mirror of
https://github.com/microsoft/autogen.git
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549a0dfb53
* limit time and memory * separate tests * lrl1 can't be limited by limit_resource * free memory when possible * passthrough=False when ensemble fails; retrain when trained_estimator is None * use callback to for resource limit * handle lower version of xgb with no callback * free mem ratio * reduce verbosity * retrain_final when max_iter==1 * remove trained_estimator from result * model_history * wheel * retrain time as best_config_train_time * ci: libomp version for xgboost on macos * limit_resource not working in windows * test pickle load * mute forecaster * notebook update * check hard * preventive callback * add use_ray
96 lines
2.4 KiB
Python
96 lines
2.4 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") 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 and lightweight autoML system",
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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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"transformers==4.4.1",
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"wandb==0.10.26",
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"torch==1.8.1",
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"datasets==1.4.1",
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"azure-storage-blob",
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"statsmodels>=0.12.2",
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"psutil==5.8.0",
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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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"ray[tune]>=1.6.0",
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"transformers",
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"datasets==1.4.1",
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"tensorboardX<=2.2",
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"torch",
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],
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"ts_forecast": ["prophet>=1.0.1", "statsmodels>=0.12.2"],
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"forecast": ["prophet>=1.0.1", "statsmodels>=0.12.2"],
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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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