.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/tabular/plot_conditional_imputer.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_tabular_plot_conditional_imputer.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_tabular_plot_conditional_imputer.py:


Conditional Data Imputation
============================

This example shows how to use :class:`~shapiq.ConditionalImputer` for
conditional (observational) imputation when computing Shapley interactions.
Conditional imputation respects feature dependencies, unlike marginal
(interventional) imputation.

.. GENERATED FROM PYTHON SOURCE LINES 10-18

.. code-block:: Python


    from __future__ import annotations

    from sklearn.ensemble import RandomForestRegressor
    from sklearn.model_selection import train_test_split

    import shapiq








.. GENERATED FROM PYTHON SOURCE LINES 19-21

Load Data and Train Model
--------------------------

.. GENERATED FROM PYTHON SOURCE LINES 21-42

.. code-block:: Python


    X, y = shapiq.load_california_housing()
    X_train, X_test, y_train, y_test = train_test_split(
        X.values,
        y.values,
        test_size=0.25,
        random_state=42,
    )
    n_features = X_train.shape[1]

    model = RandomForestRegressor(
        n_estimators=100,
        max_depth=n_features,
        max_features=2 / 3,
        max_samples=2 / 3,
        random_state=42,
    )
    model.fit(X_train, y_train)
    print(f"Train R2: {model.score(X_train, y_train):.4f}")
    print(f"Test  R2: {model.score(X_test, y_test):.4f}")





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

 .. code-block:: none

    Train R2: 0.7965
    Test  R2: 0.7431




.. GENERATED FROM PYTHON SOURCE LINES 43-52

Conditional Imputer
--------------------
Set ``imputer="conditional"`` in :class:`~shapiq.TabularExplainer`.
The imputer trains a gradient boosting model per feature to learn the
conditional distribution. Key parameters:

- ``sample_size``: samples drawn from conditional background
- ``conditional_budget``: coalitions per data point for training
- ``conditional_threshold``: quantile threshold for neighbourhood

.. GENERATED FROM PYTHON SOURCE LINES 52-64

.. code-block:: Python


    explainer = shapiq.TabularExplainer(
        model=model,
        data=X_train,
        index="SII",
        max_order=2,
        imputer="conditional",
        sample_size=100,
        conditional_budget=32,
        conditional_threshold=0.04,
    )








.. GENERATED FROM PYTHON SOURCE LINES 65-67

Explain a Single Instance
--------------------------

.. GENERATED FROM PYTHON SOURCE LINES 67-72

.. code-block:: Python


    x_explain = X_test[100]
    iv = explainer.explain(x_explain, budget=2**n_features, random_state=0)
    print(iv)





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

 .. code-block:: none

    InteractionValues(
        index=SII, max_order=2, min_order=0, estimated=False, estimation_budget=256,
        n_players=8, baseline_value=2.0701874006108745,
        Top 10 interactions:
            (0, 1): 0.07235594319404895
            (5,): 0.06398005281148228
            (2,): 0.037374147027905556
            (5, 7): 0.037311123046789234
            (1, 7): -0.029679463654197765
            (1, 5): -0.0336762113531598
            (0, 7): -0.06493048753501021
            (1,): -0.1331566617040377
            (7,): -0.17584970197462807
            (0,): -0.19972672348833737
    )




.. GENERATED FROM PYTHON SOURCE LINES 73-75

Network Plot
-------------

.. GENERATED FROM PYTHON SOURCE LINES 75-77

.. code-block:: Python


    shapiq.network_plot(interaction_values=iv, feature_names=list(X.columns))



.. image-sg:: /auto_examples/tabular/images/sphx_glr_plot_conditional_imputer_001.png
   :alt: plot conditional imputer
   :srcset: /auto_examples/tabular/images/sphx_glr_plot_conditional_imputer_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    (<Figure size 700x700 with 1 Axes>, <Axes: >)




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

   **Total running time of the script:** (0 minutes 32.687 seconds)


.. _sphx_glr_download_auto_examples_tabular_plot_conditional_imputer.py:

.. only:: html

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

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

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

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

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

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

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