""" The :mod:`sklearn.grid_search` includes utilities to fine-tune the parameters of an estimator. """ from __future__ import print_function # Author: Alexandre Gramfort , # Gael Varoquaux # Andreas Mueller # Olivier Grisel # License: BSD 3 clause from abc import ABCMeta, abstractmethod from collections import Mapping, namedtuple, Sized from functools import partial, reduce from itertools import product import operator import numpy as np from .base import BaseEstimator, is_classifier, clone from .base import MetaEstimatorMixin from .cross_validation import _check_cv as check_cv from .cross_validation import _fit_and_score from .externals.joblib import Parallel, delayed from .externals import six from .utils import check_random_state from .utils.validation import _num_samples, indexable from .metrics.scorer import check_scoring __all__ = ['GridSearchCV', 'ParameterGrid', 'fit_grid_point', 'ParameterSampler', 'RandomizedSearchCV'] class ParameterGrid(object): """Grid of parameters with a discrete number of values for each. Can be used to iterate over parameter value combinations with the Python built-in function iter. Parameters ---------- param_grid : dict of string to sequence, or sequence of such The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values. An empty dict signifies default parameters. A sequence of dicts signifies a sequence of grids to search, and is useful to avoid exploring parameter combinations that make no sense or have no effect. See the examples below. Examples -------- >>> from sklearn.grid_search import ParameterGrid >>> param_grid = {'a': [1, 2], 'b': [True, False]} >>> list(ParameterGrid(param_grid)) == ( ... [{'a': 1, 'b': True}, {'a': 1, 'b': False}, ... {'a': 2, 'b': True}, {'a': 2, 'b': False}]) True >>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}] >>> list(ParameterGrid(grid)) == [{'kernel': 'linear'}, ... {'kernel': 'rbf', 'gamma': 1}, ... {'kernel': 'rbf', 'gamma': 10}] True See also -------- :class:`GridSearchCV`: uses ``ParameterGrid`` to perform a full parallelized parameter search. """ def __init__(self, param_grid): if isinstance(param_grid, Mapping): # wrap dictionary in a singleton list to support either dict # or list of dicts param_grid = [param_grid] self.param_grid = param_grid def __iter__(self): """Iterate over the points in the grid. Returns ------- params : iterator over dict of string to any Yields dictionaries mapping each estimator parameter to one of its allowed values. """ for p in self.param_grid: # Always sort the keys of a dictionary, for reproducibility items = sorted(p.items()) if not items: yield {} else: keys, values = zip(*items) for v in product(*values): params = dict(zip(keys, v)) yield params def __len__(self): """Number of points on the grid.""" # Product function that can handle iterables (np.product can't). product = partial(reduce, operator.mul) return sum(product(len(v) for v in p.values()) if p else 1 for p in self.param_grid) class ParameterSampler(object): """Generator on parameters sampled from given distributions. Non-deterministic iterable over random candidate combinations for hyper- parameter search. Note that as of SciPy 0.12, the ``scipy.stats.distributions`` do not accept a custom RNG instance and always use the singleton RNG from ``numpy.random``. Hence setting ``random_state`` will not guarantee a deterministic iteration whenever ``scipy.stats`` distributions are used to define the parameter search space. Parameters ---------- param_distributions : dict Dictionary where the keys are parameters and values are distributions from which a parameter is to be sampled. Distributions either have to provide a ``rvs`` function to sample from them, or can be given as a list of values, where a uniform distribution is assumed. n_iter : integer Number of parameter settings that are produced. random_state : int or RandomState Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Returns ------- params : dict of string to any **Yields** dictionaries mapping each estimator parameter to as sampled value. Examples -------- >>> from sklearn.grid_search import ParameterSampler >>> from scipy.stats.distributions import expon >>> import numpy as np >>> np.random.seed(0) >>> param_grid = {'a':[1, 2], 'b': expon()} >>> param_list = list(ParameterSampler(param_grid, n_iter=4)) >>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items()) ... for d in param_list] >>> rounded_list == [{'b': 0.89856, 'a': 1}, ... {'b': 0.923223, 'a': 1}, ... {'b': 1.878964, 'a': 2}, ... {'b': 1.038159, 'a': 2}] True """ def __init__(self, param_distributions, n_iter, random_state=None): self.param_distributions = param_distributions self.n_iter = n_iter self.random_state = random_state def __iter__(self): rnd = check_random_state(self.random_state) # Always sort the keys of a dictionary, for reproducibility items = sorted(self.param_distributions.items()) for _ in range(self.n_iter): params = dict() for k, v in items: if hasattr(v, "rvs"): params[k] = v.rvs() else: params[k] = v[rnd.randint(len(v))] yield params def __len__(self): """Number of points that will be sampled.""" return self.n_iter def fit_grid_point(X, y, estimator, parameters, train, test, scorer, verbose, **fit_params): """Run fit on one set of parameters. Parameters ---------- X : array-like, sparse matrix or list Input data. y : array-like or None Targets for input data. estimator : estimator object This estimator will be cloned and then fitted. parameters : dict Parameters to be set on estimator for this grid point. train : ndarray, dtype int or bool Boolean mask or indices for training set. test : ndarray, dtype int or bool Boolean mask or indices for test set. scorer : callable or None. If provided must be a scorer callable object / function with signature ``scorer(estimator, X, y)``. verbose : int Verbosity level. **fit_params : kwargs Additional parameter passed to the fit function of the estimator. Returns ------- score : float Score of this parameter setting on given training / test split. parameters : dict The parameters that have been evaluated. n_samples_test : int Number of test samples in this split. """ score, n_samples_test, _ = _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params) return score, parameters, n_samples_test def _check_param_grid(param_grid): if hasattr(param_grid, 'items'): param_grid = [param_grid] for p in param_grid: for v in p.values(): 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.") class _CVScoreTuple (namedtuple('_CVScoreTuple', ('parameters', 'mean_validation_score', 'cv_validation_scores'))): # A raw namedtuple is very memory efficient as it packs the attributes # in a struct to get rid of the __dict__ of attributes in particular it # does not copy the string for the keys on each instance. # By deriving a namedtuple class just to introduce the __repr__ method we # would also reintroduce the __dict__ on the instance. By telling the # Python interpreter that this subclass uses static __slots__ instead of # dynamic attributes. Furthermore we don't need any additional slot in the # subclass so we set __slots__ to the empty tuple. __slots__ = () def __repr__(self): """Simple custom repr to summarize the main info""" return "mean: {0:.5f}, std: {1:.5f}, params: {2}".format( self.mean_validation_score, np.std(self.cv_validation_scores), self.parameters) class BaseSearchCV(six.with_metaclass(ABCMeta, BaseEstimator, MetaEstimatorMixin)): """Base class for hyper parameter search with cross-validation.""" @abstractmethod 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'): 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 def score(self, X, y=None): """Returns the score on the given test data and labels, if the search estimator has been refit. The ``score`` function of the best estimator is used, or the ``scoring`` parameter where unavailable. 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 """ if hasattr(self.best_estimator_, 'score'): return self.best_estimator_.score(X, y) if self.scorer_ is None: raise ValueError("No score function explicitly defined, " "and the estimator doesn't provide one %s" % self.best_estimator_) return self.scorer_(self.best_estimator_, X, y) @property def predict(self): return self.best_estimator_.predict @property def predict_proba(self): return self.best_estimator_.predict_proba @property def decision_function(self): return self.best_estimator_.decision_function @property def transform(self): return self.best_estimator_.transform def _fit(self, X, y, parameter_iterable): """Actual fitting, performing the search over parameters.""" estimator = self.estimator cv = self.cv self.scorer_ = check_scoring(self.estimator, scoring=self.scoring) n_samples = _num_samples(X) X, y = indexable(X, y) 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)) cv = check_cv(cv, X, y, classifier=is_classifier(estimator)) if self.verbose > 0: if isinstance(parameter_iterable, Sized): n_candidates = len(parameter_iterable) print("Fitting {0} folds for each of {1} candidates, totalling" " {2} fits".format(len(cv), n_candidates, n_candidates * len(cv))) base_estimator = clone(self.estimator) pre_dispatch = self.pre_dispatch out = Parallel( n_jobs=self.n_jobs, verbose=self.verbose, pre_dispatch=pre_dispatch )( delayed(_fit_and_score)(clone(base_estimator), X, y, self.scorer_, train, test, self.verbose, parameters, self.fit_params, return_parameters=True) for parameters in parameter_iterable for train, test in cv) # Out is a list of triplet: score, estimator, n_test_samples 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 class GridSearchCV(BaseSearchCV): """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. 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, 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 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, 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 cross-validation generator, optional If an integer is passed, it is the number of folds (default 3). Specific cross-validation 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 GridSearchCV 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=..., class_weight=..., coef0=..., 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. 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'): super(GridSearchCV, self).__init__( estimator, scoring, fit_params, n_jobs, iid, refit, cv, verbose, pre_dispatch) 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)) class RandomizedSearchCV(BaseSearchCV): """Randomized search on hyper parameters. RandomizedSearchCV 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. In contrast to GridSearchCV, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter. Parameters ---------- estimator : object type that implements the "fit" and "predict" methods A object of that type is instantiated for each parameter setting. param_distributions : dict Dictionary with parameters names (string) as keys and distributions or lists of parameters to try. Distributions must provide a ``rvs`` method for sampling (such as those from scipy.stats.distributions). If a list is given, it is sampled uniformly. n_iter : int, default=10 Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution. 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, 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 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, 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 cross-validation generator, optional If an integer is passed, it is the number of folds (default 3). Specific cross-validation 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 RandomizedSearchCV instance after fitting. verbose : integer Controls the verbosity: the higher, the more messages. 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. 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 held-out data, according to the scoring parameter. If `n_jobs` was set to a value higher than one, the data is copied for each parameter setting(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:`GridSearchCV`: Does exhaustive search over a grid of parameters. :class:`ParameterSampler`: A generator over parameter settins, constructed from param_distributions. """ def __init__(self, estimator, param_distributions, n_iter=10, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', random_state=None): self.param_distributions = param_distributions self.n_iter = n_iter self.random_state = random_state super(RandomizedSearchCV, self).__init__( estimator=estimator, scoring=scoring, fit_params=fit_params, n_jobs=n_jobs, iid=iid, refit=refit, cv=cv, verbose=verbose, pre_dispatch=pre_dispatch) def fit(self, X, y=None): """Run fit on the estimator with randomly drawn parameters. Parameters ---------- X : array-like, shape = [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] or [n_samples, n_output], optional Target relative to X for classification or regression; None for unsupervised learning. """ sampled_params = ParameterSampler(self.param_distributions, self.n_iter, random_state=self.random_state) return self._fit(X, y, sampled_params)