From e0c44480f63ddd2037186478287f73d9b8048284 Mon Sep 17 00:00:00 2001 From: Zoran Pandovski Date: Thu, 19 Dec 2024 17:26:44 +0100 Subject: [PATCH] Dependency update (#1244) * Pin sktime and scikit learn dependency to strict version to avoid Value errors * Compatible versions * Change sktime * Version bump --- lightwood/__about__.py | 2 +- poetry.lock | 63 ++++++++++++++---------------------------- pyproject.toml | 6 ++-- 3 files changed, 25 insertions(+), 46 deletions(-) diff --git a/lightwood/__about__.py b/lightwood/__about__.py index 38481ebd3..1b4a18446 100644 --- a/lightwood/__about__.py +++ b/lightwood/__about__.py @@ -1,6 +1,6 @@ __title__ = 'lightwood' __package_name__ = 'lightwood' -__version__ = '24.5.2.0' +__version__ = '24.12.3.0' __description__ = "Lightwood is a toolkit for automatic machine learning model building" __email__ = "community@mindsdb.com" __author__ = 'MindsDB Inc' diff --git a/poetry.lock b/poetry.lock index 3164e24f4..c2095fd4f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "adagio" @@ -774,25 +774,6 @@ files = [ {file = "Cython-3.0.10.tar.gz", hash = "sha256:dcc96739331fb854dcf503f94607576cfe8488066c61ca50dfd55836f132de99"}, ] -[[package]] -name = "dask" -version = "2024.8.0" -description = "Parallel PyData with Task Scheduling" -optional = false -python-versions = ">=3.9" -files = [ - {file = "dask-2024.8.0-py3-none-any.whl", hash = "sha256:250ea3df30d4a25958290eec4f252850091c6cfaed82d098179c3b25bba18309"}, - {file = "dask-2024.8.0.tar.gz", hash = "sha256:f1fec39373d2f101bc045529ad4e9b30e34e6eb33b7aa0fa7073aec7b1bf9eee"}, -] - -[package.extras] -array = ["numpy (>=1.21)"] -complete = ["dask[array,dataframe,diagnostics,distributed]", "lz4 (>=4.3.2)", "pyarrow (>=7.0)", "pyarrow-hotfix"] -dataframe = ["dask-expr (>=1.1,<1.2)", "dask[array]", "pandas (>=2.0)"] -diagnostics = ["bokeh (>=2.4.2)", "jinja2 (>=2.10.3)"] -distributed = ["distributed (==2024.8.0)"] -test = ["pandas[test]", "pre-commit", "pytest", "pytest-cov", "pytest-rerunfailures", "pytest-timeout", "pytest-xdist"] - [[package]] name = "dataclasses-json" version = "0.6.6" @@ -4504,49 +4485,47 @@ files = [ [[package]] name = "sktime" -version = "0.33.0" +version = "0.30.0" description = "A unified framework for machine learning with time series" optional = false python-versions = "<3.13,>=3.9" files = [ - {file = "sktime-0.33.0-py3-none-any.whl", hash = "sha256:82fc104604056f6e80dca79c80bbf9b1adb643f6a08c807a31736a773dfee830"}, - {file = "sktime-0.33.0.tar.gz", hash = "sha256:d330a82d55a6e14c517cecab144c845193f8fd84b2d787510752a7a5ca9ed2e9"}, + {file = "sktime-0.30.0-py3-none-any.whl", hash = "sha256:e6499f50422374b4d43c5bb46d4591139e66c5c15da32e5b5ae955bef8ecd210"}, + {file = "sktime-0.30.0.tar.gz", hash = "sha256:66e532e847aa71345011a3230cc7bc0e0d95f8c61308e197295477347c21cd1b"}, ] [package.dependencies] -dask = {version = "<2024.8.3", markers = ""} joblib = ">=1.2.0,<1.5" -numpy = ">=1.21,<2.1" +numpy = ">=1.21,<1.27" packaging = "*" pandas = ">=1.1,<2.3.0" -scikit-base = ">=0.6.1,<0.10.0" +scikit-base = ">=0.6.1,<0.9.0" scikit-learn = ">=0.24,<1.6.0" scipy = ">=1.2,<2.0.0" [package.extras] -alignment = ["dtaidistance (<2.4)", "dtw-python (>=1.3,<1.6)", "numba (>=0.53,<0.61)"] -all-extras = ["arch (>=5.6,<7.1.0)", "autots (>=0.6.1,<0.7)", "cloudpickle", "dash (!=2.9.0)", "dtaidistance (<2.4)", "dtw-python", "esig (==0.9.7)", "filterpy (>=1.4.5)", "gluonts (>=0.9)", "h5py", "hmmlearn (>=0.2.7)", "holidays", "keras-self-attention", "matplotlib (>=3.3.2,!=3.9.1)", "mne", "numba (>=0.53,<0.61)", "optuna (<3.7)", "pmdarima (>=1.8,!=1.8.1,<3.0.0)", "polars[pandas] (>=0.20,<2.0)", "prophet (>=1.1)", "pycatch22 (<0.4.6)", "pyod (>=0.8)", "pyts (<0.14.0)", "scikit-optimize", "scikit-posthocs (>=0.6.5)", "seaborn (>=0.11)", "seasonal", "skforecast (>=0.12.1,<0.14)", "skpro (>=2,<2.6.0)", "statsforecast (>=1.0.0,<1.8.0)", "statsmodels (>=0.12.1)", "stumpy (>=1.5.1)", "tbats (>=1.1)", "temporian (>=0.7.0,!=0.8.0,<0.9.0)", "tensorflow (>=2,<2.17)", "tsfresh (>=0.17)", "tslearn (>=0.5.2,!=0.6.0,<0.7.0)", "u8darts (>=0.29.0,<0.31)", "xarray"] -all-extras-pandas2 = ["arch (>=5.6,<7.1.0)", "autots (>=0.6.1,<0.7)", "cloudpickle", "dash (!=2.9.0)", "dtaidistance (<2.4)", "dtw-python", "esig (==0.9.7)", "filterpy (>=1.4.5)", "gluonts (>=0.9)", "h5py", "hmmlearn (>=0.2.7)", "holidays", "keras-self-attention", "matplotlib (>=3.3.2,!=3.9.1)", "mne", "numba (>=0.53,<0.61)", "optuna (<3.7)", "pmdarima (>=1.8,!=1.8.1,<3.0.0)", "polars[pandas] (>=0.20,<2.0)", "prophet (>=1.1)", "pycatch22 (<0.4.6)", "pyod (>=0.8)", "scikit-posthocs (>=0.6.5)", "seaborn (>=0.11)", "seasonal", "skforecast (>=0.12.1,<0.14)", "skpro (>=2,<2.6.0)", "statsforecast (>=1.0.0,<1.8.0)", "statsmodels (>=0.12.1)", "stumpy (>=1.5.1)", "tbats (>=1.1)", "temporian (>=0.7.0,!=0.8.0,<0.9.0)", "tensorflow (>=2,<2.17)", "tsbootstrap (>=0.1.0,<0.2)", "tsfresh (>=0.17)", "tslearn (>=0.5.2,!=0.6.0,<0.7.0)", "u8darts (>=0.29.0,<0.31)", "xarray"] -annotation = ["hmmlearn (>=0.2.7,<0.4)", "numba (>=0.53,<0.61)", "pyod (>=0.8,<1.2)"] +alignment = ["dtw-python (>=1.3,<1.6)", "numba (>=0.53,<0.60)"] +all-extras = ["arch (>=5.6,<7.1.0)", "cloudpickle", "dash (!=2.9.0)", "dask (<2024.5.3)", "dtw-python", "esig (==0.9.7)", "filterpy (>=1.4.5)", "gluonts (>=0.9)", "h5py", "hmmlearn (>=0.2.7)", "holidays", "keras-self-attention", "matplotlib (>=3.3.2)", "mne", "numba (>=0.53,<0.60)", "pmdarima (>=1.8,!=1.8.1,<3.0.0)", "prophet (>=1.1)", "pycatch22 (<0.4.6)", "pyod (>=0.8)", "pyts (<0.14.0)", "scikit-optimize", "scikit-posthocs (>=0.6.5)", "seaborn (>=0.11)", "seasonal", "skpro (>=2,<2.4.0)", "statsforecast (>=1.0.0,<1.8.0)", "statsmodels (>=0.12.1)", "stumpy (>=1.5.1)", "tbats (>=1.1)", "temporian (>=0.7.0,!=0.8.0,<0.9.0)", "tensorflow (>=2,<2.17)", "tsbootstrap (>=0.1.0,<0.2)", "tsfresh (>=0.17)", "tslearn (>=0.5.2,!=0.6.0,<0.7.0)", "xarray"] +all-extras-pandas2 = ["arch (>=5.6,<7.1.0)", "cloudpickle", "dash (!=2.9.0)", "dask (<2024.5.3)", "dtw-python", "esig (==0.9.7)", "filterpy (>=1.4.5)", "gluonts (>=0.9)", "h5py", "hmmlearn (>=0.2.7)", "holidays", "keras-self-attention", "matplotlib (>=3.3.2)", "mne", "numba (>=0.53,<0.60)", "pmdarima (>=1.8,!=1.8.1,<3.0.0)", "prophet (>=1.1)", "pycatch22 (<0.4.6)", "pyod (>=0.8)", "scikit-posthocs (>=0.6.5)", "seaborn (>=0.11)", "seasonal", "skpro (>=2,<2.4.0)", "statsforecast (>=1.0.0,<1.8.0)", "statsmodels (>=0.12.1)", "stumpy (>=1.5.1)", "tbats (>=1.1)", "temporian (>=0.7.0,!=0.8.0,<0.9.0)", "tensorflow (>=2,<2.17)", "tsbootstrap (>=0.1.0,<0.2)", "tsfresh (>=0.17)", "tslearn (>=0.5.2,!=0.6.0,<0.7.0)", "xarray"] +annotation = ["hmmlearn (>=0.2.7,<0.4)", "numba (>=0.53,<0.60)", "pyod (>=0.8,<1.2)"] binder = ["jupyter", "pandas (<2.0.0)"] -classification = ["esig (>=0.9.7,<0.10)", "numba (>=0.53,<0.61)", "tensorflow (>=2,<2.17)", "tsfresh (>=0.17,<0.21)"] -clustering = ["numba (>=0.53,<0.61)", "tslearn (>=0.5.2,!=0.6.0,<0.7.0)"] -compatibility-tests = ["catboost"] -cython-extras = ["mrseql (<0.0.3)", "mrsqm", "numba (<0.61)"] +classification = ["esig (>=0.9.7,<0.10)", "numba (>=0.53,<0.60)", "tensorflow (>=2,<2.17)", "tsfresh (>=0.17,<0.21)"] +clustering = ["numba (>=0.53,<0.60)", "tslearn (>=0.5.2,!=0.6.0,<0.7.0)"] +cython-extras = ["mrseql", "mrsqm", "numba (<0.60)"] +dataframe = ["dask (<2024.5.3)", "dask (<2024.5.3)"] datasets = ["rdata", "requests"] dev = ["backoff", "httpx", "pre-commit", "pytest", "pytest-cov", "pytest-randomly", "pytest-timeout", "pytest-xdist", "wheel"] -dl = ["FrEIA", "neuralforecast (>=1.6.4,<1.8.0)", "peft (>=0.10.0)", "pykan (>=0.2,<0.2.7)", "pytorch-forecasting (>=1.0.0,<1.2.0)", "tensorflow (>=2,<2.17)", "torch", "transformers[torch] (<4.41.0)"] -docs = ["Sphinx (!=7.2.0,<9.0.0)", "jupyter", "myst-parser", "nbsphinx (>=0.8.6)", "numpydoc", "pydata-sphinx-theme", "sphinx-copybutton", "sphinx-design (<0.7.0)", "sphinx-gallery (<0.18.0)", "sphinx-issues (<5.0.0)", "tabulate"] -forecasting = ["arch (>=5.6,<7.1)", "autots (>=0.6.1,<0.7)", "pmdarima (>=1.8,!=1.8.1,<2.1)", "prophet (>=1.1,<1.2)", "skforecast (>=0.12.1,<0.14)", "skpro (>=2,<2.6.0)", "statsforecast (>=1.0.0,<1.8.0)", "statsmodels (>=0.12.1,<0.15)", "tbats (>=1.1,<1.2)"] +dl = ["FrEIA", "neuralforecast (>=1.6.4,<1.8.0)", "pykan", "tensorflow (>=2,<2.17)", "torch", "transformers[torch] (<4.41.0)"] +docs = ["Sphinx (!=7.2.0,<8.0.0)", "jupyter", "myst-parser", "nbsphinx (>=0.8.6)", "numpydoc", "pydata-sphinx-theme", "sphinx-copybutton", "sphinx-design (<0.7.0)", "sphinx-gallery (<0.17.0)", "sphinx-issues (<5.0.0)", "tabulate"] +forecasting = ["arch (>=5.6,<7.1)", "pmdarima (>=1.8,!=1.8.1,<2.1)", "prophet (>=1.1,<1.2)", "skpro (>=2,<2.4.0)", "statsforecast (>=1.0.0,<1.8.0)", "statsmodels (>=0.12.1,<0.15)", "tbats (>=1.1,<1.2)"] mlflow = ["mlflow"] mlflow-tests = ["boto3", "botocore", "mlflow", "moto"] networks = ["keras-self-attention (>=0.51,<0.52)", "tensorflow (>=2,<2.17)"] -numpy1 = ["numpy (<3.0.0)"] pandas1 = ["pandas (<2.0.0)"] param-est = ["seasonal (>=0.3.1,<0.4)", "statsmodels (>=0.12.1,<0.15)"] -regression = ["numba (>=0.53,<0.61)", "tensorflow (>=2,<2.17)"] -tests = ["pytest (>=7.4,<8.4)", "pytest-cov (>=4.1,<5.1)", "pytest-randomly (>=3.15,<3.16)", "pytest-timeout (>=2.1,<2.4)", "pytest-xdist (>=3.3,<3.7)"] -transformations = ["esig (>=0.9.7,<0.10)", "filterpy (>=1.4.5,<1.5)", "holidays (>=0.29,<0.57)", "mne (>=1.5,<1.9)", "numba (>=0.53,<0.61)", "pycatch22 (>=0.4,<0.4.6)", "statsmodels (>=0.12.1,<0.15)", "stumpy (>=1.5.1,<1.13)", "temporian (>=0.7.0,!=0.8.0,<0.9.0)", "tsfresh (>=0.17,<0.21)"] +regression = ["numba (>=0.53,<0.60)", "tensorflow (>=2,<2.17)"] +tests = ["pytest (>=7.4,<8.3)", "pytest-cov (>=4.1,<5.1)", "pytest-randomly (>=3.15,<3.16)", "pytest-timeout (>=2.1,<2.4)", "pytest-xdist (>=3.3,<3.7)"] +transformations = ["esig (>=0.9.7,<0.10)", "filterpy (>=1.4.5,<1.5)", "holidays (>=0.29,<0.51)", "mne (>=1.5,<1.8)", "numba (>=0.53,<0.60)", "pycatch22 (>=0.4,<0.4.6)", "statsmodels (>=0.12.1,<0.15)", "stumpy (>=1.5.1,<1.13)", "temporian (>=0.7.0,!=0.8.0,<0.9.0)", "tsbootstrap (>=0.1.0,<0.2)", "tsfresh (>=0.17,<0.21)"] [[package]] name = "slicer" @@ -5735,4 +5714,4 @@ xai = ["pyod", "shap", "suod"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.12" -content-hash = "985acc0dba5919c63b5ac163be5a6cb997c234459ae83a5d1a3392140d20ce58" +content-hash = "917f58e35cf0f2680c77ba64a7eebd040059d5f8f654f3ee3393ce7611631caf" diff --git a/pyproject.toml b/pyproject.toml index 268122395..f783a19b4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api" [tool.poetry] name = "lightwood" -version = "24.12.1.0" +version = "24.12.3.0" description = "Lightwood is Legos for Machine Learning." authors = ["MindsDB Inc."] license = "GPL-3.0-only" @@ -24,10 +24,10 @@ transformers = ">=4.34.0" optuna = ">=3.1.0,<4.0.0" scipy = ">=1.5.4" psutil = ">=5.7.0" -scikit-learn = ">=1.5.0" +scikit-learn = "==1.5.2" dataclasses_json = ">=0.5.4" dill = "==0.3.6" -sktime = ">=0.30.0" +sktime = "==0.30.0" statsforecast = "~=1.6.0" torch_optimizer = "==0.1.0" black = "==24.3.0"