.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/shap/plot_main01.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download__examples_shap_plot_main01.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr__examples_shap_plot_main01.py:


01. Basic example
==================

.. GENERATED FROM PYTHON SOURCE LINES 8-200



.. image-sg:: /_examples/shap/images/sphx_glr_plot_main01_001.png
   :alt: plot main01
   :srcset: /_examples/shap/images/sphx_glr_plot_main01_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    Kernel type: <class 'shap.explainers._tree.Tree'>
    .values =
    array([[ 0.01,  1.37, -4.46],
           [-0.04, -0.05, -4.71],
           [-0.04, -0.67, -4.25],
           ...,
           [ 0.35,  0.36,  2.19],
           [-0.03, -0.06, -4.72],
           [-0.04, -0.67, -4.25]])

    .base_values =
    array([0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35,
           0.35, 0.35, 0.35, 0.35, 0.35])

    .data =
    array([[ 17.99,  10.38, 122.8 ],
           [ 20.57,  17.77, 132.9 ],
           [ 19.69,  21.25, 130.  ],
           ...,
           [ 12.47,  17.31,  80.45],
           [ 18.49,  17.52, 121.3 ],
           [ 20.59,  21.24, 137.8 ]])
    shap_values (shape): (500, 3)
    <IPython.core.display.HTML object>
    [[-0.09 -0.58 -4.2 ]
     [-0.57  0.93  3.57]
     [ 0.33  2.81 -2.97]
     ...
     [ 0.78 -0.04 -2.36]
     [ 0.25 -0.04  0.63]
     [ 0.22  1.57 -3.67]]
    None

    '\nprint(sv)\n#sns.swarmplot(data=sv, x=0, y=\'level_1\', color=\'viridis\', palette=\'viridis\')\n#sns.stripplot(data=sv, x=0, y=\'level_1\', color=\'viridis\', palette=\'viridis\')\n#plt.show()\nimport sys\nsys.exit()\n#sns.swarmplot(x=)\n\nimport sys\nsys.exit()\n\n#html = f"<head>{shap.getjs()}</head><body>"\n# Bee swarm\n# .. note: unexpected algorithm matplotlib!\n# .. note: does not return an object!\nplot_bee = shap.plots.beeswarm(shap_values, show=False)\n\n# Sow\nprint("\nBEE")\nprint(plot_bee)\n\n#print(f)\n# Waterfall\n# .. note: not working!\n#shap.plots.waterfall(shap_values[0], max_display=14)\n\n# Force plot\n# .. note: not working!\nplot_force = shap.plots.force(explainer.expected_value,\n    explainer.shap_values(X_train), X_train,\n    matplotlib=False, show=False)\n\n# Show\nprint("\nFORCE:")\nprint(plot_force)\nprint(plot_force.html())\nprint(shap.save_html(\'e.html\', plot_force))\n'





|

.. code-block:: default
   :lineno-start: 8

    # Generic
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt

    # Sklearn
    from sklearn.model_selection import train_test_split
    from sklearn.datasets import load_iris
    from sklearn.datasets import load_breast_cancer
    from sklearn.naive_bayes import GaussianNB
    from sklearn.linear_model import LogisticRegression
    from sklearn.tree import DecisionTreeClassifier
    from sklearn.ensemble import RandomForestClassifier

    # Xgboost
    from xgboost import XGBClassifier

    # ----------------------------------------
    # Load data
    # ----------------------------------------
    # Seed
    seed = 0

    # Load dataset
    bunch = load_iris()
    bunch = load_breast_cancer()
    features = list(bunch['feature_names'])

    # Create DataFrame
    data = pd.DataFrame(data=np.c_[bunch['data'], bunch['target']],
                        columns=features + ['target'])

    # Create X, y
    X = data[bunch['feature_names']]
    y = data['target']

    # Filter
    X = X.iloc[:500, :3]
    y = y.iloc[:500]


    # Split dataset
    X_train, X_test, y_train, y_test = \
        train_test_split(X, y, random_state=seed)


    # ----------------------------------------
    # Classifiers
    # ----------------------------------------
    # Train classifier
    gnb = GaussianNB()
    llr = LogisticRegression()
    dtc = DecisionTreeClassifier(random_state=seed)
    rfc = RandomForestClassifier(random_state=seed)
    xgb = XGBClassifier(
        min_child_weight=0.005,
        eta= 0.05, gamma= 0.2,
        max_depth= 4,
        n_estimators= 100)

    # Select one
    clf = xgb

    # Fit
    clf.fit(X_train, y_train)

    # ----------------------------------------
    # Find shap values
    # ----------------------------------------
    # Import
    import shap

    """
    # Create shap explainer
    if isinstance(clf,
        (DecisionTreeClassifier,
         RandomForestClassifier,
         XGBClassifier)):
        # Set Tree explainer
        explainer = shap.TreeExplainer(clf)
    elif isinstance(clf, int):
        # Set NN explainer
        explainer = shap.DeepExplainer(clf)
    else:
        # Set generic kernel explainer
        explainer = shap.KernelExplainer(clf.predict_proba, X_train)
    """

    # Get generic explainer
    explainer = shap.Explainer(clf, X_train)

    # Show kernel type
    print("\nKernel type: %s" % type(explainer))

    # Get shap values
    shap_values = explainer(X)

    print(shap_values)

    # For interactions!!
    # https://github.com/slundberg/shap/issues/501

    # Get shap values
    #shap_values = \
    #    explainer.shap_values(X_train)
    #shap_interaction_values = \
    #    explainer.shap_interaction_values(X_train)

    # Show information
    print("shap_values (shape): %s" % \
          str(shap_values.shape))
    #print("shap_values_interaction (shape): %s" % \
    #      str(shap_interaction_values.shape))


    # ----------------------------------------
    # Visualize
    # ----------------------------------------
    # Initialise
    shap.initjs()

    """
    # Dependence plot
    shap.dependence_plot(0, shap_values,
        X_train, interaction_index=None, dot_size=5,
        alpha=0.5, color='#3F75BC', show=False)
    plt.tight_layout()
    """

    print(explainer.shap_values(X_train))

    # Summary plot
    plot_summary = shap.summary_plot( \
        explainer.shap_values(X_train),
        X_train, cmap='viridis',
        show=False)

    plt.tight_layout()
    plt.show()

    print(plot_summary)


    import seaborn as sns
    sv = explainer.shap_values(X_train)
    sv = pd.DataFrame(sv, columns=X.columns)
    sv = sv.stack().reset_index()
    sv['val'] = X_train.stack().reset_index()[0]

    #import plotly.express as px

    #f = px.strip(data_frame=sv, x=0, y='level_1', color='val')
    #f.show()

    """
    print(sv)
    #sns.swarmplot(data=sv, x=0, y='level_1', color='viridis', palette='viridis')
    #sns.stripplot(data=sv, x=0, y='level_1', color='viridis', palette='viridis')
    #plt.show()
    import sys
    sys.exit()
    #sns.swarmplot(x=)

    import sys
    sys.exit()

    #html = f"<head>{shap.getjs()}</head><body>"
    # Bee swarm
    # .. note: unexpected algorithm matplotlib!
    # .. note: does not return an object!
    plot_bee = shap.plots.beeswarm(shap_values, show=False)

    # Sow
    print("\nBEE")
    print(plot_bee)

    #print(f)
    # Waterfall
    # .. note: not working!
    #shap.plots.waterfall(shap_values[0], max_display=14)

    # Force plot
    # .. note: not working!
    plot_force = shap.plots.force(explainer.expected_value,
        explainer.shap_values(X_train), X_train,
        matplotlib=False, show=False)

    # Show
    print("\nFORCE:")
    print(plot_force)
    print(plot_force.html())
    print(shap.save_html('e.html', plot_force))
    """

.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  2.068 seconds)


.. _sphx_glr_download__examples_shap_plot_main01.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: plot_main01.py <plot_main01.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: plot_main01.ipynb <plot_main01.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_