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

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

.. _sphx_glr_auto_examples_regression.py:


Regression models
===========================

Simple demonstration of the different implemented regression models in emlearn

.. GENERATED FROM PYTHON SOURCE LINES 8-25

.. code-block:: Python



    import os.path

    import emlearn
    import numpy
    import pandas
    import matplotlib.pyplot as plt
    import seaborn

    try:
        # When executed as regular .py script
        here = os.path.dirname(__file__)
    except NameError:
        # When executed as Jupyter notebook / Sphinx Gallery
        here = os.getcwd()








.. GENERATED FROM PYTHON SOURCE LINES 26-30

Create dataset
------------------------

Using a simple multi-class dataset included with scikit-learn

.. GENERATED FROM PYTHON SOURCE LINES 30-52

.. code-block:: Python

    def load_dataset():
        from sklearn import datasets
        data = datasets.load_diabetes(as_frame=True)

        df = data.data.copy()
        df.columns = data.feature_names
        df['target'] = data.target

        return df

    dataset = load_dataset()

    def plot_results(ax, model, X, y):
        from sklearn.metrics import PredictionErrorDisplay

        disp = PredictionErrorDisplay.from_estimator(
            model, X, y,
            ax=ax,
            kind="actual_vs_predicted",
        )









.. GENERATED FROM PYTHON SOURCE LINES 53-58

Train, convert and run model
------------------------------

Using the standard scikit-learn process,
and then using emlearn to convert the model to C

.. GENERATED FROM PYTHON SOURCE LINES 58-88

.. code-block:: Python

    def build_run_regressor(model, name, ax=None, feature_columns=None, color=None):
        from sklearn.model_selection import train_test_split

        target_column = 'target'
        if feature_columns is None:
            feature_columns = list(set(dataset.columns) - set([target_column]))

        # Train model
        test, train = train_test_split(dataset, test_size=0.3, random_state=3)

        model.fit(train[feature_columns], train[target_column])

        out_dir = os.path.join(here, 'regression')
        if not os.path.exists(out_dir):
            os.makedirs(out_dir)

        model_filename = os.path.join(out_dir, f'{name}_model.h')
        cmodel = emlearn.convert(model, method='loadable')
        code = cmodel.save(file=model_filename, name='model')
    
        test_pred = cmodel.predict(test[feature_columns])
        # Generate a test dataet
        test_data = numpy.array(test[feature_columns]).flatten()
        test_res = numpy.array(test_pred).flatten()
        # TODO: use tools to generate C file to compare classifier against expected results i C

        if ax is not None:
            plot_results(ax, model, X=test[feature_columns], y=test[target_column])









.. GENERATED FROM PYTHON SOURCE LINES 89-93

Run all models
--------------------------------

Some of the supported modela and configurations

.. GENERATED FROM PYTHON SOURCE LINES 93-127

.. code-block:: Python

    import sklearn.ensemble
    import sklearn.tree
    import sklearn.neural_network
    import sklearn.naive_bayes

    models = {
        'random_forest': sklearn.ensemble.RandomForestRegressor(n_estimators=10, random_state=1),
        'extra_trees': sklearn.ensemble.ExtraTreesRegressor(n_estimators=10, random_state=1), 
        'decision_tree': sklearn.tree.DecisionTreeRegressor(),

        # TODO: support MultiLayerPerceptron
        #'sklearn_mlp': sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(10,10,), max_iter=30, random_state=1),

        # TODO: support the various LinearRegression alternatives
    }

    # Based the below feature analysis - s5, bmi, bp are the most useful features for diabetes dataset
    # https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#gsphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py
    feature_columns = ['s5', 'bmi', 'bp']

    fig, axs = plt.subplots(
            #ncols=len(models),
            nrows=len(models),
            figsize=(4, 4.2*len(models)),
            sharex=False, sharey=False,
    )
    for model_no, (name, cls) in enumerate(models.items()): 
        ax = axs[model_no] 
        build_run_regressor(cls, name, ax)

        ax.set_title(name)
        ax.set_xlabel('')

    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_regression_001.png
   :alt: random_forest, extra_trees, decision_tree
   :srcset: /auto_examples/images/sphx_glr_regression_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 3.546 seconds)


.. _sphx_glr_download_auto_examples_regression.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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