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https://github.com/Doctorado-ML/STree.git
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1c869e154e
#15 Create impurity function in Stree (consistent name, same criteria as other splitter parameter) Create test for the new function Update init test Update test splitter parameters Rename old impurity function to partition_impurity close #15 * Complete implementation of splitter_type = impurity with tests Remove max_distance & min_distance splitter types * Fix mistake in computing multiclass node belief Set default criterion for split to entropy instead of gini Set default max_iter to 1e5 instead of 1e3 change up-down criterion to match SVC multiclass Fix impurity method of splitting nodes Update jupyter Notebooks
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
import setuptools
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__version__ = "0.9rc6"
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__author__ = "Ricardo Montañana Gómez"
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def readme():
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with open("README.md") as f:
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return f.read()
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setuptools.setup(
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name="STree",
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version=__version__,
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license="MIT License",
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description="Oblique decision tree with svm nodes",
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long_description=readme(),
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long_description_content_type="text/markdown",
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packages=setuptools.find_packages(),
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url="https://github.com/doctorado-ml/stree",
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author=__author__,
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author_email="ricardo.montanana@alu.uclm.es",
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keywords="scikit-learn oblique-classifier oblique-decision-tree decision-\
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tree svm svc",
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classifiers=[
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"Development Status :: 4 - Beta",
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"License :: OSI Approved :: MIT License",
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"Programming Language :: Python :: 3.8",
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"Natural Language :: English",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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"Intended Audience :: Science/Research",
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],
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install_requires=["scikit-learn>=0.23.0", "numpy", "ipympl"],
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test_suite="stree.tests",
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zip_safe=False,
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)
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