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ai-content-maker/.venv/Lib/site-packages/sklearn/model_selection/_search.py

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2024-05-03 04:18:51 +03:00
"""
The :mod:`sklearn.model_selection._search` includes utilities to fine-tune the
parameters of an estimator.
"""
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>,
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Olivier Grisel <olivier.grisel@ensta.org>
# Raghav RV <rvraghav93@gmail.com>
# License: 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 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._param_validation import HasMethods, Interval, StrOptions
from ..utils._tags import _safe_tags
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 <grid_search>`.
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 <grid_search>`.
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 <random_state>`.
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 _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
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
def _estimator_type(self):
return self.estimator._estimator_type
def _more_tags(self):
# allows cross-validation to see 'precomputed' metrics
return {
"pairwise": _safe_tags(self.estimator, "pairwise"),
"_xfail_checks": {
"check_supervised_y_2d": "DataConversionWarning not caught"
},
}
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 <metadata_routing>` 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(_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(_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(_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(_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(_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(_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(_estimator_has("inverse_transform"))
def inverse_transform(self, Xt):
"""Call inverse_transform on the estimator with the best found params.
Only available if the underlying estimator implements
``inverse_transform`` and ``refit=True``.
Parameters
----------
Xt : indexable, length n_samples
Must fulfill the input assumptions of the
underlying estimator.
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.
"""
check_is_fitted(self)
return self.best_estimator_.inverse_transform(Xt)
@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.
"""
_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, convert_multimetric):
"""Get the scorer(s) to be used.
This is used in ``fit`` and ``get_metadata_routing``.
Parameters
----------
convert_multimetric : bool
Whether to convert a dict of scorers to a _MultimetricScorer. This
is used in ``get_metadata_routing`` to include the routing info for
multiple scorers.
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
if convert_multimetric and isinstance(scorers, dict):
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)
Training vector, 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 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 across CV groups along with `X`
and `y`. For example, the :term:`sample_weight` parameter is split
because `len(sample_weights) = len(X)`.
Returns
-------
self : object
Instance of fitted estimator.
"""
estimator = self.estimator
# Here we keep a dict of scorers as is, and only convert to a
# _MultimetricScorer at a later stage. Issue:
# https://github.com/scikit-learn/scikit-learn/issues/27001
scorers, refit_metric = self._get_scorers(convert_multimetric=False)
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 callabe 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
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"])
# Use one MaskedArray and mask all the places where the param is not
# applicable for that candidate. Use defaultdict as each candidate may
# not contain all the params
param_results = defaultdict(
partial(
MaskedArray,
np.empty(
n_candidates,
),
mask=True,
dtype=object,
)
)
for cand_idx, params in enumerate(candidate_params):
for name, value in params.items():
# An all masked empty array gets created for the key
# `"param_%s" % name` at the first occurrence of `name`.
# Setting the value at an index also unmasks that index
param_results["param_%s" % name][cand_idx] = value
results.update(param_results)
# Store a list of param dicts at the key 'params'
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 <metadata_routing>` 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(convert_multimetric=True)
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
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 <grid_search>`.
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`) 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 a 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 <n_jobs>`
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 <cross_validation>` 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 (``'_<scorer_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']
"""
_required_parameters = ["estimator", "param_grid"]
_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 <randomized_parameter_search>`.
.. 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`) 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 a 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 <n_jobs>`
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 <cross_validation>` 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 <random_state>`.
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 (``'_<scorer_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': 2..., 'penalty': 'l1'}
"""
_required_parameters = ["estimator", "param_distributions"]
_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
)
)