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

.. only:: html

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

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

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

.. _sphx_glr_gyexamples_plot_grid_search.py:


.. _l-example-grid-search:

Grid search ONNX models
=======================

This example uses *OnnxTransformer* to freeze a model.
Many preprocessing are fitted, converted into :epkg:`ONNX`
and inserted into a pipeline with *OnnxTransformer*
si that they do not have to be fitted again.
The grid search will pick the best one for the task.

.. contents::
    :local:

Fit all preprocessings and serialize with ONNX
++++++++++++++++++++++++++++++++++++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 19-52

.. code-block:: default


    from sklearn.pipeline import make_pipeline
    from sklearn.decomposition import PCA
    from sklearn.preprocessing import StandardScaler
    from sklearn.datasets import load_iris
    from sklearn.linear_model import LogisticRegression
    from sklearn.model_selection import train_test_split
    from sklearn.model_selection import GridSearchCV
    from sklearn.metrics import classification_report
    from sklearn.exceptions import ConvergenceWarning
    from sklearn.utils._testing import ignore_warnings
    from skl2onnx.common.data_types import FloatTensorType
    from skl2onnx import convert_sklearn
    from mlprodict.sklapi import OnnxTransformer

    iris = load_iris()
    X, y = iris.data, iris.target
    X_train, X_test, y_train, y_test = train_test_split(X, y)

    dec_models = [
        PCA(n_components=1),
        PCA(n_components=2),
        StandardScaler(),
    ]

    onx_bytes = []

    for model in dec_models:
        model.fit(X_train)
        onx = convert_sklearn(
            model, initial_types=[('X', FloatTensorType((None, X.shape[1])))])
        onx_bytes.append(onx.SerializeToString())








.. GENERATED FROM PYTHON SOURCE LINES 53-55

Pipeline with OnnxTransformer
+++++++++++++++++++++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 55-59

.. code-block:: default


    pipe = make_pipeline(OnnxTransformer(onx_bytes[0]),
                         LogisticRegression(multi_class='ovr'))








.. GENERATED FROM PYTHON SOURCE LINES 60-65

Grid Search
+++++++++++

The serialized models are now used as a parameter
in the grid search.

.. GENERATED FROM PYTHON SOURCE LINES 65-85

.. code-block:: default


    param_grid = [{'onnxtransformer__onnx_bytes': onx_bytes,
                   'logisticregression__penalty': ['l2', 'l1'],
                   'logisticregression__solver': ['liblinear', 'saga']
                   }]


    @ignore_warnings(category=ConvergenceWarning)
    def fit(pipe, param_grid, cv=3):
        clf = GridSearchCV(pipe, param_grid, cv=3, n_jobs=1)
        clf.fit(X_train, y_train)
        return clf


    clf = fit(pipe, param_grid)

    y_true, y_pred = y_test, clf.predict(X_test)
    cl = classification_report(y_true, y_pred)
    print(cl)





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

 .. code-block:: none

                  precision    recall  f1-score   support

               0       1.00      1.00      1.00        14
               1       1.00      0.85      0.92        13
               2       0.85      1.00      0.92        11

        accuracy                           0.95        38
       macro avg       0.95      0.95      0.94        38
    weighted avg       0.96      0.95      0.95        38





.. GENERATED FROM PYTHON SOURCE LINES 86-93

Best preprocessing?
+++++++++++++++++++

We get the best parameters returned by the grid search
and we search for it in the list of serialized
preprocessing models.
And the winner is...

.. GENERATED FROM PYTHON SOURCE LINES 93-97

.. code-block:: default


    bp = clf.best_params_
    best_step = onx_bytes.index(bp["onnxtransformer__onnx_bytes"])
    print(dec_models[best_step])




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

 .. code-block:: none

    PCA(n_components=1)





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

   **Total running time of the script:** ( 0 minutes  0.857 seconds)


.. _sphx_glr_download_gyexamples_plot_grid_search.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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