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

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

.. _sphx_glr_auto_examples_classifiers.py:


Classifier comparison
===========================

Simple demonstration of the different implemented classifiers in emlearn

.. GENERATED FROM PYTHON SOURCE LINES 8-24

.. 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 25-29

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

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

.. GENERATED FROM PYTHON SOURCE LINES 29-42

.. code-block:: Python

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

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

        return df

    dataset = load_dataset()









.. GENERATED FROM PYTHON SOURCE LINES 43-48

Correctness checking
------------------------------

Compare predictions made by converted emlearn C model
with those of the trained Python model

.. GENERATED FROM PYTHON SOURCE LINES 48-117

.. code-block:: Python

    def check_correctness(out_dir, name, model_filename, test_data, test_predictions, feature_columns):
        test_res = numpy.array(test_predictions).flatten()

        test_dataset = "\n".join([
            emlearn.cgen.array_declare(f"{name}_testset_data", dtype='float', values=test_data),
            emlearn.cgen.array_declare(f"{name}_testset_results", dtype='int', values=test_res),
            emlearn.cgen.constant_declare(f'{name}_testset_features', val=len(feature_columns)),
            emlearn.cgen.constant_declare(f'{name}_testset_samples', val=len(test_predictions)),
        ])

        test_code = test_dataset + \
        f'''
        #include "{model_filename}" // emlearn generated model

        #include <stdio.h> // printf

        int
        {name}_test() {{
            const int n_features = {name}_testset_features;
            const int n_testcases = {name}_testset_samples;

            int errors = 0;

            for (int i=0; i<n_testcases; i++) {{
                const float *features = {name}_testset_data + (i*n_features);
                const int expect_result = {name}_testset_results[i*1];

                const int32_t out = model_predict(features, n_features);

                if (out != expect_result) {{
                    printf(\"test-fail sample=%d expect=%d got=%d \\n\", i, expect_result, out);
                    errors += 1;
                }}

            }}
            return errors;
        }}

        int
        main(int argc, const char *argv[])
        {{
            const int errors = {name}_test();
            return errors;
        }}'''

        test_source_file = os.path.join(out_dir, f'test_{name}.c')
        with open(test_source_file, 'w') as f:
            f.write(test_code)

        print('Generated', test_source_file)

        include_dirs = [ emlearn.includedir ]
        test_executable = emlearn.common.compile_executable(
                test_source_file,
                out_dir,
                name='test_{name}',
                include_dirs=include_dirs
        )

        import subprocess
        errors = None
        try:
            subprocess.check_output([test_executable])
            errors = 0
        except subprocess.CalledProcessError as e:
            errors = e.returncode

        return errors








.. GENERATED FROM PYTHON SOURCE LINES 118-122

Plotting tools
------------------------

Plots the decision boundaries score landscape

.. GENERATED FROM PYTHON SOURCE LINES 122-134

.. code-block:: Python

    def plot_results(ax, model, X, y):
        from sklearn.inspection import DecisionBoundaryDisplay

        # show classification boundaries
        DecisionBoundaryDisplay.from_estimator(
            model, X, alpha=0.4, ax=ax, response_method="auto",
        )

        # show datapoints
        ax.scatter(X.iloc[:, 0], X.iloc[:, 1], c=y, s=20, edgecolor="k")

 







.. GENERATED FROM PYTHON SOURCE LINES 135-140

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 140-175

.. code-block:: Python

    def build_run_classifier(ax, model, name):
        from sklearn.model_selection import train_test_split

        target_column = 'target'

        # Train model
        test, train = train_test_split(dataset, test_size=0.3, random_state=3)
        feature_columns = list(set(dataset.columns) - set([target_column]))

        # limit to 2 columns to be able to visualize
        feature_columns = ['total_phenols', 'color_intensity']
        feature_columns = ['alcohol', 'flavanoids']

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

        out_dir = os.path.join(here, 'classifiers')
        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=None, dtype='float')
        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()

        errors = check_correctness(out_dir, name, model_filename, test_data, test_pred, feature_columns)
        print(f"Tested {name}: {errors} errors")

        plot_results(ax, model, test[feature_columns], test[target_column])










.. GENERATED FROM PYTHON SOURCE LINES 176-180

Classifiers to compare
--------------------------------

Some of the supported modela and configurations

.. GENERATED FROM PYTHON SOURCE LINES 180-194

.. code-block:: Python

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

    classifiers = {
        'decision_tree': sklearn.tree.DecisionTreeClassifier(),
        'random_forest': sklearn.ensemble.RandomForestClassifier(n_estimators=10, random_state=1),
        'extra_trees': sklearn.ensemble.ExtraTreesClassifier(n_estimators=10, random_state=1),
        'gaussian_naive_bayes': sklearn.naive_bayes.GaussianNB(),
        'sklearn_mlp': sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(10,10,), max_iter=1000, random_state=1),
    }









.. GENERATED FROM PYTHON SOURCE LINES 195-199

Run all classifiers
--------------------------------



.. GENERATED FROM PYTHON SOURCE LINES 199-218

.. code-block:: Python

    def main(): 
        fig, axs = plt.subplots(
                ncols=1,
                nrows=len(classifiers),
                figsize=(4, 4*len(classifiers)),
                sharex=True, sharey=True,
        )

        for ax, (name, cls) in zip(axs, classifiers.items()):
            build_run_classifier(ax, cls, name)
            ax.set_title(name)

            if ax != axs[-1]:
                ax.set_xlabel('')

        plt.show()

    main()




.. image-sg:: /auto_examples/images/sphx_glr_classifiers_001.png
   :alt: decision_tree, random_forest, extra_trees, gaussian_naive_bayes, sklearn_mlp
   :srcset: /auto_examples/images/sphx_glr_classifiers_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Generated /home/docs/checkouts/readthedocs.org/user_builds/emlearn/checkouts/stable/examples/classifiers/test_decision_tree.c
    Tested decision_tree: 0 errors
    Generated /home/docs/checkouts/readthedocs.org/user_builds/emlearn/checkouts/stable/examples/classifiers/test_random_forest.c
    Tested random_forest: 0 errors
    Generated /home/docs/checkouts/readthedocs.org/user_builds/emlearn/checkouts/stable/examples/classifiers/test_extra_trees.c
    Tested extra_trees: 0 errors
    Generated /home/docs/checkouts/readthedocs.org/user_builds/emlearn/checkouts/stable/examples/classifiers/test_gaussian_naive_bayes.c
    Tested gaussian_naive_bayes: 0 errors
    /home/docs/checkouts/readthedocs.org/user_builds/emlearn/envs/stable/lib/python3.12/site-packages/sklearn/neural_network/_multilayer_perceptron.py:785: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.
      warnings.warn(
    Generated /home/docs/checkouts/readthedocs.org/user_builds/emlearn/checkouts/stable/examples/classifiers/test_sklearn_mlp.c
    Tested sklearn_mlp: 0 errors





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

   **Total running time of the script:** (0 minutes 1.315 seconds)


.. _sphx_glr_download_auto_examples_classifiers.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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