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from numbers import Integral, Real
from sklearn.tree._criterion import Criterion
from ..utils._param_validation import (
HasMethods,
Hidden,
Interval,
RealNotInt,
StrOptions,
)
def _estimator_has(attr):
"""Check if we can delegate a method to the underlying estimator.
First, we check the first fitted estimator if available, otherwise we
check the estimator attribute.
"""
def check(self):
if hasattr(self, "estimators_"):
return hasattr(self.estimators_[0], attr)
elif self.estimator is not None:
return hasattr(self.estimator, attr)
else: # TODO(1.4): Remove when the base_estimator deprecation cycle ends
return hasattr(self.base_estimator, attr)
return check
_bagging_parameter_constraints = {
"estimator": [HasMethods(["fit", "predict"]), None],
"n_estimators": [Interval(Integral, 1, None, closed="left")],
"max_samples": [
Interval(Integral, 1, None, closed="left"),
Interval(RealNotInt, 0, 1, closed="right"),
],
"max_features": [
Interval(Integral, 1, None, closed="left"),
Interval(RealNotInt, 0, 1, closed="right"),
],
"bootstrap": ["boolean"],
"bootstrap_features": ["boolean"],
"oob_score": ["boolean"],
"warm_start": ["boolean"],
"n_jobs": [None, Integral],
"random_state": ["random_state"],
"verbose": ["verbose"],
"base_estimator": [
HasMethods(["fit", "predict"]),
StrOptions({"deprecated"}),
None,
],
}
_adaboost_classifier_parameter_constraints = {
"estimator": [HasMethods(["fit", "predict"]), None],
"n_estimators": [Interval(Integral, 1, None, closed="left")],
"learning_rate": [Interval(Real, 0, None, closed="neither")],
"random_state": ["random_state"],
"base_estimator": [HasMethods(["fit", "predict"]), StrOptions({"deprecated"})],
"algorithm": [StrOptions({"SAMME", "SAMME.R"})],
}
_random_forest_classifier_parameter_constraints = {
"n_estimators": [Interval(Integral, 1, None, closed="left")],
"bootstrap": ["boolean"],
"oob_score": ["boolean"],
"n_jobs": [Integral, None],
"random_state": ["random_state"],
"verbose": ["verbose"],
"warm_start": ["boolean"],
"criterion": [StrOptions({"gini", "entropy", "log_loss"}), Hidden(Criterion)],
"max_samples": [
None,
Interval(Real, 0.0, 1.0, closed="right"),
Interval(Integral, 1, None, closed="left"),
],
"max_depth": [Interval(Integral, 1, None, closed="left"), None],
"min_samples_split": [
Interval(Integral, 2, None, closed="left"),
Interval(RealNotInt, 0.0, 1.0, closed="right"),
],
"min_samples_leaf": [
Interval(Integral, 1, None, closed="left"),
Interval(RealNotInt, 0.0, 1.0, closed="neither"),
],
"min_weight_fraction_leaf": [Interval(Real, 0.0, 0.5, closed="both")],
"max_features": [
Interval(Integral, 1, None, closed="left"),
Interval(RealNotInt, 0.0, 1.0, closed="right"),
StrOptions({"sqrt", "log2"}),
None,
],
"max_leaf_nodes": [Interval(Integral, 2, None, closed="left"), None],
"min_impurity_decrease": [Interval(Real, 0.0, None, closed="left")],
"ccp_alpha": [Interval(Real, 0.0, None, closed="left")],
"class_weight": [
StrOptions({"balanced_subsample", "balanced"}),
dict,
list,
None,
],
"monotonic_cst": ["array-like", None],
}
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