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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_category_feature_analysis.py:


Category Feature Analysis
=========================

It is common to model across estimation domains using categorical features.
This example demonstrates how to use the ModelSelection class to compare the performance of the
source model against models fitted independently on the category values.

.. GENERATED FROM PYTHON SOURCE LINES 10-28

.. code-block:: default

    import logging
    from functools import partial
    from typing import Dict

    import pandas as pd
    import plotly
    import sklearn
    from sklearn.compose import ColumnTransformer
    from sklearn.datasets import load_diabetes
    from sklearn.pipeline import make_pipeline, Pipeline
    from sklearn.preprocessing import OneHotEncoder, StandardScaler

    from elphick.sklearn_viz.model_selection import ModelSelection, metrics

    logging.basicConfig(level=logging.DEBUG,
                        format='%(asctime)s %(levelname)s %(module)s - %(funcName)s: %(message)s',
                        datefmt='%Y-%m-%dT%H:%M:%S%z')








.. GENERATED FROM PYTHON SOURCE LINES 29-33

Load Regression Data
--------------------

We prepare a `group` variable (a pd.Series) in order to test the performance of modelling independently.

.. GENERATED FROM PYTHON SOURCE LINES 33-41

.. code-block:: default


    diabetes = load_diabetes(as_frame=True)
    x, y = diabetes.data.copy(), diabetes.target
    x['sex'] = pd.Categorical(x['sex'].apply(lambda x: 'M' if x < 0 else 'F'))  # assumed mock classes.
    y.name = "progression"
    xy: pd.DataFrame = pd.concat([x, y], axis=1)
    group: pd.Series = x['sex']








.. GENERATED FROM PYTHON SOURCE LINES 42-44

Define the pipeline
-------------------

.. GENERATED FROM PYTHON SOURCE LINES 44-67

.. code-block:: default


    numerical_cols = x.select_dtypes(include=[float]).columns.to_list()
    categorical_cols = x.select_dtypes(include=[object, 'category']).columns.to_list()

    categorical_preprocessor = OneHotEncoder(handle_unknown="ignore", sparse_output=False)
    numerical_preprocessor = StandardScaler()
    preprocessor = ColumnTransformer(
        [
            ("one-hot-encoder", categorical_preprocessor, categorical_cols),
            ("standard_scaler", numerical_preprocessor, numerical_cols),
        ]
    )

    pp: Pipeline = make_pipeline(preprocessor)
    models_to_test: Dict = {'LR': sklearn.linear_model.LinearRegression(),
                            'LASSO': sklearn.linear_model.LassoCV()}

    ms: ModelSelection = ModelSelection(estimators=models_to_test, datasets=xy, target='progression', pre_processor=pp,
                                        k_folds=10, scorer='r2', group=group,
                                        metrics={'r2_score': metrics.r2_score, 'moe': metrics.moe_95,
                                                 'rmse': metrics.rmse,
                                                 'me': metrics.mean_error},
                                        random_state=123)







.. GENERATED FROM PYTHON SOURCE LINES 68-69

Next we'll view the plot, but we will not (yet) leverage the group variable.

.. GENERATED FROM PYTHON SOURCE LINES 69-74

.. code-block:: default


    fig = ms.plot(metrics=['moe', 'me'])
    fig.update_layout(height=700)
    fig






.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 75-79

Now, we will re-plot using group.  This is fast, since the fitting metrics were calculated when the first plot was
created, and do not need to be calculated again.

Plotting by group can (hopefully) provide evidence that metrics are consistent across groups.

.. GENERATED FROM PYTHON SOURCE LINES 79-84

.. code-block:: default


    fig = ms.plot(metrics=['moe', 'me'], show_group=True)
    fig.update_layout(height=700)
    fig






.. raw:: html

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.. GENERATED FROM PYTHON SOURCE LINES 85-90

Categorical Feature Analysis
----------------------------

This analysis will test whether better performance can be achieved by modelling the specified categorical class
separately, rather than passing it as a feature to the model.

.. GENERATED FROM PYTHON SOURCE LINES 90-95

.. code-block:: default


    fig = ms.plot_category_analysis(algorithm='LR')
    fig.update_layout(height=700)
    plotly.io.show(fig)




.. raw:: html
    :file: images/sphx_glr_category_feature_analysis_001.html





.. GENERATED FROM PYTHON SOURCE LINES 96-97

We can view more metrics...

.. GENERATED FROM PYTHON SOURCE LINES 97-104

.. code-block:: default


    fig = ms.plot_category_analysis(algorithm='LR', dataset=None,
                                    metrics=['r2_score', 'moe', 'rmse', 'me'],
                                    col_wrap=2)
    fig.update_layout(height=800)
    fig






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.. GENERATED FROM PYTHON SOURCE LINES 105-109

.. admonition:: Info

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.. _sphx_glr_download_auto_examples_category_feature_analysis.py:

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