grid_search.py

import time
import numpy as np

from sklearn.cross_validation import _safe_split, check_scoring, check_cv, indexable
from sklearn.utils.validation import _num_samples
from sklearn.base import clone, is_classifier
from sklearn.grid_search import _CVScoreTuple, _check_param_grid, ParameterGrid

from dask.base import compute
from dask.imperative import value


def _fit_and_score(estimator, X, y, scorer, train, test, verbose,
                   parameters, fit_params, return_train_score=False,
                   return_parameters=False, error_score='raise'):

    if parameters is not None:
        estimator.set_params(**parameters)

    start_time = time.time()

    X_train, y_train = _safe_split(estimator, X, y, train)
    X_test, y_test = _safe_split(estimator, X, y, test, train)

    if y_train is None:
        estimator.fit(X_train, **fit_params)
    else:
        estimator.fit(X_train, y_train, **fit_params)
    test_score = estimator.score(X_test, y_test)

    scoring_time = time.time() - start_time

    ret = [test_score, _num_samples(X_test), scoring_time]
    if return_parameters:
        ret.append(parameters)
    return ret


class BaseDaskSearchCV(object):
    def __init__(self, estimator, scoring=None,
                 fit_params=None, n_jobs=1, iid=True,
                 refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs',
                 error_score='raise'):

        self.scoring = scoring
        self.estimator = estimator
        self.n_jobs = n_jobs
        self.fit_params = fit_params if fit_params is not None else {}
        self.iid = iid
        self.refit = refit
        self.cv = cv
        self.verbose = verbose
        self.pre_dispatch = pre_dispatch
        self.error_score = error_score

    def _fit(self, X, y, parameter_iterable):
        """Actual fitting,  performing the search over parameters."""

        cv = self.cv
        self.scorer_ = check_scoring(self.estimator, scoring=self.scoring)

        X, y = indexable(X, y)

        cv = check_cv(cv, X, y, classifier=is_classifier(self.estimator))

        base_estimator = clone(self.estimator)
        out = [_fit_and_score(clone(base_estimator), X, y, self.scorer_, train,
                              test, self.verbose, parameters, self.fit_params,
                              return_parameters=True,
                              error_score=self.error_score)
               for parameters in parameter_iterable
               for train, test in cv]
        self._dask_value = value(out)

        out, = compute(value(out))
        n_fits = len(out)
        n_folds = len(cv)

        scores = list()
        grid_scores = list()
        for grid_start in range(0, n_fits, n_folds):
            n_test_samples = 0
            score = 0
            all_scores = []
            for this_score, this_n_test_samples, _, parameters in \
                    out[grid_start:grid_start + n_folds]:
                all_scores.append(this_score)
                if self.iid:
                    this_score *= this_n_test_samples
                    n_test_samples += this_n_test_samples
                score += this_score
            if self.iid:
                score /= float(n_test_samples)
            else:
                score /= float(n_folds)
            scores.append((score, parameters))
            # TODO: shall we also store the test_fold_sizes?
            grid_scores.append(_CVScoreTuple(
                parameters,
                score,
                np.array(all_scores)))
        # Store the computed scores
        self.grid_scores_ = grid_scores

        # Find the best parameters by comparing on the mean validation score:
        # note that `sorted` is deterministic in the way it breaks ties
        best = sorted(grid_scores, key=lambda x: x.mean_validation_score,
                      reverse=True)[0]
        self.best_params_ = best.parameters
        self.best_score_ = best.mean_validation_score

        if self.refit:
            # fit the best estimator using the entire dataset
            # clone first to work around broken estimators
            best_estimator = clone(base_estimator).set_params(
                **best.parameters)
            if y is not None:
                best_estimator.fit(X, y, **self.fit_params)
            else:
                best_estimator.fit(X, **self.fit_params)
            self.best_estimator_ = best_estimator
        return self

    def score(self, X, y=None):
        """Returns the score on the given data, if the estimator has been refit

        This uses the score defined by ``scoring`` where provided, and the
        ``best_estimator_.score`` method otherwise.

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            Input data, where n_samples is the number of samples and
            n_features is the number of features.

        y : array-like, shape = [n_samples] or [n_samples, n_output], optional
            Target relative to X for classification or regression;
            None for unsupervised learning.

        Returns
        -------
        score : float

        Notes
        -----
         * The long-standing behavior of this method changed in version 0.16.
         * It no longer uses the metric provided by ``estimator.score`` if the
           ``scoring`` parameter was set when fitting.

        """
        return self.best_estimator_.score(X, y)


class GridSearchCV(BaseDaskSearchCV):
    """Exhaustive search over specified parameter values for an estimator.

    Important members are fit, predict.

    GridSearchCV implements a "fit" method and a "predict" method like
    any classifier except that the parameters of the classifier
    used to predict is optimized by cross-validation.

    Read more in the :ref:`User Guide <grid_search>`.

    Parameters
    ----------
    estimator : object type that implements the "fit" and "predict" methods
        A object of that type is instantiated for each grid point.

    param_grid : dict or list of dictionaries
        Dictionary with parameters names (string) as keys and lists of
        parameter settings to try as values, or a list of such
        dictionaries, in which case the grids spanned by each dictionary
        in the list are explored. This enables searching over any sequence
        of parameter settings.

    scoring : string, callable or None, optional, default: None
        A string (see model evaluation documentation) or
        a scorer callable object / function with signature
        ``scorer(estimator, X, y)``.

    fit_params : dict, optional
        Parameters to pass to the fit method.

    n_jobs : int, default 1
        Number of jobs to run in parallel.

    pre_dispatch : int, or string, optional
        Controls the number of jobs that get dispatched during parallel
        execution. Reducing this number can be useful to avoid an
        explosion of memory consumption when more jobs get dispatched
        than CPUs can process. This parameter can be:

            - None, in which case all the jobs are immediately
              created and spawned. Use this for lightweight and
              fast-running jobs, to avoid delays due to on-demand
              spawning of the jobs

            - An int, giving the exact number of total jobs that are
              spawned

            - A string, giving an expression as a function of n_jobs,
              as in '2*n_jobs'

    iid : boolean, default=True
        If True, the data is assumed to be identically distributed across
        the folds, and the loss minimized is the total loss per sample,
        and not the mean loss across the folds.

    cv : integer or cross-validation generator, default=3
        A cross-validation generator to use. If int, determines
        the number of folds in StratifiedKFold if estimator is a classifier
        and the target y is binary or multiclass, or the number
        of folds in KFold otherwise.
        Specific cross-validation objects can be passed, see
        sklearn.cross_validation module for the list of possible objects.

    refit : boolean, default=True
        Refit the best estimator with the entire dataset.
        If "False", it is impossible to make predictions using
        this GridSearchCV instance after fitting.

    verbose : integer
        Controls the verbosity: the higher, the more messages.

    error_score : 'raise' (default) or numeric
        Value to assign to the score if an error occurs in estimator fitting.
        If set to 'raise', the error is raised. If a numeric value is given,
        FitFailedWarning is raised. This parameter does not affect the refit
        step, which will always raise the error.


    Examples
    --------
    >>> from sklearn import svm, grid_search, datasets
    >>> iris = datasets.load_iris()
    >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
    >>> svr = svm.SVC()
    >>> clf = grid_search.GridSearchCV(svr, parameters)
    >>> clf.fit(iris.data, iris.target)
    ...                             # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    GridSearchCV(cv=None, error_score=...,
           estimator=SVC(C=1.0, cache_size=..., class_weight=..., coef0=...,
                         decision_function_shape=None, degree=..., gamma=...,
                         kernel='rbf', max_iter=-1, probability=False,
                         random_state=None, shrinking=True, tol=...,
                         verbose=False),
           fit_params={}, iid=..., n_jobs=1,
           param_grid=..., pre_dispatch=..., refit=...,
           scoring=..., verbose=...)


    Attributes
    ----------
    grid_scores_ : list of named tuples
        Contains scores for all parameter combinations in param_grid.
        Each entry corresponds to one parameter setting.
        Each named tuple has the attributes:

            * ``parameters``, a dict of parameter settings
            * ``mean_validation_score``, the mean score over the
              cross-validation folds
            * ``cv_validation_scores``, the list of scores for each fold

    best_estimator_ : estimator
        Estimator that was chosen by the search, i.e. estimator
        which gave highest score (or smallest loss if specified)
        on the left out data. Not available if refit=False.

    best_score_ : float
        Score of best_estimator on the left out data.

    best_params_ : dict
        Parameter setting that gave the best results on the hold out data.

    scorer_ : function
        Scorer function used on the held out data to choose the best
        parameters for the model.

    Notes
    ------
    The parameters selected are those that maximize the score of the left out
    data, unless an explicit score is passed in which case it is used instead.

    If `n_jobs` was set to a value higher than one, the data is copied for each
    point in the grid (and not `n_jobs` times). This is done for efficiency
    reasons if individual jobs take very little time, but may raise errors if
    the dataset is large and not enough memory is available.  A workaround in
    this case is to set `pre_dispatch`. Then, the memory is copied only
    `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *
    n_jobs`.

    See Also
    ---------
    :class:`ParameterGrid`:
        generates all the combinations of a an hyperparameter grid.

    :func:`sklearn.cross_validation.train_test_split`:
        utility function to split the data into a development set usable
        for fitting a GridSearchCV instance and an evaluation set for
        its final evaluation.

    :func:`sklearn.metrics.make_scorer`:
        Make a scorer from a performance metric or loss function.

    """

    def __init__(self, estimator, param_grid, scoring=None, fit_params=None,
                 n_jobs=1, iid=True, refit=True, cv=None, verbose=0,
                 pre_dispatch='2*n_jobs', error_score='raise'):

        super(GridSearchCV, self).__init__(
            estimator, scoring, fit_params, n_jobs, iid,
            refit, cv, verbose, pre_dispatch, error_score)
        self.param_grid = param_grid
        _check_param_grid(param_grid)

    def fit(self, X, y=None):
        """Run fit with all sets of parameters.

        Parameters
        ----------

        X : array-like, shape = [n_samples, n_features]
            Training vector, where n_samples is the number of samples and
            n_features is the number of features.

        y : array-like, shape = [n_samples] or [n_samples, n_output], optional
            Target relative to X for classification or regression;
            None for unsupervised learning.

        """
        return self._fit(X, y, ParameterGrid(self.param_grid))