1080 lines
43 KiB
Python
1080 lines
43 KiB
Python
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from abc import abstractmethod
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from copy import deepcopy
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from math import ceil, floor, log
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from numbers import Integral, Real
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import numpy as np
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from ..base import _fit_context, is_classifier
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from ..metrics._scorer import get_scorer_names
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from ..utils import resample
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from ..utils._param_validation import Interval, StrOptions
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from ..utils.multiclass import check_classification_targets
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from ..utils.validation import _num_samples
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from . import ParameterGrid, ParameterSampler
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from ._search import BaseSearchCV
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from ._split import _yields_constant_splits, check_cv
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__all__ = ["HalvingGridSearchCV", "HalvingRandomSearchCV"]
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class _SubsampleMetaSplitter:
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"""Splitter that subsamples a given fraction of the dataset"""
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def __init__(self, *, base_cv, fraction, subsample_test, random_state):
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self.base_cv = base_cv
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self.fraction = fraction
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self.subsample_test = subsample_test
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self.random_state = random_state
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def split(self, X, y, **kwargs):
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for train_idx, test_idx in self.base_cv.split(X, y, **kwargs):
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train_idx = resample(
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train_idx,
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replace=False,
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random_state=self.random_state,
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n_samples=int(self.fraction * len(train_idx)),
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)
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if self.subsample_test:
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test_idx = resample(
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test_idx,
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replace=False,
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random_state=self.random_state,
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n_samples=int(self.fraction * len(test_idx)),
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)
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yield train_idx, test_idx
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def _top_k(results, k, itr):
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# Return the best candidates of a given iteration
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iteration, mean_test_score, params = (
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np.asarray(a)
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for a in (results["iter"], results["mean_test_score"], results["params"])
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)
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iter_indices = np.flatnonzero(iteration == itr)
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scores = mean_test_score[iter_indices]
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# argsort() places NaNs at the end of the array so we move NaNs to the
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# front of the array so the last `k` items are the those with the
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# highest scores.
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sorted_indices = np.roll(np.argsort(scores), np.count_nonzero(np.isnan(scores)))
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return np.array(params[iter_indices][sorted_indices[-k:]])
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class BaseSuccessiveHalving(BaseSearchCV):
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"""Implements successive halving.
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Ref:
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Almost optimal exploration in multi-armed bandits, ICML 13
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Zohar Karnin, Tomer Koren, Oren Somekh
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"""
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_parameter_constraints: dict = {
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**BaseSearchCV._parameter_constraints,
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# overwrite `scoring` since multi-metrics are not supported
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"scoring": [StrOptions(set(get_scorer_names())), callable, None],
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"random_state": ["random_state"],
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"max_resources": [
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Interval(Integral, 0, None, closed="neither"),
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StrOptions({"auto"}),
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],
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"min_resources": [
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Interval(Integral, 0, None, closed="neither"),
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StrOptions({"exhaust", "smallest"}),
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],
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"resource": [str],
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"factor": [Interval(Real, 0, None, closed="neither")],
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"aggressive_elimination": ["boolean"],
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}
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_parameter_constraints.pop("pre_dispatch") # not used in this class
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def __init__(
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self,
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estimator,
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*,
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scoring=None,
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n_jobs=None,
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refit=True,
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cv=5,
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verbose=0,
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random_state=None,
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error_score=np.nan,
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return_train_score=True,
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max_resources="auto",
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min_resources="exhaust",
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resource="n_samples",
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factor=3,
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aggressive_elimination=False,
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):
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super().__init__(
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estimator,
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scoring=scoring,
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n_jobs=n_jobs,
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refit=refit,
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cv=cv,
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verbose=verbose,
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error_score=error_score,
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return_train_score=return_train_score,
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)
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self.random_state = random_state
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self.max_resources = max_resources
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self.resource = resource
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self.factor = factor
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self.min_resources = min_resources
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self.aggressive_elimination = aggressive_elimination
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def _check_input_parameters(self, X, y, split_params):
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# We need to enforce that successive calls to cv.split() yield the same
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# splits: see https://github.com/scikit-learn/scikit-learn/issues/15149
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if not _yields_constant_splits(self._checked_cv_orig):
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raise ValueError(
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"The cv parameter must yield consistent folds across "
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"calls to split(). Set its random_state to an int, or set "
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"shuffle=False."
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)
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if (
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self.resource != "n_samples"
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and self.resource not in self.estimator.get_params()
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):
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raise ValueError(
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f"Cannot use resource={self.resource} which is not supported "
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f"by estimator {self.estimator.__class__.__name__}"
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)
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if isinstance(self, HalvingRandomSearchCV):
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if self.min_resources == self.n_candidates == "exhaust":
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# for n_candidates=exhaust to work, we need to know what
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# min_resources is. Similarly min_resources=exhaust needs to
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# know the actual number of candidates.
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raise ValueError(
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"n_candidates and min_resources cannot be both set to 'exhaust'."
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)
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self.min_resources_ = self.min_resources
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if self.min_resources_ in ("smallest", "exhaust"):
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if self.resource == "n_samples":
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n_splits = self._checked_cv_orig.get_n_splits(X, y, **split_params)
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# please see https://gph.is/1KjihQe for a justification
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magic_factor = 2
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self.min_resources_ = n_splits * magic_factor
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if is_classifier(self.estimator):
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y = self._validate_data(X="no_validation", y=y)
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check_classification_targets(y)
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n_classes = np.unique(y).shape[0]
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self.min_resources_ *= n_classes
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else:
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self.min_resources_ = 1
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# if 'exhaust', min_resources_ might be set to a higher value later
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# in _run_search
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self.max_resources_ = self.max_resources
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if self.max_resources_ == "auto":
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if not self.resource == "n_samples":
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raise ValueError(
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"resource can only be 'n_samples' when max_resources='auto'"
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)
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self.max_resources_ = _num_samples(X)
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if self.min_resources_ > self.max_resources_:
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raise ValueError(
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f"min_resources_={self.min_resources_} is greater "
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f"than max_resources_={self.max_resources_}."
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)
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if self.min_resources_ == 0:
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raise ValueError(
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f"min_resources_={self.min_resources_}: you might have passed "
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"an empty dataset X."
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)
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@staticmethod
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def _select_best_index(refit, refit_metric, results):
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"""Custom refit callable to return the index of the best candidate.
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We want the best candidate out of the last iteration. By default
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BaseSearchCV would return the best candidate out of all iterations.
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Currently, we only support for a single metric thus `refit` and
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`refit_metric` are not required.
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"""
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last_iter = np.max(results["iter"])
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last_iter_indices = np.flatnonzero(results["iter"] == last_iter)
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test_scores = results["mean_test_score"][last_iter_indices]
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# If all scores are NaNs there is no way to pick between them,
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# so we (arbitrarily) declare the zero'th entry the best one
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if np.isnan(test_scores).all():
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best_idx = 0
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else:
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best_idx = np.nanargmax(test_scores)
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return last_iter_indices[best_idx]
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@_fit_context(
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# Halving*SearchCV.estimator is not validated yet
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prefer_skip_nested_validation=False
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)
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def fit(self, X, y=None, **params):
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"""Run fit with all sets of parameters.
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Parameters
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----------
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X : array-like, shape (n_samples, n_features)
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Training vector, where `n_samples` is the number of samples and
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`n_features` is the number of features.
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y : array-like, shape (n_samples,) or (n_samples, n_output), optional
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Target relative to X for classification or regression;
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None for unsupervised learning.
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**params : dict of string -> object
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Parameters passed to the ``fit`` method of the estimator.
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Returns
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-------
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self : object
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Instance of fitted estimator.
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"""
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self._checked_cv_orig = check_cv(
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self.cv, y, classifier=is_classifier(self.estimator)
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)
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routed_params = self._get_routed_params_for_fit(params)
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self._check_input_parameters(
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X=X, y=y, split_params=routed_params.splitter.split
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)
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self._n_samples_orig = _num_samples(X)
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super().fit(X, y=y, **params)
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# Set best_score_: BaseSearchCV does not set it, as refit is a callable
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self.best_score_ = self.cv_results_["mean_test_score"][self.best_index_]
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return self
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def _run_search(self, evaluate_candidates):
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candidate_params = self._generate_candidate_params()
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if self.resource != "n_samples" and any(
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self.resource in candidate for candidate in candidate_params
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):
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# Can only check this now since we need the candidates list
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raise ValueError(
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f"Cannot use parameter {self.resource} as the resource since "
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"it is part of the searched parameters."
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)
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# n_required_iterations is the number of iterations needed so that the
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# last iterations evaluates less than `factor` candidates.
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n_required_iterations = 1 + floor(log(len(candidate_params), self.factor))
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if self.min_resources == "exhaust":
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# To exhaust the resources, we want to start with the biggest
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# min_resources possible so that the last (required) iteration
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# uses as many resources as possible
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last_iteration = n_required_iterations - 1
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self.min_resources_ = max(
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self.min_resources_,
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self.max_resources_ // self.factor**last_iteration,
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)
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# n_possible_iterations is the number of iterations that we can
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# actually do starting from min_resources and without exceeding
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# max_resources. Depending on max_resources and the number of
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# candidates, this may be higher or smaller than
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# n_required_iterations.
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n_possible_iterations = 1 + floor(
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log(self.max_resources_ // self.min_resources_, self.factor)
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)
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if self.aggressive_elimination:
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n_iterations = n_required_iterations
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else:
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n_iterations = min(n_possible_iterations, n_required_iterations)
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if self.verbose:
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print(f"n_iterations: {n_iterations}")
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print(f"n_required_iterations: {n_required_iterations}")
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print(f"n_possible_iterations: {n_possible_iterations}")
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print(f"min_resources_: {self.min_resources_}")
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print(f"max_resources_: {self.max_resources_}")
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print(f"aggressive_elimination: {self.aggressive_elimination}")
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print(f"factor: {self.factor}")
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self.n_resources_ = []
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self.n_candidates_ = []
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for itr in range(n_iterations):
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power = itr # default
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if self.aggressive_elimination:
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# this will set n_resources to the initial value (i.e. the
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# value of n_resources at the first iteration) for as many
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# iterations as needed (while candidates are being
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# eliminated), and then go on as usual.
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power = max(0, itr - n_required_iterations + n_possible_iterations)
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n_resources = int(self.factor**power * self.min_resources_)
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# guard, probably not needed
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n_resources = min(n_resources, self.max_resources_)
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self.n_resources_.append(n_resources)
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n_candidates = len(candidate_params)
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self.n_candidates_.append(n_candidates)
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if self.verbose:
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print("-" * 10)
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print(f"iter: {itr}")
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print(f"n_candidates: {n_candidates}")
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print(f"n_resources: {n_resources}")
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if self.resource == "n_samples":
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# subsampling will be done in cv.split()
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cv = _SubsampleMetaSplitter(
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base_cv=self._checked_cv_orig,
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fraction=n_resources / self._n_samples_orig,
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subsample_test=True,
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random_state=self.random_state,
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)
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else:
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# Need copy so that the n_resources of next iteration does
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# not overwrite
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candidate_params = [c.copy() for c in candidate_params]
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for candidate in candidate_params:
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candidate[self.resource] = n_resources
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cv = self._checked_cv_orig
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more_results = {
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"iter": [itr] * n_candidates,
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"n_resources": [n_resources] * n_candidates,
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}
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results = evaluate_candidates(
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candidate_params, cv, more_results=more_results
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)
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n_candidates_to_keep = ceil(n_candidates / self.factor)
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candidate_params = _top_k(results, n_candidates_to_keep, itr)
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self.n_remaining_candidates_ = len(candidate_params)
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self.n_required_iterations_ = n_required_iterations
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self.n_possible_iterations_ = n_possible_iterations
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|
|
self.n_iterations_ = n_iterations
|
||
|
|
|
||
|
|
@abstractmethod
|
||
|
|
def _generate_candidate_params(self):
|
||
|
|
pass
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
tags = deepcopy(super()._more_tags())
|
||
|
|
tags["_xfail_checks"].update(
|
||
|
|
{
|
||
|
|
"check_fit2d_1sample": (
|
||
|
|
"Fail during parameter check since min/max resources requires"
|
||
|
|
" more samples"
|
||
|
|
),
|
||
|
|
}
|
||
|
|
)
|
||
|
|
return tags
|
||
|
|
|
||
|
|
|
||
|
|
class HalvingGridSearchCV(BaseSuccessiveHalving):
|
||
|
|
"""Search over specified parameter values with successive halving.
|
||
|
|
|
||
|
|
The search strategy starts evaluating all the candidates with a small
|
||
|
|
amount of resources and iteratively selects the best candidates, using
|
||
|
|
more and more resources.
|
||
|
|
|
||
|
|
Read more in the :ref:`User guide <successive_halving_user_guide>`.
|
||
|
|
|
||
|
|
.. note::
|
||
|
|
|
||
|
|
This estimator is still **experimental** for now: the predictions
|
||
|
|
and the API might change without any deprecation cycle. To use it,
|
||
|
|
you need to explicitly import ``enable_halving_search_cv``::
|
||
|
|
|
||
|
|
>>> # explicitly require this experimental feature
|
||
|
|
>>> from sklearn.experimental import enable_halving_search_cv # noqa
|
||
|
|
>>> # now you can import normally from model_selection
|
||
|
|
>>> from sklearn.model_selection import HalvingGridSearchCV
|
||
|
|
|
||
|
|
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 (string) as keys and lists of
|
||
|
|
parameter settings to try as values, or a list of such
|
||
|
|
dictionaries, in which case the grids spanned by each dictionary
|
||
|
|
in the list are explored. This enables searching over any sequence
|
||
|
|
of parameter settings.
|
||
|
|
|
||
|
|
factor : int or float, default=3
|
||
|
|
The 'halving' parameter, which determines the proportion of candidates
|
||
|
|
that are selected for each subsequent iteration. For example,
|
||
|
|
``factor=3`` means that only one third of the candidates are selected.
|
||
|
|
|
||
|
|
resource : ``'n_samples'`` or str, default='n_samples'
|
||
|
|
Defines the resource that increases with each iteration. By default,
|
||
|
|
the resource is the number of samples. It can also be set to any
|
||
|
|
parameter of the base estimator that accepts positive integer
|
||
|
|
values, e.g. 'n_iterations' or 'n_estimators' for a gradient
|
||
|
|
boosting estimator. In this case ``max_resources`` cannot be 'auto'
|
||
|
|
and must be set explicitly.
|
||
|
|
|
||
|
|
max_resources : int, default='auto'
|
||
|
|
The maximum amount of resource that any candidate is allowed to use
|
||
|
|
for a given iteration. By default, this is set to ``n_samples`` when
|
||
|
|
``resource='n_samples'`` (default), else an error is raised.
|
||
|
|
|
||
|
|
min_resources : {'exhaust', 'smallest'} or int, default='exhaust'
|
||
|
|
The minimum amount of resource that any candidate is allowed to use
|
||
|
|
for a given iteration. Equivalently, this defines the amount of
|
||
|
|
resources `r0` that are allocated for each candidate at the first
|
||
|
|
iteration.
|
||
|
|
|
||
|
|
- 'smallest' is a heuristic that sets `r0` to a small value:
|
||
|
|
|
||
|
|
- ``n_splits * 2`` when ``resource='n_samples'`` for a regression
|
||
|
|
problem
|
||
|
|
- ``n_classes * n_splits * 2`` when ``resource='n_samples'`` for a
|
||
|
|
classification problem
|
||
|
|
- ``1`` when ``resource != 'n_samples'``
|
||
|
|
|
||
|
|
- 'exhaust' will set `r0` such that the **last** iteration uses as
|
||
|
|
much resources as possible. Namely, the last iteration will use the
|
||
|
|
highest value smaller than ``max_resources`` that is a multiple of
|
||
|
|
both ``min_resources`` and ``factor``. In general, using 'exhaust'
|
||
|
|
leads to a more accurate estimator, but is slightly more time
|
||
|
|
consuming.
|
||
|
|
|
||
|
|
Note that the amount of resources used at each iteration is always a
|
||
|
|
multiple of ``min_resources``.
|
||
|
|
|
||
|
|
aggressive_elimination : bool, default=False
|
||
|
|
This is only relevant in cases where there isn't enough resources to
|
||
|
|
reduce the remaining candidates to at most `factor` after the last
|
||
|
|
iteration. If ``True``, then the search process will 'replay' the
|
||
|
|
first iteration for as long as needed until the number of candidates
|
||
|
|
is small enough. This is ``False`` by default, which means that the
|
||
|
|
last iteration may evaluate more than ``factor`` candidates. See
|
||
|
|
:ref:`aggressive_elimination` for more details.
|
||
|
|
|
||
|
|
cv : int, cross-validation generator or iterable, default=5
|
||
|
|
Determines the cross-validation splitting strategy.
|
||
|
|
Possible inputs for cv are:
|
||
|
|
|
||
|
|
- 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.
|
||
|
|
|
||
|
|
.. note::
|
||
|
|
Due to implementation details, the folds produced by `cv` must be
|
||
|
|
the same across multiple calls to `cv.split()`. For
|
||
|
|
built-in `scikit-learn` iterators, this can be achieved by
|
||
|
|
deactivating shuffling (`shuffle=False`), or by setting the
|
||
|
|
`cv`'s `random_state` parameter to an integer.
|
||
|
|
|
||
|
|
scoring : str, callable, or None, default=None
|
||
|
|
A single string (see :ref:`scoring_parameter`) or a callable
|
||
|
|
(see :ref:`scoring`) to evaluate the predictions on the test set.
|
||
|
|
If None, the estimator's score method is used.
|
||
|
|
|
||
|
|
refit : bool, default=True
|
||
|
|
If True, refit an estimator using the best found parameters on the
|
||
|
|
whole dataset.
|
||
|
|
|
||
|
|
The refitted estimator is made available at the ``best_estimator_``
|
||
|
|
attribute and permits using ``predict`` directly on this
|
||
|
|
``HalvingGridSearchCV`` instance.
|
||
|
|
|
||
|
|
error_score : 'raise' or numeric
|
||
|
|
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. Default is ``np.nan``.
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
random_state : int, RandomState instance or None, default=None
|
||
|
|
Pseudo random number generator state used for subsampling the dataset
|
||
|
|
when `resources != 'n_samples'`. Ignored otherwise.
|
||
|
|
Pass an int for reproducible output across multiple function calls.
|
||
|
|
See :term:`Glossary <random_state>`.
|
||
|
|
|
||
|
|
n_jobs : int or None, 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.
|
||
|
|
|
||
|
|
verbose : int
|
||
|
|
Controls the verbosity: the higher, the more messages.
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
n_resources_ : list of int
|
||
|
|
The amount of resources used at each iteration.
|
||
|
|
|
||
|
|
n_candidates_ : list of int
|
||
|
|
The number of candidate parameters that were evaluated at each
|
||
|
|
iteration.
|
||
|
|
|
||
|
|
n_remaining_candidates_ : int
|
||
|
|
The number of candidate parameters that are left after the last
|
||
|
|
iteration. It corresponds to `ceil(n_candidates[-1] / factor)`
|
||
|
|
|
||
|
|
max_resources_ : int
|
||
|
|
The maximum number of resources that any candidate is allowed to use
|
||
|
|
for a given iteration. Note that since the number of resources used
|
||
|
|
at each iteration must be a multiple of ``min_resources_``, the
|
||
|
|
actual number of resources used at the last iteration may be smaller
|
||
|
|
than ``max_resources_``.
|
||
|
|
|
||
|
|
min_resources_ : int
|
||
|
|
The amount of resources that are allocated for each candidate at the
|
||
|
|
first iteration.
|
||
|
|
|
||
|
|
n_iterations_ : int
|
||
|
|
The actual number of iterations that were run. This is equal to
|
||
|
|
``n_required_iterations_`` if ``aggressive_elimination`` is ``True``.
|
||
|
|
Else, this is equal to ``min(n_possible_iterations_,
|
||
|
|
n_required_iterations_)``.
|
||
|
|
|
||
|
|
n_possible_iterations_ : int
|
||
|
|
The number of iterations that are possible starting with
|
||
|
|
``min_resources_`` resources and without exceeding
|
||
|
|
``max_resources_``.
|
||
|
|
|
||
|
|
n_required_iterations_ : int
|
||
|
|
The number of iterations that are required to end up with less than
|
||
|
|
``factor`` candidates at the last iteration, starting with
|
||
|
|
``min_resources_`` resources. This will be smaller than
|
||
|
|
``n_possible_iterations_`` when there isn't enough resources.
|
||
|
|
|
||
|
|
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``. It contains lots of information
|
||
|
|
for analysing the results of a search.
|
||
|
|
Please refer to the :ref:`User guide<successive_halving_cv_results>`
|
||
|
|
for details.
|
||
|
|
|
||
|
|
best_estimator_ : estimator or dict
|
||
|
|
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``.
|
||
|
|
|
||
|
|
best_score_ : float
|
||
|
|
Mean cross-validated score of the best_estimator.
|
||
|
|
|
||
|
|
best_params_ : dict
|
||
|
|
Parameter setting that gave the best results on the hold out data.
|
||
|
|
|
||
|
|
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_``).
|
||
|
|
|
||
|
|
scorer_ : function or a dict
|
||
|
|
Scorer function used on the held out data to choose the best
|
||
|
|
parameters for the model.
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
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
|
||
|
|
--------
|
||
|
|
:class:`HalvingRandomSearchCV`:
|
||
|
|
Random search over a set of parameters using successive halving.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The parameters selected are those that maximize the score of the held-out
|
||
|
|
data, according to the scoring parameter.
|
||
|
|
|
||
|
|
All parameter combinations scored with a NaN will share the lowest rank.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
>>> from sklearn.datasets import load_iris
|
||
|
|
>>> from sklearn.ensemble import RandomForestClassifier
|
||
|
|
>>> from sklearn.experimental import enable_halving_search_cv # noqa
|
||
|
|
>>> from sklearn.model_selection import HalvingGridSearchCV
|
||
|
|
...
|
||
|
|
>>> X, y = load_iris(return_X_y=True)
|
||
|
|
>>> clf = RandomForestClassifier(random_state=0)
|
||
|
|
...
|
||
|
|
>>> param_grid = {"max_depth": [3, None],
|
||
|
|
... "min_samples_split": [5, 10]}
|
||
|
|
>>> search = HalvingGridSearchCV(clf, param_grid, resource='n_estimators',
|
||
|
|
... max_resources=10,
|
||
|
|
... random_state=0).fit(X, y)
|
||
|
|
>>> search.best_params_ # doctest: +SKIP
|
||
|
|
{'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}
|
||
|
|
"""
|
||
|
|
|
||
|
|
_required_parameters = ["estimator", "param_grid"]
|
||
|
|
|
||
|
|
_parameter_constraints: dict = {
|
||
|
|
**BaseSuccessiveHalving._parameter_constraints,
|
||
|
|
"param_grid": [dict, list],
|
||
|
|
}
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
estimator,
|
||
|
|
param_grid,
|
||
|
|
*,
|
||
|
|
factor=3,
|
||
|
|
resource="n_samples",
|
||
|
|
max_resources="auto",
|
||
|
|
min_resources="exhaust",
|
||
|
|
aggressive_elimination=False,
|
||
|
|
cv=5,
|
||
|
|
scoring=None,
|
||
|
|
refit=True,
|
||
|
|
error_score=np.nan,
|
||
|
|
return_train_score=True,
|
||
|
|
random_state=None,
|
||
|
|
n_jobs=None,
|
||
|
|
verbose=0,
|
||
|
|
):
|
||
|
|
super().__init__(
|
||
|
|
estimator,
|
||
|
|
scoring=scoring,
|
||
|
|
n_jobs=n_jobs,
|
||
|
|
refit=refit,
|
||
|
|
verbose=verbose,
|
||
|
|
cv=cv,
|
||
|
|
random_state=random_state,
|
||
|
|
error_score=error_score,
|
||
|
|
return_train_score=return_train_score,
|
||
|
|
max_resources=max_resources,
|
||
|
|
resource=resource,
|
||
|
|
factor=factor,
|
||
|
|
min_resources=min_resources,
|
||
|
|
aggressive_elimination=aggressive_elimination,
|
||
|
|
)
|
||
|
|
self.param_grid = param_grid
|
||
|
|
|
||
|
|
def _generate_candidate_params(self):
|
||
|
|
return ParameterGrid(self.param_grid)
|
||
|
|
|
||
|
|
|
||
|
|
class HalvingRandomSearchCV(BaseSuccessiveHalving):
|
||
|
|
"""Randomized search on hyper parameters.
|
||
|
|
|
||
|
|
The search strategy starts evaluating all the candidates with a small
|
||
|
|
amount of resources and iteratively selects the best candidates, using more
|
||
|
|
and more resources.
|
||
|
|
|
||
|
|
The candidates are sampled at random from the parameter space and the
|
||
|
|
number of sampled candidates is determined by ``n_candidates``.
|
||
|
|
|
||
|
|
Read more in the :ref:`User guide<successive_halving_user_guide>`.
|
||
|
|
|
||
|
|
.. note::
|
||
|
|
|
||
|
|
This estimator is still **experimental** for now: the predictions
|
||
|
|
and the API might change without any deprecation cycle. To use it,
|
||
|
|
you need to explicitly import ``enable_halving_search_cv``::
|
||
|
|
|
||
|
|
>>> # explicitly require this experimental feature
|
||
|
|
>>> from sklearn.experimental import enable_halving_search_cv # noqa
|
||
|
|
>>> # now you can import normally from model_selection
|
||
|
|
>>> from sklearn.model_selection import HalvingRandomSearchCV
|
||
|
|
|
||
|
|
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_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_candidates : "exhaust" or int, default="exhaust"
|
||
|
|
The number of candidate parameters to sample, at the first
|
||
|
|
iteration. Using 'exhaust' will sample enough candidates so that the
|
||
|
|
last iteration uses as many resources as possible, based on
|
||
|
|
`min_resources`, `max_resources` and `factor`. In this case,
|
||
|
|
`min_resources` cannot be 'exhaust'.
|
||
|
|
|
||
|
|
factor : int or float, default=3
|
||
|
|
The 'halving' parameter, which determines the proportion of candidates
|
||
|
|
that are selected for each subsequent iteration. For example,
|
||
|
|
``factor=3`` means that only one third of the candidates are selected.
|
||
|
|
|
||
|
|
resource : ``'n_samples'`` or str, default='n_samples'
|
||
|
|
Defines the resource that increases with each iteration. By default,
|
||
|
|
the resource is the number of samples. It can also be set to any
|
||
|
|
parameter of the base estimator that accepts positive integer
|
||
|
|
values, e.g. 'n_iterations' or 'n_estimators' for a gradient
|
||
|
|
boosting estimator. In this case ``max_resources`` cannot be 'auto'
|
||
|
|
and must be set explicitly.
|
||
|
|
|
||
|
|
max_resources : int, default='auto'
|
||
|
|
The maximum number of resources that any candidate is allowed to use
|
||
|
|
for a given iteration. By default, this is set ``n_samples`` when
|
||
|
|
``resource='n_samples'`` (default), else an error is raised.
|
||
|
|
|
||
|
|
min_resources : {'exhaust', 'smallest'} or int, default='smallest'
|
||
|
|
The minimum amount of resource that any candidate is allowed to use
|
||
|
|
for a given iteration. Equivalently, this defines the amount of
|
||
|
|
resources `r0` that are allocated for each candidate at the first
|
||
|
|
iteration.
|
||
|
|
|
||
|
|
- 'smallest' is a heuristic that sets `r0` to a small value:
|
||
|
|
|
||
|
|
- ``n_splits * 2`` when ``resource='n_samples'`` for a regression
|
||
|
|
problem
|
||
|
|
- ``n_classes * n_splits * 2`` when ``resource='n_samples'`` for a
|
||
|
|
classification problem
|
||
|
|
- ``1`` when ``resource != 'n_samples'``
|
||
|
|
|
||
|
|
- 'exhaust' will set `r0` such that the **last** iteration uses as
|
||
|
|
much resources as possible. Namely, the last iteration will use the
|
||
|
|
highest value smaller than ``max_resources`` that is a multiple of
|
||
|
|
both ``min_resources`` and ``factor``. In general, using 'exhaust'
|
||
|
|
leads to a more accurate estimator, but is slightly more time
|
||
|
|
consuming. 'exhaust' isn't available when `n_candidates='exhaust'`.
|
||
|
|
|
||
|
|
Note that the amount of resources used at each iteration is always a
|
||
|
|
multiple of ``min_resources``.
|
||
|
|
|
||
|
|
aggressive_elimination : bool, default=False
|
||
|
|
This is only relevant in cases where there isn't enough resources to
|
||
|
|
reduce the remaining candidates to at most `factor` after the last
|
||
|
|
iteration. If ``True``, then the search process will 'replay' the
|
||
|
|
first iteration for as long as needed until the number of candidates
|
||
|
|
is small enough. This is ``False`` by default, which means that the
|
||
|
|
last iteration may evaluate more than ``factor`` candidates. See
|
||
|
|
:ref:`aggressive_elimination` for more details.
|
||
|
|
|
||
|
|
cv : int, cross-validation generator or an iterable, default=5
|
||
|
|
Determines the cross-validation splitting strategy.
|
||
|
|
Possible inputs for cv are:
|
||
|
|
|
||
|
|
- 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.
|
||
|
|
|
||
|
|
.. note::
|
||
|
|
Due to implementation details, the folds produced by `cv` must be
|
||
|
|
the same across multiple calls to `cv.split()`. For
|
||
|
|
built-in `scikit-learn` iterators, this can be achieved by
|
||
|
|
deactivating shuffling (`shuffle=False`), or by setting the
|
||
|
|
`cv`'s `random_state` parameter to an integer.
|
||
|
|
|
||
|
|
scoring : str, callable, or None, default=None
|
||
|
|
A single string (see :ref:`scoring_parameter`) or a callable
|
||
|
|
(see :ref:`scoring`) to evaluate the predictions on the test set.
|
||
|
|
If None, the estimator's score method is used.
|
||
|
|
|
||
|
|
refit : bool, default=True
|
||
|
|
If True, refit an estimator using the best found parameters on the
|
||
|
|
whole dataset.
|
||
|
|
|
||
|
|
The refitted estimator is made available at the ``best_estimator_``
|
||
|
|
attribute and permits using ``predict`` directly on this
|
||
|
|
``HalvingRandomSearchCV`` instance.
|
||
|
|
|
||
|
|
error_score : 'raise' or numeric
|
||
|
|
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. Default is ``np.nan``.
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
random_state : int, RandomState instance or None, default=None
|
||
|
|
Pseudo random number generator state used for subsampling the dataset
|
||
|
|
when `resources != 'n_samples'`. Also 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>`.
|
||
|
|
|
||
|
|
n_jobs : int or None, 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.
|
||
|
|
|
||
|
|
verbose : int
|
||
|
|
Controls the verbosity: the higher, the more messages.
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
n_resources_ : list of int
|
||
|
|
The amount of resources used at each iteration.
|
||
|
|
|
||
|
|
n_candidates_ : list of int
|
||
|
|
The number of candidate parameters that were evaluated at each
|
||
|
|
iteration.
|
||
|
|
|
||
|
|
n_remaining_candidates_ : int
|
||
|
|
The number of candidate parameters that are left after the last
|
||
|
|
iteration. It corresponds to `ceil(n_candidates[-1] / factor)`
|
||
|
|
|
||
|
|
max_resources_ : int
|
||
|
|
The maximum number of resources that any candidate is allowed to use
|
||
|
|
for a given iteration. Note that since the number of resources used at
|
||
|
|
each iteration must be a multiple of ``min_resources_``, the actual
|
||
|
|
number of resources used at the last iteration may be smaller than
|
||
|
|
``max_resources_``.
|
||
|
|
|
||
|
|
min_resources_ : int
|
||
|
|
The amount of resources that are allocated for each candidate at the
|
||
|
|
first iteration.
|
||
|
|
|
||
|
|
n_iterations_ : int
|
||
|
|
The actual number of iterations that were run. This is equal to
|
||
|
|
``n_required_iterations_`` if ``aggressive_elimination`` is ``True``.
|
||
|
|
Else, this is equal to ``min(n_possible_iterations_,
|
||
|
|
n_required_iterations_)``.
|
||
|
|
|
||
|
|
n_possible_iterations_ : int
|
||
|
|
The number of iterations that are possible starting with
|
||
|
|
``min_resources_`` resources and without exceeding
|
||
|
|
``max_resources_``.
|
||
|
|
|
||
|
|
n_required_iterations_ : int
|
||
|
|
The number of iterations that are required to end up with less than
|
||
|
|
``factor`` candidates at the last iteration, starting with
|
||
|
|
``min_resources_`` resources. This will be smaller than
|
||
|
|
``n_possible_iterations_`` when there isn't enough resources.
|
||
|
|
|
||
|
|
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``. It contains lots of information
|
||
|
|
for analysing the results of a search.
|
||
|
|
Please refer to the :ref:`User guide<successive_halving_cv_results>`
|
||
|
|
for details.
|
||
|
|
|
||
|
|
best_estimator_ : estimator or dict
|
||
|
|
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``.
|
||
|
|
|
||
|
|
best_score_ : float
|
||
|
|
Mean cross-validated score of the best_estimator.
|
||
|
|
|
||
|
|
best_params_ : dict
|
||
|
|
Parameter setting that gave the best results on the hold out data.
|
||
|
|
|
||
|
|
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_``).
|
||
|
|
|
||
|
|
scorer_ : function or a dict
|
||
|
|
Scorer function used on the held out data to choose the best
|
||
|
|
parameters for the model.
|
||
|
|
|
||
|
|
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.
|
||
|
|
|
||
|
|
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
|
||
|
|
--------
|
||
|
|
:class:`HalvingGridSearchCV`:
|
||
|
|
Search over a grid of parameters using successive halving.
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
The parameters selected are those that maximize the score of the held-out
|
||
|
|
data, according to the scoring parameter.
|
||
|
|
|
||
|
|
All parameter combinations scored with a NaN will share the lowest rank.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
|
||
|
|
>>> from sklearn.datasets import load_iris
|
||
|
|
>>> from sklearn.ensemble import RandomForestClassifier
|
||
|
|
>>> from sklearn.experimental import enable_halving_search_cv # noqa
|
||
|
|
>>> from sklearn.model_selection import HalvingRandomSearchCV
|
||
|
|
>>> from scipy.stats import randint
|
||
|
|
>>> import numpy as np
|
||
|
|
...
|
||
|
|
>>> X, y = load_iris(return_X_y=True)
|
||
|
|
>>> clf = RandomForestClassifier(random_state=0)
|
||
|
|
>>> np.random.seed(0)
|
||
|
|
...
|
||
|
|
>>> param_distributions = {"max_depth": [3, None],
|
||
|
|
... "min_samples_split": randint(2, 11)}
|
||
|
|
>>> search = HalvingRandomSearchCV(clf, param_distributions,
|
||
|
|
... resource='n_estimators',
|
||
|
|
... max_resources=10,
|
||
|
|
... random_state=0).fit(X, y)
|
||
|
|
>>> search.best_params_ # doctest: +SKIP
|
||
|
|
{'max_depth': None, 'min_samples_split': 10, 'n_estimators': 9}
|
||
|
|
"""
|
||
|
|
|
||
|
|
_required_parameters = ["estimator", "param_distributions"]
|
||
|
|
|
||
|
|
_parameter_constraints: dict = {
|
||
|
|
**BaseSuccessiveHalving._parameter_constraints,
|
||
|
|
"param_distributions": [dict, list],
|
||
|
|
"n_candidates": [
|
||
|
|
Interval(Integral, 0, None, closed="neither"),
|
||
|
|
StrOptions({"exhaust"}),
|
||
|
|
],
|
||
|
|
}
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
estimator,
|
||
|
|
param_distributions,
|
||
|
|
*,
|
||
|
|
n_candidates="exhaust",
|
||
|
|
factor=3,
|
||
|
|
resource="n_samples",
|
||
|
|
max_resources="auto",
|
||
|
|
min_resources="smallest",
|
||
|
|
aggressive_elimination=False,
|
||
|
|
cv=5,
|
||
|
|
scoring=None,
|
||
|
|
refit=True,
|
||
|
|
error_score=np.nan,
|
||
|
|
return_train_score=True,
|
||
|
|
random_state=None,
|
||
|
|
n_jobs=None,
|
||
|
|
verbose=0,
|
||
|
|
):
|
||
|
|
super().__init__(
|
||
|
|
estimator,
|
||
|
|
scoring=scoring,
|
||
|
|
n_jobs=n_jobs,
|
||
|
|
refit=refit,
|
||
|
|
verbose=verbose,
|
||
|
|
cv=cv,
|
||
|
|
random_state=random_state,
|
||
|
|
error_score=error_score,
|
||
|
|
return_train_score=return_train_score,
|
||
|
|
max_resources=max_resources,
|
||
|
|
resource=resource,
|
||
|
|
factor=factor,
|
||
|
|
min_resources=min_resources,
|
||
|
|
aggressive_elimination=aggressive_elimination,
|
||
|
|
)
|
||
|
|
self.param_distributions = param_distributions
|
||
|
|
self.n_candidates = n_candidates
|
||
|
|
|
||
|
|
def _generate_candidate_params(self):
|
||
|
|
n_candidates_first_iter = self.n_candidates
|
||
|
|
if n_candidates_first_iter == "exhaust":
|
||
|
|
# This will generate enough candidate so that the last iteration
|
||
|
|
# uses as much resources as possible
|
||
|
|
n_candidates_first_iter = self.max_resources_ // self.min_resources_
|
||
|
|
return ParameterSampler(
|
||
|
|
self.param_distributions,
|
||
|
|
n_candidates_first_iter,
|
||
|
|
random_state=self.random_state,
|
||
|
|
)
|