mirror of
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d4273669e6
* add sklearn regressors as learners for ts_forecast task * add direct forecasting strategy warnings and errors for duplicate rows and missing values - add preprocess for sklearn time series forecast update automl.py update test/test_forecast.py * update model.py and test_forecast.py for cv eval_method * add "hcrystalball" dependency in setup.py * update automl.py - add _validate_ts_data function for abstraction - include xgb_limitdepth as a learner * update model.py - update search space for sklearn ts regressors * update automl.py and test_forecast.py for numpy array inputs * add documentations to model.py * add documentation for removing catboost regressor * update automl.py - _validate_ts_data() function Signed-off-by: Kevin Chen <chenkevin.8787@gmail.com>
100 lines
2.6 KiB
Python
100 lines
2.6 KiB
Python
import setuptools
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import os
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here = os.path.abspath(os.path.dirname(__file__))
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with open("README.md", "r", encoding="UTF-8") as fh:
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long_description = fh.read()
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# Get the code version
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version = {}
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with open(os.path.join(here, "flaml/version.py")) as fp:
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exec(fp.read(), version)
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__version__ = version["__version__"]
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install_requires = [
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"NumPy>=1.16.2",
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"lightgbm>=2.3.1",
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"xgboost>=0.90,<=1.3.3",
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"scipy>=1.4.1",
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"pandas>=1.1.4",
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"scikit-learn>=0.24",
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]
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setuptools.setup(
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name="FLAML",
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version=__version__,
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author="Microsoft Corporation",
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author_email="hpo@microsoft.com",
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description="A fast library for automated machine learning and tuning",
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long_description=long_description,
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long_description_content_type="text/markdown",
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url="https://github.com/microsoft/FLAML",
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packages=setuptools.find_packages(include=["flaml*"]),
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install_requires=install_requires,
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extras_require={
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"notebook": [
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"openml==0.10.2",
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"jupyter",
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"matplotlib",
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"rgf-python",
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"catboost>=0.26",
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],
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"test": [
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"flake8>=3.8.4",
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"pytest>=6.1.1",
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"coverage>=5.3",
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"pre-commit",
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"catboost>=0.26",
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"rgf-python",
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"optuna==2.8.0",
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"vowpalwabbit",
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"openml",
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"statsmodels>=0.12.2",
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"psutil==5.8.0",
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"dataclasses",
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"transformers>=4.14",
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"datasets",
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"torch",
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"nltk",
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"rouge_score",
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"hcrystalball==0.1.10",
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"seqeval",
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],
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"catboost": ["catboost>=0.26"],
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"blendsearch": ["optuna==2.8.0"],
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"ray": [
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"ray[tune]==1.6.0",
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"pyyaml<5.3.1",
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],
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"azureml": [
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"azureml-mlflow",
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],
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"nni": [
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"nni",
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],
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"vw": [
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"vowpalwabbit",
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],
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"nlp": [
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"transformers>=4.14",
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"datasets",
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"torch",
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"seqeval",
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"nltk",
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"rouge_score",
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],
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"ts_forecast": ["prophet>=1.0.1", "statsmodels>=0.12.2", "hcrystalball==0.1.10"],
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"forecast": ["prophet>=1.0.1", "statsmodels>=0.12.2", "hcrystalball==0.1.10"],
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"benchmark": ["catboost>=0.26", "psutil==5.8.0", "xgboost==1.3.3"],
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},
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classifiers=[
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"Programming Language :: Python :: 3",
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"License :: OSI Approved :: MIT License",
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"Operating System :: OS Independent",
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],
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python_requires=">=3.6",
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)
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