""" The :mod:`sklearn.model_selection._search` includes utilities to fine-tune the parameters of an estimator. """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import numbers import operator import time import warnings from abc import ABCMeta, abstractmethod from collections import defaultdict from collections.abc import Iterable, Mapping, Sequence from copy import deepcopy from functools import partial, reduce from itertools import product import numpy as np from numpy.ma import MaskedArray from scipy.stats import rankdata from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone, is_classifier from ..exceptions import NotFittedError from ..metrics import check_scoring from ..metrics._scorer import ( _check_multimetric_scoring, _MultimetricScorer, get_scorer_names, ) from ..utils import Bunch, check_random_state from ..utils._estimator_html_repr import _VisualBlock from ..utils._param_validation import HasMethods, Interval, StrOptions from ..utils._tags import get_tags from ..utils.deprecation import _deprecate_Xt_in_inverse_transform from ..utils.metadata_routing import ( MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing, ) from ..utils.metaestimators import available_if from ..utils.parallel import Parallel, delayed from ..utils.random import sample_without_replacement from ..utils.validation import _check_method_params, check_is_fitted, indexable from ._split import check_cv from ._validation import ( _aggregate_score_dicts, _fit_and_score, _insert_error_scores, _normalize_score_results, _warn_or_raise_about_fit_failures, ) __all__ = ["GridSearchCV", "ParameterGrid", "ParameterSampler", "RandomizedSearchCV"] class ParameterGrid: """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. The order of the generated parameter combinations is deterministic. Read more in the :ref:`User Guide `. Parameters ---------- param_grid : dict of str 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.model_selection 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 >>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1} True See Also -------- GridSearchCV : Uses :class:`ParameterGrid` to perform a full parallelized parameter search. """ def __init__(self, param_grid): if not isinstance(param_grid, (Mapping, Iterable)): raise TypeError( f"Parameter grid should be a dict or a list, got: {param_grid!r} of" f" type {type(param_grid).__name__}" ) if isinstance(param_grid, Mapping): # wrap dictionary in a singleton list to support either dict # or list of dicts param_grid = [param_grid] # check if all entries are dictionaries of lists for grid in param_grid: if not isinstance(grid, dict): raise TypeError(f"Parameter grid is not a dict ({grid!r})") for key, value in grid.items(): if isinstance(value, np.ndarray) and value.ndim > 1: raise ValueError( f"Parameter array for {key!r} should be one-dimensional, got:" f" {value!r} with shape {value.shape}" ) if isinstance(value, str) or not isinstance( value, (np.ndarray, Sequence) ): raise TypeError( f"Parameter grid for parameter {key!r} needs to be a list or a" f" numpy array, but got {value!r} (of type " f"{type(value).__name__}) instead. Single values " "need to be wrapped in a list with one element." ) if len(value) == 0: raise ValueError( f"Parameter grid for parameter {key!r} need " f"to be a non-empty sequence, got: {value!r}" ) self.param_grid = param_grid def __iter__(self): """Iterate over the points in the grid. Returns ------- params : iterator over dict of str 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.prod 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 ) def __getitem__(self, ind): """Get the parameters that would be ``ind``th in iteration Parameters ---------- ind : int The iteration index Returns ------- params : dict of str to any Equal to list(self)[ind] """ # This is used to make discrete sampling without replacement memory # efficient. for sub_grid in self.param_grid: # XXX: could memoize information used here if not sub_grid: if ind == 0: return {} else: ind -= 1 continue # Reverse so most frequent cycling parameter comes first keys, values_lists = zip(*sorted(sub_grid.items())[::-1]) sizes = [len(v_list) for v_list in values_lists] total = np.prod(sizes) if ind >= total: # Try the next grid ind -= total else: out = {} for key, v_list, n in zip(keys, values_lists, sizes): ind, offset = divmod(ind, n) out[key] = v_list[offset] return out raise IndexError("ParameterGrid index out of range") class ParameterSampler: """Generator on parameters sampled from given distributions. Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Read more in the :ref:`User Guide `. Parameters ---------- param_distributions : dict Dictionary with parameters names (`str`) 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. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above. n_iter : int Number of parameter settings that are produced. random_state : int, RandomState instance or None, default=None Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. Returns ------- params : dict of str to any **Yields** dictionaries mapping each estimator parameter to as sampled value. Examples -------- >>> from sklearn.model_selection import ParameterSampler >>> from scipy.stats.distributions import expon >>> import numpy as np >>> rng = np.random.RandomState(0) >>> param_grid = {'a':[1, 2], 'b': expon()} >>> param_list = list(ParameterSampler(param_grid, n_iter=4, ... random_state=rng)) >>> 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): if not isinstance(param_distributions, (Mapping, Iterable)): raise TypeError( "Parameter distribution is not a dict or a list," f" got: {param_distributions!r} of type " f"{type(param_distributions).__name__}" ) if isinstance(param_distributions, Mapping): # wrap dictionary in a singleton list to support either dict # or list of dicts param_distributions = [param_distributions] for dist in param_distributions: if not isinstance(dist, dict): raise TypeError( "Parameter distribution is not a dict ({!r})".format(dist) ) for key in dist: if not isinstance(dist[key], Iterable) and not hasattr( dist[key], "rvs" ): raise TypeError( f"Parameter grid for parameter {key!r} is not iterable " f"or a distribution (value={dist[key]})" ) self.n_iter = n_iter self.random_state = random_state self.param_distributions = param_distributions def _is_all_lists(self): return all( all(not hasattr(v, "rvs") for v in dist.values()) for dist in self.param_distributions ) def __iter__(self): rng = check_random_state(self.random_state) # if all distributions are given as lists, we want to sample without # replacement if self._is_all_lists(): # look up sampled parameter settings in parameter grid param_grid = ParameterGrid(self.param_distributions) grid_size = len(param_grid) n_iter = self.n_iter if grid_size < n_iter: warnings.warn( "The total space of parameters %d is smaller " "than n_iter=%d. Running %d iterations. For exhaustive " "searches, use GridSearchCV." % (grid_size, self.n_iter, grid_size), UserWarning, ) n_iter = grid_size for i in sample_without_replacement(grid_size, n_iter, random_state=rng): yield param_grid[i] else: for _ in range(self.n_iter): dist = rng.choice(self.param_distributions) # Always sort the keys of a dictionary, for reproducibility items = sorted(dist.items()) params = dict() for k, v in items: if hasattr(v, "rvs"): params[k] = v.rvs(random_state=rng) else: params[k] = v[rng.randint(len(v))] yield params def __len__(self): """Number of points that will be sampled.""" if self._is_all_lists(): grid_size = len(ParameterGrid(self.param_distributions)) return min(self.n_iter, grid_size) else: return self.n_iter def _check_refit(search_cv, attr): if not search_cv.refit: raise AttributeError( f"This {type(search_cv).__name__} instance was initialized with " f"`refit=False`. {attr} is available only after refitting on the best " "parameters. You can refit an estimator manually using the " "`best_params_` attribute" ) def _search_estimator_has(attr): """Check if we can delegate a method to the underlying estimator. Calling a prediction method will only be available if `refit=True`. In such case, we check first the fitted best estimator. If it is not fitted, we check the unfitted estimator. Checking the unfitted estimator allows to use `hasattr` on the `SearchCV` instance even before calling `fit`. """ def check(self): _check_refit(self, attr) if hasattr(self, "best_estimator_"): # raise an AttributeError if `attr` does not exist getattr(self.best_estimator_, attr) return True # raise an AttributeError if `attr` does not exist getattr(self.estimator, attr) return True return check def _yield_masked_array_for_each_param(candidate_params): """ Yield a masked array for each candidate param. `candidate_params` is a sequence of params which were used in a `GridSearchCV`. We use masked arrays for the results, as not all params are necessarily present in each element of `candidate_params`. For example, if using `GridSearchCV` with a `SVC` model, then one might search over params like: - kernel=["rbf"], gamma=[0.1, 1] - kernel=["poly"], degree=[1, 2] and then param `'gamma'` would not be present in entries of `candidate_params` corresponding to `kernel='poly'`. """ n_candidates = len(candidate_params) param_results = defaultdict(dict) for cand_idx, params in enumerate(candidate_params): for name, value in params.items(): param_results["param_%s" % name][cand_idx] = value for key, param_result in param_results.items(): param_list = list(param_result.values()) try: arr = np.array(param_list) except ValueError: # This can happen when param_list contains lists of different # lengths, for example: # param_list=[[1], [2, 3]] arr_dtype = np.dtype(object) else: # There are two cases when we don't use the automatically inferred # dtype when creating the array and we use object instead: # - string dtype # - when array.ndim > 1, that means that param_list was something # like a list of same-size sequences, which gets turned into a # multi-dimensional array but we want a 1d array arr_dtype = arr.dtype if arr.dtype.kind != "U" and arr.ndim == 1 else object # Use one MaskedArray and mask all the places where the param is not # applicable for that candidate (which may not contain all the params). ma = MaskedArray(np.empty(n_candidates, dtype=arr_dtype), mask=True) for index, value in param_result.items(): # Setting the value at an index unmasks that index ma[index] = value yield (key, ma) class BaseSearchCV(MetaEstimatorMixin, BaseEstimator, metaclass=ABCMeta): """Abstract base class for hyper parameter search with cross-validation.""" _parameter_constraints: dict = { "estimator": [HasMethods(["fit"])], "scoring": [ StrOptions(set(get_scorer_names())), callable, list, tuple, dict, None, ], "n_jobs": [numbers.Integral, None], "refit": ["boolean", str, callable], "cv": ["cv_object"], "verbose": ["verbose"], "pre_dispatch": [numbers.Integral, str], "error_score": [StrOptions({"raise"}), numbers.Real], "return_train_score": ["boolean"], } @abstractmethod def __init__( self, estimator, *, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch="2*n_jobs", error_score=np.nan, return_train_score=True, ): self.scoring = scoring self.estimator = estimator self.n_jobs = n_jobs self.refit = refit self.cv = cv self.verbose = verbose self.pre_dispatch = pre_dispatch self.error_score = error_score self.return_train_score = return_train_score @property # TODO(1.8) remove this property def _estimator_type(self): return self.estimator._estimator_type def __sklearn_tags__(self): tags = super().__sklearn_tags__() sub_estimator_tags = get_tags(self.estimator) tags.estimator_type = sub_estimator_tags.estimator_type tags.classifier_tags = deepcopy(sub_estimator_tags.classifier_tags) tags.regressor_tags = deepcopy(sub_estimator_tags.regressor_tags) # allows cross-validation to see 'precomputed' metrics tags.input_tags.pairwise = sub_estimator_tags.input_tags.pairwise tags.input_tags.sparse = sub_estimator_tags.input_tags.sparse tags.array_api_support = sub_estimator_tags.array_api_support return tags def score(self, X, y=None, **params): """Return 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 of 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 of shape (n_samples, n_output) \ or (n_samples,), default=None Target relative to X for classification or regression; None for unsupervised learning. **params : dict Parameters to be passed to the underlying scorer(s). .. versionadded:: 1.4 Only available if `enable_metadata_routing=True`. See :ref:`Metadata Routing User Guide ` for more details. Returns ------- score : float The score defined by ``scoring`` if provided, and the ``best_estimator_.score`` method otherwise. """ _check_refit(self, "score") check_is_fitted(self) _raise_for_params(params, self, "score") if _routing_enabled(): score_params = process_routing(self, "score", **params).scorer["score"] else: score_params = dict() if self.scorer_ is None: raise ValueError( "No score function explicitly defined, " "and the estimator doesn't provide one %s" % self.best_estimator_ ) if isinstance(self.scorer_, dict): if self.multimetric_: scorer = self.scorer_[self.refit] else: scorer = self.scorer_ return scorer(self.best_estimator_, X, y, **score_params) # callable score = self.scorer_(self.best_estimator_, X, y, **score_params) if self.multimetric_: score = score[self.refit] return score @available_if(_search_estimator_has("score_samples")) def score_samples(self, X): """Call score_samples on the estimator with the best found parameters. Only available if ``refit=True`` and the underlying estimator supports ``score_samples``. .. versionadded:: 0.24 Parameters ---------- X : iterable Data to predict on. Must fulfill input requirements of the underlying estimator. Returns ------- y_score : ndarray of shape (n_samples,) The ``best_estimator_.score_samples`` method. """ check_is_fitted(self) return self.best_estimator_.score_samples(X) @available_if(_search_estimator_has("predict")) def predict(self, X): """Call predict on the estimator with the best found parameters. Only available if ``refit=True`` and the underlying estimator supports ``predict``. Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. Returns ------- y_pred : ndarray of shape (n_samples,) The predicted labels or values for `X` based on the estimator with the best found parameters. """ check_is_fitted(self) return self.best_estimator_.predict(X) @available_if(_search_estimator_has("predict_proba")) def predict_proba(self, X): """Call predict_proba on the estimator with the best found parameters. Only available if ``refit=True`` and the underlying estimator supports ``predict_proba``. Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. Returns ------- y_pred : ndarray of shape (n_samples,) or (n_samples, n_classes) Predicted class probabilities for `X` based on the estimator with the best found parameters. The order of the classes corresponds to that in the fitted attribute :term:`classes_`. """ check_is_fitted(self) return self.best_estimator_.predict_proba(X) @available_if(_search_estimator_has("predict_log_proba")) def predict_log_proba(self, X): """Call predict_log_proba on the estimator with the best found parameters. Only available if ``refit=True`` and the underlying estimator supports ``predict_log_proba``. Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. Returns ------- y_pred : ndarray of shape (n_samples,) or (n_samples, n_classes) Predicted class log-probabilities for `X` based on the estimator with the best found parameters. The order of the classes corresponds to that in the fitted attribute :term:`classes_`. """ check_is_fitted(self) return self.best_estimator_.predict_log_proba(X) @available_if(_search_estimator_has("decision_function")) def decision_function(self, X): """Call decision_function on the estimator with the best found parameters. Only available if ``refit=True`` and the underlying estimator supports ``decision_function``. Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. Returns ------- y_score : ndarray of shape (n_samples,) or (n_samples, n_classes) \ or (n_samples, n_classes * (n_classes-1) / 2) Result of the decision function for `X` based on the estimator with the best found parameters. """ check_is_fitted(self) return self.best_estimator_.decision_function(X) @available_if(_search_estimator_has("transform")) def transform(self, X): """Call transform on the estimator with the best found parameters. Only available if the underlying estimator supports ``transform`` and ``refit=True``. Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. Returns ------- Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) `X` transformed in the new space based on the estimator with the best found parameters. """ check_is_fitted(self) return self.best_estimator_.transform(X) @available_if(_search_estimator_has("inverse_transform")) def inverse_transform(self, X=None, Xt=None): """Call inverse_transform on the estimator with the best found params. Only available if the underlying estimator implements ``inverse_transform`` and ``refit=True``. Parameters ---------- X : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. Xt : indexable, length n_samples Must fulfill the input assumptions of the underlying estimator. .. deprecated:: 1.5 `Xt` was deprecated in 1.5 and will be removed in 1.7. Use `X` instead. Returns ------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) Result of the `inverse_transform` function for `Xt` based on the estimator with the best found parameters. """ X = _deprecate_Xt_in_inverse_transform(X, Xt) check_is_fitted(self) return self.best_estimator_.inverse_transform(X) @property def n_features_in_(self): """Number of features seen during :term:`fit`. Only available when `refit=True`. """ # For consistency with other estimators we raise a AttributeError so # that hasattr() fails if the search estimator isn't fitted. try: check_is_fitted(self) except NotFittedError as nfe: raise AttributeError( "{} object has no n_features_in_ attribute.".format( self.__class__.__name__ ) ) from nfe return self.best_estimator_.n_features_in_ @property def classes_(self): """Class labels. Only available when `refit=True` and the estimator is a classifier. """ _search_estimator_has("classes_")(self) return self.best_estimator_.classes_ def _run_search(self, evaluate_candidates): """Repeatedly calls `evaluate_candidates` to conduct a search. This method, implemented in sub-classes, makes it possible to customize the scheduling of evaluations: GridSearchCV and RandomizedSearchCV schedule evaluations for their whole parameter search space at once but other more sequential approaches are also possible: for instance is possible to iteratively schedule evaluations for new regions of the parameter search space based on previously collected evaluation results. This makes it possible to implement Bayesian optimization or more generally sequential model-based optimization by deriving from the BaseSearchCV abstract base class. For example, Successive Halving is implemented by calling `evaluate_candidates` multiples times (once per iteration of the SH process), each time passing a different set of candidates with `X` and `y` of increasing sizes. Parameters ---------- evaluate_candidates : callable This callback accepts: - a list of candidates, where each candidate is a dict of parameter settings. - an optional `cv` parameter which can be used to e.g. evaluate candidates on different dataset splits, or evaluate candidates on subsampled data (as done in the SucessiveHaling estimators). By default, the original `cv` parameter is used, and it is available as a private `_checked_cv_orig` attribute. - an optional `more_results` dict. Each key will be added to the `cv_results_` attribute. Values should be lists of length `n_candidates` It returns a dict of all results so far, formatted like ``cv_results_``. Important note (relevant whether the default cv is used or not): in randomized splitters, and unless the random_state parameter of cv was set to an int, calling cv.split() multiple times will yield different splits. Since cv.split() is called in evaluate_candidates, this means that candidates will be evaluated on different splits each time evaluate_candidates is called. This might be a methodological issue depending on the search strategy that you're implementing. To prevent randomized splitters from being used, you may use _split._yields_constant_splits() Examples -------- :: def _run_search(self, evaluate_candidates): 'Try C=0.1 only if C=1 is better than C=10' all_results = evaluate_candidates([{'C': 1}, {'C': 10}]) score = all_results['mean_test_score'] if score[0] < score[1]: evaluate_candidates([{'C': 0.1}]) """ raise NotImplementedError("_run_search not implemented.") def _check_refit_for_multimetric(self, scores): """Check `refit` is compatible with `scores` is valid""" multimetric_refit_msg = ( "For multi-metric scoring, the parameter refit must be set to a " "scorer key or a callable to refit an estimator with the best " "parameter setting on the whole data and make the best_* " "attributes available for that metric. If this is not needed, " f"refit should be set to False explicitly. {self.refit!r} was " "passed." ) valid_refit_dict = isinstance(self.refit, str) and self.refit in scores if ( self.refit is not False and not valid_refit_dict and not callable(self.refit) ): raise ValueError(multimetric_refit_msg) @staticmethod def _select_best_index(refit, refit_metric, results): """Select index of the best combination of hyperparemeters.""" if callable(refit): # If callable, refit is expected to return the index of the best # parameter set. best_index = refit(results) if not isinstance(best_index, numbers.Integral): raise TypeError("best_index_ returned is not an integer") if best_index < 0 or best_index >= len(results["params"]): raise IndexError("best_index_ index out of range") else: best_index = results[f"rank_test_{refit_metric}"].argmin() return best_index def _get_scorers(self): """Get the scorer(s) to be used. This is used in ``fit`` and ``get_metadata_routing``. Returns ------- scorers, refit_metric """ refit_metric = "score" if callable(self.scoring): scorers = self.scoring elif self.scoring is None or isinstance(self.scoring, str): scorers = check_scoring(self.estimator, self.scoring) else: scorers = _check_multimetric_scoring(self.estimator, self.scoring) self._check_refit_for_multimetric(scorers) refit_metric = self.refit scorers = _MultimetricScorer( scorers=scorers, raise_exc=(self.error_score == "raise") ) return scorers, refit_metric def _get_routed_params_for_fit(self, params): """Get the parameters to be used for routing. This is a method instead of a snippet in ``fit`` since it's used twice, here in ``fit``, and in ``HalvingRandomSearchCV.fit``. """ if _routing_enabled(): routed_params = process_routing(self, "fit", **params) else: params = params.copy() groups = params.pop("groups", None) routed_params = Bunch( estimator=Bunch(fit=params), splitter=Bunch(split={"groups": groups}), scorer=Bunch(score={}), ) return routed_params @_fit_context( # *SearchCV.estimator is not validated yet prefer_skip_nested_validation=False ) def fit(self, X, y=None, **params): """Run fit with all sets of parameters. Parameters ---------- X : array-like of shape (n_samples, n_features) or (n_samples, n_samples) Training vectors, where `n_samples` is the number of samples and `n_features` is the number of features. For precomputed kernel or distance matrix, the expected shape of X is (n_samples, n_samples). y : array-like of shape (n_samples, n_output) \ or (n_samples,), default=None Target relative to X for classification or regression; None for unsupervised learning. **params : dict of str -> object Parameters passed to the ``fit`` method of the estimator, the scorer, and the CV splitter. If a fit parameter is an array-like whose length is equal to `num_samples` then it will be split by cross-validation along with `X` and `y`. For example, the :term:`sample_weight` parameter is split because `len(sample_weights) = len(X)`. However, this behavior does not apply to `groups` which is passed to the splitter configured via the `cv` parameter of the constructor. Thus, `groups` is used *to perform the split* and determines which samples are assigned to the each side of the a split. Returns ------- self : object Instance of fitted estimator. """ estimator = self.estimator scorers, refit_metric = self._get_scorers() X, y = indexable(X, y) params = _check_method_params(X, params=params) routed_params = self._get_routed_params_for_fit(params) cv_orig = check_cv(self.cv, y, classifier=is_classifier(estimator)) n_splits = cv_orig.get_n_splits(X, y, **routed_params.splitter.split) base_estimator = clone(self.estimator) parallel = Parallel(n_jobs=self.n_jobs, pre_dispatch=self.pre_dispatch) fit_and_score_kwargs = dict( scorer=scorers, fit_params=routed_params.estimator.fit, score_params=routed_params.scorer.score, return_train_score=self.return_train_score, return_n_test_samples=True, return_times=True, return_parameters=False, error_score=self.error_score, verbose=self.verbose, ) results = {} with parallel: all_candidate_params = [] all_out = [] all_more_results = defaultdict(list) def evaluate_candidates(candidate_params, cv=None, more_results=None): cv = cv or cv_orig candidate_params = list(candidate_params) n_candidates = len(candidate_params) if self.verbose > 0: print( "Fitting {0} folds for each of {1} candidates," " totalling {2} fits".format( n_splits, n_candidates, n_candidates * n_splits ) ) out = parallel( delayed(_fit_and_score)( clone(base_estimator), X, y, train=train, test=test, parameters=parameters, split_progress=(split_idx, n_splits), candidate_progress=(cand_idx, n_candidates), **fit_and_score_kwargs, ) for (cand_idx, parameters), (split_idx, (train, test)) in product( enumerate(candidate_params), enumerate(cv.split(X, y, **routed_params.splitter.split)), ) ) if len(out) < 1: raise ValueError( "No fits were performed. " "Was the CV iterator empty? " "Were there no candidates?" ) elif len(out) != n_candidates * n_splits: raise ValueError( "cv.split and cv.get_n_splits returned " "inconsistent results. Expected {} " "splits, got {}".format(n_splits, len(out) // n_candidates) ) _warn_or_raise_about_fit_failures(out, self.error_score) # For callable self.scoring, the return type is only know after # calling. If the return type is a dictionary, the error scores # can now be inserted with the correct key. The type checking # of out will be done in `_insert_error_scores`. if callable(self.scoring): _insert_error_scores(out, self.error_score) all_candidate_params.extend(candidate_params) all_out.extend(out) if more_results is not None: for key, value in more_results.items(): all_more_results[key].extend(value) nonlocal results results = self._format_results( all_candidate_params, n_splits, all_out, all_more_results ) return results self._run_search(evaluate_candidates) # multimetric is determined here because in the case of a callable # self.scoring the return type is only known after calling first_test_score = all_out[0]["test_scores"] self.multimetric_ = isinstance(first_test_score, dict) # check refit_metric now for a callable scorer that is multimetric if callable(self.scoring) and self.multimetric_: self._check_refit_for_multimetric(first_test_score) refit_metric = self.refit # For multi-metric evaluation, store the best_index_, best_params_ and # best_score_ iff refit is one of the scorer names # In single metric evaluation, refit_metric is "score" if self.refit or not self.multimetric_: self.best_index_ = self._select_best_index( self.refit, refit_metric, results ) if not callable(self.refit): # With a non-custom callable, we can select the best score # based on the best index self.best_score_ = results[f"mean_test_{refit_metric}"][ self.best_index_ ] self.best_params_ = results["params"][self.best_index_] if self.refit: # here we clone the estimator as well as the parameters, since # sometimes the parameters themselves might be estimators, e.g. # when we search over different estimators in a pipeline. # ref: https://github.com/scikit-learn/scikit-learn/pull/26786 self.best_estimator_ = clone(base_estimator).set_params( **clone(self.best_params_, safe=False) ) refit_start_time = time.time() if y is not None: self.best_estimator_.fit(X, y, **routed_params.estimator.fit) else: self.best_estimator_.fit(X, **routed_params.estimator.fit) refit_end_time = time.time() self.refit_time_ = refit_end_time - refit_start_time if hasattr(self.best_estimator_, "feature_names_in_"): self.feature_names_in_ = self.best_estimator_.feature_names_in_ # Store the only scorer not as a dict for single metric evaluation if isinstance(scorers, _MultimetricScorer): self.scorer_ = scorers._scorers else: self.scorer_ = scorers self.cv_results_ = results self.n_splits_ = n_splits return self def _format_results(self, candidate_params, n_splits, out, more_results=None): n_candidates = len(candidate_params) out = _aggregate_score_dicts(out) results = dict(more_results or {}) for key, val in results.items(): # each value is a list (as per evaluate_candidate's convention) # we convert it to an array for consistency with the other keys results[key] = np.asarray(val) def _store(key_name, array, weights=None, splits=False, rank=False): """A small helper to store the scores/times to the cv_results_""" # When iterated first by splits, then by parameters # We want `array` to have `n_candidates` rows and `n_splits` cols. array = np.array(array, dtype=np.float64).reshape(n_candidates, n_splits) if splits: for split_idx in range(n_splits): # Uses closure to alter the results results["split%d_%s" % (split_idx, key_name)] = array[:, split_idx] array_means = np.average(array, axis=1, weights=weights) results["mean_%s" % key_name] = array_means if key_name.startswith(("train_", "test_")) and np.any( ~np.isfinite(array_means) ): warnings.warn( ( f"One or more of the {key_name.split('_')[0]} scores " f"are non-finite: {array_means}" ), category=UserWarning, ) # Weighted std is not directly available in numpy array_stds = np.sqrt( np.average( (array - array_means[:, np.newaxis]) ** 2, axis=1, weights=weights ) ) results["std_%s" % key_name] = array_stds if rank: # When the fit/scoring fails `array_means` contains NaNs, we # will exclude them from the ranking process and consider them # as tied with the worst performers. if np.isnan(array_means).all(): # All fit/scoring routines failed. rank_result = np.ones_like(array_means, dtype=np.int32) else: min_array_means = np.nanmin(array_means) - 1 array_means = np.nan_to_num(array_means, nan=min_array_means) rank_result = rankdata(-array_means, method="min").astype( np.int32, copy=False ) results["rank_%s" % key_name] = rank_result _store("fit_time", out["fit_time"]) _store("score_time", out["score_time"]) # Store a list of param dicts at the key 'params' for param, ma in _yield_masked_array_for_each_param(candidate_params): results[param] = ma results["params"] = candidate_params test_scores_dict = _normalize_score_results(out["test_scores"]) if self.return_train_score: train_scores_dict = _normalize_score_results(out["train_scores"]) for scorer_name in test_scores_dict: # Computed the (weighted) mean and std for test scores alone _store( "test_%s" % scorer_name, test_scores_dict[scorer_name], splits=True, rank=True, weights=None, ) if self.return_train_score: _store( "train_%s" % scorer_name, train_scores_dict[scorer_name], splits=True, ) return results def get_metadata_routing(self): """Get metadata routing of this object. Please check :ref:`User Guide ` on how the routing mechanism works. .. versionadded:: 1.4 Returns ------- routing : MetadataRouter A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating routing information. """ router = MetadataRouter(owner=self.__class__.__name__) router.add( estimator=self.estimator, method_mapping=MethodMapping().add(caller="fit", callee="fit"), ) scorer, _ = self._get_scorers() router.add( scorer=scorer, method_mapping=MethodMapping() .add(caller="score", callee="score") .add(caller="fit", callee="score"), ) router.add( splitter=self.cv, method_mapping=MethodMapping().add(caller="fit", callee="split"), ) return router def _sk_visual_block_(self): if hasattr(self, "best_estimator_"): key, estimator = "best_estimator_", self.best_estimator_ else: key, estimator = "estimator", self.estimator return _VisualBlock( "parallel", [estimator], names=[f"{key}: {estimator.__class__.__name__}"], name_details=[str(estimator)], ) class GridSearchCV(BaseSearchCV): """Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid. Read more in the :ref:`User Guide `. Parameters ---------- estimator : estimator object This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. param_grid : dict or list of dictionaries Dictionary with parameters names (`str`) 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 : str, callable, list, tuple or dict, default=None Strategy to evaluate the performance of the cross-validated model on the test set. If `scoring` represents a single score, one can use: - a single string (see :ref:`scoring_parameter`); - a callable (see :ref:`scoring_callable`) that returns a single value. If `scoring` represents multiple scores, one can use: - a list or tuple of unique strings; - a callable returning a dictionary where the keys are the metric names and the values are the metric scores; - a dictionary with metric names as keys and callables as values. See :ref:`multimetric_grid_search` for an example. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None refit : bool, str, or callable, default=True Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a `str` denoting the scorer that would be used to find the best parameters for refitting the estimator at the end. Where there are considerations other than maximum score in choosing a best estimator, ``refit`` can be set to a function which returns the selected ``best_index_`` given ``cv_results_``. In that case, the ``best_estimator_`` and ``best_params_`` will be set according to the returned ``best_index_`` while the ``best_score_`` attribute will not be available. The refitted estimator is made available at the ``best_estimator_`` attribute and permits using ``predict`` directly on this ``GridSearchCV`` instance. Also for multiple metric evaluation, the attributes ``best_index_``, ``best_score_`` and ``best_params_`` will only be available if ``refit`` is set and all of them will be determined w.r.t this specific scorer. See ``scoring`` parameter to know more about multiple metric evaluation. See :ref:`sphx_glr_auto_examples_model_selection_plot_grid_search_digits.py` to see how to design a custom selection strategy using a callable via `refit`. .. versionchanged:: 0.20 Support for callable added. cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold. verbose : int Controls the verbosity: the higher, the more messages. - >1 : the computation time for each fold and parameter candidate is displayed; - >2 : the score is also displayed; - >3 : the fold and candidate parameter indexes are also displayed together with the starting time of the computation. pre_dispatch : int, or str, default='2*n_jobs' 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 str, giving an expression as a function of n_jobs, as in '2*n_jobs' error_score : 'raise' or numeric, default=np.nan 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. return_train_score : bool, default=False If ``False``, the ``cv_results_`` attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance. .. versionadded:: 0.19 .. versionchanged:: 0.21 Default value was changed from ``True`` to ``False`` Attributes ---------- cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. For instance the below given table +------------+-----------+------------+-----------------+---+---------+ |param_kernel|param_gamma|param_degree|split0_test_score|...|rank_t...| +============+===========+============+=================+===+=========+ | 'poly' | -- | 2 | 0.80 |...| 2 | +------------+-----------+------------+-----------------+---+---------+ | 'poly' | -- | 3 | 0.70 |...| 4 | +------------+-----------+------------+-----------------+---+---------+ | 'rbf' | 0.1 | -- | 0.80 |...| 3 | +------------+-----------+------------+-----------------+---+---------+ | 'rbf' | 0.2 | -- | 0.93 |...| 1 | +------------+-----------+------------+-----------------+---+---------+ will be represented by a ``cv_results_`` dict of:: { 'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'], mask = [False False False False]...) 'param_gamma': masked_array(data = [-- -- 0.1 0.2], mask = [ True True False False]...), 'param_degree': masked_array(data = [2.0 3.0 -- --], mask = [False False True True]...), 'split0_test_score' : [0.80, 0.70, 0.80, 0.93], 'split1_test_score' : [0.82, 0.50, 0.70, 0.78], 'mean_test_score' : [0.81, 0.60, 0.75, 0.85], 'std_test_score' : [0.01, 0.10, 0.05, 0.08], 'rank_test_score' : [2, 4, 3, 1], 'split0_train_score' : [0.80, 0.92, 0.70, 0.93], 'split1_train_score' : [0.82, 0.55, 0.70, 0.87], 'mean_train_score' : [0.81, 0.74, 0.70, 0.90], 'std_train_score' : [0.01, 0.19, 0.00, 0.03], 'mean_fit_time' : [0.73, 0.63, 0.43, 0.49], 'std_fit_time' : [0.01, 0.02, 0.01, 0.01], 'mean_score_time' : [0.01, 0.06, 0.04, 0.04], 'std_score_time' : [0.00, 0.00, 0.00, 0.01], 'params' : [{'kernel': 'poly', 'degree': 2}, ...], } NOTE The key ``'params'`` is used to store a list of parameter settings dicts for all the parameter candidates. The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and ``std_score_time`` are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the ``cv_results_`` dict at the keys ending with that scorer's name (``'_'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.) 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``. See ``refit`` parameter for more information on allowed values. best_score_ : float Mean cross-validated score of the best_estimator For multi-metric evaluation, this is present only if ``refit`` is specified. This attribute is not available if ``refit`` is a function. best_params_ : dict Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is present only if ``refit`` is specified. best_index_ : int The index (of the ``cv_results_`` arrays) which corresponds to the best candidate parameter setting. The dict at ``search.cv_results_['params'][search.best_index_]`` gives the parameter setting for the best model, that gives the highest mean score (``search.best_score_``). For multi-metric evaluation, this is present only if ``refit`` is specified. scorer_ : function or a dict Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated ``scoring`` dict which maps the scorer key to the scorer callable. n_splits_ : int The number of cross-validation splits (folds/iterations). refit_time_ : float Seconds used for refitting the best model on the whole dataset. This is present only if ``refit`` is not False. .. versionadded:: 0.20 multimetric_ : bool Whether or not the scorers compute several metrics. classes_ : ndarray of shape (n_classes,) The classes labels. This is present only if ``refit`` is specified and the underlying estimator is a classifier. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if `best_estimator_` is defined (see the documentation for the `refit` parameter for more details) and that `best_estimator_` exposes `n_features_in_` when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if `best_estimator_` is defined (see the documentation for the `refit` parameter for more details) and that `best_estimator_` exposes `feature_names_in_` when fit. .. versionadded:: 1.0 See Also -------- ParameterGrid : Generates all the combinations of a hyperparameter grid. 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. sklearn.metrics.make_scorer : Make a scorer from a performance metric or loss function. 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`. Examples -------- >>> from sklearn import svm, datasets >>> from sklearn.model_selection import GridSearchCV >>> iris = datasets.load_iris() >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]} >>> svc = svm.SVC() >>> clf = GridSearchCV(svc, parameters) >>> clf.fit(iris.data, iris.target) GridSearchCV(estimator=SVC(), param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')}) >>> sorted(clf.cv_results_.keys()) ['mean_fit_time', 'mean_score_time', 'mean_test_score',... 'param_C', 'param_kernel', 'params',... 'rank_test_score', 'split0_test_score',... 'split2_test_score', ... 'std_fit_time', 'std_score_time', 'std_test_score'] """ _parameter_constraints: dict = { **BaseSearchCV._parameter_constraints, "param_grid": [dict, list], } def __init__( self, estimator, param_grid, *, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch="2*n_jobs", error_score=np.nan, return_train_score=False, ): super().__init__( estimator=estimator, scoring=scoring, n_jobs=n_jobs, refit=refit, cv=cv, verbose=verbose, pre_dispatch=pre_dispatch, error_score=error_score, return_train_score=return_train_score, ) self.param_grid = param_grid def _run_search(self, evaluate_candidates): """Search all candidates in param_grid""" evaluate_candidates(ParameterGrid(self.param_grid)) class RandomizedSearchCV(BaseSearchCV): """Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and "inverse_transform" if they are implemented in the estimator used. The parameters of the estimator used to apply these methods are optimized by cross-validated search over parameter settings. 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. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters. Read more in the :ref:`User Guide `. .. versionadded:: 0.14 Parameters ---------- estimator : estimator object An object of that type is instantiated for each grid point. This is assumed to implement the scikit-learn estimator interface. Either estimator needs to provide a ``score`` function, or ``scoring`` must be passed. param_distributions : dict or list of dicts Dictionary with parameters names (`str`) 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. If a list of dicts is given, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above. n_iter : int, default=10 Number of parameter settings that are sampled. n_iter trades off runtime vs quality of the solution. scoring : str, callable, list, tuple or dict, default=None Strategy to evaluate the performance of the cross-validated model on the test set. If `scoring` represents a single score, one can use: - a single string (see :ref:`scoring_parameter`); - a callable (see :ref:`scoring_callable`) that returns a single value. If `scoring` represents multiple scores, one can use: - a list or tuple of unique strings; - a callable returning a dictionary where the keys are the metric names and the values are the metric scores; - a dictionary with metric names as keys and callables as values. See :ref:`multimetric_grid_search` for an example. If None, the estimator's score method is used. n_jobs : int, default=None Number of jobs to run in parallel. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary ` for more details. .. versionchanged:: v0.20 `n_jobs` default changed from 1 to None refit : bool, str, or callable, default=True Refit an estimator using the best found parameters on the whole dataset. For multiple metric evaluation, this needs to be a `str` denoting the scorer that would be used to find the best parameters for refitting the estimator at the end. Where there are considerations other than maximum score in choosing a best estimator, ``refit`` can be set to a function which returns the selected ``best_index_`` given the ``cv_results_``. In that case, the ``best_estimator_`` and ``best_params_`` will be set according to the returned ``best_index_`` while the ``best_score_`` attribute will not be available. The refitted estimator is made available at the ``best_estimator_`` attribute and permits using ``predict`` directly on this ``RandomizedSearchCV`` instance. Also for multiple metric evaluation, the attributes ``best_index_``, ``best_score_`` and ``best_params_`` will only be available if ``refit`` is set and all of them will be determined w.r.t this specific scorer. See ``scoring`` parameter to know more about multiple metric evaluation. .. versionchanged:: 0.20 Support for callable added. cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are: - None, to use the default 5-fold cross validation, - integer, to specify the number of folds in a `(Stratified)KFold`, - :term:`CV splitter`, - An iterable yielding (train, test) splits as arrays of indices. For integer/None inputs, if the estimator is a classifier and ``y`` is either binary or multiclass, :class:`StratifiedKFold` is used. In all other cases, :class:`KFold` is used. These splitters are instantiated with `shuffle=False` so the splits will be the same across calls. Refer :ref:`User Guide ` for the various cross-validation strategies that can be used here. .. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold. verbose : int Controls the verbosity: the higher, the more messages. - >1 : the computation time for each fold and parameter candidate is displayed; - >2 : the score is also displayed; - >3 : the fold and candidate parameter indexes are also displayed together with the starting time of the computation. pre_dispatch : int, or str, default='2*n_jobs' 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 str, giving an expression as a function of n_jobs, as in '2*n_jobs' random_state : int, RandomState instance or None, default=None Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See :term:`Glossary `. error_score : 'raise' or numeric, default=np.nan 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. return_train_score : bool, default=False If ``False``, the ``cv_results_`` attribute will not include training scores. Computing training scores is used to get insights on how different parameter settings impact the overfitting/underfitting trade-off. However computing the scores on the training set can be computationally expensive and is not strictly required to select the parameters that yield the best generalization performance. .. versionadded:: 0.19 .. versionchanged:: 0.21 Default value was changed from ``True`` to ``False`` Attributes ---------- cv_results_ : dict of numpy (masked) ndarrays A dict with keys as column headers and values as columns, that can be imported into a pandas ``DataFrame``. For instance the below given table +--------------+-------------+-------------------+---+---------------+ | param_kernel | param_gamma | split0_test_score |...|rank_test_score| +==============+=============+===================+===+===============+ | 'rbf' | 0.1 | 0.80 |...| 1 | +--------------+-------------+-------------------+---+---------------+ | 'rbf' | 0.2 | 0.84 |...| 3 | +--------------+-------------+-------------------+---+---------------+ | 'rbf' | 0.3 | 0.70 |...| 2 | +--------------+-------------+-------------------+---+---------------+ will be represented by a ``cv_results_`` dict of:: { 'param_kernel' : masked_array(data = ['rbf', 'rbf', 'rbf'], mask = False), 'param_gamma' : masked_array(data = [0.1 0.2 0.3], mask = False), 'split0_test_score' : [0.80, 0.84, 0.70], 'split1_test_score' : [0.82, 0.50, 0.70], 'mean_test_score' : [0.81, 0.67, 0.70], 'std_test_score' : [0.01, 0.24, 0.00], 'rank_test_score' : [1, 3, 2], 'split0_train_score' : [0.80, 0.92, 0.70], 'split1_train_score' : [0.82, 0.55, 0.70], 'mean_train_score' : [0.81, 0.74, 0.70], 'std_train_score' : [0.01, 0.19, 0.00], 'mean_fit_time' : [0.73, 0.63, 0.43], 'std_fit_time' : [0.01, 0.02, 0.01], 'mean_score_time' : [0.01, 0.06, 0.04], 'std_score_time' : [0.00, 0.00, 0.00], 'params' : [{'kernel' : 'rbf', 'gamma' : 0.1}, ...], } NOTE The key ``'params'`` is used to store a list of parameter settings dicts for all the parameter candidates. The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and ``std_score_time`` are all in seconds. For multi-metric evaluation, the scores for all the scorers are available in the ``cv_results_`` dict at the keys ending with that scorer's name (``'_'``) instead of ``'_score'`` shown above. ('split0_test_precision', 'mean_train_precision' etc.) 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``. For multi-metric evaluation, this attribute is present only if ``refit`` is specified. See ``refit`` parameter for more information on allowed values. best_score_ : float Mean cross-validated score of the best_estimator. For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information. This attribute is not available if ``refit`` is a function. best_params_ : dict Parameter setting that gave the best results on the hold out data. For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information. best_index_ : int The index (of the ``cv_results_`` arrays) which corresponds to the best candidate parameter setting. The dict at ``search.cv_results_['params'][search.best_index_]`` gives the parameter setting for the best model, that gives the highest mean score (``search.best_score_``). For multi-metric evaluation, this is not available if ``refit`` is ``False``. See ``refit`` parameter for more information. scorer_ : function or a dict Scorer function used on the held out data to choose the best parameters for the model. For multi-metric evaluation, this attribute holds the validated ``scoring`` dict which maps the scorer key to the scorer callable. n_splits_ : int The number of cross-validation splits (folds/iterations). refit_time_ : float Seconds used for refitting the best model on the whole dataset. This is present only if ``refit`` is not False. .. versionadded:: 0.20 multimetric_ : bool Whether or not the scorers compute several metrics. classes_ : ndarray of shape (n_classes,) The classes labels. This is present only if ``refit`` is specified and the underlying estimator is a classifier. n_features_in_ : int Number of features seen during :term:`fit`. Only defined if `best_estimator_` is defined (see the documentation for the `refit` parameter for more details) and that `best_estimator_` exposes `n_features_in_` when fit. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Only defined if `best_estimator_` is defined (see the documentation for the `refit` parameter for more details) and that `best_estimator_` exposes `feature_names_in_` when fit. .. versionadded:: 1.0 See Also -------- GridSearchCV : Does exhaustive search over a grid of parameters. ParameterSampler : A generator over parameter settings, constructed from param_distributions. 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`. Examples -------- >>> from sklearn.datasets import load_iris >>> from sklearn.linear_model import LogisticRegression >>> from sklearn.model_selection import RandomizedSearchCV >>> from scipy.stats import uniform >>> iris = load_iris() >>> logistic = LogisticRegression(solver='saga', tol=1e-2, max_iter=200, ... random_state=0) >>> distributions = dict(C=uniform(loc=0, scale=4), ... penalty=['l2', 'l1']) >>> clf = RandomizedSearchCV(logistic, distributions, random_state=0) >>> search = clf.fit(iris.data, iris.target) >>> search.best_params_ {'C': np.float64(2...), 'penalty': 'l1'} """ _parameter_constraints: dict = { **BaseSearchCV._parameter_constraints, "param_distributions": [dict, list], "n_iter": [Interval(numbers.Integral, 1, None, closed="left")], "random_state": ["random_state"], } def __init__( self, estimator, param_distributions, *, n_iter=10, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch="2*n_jobs", random_state=None, error_score=np.nan, return_train_score=False, ): self.param_distributions = param_distributions self.n_iter = n_iter self.random_state = random_state super().__init__( estimator=estimator, scoring=scoring, n_jobs=n_jobs, refit=refit, cv=cv, verbose=verbose, pre_dispatch=pre_dispatch, error_score=error_score, return_train_score=return_train_score, ) def _run_search(self, evaluate_candidates): """Search n_iter candidates from param_distributions""" evaluate_candidates( ParameterSampler( self.param_distributions, self.n_iter, random_state=self.random_state ) )