOCS"] -create_figure = model_util.create_figure -# provides the forecast for a pretrained model -forecast = model_util.forecast - -DATASETS = GLOBAL_CONFIG["DATASETS"] -MODELS = GLOBAL_CONFIG["MODELS"] -INFERENCE_APPROACHES = GLOBAL_CONFIG["INFERENCE_APPROACHES"] -METRICS = GLOBAL_CONFIG["INFERENCE_METRICS"] - - -def tsforecasting_with_fmdls(): - st.set_page_config( - page_title=GLOBAL_CONFIG["title"], - page_icon="🔮", - layout="wide", - initial_sidebar_state="expanded", - ) - - st.title(GLOBAL_CONFIG["title"]) - st.write("", unsafe_allow_html=True) - - st.write(GLOBAL_CONFIG["intro"]) - - # required_cols = ["ds", "y"] - - with st.sidebar: - st.subheader("Select the pre-trained model to use.") - model_name = st.radio( - "Model:", - MODELS.keys(), - ) - - with st.sidebar: - st.subheader("Select which dataset to use for tuning and testing.") - dataset_name = st.radio( - "Dataset:", - DATASETS.keys(), - ) - - with st.sidebar: - st.subheader("Select the tuning approach.") - approach_name = st.radio( - "Tuning Approach:", - INFERENCE_APPROACHES.keys(), - ) - - dataset_meta = DATASETS[dataset_name] - model_meta = MODELS[model_name] - approach_meta = INFERENCE_APPROACHES[approach_name] - - # tabs - tab_forecast, tab_leaderboard, tab_docs = st.tabs( - [ - ":chart: Forecast", - ":runner: Leader Board", - ":information_source: Documentation", - ] - ) - - with tab_forecast: - # ### Dataframe of selected data - # st.json(GLOBAL_CONFIG) - - st.header(dataset_name) - - # obtain forecast - # forecasts = model_util.forecast(**dataset_meta, **model_meta) - # plot forecast and ground true - # num_cols = 1 - # columns = st.columns(num_cols) - - col1, col2 = st.columns([0.7, 0.3]) - - with col1: - st.subheader("Forecast results") - for idx, channel in enumerate(dataset_meta["channel_plots"]): - # col = columns[idx % num_cols] - st.plotly_chart( - model_util.create_figure(**dataset_meta, **model_meta, **approach_meta, channel=channel), - use_container_width=True, - fig_size=(1600, 200), - ) - - with col2: - st.subheader("Performance") - df_perf = model_util.get_performance(metrics=METRICS, **dataset_meta, **model_meta, **approach_meta) - - df_perf_styled = df_perf.style.set_table_styles( - [ - {"selector": "th", "props": "background-color: whitesmoke;"}, - ] - ).format(precision=3) # .style.hide(axis="index") - st.write(df_perf_styled.to_html(), unsafe_allow_html=True) - st.write("") - - # st.dataframe(df_perf) - # add output of results - - st.subheader("Dataset") - df = model_util.csv_to_df(dataset_meta) - st.dataframe(df) - - with tab_leaderboard: - st.subheader("Current Leader Board") - # leaderboard = pd.DataFrame( - # np.random.randn(len(INFERENCE_APPROACHES), len(METRICS)), - # columns=METRICS, - # index=INFERENCE_APPROACHES, - # ) - - table_source = r""" -\begin{tabular}{cc|c|cc|cc|cc|cc|cc|ccc} -\cline{2-15} -&\multicolumn{2}{c|}{Models} & \multicolumn{2}{c}{\textbf{\citsm-Best}} & \multicolumn{2}{c|}{DLinear} & \multicolumn{2}{c|}{PatchTST}& \multicolumn{2}{c|}{FEDformer}& \multicolumn{2}{c|}{Autoformer}& \multicolumn{2}{c}{Informer} \\ -\cline{2-15} -&\multicolumn{2}{c|}{Metric}&MSE&MAE&MSE&MAE&MSE&MAE&MSE&MAE&MSE&MAE&MSE&MAE\\ -\cline{2-15} -&\multirow{4}*{\rotatebox{90}{ETTH1}} & 96 & \textbf{0.368$\pm$0.001} & \textbf{0.398$\pm$0.001} & 0.375 & \uline{0.399} & \uline{0.370} & 0.400 & 0.376 & 0.419 & 0.449 & 0.459 & 0.865 & 0.713 \\ -&\multicolumn{1}{c|}{} & 192 & \textbf{0.399$\pm$0.002} & \uline{0.418$\pm$0.001} & \uline{0.405} & \textbf{0.416} & 0.413 & 0.429 & 0.420 & 0.448 & 0.500 & 0.482 & 1.008 & 0.792 \\ -&\multicolumn{1}{c|}{} & 336 & \textbf{0.421$\pm$0.004} & \textbf{0.436$\pm$0.003} & 0.439 & 0.443 & \uline{0.422} & \uline{0.440} & 0.459 & 0.465 & 0.521 & 0.496 & 1.107 & 0.809 \\ -&\multicolumn{1}{c|}{} & 720 & \textbf{0.444$\pm$0.003} & \textbf{0.467$\pm$0.002} & 0.472 & 0.490 & \uline{0.447} & \uline{0.468} & 0.506 & 0.507 & 0.514 & 0.512 & 1.181 & 0.865 \\ -\cline{2-15} -&\multirow{4}*{\rotatebox{90}{ETTH2}} & 96 & \uline{0.276$\pm$0.006} & \textbf{0.337$\pm$0.003} & 0.289 & \uline{0.353} & \textbf{0.274} & \textbf{0.337} & 0.346 & 0.388 & 0.358 & 0.397 & 3.755 & 1.525 \\ -&\multicolumn{1}{c|}{} & 192 & \textbf{0.330$\pm$0.003} & \textbf{0.374$\pm$0.001} & 0.383 & 0.418 & \uline{0.341} & \uline{0.382} & 0.429 & 0.439 & 0.456 & 0.452 & 5.602 & 1.931 \\ -&\multicolumn{1}{c|}{} & 336 & \uline{0.357$\pm$0.001} & \uline{0.401$\pm$0.002} & 0.448 & 0.465 & \textbf{0.329} & \textbf{0.384} & 0.496 & 0.487 & 0.482 & 0.486 & 4.721 & 1.835 \\ -&\multicolumn{1}{c|}{} & 720 & \uline{0.395$\pm$0.003} & \uline{0.436$\pm$0.003} & 0.605 & 0.551 & \textbf{0.379} & \textbf{0.422} & 0.463 & 0.474 & 0.515 & 0.511 & 3.647 & 1.625 \\ -\cline{2-15} -&\multirow{4}*{\rotatebox{90}{ETTM1}} & 96 & \textbf{0.291$\pm$0.002} & \uline{0.346$\pm$0.002} & 0.299 & \textbf{0.343} & \uline{0.293} & \uline{0.346} & 0.379 & 0.419 & 0.505 & 0.475 & 0.672 & 0.571 \\ -&\multicolumn{1}{c|}{} & 192 & \textbf{0.333$\pm$0.002} & \uline{0.369$\pm$0.002} & \uline{0.335} & \textbf{0.365} & \textbf{0.333} & 0.370 & 0.426 & 0.441 & 0.553 & 0.496 & 0.795 & 0.669 \\ -&\multicolumn{1}{c|}{} & 336 & \textbf{0.365$\pm$0.005} & \textbf{0.385$\pm$0.004} & \uline{0.369} & \uline{0.386} & \uline{0.369} & 0.392 & 0.445 & 0.459 & 0.621 & 0.537 & 1.212 & 0.871 \\ -&\multicolumn{1}{c|}{} & 720 & \textbf{0.416$\pm$0.002} & \textbf{0.413$\pm$0.001} & \uline{0.425} & 0.421 & \textbf{0.416} & \uline{0.420} & 0.543 & 0.490 & 0.671 & 0.561 & 1.166 & 0.823 \\ -\cline{2-15} -&\multirow{4}*{\rotatebox{90}{ETTM2}} & 96 & \textbf{0.164$\pm$0.002} & \textbf{0.255$\pm$0.002} & 0.167 & 0.260 & \uline{0.166} & \uline{0.256} & 0.203 & 0.287 & 0.255 & 0.339 & 0.365 & 0.453 \\ -&\multicolumn{1}{c|}{} & 192 & \textbf{0.219$\pm$0.002} & \textbf{0.293$\pm$0.002} & 0.224 & 0.303 & \uline{0.223} & \uline{0.296} & 0.269 & 0.328 & 0.281 & 0.340 & 0.533 & 0.563 \\ -&\multicolumn{1}{c|}{} & 336 & \textbf{0.273$\pm$0.003} & \textbf{0.329$\pm$0.003} & 0.281 & \uline{0.342} & \uline{0.274} & \textbf{0.329} & 0.325 & 0.366 & 0.339 & 0.372 & 1.363 & 0.887 \\ -&\multicolumn{1}{c|}{} & 720 & \textbf{0.358$\pm$0.002} & \textbf{0.380$\pm$0.001} & 0.397 & 0.421 & \uline{0.362} & \uline{0.385} & 0.421 & 0.415 & 0.433 & 0.432 & 3.379 & 1.338 \\ -\cline{2-15} -&\multirow{4}*{\rotatebox{90}{Electricity}} & 96 & \textbf{0.129$\pm$1e-4} & \uline{0.224$\pm$0.001} & \uline{0.140} & 0.237 & \textbf{0.129} & \textbf{0.222} & 0.193 & 0.308 & 0.201 & 0.317 & 0.274 & 0.368 \\ -&\multicolumn{1}{c|}{} & 192 & \textbf{0.146$\pm$0.001} & \uline{0.242$\pm$1e-4} & 0.153 & 0.249 & \uline{0.147} & \textbf{0.240} & 0.201 & 0.315 & 0.222 & 0.334 & 0.296 & 0.386 \\ -&\multicolumn{1}{c|}{} & 336 & \textbf{0.158$\pm$0.001} & \textbf{0.256$\pm$0.001} & 0.169 & 0.267 & \uline{0.163} & \uline{0.259} & 0.214 & 0.329 & 0.231 & 0.338 & 0.300 & 0.394 \\ -&\multicolumn{1}{c|}{} & 720 & \textbf{0.186$\pm$0.001} & \textbf{0.282$\pm$0.001} & 0.203 & 0.301 & \uline{0.197} & \uline{0.290} & 0.246 & 0.355 & 0.254 & 0.361 & 0.373 & 0.439 \\ -\cline{2-15} -&\multirow{4}*{\rotatebox{90}{Traffic}} & 96 & \textbf{0.356$\pm$0.002} & \textbf{0.248$\pm$0.002} & 0.410 & 0.282 & \uline{0.360} & \uline{0.249} & 0.587 & 0.366 & 0.613 & 0.388 & 0.719 & 0.391 \\ -&\multicolumn{1}{c|}{} & 192 & \textbf{0.377$\pm$0.003} & \uline{0.257$\pm$0.002} & 0.423 & 0.287 & \uline{0.379} & \textbf{0.256} & 0.604 & 0.373 & 0.616 & 0.382 & 0.696 & 0.379 \\ -&\multicolumn{1}{c|}{} & 336 & \textbf{0.385$\pm$0.002} & \textbf{0.262$\pm$0.001} & 0.436 & 0.296 & \uline{0.392} & \uline{0.264} & 0.621 & 0.383 & 0.622 & 0.337 & 0.777 & 0.420 \\ -&\multicolumn{1}{c|}{} & 720 & \textbf{0.424$\pm$0.001} & \textbf{0.283$\pm$0.001} & 0.466 & 0.315 & \uline{0.432} & \uline{0.286} & 0.626 & 0.382 & 0.660 & 0.408 & 0.864 & 0.472 \\ -\cline{2-15} -&\multirow{4}*{\rotatebox{90}{Weather}} & 96 & \textbf{0.146$\pm$0.001} & \textbf{0.197$\pm$0.002} & 0.176 & 0.237 & \uline{0.149} & \uline{0.198} & 0.217 & 0.296 & 0.266 & 0.336 & 0.300 & 0.384 \\ -&\multicolumn{1}{c|}{} & 192 & \textbf{0.191$\pm$0.001} & \textbf{0.240$\pm$0.001} & 0.220 & 0.282 & \uline{0.194} & \uline{0.241} & 0.276 & 0.336 & 0.307 & 0.367 & 0.598 & 0.544 \\ -&\multicolumn{1}{c|}{} & 336 & \textbf{0.243$\pm$0.001} & \textbf{0.279$\pm$0.002} & 0.265 & 0.319 & \uline{0.245} & \uline{0.282} & 0.339 & 0.380 & 0.359 & 0.395 & 0.578 & 0.523 \\ -&\multicolumn{1}{c|}{} & 720 & \uline{0.316$\pm$0.001} & \textbf{0.333$\pm$0.002} & 0.323 & 0.362 & \textbf{0.314} & \uline{0.334} & 0.403 & 0.428 & 0.419 & 0.428 & 1.059 & 0.741 \\ -\cline{2-15} -% &\multicolumn{4}{c|}{\makecell{\textbf{\citsm-Best} \textbf{\% improvement}}}& \textbf{8\%} & \textbf{6.8\%}& \textbf{0.7\%} & \textbf{0.4\%} & \textbf{22.9\%} & \textbf{18.2\%} & \textbf{30.1\%} & \textbf{22.7\%} & \textbf{64\%} & \textbf{50.3\%} \\ -% &\multicolumn{4}{c|}{\makecell{\textbf{\citsm-Best} \textbf{\% improvement (MSE)}}}& \multicolumn{2}{c}{\textbf{8\%}} & \multicolumn{2}{c}{ \textbf{0.7\%}} & \multicolumn{2}{c}{\textbf{22.9\%}} & \multicolumn{2}{c}{\textbf{30.1\%}} & \multicolumn{2}{c}{\textbf{64\%}} \\ -&\multicolumn{4}{c|}{\makecell{\textbf{\citsm-Best} \textbf{\% improvement (MSE)}}}& \multicolumn{2}{c}{\textbf{8\%}} & \multicolumn{2}{c}{ \textbf{1\%}} & \multicolumn{2}{c}{\textbf{23\%}} & \multicolumn{2}{c}{\textbf{30\%}} & \multicolumn{2}{c}{\textbf{64\%}} \\ -\cline{2-15} -\end{tabular} -""" - - out = re.sub(r"\\textbf{([^&]*)}", r"\1", table_source) - out = re.sub(r"\\uline{([^&]*)}", r"\1", out) - out = re.sub(r"\s*|\$\\pm\$[^&]*|\\cline{.*}", "", out) - vals = np.array([r.split("&")[3:] for r in out.split(r"\\")[2:30]]).astype(float) - - leaderboard = pd.DataFrame( - index=pd.MultiIndex.from_product( - [ - [ - "ETTh1", - "ETTh2", - "ETTm1", - "ETTm2", - "Electricity", - "Traffic", - "Weather", - ], - [96, 192, 336, 720], - ], - names=["Dataset", "Prediction length"], - ), - columns=pd.MultiIndex.from_product( - [ - [ - "PatchTSMixer", - "DLinear", - "PatchTST", - "FEDformer", - "Autoformer", - "Informer", - ], - ["MSE", "MAE"], - ], - names=["Model", "Metric"], - ), - data=vals, - ) - - def highlight_best(s, props=""): - for metric in ["MSE", "MAE"]: - mask = s.index.droplevel(0) == metric - s[mask] = s[mask] == np.nanmin(s.values[mask]) - - ret = np.where(s, props, "") - return ret - - def highlight_second_best(s, props=""): - for metric in ["MSE", "MAE"]: - mask = s.index.droplevel(0) == metric - v_ord = np.sort(s.values[mask]) - v_min = v_ord[0] - v_second = v_ord[1] - s[mask] = (s[mask] == v_second) & (v_min != v_second) - - ret = np.where(s, props, "") - return ret - - # (df.style.apply(highlight_max, axis=0, props='background-color:green;', subset=['A','B']) - # .apply(highlight_min, axis=0, props='background-color:red;', subset=['A','B']) - - # st.dataframe(leaderboard, hide_index=False) - - leaderboard = leaderboard[ - [ - "PatchTSMixer", - "PatchTST", - "DLinear", - "FEDformer", - "Autoformer", - "Informer", - ] - ] - - leaderboard_styled = ( - leaderboard.style.set_table_styles( - [ - {"selector": "th", "props": "background-color: whitesmoke;"}, - ] - ) - .format(precision=3) - .apply(highlight_best, axis=1, props="font-weight: bold;") - .apply(highlight_second_best, axis=1, props="text-decoration: underline;") - ) - - st.write(leaderboard_styled.to_html(), unsafe_allow_html=True) - # st.dataframe(leaderboard_styled) - - st.write("Source: https://arxiv.org/abs/2306.09364") - - with tab_docs: - # drop [0] below if more than one tab - tab_desc = st.tabs(["Description of the models"])[0] - - with tab_desc: - for model_name in MODELS.keys(): - st.header(model_name) - model_card_name = MODELS[model_name]["card"] - st.subheader("Abstract") - st.write(f"""{MODEL_DOCS[model_card_name]['Abstract']}""") - st.subheader("Model Architecture") - st.image(**MODEL_DOCS[model_card_name]["figure"]) - st.write(f"""{MODEL_DOCS[model_card_name]['Model Architecture']}""") - # st.subheader("Secondary use") - # st.write(f"""{MODEL_DOCS[model_card_name]['Secondary use']}""") - # st.subheader("Limitations") - # st.write(f"""{MODEL_DOCS[model_card_name]['Limitations']}""") - # st.subheader("Training data") - # st.write(f"""{MODEL_DOCS[model_card_name]['Training data']}""") - st.subheader("BibTex/Citation Info") - st.code(f"""{MODEL_DOCS[model_card_name]['Citation Info']}""") - - -if __name__ == "__main__": - tsforecasting_with_fmdls() diff --git a/tsfmhfdemos/neurips/backends/__init__.py b/tsfmhfdemos/neurips/backends/__init__.py deleted file mode 100644 index 4f85bd0b..00000000 --- a/tsfmhfdemos/neurips/backends/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -# Copyright contributors to the TSFM project -# diff --git a/tsfmhfdemos/neurips/backends/v1/__init__.py b/tsfmhfdemos/neurips/backends/v1/__init__.py deleted file mode 100644 index 306d315c..00000000 --- a/tsfmhfdemos/neurips/backends/v1/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright contributors to the TSFM project -# - -import os - -import toml - - -_config_file = os.path.join(os.path.dirname(__file__), "config.toml") -GLOBAL_CONFIG = toml.load(open(_config_file)) diff --git a/tsfmhfdemos/neurips/backends/v1/config.toml b/tsfmhfdemos/neurips/backends/v1/config.toml deleted file mode 100644 index 7b2d3580..00000000 --- a/tsfmhfdemos/neurips/backends/v1/config.toml +++ /dev/null @@ -1,200 +0,0 @@ -# Copyright contributors to the TSFM project -# - -# These are top-level (app.py) globals - -title = "IBM Research Foundation Models for Time Series Forecasting" -intro = """ -This demo highlights the use of two popular Time Series FM backbones developed at IBM Research, -the first patched time-series transformer (PatchTST, ICLR 23), -and the first patched MLP-Mixer for time series (PatchTSMixer, KDD 23). -Our line of work not only attempts to improve the state-of-the-art accuracies (SOTA), but also -focuses on achieving it with extremely reduced memory and compute requirements. - -This demo focusses on demonstraing the usefulness of first pre-training a model on related but -independent data and then using that model in conjunction with various tunning approaches on different target datasets. -Each pre-trained model has been pretrained on the hourly aggregated electricity dataset from the UCI repository (https://zenodo.org/records/4656140). -Then, we tune that model using the selected dataset and tuning approach. Forecasts and performance evaluation of the tuned model are shown below. -""" - -# These are the leader board metric to report -INFERENCE_METRICS = ["mse", "mae", "smape"] - -## Model cards for documentation tab -[MODEL_DOCS.PatchTST] - -Abstract = """ -We propose an efficient design of Transformer-based models for multivariate time series forecasting and self-supervised representation learning. -It is based on two key components: (i) segmentation of time series into subseries-level patches which are served as input tokens to Transformer; -(ii) channel-independence where each channel contains a single univariate time series that shares the same embedding and Transformer weights across all the series. -Patching design naturally has three-fold benefit: local semantic information is retained in the embedding; -computation and memory usage of the attention maps are quadratically reduced given the same look-back window; -and the model can attend longer history. -Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly -when compared with that of SOTA Transformer-based models. -We also apply our model to self-supervised pre-training tasks and attain excellent fine-tuning performance, -which outperforms supervised training on large datasets. -Transferring of masked pre-trained representation on one dataset to others also produces SOTA forecasting accuracy. -""" - -"Model Architecture" = """ -For more details, please see our paper: https://arxiv.org/abs/2211.14730 -""" - -figure = {image = "backends/v1/figures/patchTST.png", caption = "PatchTST Model Architecture", use_column_width="auto"} - -# "Secondary use" = """ -# Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor -# incididunt ut labore et dolore magna aliqua. Vulputate mi sit amet mauris commodo -# quis imperdiet. Augue eget arcu dictum varius duis at consectetur lorem donec. -# Volutpat diam ut venenatis tellus in metus. Interdum velit. -# """ -# Limitations = """ -# Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor -# incididunt ut labore et dolore magna aliqua. Vulputate mi sit amet mauris commodo -# quis imperdiet. Augue eget arcu dictum varius duis at consectetur lorem donec. -# Volutpat diam ut venenatis tellus in metus. Interdum velit. -# """ -# "Training data" = """ -# Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor -# incididunt ut labore et dolore magna aliqua. Vulputate mi sit amet mauris commodo -# quis imperdiet. Augue eget arcu dictum varius duis at consectetur lorem donec. -# Volutpat diam ut venenatis tellus in metus. Interdum velit. -# """ - -"Citation Info" = """ -@inproceedings{Yuqietal-2023-PatchTST, - title = {A Time Series is Worth 64 Words: Long-term Forecasting with Transformers}, - author = {Nie, Yuqi and - H. Nguyen, Nam and - Sinthong, Phanwadee and - Kalagnanam, Jayant}, - booktitle = {International Conference on Learning Representations}, - year = {2023} -} -""" - -[MODEL_DOCS.PatchTSMixer] - - -Abstract = """ -Transformers have gained popularity in time series forecasting for their ability to capture long-sequence interactions. -However, their memory and compute-intensive requirements pose a critical bottleneck for long-term forecasting, -despite numerous advancements in compute-aware self-attention modules. -To address this, we propose TSMixer, a lightweight neural architecture exclusively composed of multi-layer perceptron (MLP) modules. -TSMixer is designed for multivariate forecasting and representation learning on patched time series, -providing an efficient alternative to Transformers. Our model draws inspiration from the success of MLP-Mixer models in computer vision. -We demonstrate the challenges involved in adapting Vision MLP-Mixer for time series -and introduce empirically validated components to enhance accuracy. -This includes a novel design paradigm of attaching online reconciliation heads to the MLP-Mixer backbone, -for explicitly modeling the time-series properties such as hierarchy and channel-correlations. -We also propose a Hybrid channel modeling approach to effectively handle noisy channel interactions and generalization across diverse datasets, -a common challenge in existing patch channel-mixing methods. -Additionally, a simple gated attention mechanism is introduced in the backbone to prioritize important features. -By incorporat- ing these lightweight components, we significantly enhance the learning capability of simple MLP structures, -outperforming complex Transformer models with minimal computing usage. -Moreover, TSMixer’s modular design enables compatibility with both supervised and masked self-supervised learning methods, -making it a promising building block for time-series Foundation Models. -TSMixer outperforms state-of-the-art MLP and Transformer models in forecasting by a considerable margin of 8-60%. -It also outperforms the latest strong benchmarks of Patch-Transformer models (by 1-2%) with a significant reduction in memory and runtime (2- 3X). -""" - -"Model Architecture" = """ -For more details, please see our paper: https://arxiv.org/abs/2306.09364 -""" -figure = {image = "backends/v1/figures/patchTSMixer.png", caption = "PatchTSMixer Model Architecture", use_column_width="auto"} - -# "Secondary use" = """ -# Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor -# incididunt ut labore et dolore magna aliqua. Vulputate mi sit amet mauris commodo -# quis imperdiet. Augue eget arcu dictum varius duis at consectetur lorem donec. -# Volutpat diam ut venenatis tellus in metus. Interdum velit. -# """ -# Limitations = """ -# Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor -# incididunt ut labore et dolore magna aliqua. Vulputate mi sit amet mauris commodo -# quis imperdiet. Augue eget arcu dictum varius duis at consectetur lorem donec. -# Volutpat diam ut venenatis tellus in metus. Interdum velit. -# """ -# "Training data" = """ -# Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor -# incididunt ut labore et dolore magna aliqua. Vulputate mi sit amet mauris commodo -# quis imperdiet. Augue eget arcu dictum varius duis at consectetur lorem donec. -# Volutpat diam ut venenatis tellus in metus. Interdum velit. -# """ - -"Citation Info" = """ -@inproceedings{Ekambaram_2023, series={KDD ’23}, - title={TSMixer: Lightweight MLP-Mixer Model for Multivariate Time Series Forecasting}, - url={http://dx.doi.org/10.1145/3580305.3599533}, - DOI={10.1145/3580305.3599533}, - booktitle={Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining}, - publisher={ACM}, - author={Ekambaram, Vijay and Jati, Arindam and Nguyen, Nam and Sinthong, Phanwadee and Kalagnanam, Jayant}, - year={2023}, - month=aug, collection={KDD ’23} } -""" - -# Mapping of models and associated inference/training -# parameters. Note that first level keys of this mapping will -# be rendered in the UI -# model paths need to conform to HF conventions -# and it seems they MUST be relative to the same directory as -# app.py (e.g., they can't refer to directories under backends) - -[MODELS.PatchTST] - -card = "PatchTST" -pretrained_model_path = "models/patchtst/electricity/model" -# finetuned_model_path = "model/forecasting" -preprocessor_path = "models/patchtst/electricity/preprocessor" - -[MODELS.PatchTSMixer] - -card = "PatchTSMixer" -pretrained_model_path = "models/patchtsmixer/electricity/model" -# finetuned_model_path = "model/forecasting" -preprocessor_path = "models/patchtsmixer/electricity/preprocessor" - - -# The data sets in play. These keys will render in the demo page -[DATASETS."ETTh1"] -dataset_name="ETTh1" -uri="https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv" -timestamp_column="date" -id_columns=[] -forecast_columns=["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"] -channel_plots=["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"] # ["HUFL", "HULL", "MUFL", "OT"] -periodicity="1h" -test_start_index = 11520 # 12 * 30 * 24 + 4 * 30 * 24 -test_end_index = 14400 # 12 * 30 * 24 + 8 * 30 * 24 - - -[DATASETS."ETTh2"] -dataset_name="ETTh2" -uri="https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh2.csv" -timestamp_column="date" -id_columns=[] -forecast_columns=["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"] -channel_plots=["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"] -periodicity="1h" -test_start_index = 11520 # 12 * 30 * 24 + 4 * 30 * 24 -test_end_index = 14400 # 12 * 30 * 24 + 8 * 30 * 24 - - - -[INFERENCE_APPROACHES] - 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