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.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/80_extending/example_extending_regression.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_examples_80_extending_example_extending_regression.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_examples_80_extending_example_extending_regression.py:


================================================
Extending Auto-Sklearn with Regression Component
================================================

The following example demonstrates how to create a new regression
component for using in auto-sklearn.

.. GENERATED FROM PYTHON SOURCE LINES 9-38

.. code-block:: default

    from typing import Optional
    from pprint import pprint

    from ConfigSpace.configuration_space import ConfigurationSpace
    from ConfigSpace.hyperparameters import (
        UniformFloatHyperparameter,
        UniformIntegerHyperparameter,
        CategoricalHyperparameter,
    )
    from ConfigSpace.conditions import EqualsCondition

    import sklearn.metrics

    from autosklearn.askl_typing import FEAT_TYPE_TYPE
    import autosklearn.regression
    import autosklearn.pipeline.components.regression
    from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
    from autosklearn.pipeline.constants import (
        SPARSE,
        DENSE,
        SIGNED_DATA,
        UNSIGNED_DATA,
        PREDICTIONS,
    )

    from sklearn.datasets import load_diabetes
    from sklearn.model_selection import train_test_split









.. GENERATED FROM PYTHON SOURCE LINES 39-41

Implement kernel ridge regression component for auto-sklearn
============================================================

.. GENERATED FROM PYTHON SOURCE LINES 41-130

.. code-block:: default



    class KernelRidgeRegression(AutoSklearnRegressionAlgorithm):
        def __init__(self, alpha, kernel, gamma, degree, coef0, random_state=None):
            self.alpha = alpha
            self.kernel = kernel
            self.gamma = gamma
            self.degree = degree
            self.coef0 = coef0
            self.random_state = random_state
            self.estimator = None

        def fit(self, X, y):
            self.alpha = float(self.alpha)
            self.gamma = float(self.gamma)
            self.degree = int(self.degree)
            self.coef0 = float(self.coef0)

            import sklearn.kernel_ridge

            self.estimator = sklearn.kernel_ridge.KernelRidge(
                alpha=self.alpha,
                kernel=self.kernel,
                gamma=self.gamma,
                degree=self.degree,
                coef0=self.coef0,
            )
            self.estimator.fit(X, y)
            return self

        def predict(self, X):
            if self.estimator is None:
                raise NotImplementedError
            return self.estimator.predict(X)

        @staticmethod
        def get_properties(dataset_properties=None):
            return {
                "shortname": "KRR",
                "name": "Kernel Ridge Regression",
                "handles_regression": True,
                "handles_classification": False,
                "handles_multiclass": False,
                "handles_multilabel": False,
                "handles_multioutput": True,
                "is_deterministic": True,
                "input": (SPARSE, DENSE, UNSIGNED_DATA, SIGNED_DATA),
                "output": (PREDICTIONS,),
            }

        @staticmethod
        def get_hyperparameter_search_space(
            feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None
        ):
            cs = ConfigurationSpace()
            alpha = UniformFloatHyperparameter(
                name="alpha", lower=10**-5, upper=1, log=True, default_value=1.0
            )
            kernel = CategoricalHyperparameter(
                name="kernel",
                # We restrict ourselves to two possible kernels for this example
                choices=["polynomial", "rbf"],
                default_value="polynomial",
            )
            gamma = UniformFloatHyperparameter(
                name="gamma", lower=0.00001, upper=1, default_value=0.1, log=True
            )
            degree = UniformIntegerHyperparameter(
                name="degree", lower=2, upper=5, default_value=3
            )
            coef0 = UniformFloatHyperparameter(
                name="coef0",
                lower=1e-2,
                upper=1e2,
                log=True,
                default_value=1,
            )
            cs.add_hyperparameters([alpha, kernel, gamma, degree, coef0])
            degree_condition = EqualsCondition(degree, kernel, "polynomial")
            coef0_condition = EqualsCondition(coef0, kernel, "polynomial")
            cs.add_conditions([degree_condition, coef0_condition])
            return cs


    # Add KRR component to auto-sklearn.
    autosklearn.pipeline.components.regression.add_regressor(KernelRidgeRegression)
    cs = KernelRidgeRegression.get_hyperparameter_search_space()
    print(cs)





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

 .. code-block:: none

    Configuration space object:
      Hyperparameters:
        alpha, Type: UniformFloat, Range: [1e-05, 1.0], Default: 1.0, on log-scale
        coef0, Type: UniformFloat, Range: [0.01, 100.0], Default: 1.0, on log-scale
        degree, Type: UniformInteger, Range: [2, 5], Default: 3
        gamma, Type: UniformFloat, Range: [1e-05, 1.0], Default: 0.1, on log-scale
        kernel, Type: Categorical, Choices: {polynomial, rbf}, Default: polynomial
      Conditions:
        coef0 | kernel == 'polynomial'
        degree | kernel == 'polynomial'





.. GENERATED FROM PYTHON SOURCE LINES 131-133

Generate data
=============

.. GENERATED FROM PYTHON SOURCE LINES 133-137

.. code-block:: default


    X, y = load_diabetes(return_X_y=True)
    X_train, X_test, y_train, y_test = train_test_split(X, y)








.. GENERATED FROM PYTHON SOURCE LINES 138-140

Fit the model using KRR
=======================

.. GENERATED FROM PYTHON SOURCE LINES 140-152

.. code-block:: default


    reg = autosklearn.regression.AutoSklearnRegressor(
        time_left_for_this_task=30,
        per_run_time_limit=10,
        include={"regressor": ["KernelRidgeRegression"]},
        # Bellow two flags are provided to speed up calculations
        # Not recommended for a real implementation
        initial_configurations_via_metalearning=0,
        smac_scenario_args={"runcount_limit": 5},
    )
    reg.fit(X_train, y_train)





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

 .. code-block:: none


    AutoSklearnRegressor(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
                         include={'regressor': ['KernelRidgeRegression']},
                         initial_configurations_via_metalearning=0,
                         per_run_time_limit=10,
                         smac_scenario_args={'runcount_limit': 5},
                         time_left_for_this_task=30)



.. GENERATED FROM PYTHON SOURCE LINES 153-155

Print prediction score and statistics
=====================================

.. GENERATED FROM PYTHON SOURCE LINES 155-158

.. code-block:: default

    y_pred = reg.predict(X_test)
    print("r2 score: ", sklearn.metrics.r2_score(y_pred, y_test))
    pprint(reg.show_models(), indent=4)




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

 .. code-block:: none

    r2 score:  -1.2119940941318412
    {   5: {   'cost': 0.5316320422975496,
               'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7f05d20441c0>,
               'ensemble_weight': 1.0,
               'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7f05d1bf63d0>,
               'model_id': 5,
               'rank': 1,
               'regressor': <autosklearn.pipeline.components.regression.RegressorChoice object at 0x7f05d1bf69d0>,
               'sklearn_regressor': KernelRidge(alpha=0.12092279509584172, coef0=0.8084604056638888, degree=2,
                gamma=0.0005846787584894724, kernel='polynomial')}}





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

   **Total running time of the script:** ( 0 minutes  11.587 seconds)


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