.. _model_selection_tut:

============================================================
Model selection: choosing estimators and their parameters
============================================================

Score, and cross-validated scores
==================================

As we have seen, every estimator exposes a ``score`` method that can judge
the quality of the fit (or the prediction) on new data. **Bigger is
better**.

::

    >>> from sklearn import datasets, svm
    >>> digits = datasets.load_digits()
    >>> X_digits = digits.data
    >>> y_digits = digits.target
    >>> svc = svm.SVC(C=1, kernel='linear')
    >>> svc.fit(X_digits[:-100], y_digits[:-100]).score(X_digits[-100:], y_digits[-100:])
    0.97999999999999998

To get a better measure of prediction accuracy (which we can use as a
proxy for goodness of fit of the model), we can successively split the
data in *folds* that we use for training and testing::

    >>> import numpy as np
    >>> X_folds = np.array_split(X_digits, 3)
    >>> y_folds = np.array_split(y_digits, 3)
    >>> scores = list()
    >>> for k in range(3):
    ...     # We use 'list' to copy, in order to 'pop' later on
    ...     X_train = list(X_folds)
    ...     X_test  = X_train.pop(k)
    ...     X_train = np.concatenate(X_train)
    ...     y_train = list(y_folds)
    ...     y_test  = y_train.pop(k)
    ...     y_train = np.concatenate(y_train)
    ...     scores.append(svc.fit(X_train, y_train).score(X_test, y_test))
    >>> print(scores)
    [0.93489148580968284, 0.95659432387312182, 0.93989983305509184]

.. currentmodule:: sklearn.model_selection

This is called a :class:`KFold` cross-validation.

.. _cv_generators_tut:

Cross-validation generators
=============================

Scikit-learn has a collection of classes which can be used to generate lists of
train/test indices for popular cross-validation strategies.

They expose a ``split`` method which accepts the input
dataset to be split and yields the train/test set indices for each iteration
of the chosen cross-validation strategy.

This example shows an example usage of the ``split`` method.

    >>> from sklearn.model_selection import KFold, cross_val_score
    >>> X = ["a", "a", "b", "c", "c", "c"]
    >>> k_fold = KFold(n_splits=3)
    >>> for train_indices, test_indices in k_fold.split(X):
    ...      print('Train: %s | test: %s' % (train_indices, test_indices))
    Train: [2 3 4 5] | test: [0 1]
    Train: [0 1 4 5] | test: [2 3]
    Train: [0 1 2 3] | test: [4 5]

The cross-validation can then be performed easily::

    >>> kfold = KFold(n_splits=3)
    >>> [svc.fit(X_digits[train], y_digits[train]).score(X_digits[test], y_digits[test])
    ...          for train, test in k_fold.split(X_digits)]
    [0.93489148580968284, 0.95659432387312182, 0.93989983305509184]

The cross-validation score can be directly calculated using the
:func:`cross_val_score` helper. Given an estimator, the cross-validation object
and the input dataset, the :func:`cross_val_score` splits the data repeatedly into
a training and a testing set, trains the estimator using the training set and
computes the scores based on the testing set for each iteration of cross-validation.

By default the estimator's ``score`` method is used to compute the individual scores.

Refer the :ref:`metrics module <metrics>` to learn more on the available scoring
methods.

    >>> cross_val_score(svc, X_digits, y_digits, cv=k_fold, n_jobs=-1)
    array([ 0.93489149,  0.95659432,  0.93989983])

`n_jobs=-1` means that the computation will be dispatched on all the CPUs
of the computer.

Alternatively, the ``scoring`` argument can be provided to specify an alternative
scoring method.

    >>> cross_val_score(svc, X_digits, y_digits, cv=k_fold,
    ...                 scoring='precision_macro')
    array([ 0.93969761,  0.95911415,  0.94041254])

   **Cross-validation generators**


.. list-table::

   *

    - :class:`KFold` **(n_splits, shuffle, random_state)**

    - :class:`StratifiedKFold` **(n_iter, test_size, train_size, random_state)**

    - :class:`GroupKFold` **(n_splits, shuffle, random_state)**


   *

    - Splits it into K folds, trains on K-1 and then tests on the left-out.

    - Same as K-Fold but preserves the class distribution within each fold.

    - Ensures that the same group is not in both testing and training sets.


.. list-table::

   *

    - :class:`ShuffleSplit` **(n_iter, test_size, train_size, random_state)**

    - :class:`StratifiedShuffleSplit`

    - :class:`GroupShuffleSplit`

   *

    - Generates train/test indices based on random permutation.

    - Same as shuffle split but preserves the class distribution within each iteration.

    - Ensures that the same group is not in both testing and training sets.


.. list-table::

   *

    - :class:`LeaveOneGroupOut` **()**

    - :class:`LeavePGroupsOut`  **(p)**

    - :class:`LeaveOneOut` **()**



   *

    - Takes a group array to group observations.

    - Leave P groups out.

    - Leave one observation out.



.. list-table::

   *

    - :class:`LeavePOut` **(p)**

    - :class:`PredefinedSplit`

   *

    - Leave P observations out.

    - Generates train/test indices based on predefined splits.


.. currentmodule:: sklearn.svm

.. topic:: **Exercise**
   :class: green

   .. image:: /auto_examples/exercises/images/sphx_glr_plot_cv_digits_001.png
        :target: ../../auto_examples/exercises/plot_cv_digits.html
        :align: right
        :scale: 90

   On the digits dataset, plot the cross-validation score of a :class:`SVC`
   estimator with an linear kernel as a function of parameter ``C`` (use a
   logarithmic grid of points, from 1 to 10).

   .. literalinclude:: ../../auto_examples/exercises/plot_cv_digits.py
       :lines: 13-23

   **Solution:** :ref:`sphx_glr_auto_examples_exercises_plot_cv_digits.py`



Grid-search and cross-validated estimators
============================================

Grid-search
-------------

.. currentmodule:: sklearn.model_selection

scikit-learn provides an object that, given data, computes the score
during the fit of an estimator on a parameter grid and chooses the
parameters to maximize the cross-validation score. This object takes an
estimator during the construction and exposes an estimator API::

    >>> from sklearn.model_selection import GridSearchCV, cross_val_score
    >>> Cs = np.logspace(-6, -1, 10)
    >>> clf = GridSearchCV(estimator=svc, param_grid=dict(C=Cs),
    ...                    n_jobs=-1)
    >>> clf.fit(X_digits[:1000], y_digits[:1000])        # doctest: +ELLIPSIS
    GridSearchCV(cv=None,...
    >>> clf.best_score_                                  # doctest: +ELLIPSIS
    0.925...
    >>> clf.best_estimator_.C                            # doctest: +ELLIPSIS
    0.0077...

    >>> # Prediction performance on test set is not as good as on train set
    >>> clf.score(X_digits[1000:], y_digits[1000:])      # doctest: +ELLIPSIS
    0.943...


By default, the :class:`GridSearchCV` uses a 3-fold cross-validation. However,
if it detects that a classifier is passed, rather than a regressor, it uses
a stratified 3-fold.

.. topic:: Nested cross-validation

    ::

        >>> cross_val_score(clf, X_digits, y_digits)
        ...                                               # doctest: +ELLIPSIS
        array([ 0.938...,  0.963...,  0.944...])

    Two cross-validation loops are performed in parallel: one by the
    :class:`GridSearchCV` estimator to set ``gamma`` and the other one by
    ``cross_val_score`` to measure the prediction performance of the
    estimator. The resulting scores are unbiased estimates of the
    prediction score on new data.

.. warning::

    You cannot nest objects with parallel computing (``n_jobs`` different
    than 1).

.. _cv_estimators_tut:

Cross-validated estimators
----------------------------

Cross-validation to set a parameter can be done more efficiently on an
algorithm-by-algorithm basis. This is why, for certain estimators,
scikit-learn exposes :ref:`cross_validation` estimators that set their
parameter automatically by cross-validation::

    >>> from sklearn import linear_model, datasets
    >>> lasso = linear_model.LassoCV()
    >>> diabetes = datasets.load_diabetes()
    >>> X_diabetes = diabetes.data
    >>> y_diabetes = diabetes.target
    >>> lasso.fit(X_diabetes, y_diabetes)
    LassoCV(alphas=None, copy_X=True, cv=None, eps=0.001, fit_intercept=True,
        max_iter=1000, n_alphas=100, n_jobs=1, normalize=False, positive=False,
        precompute='auto', random_state=None, selection='cyclic', tol=0.0001,
        verbose=False)
    >>> # The estimator chose automatically its lambda:
    >>> lasso.alpha_ # doctest: +ELLIPSIS
    0.01229...

These estimators are called similarly to their counterparts, with 'CV'
appended to their name.

.. topic:: **Exercise**
   :class: green

   On the diabetes dataset, find the optimal regularization parameter
   alpha.

   **Bonus**: How much can you trust the selection of alpha?

   .. literalinclude:: ../../auto_examples/exercises/plot_cv_diabetes.py
       :lines: 17-24

   **Solution:** :ref:`sphx_glr_auto_examples_exercises_plot_cv_diabetes.py`
