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fa5ccea862
* extract code from text * solve_problem; request_timeout in config * improve * move import statement * improve code * generate assertions * constant * configs for implement; voting * doc * execute code in docker * success indicator of code executation in docker * success indicator * execute code * strip n * add cost in generate_code * add docstr * filename * bytes * check docker version * print log * python test * remove api key address * rename exit code * success exit code * datasets * exit code * recover openai tests * cache and pattern match * wait * wait * cache and test * timeout test * python image name and skip macos * windows image * docker images * volume path and yaml * win path -> posix * extensions * path * path * path * path * path * path * path * path * path * path * path * skip windows * path * timeout in windows * use_docker * use_docker * hot fix from #1000 --------- Co-authored-by: Qingyun Wu <qingyun.wu@psu.edu>
134 lines
3.7 KiB
Python
134 lines
3.7 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>=1.17.0rc1",
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"lightgbm>=2.3.1",
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"xgboost>=0.90",
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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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package_data={
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"flaml.default": ["*/*.json"],
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},
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include_package_data=True,
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install_requires=install_requires,
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extras_require={
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"notebook": [
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"jupyter",
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"matplotlib",
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"openml==0.10.2",
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],
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"spark": [
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"pyspark>=3.2.0",
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"joblibspark>=0.5.0",
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],
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"test": [
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"flake8>=3.8.4",
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"thop",
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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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"torch",
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"torchvision",
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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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"openml==0.10.2",
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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[torch]==4.26",
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"datasets",
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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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"pytorch-forecasting>=0.9.0,<=0.10.1",
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"mlflow",
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"pyspark>=3.2.0",
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"joblibspark>=0.5.0",
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"nbconvert",
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"nbformat",
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"ipykernel",
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"pytorch-lightning<1.9.1", # test_forecast_panel
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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]~=1.13",
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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>=8.10.0, <9.0.0",
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],
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"hf": [
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"transformers[torch]==4.26",
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"datasets",
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"nltk",
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"rouge_score",
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"seqeval",
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],
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"nlp": [ # for backward compatibility; hf is the new option name
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"transformers[torch]==4.26",
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"datasets",
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"nltk",
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"rouge_score",
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"seqeval",
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],
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"ts_forecast": [
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"holidays<0.14", # to prevent installation error for prophet
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"prophet>=1.0.1",
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"statsmodels>=0.12.2",
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"hcrystalball==0.1.10",
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],
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"forecast": [
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"holidays<0.14", # to prevent installation error for prophet
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"prophet>=1.0.1",
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"statsmodels>=0.12.2",
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"hcrystalball==0.1.10",
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"pytorch-forecasting>=0.9.0",
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],
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"benchmark": ["catboost>=0.26", "psutil==5.8.0", "xgboost==1.3.3"],
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"openai": ["openai==0.27.4", "diskcache"],
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"autogen": ["openai==0.27.4", "diskcache", "docker"],
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"synapse": ["joblibspark>=0.5.0", "optuna==2.8.0", "pyspark>=3.2.0"],
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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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