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        "author": "Yohann Lereclus",
        "author_email": "lereclus84L@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "plot_metric\n===========\n\n|PyPI-Versions| |doc_badge|\n\nLibrairie to simplify plotting of metric like ROC curve, confusion matrix etc..\n\nInstallation\n------------\nUsing pip :\n\n.. code:: sh\n\n    pip install plot-metric\n\n\nExample BinaryClassification\n-------\n\nSimple binary classification\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nLet's load a simple dataset and make a train & test set :\n\n.. code:: python\n\n    from sklearn.datasets import make_classification\n    from sklearn.model_selection import train_test_split\n\n    X, y = make_classification(n_samples=1000, n_classes=2, weights=[1,1], random_state=1)\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=2)\n\n\nTrain our classifier and predict our test set :\n\n.. code:: python\n\n    from sklearn.ensemble import RandomForestClassifier\n\n    clf = RandomForestClassifier(n_estimators=50, random_state=23)\n    model = clf.fit(X_train, y_train)\n\n    # Use predict_proba to predict probability of the class\n    y_pred = clf.predict_proba(X_test)[:,1]\n\n\nWe can now use ``plot_metric`` to plot ROC Curve, distribution class and classification matrix :\n\n.. code:: python\n\n    # Visualisation with plot_metric\n    bc = BinaryClassification(y_test, y_pred, labels=[\"Class 1\", \"Class 2\"])\n\n    # Figures\n    plt.figure(figsize=(15,10))\n    plt.subplot2grid(shape=(2,6), loc=(0,0), colspan=2)\n    bc.plot_roc_curve()\n    plt.subplot2grid((2,6), (0,2), colspan=2)\n    bc.plot_precision_recall_curve()\n    plt.subplot2grid((2,6), (0,4), colspan=2)\n    bc.plot_class_distribution()\n    plt.subplot2grid((2,6), (1,1), colspan=2)\n    bc.plot_confusion_matrix()\n    plt.subplot2grid((2,6), (1,3), colspan=2)\n    bc.plot_confusion_matrix(normalize=True)\n    plt.show()\n    bc.print_report()\n\n    >>>                    ________________________\n    >>>                   |  Classification Report |\n    >>>                    \u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\u203e\n    >>>               precision    recall  f1-score   support\n    >>>            0       1.00      0.93      0.96        43\n    >>>            1       0.96      1.00      0.98        71\n    >>>    micro avg       0.97      0.97      0.97       114\n    >>>    macro avg       0.98      0.97      0.97       114\n    >>> weighted avg       0.97      0.97      0.97       114\n\n\n.. image:: example/images/example_binary_classification.png\n\nCustom parameters\n~~~~~~~~~~~~~~~~~\n\nIt is possible to customize a lot of figures parameters. You can find all parameters with documentation on the official package documentation : https://plot-metric.readthedocs.io/en/latest/\nOr you can retrieve a python dictionnary with all available parameters with the following :\n\n.. code:: python\n\n    # Use the function get_function_parameters(function) to get parameters\n    bc.get_function_parameters(bc.plot_roc_curve)\n\n    >>> {'threshold': None,\n        'plot_threshold': True,\n        'beta': 1,\n        'linewidth': 2,\n        'fscore_iso': [0.2, 0.4, 0.6, 0.8],\n        'iso_alpha': 0.7,\n        'y_text_margin': 0.03,\n        'x_text_margin': 0.2,\n        'c_pr_curve': 'black',\n        'c_mean_prec': 'red',\n        'c_thresh': 'black',\n        'c_f1_iso': 'grey',\n        'c_thresh_point': 'red',\n        'ls_pr_curve': '-',\n        'ls_mean_prec': '--',\n        'ls_thresh': ':',\n        'ls_fscore_iso': ':',\n        'marker_pr_curve': None}\n\nFrom a custom dictionnary you can set all parameters you want and plot a figures :\n\n.. code:: python\n\n    # Example custom param using dictionnary\n    param_pr_plot = {\n        'c_pr_curve':'blue',\n        'c_mean_prec':'cyan',\n        'c_thresh_lines':'red',\n        'c_f1_iso':'green',\n        'beta': 2,\n    }\n\n    plt.figure(figsize=(6,6))\n    bc.plot_precision_recall_curve(**param_pr_plot)\n    plt.show()\n\n.. image:: example/images/example_binary_class_PRCurve_custom.png\n\n.. |PyPI-Versions| image:: https://img.shields.io/badge/plot__metric-v0.0.4-blue.svg\n    :target: https://pypi.org/project/plot-metric/\n\n.. |doc_badge| image:: https://readthedocs.org/projects/plot-metric/badge/?version=latest\n    :target: https://plot-metric.readthedocs.io/en/latest/?badge=latest\n    :alt: Documentation Status\n\n\n",
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