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Merge branch 'master' into feature/lora

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Tanmay Shishodia
2024-08-03 11:00:58 +05:30
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README.md
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@@ -18,6 +18,7 @@ We recommend at least 16GB RAM to load TimesFM dependencies.
## Update - July 15, 2024 ## Update - July 15, 2024
- To install TimesFM, you can now simply do: `pip install timesfm`.
- 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 [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. More details [here](https://github.com/google-research/timesfm?tab=readme-ov-file#covariates-support). - 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).
@@ -202,7 +203,7 @@ forecast_df = tfm.forecast_on_df(
## Covariates Support ## 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`. 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](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb).
Let's take a toy example of forecasting sales for a grocery store: Let's take a toy example of forecasting sales for a grocery store:
@@ -241,11 +242,11 @@ 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. **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 produce 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](https://github.com/google-research/timesfm/blob/master/notebooks/covariates.ipynb).
## Finetuning ## Finetuning
We have provided an example of finetuning the model on a new dataset in `notebooks/finetuning.ipynb`. We have provided an example of finetuning the model on a new dataset in [notebooks/finetuning.ipynb](https://github.com/google-research/timesfm/blob/master/notebooks/finetuning.ipynb).
## Contribution Style guide ## Contribution Style guide
poetry.lock
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@@ -3682,13 +3687,13 @@ testing = ["pytest", "pytest-benchmark"]
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all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"] all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
benchmark = ["optimum-benchmark (>=0.2.0)"]
codecarbon = ["codecarbon (==1.2.0)"] codecarbon = ["codecarbon (==1.2.0)"]
deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"] deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "scipy (<1.13.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"] dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"] dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm (<=0.9.16)", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"] flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)", "scipy (<1.13.0)"]
flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"] flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
ftfy = ["ftfy"] ftfy = ["ftfy"]
@@ -5924,25 +5931,26 @@ natten = ["natten (>=0.14.6,<0.15.0)"]
onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"] onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"] onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
optuna = ["optuna"] 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)"] ray = ["ray[tune] (>=2.7.0)"]
retrieval = ["datasets (!=2.5.0)", "faiss-cpu"] retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
ruff = ["ruff (==0.4.4)"]
sagemaker = ["sagemaker (>=2.31.0)"] sagemaker = ["sagemaker (>=2.31.0)"]
sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"] sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
serving = ["fastapi", "pydantic", "starlette", "uvicorn"] serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
sigopt = ["sigopt"] sigopt = ["sigopt"]
sklearn = ["scikit-learn"] sklearn = ["scikit-learn"]
speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"] testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "nltk", "parameterized", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-rich", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.4.4)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"] tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>2.9,<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 (<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)"] tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
timm = ["timm"] timm = ["timm (<=0.9.16)"]
tokenizers = ["tokenizers (>=0.19,<0.20)"] tokenizers = ["tokenizers (>=0.19,<0.20)"]
torch = ["accelerate (>=0.21.0)", "torch"] torch = ["accelerate (>=0.21.0)", "torch"]
torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"] torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"] 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)"] video = ["av (==9.2.0)", "decord (==0.6.0)"]
vision = ["Pillow (>=10.0.1,<=15.0)"] vision = ["Pillow (>=10.0.1,<=15.0)"]
@@ -6044,13 +6052,13 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]] [[package]]
name = "utilsforecast" name = "utilsforecast"
version = "0.1.10" version = "0.2.0"
description = "Forecasting utilities" description = "Forecasting utilities"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "utilsforecast-0.1.10-py3-none-any.whl", hash = "sha256:186cad81be70466a883a18c284ac1697118af6d896af1c0ab32fb4b124df7194"}, {file = "utilsforecast-0.2.0-py3-none-any.whl", hash = "sha256:a4825bf8da547e3dc552f9b9a7a8159341a118c3a5d122191f09bc3683cba433"},
{file = "utilsforecast-0.1.10.tar.gz", hash = "sha256:0f19ba507dcc642af190968268ea5407d31b7cfa7e4b9d81f9e9344c96069834"}, {file = "utilsforecast-0.2.0.tar.gz", hash = "sha256:3db4245da4e361f26c8eaeef216c2d1206b20defbb033bf11d3e66ce2b1d6ef8"},
] ]
[package.dependencies] [package.dependencies]
@@ -6059,10 +6067,9 @@ packaging = "*"
pandas = ">=1.1.1" pandas = ">=1.1.1"
[package.extras] [package.extras]
dev = ["datasetsforecast (==0.0.8)", "nbdev", "numba", "pandas[plot]", "plotly", "plotly-resampler", "polars", "pyarrow", "scipy"] dev = ["datasetsforecast (==0.0.8)", "nbdev", "pandas[plot]", "plotly", "plotly-resampler", "polars[numpy]", "pyarrow", "scipy"]
plotting = ["pandas[plot]", "plotly", "plotly-resampler"] plotting = ["pandas[plot]", "plotly", "plotly-resampler"]
polars = ["polars"] polars = ["polars[numpy]"]
scalers = ["numba", "scipy"]
[[package]] [[package]]
name = "wandb" name = "wandb"
pyproject.toml
+10 -10
View File
@@ -1,7 +1,7 @@
[tool.poetry] [tool.poetry]
name = "timesfm" name = "timesfm"
packages = [ packages = [
{ include = "*", from = "src" }, { include = "timesfm", from = "src" },
] ]
description = "Open weights time-series foundation model from Google Research." description = "Open weights time-series foundation model from Google Research."
version = "1.0.1" version = "1.0.1"
@@ -30,15 +30,15 @@ include = [
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = ">=3.10,<3.11" python = ">=3.10,<3.11"
einshape = "1.0.0" einshape = ">=1.0.0"
numpy = "1.26.4" numpy = ">=1.26.4"
pandas = "2.1.4" pandas = ">=2.1.4"
paxml = "1.4.0" paxml = ">=1.4.0"
utilsforecast = "0.1.10" utilsforecast = ">=0.1.10"
jax = {version = "0.4.26", extras = ["cuda12"]} jax = {version = ">=0.4.26", extras = ["cuda12"]}
jaxlib = "0.4.26" jaxlib = ">=0.4.26"
huggingface_hub = {version = "0.23.0", extras = ["cli"]} huggingface_hub = {version = ">=0.23.0", extras = ["cli"]}
scikit-learn = "1.0.2" scikit-learn = ">=1.2.2"
typer = "^0.12.3" typer = "^0.12.3"
wandb = "^0.17.5" wandb = "^0.17.5"
src/timesfm/xreg_lib.py
+53 -99
View File
@@ -11,7 +11,6 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and # See the License for the specific language governing permissions and
# limitations under the License. # limitations under the License.
"""Helper functions for in-context covariates and regression.""" """Helper functions for in-context covariates and regression."""
import itertools import itertools
@@ -36,9 +35,8 @@ def _unnest(nested: Sequence[Sequence[Any]]) -> np.ndarray:
def _repeat(elements: Iterable[Any], counts: Iterable[int]) -> np.ndarray: def _repeat(elements: Iterable[Any], counts: Iterable[int]) -> np.ndarray:
return np.array( return np.array(
list( list(
itertools.chain.from_iterable(map(itertools.repeat, elements, counts)) itertools.chain.from_iterable(map(itertools.repeat, elements,
) counts))))
)
def _to_padded_jax_array(x: np.ndarray) -> jax.Array: def _to_padded_jax_array(x: np.ndarray) -> jax.Array:
@@ -86,21 +84,16 @@ class BatchedInContextXRegBase:
train_lens: Sequence[int], train_lens: Sequence[int],
test_lens: Sequence[int], test_lens: Sequence[int],
train_dynamic_numerical_covariates: ( train_dynamic_numerical_covariates: (
Mapping[str, Sequence[Sequence[float]]] | None Mapping[str, Sequence[Sequence[float]]] | None) = None,
) = None,
train_dynamic_categorical_covariates: ( train_dynamic_categorical_covariates: (
Mapping[str, Sequence[Sequence[Category]]] | None Mapping[str, Sequence[Sequence[Category]]] | None) = None,
) = None,
test_dynamic_numerical_covariates: ( test_dynamic_numerical_covariates: (
Mapping[str, Sequence[Sequence[float]]] | None Mapping[str, Sequence[Sequence[float]]] | None) = None,
) = None,
test_dynamic_categorical_covariates: ( test_dynamic_categorical_covariates: (
Mapping[str, Sequence[Sequence[Category]]] | None Mapping[str, Sequence[Sequence[Category]]] | None) = None,
) = None,
static_numerical_covariates: Mapping[str, Sequence[float]] | None = None, static_numerical_covariates: Mapping[str, Sequence[float]] | None = None,
static_categorical_covariates: ( static_categorical_covariates: (Mapping[str, Sequence[Category]] |
Mapping[str, Sequence[Category]] | None None) = None,
) = None,
) -> None: ) -> None:
"""Initializes with the exogenous covariate inputs. """Initializes with the exogenous covariate inputs.
@@ -187,17 +180,13 @@ class BatchedInContextXRegBase:
self.train_lens = train_lens self.train_lens = train_lens
self.test_lens = test_lens self.test_lens = test_lens
self.train_dynamic_numerical_covariates = ( self.train_dynamic_numerical_covariates = (
train_dynamic_numerical_covariates or {} train_dynamic_numerical_covariates or {})
)
self.train_dynamic_categorical_covariates = ( self.train_dynamic_categorical_covariates = (
train_dynamic_categorical_covariates or {} train_dynamic_categorical_covariates or {})
) self.test_dynamic_numerical_covariates = (test_dynamic_numerical_covariates
self.test_dynamic_numerical_covariates = ( or {})
test_dynamic_numerical_covariates or {}
)
self.test_dynamic_categorical_covariates = ( self.test_dynamic_categorical_covariates = (
test_dynamic_categorical_covariates or {} test_dynamic_categorical_covariates or {})
)
self.static_numerical_covariates = static_numerical_covariates or {} self.static_numerical_covariates = static_numerical_covariates or {}
self.static_categorical_covariates = static_categorical_covariates or {} self.static_categorical_covariates = static_categorical_covariates or {}
@@ -205,31 +194,23 @@ class BatchedInContextXRegBase:
"""Verifies the validity of the covariate inputs.""" """Verifies the validity of the covariate inputs."""
# Check presence. # Check presence.
if ( if (self.train_dynamic_numerical_covariates and
self.train_dynamic_numerical_covariates not self.test_dynamic_numerical_covariates) or (
and not self.test_dynamic_numerical_covariates not self.train_dynamic_numerical_covariates and
) or ( self.test_dynamic_numerical_covariates):
not self.train_dynamic_numerical_covariates
and self.test_dynamic_numerical_covariates
):
raise ValueError( raise ValueError(
"train_dynamic_numerical_covariates and" "train_dynamic_numerical_covariates and"
" test_dynamic_numerical_covariates must be both present or both" " test_dynamic_numerical_covariates must be both present or both"
" absent." " absent.")
)
if ( if (self.train_dynamic_categorical_covariates and
self.train_dynamic_categorical_covariates not self.test_dynamic_categorical_covariates) or (
and not self.test_dynamic_categorical_covariates not self.train_dynamic_categorical_covariates and
) or ( self.test_dynamic_categorical_covariates):
not self.train_dynamic_categorical_covariates
and self.test_dynamic_categorical_covariates
):
raise ValueError( raise ValueError(
"train_dynamic_categorical_covariates and" "train_dynamic_categorical_covariates and"
" test_dynamic_categorical_covariates must be both present or both" " test_dynamic_categorical_covariates must be both present or both"
" absent." " absent.")
)
# Check keys. # Check keys.
for dict_a, dict_b, dict_a_name, dict_b_name in ( for dict_a, dict_b, dict_a_name, dict_b_name in (
@@ -248,46 +229,38 @@ class BatchedInContextXRegBase:
): ):
if w := set(dict_a.keys()) - set(dict_b.keys()): if w := set(dict_a.keys()) - set(dict_b.keys()):
raise ValueError( raise ValueError(
f"{dict_a_name} has keys not present in {dict_b_name}: {w}" f"{dict_a_name} has keys not present in {dict_b_name}: {w}")
)
if w := set(dict_b.keys()) - set(dict_a.keys()): if w := set(dict_b.keys()) - set(dict_a.keys()):
raise ValueError( raise ValueError(
f"{dict_b_name} has keys not present in {dict_a_name}: {w}" f"{dict_b_name} has keys not present in {dict_a_name}: {w}")
)
# Check shapes. # Check shapes.
if assert_covariate_shapes: if assert_covariate_shapes:
if len(self.targets) != len(self.train_lens): if len(self.targets) != len(self.train_lens):
raise ValueError( raise ValueError(
"targets and train_lens must have the same number of elements." "targets and train_lens must have the same number of elements.")
)
if len(self.train_lens) != len(self.test_lens): if len(self.train_lens) != len(self.test_lens):
raise ValueError( raise ValueError(
"train_lens and test_lens must have the same number of elements." "train_lens and test_lens must have the same number of elements.")
)
for i, (target, train_len) in enumerate( for i, (target, train_len) in enumerate(zip(self.targets,
zip(self.targets, self.train_lens) self.train_lens)):
):
if len(target) != train_len: if len(target) != train_len:
raise ValueError( raise ValueError(
f"targets[{i}] has length {len(target)} != expected {train_len}." f"targets[{i}] has length {len(target)} != expected {train_len}.")
)
for key, values in self.static_numerical_covariates.items(): for key, values in self.static_numerical_covariates.items():
if len(values) != len(self.train_lens): if len(values) != len(self.train_lens):
raise ValueError( raise ValueError(
f"static_numerical_covariates has key {key} with number of" f"static_numerical_covariates has key {key} with number of"
f" examples {len(values)} != expected {len(self.train_lens)}." f" examples {len(values)} != expected {len(self.train_lens)}.")
)
for key, values in self.static_categorical_covariates.items(): for key, values in self.static_categorical_covariates.items():
if len(values) != len(self.train_lens): if len(values) != len(self.train_lens):
raise ValueError( raise ValueError(
f"static_categorical_covariates has key {key} with number of" f"static_categorical_covariates has key {key} with number of"
f" examples {len(values)} != expected {len(self.train_lens)}." f" examples {len(values)} != expected {len(self.train_lens)}.")
)
for lens, dict_cov, dict_cov_name in ( for lens, dict_cov, dict_cov_name in (
( (
@@ -315,14 +288,12 @@ class BatchedInContextXRegBase:
if len(cov_values) != len(lens): if len(cov_values) != len(lens):
raise ValueError( raise ValueError(
f"{dict_cov_name} has key {key} with number of examples" f"{dict_cov_name} has key {key} with number of examples"
f" {len(cov_values)} != expected {len(lens)}." f" {len(cov_values)} != expected {len(lens)}.")
)
for i, cov_value in enumerate(cov_values): for i, cov_value in enumerate(cov_values):
if len(cov_value) != lens[i]: if len(cov_value) != lens[i]:
raise ValueError( raise ValueError(
f"{dict_cov_name} has key {key} with its {i}-th example" f"{dict_cov_name} has key {key} with its {i}-th example"
f" length {len(cov_value)} != expected {lens[i]}." f" length {len(cov_value)} != expected {lens[i]}.")
)
def create_covariate_matrix( def create_covariate_matrix(
self, self,
@@ -356,11 +327,9 @@ class BatchedInContextXRegBase:
# Numerical features. # Numerical features.
for name in sorted(self.train_dynamic_numerical_covariates): for name in sorted(self.train_dynamic_numerical_covariates):
x_train.append( x_train.append(
_unnest(self.train_dynamic_numerical_covariates[name])[:, np.newaxis] _unnest(self.train_dynamic_numerical_covariates[name])[:, np.newaxis])
)
x_test.append( x_test.append(
_unnest(self.test_dynamic_numerical_covariates[name])[:, np.newaxis] _unnest(self.test_dynamic_numerical_covariates[name])[:, np.newaxis])
)
for covs in self.static_numerical_covariates.values(): for covs in self.static_numerical_covariates.values():
x_train.append(_repeat(covs, self.train_lens)[:, np.newaxis]) x_train.append(_repeat(covs, self.train_lens)[:, np.newaxis])
@@ -372,25 +341,22 @@ class BatchedInContextXRegBase:
# Normalize for robustness. # Normalize for robustness.
x_mean = np.mean(x_train, axis=0, keepdims=True) x_mean = np.mean(x_train, axis=0, keepdims=True)
x_std = np.where( x_std = np.where((w := np.std(x_train, axis=0, keepdims=True)) > _TOL, w,
(w := np.std(x_train, axis=0, keepdims=True)) > _TOL, w, 1.0 1.0)
)
x_train = [(x_train - x_mean) / x_std] x_train = [(x_train - x_mean) / x_std]
x_test = [(x_test - x_mean) / x_std] x_test = [(x_test - x_mean) / x_std]
# Categorical features. Encode one by one. # Categorical features. Encode one by one.
one_hot_encoder = preprocessing.OneHotEncoder( one_hot_encoder = preprocessing.OneHotEncoder(
drop=one_hot_encoder_drop, drop=one_hot_encoder_drop,
sparse=False, sparse_output=False,
handle_unknown="ignore", handle_unknown="ignore",
) )
for name in sorted(self.train_dynamic_categorical_covariates.keys()): for name in sorted(self.train_dynamic_categorical_covariates.keys()):
ohe_train = _unnest(self.train_dynamic_categorical_covariates[name])[ ohe_train = _unnest(
:, np.newaxis self.train_dynamic_categorical_covariates[name])[:, np.newaxis]
] ohe_test = _unnest(
ohe_test = _unnest(self.test_dynamic_categorical_covariates[name])[ self.test_dynamic_categorical_covariates[name])[:, np.newaxis]
:, np.newaxis
]
x_train.append(np.array(one_hot_encoder.fit_transform(ohe_train))) x_train.append(np.array(one_hot_encoder.fit_transform(ohe_train)))
x_test.append(np.array(one_hot_encoder.transform(ohe_test))) x_test.append(np.array(one_hot_encoder.transform(ohe_test)))
@@ -426,12 +392,8 @@ class BatchedInContextXRegLinear(BatchedInContextXRegBase):
debug_info: bool = False, debug_info: bool = False,
assert_covariates: bool = False, assert_covariates: bool = False,
assert_covariate_shapes: bool = False, assert_covariate_shapes: bool = False,
) -> ( ) -> (list[np.ndarray] | tuple[list[np.ndarray], list[np.ndarray], jax.Array,
list[np.ndarray] jax.Array, jax.Array]):
| tuple[
list[np.ndarray], list[np.ndarray], jax.Array, jax.Array, jax.Array
]
):
"""Fits a linear model for in-context regression. """Fits a linear model for in-context regression.
Args: Args:
@@ -495,14 +457,10 @@ class BatchedInContextXRegLinear(BatchedInContextXRegBase):
x_train = _to_padded_jax_array(x_train) x_train = _to_padded_jax_array(x_train)
flat_targets = _to_padded_jax_array(flat_targets) flat_targets = _to_padded_jax_array(flat_targets)
x_test = _to_padded_jax_array(x_test) x_test = _to_padded_jax_array(x_test)
beta_hat = ( beta_hat = (jnp.linalg.pinv(
jnp.linalg.pinv(
x_train.T @ x_train + ridge * jnp.eye(x_train.shape[1]), x_train.T @ x_train + ridge * jnp.eye(x_train.shape[1]),
hermitian=True, hermitian=True,
) ) @ x_train.T @ flat_targets)
@ x_train.T
@ flat_targets
)
y_hat = x_test @ beta_hat y_hat = x_test @ beta_hat
y_hat_context = x_train_raw @ beta_hat if debug_info else None y_hat_context = x_train_raw @ beta_hat if debug_info else None
@@ -511,18 +469,14 @@ class BatchedInContextXRegLinear(BatchedInContextXRegBase):
# Reconstruct the ragged 2-dim batched forecasts from flattened linear fits. # Reconstruct the ragged 2-dim batched forecasts from flattened linear fits.
train_index, test_index = 0, 0 train_index, test_index = 0, 0
for train_index_delta, test_index_delta in zip( for train_index_delta, test_index_delta in zip(self.train_lens,
self.train_lens, self.test_lens self.test_lens):
): outputs.append(np.array(y_hat[test_index:(test_index +
outputs.append( test_index_delta)]))
np.array(y_hat[test_index : (test_index + test_index_delta)])
)
if debug_info: if debug_info:
outputs_context.append( outputs_context.append(
np.array( np.array(y_hat_context[train_index:(train_index +
y_hat_context[train_index : (train_index + train_index_delta)] train_index_delta)]))
)
)
train_index += train_index_delta train_index += train_index_delta
test_index += test_index_delta test_index += test_index_delta