Merge branch 'master' into feature/lora
This commit is contained in:
Tanmay Shishodia
@@ -18,6 +18,7 @@ We recommend at least 16GB RAM to load TimesFM dependencies.
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## Update - July 15, 2024
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- To install TimesFM, you can now simply do: `pip install timesfm`.
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- 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.
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- 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).
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@@ -202,7 +203,7 @@ forecast_df = tfm.forecast_on_df(
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## Covariates Support
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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`.
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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).
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Let's take a toy example of forecasting sales for a grocery store:
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@@ -241,11 +242,11 @@ In this example, besides the `Daily_sales`, we also have covariates `Category`,
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**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.
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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`.
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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).
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## Finetuning
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We have provided an example of finetuning the model on a new dataset in `notebooks/finetuning.ipynb`.
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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).
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## Contribution Style guide
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Generated
+156
-149
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[[package]]
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name = "cachetools"
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version = "5.3.3"
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version = "5.4.0"
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description = "Extensible memoizing collections and decorators"
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optional = false
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[[package]]
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@@ -909,13 +909,13 @@ lazy-imports = ["etils[ecolab]"]
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[[package]]
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name = "exceptiongroup"
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version = "1.2.1"
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]
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[package.extras]
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@@ -1313,61 +1313,61 @@ test = ["coverage", "mock (>=4)", "pytest (>=7)", "pytest-cov", "pytest-mock (>=
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[[package]]
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name = "grpcio"
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version = "1.64.1"
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description = "HTTP/2-based RPC framework"
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{file = "grpcio-1.65.1-cp39-cp39-linux_armv7l.whl", hash = "sha256:cb5175f45c980ff418998723ea1b3869cce3766d2ab4e4916fbd3cedbc9d0ed3"},
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{file = "grpcio-1.65.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:2f56b5a68fdcf17a0a1d524bf177218c3c69b3947cb239ea222c6f1867c3ab68"},
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{file = "grpcio-1.65.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:941596d419b9736ab548aa0feb5bbba922f98872668847bf0720b42d1d227b9e"},
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{file = "grpcio-1.65.1-cp39-cp39-win32.whl", hash = "sha256:5fd7337a823b890215f07d429f4f193d24b80d62a5485cf88ee06648591a0c57"},
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{file = "grpcio-1.65.1-cp39-cp39-win_amd64.whl", hash = "sha256:1bceeec568372cbebf554eae1b436b06c2ff24cfaf04afade729fb9035408c6c"},
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{file = "grpcio-1.65.1.tar.gz", hash = "sha256:3c492301988cd720cd145d84e17318d45af342e29ef93141228f9cd73222368b"},
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||||
]
|
||||
|
||||
[package.extras]
|
||||
protobuf = ["grpcio-tools (>=1.64.1)"]
|
||||
protobuf = ["grpcio-tools (>=1.65.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "h11"
|
||||
@@ -1460,13 +1460,13 @@ socks = ["socksio (==1.*)"]
|
||||
|
||||
[[package]]
|
||||
name = "huggingface-hub"
|
||||
version = "0.23.0"
|
||||
version = "0.24.0"
|
||||
description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
|
||||
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-0.24.0-py3-none-any.whl", hash = "sha256:7ad92edefb93d8145c061f6df8d99df2ff85f8379ba5fac8a95aca0642afa5d7"},
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -1480,17 +1480,17 @@ tqdm = ">=4.42.1"
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||||
typing-extensions = ">=3.7.4.3"
|
||||
|
||||
[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 (>=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)"]
|
||||
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"]
|
||||
hf-transfer = ["hf-transfer (>=0.1.4)"]
|
||||
inference = ["aiohttp", "minijinja (>=1.0)"]
|
||||
quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
|
||||
quality = ["mypy (==1.5.1)", "ruff (>=0.5.0)"]
|
||||
tensorflow = ["graphviz", "pydot", "tensorflow"]
|
||||
tensorflow-testing = ["keras (<3.0)", "tensorflow"]
|
||||
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)"]
|
||||
torch = ["safetensors", "torch"]
|
||||
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)"]
|
||||
torch = ["safetensors[torch]", "torch"]
|
||||
typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
|
||||
|
||||
[[package]]
|
||||
@@ -2105,13 +2105,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
|
||||
|
||||
[[package]]
|
||||
name = "jupyterlab"
|
||||
version = "4.2.3"
|
||||
version = "4.2.4"
|
||||
description = "JupyterLab computational environment"
|
||||
optional = false
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||||
python-versions = ">=3.8"
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||||
files = [
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|
||||
|
||||
[package.dependencies]
|
||||
@@ -2135,7 +2135,7 @@ dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov",
|
||||
docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<7.3.0)", "sphinx-copybutton"]
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||||
docs-screenshots = ["altair (==5.3.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.2)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.1.post2)", "matplotlib (==3.8.3)", "nbconvert (>=7.0.0)", "pandas (==2.2.1)", "scipy (==1.12.0)", "vega-datasets (==0.9.0)"]
|
||||
test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
|
||||
upgrade-extension = ["copier (>=8,<10)", "jinja2-time (<0.3)", "pydantic (<2.0)", "pyyaml-include (<2.0)", "tomli-w (<2.0)"]
|
||||
upgrade-extension = ["copier (>=9,<10)", "jinja2-time (<0.3)", "pydantic (<3.0)", "pyyaml-include (<3.0)", "tomli-w (<2.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "jupyterlab-pygments"
|
||||
@@ -2150,13 +2150,13 @@ files = [
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||||
|
||||
[[package]]
|
||||
name = "jupyterlab-server"
|
||||
version = "2.27.2"
|
||||
version = "2.27.3"
|
||||
description = "A set of server components for JupyterLab and JupyterLab like applications."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -3401,67 +3401,72 @@ files = [
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||||
|
||||
[[package]]
|
||||
name = "pandas"
|
||||
version = "2.1.4"
|
||||
version = "2.2.2"
|
||||
description = "Powerful data structures for data analysis, time series, and statistics"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
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||||
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||||
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||||
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||||
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mysql = ["SQLAlchemy (>=2.0.0)", "pymysql (>=1.0.2)"]
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output-formatting = ["jinja2 (>=3.1.2)", "tabulate (>=0.9.0)"]
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postgresql = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "psycopg2 (>=2.9.6)"]
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sql-other = ["SQLAlchemy (>=2.0.0)", "adbc-driver-postgresql (>=0.8.0)", "adbc-driver-sqlite (>=0.8.0)"]
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xml = ["lxml (>=4.9.2)"]
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[[package]]
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name = "pandocfilters"
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@@ -3682,13 +3687,13 @@ testing = ["pytest", "pytest-benchmark"]
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||||
[[package]]
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name = "portalocker"
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version = "2.10.0"
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version = "2.10.1"
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description = "Wraps the portalocker recipe for easy usage"
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optional = false
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python-versions = ">=3.8"
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files = [
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[package.dependencies]
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@@ -5056,18 +5061,19 @@ test = ["pytest"]
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[[package]]
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name = "setuptools"
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version = "70.3.0"
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python-versions = ">=3.8"
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[package.extras]
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||||
test = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "importlib-metadata", "ini2toml[lite] (>=0.14)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "jaraco.test", "mypy (==1.10.0)", "packaging (>=23.2)", "pip (>=19.1)", "pyproject-hooks (!=1.1)", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-home (>=0.5)", "pytest-mypy", "pytest-perf", "pytest-ruff (<0.4)", "pytest-ruff (>=0.2.1)", "pytest-ruff (>=0.3.2)", "pytest-subprocess", "pytest-timeout", "pytest-xdist (>=3)", "tomli", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
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||||
|
||||
[[package]]
|
||||
name = "shellingham"
|
||||
@@ -5145,13 +5151,13 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
|
||||
|
||||
[[package]]
|
||||
name = "sympy"
|
||||
version = "1.13.0"
|
||||
version = "1.13.1"
|
||||
description = "Computer algebra system (CAS) in Python"
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||||
optional = false
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||||
python-versions = ">=3.8"
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files = [
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{file = "sympy-1.13.0-py3-none-any.whl", hash = "sha256:6b0b32a4673fb91bd3cac3b55406c8e01d53ae22780be467301cc452f6680c92"},
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{file = "sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f"},
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||||
]
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||||
[package.dependencies]
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||||
@@ -5882,19 +5888,19 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
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||||
|
||||
[[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"
|
||||
@@ -5906,14 +5912,15 @@ 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"]
|
||||
@@ -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"]
|
||||
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)"]
|
||||
|
||||
@@ -6044,13 +6052,13 @@ 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]
|
||||
@@ -6059,10 +6067,9 @@ 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 = "wandb"
|
||||
|
||||
+10
-10
@@ -1,7 +1,7 @@
|
||||
[tool.poetry]
|
||||
name = "timesfm"
|
||||
packages = [
|
||||
{ include = "*", from = "src" },
|
||||
{ include = "timesfm", from = "src" },
|
||||
]
|
||||
description = "Open weights time-series foundation model from Google Research."
|
||||
version = "1.0.1"
|
||||
@@ -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"
|
||||
typer = "^0.12.3"
|
||||
wandb = "^0.17.5"
|
||||
|
||||
|
||||
+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