""" The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters of an estimator. """ # Author: Alexandre Gramfort , # Gael Varoquaux # License: BSD Style. import copy from itertools import product import time import numpy as np import scipy.sparse as sp from .base import BaseEstimator, is_classifier, clone from .cross_validation import check_cv from .externals.joblib import Parallel, delayed, logger from .utils import deprecated class IterGrid(object): """Generators on the combination of the various parameter lists given Parameters ---------- param_grid: dict of string to sequence The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values. Returns ------- params: dict of string to any **Yields** dictionaries mapping each estimator parameter to one of its allowed values. Examples -------- >>> from sklearn.grid_search import IterGrid >>> param_grid = {'a':[1, 2], 'b':[True, False]} >>> list(IterGrid(param_grid)) #doctest: +NORMALIZE_WHITESPACE [{'a': 1, 'b': True}, {'a': 1, 'b': False}, {'a': 2, 'b': True}, {'a': 2, 'b': False}] See also -------- :class:`GridSearchCV`: uses ``IterGrid`` to perform a full parallelized grid search. """ def __init__(self, param_grid): self.param_grid = param_grid def __iter__(self): param_grid = self.param_grid if hasattr(param_grid, 'items'): # wrap dictionary in a singleton list param_grid = [param_grid] for p in param_grid: # Always sort the keys of a dictionary, for reproducibility items = sorted(p.items()) keys, values = zip(*items) for v in product(*values): params = dict(zip(keys, v)) yield params def fit_grid_point(X, y, base_clf, clf_params, train, test, loss_func, score_func, verbose, **fit_params): """Run fit on one set of parameters Returns the score and the instance of the classifier """ if verbose > 1: start_time = time.time() msg = '%s' % (', '.join('%s=%s' % (k, v) for k, v in clf_params.iteritems())) print "[GridSearchCV] %s %s" % (msg, (64 - len(msg)) * '.') # update parameters of the classifier after a copy of its base structure # FIXME we should be doing a clone here clf = copy.deepcopy(base_clf) clf.set_params(**clf_params) if isinstance(X, list) or isinstance(X, tuple): X_train = [X[i] for i, cond in enumerate(train) if cond] X_test = [X[i] for i, cond in enumerate(test) if cond] else: if sp.issparse(X): # For sparse matrices, slicing only works with indices # (no masked array). Convert to CSR format for efficiency and # because some sparse formats don't support row slicing. X = sp.csr_matrix(X) ind = np.arange(X.shape[0]) train = ind[train] test = ind[test] if hasattr(base_clf, 'kernel') and hasattr(base_clf.kernel, '__call__'): # cannot compute the kernel values with custom function raise ValueError( "Cannot use a custom kernel function. " "Precompute the kernel matrix instead.") if getattr(base_clf, 'kernel', '') == 'precomputed': # X is a precomputed square kernel matrix if X.shape[0] != X.shape[1]: raise ValueError("X should be a square kernel matrix") X_train = X[np.ix_(train, train)] X_test = X[np.ix_(test, train)] else: X_train = X[train] X_test = X[test] if y is not None: y_test = y[test] y_train = y[train] else: y_test = None y_train = None clf.fit(X_train, y_train, **fit_params) if loss_func is not None: y_pred = clf.predict(X_test) this_score = -loss_func(y_test, y_pred) elif score_func is not None: y_pred = clf.predict(X_test) this_score = score_func(y_test, y_pred) else: this_score = clf.score(X_test, y_test) if y is not None: if hasattr(y, 'shape'): this_n_test_samples = y.shape[0] else: this_n_test_samples = len(y) else: if hasattr(X, 'shape'): this_n_test_samples = X.shape[0] else: this_n_test_samples = len(X) if verbose > 2: msg += ", score=%f" % this_score if verbose > 1: end_msg = "%s -%s" % (msg, logger.short_format_time(time.time() - start_time)) print "[GridSearchCV] %s %s" % ((64 - len(end_msg)) * '.', end_msg) return this_score, clf_params, this_n_test_samples def _check_param_grid(param_grid): if hasattr(param_grid, 'items'): param_grid = [param_grid] for p in param_grid: for v in p.itervalues(): if isinstance(v, np.ndarray) and v.ndim > 1: raise ValueError("Parameter array should be one-dimensional.") check = [isinstance(v, k) for k in (list, tuple, np.ndarray)] if not True in check: raise ValueError("Parameter values should be a list.") if len(v) == 0: raise ValueError("Parameter values should be a non-empty list.") def _has_one_grid_point(param_grid): if hasattr(param_grid, 'items'): param_grid = [param_grid] for p in param_grid: for v in p.itervalues(): if len(v) > 1: return False return True class GridSearchCV(BaseEstimator): """Grid search on the parameters of a classifier 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. 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 Dictionary with parameters names (string) as keys and lists of parameter settings to try as values. loss_func: callable, optional function that takes 2 arguments and compares them in order to evaluate the performance of prediciton (small is good) if None is passed, the score of the estimator is maximized score_func: callable, optional A function that takes 2 arguments and compares them in order to evaluate the performance of prediction (high is good). If None is passed, the score of the estimator is maximized. fit_params : dict, optional parameters to pass to the fit method n_jobs: int, optional number of jobs to run in parallel (default 1) 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 immediatly 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, optional 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 crossvalidation generator, optional If an integer is passed, it is the number of fold (default 3). Specific crossvalidation objects can be passed, see sklearn.cross_validation module for the list of possible objects refit: boolean refit the best estimator with the entire dataset. If "False", it is impossible to make predictions using this GridSearch instance after fitting. verbose: integer Controls the verbosity: the higher, the more messages. 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, estimator=SVC(C=1.0, cache_size=..., coef0=..., degree=..., gamma=..., kernel='rbf', probability=False, shrinking=True, tol=...), fit_params={}, iid=True, loss_func=None, n_jobs=1, param_grid=..., ...) Attributes ---------- `grid_scores_` : dict of any to float Contains scores for all parameter combinations in param_grid. `best_estimator_` : estimator Estimator that was choosen by grid search, i.e. estimator which gave highest score (or smallest loss if specified) on the left out data. `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. Notes ------ The parameters selected are those that maximize the score of the left out data, unless an explicit score_func is passed in which case it is used instead. If a loss function loss_func is passed, it overrides the score functions and is minimized. 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:`IterGrid`: 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. """ def __init__(self, estimator, param_grid, loss_func=None, score_func=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', ): if not hasattr(estimator, 'fit') or \ not (hasattr(estimator, 'predict') or hasattr(estimator, 'score')): raise TypeError("estimator should a be an estimator implementing" " 'fit' and 'predict' or 'score' methods," " %s (type %s) was passed" % (estimator, type(estimator))) if loss_func is None and score_func is None: if not hasattr(estimator, 'score'): raise TypeError( "If no loss_func is specified, the estimator passed " "should have a 'score' method. The estimator %s " "does not." % estimator) _check_param_grid(param_grid) self.estimator = estimator self.param_grid = param_grid self.loss_func = loss_func self.score_func = score_func 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 def _set_methods(self): if hasattr(self._best_estimator_, 'predict'): self.predict = self._best_estimator_.predict if hasattr(self._best_estimator_, 'predict_proba'): self.predict_proba = self._best_estimator_.predict_proba def fit(self, X, y=None, **params): """Run fit with all sets of parameters Returns the best classifier Parameters ---------- X: array, [n_samples, n_features] Training vector, where n_samples in the number of samples and n_features is the number of features. y: array-like, shape = [n_samples], optional Target vector relative to X for classification; None for unsupervised learning. """ self._set_params(**params) estimator = self.estimator cv = self.cv if hasattr(X, 'shape'): n_samples = X.shape[0] else: # support list of unstructured objects on which feature # extraction will be applied later in the tranformer chain n_samples = len(X) if y is not None: if len(y) != n_samples: raise ValueError('Target variable (y) has a different number ' 'of samples (%i) than data (X: %i samples)' % (len(y), n_samples)) y = np.asarray(y) cv = check_cv(cv, X, y, classifier=is_classifier(estimator)) grid = IterGrid(self.param_grid) base_clf = clone(self.estimator) # Return early if there is only one grid point. if _has_one_grid_point(self.param_grid): params = iter(grid).next() base_clf.set_params(**params) base_clf.fit(X, y) self._best_estimator_ = base_clf self._set_methods() return self pre_dispatch = self.pre_dispatch out = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, pre_dispatch=pre_dispatch)( delayed(fit_grid_point)( X, y, base_clf, clf_params, train, test, self.loss_func, self.score_func, self.verbose, **self.fit_params) for clf_params in grid for train, test in cv) # Out is a list of triplet: score, estimator, n_test_samples n_grid_points = len(list(grid)) n_fits = len(out) n_folds = n_fits // n_grid_points scores = list() cv_scores = list() for grid_start in range(0, n_fits, n_folds): n_test_samples = 0 score = 0 these_points = list() for this_score, clf_params, this_n_test_samples in \ out[grid_start:grid_start + n_folds]: these_points.append(this_score) if self.iid: this_score *= this_n_test_samples score += this_score n_test_samples += this_n_test_samples if self.iid: score /= float(n_test_samples) scores.append((score, clf_params)) cv_scores.append(these_points) cv_scores = np.asarray(cv_scores) # Note: we do not use max(out) to make ties deterministic even if # comparison on estimator instances is not deterministic best_score = -np.inf for score, params in scores: if score > best_score: best_score = score best_params = params if best_score is None: raise ValueError('Best score could not be found') self.best_score_ = best_score self.best_params_ = best_params if self.refit: # fit the best estimator using the entire dataset # clone first to work around broken estimators best_estimator = clone(base_clf).set_params(**best_params) best_estimator.fit(X, y, **self.fit_params) self._best_estimator_ = best_estimator self._set_methods() # Store the computed scores # XXX: the name is too specific, it shouldn't have # 'grid' in it. Also, we should be retrieving/storing variance self.grid_scores_ = [ (clf_params, score, all_scores) for clf_params, (score, _), all_scores in zip(grid, scores, cv_scores)] return self def score(self, X, y=None): if hasattr(self.best_estimator_, 'score'): return self.best_estimator_.score(X, y) if self.score_func is None: raise ValueError("No score function explicitly defined, " "and the estimator doesn't provide one %s" % self.best_estimator_) y_predicted = self.predict(X) return self.score_func(y, y_predicted) # TODO around 0.13: remove this property, make it an attribute @property def best_estimator_(self): if hasattr(self, '_best_estimator_'): return self._best_estimator_ else: raise RuntimeError("Grid search has to be run with 'refit=True'" " to make predictions or obtain an instance of the best " " estimator. To obtain the best parameter settings, " " use ``best_params_``.") @property @deprecated('GridSearchCV.best_estimator is deprecated' ' and will be removed in version 0.12.' ' Please use ``GridSearchCV.best_estimator_`` instead.') def best_estimator(self): return self.best_estimator_ @property @deprecated('GridSearchCV.best_score is deprecated' ' and will be removed in version 0.12.' ' Please use ``GridSearchCV.best_score_`` instead.') def best_score(self): return self.best_score_