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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_Interpretation.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_Interpretation.py:


=====================
6. Interpretation
=====================
The purpose of this notebook is to apply various post-hoc interpretation
methods on our model. For this purose, we will rebuild our DecisionTree model,
train it. After this we will apply SHAP, PDP and ALE on the trained DecisionTree model.

.. GENERATED FROM PYTHON SOURCE LINES 10-41

.. code-block:: Python


    import numpy as np
    import pandas as pd

    #np.bool = np.bool_

    import shap

    import matplotlib as mpl
    import matplotlib.pyplot as plt

    import matplotlib.colors as mcolors

    from easy_mpl import pie
    from easy_mpl import bar_chart
    from easy_mpl.utils import create_subplots

    from shap.plots import waterfall
    from shap import summary_plot, Explanation

    from ai4water import Model
    from ai4water.utils.utils import TrainTestSplit
    from ai4water.postprocessing import PartialDependencePlot

    from utils import LABEL_MAP
    from utils import version_info
    from utils import shap_scatter
    from utils import make_classes
    from utils import shap_scatter_plots
    from utils import prepare_data, set_rcParams, plot_ale, SAVE








.. GENERATED FROM PYTHON SOURCE LINES 42-46

.. code-block:: Python


    for lib, ver in version_info().items():
        print(lib, ver)





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

 .. code-block:: none

    python 3.12.10 (main, May  6 2025, 10:49:23) [GCC 11.4.0]
    os posix
    ai4water 1.07
    lightgbm 4.6.0
    catboost 1.2.10
    xgboost 3.2.0
    easy_mpl 0.21.5
    SeqMetrics 2.0.0
    numpy 1.26.4
    pandas 2.2.3
    matplotlib 3.10.8
    h5py 3.16.0
    sklearn 1.3.1
    optuna 4.8.0
    skopt 0.10.2
    plotly 6.6.0
    seaborn 0.13.2
    crepes 0.9.0
    mapie 0.9.2
    shap 0.49.1
    scipy 1.17.1




.. GENERATED FROM PYTHON SOURCE LINES 47-50

.. code-block:: Python


    set_rcParams()








.. GENERATED FROM PYTHON SOURCE LINES 51-59

.. code-block:: Python

    inputs = ['Solution pH', 'Time (m)', 'Anions', 'Ni (At%)', 'HA (mg/L)',
              'loading (g)', 'Pore size (nm)', 'O (At%)',
              'Light intensity (watt)', 'Mo (At%)', 'Dye concentration (mg/L)']
    data, encoders = prepare_data(inputs=inputs,
                                  outputs="k")

    print(data.shape)





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

 .. code-block:: none

    (1527, 12)




.. GENERATED FROM PYTHON SOURCE LINES 60-80

.. code-block:: Python


    input_features = data.columns.tolist()[0:-1]
    output_features = data.columns.tolist()[-1:]

    TrainX, TestX, TrainY, TestY = TrainTestSplit(seed=313).split_by_random(
        data[input_features],
        data[output_features]
    )

    print(TrainX.shape, TrainY.shape, TestX.shape, TestY.shape)

    model = Model(
        model = "DecisionTreeRegressor",
        input_features=input_features,
        output_features=output_features,
        verbosity=-1,
    )

    model.fit(TrainX, TrainY.values)





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

 .. code-block:: none

    (1068, 11) (1068, 1) (459, 11) (459, 1)


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-4" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>DecisionTreeRegressor(random_state=313)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" checked><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeRegressor</label><div class="sk-toggleable__content"><pre>DecisionTreeRegressor(random_state=313)</pre></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 81-83

.. code-block:: Python

    train_p = model.predict(TrainX, process_results=False)








.. GENERATED FROM PYTHON SOURCE LINES 84-86

.. code-block:: Python

    test_p = model.predict(TestX, process_results=False)








.. GENERATED FROM PYTHON SOURCE LINES 87-88

Average prediction on training data

.. GENERATED FROM PYTHON SOURCE LINES 88-90

.. code-block:: Python

    print(train_p.mean())





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

 .. code-block:: none

    0.006940225438833623




.. GENERATED FROM PYTHON SOURCE LINES 91-92

default feature importance from decision tree

.. GENERATED FROM PYTHON SOURCE LINES 92-95

.. code-block:: Python


    print(model._model.feature_importances_)





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

 .. code-block:: none

    [7.40882935e-02 1.94588607e-01 9.18145954e-02 3.19367156e-01
     3.43836446e-02 9.41130328e-02 5.70019588e-03 1.71579275e-02
     3.37566239e-02 2.51049444e-04 1.34778874e-01]




.. GENERATED FROM PYTHON SOURCE LINES 96-103

.. code-block:: Python

    bar_chart(model._model.feature_importances_,
              [LABEL_MAP[n] if n in LABEL_MAP else n for n in model.input_features],
              sort=True,
              show=False)
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_001.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_001.png, /auto_examples/images/sphx_glr_Interpretation_001_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 104-106

SHAP
======

.. GENERATED FROM PYTHON SOURCE LINES 106-112

.. code-block:: Python


    exp = shap.TreeExplainer(model=model._model,
                             data=TrainX,
                             feature_names=input_features)

    print(exp.expected_value)




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

 .. code-block:: none

    0.0066904566801259955




.. GENERATED FROM PYTHON SOURCE LINES 113-126

.. code-block:: Python


    shap_values = exp.shap_values(TrainX, TrainY)

    summary_plot(shap_values,
        TrainX,
        max_display=34,
        feature_names=[LABEL_MAP[n] if n in LABEL_MAP else n for n in input_features],
        show=False)
    if SAVE:
        plt.savefig("results/figures/shap_summary.png", dpi=600, bbox_inches="tight")
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_002.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_002.png, /auto_examples/images/sphx_glr_Interpretation_002_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 127-138

.. code-block:: Python


    sv_bar = np.mean(np.abs(shap_values), axis=0)

    classes, colors, colors_ = make_classes(exp)

    df_with_classes = pd.DataFrame({'features': exp.feature_names,
                       'classes': classes,
                       'mean_shap': sv_bar})

    print(df_with_classes)





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

 .. code-block:: none

                        features                     classes  mean_shap
    0                Solution pH     Experimental Conditions   0.001051
    1                   Time (m)     Experimental Conditions   0.001239
    2                     Anions     Experimental Conditions   0.000390
    3                   Ni (At%)          Atomic Composition   0.003584
    4                  HA (mg/L)     Experimental Conditions   0.000281
    5                loading (g)     Experimental Conditions   0.001199
    6             Pore size (nm)  Physicochemical Properties   0.000322
    7                    O (At%)          Atomic Composition   0.000349
    8     Light intensity (watt)     Experimental Conditions   0.000395
    9                   Mo (At%)          Atomic Composition   0.000014
    10  Dye concentration (mg/L)     Experimental Conditions   0.001875




.. GENERATED FROM PYTHON SOURCE LINES 139-170

.. code-block:: Python

    f, ax = plt.subplots(figsize=(7,9))
    ax = bar_chart(
        sv_bar,
        [LABEL_MAP[n] if n in LABEL_MAP else n for n in exp.feature_names],
        bar_labels=np.round(sv_bar, 4),
        bar_label_kws={'label_type':'edge',
                       'fontsize': 10,
                       'weight': 'bold',
                       "fmt": '%.4f',
                       'padding': 1.5
                       },
        show=False,
        sort=True,
        color=colors_,
        ax = ax
    )
    ax.spines[['top', 'right']].set_visible(False)
    ax.set_xlabel(xlabel='mean(|SHAP value|)')
    ax.set_xticklabels(ax.get_xticks().astype(float))
    ax.set_yticklabels(ax.get_yticklabels())

    labels = df_with_classes['classes'].unique()
    handles = [plt.Rectangle((0,0),1,1,
                             color=colors[l]) for l in labels]
    plt.legend(handles, labels, loc='lower right')
    ax.xaxis.set_major_locator(plt.MaxNLocator(4))
    if SAVE:
        plt.savefig("results/figures/shap_bar.png", dpi=600, bbox_inches="tight")
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_003.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_003.png, /auto_examples/images/sphx_glr_Interpretation_003_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 171-215

.. code-block:: Python


    seg_colors = (colors.values())
    # Change the saturation of seg_colors to 70% for the interior segments
    rgb = mcolors.to_rgba_array(seg_colors)[:,:-1]
    hsv = mcolors.rgb_to_hsv(rgb)
    hsv[:,1] = 0.7 * hsv[:, 1]
    interior_colors = mcolors.hsv_to_rgb(hsv)

    fractions = np.array([
    df_with_classes.loc[df_with_classes['classes']=='Experimental Conditions']['mean_shap'].sum(),
    df_with_classes.loc[df_with_classes['classes']=='Physicochemical Properties']['mean_shap'].sum(),
    df_with_classes.loc[df_with_classes['classes']=='Atomic Composition']['mean_shap'].sum(),
    ])

    dye_frac = df_with_classes.loc[df_with_classes['classes']=='Dye Properties']['mean_shap'].sum()
    labels = ['Experimental \nConditions', 'Physicochemical \nProperties',
                   'Atomic \nComposition']

    if dye_frac > 0.0:
        fractions = np.array(fractions.tolist().append(dye_frac))
        labels.append('Dye Properties')

    fractions /=fractions.sum()

    _, texts= pie(fractions=fractions,
        colors=seg_colors,
        labels=labels,
        wedgeprops=dict(edgecolor="w", width=0.03), radius=1,
        autopct=None,
        textprops = dict(fontsize=12),
        startangle=90, counterclock=False, show=False)
    texts[0].set_fontsize(12)
    _, texts, autotexts = pie(fractions=fractions,
        colors=interior_colors,
           autopct='%1.0f%%',
        textprops = dict(fontsize=24),
        wedgeprops=dict(edgecolor="w"), radius=1-2*0.03,
        startangle=90, counterclock=False, ax=plt.gca(), show=False)
    texts[0].set_fontsize(12)
    if SAVE:
        plt.savefig("results/figures/shap_pie.png", dpi=600, bbox_inches="tight")
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_004.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_004.png, /auto_examples/images/sphx_glr_Interpretation_004_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 216-220

.. code-block:: Python


    index = train_p.argmax()
    print(index, train_p.max())





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

 .. code-block:: none

    355 0.036809739452414975




.. GENERATED FROM PYTHON SOURCE LINES 221-234

.. code-block:: Python

    e = Explanation(
        shap_values[index],
        base_values=exp.expected_value,
        data=TrainX.values[index],
        feature_names=input_features
    )

    waterfall(e, max_display=20, show=False)
    if SAVE:
        plt.savefig(f"results/figures/shap_local_{index}.png", dpi=600, bbox_inches="tight")
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_005.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_005.png, /auto_examples/images/sphx_glr_Interpretation_005_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 235-238

The following figures show SHAP interaction plots. These
figures depict the inteaction effect of two features on model performance.
In these figures, the numbers in legends for Anions, have following meanings

.. GENERATED FROM PYTHON SOURCE LINES 238-240

.. code-block:: Python

    encoders['Anions'].inverse_transform(np.array([0,1,2,3,4, 5, 5]).reshape(-1,1))





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

 .. code-block:: none


    array(['N/A', 'Na2HPO4', 'Na2SO4', 'NaCO3', 'NaCl', 'NaHCO3', 'NaHCO3'],
          dtype=object)



.. GENERATED FROM PYTHON SOURCE LINES 241-249

Similarly for catalyst, the numbers in legend have following meanings
Pt-BFO : 6
Pd-BFO: 4
LM : 2
Ag-BFO : 0
Photolysis : 5
LTH : 3
BFO : 1

.. GENERATED FROM PYTHON SOURCE LINES 251-254

Dye Concentration
------------------
It represents initial concentration of dye.

.. GENERATED FROM PYTHON SOURCE LINES 254-261

.. code-block:: Python

    feature_name = 'Dye concentration (mg/L)'

    if feature_name in TrainX:
        shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_006.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_006.png, /auto_examples/images/sphx_glr_Interpretation_006_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 262-265

Ni (At%)
------------------


.. GENERATED FROM PYTHON SOURCE LINES 265-271

.. code-block:: Python

    feature_name = 'Ni (At%)'
    if feature_name in TrainX:
        shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_007.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_007.png, /auto_examples/images/sphx_glr_Interpretation_007_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 272-275

loading
-----------
It represents how much photocatalyst is present.

.. GENERATED FROM PYTHON SOURCE LINES 275-281

.. code-block:: Python


    feature_name = 'loading (g)'
    shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_008.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_008.png, /auto_examples/images/sphx_glr_Interpretation_008_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 282-284

Time
-------

.. GENERATED FROM PYTHON SOURCE LINES 284-290

.. code-block:: Python


    feature_name = 'Time (m)'
    shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_009.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_009.png, /auto_examples/images/sphx_glr_Interpretation_009_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 291-293

Solution pH
--------------

.. GENERATED FROM PYTHON SOURCE LINES 293-299

.. code-block:: Python


    feature_name = 'Solution pH'
    shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_010.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_010.png, /auto_examples/images/sphx_glr_Interpretation_010_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 300-302

Light intensiy
---------------

.. GENERATED FROM PYTHON SOURCE LINES 302-309

.. code-block:: Python


    feature_name =  'Light intensity (watt)'
    shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)





.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_011.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_011.png, /auto_examples/images/sphx_glr_Interpretation_011_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 310-312

Oxygen
---------------

.. GENERATED FROM PYTHON SOURCE LINES 312-318

.. code-block:: Python


    feature_name = 'O (At%)'
    shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_012.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_012.png, /auto_examples/images/sphx_glr_Interpretation_012_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 319-321

Humic Acid
-----------

.. GENERATED FROM PYTHON SOURCE LINES 321-327

.. code-block:: Python


    feature_name = 'HA (mg/L)'
    shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_013.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_013.png, /auto_examples/images/sphx_glr_Interpretation_013_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 328-330

Pore size
-----------

.. GENERATED FROM PYTHON SOURCE LINES 330-337

.. code-block:: Python


    feature_name = 'Pore size (nm)'
    if feature_name in TrainX:
        shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_014.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_014.png, /auto_examples/images/sphx_glr_Interpretation_014_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 338-340

Anions
--------

.. GENERATED FROM PYTHON SOURCE LINES 340-346

.. code-block:: Python


    feature_name = 'Anions'
    shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_015.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_015.png, /auto_examples/images/sphx_glr_Interpretation_015_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 347-353

.. code-block:: Python

    feature_name = 'Mass ratio (Catalyst/Dye)'
    if feature_name in TrainX:
        shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)








.. GENERATED FROM PYTHON SOURCE LINES 354-356

S
---

.. GENERATED FROM PYTHON SOURCE LINES 356-364

.. code-block:: Python


    feature_name = 'S (At%)'
    if feature_name in TrainX:
        shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)









.. GENERATED FROM PYTHON SOURCE LINES 365-367

Surface Area
--------------

.. GENERATED FROM PYTHON SOURCE LINES 367-374

.. code-block:: Python


    feature_name = 'Surface area (m2/g)'
    if feature_name in TrainX:
        shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)








.. GENERATED FROM PYTHON SOURCE LINES 375-377

Mo
-----------

.. GENERATED FROM PYTHON SOURCE LINES 377-382

.. code-block:: Python

    feature_name =  'Mo (At%)'
    if feature_name in TrainX:
        shap_scatter_plots(shap_values, TrainX, feature_name,
                       encoders=encoders,
                       save=SAVE)



.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_016.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_016.png, /auto_examples/images/sphx_glr_Interpretation_016_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 383-476

.. code-block:: Python

    fig, ((ax1, ax2, ax3, ax4), (ax5, ax6, ax7, ax8)) = plt.subplots(
        2,4, figsize=(15, 8))

    ax = shap_scatter(
        shap_values[:, 5],
        TrainX.loc[:, 'loading (g)'],
        TrainX.loc[:, 'Ni (At%)'],
        feature_name='Cat. Loading (g/L)',
        ax=ax1,
        show=False
    )
    ax.set_ylabel('')
    #ax.set_xlim(ax.get_xlim()[0], 0.32)

    ax = shap_scatter(
        shap_values[:, 5],
        TrainX.loc[:, 'loading (g)'],
        TrainX.loc[:, 'Pore size (nm)'],
        feature_name='Cat. Loading (g/L)',
        ax=ax2,
        show=False
    )
    ax.set_ylabel('')
    #ax.set_xlim(2.5, 12.5)

    ax = shap_scatter(
        shap_values[:, 5],
        TrainX.loc[:, 'loading (g)'],
        TrainX.loc[:, 'Solution pH'],
        feature_name='Cat. Loading (g/L)',
        ax=ax3,
        show=False,
    )
    ax.set_ylabel('')
    #ax.set_xlim(ax.get_xlim()[0], 62)


    ax = shap_scatter(
        shap_values[:, 5],
        TrainX.loc[:, 'loading (g)'],
        TrainX.loc[:, 'Mo (At%)'],
        feature_name='Cat. Loading (g/L)',
        ax=ax4,
        show=False,
    )
    ax.set_ylabel('')
    #ax.set_xlim(2.5, 12.5)

    ax = shap_scatter(
        shap_values[:, 3],
        TrainX.loc[:, 'Ni (At%)'],
        TrainX.loc[:, 'Pore size (nm)'],
        feature_name='Ni (At%)',
        ax=ax5,
        show=False
    )
    ax.set_ylabel('')

    ax = shap_scatter(
        shap_values[:, 3],
        TrainX.loc[:, 'Ni (At%)'],
        TrainX.loc[:, 'Solution pH'],
        feature_name='Ni (At%)',
        ax=ax6,
        show=False
    )
    ax.set_ylabel('')
    #ax.set_xlim(2.5, 12.5)

    ax = shap_scatter(
        shap_values[:, 3],
        TrainX.loc[:, 'Ni (At%)'],
        TrainX.loc[:, 'Mo (At%)'],
        feature_name='Ni (At%)',
        ax=ax7,
        show=False
    )
    ax.set_ylabel('')

    ax = shap_scatter(
        shap_values[:, 0],
        TrainX.loc[:, 'Solution pH'],
        TrainX.loc[:, 'O (At%)'],
        feature_name='Solution pH',
        ax=ax8,
        show=False
    )
    ax.set_ylabel('')
    plt.tight_layout()
    if SAVE:
        plt.savefig("results/figures/shap_dep.png", dpi=600, bbox_inches="tight")
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_017.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_017.png, /auto_examples/images/sphx_glr_Interpretation_017_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 477-479

Partial Dependence Plot
==========================

.. GENERATED FROM PYTHON SOURCE LINES 479-490

.. code-block:: Python



    pdp = PartialDependencePlot(
        model.predict,
        TrainX,
        num_points=20,
        feature_names=TrainX.columns.tolist(),
        show=False,
        save=False
    )








.. GENERATED FROM PYTHON SOURCE LINES 491-522

.. code-block:: Python

    mpl.rcParams.update(mpl.rcParamsDefault)
    colors = ["#DB0007", "#670E36", "#e30613", "#0057B8", "#6C1D45",
              "#034694", "#1B458F", "#003399", "#FFCD00", "#003090",
              "#C8102E", "#6CABDD", "#DA291C", "#241F20", "#00A650",
              "#D71920", "#132257", "#ED2127", "#7A263A", "#FDB913",
              "#DB0007", "#670E36", "#e30613", "#0057B8", "#6C1D45",
              "#034694", "#1B458F", "#003399", "#FFCD00", "#003090",
              ]

    f, axes = create_subplots(TrainX.shape[1], figsize=(10, 12))

    for ax, feature, clr in zip(axes.flat, TrainX.columns, colors):

        pdp_vals, ice_vals = pdp.calc_pdp_1dim(TrainX.values, feature)

        ax = pdp.plot_pdp_1dim(pdp_vals, ice_vals, TrainX.values,
                                feature,
                                pdp_line_kws={
                                    'color': clr, 'zorder': 3},
                                ice_color="gray",
                                ice_lines_kws=dict(zorder=2, alpha=0.15),
                                ax=ax,
                                show=False,
                                )
        ax.set_xlabel(LABEL_MAP.get(feature, feature))
        ax.set_ylabel(f"E[f(x) | " + feature + "]")
    if SAVE:
        plt.savefig("results/figures/pdp.png", dpi=600, bbox_inches="tight")
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_018.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_018.png, /auto_examples/images/sphx_glr_Interpretation_018_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 523-525

Accumulated Local Effects
==========================

.. GENERATED FROM PYTHON SOURCE LINES 525-539

.. code-block:: Python


    class MyModel:
        def predict(self, X):
            return model.predict(X).reshape(-1,)

    f, axes = create_subplots(TrainX.shape[1], figsize=(10, 12))

    for ax, feature, clr in zip(axes.flat, TrainX.columns, colors):
        plot_ale(MyModel().predict, TrainX, feature,
                 ax=ax, show=False, color=clr, )

    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_019.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_019.png, /auto_examples/images/sphx_glr_Interpretation_019_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 540-544

All Features model interpretation
==================================
For the sake of comparison, we also show interpretation of model which uses
all features as input.

.. GENERATED FROM PYTHON SOURCE LINES 544-551

.. code-block:: Python


    set_rcParams()

    data, encoders = prepare_data(outputs="k")

    print(data.shape)





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

 .. code-block:: none

    (1527, 35)




.. GENERATED FROM PYTHON SOURCE LINES 552-573

.. code-block:: Python


    input_features = data.columns.tolist()[0:-1]
    output_features = data.columns.tolist()[-1:]

    TrainX, TestX, TrainY, TestY = TrainTestSplit(seed=313).split_by_random(
        data[input_features],
        data[output_features]
    )

    print(TrainX.shape, TrainY.shape, TestX.shape, TestY.shape)


    model = Model(
        model = "DecisionTreeRegressor",
        input_features=input_features,
        output_features=output_features,
        verbosity=-1,
    )

    model.fit(TrainX, TrainY.values)





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

 .. code-block:: none

    (1068, 34) (1068, 1) (459, 34) (459, 1)


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-5" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>DecisionTreeRegressor(random_state=313)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" checked><label for="sk-estimator-id-7" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeRegressor</label><div class="sk-toggleable__content"><pre>DecisionTreeRegressor(random_state=313)</pre></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 574-576

.. code-block:: Python

    train_p = model.predict(TrainX, process_results=False)








.. GENERATED FROM PYTHON SOURCE LINES 577-579

.. code-block:: Python

    test_p = model.predict(TestX, process_results=False)








.. GENERATED FROM PYTHON SOURCE LINES 580-581

Average prediction on training data

.. GENERATED FROM PYTHON SOURCE LINES 581-583

.. code-block:: Python

    print(train_p.mean())





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

 .. code-block:: none

    0.0069402254388336235




.. GENERATED FROM PYTHON SOURCE LINES 584-585

default feature importance from decision tree

.. GENERATED FROM PYTHON SOURCE LINES 585-588

.. code-block:: Python


    print(model._model.feature_importances_)





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

 .. code-block:: none

    [3.81006042e-05 1.73386029e-06 1.19712970e-02 0.00000000e+00
     7.23370042e-03 1.99232034e-05 3.97314739e-04 2.75412342e-01
     1.25751993e-03 4.10362230e-02 9.97703911e-04 0.00000000e+00
     2.34364582e-05 2.25969210e-04 1.03530578e-02 2.14975624e-03
     5.75046118e-04 0.00000000e+00 8.39858878e-02 3.36541611e-02
     2.61657725e-03 1.93957659e-01 0.00000000e+00 0.00000000e+00
     0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00
     0.00000000e+00 0.00000000e+00 1.34414955e-01 7.38624495e-02
     3.42792786e-02 9.15359069e-02]




.. GENERATED FROM PYTHON SOURCE LINES 589-599

.. code-block:: Python

    fig, ax = plt.subplots(figsize=(6, 8))
    bar_chart(model._model.feature_importances_,
              [LABEL_MAP[n] if n in LABEL_MAP else n for n in model.input_features],
              sort=True,
              show=False,
              ax=ax)
    plt.tight_layout()
    plt.show()





.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_020.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_020.png, /auto_examples/images/sphx_glr_Interpretation_020_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 600-602

SHAP all features
==================

.. GENERATED FROM PYTHON SOURCE LINES 602-608

.. code-block:: Python


    exp = shap.TreeExplainer(model=model._model,
                             data=TrainX,
                             feature_names=input_features)

    print(exp.expected_value)




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

 .. code-block:: none

    0.00671328508398034




.. GENERATED FROM PYTHON SOURCE LINES 609-622

.. code-block:: Python


    shap_values = exp.shap_values(TrainX, TrainY)

    summary_plot(shap_values,
        TrainX,
                 max_display=34,
    feature_names=[LABEL_MAP[n] if n in LABEL_MAP else n for n in input_features],
        show=False)
    if SAVE:
        plt.savefig("results/figures/shap_summary_all.png", dpi=600, bbox_inches="tight")
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/images/sphx_glr_Interpretation_021.png
   :alt: Interpretation
   :srcset: /auto_examples/images/sphx_glr_Interpretation_021.png, /auto_examples/images/sphx_glr_Interpretation_021_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 623-710

.. code-block:: Python


    # sv_bar = np.mean(np.abs(shap_values), axis=0)

    # classes, colors, colors_ = make_classes(exp)

    # df_with_classes = pd.DataFrame({'features': exp.feature_names,
    #                    'classes': classes,
    #                    'mean_shap': sv_bar})

    # print(df_with_classes)

    # # %%
    # f, ax = plt.subplots(figsize=(7,9))
    # ax = bar_chart(
    #     sv_bar,
    #     [LABEL_MAP[n] if n in LABEL_MAP else n for n in exp.feature_names],
    #     bar_labels=np.round(sv_bar, 4),
    #     bar_label_kws={'label_type':'edge',
    #                    'fontsize': 10,
    #                    'weight': 'bold',
    #                    "fmt": '%.4f',
    #                    'padding': 1.5
    #                    },
    #     show=False,
    #     sort=True,
    #     color=colors_,
    #     ax = ax
    # )
    # ax.spines[['top', 'right']].set_visible(False)
    # ax.set_xlabel(xlabel='mean(|SHAP value|)')
    # ax.set_xticklabels(ax.get_xticks().astype(float))
    # ax.set_yticklabels(ax.get_yticklabels())

    # labels = df_with_classes['classes'].unique()
    # handles = [plt.Rectangle((0,0),1,1,
    #                          color=colors[l]) for l in labels]
    # plt.legend(handles, labels, loc='lower right')
    # ax.xaxis.set_major_locator(plt.MaxNLocator(4))
    # if SAVE:
    #     plt.savefig("results/figures/shap_bar_all.png", dpi=600, bbox_inches="tight")
    # plt.tight_layout()
    # plt.show()

    # # %%

    # seg_colors = (colors.values())
    # # Change the saturation of seg_colors to 70% for the interior segments
    # rgb = mcolors.to_rgba_array(seg_colors)[:,:-1]
    # hsv = mcolors.rgb_to_hsv(rgb)
    # hsv[:,1] = 0.7 * hsv[:, 1]
    # interior_colors = mcolors.hsv_to_rgb(hsv)

    # fractions = np.array([
    # df_with_classes.loc[df_with_classes['classes']=='Experimental Conditions']['mean_shap'].sum(),
    # df_with_classes.loc[df_with_classes['classes']=='Physicochemical Properties']['mean_shap'].sum(),
    # df_with_classes.loc[df_with_classes['classes']=='Atomic Composition']['mean_shap'].sum(),
    # ])

    # dye_frac = df_with_classes.loc[df_with_classes['classes']=='Dye Properties']['mean_shap'].sum()
    # labels = ['Experimental \nConditions', 'Physicochemical \nProperties',
    #                'Atomic \nComposition']

    # if dye_frac > 0.0:
    #     fractions = np.array(fractions.tolist().append(dye_frac))
    #     labels.append('Dye Properties')

    # fractions /=fractions.sum()

    # _, texts= pie(fractions=fractions,
    #     colors=seg_colors,
    #     labels=labels,
    #     wedgeprops=dict(edgecolor="w", width=0.03), radius=1,
    #     autopct=None,
    #     textprops = dict(fontsize=12),
    #     startangle=90, counterclock=False, show=False)
    # texts[0].set_fontsize(12)
    # _, texts, autotexts = pie(fractions=fractions,
    #     colors=interior_colors,
    #        autopct='%1.0f%%',
    #     textprops = dict(fontsize=24),
    #     wedgeprops=dict(edgecolor="w"), radius=1-2*0.03,
    #     startangle=90, counterclock=False, ax=plt.gca(), show=False)
    # texts[0].set_fontsize(12)
    # if SAVE:
    #     plt.savefig("results/figures/shap_pie_all.png", dpi=600, bbox_inches="tight")
    # plt.tight_layout()
    # plt.show()








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

   **Total running time of the script:** (0 minutes 56.549 seconds)


.. _sphx_glr_download_auto_examples_Interpretation.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: Interpretation.zip <Interpretation.zip>`


.. only:: html

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

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