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    "info": {
        "author": "Ryota KAMOSHIDA",
        "author_email": "malss@malss.com",
        "bugtrack_url": null,
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            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "MAchine Learning Support System\n###############################\n\n``malss`` is a python module to facilitate machine learning tasks.\nThis module is written to be compatible with the `scikit-learn algorithms <http://scikit-learn.org/stable/supervised_learning.html>`_ and the other scikit-learn-compatible algorithms.\n\n.. image:: https://travis-ci.org/canard0328/malss.svg?branch=master\n    :target: https://travis-ci.org/canard0328/malss\n\nDependencies\n************\n\nmalss requires:\n\n* python (>= 3.6)\n* numpy (>= 1.10.2)\n* scipy (>= 0.16.1)\n* scikit-learn (>= 0.19)\n* matplotlib (>= 1.5.1)\n* pandas (>= 0.14.1)\n* jinja2 (>= 2.8)\n* PyQt5 (>= 5.12) (only for interactive mode)\n\nAll modules except PyQt5 are automatically installed when installing malss.\n\nInstallation\n************\n\n  pip install malss\n\nFor interactive mode, you need to install PyQt5 using pip.\n\n  pip install PyQt5\n\nExample\n*******\n\nClassification:\n\n.. code-block:: python\n\n  from malss import MALSS\n  from sklearn.datasets import load_iris\n  iris = load_iris()\n  clf = MALSS(task='classification', lang='en')\n  clf.fit(iris.data, iris.target, 'classification_result')\n  clf.generate_module_sample('classification_module_sample.py')\n\nRegression:\n\n.. code-block:: python\n\n  from malss import MALSS\n  from sklearn.datasets import load_boston\n  boston = load_boston()\n  clf = MALSS(task='regression', lang='en')\n  clf.fit(boston.data, boston.target, 'regression_result')\n  clf.generate_module_sample('regression_module_sample.py')\n\nChange algorithm:\n\n.. code-block:: python\n\n  from malss import MALSS\n  from sklearn.datasets import load_iris\n  from sklearn.ensemble import RandomForestClassifier as RF\n  iris = load_iris()\n  clf = MALSS(task='classification', lang='en')\n  clf.fit(iris.data, iris.target, algorithm_selection_only=True)\n  algorithms = clf.get_algorithms()\n  # check algorithms here\n  clf.remove_algorithm(0)  # remove the first algorithm\n  # add random forest classifier\n  clf.add_algorithm(RF(n_jobs=3),\n                    [{'n_estimators': [10, 30, 50],\n                      'max_depth': [3, 5, None],\n                      'max_features': [0.3, 0.6, 'auto']}],\n                    'Random Forest')\n  clf.fit(iris.data, iris.target, 'classification_result')\n  clf.generate_module_sample('classification_module_sample.py')\n\nInteractive mode:\n\nIn the interactive mode, you can interactively analyze data through a GUI.\n\n.. code-block:: python\n\n  from malss import MALSS\n\n  MALSS(lang='en', interactive=True)\n\n",
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