.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/20_basics/example_tabular_regression.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_examples_20_basics_example_tabular_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_20_basics_example_tabular_regression.py:


======================
Tabular Regression
======================

The following example shows how to fit a sample regression model
with AutoPyTorch

.. GENERATED FROM PYTHON SOURCE LINES 9-27

.. code-block:: default

    import os
    import tempfile as tmp
    import warnings

    import sklearn.datasets
    import sklearn.model_selection

    os.environ['JOBLIB_TEMP_FOLDER'] = tmp.gettempdir()
    os.environ['OMP_NUM_THREADS'] = '1'
    os.environ['OPENBLAS_NUM_THREADS'] = '1'
    os.environ['MKL_NUM_THREADS'] = '1'

    warnings.simplefilter(action='ignore', category=UserWarning)
    warnings.simplefilter(action='ignore', category=FutureWarning)

    from autoPyTorch.api.tabular_regression import TabularRegressionTask









.. GENERATED FROM PYTHON SOURCE LINES 28-30

Data Loading
============

.. GENERATED FROM PYTHON SOURCE LINES 30-37

.. code-block:: default

    X, y = sklearn.datasets.fetch_openml(name='boston', return_X_y=True, as_frame=True)
    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
        X,
        y,
        random_state=1,
    )








.. GENERATED FROM PYTHON SOURCE LINES 38-40

Build and fit a regressor
==========================

.. GENERATED FROM PYTHON SOURCE LINES 40-42

.. code-block:: default

    api = TabularRegressionTask()








.. GENERATED FROM PYTHON SOURCE LINES 43-45

Search for an ensemble of machine learning algorithms
=====================================================

.. GENERATED FROM PYTHON SOURCE LINES 45-56

.. code-block:: default

    api.search(
        X_train=X_train,
        y_train=y_train,
        X_test=X_test.copy(),
        y_test=y_test.copy(),
        optimize_metric='r2',
        total_walltime_limit=300,
        func_eval_time_limit_secs=50,
        dataset_name="Boston"
    )





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

 .. code-block:: none


    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f9b36e8d220>



.. GENERATED FROM PYTHON SOURCE LINES 57-59

Print the final ensemble performance before refit
=================================================

.. GENERATED FROM PYTHON SOURCE LINES 59-66

.. code-block:: default

    y_pred = api.predict(X_test)
    score = api.score(y_pred, y_test)
    print(score)

    # Print statistics from search
    print(api.sprint_statistics())





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

 .. code-block:: none

    {'r2': 0.9407884171054208}
    autoPyTorch results:
            Dataset name: Boston
            Optimisation Metric: r2
            Best validation score: 0.8670098636440993
            Number of target algorithm runs: 23
            Number of successful target algorithm runs: 22
            Number of crashed target algorithm runs: 0
            Number of target algorithms that exceeded the time limit: 1
            Number of target algorithms that exceeded the memory limit: 0





.. GENERATED FROM PYTHON SOURCE LINES 67-69

Refit the models on the full dataset.
=====================================

.. GENERATED FROM PYTHON SOURCE LINES 69-84

.. code-block:: default


    api.refit(
        X_train=X_train,
        y_train=y_train,
        X_test=X_test,
        y_test=y_test,
        dataset_name="Boston",
        total_walltime_limit=500,
        run_time_limit_secs=50
        # you can change the resampling strategy to
        # for example, CrossValTypes.k_fold_cross_validation
        # to fit k fold models and have a voting classifier
        # resampling_strategy=CrossValTypes.k_fold_cross_validation
    )





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

 .. code-block:: none


    <autoPyTorch.api.tabular_regression.TabularRegressionTask object at 0x7f9b36e8d220>



.. GENERATED FROM PYTHON SOURCE LINES 85-87

Print the final ensemble performance after refit
================================================

.. GENERATED FROM PYTHON SOURCE LINES 87-94

.. code-block:: default


    y_pred = api.predict(X_test)
    score = api.score(y_pred, y_test)
    print(score)

    # Print the final ensemble built by AutoPyTorch
    print(api.show_models())




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

 .. code-block:: none

    {'r2': 0.9378277102071694}
    |    | Preprocessing                                                                                    | Estimator                                                       |   Weight |
    |---:|:-------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|---------:|
    |  0 | None                                                                                             | CBLearner                                                       |     0.44 |
    |  1 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.42 |
    |  2 | SimpleImputer,Variance Threshold,NoCoalescer,OneHotEncoder,StandardScaler,NoFeaturePreprocessing | no embedding,ShapedMLPBackbone,FullyConnectedHead,nn.Sequential |     0.1  |
    |  3 | None                                                                                             | LGBMLearner                                                     |     0.04 |





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

   **Total running time of the script:** ( 6 minutes  52.064 seconds)


.. _sphx_glr_download_examples_20_basics_example_tabular_regression.py:

.. only:: html

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


    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/automl/Auto-PyTorch/development?urlpath=lab/tree/notebooks/examples/20_basics/example_tabular_regression.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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