From 5383730aa4b6f9c26e4cfc2d48f145132f503cef Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 13:36:35 +0200 Subject: [PATCH 01/23] Github action poetry piblish, poetry --- .github/workflows/main.yml | 29 + .gitignore | 2 + poetry.lock | 5718 ++++++++++++++++++++++++++++++++++++ pyproject.toml | 50 +- 4 files changed, 5781 insertions(+), 18 deletions(-) create mode 100644 .github/workflows/main.yml create mode 100644 .gitignore create mode 100644 poetry.lock diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml new file mode 100644 index 0000000..98e494e --- /dev/null +++ b/.github/workflows/main.yml @@ -0,0 +1,29 @@ + +name: Poetry publish + +on: + push: + branches: [ "master" ] + pull_request: + branches: [ "master" ] + +jobs: + buildPush: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.10' + - name: Install Poetry + run: | + python -m pip install 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name = "timesfm" +version = "0.0.2" description = "Open weights time-series foundation model from Google Research." -version = "0.0.1" -dependencies = [ - "einshape>=1.0.0", - "paxml>=1.4.0", - "praxis>=1.4.0", - "jax==0.4.26", - "numpy>=1.26.4", - "pandas>=2.1.4", -] authors = [ - {name = "Rajat Sen", email = "senrajat@google.com"}, - {name = "Yichen Zhou", email = "yichenzhou@google.com"}, - {name = "Abhimanyu Das", email = "abhidas@google.com"}, - {name = "Petros Mol", email = "pmol@google.com"}, + "Rajat Sen ", + "Yichen Zhou ", + "Abhimanyu Das ", + "Petros Mol ", + "Justin Güse ", ] +readme = "README.md" +keywords = ["time series", "timesfm", "forecast", "time series model"] +homepage = "https://github.com/google-research/timesfm" +repository = "https://github.com/google-research/timesfm" +classifiers = [ + "Environment :: Console", + "Framework :: Flake8", + "Operating System :: OS Independent", + "Topic :: Software Development :: Documentation", + "Topic :: Software Development :: Libraries :: Python Modules", + "Topic :: Software Development :: Quality Assurance", +] +include = [ + "LICENSE", +] + +[tool.poetry.dependencies] +python = ">=3.10,<3.11" +einshape = ">=1.0.0" +numpy = ">=1.26.4" +pandas = ">=2.1.4" +paxml = "1.4.0" +jax = "0.4.26" [build-system] -requires = ["setuptools"] -build-backend = "setuptools.build_meta" +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" From 653679949533eff7e6b1e32fee9a7b69ee12ebec Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 13:37:53 +0200 Subject: [PATCH 02/23] run number --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 98e494e..f8f99f1 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -22,7 +22,7 @@ jobs: pip install poetry - name: Set Version number run: | - poetry version ${{ github.event_name }} + poetry version ${{ github.run_number }} - name: Build and Publish to PyPI run: | poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }} From e402b93272d30eb3bf2ba4a373a7e3e4803de9e8 Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 13:39:22 +0200 Subject: [PATCH 03/23] github run number for version --- .github/workflows/main.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index f8f99f1..268e146 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -22,7 +22,7 @@ jobs: pip install poetry - name: Set Version number run: | - poetry version ${{ github.run_number }} + poetry version 0.0.${{ github.run_number }} - name: Build and Publish to PyPI run: | poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }} From a94df6bc22f0448f186eef88f5a0523868d72257 Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 13:43:49 +0200 Subject: [PATCH 04/23] package path --- pyproject.toml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index a12e635..5af18f0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,8 @@ [tool.poetry] name = "timesfm" +packages = [ + { include = "timesfm", from = "src" }, +] version = "0.0.2" description = "Open weights time-series foundation model from Google Research." authors = [ From 660c6757e7755b95b4236978a7bc5141ac91bdef Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 13:45:26 +0200 Subject: [PATCH 05/23] poetry package path --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 5af18f0..0fb8934 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ [tool.poetry] name = "timesfm" packages = [ - { include = "timesfm", from = "src" }, + { include = "*", from = "src" }, ] version = "0.0.2" description = "Open weights time-series foundation model from Google Research." From a7cc934536ad4340f841fd5bca6791ca501cd2b3 Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 13:47:22 +0200 Subject: [PATCH 06/23] move init file --- __init__.py => src/__init__.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) rename __init__.py => src/__init__.py (83%) diff --git a/__init__.py b/src/__init__.py similarity index 83% rename from __init__.py rename to src/__init__.py index 8275b16..646426d 100644 --- a/__init__.py +++ b/src/__init__.py @@ -16,6 +16,5 @@ from __future__ import absolute_import -from .src.patched_decoder import PatchedTimeSeriesDecoder -from .src.timesfm import TimesFm -from .src.timesfm import freq_map +from .patched_decoder import PatchedTimeSeriesDecoder +from .timesfm import TimesFm, freq_map From 8e40b0f378657e1fe817f0e35a2f8eff0ed4160c Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 13:50:14 +0200 Subject: [PATCH 07/23] dependency --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git 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"scipy"] +plotting = ["pandas[plot]", "plotly", "plotly-resampler"] +polars = ["polars"] +scalers = ["numba", "scipy"] + [[package]] name = "wcwidth" version = "0.2.13" @@ -5715,4 +5737,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.11" -content-hash = "73c0af966dab47ab642141e6dd2078d07f26e5b47ab698f5b54adbe8b1f4cc1c" +content-hash = "5b5898ee489089ecfb4258a3598877ccc2e458ee6c6029ebb0c9f58c0f2b16fa" From f82e9343e4b052db2e98d264e9f66a8623764d93 Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 15:04:15 +0200 Subject: [PATCH 10/23] change versioning to pyproject.toml, README changes for poetry --- .github/workflows/main.yml | 7 ++++--- README.md | 27 +++++++++++++++++++++++++++ pyproject.toml | 2 +- 3 files changed, 32 insertions(+), 4 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 268e146..9ff93a0 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -20,9 +20,10 @@ jobs: run: | python -m pip install --upgrade pip pip install poetry - - name: Set Version number - run: | - poetry version 0.0.${{ github.run_number }} + # use the hardcoded version number for now + # - name: Set Version number + # run: | + # poetry version 0.0.${{ github.run_number }} - name: Build and Publish to PyPI run: | poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }} diff --git a/README.md b/README.md index 41d3f82..72d0de8 100644 --- a/README.md +++ b/README.md @@ -30,6 +30,27 @@ Please look into the README files in the respective benchmark directories within ## Installation +### Installation as a package + +To install the CPU version of TimesFM as a package, you can run the following command without cloning this repo: + +`pip install timesfm` + +The package is currently using https://pypi.org/project/timesfm/. + +### Poetry (CPU) installation + +To install the CPU environment using Poetry, you can run the following command: + +``` +pip install poetry # optional +poetry install +``` + +This will install the environment in the local .venv folder (depends on the configuration) and matches the python command to the poetry environment. If this is not the case, you can use `poetry run python` to use the local environment. + +### Conda / GPU installation + For calling TimesFM, We have two environment files. Inside `timesfm`, for GPU installation (assuming CUDA 12 has been setup), you can create a conda environment `tfm_env` from the base folder through: @@ -60,6 +81,12 @@ Please use the environment files under `experiments` instead. 2. The dependency `lingvo` does not support ARM architectures, and the code is not working for machines with Apple silicon. We are aware of this issue and are working on a solution. Stay tuned. +#### Building the package and publishing to PyPI + +The package can be built using the command `poetry build`. + +To build and publish it to PyPI, the command `poetry publish` can be used. This command will require the user to have the necessary permissions to publish to the PyPI repository. + ## Usage ### Initialize the model and load a checkpoint. diff --git a/pyproject.toml b/pyproject.toml index a1b4923..168ddcd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,7 @@ name = "timesfm" packages = [ { include = "*", from = "src" }, ] -version = "0.0.2" +version = "0.0.1" description = "Open weights time-series foundation model from Google Research." authors = [ "Rajat Sen ", From 84955b11dc794d4d380ced327496a2e125fadafb Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 15:11:19 +0200 Subject: [PATCH 11/23] triggering a github run_number build --- .github/workflows/main.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 9ff93a0..e710f86 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -21,9 +21,9 @@ jobs: python -m pip install --upgrade pip pip install poetry # use the hardcoded version number for now - # - name: Set Version number - # run: | - # poetry version 0.0.${{ github.run_number }} + - name: Set Version number + run: | + poetry version 0.0.${{ github.run_number }} - name: Build and Publish to PyPI run: | poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }} From 16b899c4a5cce29c6746346551c2d41beb47d70e Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 24 May 2024 15:12:50 +0200 Subject: [PATCH 12/23] revert back to pyproject version number --- .github/workflows/main.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index e710f86..9ff93a0 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -21,9 +21,9 @@ jobs: python -m pip install --upgrade pip pip install poetry # use the hardcoded version number for now - - name: Set Version number - run: | - poetry version 0.0.${{ github.run_number }} + # - name: Set Version number + # run: | + # poetry version 0.0.${{ github.run_number }} - name: Build and Publish to PyPI run: | poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }} From c0b93d4b6aa645a2397decba13927141c44306c8 Mon Sep 17 00:00:00 2001 From: periodLeo Date: Tue, 9 Jul 2024 20:16:43 +0900 Subject: [PATCH 13/23] added a verbose parameter to TimesFM.forecast_on_df(), in order to make it possible to desactivate print outputs. --- src/timesfm/timesfm.py | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/src/timesfm/timesfm.py b/src/timesfm/timesfm.py index 5dc7611..5bf3261 100644 --- a/src/timesfm/timesfm.py +++ b/src/timesfm/timesfm.py @@ -518,6 +518,7 @@ class TimesFm: model_name: str = "timesfm", window_size: int | None = None, num_jobs: int = 1, + verbose: bool = True, ) -> pd.DataFrame: """Forecasts on a list of time series. @@ -535,6 +536,7 @@ class TimesFm: window_size: window size of trend + residual decomposition. If None then we do not do decomposition. num_jobs: number of parallel processes to use for dataframe processing. + verbose: output model states in terminal. Returns: Future forecasts dataframe. @@ -554,7 +556,8 @@ class TimesFm: new_inputs = [] uids = [] if num_jobs == 1: - print("Processing dataframe with single process.") + if verbose: + print("Processing dataframe with single process.") for key, group in df_sorted.groupby("unique_id"): inp, uid = process_group( key, @@ -567,7 +570,8 @@ class TimesFm: else: if num_jobs == -1: num_jobs = multiprocessing.cpu_count() - print("Processing dataframe with multiple processes.") + if verbose: + print("Processing dataframe with multiple processes.") with multiprocessing.Pool(processes=num_jobs) as pool: results = pool.starmap( process_group, @@ -577,12 +581,14 @@ class TimesFm: ], ) new_inputs, uids = zip(*results) - print("Finished preprocessing dataframe.") + if verbose: + print("Finished preprocessing dataframe.") freq_inps = [freq_map(freq)] * len(new_inputs) _, full_forecast = self.forecast( new_inputs, freq=freq_inps, window_size=window_size ) - print("Finished forecasting.") + if verbose: + print("Finished forecasting.") fcst_df = make_future_dataframe( uids=uids, last_times=df_sorted.groupby("unique_id")["ds"].tail(1), From a8901d243127f77df9b90f9ae3d8619d681a50a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Justin=20G=C3=BCse?= Date: Fri, 12 Jul 2024 11:07:53 +0000 Subject: [PATCH 14/23] Support for cpu/gpu version --- .github/workflows/main.yml | 2 +- .gitignore | 3 +- README.md | 41 +- environment.yml | 18 - environment_cpu.yml | 18 - poetry.lock | 1280 +++++++++++++++++++++--------------- pyproject.toml | 29 +- 7 files changed, 760 insertions(+), 631 deletions(-) delete mode 100644 environment.yml delete mode 100644 environment_cpu.yml diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 9ff93a0..41c9e4e 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -20,7 +20,7 @@ jobs: run: | python -m pip install --upgrade pip pip install poetry - # use the hardcoded version number for now + # use the hardcoded version number for now. if this is merged to the main repo, you can uncomment these lines to automatically tag the version number with the github run number # - name: Set Version number # run: | # poetry version 0.0.${{ github.run_number }} diff --git a/.gitignore b/.gitignore index 1a7eb27..cc38b4f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,3 @@ .venv/ -dist/ \ No newline at end of file +dist/ +**__pycache__/** */ \ No newline at end of file diff --git a/README.md b/README.md index e59ad5d..3bdbd9e 100644 --- a/README.md +++ b/README.md @@ -14,6 +14,8 @@ to download model checkpoints. This is not an officially supported Google product. +We recommend at least 16GB RAM to load TimesFM dependencies. + ## Checkpoint timesfm-1.0-200m timesfm-1.0-200m is the first open model checkpoint: @@ -32,51 +34,28 @@ Please look into the README files in the respective benchmark directories within ### Installation as a package -To install the CPU version of TimesFM as a package, you can run the following command without cloning this repo: +To install the GPU/default version of TimesFM as a package, you can run the following command without cloning this repo: `pip install timesfm` The package is currently using https://pypi.org/project/timesfm/. -### Poetry (CPU) installation +To install the CPU version of TimesFM, you can run the command `pip install timesfm[cpu]` -To install the CPU environment using Poetry, you can run the following command: +### Local installation + +To from the current repository/local version (like you would have previously done with `pip -e .`), you can run the command ``` pip install poetry # optional poetry install ``` +If you prefer the CPU version, run the command `poetry install -E cpu` + This will install the environment in the local .venv folder (depends on the configuration) and matches the python command to the poetry environment. If this is not the case, you can use `poetry run python` to use the local environment. -### Conda / GPU installation - -We recommend at least 16GB RAM to load TimesFM dependencies. - -For calling TimesFM, We have two environment files. Inside `timesfm`, for -GPU installation (assuming CUDA 12 has been setup), you can create a conda -environment `tfm_env` from the base folder through: - -``` -conda env create --file=environment.yml -``` - -For a CPU setup please use, - -``` -conda env create --file=environment_cpu.yml -``` -to create the environment instead. - -Follow by - -``` -conda activate tfm_env -pip install -e . -``` -to install the package. - -**Note**: +## Notes 1. Running the provided benchmarks would require additional dependencies. Please use the environment files under `experiments` instead. diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 7a1d6fe..0000000 --- a/environment.yml +++ /dev/null @@ -1,18 +0,0 @@ -name: tfm_env - -channels: - - conda-forge - - defaults - - anaconda -dependencies: - - jupyterlab - - pip - - python=3.10 - - pip: - - huggingface_hub[cli] - - utilsforecast - - praxis - - paxml - - jax[cuda12]==0.4.26 - - einshape - - scikit-learn diff --git a/environment_cpu.yml b/environment_cpu.yml deleted file mode 100644 index d808176..0000000 --- a/environment_cpu.yml +++ /dev/null @@ -1,18 +0,0 @@ -name: tfm_env - -channels: - - conda-forge - - defaults - - anaconda -dependencies: - - jupyterlab - - pip - - python=3.10 - - pip: - - huggingface_hub[cli] - - utilsforecast - - praxis - - paxml - - jax[cpu]==0.4.26 - - einshape - - scikit-learn diff --git a/poetry.lock b/poetry.lock index 85112cd..73a94e1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -13,13 +13,13 @@ files 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flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] ftfy = ["ftfy"] @@ -5422,25 +5614,26 @@ natten = ["natten (>=0.14.6,<0.15.0)"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] optuna = ["optuna"] -quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"] +quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "isort (>=5.5.4)", "ruff (==0.4.4)", "urllib3 (<2.0.0)"] ray = ["ray[tune] (>=2.7.0)"] retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] +ruff = ["ruff (==0.4.4)"] sagemaker = ["sagemaker (>=2.31.0)"] sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"] serving = ["fastapi", "pydantic", "starlette", "uvicorn"] sigopt = ["sigopt"] sklearn = ["scikit-learn"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] -testing = ["GitPython 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"tensorflow-text (<2.16)", "tf2onnx"] -tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] +tf-cpu = ["keras (>2.9,<2.16)", "keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>2.9,<2.16)", "tensorflow-probability (<0.24)", "tensorflow-text (<2.16)", "tf2onnx"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] -timm = ["timm"] +timm = ["timm (<=0.9.16)"] tokenizers = ["tokenizers (>=0.19,<0.20)"] torch = ["accelerate (>=0.21.0)", "torch"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] -torchhub = ["filelock", "huggingface-hub (>=0.23.0,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] +torchhub = ["filelock", "huggingface-hub (>=0.23.2,<1.0)", "importlib-metadata", "numpy (>=1.17,<2.0)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"] video = ["av (==9.2.0)", "decord (==0.6.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"] @@ -5472,13 +5665,13 @@ files = [ [[package]] name = "typing-extensions" -version = "4.12.0" +version = "4.12.2" description = "Backported and Experimental Type Hints for Python 3.8+" optional = false python-versions = ">=3.8" files = [ - {file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"}, - {file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"}, + {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = 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"pandas[plot]", "plotly", "plotly-resampler", "polars", "pyarrow", "scipy"] +dev = ["datasetsforecast (==0.0.8)", "nbdev", "numba", "numpy (<2)", "pandas[plot]", "plotly", "plotly-resampler", "polars", "pyarrow", "scipy"] plotting = ["pandas[plot]", "plotly", "plotly-resampler"] -polars = ["polars"] +polars = ["numpy (<2)", "polars"] scalers = ["numba", "scipy"] [[package]] @@ -5558,18 +5751,18 @@ files = [ [[package]] name = "webcolors" -version = "1.13" +version = "24.6.0" description = "A library for working with the color formats defined by HTML and CSS." optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" files = [ - {file = "webcolors-1.13-py3-none-any.whl", hash = "sha256:29bc7e8752c0a1bd4a1f03c14d6e6a72e93d82193738fa860cbff59d0fcc11bf"}, - {file = "webcolors-1.13.tar.gz", hash = "sha256:c225b674c83fa923be93d235330ce0300373d02885cef23238813b0d5668304a"}, + {file = "webcolors-24.6.0-py3-none-any.whl", hash = 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-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] + +[extras] +cpu = ["Jax", "jax"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.11" -content-hash = "5b5898ee489089ecfb4258a3598877ccc2e458ee6c6029ebb0c9f58c0f2b16fa" +content-hash = "d792f3687131f21fc4929daa27c412fa6657b7c329ce3df5a6b92c3dbbc07b9f" diff --git a/pyproject.toml b/pyproject.toml index 0bac628..cbb7cd0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,25 +1,10 @@ -# This project can be installed with `python3 -m pip install -e .` from the main directory. - -[project] -name = "timesfm-jax" +[tool.poetry] +name = "timesfm" packages = [ { include = "*", from = "src" }, ] -version = "0.0.1" description = "Open weights time-series foundation model from Google Research." -version = "1.0.1" -dependencies = [ - "jax==0.4.26", - "paxml==1.4.0", - "praxis==1.4.0", - "jaxlib==0.4.26", - "numpy==1.26.4", - "pandas==2.1.4", - "einshape==1.0.0", - "utilsforecast==0.1.10", - "huggingface_hub[cli]==0.23.0", - "scikit-learn==1.5.1", -] +version = "0.0.12" authors = [ "Rajat Sen ", "Yichen Zhou ", @@ -49,9 +34,13 @@ einshape = ">=1.0.0" numpy = ">=1.26.4" pandas = ">=2.1.4" paxml = "1.4.0" -jax = "0.4.26" -jaxlib = "0.4.26" utilsforecast = "^0.1.10" +jax = {version = "0.4.26", extras = ["cuda12"]} +# unfortunately we have to solve it using https://stackoverflow.com/questions/72037181/how-to-add-optional-dependencies-of-a-library-as-extra-in-poetry-and-pyproject +Jax = {version = "0.4.26", extras = ["cpu"], optional = true} + +[tool.poetry.extras] +cpu = ['Jax'] [build-system] From 57d5cd08f7a56b293a3e5a05269e9a33bce32ec4 Mon Sep 17 00:00:00 2001 From: Rajat Sen Date: Fri, 12 Jul 2024 16:11:16 +0000 Subject: [PATCH 15/23] normalization changes to data loader --- src/timesfm/data_loader.py | 22 ++++++++-------------- 1 file changed, 8 insertions(+), 14 deletions(-) diff --git a/src/timesfm/data_loader.py b/src/timesfm/data_loader.py index eeace7b..d81b130 100644 --- a/src/timesfm/data_loader.py +++ b/src/timesfm/data_loader.py @@ -11,13 +11,11 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """TF dataloaders for general timeseries datasets. The expected input format is csv file with a datetime index. """ - from absl import logging import numpy as np import pandas as pd @@ -79,9 +77,8 @@ class TimeSeriesdata(object): self.data_df['ccol'] = np.zeros(self.data_df.shape[0]) cat_cov_cols = ['ccol'] self.data_df.fillna(0, inplace=True) - self.data_df.set_index( - pd.DatetimeIndex(self.data_df[datetime_col]), inplace=True - ) + self.data_df.set_index(pd.DatetimeIndex(self.data_df[datetime_col]), + inplace=True) self.num_cov_cols = num_cov_cols self.cat_cov_cols = cat_cov_cols self.ts_cols = ts_cols @@ -94,18 +91,16 @@ class TimeSeriesdata(object): data_df_idx[-1] + pd.Timedelta(1, freq=freq), periods=pred_len + 1, freq=freq, - ) - ) + )) self.time_df = time_features.TimeCovariates( - date_index, holiday=holiday - ).get_covariates() + date_index, holiday=holiday).get_covariates() self.hist_len = hist_len self.pred_len = pred_len self.batch_size = batch_size self.freq = freq self.normalize = normalize self.data_mat = self.data_df[self.ts_cols].to_numpy().transpose() - self.data_mat = self.data_mat[:, 0 : self.test_range[1]] + self.data_mat = self.data_mat[:, 0:self.test_range[1]] self.time_mat = self.time_df.to_numpy().transpose() self.num_feat_mat = self.data_df[num_cov_cols].to_numpy().transpose() self.cat_feat_mat, self.cat_sizes = self._get_cat_cols(cat_cov_cols) @@ -135,7 +130,7 @@ class TimeSeriesdata(object): def _normalize_data(self): self.scaler = StandardScaler() - train_mat = self.data_mat[:, self.train_range[0] : self.train_range[1]] + train_mat = self.data_mat[:, 0:self.train_range[1]] self.scaler = self.scaler.fit(train_mat.transpose()) self.data_mat = self.scaler.transform(self.data_mat.transpose()).transpose() @@ -253,9 +248,8 @@ class TimeSeriesdata(object): gen_fn = self.train_gen else: gen_fn = lambda: self.test_val_gen(mode, shift) - output_types = tuple( - [tf.float32] * 2 + [tf.int32] + [tf.float32] * 2 + [tf.int32] * 2 - ) + output_types = tuple([tf.float32] * 2 + [tf.int32] + [tf.float32] * 2 + + [tf.int32] * 2) dataset = tf.data.Dataset.from_generator(gen_fn, output_types) dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE) return dataset From ac92e0ea69be734c1cc9d5b37d6f462f2c383ee8 Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 12 Jul 2024 19:51:46 +0200 Subject: [PATCH 16/23] Restoring environment*.yml's, adding new dependencies, remove the [cli] option --- .github/workflows/main.yml | 6 +- environment.yml | 18 ++ environment_cpu.yml | 18 ++ poetry.lock | 457 +++++++++++++------------------------ pyproject.toml | 21 +- 5 files changed, 201 insertions(+), 319 deletions(-) create mode 100644 environment.yml create mode 100644 environment_cpu.yml diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 41c9e4e..800631b 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -21,9 +21,9 @@ jobs: python -m pip install --upgrade pip pip install poetry # use the hardcoded version number for now. if this is merged to the main repo, you can uncomment these lines to automatically tag the version number with the github run number - # - name: Set Version number - # run: | - # poetry version 0.0.${{ github.run_number }} + - name: Set Version number + run: | + poetry version 0.1.${{ github.run_number }} - name: Build and Publish to PyPI run: | poetry config pypi-token.pypi ${{ secrets.PYPI_API_TOKEN }} diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..7a1d6fe --- /dev/null +++ b/environment.yml @@ -0,0 +1,18 @@ +name: tfm_env + +channels: + - conda-forge + - defaults + - anaconda +dependencies: + - jupyterlab + - pip + - python=3.10 + - pip: + - huggingface_hub[cli] + - utilsforecast + - praxis + - paxml + - jax[cuda12]==0.4.26 + - einshape + - scikit-learn diff --git a/environment_cpu.yml b/environment_cpu.yml new file mode 100644 index 0000000..d808176 --- /dev/null +++ b/environment_cpu.yml @@ -0,0 +1,18 @@ +name: tfm_env + +channels: + - conda-forge + - defaults + - anaconda +dependencies: + - jupyterlab + 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-version = "0.1.12" +version = "0.1.10" description = "Forecasting utilities" optional = false python-versions = ">=3.8" files = [ - {file = "utilsforecast-0.1.12-py3-none-any.whl", hash = "sha256:728de990034b1fc27fe45be268e15154aa7bf32b62ab065646e11c4066da5e22"}, - {file = "utilsforecast-0.1.12.tar.gz", hash = "sha256:04f065158b70a343d60e197dc7973f24a22b82254a1b660e3f9b950d08e611ff"}, + {file = "utilsforecast-0.1.10-py3-none-any.whl", hash = "sha256:186cad81be70466a883a18c284ac1697118af6d896af1c0ab32fb4b124df7194"}, + {file = "utilsforecast-0.1.10.tar.gz", hash = "sha256:0f19ba507dcc642af190968268ea5407d31b7cfa7e4b9d81f9e9344c96069834"}, ] [package.dependencies] @@ -5733,9 +5585,9 @@ packaging = "*" pandas = ">=1.1.1" [package.extras] -dev = ["datasetsforecast (==0.0.8)", "nbdev", "numba", "numpy (<2)", "pandas[plot]", "plotly", "plotly-resampler", "polars", "pyarrow", "scipy"] +dev = ["datasetsforecast (==0.0.8)", "nbdev", "numba", "pandas[plot]", "plotly", "plotly-resampler", "polars", "pyarrow", "scipy"] plotting = ["pandas[plot]", "plotly", "plotly-resampler"] -polars = ["numpy (<2)", "polars"] +polars = ["polars"] scalers = ["numba", "scipy"] [[package]] @@ -5927,10 +5779,7 @@ files = [ doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] -[extras] -cpu = ["Jax", "jax"] - [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.11" -content-hash = "d792f3687131f21fc4929daa27c412fa6657b7c329ce3df5a6b92c3dbbc07b9f" +content-hash = "5af00562d4d4673c273707e744e7cb7ab12b7cbb003233b5d434bc9aeafb4538" diff --git a/pyproject.toml b/pyproject.toml index cbb7cd0..e3c63f0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,19 +30,16 @@ include = [ [tool.poetry.dependencies] python = ">=3.10,<3.11" -einshape = ">=1.0.0" -numpy = ">=1.26.4" -pandas = ">=2.1.4" +einshape = "1.0.0" +numpy = "1.26.4" +pandas = "2.1.4" paxml = "1.4.0" -utilsforecast = "^0.1.10" -jax = {version = "0.4.26", extras = ["cuda12"]} -# unfortunately we have to solve it using https://stackoverflow.com/questions/72037181/how-to-add-optional-dependencies-of-a-library-as-extra-in-poetry-and-pyproject -Jax = {version = "0.4.26", extras = ["cpu"], optional = true} - -[tool.poetry.extras] -cpu = ['Jax'] - +utilsforecast = "0.1.10" +jax = "0.4.26" +jaxlib = "0.4.26" +huggingface_hub = {version = "0.23.0", extras = ["cli"]} +scikit-learn = "1.5.1" [build-system] requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +build-backend = "poetry.core.masonry.api" \ No newline at end of file From d277f3bd69073d2558413c24f124762572295f5b Mon Sep 17 00:00:00 2001 From: Justin Guese Date: Fri, 12 Jul 2024 19:53:08 +0200 Subject: [PATCH 17/23] removing cpu/gpu package version hints from readme, instead going with "one package" phrasing --- README.md | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/README.md b/README.md index 3bdbd9e..5549b0c 100644 --- a/README.md +++ b/README.md @@ -34,14 +34,10 @@ Please look into the README files in the respective benchmark directories within ### Installation as a package -To install the GPU/default version of TimesFM as a package, you can run the following command without cloning this repo: +To install the TimesFM as a package, you can run the following command without cloning this repo: `pip install timesfm` -The package is currently using https://pypi.org/project/timesfm/. - -To install the CPU version of TimesFM, you can run the command `pip install timesfm[cpu]` - ### Local installation To from the current repository/local version (like you would have previously done with `pip -e .`), you can run the command @@ -51,8 +47,6 @@ pip install poetry # optional poetry install ``` -If you prefer the CPU version, run the command `poetry install -E cpu` - This will install the environment in the local .venv folder (depends on the configuration) and matches the python command to the poetry environment. If this is not the case, you can use `poetry run python` to use the local environment. ## Notes From 0a40da3c522df2c9811a2293b63eddfdbf3d0fde Mon Sep 17 00:00:00 2001 From: Yichen Zhou Date: Fri, 12 Jul 2024 13:42:46 -0700 Subject: [PATCH 18/23] Update pyproject.toml Fix jax dependency issue --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 91e1605..c4ec2e8 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ name = "timesfm-jax" description = "Open weights time-series foundation model from Google Research." version = "1.0.1" dependencies = [ - "jax==0.4.26", + "jax[cuda12]==0.4.26", "paxml==1.4.0", "praxis==1.4.0", "jaxlib==0.4.26", From e34e976efd7d479332e624915df70fa26c2aec09 Mon Sep 17 00:00:00 2001 From: Yichen Zhou Date: Fri, 12 Jul 2024 14:22:01 -0700 Subject: [PATCH 19/23] Update pyproject.toml fix scikit-learn version --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index c4ec2e8..e8cc3fd 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -14,7 +14,7 @@ dependencies = [ "einshape==1.0.0", "utilsforecast==0.1.10", "huggingface_hub[cli]==0.23.0", - "scikit-learn==1.5.1", + "scikit-learn==1.0.2", ] authors = [ {name = "Rajat Sen", email = "senrajat@google.com"}, From 4140bfa921100e2a36a25daf4bc12f9df681cfe5 Mon Sep 17 00:00:00 2001 From: Yichen Zhou Date: Fri, 12 Jul 2024 14:22:47 -0700 Subject: [PATCH 20/23] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index e8cc3fd..0b748bc 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,7 +1,7 @@ # This project can be installed with `python3 -m pip install -e .` from the main directory. [project] -name = "timesfm-jax" +name = "timesfm" description = "Open weights time-series foundation model from Google Research." version = "1.0.1" dependencies = [ From f3f8c4582aeae07c5dd34fa053df4a672078ce85 Mon Sep 17 00:00:00 2001 From: siriuz42 Date: Fri, 12 Jul 2024 22:21:28 +0000 Subject: [PATCH 21/23] tutorial for xreg --- notebooks/covariates.ipynb | 403 +++++++++++++++++++++++++++++++++++++ 1 file changed, 403 insertions(+) create mode 100644 notebooks/covariates.ipynb diff --git a/notebooks/covariates.ipynb b/notebooks/covariates.ipynb new file mode 100644 index 0000000..63bdf1d --- /dev/null +++ b/notebooks/covariates.ipynb @@ -0,0 +1,403 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TimesFM with Covariates\n", + "\n", + "This toturial notebook demonstrates how to utilize exogenous covariates with TimesFM when making forecasts. Before running this notebook, make sure:\n", + "\n", + "- You've read through the README of TimesFM.\n", + "- A local kernel with Python 3.10 is up and running." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup the environment and install TimesFM." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'\n", + "os.environ['JAX_PMAP_USE_TENSORSTORE'] = 'false'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install timesfm\n", + "import timesfm" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the checkpoint\n", + "\n", + "**Notice:** Please set up the backend as per your machine (\"cpu\", \"gpu\" or \"tpu\"). This notebook will run by default on CPU.\n", + "\n", + "We load the 1.0-200m model checkpoint from HuggingFace." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "timesfm_backend = \"cpu\" # @param\n", + "\n", + "from jax._src import config\n", + "config.update(\n", + " \"jax_platforms\", {\"cpu\": \"cpu\", \"gpu\": \"cuda\", \"tpu\": \"\"}[timesfm_backend]\n", + ")\n", + "\n", + "model = timesfm.TimesFm(\n", + " context_len=512,\n", + " horizon_len=128,\n", + " input_patch_len=32,\n", + " output_patch_len=128,\n", + " num_layers=20,\n", + " model_dims=1280,\n", + " backend=timesfm_backend,\n", + ")\n", + "model.load_from_checkpoint(repo_id=\"google/timesfm-1.0-200m\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Covariates\n", + "\n", + "Let's take a toy example of forecasting sales for a grocery store: \n", + "\n", + "**Task:** Given the observed the daily sales of this week (7 days), forecast the daily sales of next week (7 days).\n", + "\n", + "```\n", + "Product: ice cream\n", + "Daily_sales: [30, 30, 4, 5, 7, 8, 10]\n", + "Category: food\n", + "Base_price: 1.99\n", + "Weekday: [0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6]\n", + "Has_promotion: [Yes, Yes, No, No, No, Yes, Yes, No, No, No, No, No, No, No]\n", + "Daily_temperature: [31.0, 24.3, 19.4, 26.2, 24.6, 30.0, 31.1, 32.4, 30.9, 26.0, 25.0, 27.8, 29.5, 31.2]\n", + "```\n", + "\n", + "```\n", + "Product: sunscreen\n", + "Daily_sales: [5, 7, 12, 13, 5, 6, 10]\n", + "Category: skin product\n", + "Base_price: 29.99\n", + "Weekday: [0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6]\n", + "Has_promotion: [No, No, Yes, Yes, No, No, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes]\n", + "Daily_temperature: [31.0, 24.3, 19.4, 26.2, 24.6, 30.0, 31.1, 32.4, 30.9, 26.0, 25.0, 27.8, 29.5, 31.2]\n", + "```\n", + "\n", + "In this example, besides the `Daily_sales`, we also have covariates `Category`, `Base_price`, `Weekday`, `Has_promotion`, `Daily_temperature`. Let's introduce some concepts:\n", + "\n", + "**Static covariates** are covariates for each time series. \n", + "- In our example, `Category` is a **static categorical covariate**, \n", + "- `Base_price` is a **static numerical covariates**.\n", + "\n", + "**Dynamic covariates** are covaraites for each time stamps.\n", + "- Date / time related features can be usually treated as dynamic covariates.\n", + "- In our example, `Weekday` and `Has_promotion` are **dynamic categorical covariates**.\n", + "- `Daily_temperate` is a **dynamic numerical covariate**.\n", + "\n", + "**Notice:** Here we make it mandatory that the dynamic covariates need to cover both the forecasting context and horizon. For example, all dynamic covariates in the example have 14 values: the first 7 correspond to the observed 7 days, and the last 7 correspond to the next 7 days." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# TimesFM with Covariates\n", + "\n", + "\n", + "The strategy we take here is to treat covariates as batched in-context exogenous regressors (XReg) and fit linear models on them outside of TimesFM. The final forecast will be the sum of the TimesFM forecast and the linear model forecast.\n", + "\n", + " In simple words, we consider these two options.\n", + "\n", + "**Option 1:** Get the TimesFM forecast, and fit the linear model regressing the residuals on the covariates (\"timesfm + xreg\").\n", + "\n", + "**Option 2:** Fit the linear model of the time series itself on the covariates, then forecast the residuals using TimesFM (\"xreg + timesfm\").\n", + "\n", + "Let's take a code at the example of Electricity Price Forecasting (EPF). \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "from collections import defaultdict" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE.csv')\n", + "df['ds'] = pd.to_datetime(df['ds'])\n", + "df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This dataset has a few covariates beside the hourly target `y`:\n", + "\n", + "- `unique_id`: a static categorical covariate indicating the country.\n", + "- `gen_forecast`: a dynamic numerical covariate indicating the estimated electricity to be generated.\n", + "- `system_load`: the observed system load. Notice that this **CANNOT** be considered as a dynamic numerical covariate because we cannot know its values over the forecasting horizon in advance.\n", + "- `weekday`: a dynamic categorical covariate.\\\n", + "\n", + "Let's now make some forecasting tasks for TimesFM based on this dataset. For simplicity we create forecast contexts of 120 time points (hours) and forecast horizons of 24 time points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Data pipelining\n", + "def get_batched_data_fn(\n", + " batch_size: int = 128, \n", + " context_len: int = 120, \n", + " horizon_len: int = 24,\n", + "):\n", + " examples = defaultdict(list)\n", + "\n", + " num_examples = 0\n", + " for country in (\"FR\", \"BE\"):\n", + " sub_df = df[df[\"unique_id\"] == country]\n", + " for start in range(0, len(sub_df) - (context_len + horizon_len), horizon_len):\n", + " num_examples += 1\n", + " examples[\"country\"].append(country)\n", + " examples[\"inputs\"].append(sub_df[\"y\"][start:(context_end := start + context_len)].tolist())\n", + " examples[\"gen_forecast\"].append(sub_df[\"gen_forecast\"][start:context_end + horizon_len].tolist())\n", + " examples[\"week_day\"].append(sub_df[\"week_day\"][start:context_end + horizon_len].tolist())\n", + " examples[\"outputs\"].append(sub_df[\"y\"][context_end:(context_end + horizon_len)].tolist())\n", + " \n", + " def data_fn():\n", + " for i in range(1 + (num_examples - 1) // batch_size):\n", + " yield {k: v[(i * batch_size) : ((i + 1) * batch_size)] for k, v in examples.items()}\n", + " \n", + " return data_fn\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define metrics\n", + "def mse(y_pred, y_true):\n", + " y_pred = np.array(y_pred)\n", + " y_true = np.array(y_true)\n", + " return np.mean(np.square(y_pred - y_true), axis=1, keepdims=True)\n", + "\n", + "def mae(y_pred, y_true):\n", + " y_pred = np.array(y_pred)\n", + " y_true = np.array(y_true)\n", + " return np.mean(np.abs(y_pred - y_true), axis=1, keepdims=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's try `model.forecast_with_covariates`. \n", + "\n", + "In particular, the output is a tuple whose first element is the new forecast." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Benchmark\n", + "batch_size = 128\n", + "context_len = 120\n", + "horizon_len = 24\n", + "input_data = get_batched_data_fn(batch_size = 128)\n", + "metrics = defaultdict(list)\n", + "import time\n", + "\n", + "for i, example in enumerate(input_data()):\n", + " raw_forecast, _ = model.forecast(\n", + " inputs=example[\"inputs\"], freq=[0] * len(example[\"inputs\"])\n", + " )\n", + " start_time = time.time()\n", + " # Forecast with covariates\n", + " # Output: new forecast, forecast by the xreg\n", + " cov_forecast, ols_forecast = model.forecast_with_covariates( \n", + " inputs=example[\"inputs\"],\n", + " dynamic_numerical_covariates={\n", + " \"gen_forecast\": example[\"gen_forecast\"],\n", + " },\n", + " dynamic_categorical_covariates={\n", + " \"week_day\": example[\"week_day\"],\n", + " },\n", + " static_numerical_covariates={},\n", + " static_categorical_covariates={\n", + " \"country\": example[\"country\"]\n", + " },\n", + " freq=[0] * len(example[\"inputs\"]),\n", + " xreg_mode=\"xreg + timesfm\", # default\n", + " ridge=0.0,\n", + " force_on_cpu=False,\n", + " normalize_xreg_target_per_input=True, # default\n", + " )\n", + " print(\n", + " f\"\\rFinished batch {i} linear in {time.time() - start_time} seconds\",\n", + " end=\"\",\n", + " )\n", + " metrics[\"eval_mae_timesfm\"].extend(\n", + " mae(raw_forecast[:, :horizon_len], example[\"outputs\"])\n", + " )\n", + " metrics[\"eval_mae_xreg_timesfm\"].extend(mae(cov_forecast, example[\"outputs\"]))\n", + " metrics[\"eval_mae_xreg\"].extend(mae(ols_forecast, example[\"outputs\"]))\n", + " metrics[\"eval_mse_timesfm\"].extend(\n", + " mse(raw_forecast[:, :horizon_len], example[\"outputs\"])\n", + " )\n", + " metrics[\"eval_mse_xreg_timesfm\"].extend(mse(cov_forecast, example[\"outputs\"]))\n", + " metrics[\"eval_mse_xreg\"].extend(mse(ols_forecast, example[\"outputs\"]))\n", + "\n", + "print()\n", + "\n", + "for k, v in metrics.items():\n", + " print(f\"{k}: {np.mean(v)}\")\n", + "\n", + "# My output:\n", + "# eval_mae_timesfm: 6.762283045916956\n", + "# eval_mae_xreg_timesfm: 5.39219617611074\n", + "# eval_mae_xreg: 37.15275842572484\n", + "# eval_mse_timesfm: 166.7771466306823\n", + "# eval_mse_xreg_timesfm: 120.64757721021306\n", + "# eval_mse_xreg: 1672.2116821201796" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should see results close to \n", + "```\n", + "eval_mae_timesfm: 6.762283045916956\n", + "eval_mae_xreg_timesfm: 5.39219617611074\n", + "eval_mae_xreg: 37.15275842572484\n", + "eval_mse_timesfm: 166.7771466306823\n", + "eval_mse_xreg_timesfm: 120.64757721021306\n", + "eval_mse_xreg: 1672.2116821201796\n", + "```\n", + "\n", + "With the covariates, the TimesFM forecast Mean Absolute Error improves from 6.76 to 5.39, and Mean Squred Error from 166.78 to 120.65. The results of purely fitting the linear model are also provided for reference." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Formatting Your Request\n", + "\n", + "It is quite crucial to get the covariates properly formatted so that we can call this `model.forecast_with_covariates`. Please see its docstring for details. Here let's also grab a batch from a toy data input pipeline for quick explanations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "toy_input_pipeline = get_batched_data_fn(batch_size=2, context_len=5, horizon_len=2)\n", + "print(next(toy_input_pipeline()))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You should see something similar to this\n", + "```\n", + "{\n", + " 'country': ['FR', 'FR'], \n", + " 'inputs': [[53.48, 51.93, 48.76, 42.27, 38.41], [48.76, 42.27, 38.41, 35.72, 32.66]], \n", + " 'gen_forecast': [[76905.0, 75492.0, 74394.0, 72639.0, 69347.0, 67960.0, 67564.0], [74394.0, 72639.0, 69347.0, 67960.0, 67564.0, 67277.0, 67019.0]], \n", + " 'week_day': [[3, 3, 3, 3, 3, 3, 3], [3, 3, 3, 3, 3, 3, 3]], \n", + " 'outputs': [[35.72, 32.66], [32.83, 30.06]],\n", + "}\n", + "```\n", + "\n", + "Notice:\n", + "- We have two examples in this batch.\n", + "- For each example we support different context lengths and horizon lengths just as `model.forecast`. Although it is not demonstrated in this dataset.\n", + "- If dynamic covariates are present, the horizon lengths will be inferred from them, e.g. how many values are provided in additional to the ones corresponding to the inputs. Make sure all your dynamic covariates have the same length per example.\n", + "- The static covariates are one per example.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More Applications\n", + "\n", + "### Past Dynamic Covariates\n", + "\n", + "Past dynamic covariates are covariates that are only available for the context. For instance in our example `system_load` is a past dynamic covariate. Time series models generally can handle this, however it is something the batched in context regression cannot address, because these regressors are not available in the future. If you do have those covariates and consider them very meaningful, there are two hacky options to try immediately:\n", + "\n", + "1. Shift and repeat these past dynamic covariates to use their delayed version. For example, if you think the `system_load` for this week is meaningful for forecasting next week, you can create a `delay_7_system_load` by shifting 7 timestamps and use this as one dynamic numerical covariate for TimesFM.\n", + "2. Bootstrap, that is to run TimesFM once to forecast these past dynamic covariates into the horizon, then call TimesFM again using these forecasts as the future part for these dynamic covariates.\n", + "\n", + "### Multivariate Time Series\n", + "\n", + "For multivariate time series, if we need univariate forecast, we can try treating the main time series as the target and use the rest as the dynamic covariates." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cuda-gpt", + "language": "python", + "name": "cuda" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.14" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 83815d9fb1d5284833798138a5fb5cb6ae41333c Mon Sep 17 00:00:00 2001 From: Rajat Sen Date: Mon, 15 Jul 2024 18:03:50 +0000 Subject: [PATCH 22/23] update readme to reflect recent feature updates. --- README.md | 48 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) diff --git a/README.md b/README.md index c5a1c43..47db7c1 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,11 @@ This is not an officially supported Google product. We recommend at least 16GB RAM to load TimesFM dependencies. +## Update - July 15, 2024 + +- Launched [finetuning support](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning.ipynb) that lets you finetune the weights of the pretrained TimesFM model on your own data. +- Launched [~zero-shot covariate support](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb) with external regressors. + ## Checkpoint timesfm-1.0-200m timesfm-1.0-200m is the first open model checkpoint: @@ -195,6 +200,49 @@ forecast_df = tfm.forecast_on_df( ) ``` +## Covariates Support + +We now have an external regressors library on top of TimesFM that can support static covariates as well as dynamic covariates available in the future. We have an usage example in `notebooks/covariates.ipynb`. + +Let's take a toy example of forecasting sales for a grocery store: + +**Task:** Given the observed the daily sales of this week (7 days), forecast the daily sales of next week (7 days). + +``` +Product: ice cream +Daily_sales: [30, 30, 4, 5, 7, 8, 10] +Category: food +Base_price: 1.99 +Weekday: [0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6] +Has_promotion: [Yes, Yes, No, No, No, Yes, Yes, No, No, No, No, No, No, No] +Daily_temperature: [31.0, 24.3, 19.4, 26.2, 24.6, 30.0, 31.1, 32.4, 30.9, 26.0, 25.0, 27.8, 29.5, 31.2] +``` + +``` +Product: sunscreen +Daily_sales: [5, 7, 12, 13, 5, 6, 10] +Category: skin product +Base_price: 29.99 +Weekday: [0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6] +Has_promotion: [No, No, Yes, Yes, No, No, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes] +Daily_temperature: [31.0, 24.3, 19.4, 26.2, 24.6, 30.0, 31.1, 32.4, 30.9, 26.0, 25.0, 27.8, 29.5, 31.2] +``` + +In this example, besides the `Daily_sales`, we also have covariates `Category`, `Base_price`, `Weekday`, `Has_promotion`, `Daily_temperature`. Let's introduce some concepts: + +**Static covariates** are covariates for each time series. +- In our example, `Category` is a **static categorical covariate**, +- `Base_price` is a **static numerical covariates**. + +**Dynamic covariates** are covaraites for each time stamps. +- Date / time related features can be usually treated as dynamic covariates. +- In our example, `Weekday` and `Has_promotion` are **dynamic categorical covariates**. +- `Daily_temperate` is a **dynamic numerical covariate**. + +**Notice:** Here we make it mandatory that the dynamic covariates need to cover both the forecasting context and horizon. For example, all dynamic covariates in the example have 14 values: the first 7 correspond to the observed 7 days, and the last 7 correspond to the next 7 days. + +We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produc forecasts that take into the account the covariates. To learn more, check out the example in `notebooks/covariates.ipynb`. + ## Finetuning We have provided an example of finetuning the model on a new dataset in `notebooks/finetuning.ipynb`. From 23f7b64f8c5d3f721b74126331c7711cd8f5ab03 Mon Sep 17 00:00:00 2001 From: Rajat Sen Date: Mon, 15 Jul 2024 18:36:58 +0000 Subject: [PATCH 23/23] minor update to the readme. --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 47db7c1..0191b86 100644 --- a/README.md +++ b/README.md @@ -19,7 +19,7 @@ We recommend at least 16GB RAM to load TimesFM dependencies. ## Update - July 15, 2024 - Launched [finetuning support](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning.ipynb) that lets you finetune the weights of the pretrained TimesFM model on your own data. -- Launched [~zero-shot covariate support](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb) with external regressors. +- Launched [~zero-shot covariate support](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb) with external regressors. More details [here](https://github.com/google-research/timesfm?tab=readme-ov-file#covariates-support). ## Checkpoint timesfm-1.0-200m @@ -241,7 +241,7 @@ In this example, besides the `Daily_sales`, we also have covariates `Category`, **Notice:** Here we make it mandatory that the dynamic covariates need to cover both the forecasting context and horizon. For example, all dynamic covariates in the example have 14 values: the first 7 correspond to the observed 7 days, and the last 7 correspond to the next 7 days. -We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produc forecasts that take into the account the covariates. To learn more, check out the example in `notebooks/covariates.ipynb`. +We can now provide the past data of the two products along with static and dynamic covariates as a batch input to TimesFM and produce forecasts that take into the account the covariates. To learn more, check out the example in `notebooks/covariates.ipynb`. ## Finetuning