.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "_examples/shap/plot_main05_summaryplot.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download__examples_shap_plot_main05_summaryplot.py>`
        to download the full example code

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

.. _sphx_glr__examples_shap_plot_main05_summaryplot.py:


05. Summary plot
=============================

.. GENERATED FROM PYTHON SOURCE LINES 5-67

.. code-block:: default
   :lineno-start: 6


    # Libraries
    import shap
    import pandas as pd

    import matplotlib.pyplot as plt


    try:
        __file__
        TERMINAL = True
    except:
        TERMINAL = False


    # ------------------------
    # Methods
    # ------------------------
    def load_shap_file():
        """Load shap file.

        .. note: The timestep does not indicate time step but matrix
                 index index. Since the matrix index for time steps
                 started in negative t=-T and ended in t=0 the
                 transformation should be taken into account.

        """
        from pathlib import Path
        # Load data
        path = Path('../../datasets/shap/')
        data = pd.read_csv(path / 'shap.csv')
        data = data.iloc[:, 1:]
        data = data.rename(columns={'timestep': 'indice'})
        data['timestep'] = data.indice - (data.indice.nunique() - 1)
        return data


    # -----------------------------------------------------
    #                       Main
    # -----------------------------------------------------
    # Load data
    # data = create_random_shap(10, 6, 4)
    data = load_shap_file()
    #data = data[data['sample'] < 100]

    shap_values = pd.pivot_table(data,
                                 values='shap_values',
                                 index=['sample', 'timestep'],
                                 columns=['features'])

    feature_values = pd.pivot_table(data,
                                    values='feature_values',
                                    index=['sample', 'timestep'],
                                    columns=['features'])

    # Show
    if TERMINAL:
        print("\nShow:")
        print(data)
        print(shap_values)
        print(feature_values)








.. GENERATED FROM PYTHON SOURCE LINES 68-69

Let's see how data looks like

.. GENERATED FROM PYTHON SOURCE LINES 69-71

.. code-block:: default
   :lineno-start: 69

    data.head(10)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>sample</th>
          <th>indice</th>
          <th>features</th>
          <th>feature_values</th>
          <th>shap_values</th>
          <th>timestep</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>0</td>
          <td>0</td>
          <td>Ward Lactate</td>
          <td>0.0</td>
          <td>0.000652</td>
          <td>-6</td>
        </tr>
        <tr>
          <th>1</th>
          <td>0</td>
          <td>0</td>
          <td>Ward Glucose</td>
          <td>0.0</td>
          <td>-0.000596</td>
          <td>-6</td>
        </tr>
        <tr>
          <th>2</th>
          <td>0</td>
          <td>0</td>
          <td>Ward sO2</td>
          <td>0.0</td>
          <td>0.000231</td>
          <td>-6</td>
        </tr>
        <tr>
          <th>3</th>
          <td>0</td>
          <td>0</td>
          <td>White blood cell count, blood</td>
          <td>0.0</td>
          <td>0.000582</td>
          <td>-6</td>
        </tr>
        <tr>
          <th>4</th>
          <td>0</td>
          <td>0</td>
          <td>Platelets</td>
          <td>0.0</td>
          <td>-0.001705</td>
          <td>-6</td>
        </tr>
        <tr>
          <th>5</th>
          <td>0</td>
          <td>0</td>
          <td>Haemoglobin</td>
          <td>0.0</td>
          <td>-0.000918</td>
          <td>-6</td>
        </tr>
        <tr>
          <th>6</th>
          <td>0</td>
          <td>0</td>
          <td>Mean cell volume, blood</td>
          <td>0.0</td>
          <td>-0.000654</td>
          <td>-6</td>
        </tr>
        <tr>
          <th>7</th>
          <td>0</td>
          <td>0</td>
          <td>Haematocrit</td>
          <td>0.0</td>
          <td>-0.000487</td>
          <td>-6</td>
        </tr>
        <tr>
          <th>8</th>
          <td>0</td>
          <td>0</td>
          <td>Mean cell haemoglobin conc, blood</td>
          <td>0.0</td>
          <td>0.000090</td>
          <td>-6</td>
        </tr>
        <tr>
          <th>9</th>
          <td>0</td>
          <td>0</td>
          <td>Mean cell haemoglobin level, blood</td>
          <td>0.0</td>
          <td>-0.000296</td>
          <td>-6</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 72-73

Let's see how shap_values looks like

.. GENERATED FROM PYTHON SOURCE LINES 73-75

.. code-block:: default
   :lineno-start: 73

    shap_values.iloc[:10, :5]






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>features</th>
          <th>Alanine Transaminase</th>
          <th>Albumin</th>
          <th>Alkaline Phosphatase</th>
          <th>Bilirubin</th>
          <th>C-Reactive Protein</th>
        </tr>
        <tr>
          <th>sample</th>
          <th>timestep</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="7" valign="top">0</th>
          <th>-6</th>
          <td>-0.001809</td>
          <td>0.000411</td>
          <td>0.000486</td>
          <td>0.000500</td>
          <td>0.010186</td>
        </tr>
        <tr>
          <th>-5</th>
          <td>-0.001363</td>
          <td>0.000563</td>
          <td>0.000803</td>
          <td>-0.000133</td>
          <td>0.005363</td>
        </tr>
        <tr>
          <th>-4</th>
          <td>0.001180</td>
          <td>0.000101</td>
          <td>0.000859</td>
          <td>-0.001680</td>
          <td>-0.016017</td>
        </tr>
        <tr>
          <th>-3</th>
          <td>0.004938</td>
          <td>-0.001043</td>
          <td>0.000570</td>
          <td>-0.003175</td>
          <td>-0.044723</td>
        </tr>
        <tr>
          <th>-2</th>
          <td>0.006206</td>
          <td>-0.001760</td>
          <td>0.000382</td>
          <td>-0.003976</td>
          <td>-0.062485</td>
        </tr>
        <tr>
          <th>-1</th>
          <td>-0.001391</td>
          <td>-0.004886</td>
          <td>0.002457</td>
          <td>0.010031</td>
          <td>0.056280</td>
        </tr>
        <tr>
          <th>0</th>
          <td>0.003583</td>
          <td>0.023502</td>
          <td>0.000534</td>
          <td>0.001672</td>
          <td>-0.010238</td>
        </tr>
        <tr>
          <th rowspan="3" valign="top">1</th>
          <th>-6</th>
          <td>0.000325</td>
          <td>-0.000812</td>
          <td>-0.000210</td>
          <td>-0.000157</td>
          <td>0.000971</td>
        </tr>
        <tr>
          <th>-5</th>
          <td>0.000247</td>
          <td>-0.002281</td>
          <td>-0.000301</td>
          <td>-0.000036</td>
          <td>-0.000035</td>
        </tr>
        <tr>
          <th>-4</th>
          <td>-0.000316</td>
          <td>-0.000034</td>
          <td>-0.000307</td>
          <td>0.000464</td>
          <td>-0.009348</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 76-77

Let's see how feature_values looks like

.. GENERATED FROM PYTHON SOURCE LINES 77-79

.. code-block:: default
   :lineno-start: 77

    feature_values.iloc[:10, :5]






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>features</th>
          <th>Alanine Transaminase</th>
          <th>Albumin</th>
          <th>Alkaline Phosphatase</th>
          <th>Bilirubin</th>
          <th>C-Reactive Protein</th>
        </tr>
        <tr>
          <th>sample</th>
          <th>timestep</th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th rowspan="7" valign="top">0</th>
          <th>-6</th>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>-5</th>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>-4</th>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>-3</th>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>-2</th>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>-1</th>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>0</th>
          <td>-0.982956</td>
          <td>0.237113</td>
          <td>-0.956016</td>
          <td>-0.982152</td>
          <td>-0.726284</td>
        </tr>
        <tr>
          <th rowspan="3" valign="top">1</th>
          <th>-6</th>
          <td>-0.994370</td>
          <td>-0.587629</td>
          <td>-0.956533</td>
          <td>-0.988451</td>
          <td>-0.398008</td>
        </tr>
        <tr>
          <th>-5</th>
          <td>-0.993445</td>
          <td>-0.587629</td>
          <td>-0.954463</td>
          <td>-0.990551</td>
          <td>-0.190805</td>
        </tr>
        <tr>
          <th>-4</th>
          <td>-0.994370</td>
          <td>-0.628866</td>
          <td>-0.963260</td>
          <td>-0.990551</td>
          <td>-0.307893</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 80-84

Display using ``shap.summary_plot``
-----------------------------------------------

The first option is to use the ``shap`` library to plot the results.

.. GENERATED FROM PYTHON SOURCE LINES 84-93

.. code-block:: default
   :lineno-start: 85


    # Let's define/extract some useful variables.
    N = 10  # max loops filter
    TIMESTEPS = len(shap_values.index.unique(level='timestep'))  # number of timesteps
    SAMPLES = len(shap_values.index.unique(level='sample'))  # number of samples

    shap_min = data.shap_values.min()
    shap_max = data.shap_values.max()








.. GENERATED FROM PYTHON SOURCE LINES 94-95

Now, let's display the shap values for all features in each timestep.

.. GENERATED FROM PYTHON SOURCE LINES 95-115

.. code-block:: default
   :lineno-start: 98




    # For each timestep (visualise all features)
    steps = shap_values.index.get_level_values('timestep').unique()
    for i, step in enumerate(steps):
        # Get interesting indexes
        indice = shap_values.index.get_level_values('timestep') == step

        # Create auxiliary matrices
        shap_aux = shap_values.iloc[indice]
        feat_aux = feature_values.iloc[indice]

        # Display
        plt.figure()
        plt.title("Timestep: %s" % step)
        shap.summary_plot(shap_aux.to_numpy(), feat_aux, show=False)
        plt.xlim(shap_min, shap_max)





.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_001.png
         :alt: Timestep: -6
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_002.png
         :alt: Timestep: -5
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_003.png
         :alt: Timestep: -4
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_004.png
         :alt: Timestep: -3
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_004.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_005.png
         :alt: Timestep: -2
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_005.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_006.png
         :alt: Timestep: -1
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_006.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_007.png
         :alt: Timestep: 0
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_007.png
         :class: sphx-glr-multi-img





.. GENERATED FROM PYTHON SOURCE LINES 116-117

Now, let's display the shap values for all timesteps of each feature.

.. GENERATED FROM PYTHON SOURCE LINES 117-141

.. code-block:: default
   :lineno-start: 118


    # For each feature (visualise all time-steps)
    for i, f in enumerate(shap_values.columns[:N]):
        # Show
        # print('%2d. %s' % (i, f))

        # Create auxiliary matrices (select feature and reshape)
        shap_aux = shap_values.iloc[:, i] \
            .to_numpy().reshape(-1, TIMESTEPS)
        feat_aux = feature_values.iloc[:, i] \
            .to_numpy().reshape(-1, TIMESTEPS)
        feat_aux = pd.DataFrame(feat_aux,
            columns=['timestep %s' % j for j in range(-TIMESTEPS+1, 1)]
            )

        # Show
        plt.figure()
        plt.title("Feature: %s" % f)
        shap.summary_plot(shap_aux, feat_aux, sort=False, show=False, plot_type='violin')
        plt.xlim(shap_min, shap_max)
        plt.gca().invert_yaxis()

    # Show
    plt.show()



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_008.png
         :alt: Feature: Alanine Transaminase
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_008.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_009.png
         :alt: Feature: Albumin
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_009.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_010.png
         :alt: Feature: Alkaline Phosphatase
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_010.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_011.png
         :alt: Feature: Bilirubin
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_011.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_012.png
         :alt: Feature: C-Reactive Protein
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_012.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_013.png
         :alt: Feature: Chloride
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_013.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_014.png
         :alt: Feature: Creatinine
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_014.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_015.png
         :alt: Feature: D-Dimer
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_015.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_016.png
         :alt: Feature: Eosinophils
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_016.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_017.png
         :alt: Feature: Ferritin
         :srcset: /_examples/shap/images/sphx_glr_plot_main05_summaryplot_017.png
         :class: sphx-glr-multi-img






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

   **Total running time of the script:** ( 0 minutes  7.590 seconds)


.. _sphx_glr_download__examples_shap_plot_main05_summaryplot.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_main05_summaryplot.py <plot_main05_summaryplot.py>`



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

     :download:`Download Jupyter notebook: plot_main05_summaryplot.ipynb <plot_main05_summaryplot.ipynb>`


.. only:: html

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

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