Merge pull request #112 from google-research/rajat_dev
Upgrading versions and minor change to xreg.
This commit is contained in:
Rajat Sen
Generated
+118
-120
@@ -1414,13 +1414,13 @@ socks = ["socksio (==1.*)"]
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[[package]]
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name = "huggingface-hub"
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version = "0.23.0"
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version = "0.24.0"
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description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
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optional = false
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python-versions = ">=3.8.0"
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files = [
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{file = "huggingface_hub-0.23.0-py3-none-any.whl", hash = "sha256:075c30d48ee7db2bba779190dc526d2c11d422aed6f9044c5e2fdc2c432fdb91"},
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{file = "huggingface_hub-0.23.0.tar.gz", hash = "sha256:7126dedd10a4c6fac796ced4d87a8cf004efc722a5125c2c09299017fa366fa9"},
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{file = "huggingface_hub-0.24.0-py3-none-any.whl", hash = "sha256:7ad92edefb93d8145c061f6df8d99df2ff85f8379ba5fac8a95aca0642afa5d7"},
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{file = "huggingface_hub-0.24.0.tar.gz", hash = "sha256:6c7092736b577d89d57b3cdfea026f1b0dc2234ae783fa0d59caf1bf7d52dfa7"},
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]
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[package.dependencies]
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@@ -1434,17 +1434,17 @@ tqdm = ">=4.42.1"
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typing-extensions = ">=3.7.4.3"
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[package.extras]
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all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
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all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
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cli = ["InquirerPy (==0.3.4)"]
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dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
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dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.5.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
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fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
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hf-transfer = ["hf-transfer (>=0.1.4)"]
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inference = ["aiohttp", "minijinja (>=1.0)"]
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quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
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quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
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tensorflow = ["graphviz", "pydot", "tensorflow"]
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tensorflow-testing = ["keras (<3.0)", "tensorflow"]
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testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
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torch = ["safetensors", "torch"]
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testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest (>=8.1.1,<8.2.2)", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-mock", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
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torch = ["safetensors[torch]", "torch"]
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typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
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[[package]]
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@@ -3344,67 +3344,72 @@ files = [
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[[package]]
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name = "pandas"
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version = "2.1.4"
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version = "2.2.2"
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description = "Powerful data structures for data analysis, time series, and statistics"
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optional = false
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python-versions = ">=3.9"
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files = [
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]
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[package.dependencies]
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numpy = {version = ">=1.22.4,<2", markers = "python_version < \"3.11\""}
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numpy = {version = ">=1.22.4", markers = "python_version < \"3.11\""}
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python-dateutil = ">=2.8.2"
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pytz = ">=2020.1"
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tzdata = ">=2022.1"
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tzdata = ">=2022.7"
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[package.extras]
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all = ["PyQt5 (>=5.15.6)", "SQLAlchemy (>=1.4.36)", "beautifulsoup4 (>=4.11.1)", "bottleneck (>=1.3.4)", "dataframe-api-compat (>=0.1.7)", "fastparquet (>=0.8.1)", "fsspec (>=2022.05.0)", "gcsfs (>=2022.05.0)", "html5lib (>=1.1)", "hypothesis (>=6.46.1)", "jinja2 (>=3.1.2)", "lxml (>=4.8.0)", "matplotlib (>=3.6.1)", "numba (>=0.55.2)", "numexpr (>=2.8.0)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.10)", "pandas-gbq (>=0.17.5)", "psycopg2 (>=2.9.3)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.5)", "pytest (>=7.3.2)", "pytest-xdist (>=2.2.0)", "pyxlsb (>=1.0.9)", "qtpy (>=2.2.0)", "s3fs (>=2022.05.0)", "scipy (>=1.8.1)", "tables (>=3.7.0)", "tabulate (>=0.8.10)", "xarray (>=2022.03.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=3.0.3)", "zstandard (>=0.17.0)"]
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aws = ["s3fs (>=2022.05.0)"]
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clipboard = ["PyQt5 (>=5.15.6)", "qtpy (>=2.2.0)"]
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compression = ["zstandard (>=0.17.0)"]
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computation = ["scipy (>=1.8.1)", "xarray (>=2022.03.0)"]
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||||
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[[package]]
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@@ -4617,56 +4622,48 @@ torch = ["safetensors[numpy]", "torch (>=1.10)"]
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[[package]]
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[package.extras]
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|
||||
tests = ["black (>=24.3.0)", "matplotlib (>=3.3.4)", "mypy (>=1.9)", "numpydoc (>=1.2.0)", "pandas (>=1.1.5)", "polars (>=0.20.23)", "pooch (>=1.6.0)", "pyamg (>=4.0.0)", "pyarrow (>=12.0.0)", "pytest (>=7.1.2)", "pytest-cov (>=2.9.0)", "ruff (>=0.2.1)", "scikit-image (>=0.17.2)"]
|
||||
|
||||
[[package]]
|
||||
name = "scipy"
|
||||
@@ -5617,19 +5614,19 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
|
||||
|
||||
[[package]]
|
||||
name = "transformers"
|
||||
version = "4.41.2"
|
||||
version = "4.42.4"
|
||||
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
||||
files = [
|
||||
{file = "transformers-4.41.2-py3-none-any.whl", hash = "sha256:05555d20e43f808de1ef211ab64803cdb513170cef70d29a888b589caebefc67"},
|
||||
{file = "transformers-4.41.2.tar.gz", hash = "sha256:80a4db216533d573e9cc7388646c31ed9480918feb7c55eb211249cb23567f87"},
|
||||
{file = "transformers-4.42.4-py3-none-any.whl", hash = "sha256:6d59061392d0f1da312af29c962df9017ff3c0108c681a56d1bc981004d16d24"},
|
||||
{file = "transformers-4.42.4.tar.gz", hash = "sha256:f956e25e24df851f650cb2c158b6f4352dfae9d702f04c113ed24fc36ce7ae2d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
filelock = "*"
|
||||
huggingface-hub = ">=0.23.0,<1.0"
|
||||
numpy = ">=1.17"
|
||||
huggingface-hub = ">=0.23.2,<1.0"
|
||||
numpy = ">=1.17,<2.0"
|
||||
packaging = ">=20.0"
|
||||
pyyaml = ">=5.1"
|
||||
regex = "!=2019.12.17"
|
||||
@@ -5641,14 +5638,15 @@ tqdm = ">=4.27"
|
||||
[package.extras]
|
||||
accelerate = ["accelerate (>=0.21.0)"]
|
||||
agents = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch"]
|
||||
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)"]
|
||||
benchmark = ["optimum-benchmark (>=0.2.0)"]
|
||||
codecarbon = ["codecarbon (==1.2.0)"]
|
||||
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"]
|
||||
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-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-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)"]
|
||||
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 (==0.4.4)", "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 (==0.4.4)", "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 (==0.4.4)", "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-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
|
||||
ftfy = ["ftfy"]
|
||||
@@ -5659,25 +5657,26 @@ 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 (<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-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)"]
|
||||
|
||||
@@ -5762,13 +5761,13 @@ zstd = ["zstandard (>=0.18.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "utilsforecast"
|
||||
version = "0.1.10"
|
||||
version = "0.2.0"
|
||||
description = "Forecasting utilities"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "utilsforecast-0.1.10-py3-none-any.whl", hash = "sha256:186cad81be70466a883a18c284ac1697118af6d896af1c0ab32fb4b124df7194"},
|
||||
{file = "utilsforecast-0.1.10.tar.gz", hash = "sha256:0f19ba507dcc642af190968268ea5407d31b7cfa7e4b9d81f9e9344c96069834"},
|
||||
{file = "utilsforecast-0.2.0-py3-none-any.whl", hash = "sha256:a4825bf8da547e3dc552f9b9a7a8159341a118c3a5d122191f09bc3683cba433"},
|
||||
{file = "utilsforecast-0.2.0.tar.gz", hash = "sha256:3db4245da4e361f26c8eaeef216c2d1206b20defbb033bf11d3e66ce2b1d6ef8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -5777,10 +5776,9 @@ packaging = "*"
|
||||
pandas = ">=1.1.1"
|
||||
|
||||
[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"]
|
||||
polars = ["polars"]
|
||||
scalers = ["numba", "scipy"]
|
||||
polars = ["polars[numpy]"]
|
||||
|
||||
[[package]]
|
||||
name = "wcwidth"
|
||||
@@ -5974,4 +5972,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<3.11"
|
||||
content-hash = "640ff49d7d249bebced6f89477c72046ec94763c34cc6c1fdecd369c99b57e91"
|
||||
content-hash = "734b625d8c483c4cdced33cc30e90a5199fa1b27724677de68b0429013365853"
|
||||
|
||||
+9
-9
@@ -30,15 +30,15 @@ include = [
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.10,<3.11"
|
||||
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"]}
|
||||
jaxlib = "0.4.26"
|
||||
huggingface_hub = {version = "0.23.0", extras = ["cli"]}
|
||||
scikit-learn = "1.0.2"
|
||||
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"]}
|
||||
jaxlib = ">=0.4.26"
|
||||
huggingface_hub = {version = ">=0.23.0", extras = ["cli"]}
|
||||
scikit-learn = ">=1.2.2"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
|
||||
+58
-104
@@ -11,7 +11,6 @@
|
||||
# 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.
|
||||
|
||||
"""Helper functions for in-context covariates and regression."""
|
||||
|
||||
import itertools
|
||||
@@ -36,20 +35,19 @@ def _unnest(nested: Sequence[Sequence[Any]]) -> np.ndarray:
|
||||
def _repeat(elements: Iterable[Any], counts: Iterable[int]) -> np.ndarray:
|
||||
return np.array(
|
||||
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:
|
||||
if x.ndim == 1:
|
||||
(i,) = x.shape
|
||||
di = 2 ** math.ceil(math.log2(i)) - i
|
||||
di = 2**math.ceil(math.log2(i)) - i
|
||||
return jnp.pad(x, ((0, di),), mode="constant", constant_values=0.0)
|
||||
elif x.ndim == 2:
|
||||
i, j = x.shape
|
||||
di = 2 ** math.ceil(math.log2(i)) - i
|
||||
dj = 2 ** math.ceil(math.log2(j)) - j
|
||||
di = 2**math.ceil(math.log2(i)) - i
|
||||
dj = 2**math.ceil(math.log2(j)) - j
|
||||
return jnp.pad(x, ((0, di), (0, dj)), mode="constant", constant_values=0.0)
|
||||
else:
|
||||
raise ValueError(f"Unsupported array shape: {x.shape}")
|
||||
@@ -86,21 +84,16 @@ class BatchedInContextXRegBase:
|
||||
train_lens: Sequence[int],
|
||||
test_lens: Sequence[int],
|
||||
train_dynamic_numerical_covariates: (
|
||||
Mapping[str, Sequence[Sequence[float]]] | None
|
||||
) = None,
|
||||
Mapping[str, Sequence[Sequence[float]]] | None) = None,
|
||||
train_dynamic_categorical_covariates: (
|
||||
Mapping[str, Sequence[Sequence[Category]]] | None
|
||||
) = None,
|
||||
Mapping[str, Sequence[Sequence[Category]]] | None) = None,
|
||||
test_dynamic_numerical_covariates: (
|
||||
Mapping[str, Sequence[Sequence[float]]] | None
|
||||
) = None,
|
||||
Mapping[str, Sequence[Sequence[float]]] | None) = None,
|
||||
test_dynamic_categorical_covariates: (
|
||||
Mapping[str, Sequence[Sequence[Category]]] | None
|
||||
) = None,
|
||||
Mapping[str, Sequence[Sequence[Category]]] | None) = None,
|
||||
static_numerical_covariates: Mapping[str, Sequence[float]] | None = None,
|
||||
static_categorical_covariates: (
|
||||
Mapping[str, Sequence[Category]] | None
|
||||
) = None,
|
||||
static_categorical_covariates: (Mapping[str, Sequence[Category]] |
|
||||
None) = None,
|
||||
) -> None:
|
||||
"""Initializes with the exogenous covariate inputs.
|
||||
|
||||
@@ -187,17 +180,13 @@ class BatchedInContextXRegBase:
|
||||
self.train_lens = train_lens
|
||||
self.test_lens = test_lens
|
||||
self.train_dynamic_numerical_covariates = (
|
||||
train_dynamic_numerical_covariates or {}
|
||||
)
|
||||
train_dynamic_numerical_covariates or {})
|
||||
self.train_dynamic_categorical_covariates = (
|
||||
train_dynamic_categorical_covariates or {}
|
||||
)
|
||||
self.test_dynamic_numerical_covariates = (
|
||||
test_dynamic_numerical_covariates or {}
|
||||
)
|
||||
train_dynamic_categorical_covariates or {})
|
||||
self.test_dynamic_numerical_covariates = (test_dynamic_numerical_covariates
|
||||
or {})
|
||||
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_categorical_covariates = static_categorical_covariates or {}
|
||||
|
||||
@@ -205,31 +194,23 @@ class BatchedInContextXRegBase:
|
||||
"""Verifies the validity of the covariate inputs."""
|
||||
|
||||
# Check presence.
|
||||
if (
|
||||
self.train_dynamic_numerical_covariates
|
||||
and not self.test_dynamic_numerical_covariates
|
||||
) or (
|
||||
not self.train_dynamic_numerical_covariates
|
||||
and self.test_dynamic_numerical_covariates
|
||||
):
|
||||
if (self.train_dynamic_numerical_covariates and
|
||||
not self.test_dynamic_numerical_covariates) or (
|
||||
not self.train_dynamic_numerical_covariates and
|
||||
self.test_dynamic_numerical_covariates):
|
||||
raise ValueError(
|
||||
"train_dynamic_numerical_covariates and"
|
||||
" test_dynamic_numerical_covariates must be both present or both"
|
||||
" absent."
|
||||
)
|
||||
" absent.")
|
||||
|
||||
if (
|
||||
self.train_dynamic_categorical_covariates
|
||||
and not self.test_dynamic_categorical_covariates
|
||||
) or (
|
||||
not self.train_dynamic_categorical_covariates
|
||||
and self.test_dynamic_categorical_covariates
|
||||
):
|
||||
if (self.train_dynamic_categorical_covariates and
|
||||
not self.test_dynamic_categorical_covariates) or (
|
||||
not self.train_dynamic_categorical_covariates and
|
||||
self.test_dynamic_categorical_covariates):
|
||||
raise ValueError(
|
||||
"train_dynamic_categorical_covariates and"
|
||||
" test_dynamic_categorical_covariates must be both present or both"
|
||||
" absent."
|
||||
)
|
||||
" absent.")
|
||||
|
||||
# Check keys.
|
||||
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()):
|
||||
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()):
|
||||
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.
|
||||
if assert_covariate_shapes:
|
||||
if len(self.targets) != len(self.train_lens):
|
||||
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):
|
||||
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(
|
||||
zip(self.targets, self.train_lens)
|
||||
):
|
||||
for i, (target, train_len) in enumerate(zip(self.targets,
|
||||
self.train_lens)):
|
||||
if len(target) != train_len:
|
||||
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():
|
||||
if len(values) != len(self.train_lens):
|
||||
raise ValueError(
|
||||
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():
|
||||
if len(values) != len(self.train_lens):
|
||||
raise ValueError(
|
||||
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 (
|
||||
(
|
||||
@@ -315,14 +288,12 @@ class BatchedInContextXRegBase:
|
||||
if len(cov_values) != len(lens):
|
||||
raise ValueError(
|
||||
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):
|
||||
if len(cov_value) != lens[i]:
|
||||
raise ValueError(
|
||||
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(
|
||||
self,
|
||||
@@ -356,11 +327,9 @@ class BatchedInContextXRegBase:
|
||||
# Numerical features.
|
||||
for name in sorted(self.train_dynamic_numerical_covariates):
|
||||
x_train.append(
|
||||
_unnest(self.train_dynamic_numerical_covariates[name])[:, np.newaxis]
|
||||
)
|
||||
_unnest(self.train_dynamic_numerical_covariates[name])[:, np.newaxis])
|
||||
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():
|
||||
x_train.append(_repeat(covs, self.train_lens)[:, np.newaxis])
|
||||
@@ -372,25 +341,22 @@ class BatchedInContextXRegBase:
|
||||
|
||||
# Normalize for robustness.
|
||||
x_mean = np.mean(x_train, axis=0, keepdims=True)
|
||||
x_std = np.where(
|
||||
(w := np.std(x_train, axis=0, keepdims=True)) > _TOL, w, 1.0
|
||||
)
|
||||
x_std = np.where((w := np.std(x_train, axis=0, keepdims=True)) > _TOL, w,
|
||||
1.0)
|
||||
x_train = [(x_train - x_mean) / x_std]
|
||||
x_test = [(x_test - x_mean) / x_std]
|
||||
|
||||
# Categorical features. Encode one by one.
|
||||
one_hot_encoder = preprocessing.OneHotEncoder(
|
||||
drop=one_hot_encoder_drop,
|
||||
sparse=False,
|
||||
sparse_output=False,
|
||||
handle_unknown="ignore",
|
||||
)
|
||||
for name in sorted(self.train_dynamic_categorical_covariates.keys()):
|
||||
ohe_train = _unnest(self.train_dynamic_categorical_covariates[name])[
|
||||
:, np.newaxis
|
||||
]
|
||||
ohe_test = _unnest(self.test_dynamic_categorical_covariates[name])[
|
||||
:, np.newaxis
|
||||
]
|
||||
ohe_train = _unnest(
|
||||
self.train_dynamic_categorical_covariates[name])[:, np.newaxis]
|
||||
ohe_test = _unnest(
|
||||
self.test_dynamic_categorical_covariates[name])[:, np.newaxis]
|
||||
x_train.append(np.array(one_hot_encoder.fit_transform(ohe_train)))
|
||||
x_test.append(np.array(one_hot_encoder.transform(ohe_test)))
|
||||
|
||||
@@ -426,12 +392,8 @@ class BatchedInContextXRegLinear(BatchedInContextXRegBase):
|
||||
debug_info: bool = False,
|
||||
assert_covariates: bool = False,
|
||||
assert_covariate_shapes: bool = False,
|
||||
) -> (
|
||||
list[np.ndarray]
|
||||
| tuple[
|
||||
list[np.ndarray], list[np.ndarray], jax.Array, jax.Array, jax.Array
|
||||
]
|
||||
):
|
||||
) -> (list[np.ndarray] | tuple[list[np.ndarray], list[np.ndarray], jax.Array,
|
||||
jax.Array, jax.Array]):
|
||||
"""Fits a linear model for in-context regression.
|
||||
|
||||
Args:
|
||||
@@ -495,14 +457,10 @@ class BatchedInContextXRegLinear(BatchedInContextXRegBase):
|
||||
x_train = _to_padded_jax_array(x_train)
|
||||
flat_targets = _to_padded_jax_array(flat_targets)
|
||||
x_test = _to_padded_jax_array(x_test)
|
||||
beta_hat = (
|
||||
jnp.linalg.pinv(
|
||||
x_train.T @ x_train + ridge * jnp.eye(x_train.shape[1]),
|
||||
hermitian=True,
|
||||
)
|
||||
@ x_train.T
|
||||
@ flat_targets
|
||||
)
|
||||
beta_hat = (jnp.linalg.pinv(
|
||||
x_train.T @ x_train + ridge * jnp.eye(x_train.shape[1]),
|
||||
hermitian=True,
|
||||
) @ x_train.T @ flat_targets)
|
||||
y_hat = x_test @ beta_hat
|
||||
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.
|
||||
train_index, test_index = 0, 0
|
||||
for train_index_delta, test_index_delta in zip(
|
||||
self.train_lens, self.test_lens
|
||||
):
|
||||
outputs.append(
|
||||
np.array(y_hat[test_index : (test_index + test_index_delta)])
|
||||
)
|
||||
for train_index_delta, test_index_delta in zip(self.train_lens,
|
||||
self.test_lens):
|
||||
outputs.append(np.array(y_hat[test_index:(test_index +
|
||||
test_index_delta)]))
|
||||
if debug_info:
|
||||
outputs_context.append(
|
||||
np.array(
|
||||
y_hat_context[train_index : (train_index + train_index_delta)]
|
||||
)
|
||||
)
|
||||
np.array(y_hat_context[train_index:(train_index +
|
||||
train_index_delta)]))
|
||||
train_index += train_index_delta
|
||||
test_index += test_index_delta
|
||||
|
||||
|
||||
Reference in New Issue
Block a user