438 lines
16 KiB
Python
438 lines
16 KiB
Python
"""
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The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters
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of an estimator.
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"""
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# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
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# Gael Varoquaux <gael.varoquaux@normalesup.org>
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# License: BSD Style.
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from itertools import product
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import time
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import numpy as np
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from .base import BaseEstimator, is_classifier, clone
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from .base import MetaEstimatorMixin
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from .cross_validation import check_cv
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from .externals.joblib import Parallel, delayed, logger
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from .utils import safe_mask, check_arrays
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from .utils.validation import _num_samples
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__all__ = ['GridSearchCV', 'IterGrid', 'fit_grid_point']
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class IterGrid(object):
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"""Generators on the combination of the various parameter lists given
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Parameters
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----------
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param_grid: dict of string to sequence
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The parameter grid to explore, as a dictionary mapping estimator
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parameters to sequences of allowed values.
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Returns
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-------
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params: dict of string to any
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**Yields** dictionaries mapping each estimator parameter to one of its
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allowed values.
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Examples
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--------
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>>> from sklearn.grid_search import IterGrid
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>>> param_grid = {'a':[1, 2], 'b':[True, False]}
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>>> list(IterGrid(param_grid)) #doctest: +NORMALIZE_WHITESPACE
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[{'a': 1, 'b': True}, {'a': 1, 'b': False},
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{'a': 2, 'b': True}, {'a': 2, 'b': False}]
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See also
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--------
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:class:`GridSearchCV`:
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uses ``IterGrid`` to perform a full parallelized grid search.
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"""
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def __init__(self, param_grid):
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self.param_grid = param_grid
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def __iter__(self):
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param_grid = self.param_grid
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if hasattr(param_grid, 'items'):
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# wrap dictionary in a singleton list
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param_grid = [param_grid]
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for p in param_grid:
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# Always sort the keys of a dictionary, for reproducibility
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items = sorted(p.items())
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keys, values = zip(*items)
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for v in product(*values):
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params = dict(zip(keys, v))
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yield params
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def fit_grid_point(X, y, base_clf, clf_params, train, test, loss_func,
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score_func, verbose, **fit_params):
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"""Run fit on one set of parameters
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Returns the score and the instance of the classifier
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"""
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if verbose > 1:
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start_time = time.time()
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msg = '%s' % (', '.join('%s=%s' % (k, v)
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for k, v in clf_params.iteritems()))
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print "[GridSearchCV] %s %s" % (msg, (64 - len(msg)) * '.')
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# update parameters of the classifier after a copy of its base structure
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clf = clone(base_clf)
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clf.set_params(**clf_params)
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if hasattr(base_clf, 'kernel') and hasattr(base_clf.kernel, '__call__'):
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# cannot compute the kernel values with custom function
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raise ValueError("Cannot use a custom kernel function. "
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"Precompute the kernel matrix instead.")
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if not hasattr(X, "shape"):
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if getattr(base_clf, "_pairwise", False):
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raise ValueError("Precomputed kernels or affinity matrices have "
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"to be passed as arrays or sparse matrices.")
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X_train = [X[idx] for idx in train]
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X_test = [X[idx] for idx in test]
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else:
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if getattr(base_clf, "_pairwise", False):
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# X is a precomputed square kernel matrix
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if X.shape[0] != X.shape[1]:
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raise ValueError("X should be a square kernel matrix")
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X_train = X[np.ix_(train, train)]
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X_test = X[np.ix_(test, train)]
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else:
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X_train = X[safe_mask(X, train)]
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X_test = X[safe_mask(X, test)]
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if y is not None:
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y_test = y[safe_mask(y, test)]
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y_train = y[safe_mask(y, train)]
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clf.fit(X_train, y_train, **fit_params)
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if loss_func is not None:
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y_pred = clf.predict(X_test)
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this_score = -loss_func(y_test, y_pred)
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elif score_func is not None:
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y_pred = clf.predict(X_test)
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this_score = score_func(y_test, y_pred)
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else:
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this_score = clf.score(X_test, y_test)
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else:
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clf.fit(X_train, **fit_params)
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this_score = clf.score(X_test)
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if verbose > 2:
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msg += ", score=%f" % this_score
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if verbose > 1:
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end_msg = "%s -%s" % (msg,
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logger.short_format_time(time.time() -
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start_time))
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print "[GridSearchCV] %s %s" % ((64 - len(end_msg)) * '.', end_msg)
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return this_score, clf_params, _num_samples(X)
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def _check_param_grid(param_grid):
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if hasattr(param_grid, 'items'):
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param_grid = [param_grid]
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for p in param_grid:
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for v in p.itervalues():
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if isinstance(v, np.ndarray) and v.ndim > 1:
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raise ValueError("Parameter array should be one-dimensional.")
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check = [isinstance(v, k) for k in (list, tuple, np.ndarray)]
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if not True in check:
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raise ValueError("Parameter values should be a list.")
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if len(v) == 0:
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raise ValueError("Parameter values should be a non-empty "
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"list.")
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def _has_one_grid_point(param_grid):
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if hasattr(param_grid, 'items'):
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param_grid = [param_grid]
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for p in param_grid:
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for v in p.itervalues():
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if len(v) > 1:
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return False
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return True
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class GridSearchCV(BaseEstimator, MetaEstimatorMixin):
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"""Grid search on the parameters of a classifier
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Important members are fit, predict.
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GridSearchCV implements a "fit" method and a "predict" method like
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any classifier except that the parameters of the classifier
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used to predict is optimized by cross-validation.
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Parameters
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----------
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estimator: object type that implements the "fit" and "predict" methods
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A object of that type is instantiated for each grid point.
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param_grid: dict or list of dictionaries
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Dictionary with parameters names (string) as keys and lists of
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parameter settings to try as values, or a list of such
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dictionaries, in which case the grids spanned by each dictionary
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in the list are explored.
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loss_func: callable, optional
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function that takes 2 arguments and compares them in
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order to evaluate the performance of prediciton (small is good)
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if None is passed, the score of the estimator is maximized
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score_func: callable, optional
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A function that takes 2 arguments and compares them in
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order to evaluate the performance of prediction (high is good).
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If None is passed, the score of the estimator is maximized.
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fit_params : dict, optional
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parameters to pass to the fit method
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n_jobs: int, optional
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number of jobs to run in parallel (default 1)
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pre_dispatch: int, or string, optional
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Controls the number of jobs that get dispatched during parallel
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execution. Reducing this number can be useful to avoid an
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explosion of memory consumption when more jobs get dispatched
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than CPUs can process. This parameter can be:
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- None, in which case all the jobs are immediatly
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created and spawned. Use this for lightweight and
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fast-running jobs, to avoid delays due to on-demand
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spawning of the jobs
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- An int, giving the exact number of total jobs that are
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spawned
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- A string, giving an expression as a function of n_jobs,
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as in '2*n_jobs'
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iid: boolean, optional
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If True, the data is assumed to be identically distributed across
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the folds, and the loss minimized is the total loss per sample,
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and not the mean loss across the folds.
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cv : integer or crossvalidation generator, optional
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If an integer is passed, it is the number of fold (default 3).
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Specific crossvalidation objects can be passed, see
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sklearn.cross_validation module for the list of possible objects
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refit: boolean
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refit the best estimator with the entire dataset.
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If "False", it is impossible to make predictions using
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this GridSearch instance after fitting.
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verbose: integer
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Controls the verbosity: the higher, the more messages.
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Examples
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--------
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>>> from sklearn import svm, grid_search, datasets
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>>> iris = datasets.load_iris()
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>>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}
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>>> svr = svm.SVC()
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>>> clf = grid_search.GridSearchCV(svr, parameters)
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>>> clf.fit(iris.data, iris.target)
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... # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
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GridSearchCV(cv=None,
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estimator=SVC(C=1.0, cache_size=..., coef0=..., degree=...,
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gamma=..., kernel='rbf', max_iter=-1, probability=False,
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shrinking=True, tol=...),
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fit_params={}, iid=True, loss_func=None, n_jobs=1,
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param_grid=...,
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...)
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Attributes
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----------
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`grid_scores_` : dict of any to float
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Contains scores for all parameter combinations in param_grid.
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`best_estimator_` : estimator
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Estimator that was choosen by grid search, i.e. estimator
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which gave highest score (or smallest loss if specified)
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on the left out data.
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`best_score_` : float
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score of best_estimator on the left out data.
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`best_params_` : dict
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Parameter setting that gave the best results on the hold out data.
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Notes
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------
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The parameters selected are those that maximize the score of the left out
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data, unless an explicit score_func is passed in which case it is used
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instead. If a loss function loss_func is passed, it overrides the score
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functions and is minimized.
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If `n_jobs` was set to a value higher than one, the data is copied for each
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point in the grid (and not `n_jobs` times). This is done for efficiency
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reasons if individual jobs take very little time, but may raise errors if
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the dataset is large and not enough memory is available. A workaround in
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this case is to set `pre_dispatch`. Then, the memory is copied only
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`pre_dispatch` many times. A reasonable value for `pre_dispatch` is 2 *
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`n_jobs`.
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See Also
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---------
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:class:`IterGrid`:
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generates all the combinations of a an hyperparameter grid.
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:func:`sklearn.cross_validation.train_test_split`:
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utility function to split the data into a development set usable
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for fitting a GridSearchCV instance and an evaluation set for
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its final evaluation.
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"""
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def __init__(self, estimator, param_grid, loss_func=None, score_func=None,
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fit_params=None, n_jobs=1, iid=True, refit=True, cv=None,
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verbose=0, pre_dispatch='2*n_jobs'):
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if (not hasattr(estimator, 'score') and
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(not hasattr(estimator, 'predict')
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or (loss_func is None and score_func is None))):
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raise TypeError("The provided estimator %s does not implement a "
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"score function. In this case, it needs to "
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"implement a predict fuction and you have to "
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"provide either a score_func or a loss_func."
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% type(estimator))
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_check_param_grid(param_grid)
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self.estimator = estimator
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self.param_grid = param_grid
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self.loss_func = loss_func
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self.score_func = score_func
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self.n_jobs = n_jobs
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self.fit_params = fit_params if fit_params is not None else {}
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self.iid = iid
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self.refit = refit
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self.cv = cv
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self.verbose = verbose
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self.pre_dispatch = pre_dispatch
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def _set_methods(self):
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if hasattr(self.best_estimator_, 'predict'):
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self.predict = self.best_estimator_.predict
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if hasattr(self.best_estimator_, 'predict_proba'):
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self.predict_proba = self.best_estimator_.predict_proba
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def fit(self, X, y=None, **params):
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"""Run fit with all sets of parameters
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Returns the best classifier
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Parameters
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----------
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X: array, [n_samples, n_features]
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Training vector, where n_samples in the number of samples and
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n_features is the number of features.
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y: array-like, shape = [n_samples], optional
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Target vector relative to X for classification;
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None for unsupervised learning.
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"""
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estimator = self.estimator
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cv = self.cv
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X, y = check_arrays(X, y, sparse_format="csr", allow_lists=True)
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cv = check_cv(cv, X, y, classifier=is_classifier(estimator))
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grid = IterGrid(self.param_grid)
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base_clf = clone(self.estimator)
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# Return early if there is only one grid point.
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if _has_one_grid_point(self.param_grid):
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params = next(iter(grid))
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base_clf.set_params(**params)
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if y is not None:
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base_clf.fit(X, y)
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else:
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base_clf.fit(X)
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self.best_estimator_ = base_clf
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self._set_methods()
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return self
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pre_dispatch = self.pre_dispatch
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out = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
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pre_dispatch=pre_dispatch)(
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delayed(fit_grid_point)(
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X, y, base_clf, clf_params, train, test,
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self.loss_func, self.score_func, self.verbose,
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**self.fit_params)
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for clf_params in grid for train, test in cv)
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# Out is a list of triplet: score, estimator, n_test_samples
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n_grid_points = len(list(grid))
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n_fits = len(out)
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n_folds = n_fits // n_grid_points
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scores = list()
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cv_scores = list()
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for grid_start in range(0, n_fits, n_folds):
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n_test_samples = 0
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score = 0
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these_points = list()
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for this_score, clf_params, this_n_test_samples in \
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out[grid_start:grid_start + n_folds]:
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these_points.append(this_score)
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if self.iid:
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this_score *= this_n_test_samples
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score += this_score
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n_test_samples += this_n_test_samples
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if self.iid:
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score /= float(n_test_samples)
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scores.append((score, clf_params))
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cv_scores.append(these_points)
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cv_scores = np.asarray(cv_scores)
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# Note: we do not use max(out) to make ties deterministic even if
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# comparison on estimator instances is not deterministic
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best_score = -np.inf
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for score, params in scores:
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if score > best_score:
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best_score = score
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best_params = params
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self.best_score_ = best_score
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self.best_params_ = best_params
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if self.refit:
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# fit the best estimator using the entire dataset
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# clone first to work around broken estimators
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best_estimator = clone(base_clf).set_params(**best_params)
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if y is not None:
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best_estimator.fit(X, y, **self.fit_params)
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else:
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best_estimator.fit(X, **self.fit_params)
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self.best_estimator_ = best_estimator
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self._set_methods()
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# Store the computed scores
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# XXX: the name is too specific, it shouldn't have
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# 'grid' in it. Also, we should be retrieving/storing variance
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self.grid_scores_ = [(clf_params, score, all_scores)
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for clf_params, (score, _), all_scores
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in zip(grid, scores, cv_scores)]
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return self
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def score(self, X, y=None):
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if hasattr(self.best_estimator_, 'score'):
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return self.best_estimator_.score(X, y)
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if self.score_func is None:
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raise ValueError("No score function explicitly defined, "
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"and the estimator doesn't provide one %s"
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% self.best_estimator_)
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y_predicted = self.predict(X)
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return self.score_func(y, y_predicted)
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