1478 lines
52 KiB
Python
1478 lines
52 KiB
Python
"""Base classes for all estimators."""
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# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
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# License: BSD 3 clause
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import copy
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import functools
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import inspect
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import platform
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import re
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import warnings
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from collections import defaultdict
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import numpy as np
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from . import __version__
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from ._config import config_context, get_config
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from .exceptions import InconsistentVersionWarning
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from .utils._estimator_html_repr import _HTMLDocumentationLinkMixin, estimator_html_repr
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from .utils._metadata_requests import _MetadataRequester, _routing_enabled
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from .utils._param_validation import validate_parameter_constraints
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from .utils._set_output import _SetOutputMixin
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from .utils._tags import (
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_DEFAULT_TAGS,
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)
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from .utils.fixes import _IS_32BIT
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from .utils.validation import (
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_check_feature_names_in,
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_check_y,
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_generate_get_feature_names_out,
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_get_feature_names,
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_is_fitted,
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_num_features,
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check_array,
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check_is_fitted,
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check_X_y,
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)
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def clone(estimator, *, safe=True):
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"""Construct a new unfitted estimator with the same parameters.
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Clone does a deep copy of the model in an estimator
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without actually copying attached data. It returns a new estimator
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with the same parameters that has not been fitted on any data.
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.. versionchanged:: 1.3
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Delegates to `estimator.__sklearn_clone__` if the method exists.
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Parameters
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----------
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estimator : {list, tuple, set} of estimator instance or a single \
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estimator instance
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The estimator or group of estimators to be cloned.
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safe : bool, default=True
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If safe is False, clone will fall back to a deep copy on objects
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that are not estimators. Ignored if `estimator.__sklearn_clone__`
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exists.
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Returns
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-------
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estimator : object
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The deep copy of the input, an estimator if input is an estimator.
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Notes
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-----
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If the estimator's `random_state` parameter is an integer (or if the
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estimator doesn't have a `random_state` parameter), an *exact clone* is
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returned: the clone and the original estimator will give the exact same
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results. Otherwise, *statistical clone* is returned: the clone might
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return different results from the original estimator. More details can be
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found in :ref:`randomness`.
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Examples
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--------
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>>> from sklearn.base import clone
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>>> from sklearn.linear_model import LogisticRegression
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>>> X = [[-1, 0], [0, 1], [0, -1], [1, 0]]
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>>> y = [0, 0, 1, 1]
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>>> classifier = LogisticRegression().fit(X, y)
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>>> cloned_classifier = clone(classifier)
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>>> hasattr(classifier, "classes_")
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True
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>>> hasattr(cloned_classifier, "classes_")
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False
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>>> classifier is cloned_classifier
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False
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"""
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if hasattr(estimator, "__sklearn_clone__") and not inspect.isclass(estimator):
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return estimator.__sklearn_clone__()
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return _clone_parametrized(estimator, safe=safe)
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def _clone_parametrized(estimator, *, safe=True):
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"""Default implementation of clone. See :func:`sklearn.base.clone` for details."""
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estimator_type = type(estimator)
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if estimator_type is dict:
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return {k: clone(v, safe=safe) for k, v in estimator.items()}
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elif estimator_type in (list, tuple, set, frozenset):
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return estimator_type([clone(e, safe=safe) for e in estimator])
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elif not hasattr(estimator, "get_params") or isinstance(estimator, type):
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if not safe:
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return copy.deepcopy(estimator)
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else:
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if isinstance(estimator, type):
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raise TypeError(
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"Cannot clone object. "
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+ "You should provide an instance of "
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+ "scikit-learn estimator instead of a class."
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)
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else:
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raise TypeError(
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"Cannot clone object '%s' (type %s): "
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"it does not seem to be a scikit-learn "
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"estimator as it does not implement a "
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"'get_params' method." % (repr(estimator), type(estimator))
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)
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klass = estimator.__class__
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new_object_params = estimator.get_params(deep=False)
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for name, param in new_object_params.items():
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new_object_params[name] = clone(param, safe=False)
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new_object = klass(**new_object_params)
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try:
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new_object._metadata_request = copy.deepcopy(estimator._metadata_request)
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except AttributeError:
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pass
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params_set = new_object.get_params(deep=False)
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# quick sanity check of the parameters of the clone
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for name in new_object_params:
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param1 = new_object_params[name]
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param2 = params_set[name]
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if param1 is not param2:
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raise RuntimeError(
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"Cannot clone object %s, as the constructor "
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"either does not set or modifies parameter %s" % (estimator, name)
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)
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# _sklearn_output_config is used by `set_output` to configure the output
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# container of an estimator.
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if hasattr(estimator, "_sklearn_output_config"):
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new_object._sklearn_output_config = copy.deepcopy(
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estimator._sklearn_output_config
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)
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return new_object
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class BaseEstimator(_HTMLDocumentationLinkMixin, _MetadataRequester):
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"""Base class for all estimators in scikit-learn.
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Inheriting from this class provides default implementations of:
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- setting and getting parameters used by `GridSearchCV` and friends;
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- textual and HTML representation displayed in terminals and IDEs;
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- estimator serialization;
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- parameters validation;
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- data validation;
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- feature names validation.
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Read more in the :ref:`User Guide <rolling_your_own_estimator>`.
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Notes
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-----
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All estimators should specify all the parameters that can be set
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at the class level in their ``__init__`` as explicit keyword
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arguments (no ``*args`` or ``**kwargs``).
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.base import BaseEstimator
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>>> class MyEstimator(BaseEstimator):
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... def __init__(self, *, param=1):
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... self.param = param
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... def fit(self, X, y=None):
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... self.is_fitted_ = True
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... return self
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... def predict(self, X):
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... return np.full(shape=X.shape[0], fill_value=self.param)
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>>> estimator = MyEstimator(param=2)
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>>> estimator.get_params()
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{'param': 2}
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>>> X = np.array([[1, 2], [2, 3], [3, 4]])
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>>> y = np.array([1, 0, 1])
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>>> estimator.fit(X, y).predict(X)
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array([2, 2, 2])
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>>> estimator.set_params(param=3).fit(X, y).predict(X)
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array([3, 3, 3])
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"""
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@classmethod
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def _get_param_names(cls):
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"""Get parameter names for the estimator"""
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# fetch the constructor or the original constructor before
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# deprecation wrapping if any
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init = getattr(cls.__init__, "deprecated_original", cls.__init__)
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if init is object.__init__:
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# No explicit constructor to introspect
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return []
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# introspect the constructor arguments to find the model parameters
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# to represent
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init_signature = inspect.signature(init)
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# Consider the constructor parameters excluding 'self'
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parameters = [
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p
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for p in init_signature.parameters.values()
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if p.name != "self" and p.kind != p.VAR_KEYWORD
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]
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for p in parameters:
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if p.kind == p.VAR_POSITIONAL:
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raise RuntimeError(
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"scikit-learn estimators should always "
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"specify their parameters in the signature"
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" of their __init__ (no varargs)."
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" %s with constructor %s doesn't "
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" follow this convention." % (cls, init_signature)
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)
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# Extract and sort argument names excluding 'self'
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return sorted([p.name for p in parameters])
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def get_params(self, deep=True):
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"""
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Get parameters for this estimator.
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Parameters
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----------
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deep : bool, default=True
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If True, will return the parameters for this estimator and
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contained subobjects that are estimators.
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Returns
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-------
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params : dict
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Parameter names mapped to their values.
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"""
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out = dict()
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for key in self._get_param_names():
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value = getattr(self, key)
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if deep and hasattr(value, "get_params") and not isinstance(value, type):
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deep_items = value.get_params().items()
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out.update((key + "__" + k, val) for k, val in deep_items)
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out[key] = value
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return out
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def set_params(self, **params):
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"""Set the parameters of this estimator.
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The method works on simple estimators as well as on nested objects
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(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
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parameters of the form ``<component>__<parameter>`` so that it's
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possible to update each component of a nested object.
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Parameters
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----------
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**params : dict
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Estimator parameters.
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Returns
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-------
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self : estimator instance
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Estimator instance.
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"""
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if not params:
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# Simple optimization to gain speed (inspect is slow)
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return self
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valid_params = self.get_params(deep=True)
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nested_params = defaultdict(dict) # grouped by prefix
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for key, value in params.items():
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key, delim, sub_key = key.partition("__")
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if key not in valid_params:
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local_valid_params = self._get_param_names()
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raise ValueError(
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f"Invalid parameter {key!r} for estimator {self}. "
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f"Valid parameters are: {local_valid_params!r}."
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)
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if delim:
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nested_params[key][sub_key] = value
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else:
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setattr(self, key, value)
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valid_params[key] = value
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for key, sub_params in nested_params.items():
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valid_params[key].set_params(**sub_params)
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return self
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def __sklearn_clone__(self):
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return _clone_parametrized(self)
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def __repr__(self, N_CHAR_MAX=700):
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# N_CHAR_MAX is the (approximate) maximum number of non-blank
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# characters to render. We pass it as an optional parameter to ease
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# the tests.
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from .utils._pprint import _EstimatorPrettyPrinter
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N_MAX_ELEMENTS_TO_SHOW = 30 # number of elements to show in sequences
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# use ellipsis for sequences with a lot of elements
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pp = _EstimatorPrettyPrinter(
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compact=True,
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indent=1,
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indent_at_name=True,
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n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW,
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)
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repr_ = pp.pformat(self)
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# Use bruteforce ellipsis when there are a lot of non-blank characters
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n_nonblank = len("".join(repr_.split()))
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if n_nonblank > N_CHAR_MAX:
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lim = N_CHAR_MAX // 2 # apprx number of chars to keep on both ends
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regex = r"^(\s*\S){%d}" % lim
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# The regex '^(\s*\S){%d}' % n
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# matches from the start of the string until the nth non-blank
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# character:
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# - ^ matches the start of string
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# - (pattern){n} matches n repetitions of pattern
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# - \s*\S matches a non-blank char following zero or more blanks
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left_lim = re.match(regex, repr_).end()
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right_lim = re.match(regex, repr_[::-1]).end()
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if "\n" in repr_[left_lim:-right_lim]:
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# The left side and right side aren't on the same line.
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# To avoid weird cuts, e.g.:
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# categoric...ore',
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# we need to start the right side with an appropriate newline
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# character so that it renders properly as:
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# categoric...
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# handle_unknown='ignore',
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# so we add [^\n]*\n which matches until the next \n
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regex += r"[^\n]*\n"
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right_lim = re.match(regex, repr_[::-1]).end()
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ellipsis = "..."
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if left_lim + len(ellipsis) < len(repr_) - right_lim:
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# Only add ellipsis if it results in a shorter repr
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repr_ = repr_[:left_lim] + "..." + repr_[-right_lim:]
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return repr_
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def __getstate__(self):
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if getattr(self, "__slots__", None):
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raise TypeError(
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"You cannot use `__slots__` in objects inheriting from "
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"`sklearn.base.BaseEstimator`."
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)
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try:
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state = super().__getstate__()
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if state is None:
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# For Python 3.11+, empty instance (no `__slots__`,
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# and `__dict__`) will return a state equal to `None`.
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state = self.__dict__.copy()
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except AttributeError:
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# Python < 3.11
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state = self.__dict__.copy()
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if type(self).__module__.startswith("sklearn."):
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return dict(state.items(), _sklearn_version=__version__)
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else:
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return state
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def __setstate__(self, state):
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if type(self).__module__.startswith("sklearn."):
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pickle_version = state.pop("_sklearn_version", "pre-0.18")
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if pickle_version != __version__:
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warnings.warn(
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InconsistentVersionWarning(
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estimator_name=self.__class__.__name__,
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current_sklearn_version=__version__,
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original_sklearn_version=pickle_version,
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),
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)
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try:
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super().__setstate__(state)
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except AttributeError:
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self.__dict__.update(state)
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def _more_tags(self):
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return _DEFAULT_TAGS
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def _get_tags(self):
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collected_tags = {}
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for base_class in reversed(inspect.getmro(self.__class__)):
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if hasattr(base_class, "_more_tags"):
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# need the if because mixins might not have _more_tags
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# but might do redundant work in estimators
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# (i.e. calling more tags on BaseEstimator multiple times)
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more_tags = base_class._more_tags(self)
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collected_tags.update(more_tags)
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return collected_tags
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def _check_n_features(self, X, reset):
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"""Set the `n_features_in_` attribute, or check against it.
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Parameters
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----------
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X : {ndarray, sparse matrix} of shape (n_samples, n_features)
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The input samples.
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reset : bool
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If True, the `n_features_in_` attribute is set to `X.shape[1]`.
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If False and the attribute exists, then check that it is equal to
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`X.shape[1]`. If False and the attribute does *not* exist, then
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the check is skipped.
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.. note::
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It is recommended to call reset=True in `fit` and in the first
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call to `partial_fit`. All other methods that validate `X`
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should set `reset=False`.
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"""
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try:
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n_features = _num_features(X)
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except TypeError as e:
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if not reset and hasattr(self, "n_features_in_"):
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raise ValueError(
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"X does not contain any features, but "
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f"{self.__class__.__name__} is expecting "
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f"{self.n_features_in_} features"
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) from e
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# If the number of features is not defined and reset=True,
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# then we skip this check
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return
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if reset:
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self.n_features_in_ = n_features
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return
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if not hasattr(self, "n_features_in_"):
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# Skip this check if the expected number of expected input features
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# was not recorded by calling fit first. This is typically the case
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# for stateless transformers.
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return
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if n_features != self.n_features_in_:
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raise ValueError(
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f"X has {n_features} features, but {self.__class__.__name__} "
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f"is expecting {self.n_features_in_} features as input."
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)
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def _check_feature_names(self, X, *, reset):
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"""Set or check the `feature_names_in_` attribute.
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.. versionadded:: 1.0
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Parameters
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----------
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X : {ndarray, dataframe} of shape (n_samples, n_features)
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The input samples.
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reset : bool
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Whether to reset the `feature_names_in_` attribute.
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If False, the input will be checked for consistency with
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feature names of data provided when reset was last True.
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.. note::
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It is recommended to call `reset=True` in `fit` and in the first
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call to `partial_fit`. All other methods that validate `X`
|
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should set `reset=False`.
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"""
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if reset:
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feature_names_in = _get_feature_names(X)
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if feature_names_in is not None:
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self.feature_names_in_ = feature_names_in
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elif hasattr(self, "feature_names_in_"):
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# Delete the attribute when the estimator is fitted on a new dataset
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# that has no feature names.
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delattr(self, "feature_names_in_")
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return
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fitted_feature_names = getattr(self, "feature_names_in_", None)
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X_feature_names = _get_feature_names(X)
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if fitted_feature_names is None and X_feature_names is None:
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# no feature names seen in fit and in X
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return
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if X_feature_names is not None and fitted_feature_names is None:
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warnings.warn(
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f"X has feature names, but {self.__class__.__name__} was fitted without"
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" feature names"
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)
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return
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|
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if X_feature_names is None and fitted_feature_names is not None:
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warnings.warn(
|
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"X does not have valid feature names, but"
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f" {self.__class__.__name__} was fitted with feature names"
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)
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return
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|
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# validate the feature names against the `feature_names_in_` attribute
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|
if len(fitted_feature_names) != len(X_feature_names) or np.any(
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fitted_feature_names != X_feature_names
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):
|
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message = (
|
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"The feature names should match those that were passed during fit.\n"
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)
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fitted_feature_names_set = set(fitted_feature_names)
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X_feature_names_set = set(X_feature_names)
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unexpected_names = sorted(X_feature_names_set - fitted_feature_names_set)
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missing_names = sorted(fitted_feature_names_set - X_feature_names_set)
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|
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def add_names(names):
|
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output = ""
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max_n_names = 5
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|
for i, name in enumerate(names):
|
|
if i >= max_n_names:
|
|
output += "- ...\n"
|
|
break
|
|
output += f"- {name}\n"
|
|
return output
|
|
|
|
if unexpected_names:
|
|
message += "Feature names unseen at fit time:\n"
|
|
message += add_names(unexpected_names)
|
|
|
|
if missing_names:
|
|
message += "Feature names seen at fit time, yet now missing:\n"
|
|
message += add_names(missing_names)
|
|
|
|
if not missing_names and not unexpected_names:
|
|
message += (
|
|
"Feature names must be in the same order as they were in fit.\n"
|
|
)
|
|
|
|
raise ValueError(message)
|
|
|
|
def _validate_data(
|
|
self,
|
|
X="no_validation",
|
|
y="no_validation",
|
|
reset=True,
|
|
validate_separately=False,
|
|
cast_to_ndarray=True,
|
|
**check_params,
|
|
):
|
|
"""Validate input data and set or check the `n_features_in_` attribute.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix, dataframe} of shape \
|
|
(n_samples, n_features), default='no validation'
|
|
The input samples.
|
|
If `'no_validation'`, no validation is performed on `X`. This is
|
|
useful for meta-estimator which can delegate input validation to
|
|
their underlying estimator(s). In that case `y` must be passed and
|
|
the only accepted `check_params` are `multi_output` and
|
|
`y_numeric`.
|
|
|
|
y : array-like of shape (n_samples,), default='no_validation'
|
|
The targets.
|
|
|
|
- If `None`, `check_array` is called on `X`. If the estimator's
|
|
requires_y tag is True, then an error will be raised.
|
|
- If `'no_validation'`, `check_array` is called on `X` and the
|
|
estimator's requires_y tag is ignored. This is a default
|
|
placeholder and is never meant to be explicitly set. In that case
|
|
`X` must be passed.
|
|
- Otherwise, only `y` with `_check_y` or both `X` and `y` are
|
|
checked with either `check_array` or `check_X_y` depending on
|
|
`validate_separately`.
|
|
|
|
reset : bool, default=True
|
|
Whether to reset the `n_features_in_` attribute.
|
|
If False, the input will be checked for consistency with data
|
|
provided when reset was last True.
|
|
.. note::
|
|
It is recommended to call reset=True in `fit` and in the first
|
|
call to `partial_fit`. All other methods that validate `X`
|
|
should set `reset=False`.
|
|
|
|
validate_separately : False or tuple of dicts, default=False
|
|
Only used if y is not None.
|
|
If False, call validate_X_y(). Else, it must be a tuple of kwargs
|
|
to be used for calling check_array() on X and y respectively.
|
|
|
|
`estimator=self` is automatically added to these dicts to generate
|
|
more informative error message in case of invalid input data.
|
|
|
|
cast_to_ndarray : bool, default=True
|
|
Cast `X` and `y` to ndarray with checks in `check_params`. If
|
|
`False`, `X` and `y` are unchanged and only `feature_names_in_` and
|
|
`n_features_in_` are checked.
|
|
|
|
**check_params : kwargs
|
|
Parameters passed to :func:`sklearn.utils.check_array` or
|
|
:func:`sklearn.utils.check_X_y`. Ignored if validate_separately
|
|
is not False.
|
|
|
|
`estimator=self` is automatically added to these params to generate
|
|
more informative error message in case of invalid input data.
|
|
|
|
Returns
|
|
-------
|
|
out : {ndarray, sparse matrix} or tuple of these
|
|
The validated input. A tuple is returned if both `X` and `y` are
|
|
validated.
|
|
"""
|
|
self._check_feature_names(X, reset=reset)
|
|
|
|
if y is None and self._get_tags()["requires_y"]:
|
|
raise ValueError(
|
|
f"This {self.__class__.__name__} estimator "
|
|
"requires y to be passed, but the target y is None."
|
|
)
|
|
|
|
no_val_X = isinstance(X, str) and X == "no_validation"
|
|
no_val_y = y is None or isinstance(y, str) and y == "no_validation"
|
|
|
|
if no_val_X and no_val_y:
|
|
raise ValueError("Validation should be done on X, y or both.")
|
|
|
|
default_check_params = {"estimator": self}
|
|
check_params = {**default_check_params, **check_params}
|
|
|
|
if not cast_to_ndarray:
|
|
if not no_val_X and no_val_y:
|
|
out = X
|
|
elif no_val_X and not no_val_y:
|
|
out = y
|
|
else:
|
|
out = X, y
|
|
elif not no_val_X and no_val_y:
|
|
out = check_array(X, input_name="X", **check_params)
|
|
elif no_val_X and not no_val_y:
|
|
out = _check_y(y, **check_params)
|
|
else:
|
|
if validate_separately:
|
|
# We need this because some estimators validate X and y
|
|
# separately, and in general, separately calling check_array()
|
|
# on X and y isn't equivalent to just calling check_X_y()
|
|
# :(
|
|
check_X_params, check_y_params = validate_separately
|
|
if "estimator" not in check_X_params:
|
|
check_X_params = {**default_check_params, **check_X_params}
|
|
X = check_array(X, input_name="X", **check_X_params)
|
|
if "estimator" not in check_y_params:
|
|
check_y_params = {**default_check_params, **check_y_params}
|
|
y = check_array(y, input_name="y", **check_y_params)
|
|
else:
|
|
X, y = check_X_y(X, y, **check_params)
|
|
out = X, y
|
|
|
|
if not no_val_X and check_params.get("ensure_2d", True):
|
|
self._check_n_features(X, reset=reset)
|
|
|
|
return out
|
|
|
|
def _validate_params(self):
|
|
"""Validate types and values of constructor parameters
|
|
|
|
The expected type and values must be defined in the `_parameter_constraints`
|
|
class attribute, which is a dictionary `param_name: list of constraints`. See
|
|
the docstring of `validate_parameter_constraints` for a description of the
|
|
accepted constraints.
|
|
"""
|
|
validate_parameter_constraints(
|
|
self._parameter_constraints,
|
|
self.get_params(deep=False),
|
|
caller_name=self.__class__.__name__,
|
|
)
|
|
|
|
@property
|
|
def _repr_html_(self):
|
|
"""HTML representation of estimator.
|
|
|
|
This is redundant with the logic of `_repr_mimebundle_`. The latter
|
|
should be favorted in the long term, `_repr_html_` is only
|
|
implemented for consumers who do not interpret `_repr_mimbundle_`.
|
|
"""
|
|
if get_config()["display"] != "diagram":
|
|
raise AttributeError(
|
|
"_repr_html_ is only defined when the "
|
|
"'display' configuration option is set to "
|
|
"'diagram'"
|
|
)
|
|
return self._repr_html_inner
|
|
|
|
def _repr_html_inner(self):
|
|
"""This function is returned by the @property `_repr_html_` to make
|
|
`hasattr(estimator, "_repr_html_") return `True` or `False` depending
|
|
on `get_config()["display"]`.
|
|
"""
|
|
return estimator_html_repr(self)
|
|
|
|
def _repr_mimebundle_(self, **kwargs):
|
|
"""Mime bundle used by jupyter kernels to display estimator"""
|
|
output = {"text/plain": repr(self)}
|
|
if get_config()["display"] == "diagram":
|
|
output["text/html"] = estimator_html_repr(self)
|
|
return output
|
|
|
|
|
|
class ClassifierMixin:
|
|
"""Mixin class for all classifiers in scikit-learn.
|
|
|
|
This mixin defines the following functionality:
|
|
|
|
- `_estimator_type` class attribute defaulting to `"classifier"`;
|
|
- `score` method that default to :func:`~sklearn.metrics.accuracy_score`.
|
|
- enforce that `fit` requires `y` to be passed through the `requires_y` tag.
|
|
|
|
Read more in the :ref:`User Guide <rolling_your_own_estimator>`.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.base import BaseEstimator, ClassifierMixin
|
|
>>> # Mixin classes should always be on the left-hand side for a correct MRO
|
|
>>> class MyEstimator(ClassifierMixin, BaseEstimator):
|
|
... def __init__(self, *, param=1):
|
|
... self.param = param
|
|
... def fit(self, X, y=None):
|
|
... self.is_fitted_ = True
|
|
... return self
|
|
... def predict(self, X):
|
|
... return np.full(shape=X.shape[0], fill_value=self.param)
|
|
>>> estimator = MyEstimator(param=1)
|
|
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
|
|
>>> y = np.array([1, 0, 1])
|
|
>>> estimator.fit(X, y).predict(X)
|
|
array([1, 1, 1])
|
|
>>> estimator.score(X, y)
|
|
0.66...
|
|
"""
|
|
|
|
_estimator_type = "classifier"
|
|
|
|
def score(self, X, y, sample_weight=None):
|
|
"""
|
|
Return the mean accuracy on the given test data and labels.
|
|
|
|
In multi-label classification, this is the subset accuracy
|
|
which is a harsh metric since you require for each sample that
|
|
each label set be correctly predicted.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Test samples.
|
|
|
|
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
|
|
True labels for `X`.
|
|
|
|
sample_weight : array-like of shape (n_samples,), default=None
|
|
Sample weights.
|
|
|
|
Returns
|
|
-------
|
|
score : float
|
|
Mean accuracy of ``self.predict(X)`` w.r.t. `y`.
|
|
"""
|
|
from .metrics import accuracy_score
|
|
|
|
return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
|
|
|
|
def _more_tags(self):
|
|
return {"requires_y": True}
|
|
|
|
|
|
class RegressorMixin:
|
|
"""Mixin class for all regression estimators in scikit-learn.
|
|
|
|
This mixin defines the following functionality:
|
|
|
|
- `_estimator_type` class attribute defaulting to `"regressor"`;
|
|
- `score` method that default to :func:`~sklearn.metrics.r2_score`.
|
|
- enforce that `fit` requires `y` to be passed through the `requires_y` tag.
|
|
|
|
Read more in the :ref:`User Guide <rolling_your_own_estimator>`.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.base import BaseEstimator, RegressorMixin
|
|
>>> # Mixin classes should always be on the left-hand side for a correct MRO
|
|
>>> class MyEstimator(RegressorMixin, BaseEstimator):
|
|
... def __init__(self, *, param=1):
|
|
... self.param = param
|
|
... def fit(self, X, y=None):
|
|
... self.is_fitted_ = True
|
|
... return self
|
|
... def predict(self, X):
|
|
... return np.full(shape=X.shape[0], fill_value=self.param)
|
|
>>> estimator = MyEstimator(param=0)
|
|
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
|
|
>>> y = np.array([-1, 0, 1])
|
|
>>> estimator.fit(X, y).predict(X)
|
|
array([0, 0, 0])
|
|
>>> estimator.score(X, y)
|
|
0.0
|
|
"""
|
|
|
|
_estimator_type = "regressor"
|
|
|
|
def score(self, X, y, sample_weight=None):
|
|
"""Return the coefficient of determination of the prediction.
|
|
|
|
The coefficient of determination :math:`R^2` is defined as
|
|
:math:`(1 - \\frac{u}{v})`, where :math:`u` is the residual
|
|
sum of squares ``((y_true - y_pred)** 2).sum()`` and :math:`v`
|
|
is the total sum of squares ``((y_true - y_true.mean()) ** 2).sum()``.
|
|
The best possible score is 1.0 and it can be negative (because the
|
|
model can be arbitrarily worse). A constant model that always predicts
|
|
the expected value of `y`, disregarding the input features, would get
|
|
a :math:`R^2` score of 0.0.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Test samples. For some estimators this may be a precomputed
|
|
kernel matrix or a list of generic objects instead with shape
|
|
``(n_samples, n_samples_fitted)``, where ``n_samples_fitted``
|
|
is the number of samples used in the fitting for the estimator.
|
|
|
|
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
|
|
True values for `X`.
|
|
|
|
sample_weight : array-like of shape (n_samples,), default=None
|
|
Sample weights.
|
|
|
|
Returns
|
|
-------
|
|
score : float
|
|
:math:`R^2` of ``self.predict(X)`` w.r.t. `y`.
|
|
|
|
Notes
|
|
-----
|
|
The :math:`R^2` score used when calling ``score`` on a regressor uses
|
|
``multioutput='uniform_average'`` from version 0.23 to keep consistent
|
|
with default value of :func:`~sklearn.metrics.r2_score`.
|
|
This influences the ``score`` method of all the multioutput
|
|
regressors (except for
|
|
:class:`~sklearn.multioutput.MultiOutputRegressor`).
|
|
"""
|
|
|
|
from .metrics import r2_score
|
|
|
|
y_pred = self.predict(X)
|
|
return r2_score(y, y_pred, sample_weight=sample_weight)
|
|
|
|
def _more_tags(self):
|
|
return {"requires_y": True}
|
|
|
|
|
|
class ClusterMixin:
|
|
"""Mixin class for all cluster estimators in scikit-learn.
|
|
|
|
- `_estimator_type` class attribute defaulting to `"clusterer"`;
|
|
- `fit_predict` method returning the cluster labels associated to each sample.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.base import BaseEstimator, ClusterMixin
|
|
>>> class MyClusterer(ClusterMixin, BaseEstimator):
|
|
... def fit(self, X, y=None):
|
|
... self.labels_ = np.ones(shape=(len(X),), dtype=np.int64)
|
|
... return self
|
|
>>> X = [[1, 2], [2, 3], [3, 4]]
|
|
>>> MyClusterer().fit_predict(X)
|
|
array([1, 1, 1])
|
|
"""
|
|
|
|
_estimator_type = "clusterer"
|
|
|
|
def fit_predict(self, X, y=None, **kwargs):
|
|
"""
|
|
Perform clustering on `X` and returns cluster labels.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Input data.
|
|
|
|
y : Ignored
|
|
Not used, present for API consistency by convention.
|
|
|
|
**kwargs : dict
|
|
Arguments to be passed to ``fit``.
|
|
|
|
.. versionadded:: 1.4
|
|
|
|
Returns
|
|
-------
|
|
labels : ndarray of shape (n_samples,), dtype=np.int64
|
|
Cluster labels.
|
|
"""
|
|
# non-optimized default implementation; override when a better
|
|
# method is possible for a given clustering algorithm
|
|
self.fit(X, **kwargs)
|
|
return self.labels_
|
|
|
|
def _more_tags(self):
|
|
return {"preserves_dtype": []}
|
|
|
|
|
|
class BiclusterMixin:
|
|
"""Mixin class for all bicluster estimators in scikit-learn.
|
|
|
|
This mixin defines the following functionality:
|
|
|
|
- `biclusters_` property that returns the row and column indicators;
|
|
- `get_indices` method that returns the row and column indices of a bicluster;
|
|
- `get_shape` method that returns the shape of a bicluster;
|
|
- `get_submatrix` method that returns the submatrix corresponding to a bicluster.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.base import BaseEstimator, BiclusterMixin
|
|
>>> class DummyBiClustering(BiclusterMixin, BaseEstimator):
|
|
... def fit(self, X, y=None):
|
|
... self.rows_ = np.ones(shape=(1, X.shape[0]), dtype=bool)
|
|
... self.columns_ = np.ones(shape=(1, X.shape[1]), dtype=bool)
|
|
... return self
|
|
>>> X = np.array([[1, 1], [2, 1], [1, 0],
|
|
... [4, 7], [3, 5], [3, 6]])
|
|
>>> bicluster = DummyBiClustering().fit(X)
|
|
>>> hasattr(bicluster, "biclusters_")
|
|
True
|
|
>>> bicluster.get_indices(0)
|
|
(array([0, 1, 2, 3, 4, 5]), array([0, 1]))
|
|
"""
|
|
|
|
@property
|
|
def biclusters_(self):
|
|
"""Convenient way to get row and column indicators together.
|
|
|
|
Returns the ``rows_`` and ``columns_`` members.
|
|
"""
|
|
return self.rows_, self.columns_
|
|
|
|
def get_indices(self, i):
|
|
"""Row and column indices of the `i`'th bicluster.
|
|
|
|
Only works if ``rows_`` and ``columns_`` attributes exist.
|
|
|
|
Parameters
|
|
----------
|
|
i : int
|
|
The index of the cluster.
|
|
|
|
Returns
|
|
-------
|
|
row_ind : ndarray, dtype=np.intp
|
|
Indices of rows in the dataset that belong to the bicluster.
|
|
col_ind : ndarray, dtype=np.intp
|
|
Indices of columns in the dataset that belong to the bicluster.
|
|
"""
|
|
rows = self.rows_[i]
|
|
columns = self.columns_[i]
|
|
return np.nonzero(rows)[0], np.nonzero(columns)[0]
|
|
|
|
def get_shape(self, i):
|
|
"""Shape of the `i`'th bicluster.
|
|
|
|
Parameters
|
|
----------
|
|
i : int
|
|
The index of the cluster.
|
|
|
|
Returns
|
|
-------
|
|
n_rows : int
|
|
Number of rows in the bicluster.
|
|
|
|
n_cols : int
|
|
Number of columns in the bicluster.
|
|
"""
|
|
indices = self.get_indices(i)
|
|
return tuple(len(i) for i in indices)
|
|
|
|
def get_submatrix(self, i, data):
|
|
"""Return the submatrix corresponding to bicluster `i`.
|
|
|
|
Parameters
|
|
----------
|
|
i : int
|
|
The index of the cluster.
|
|
data : array-like of shape (n_samples, n_features)
|
|
The data.
|
|
|
|
Returns
|
|
-------
|
|
submatrix : ndarray of shape (n_rows, n_cols)
|
|
The submatrix corresponding to bicluster `i`.
|
|
|
|
Notes
|
|
-----
|
|
Works with sparse matrices. Only works if ``rows_`` and
|
|
``columns_`` attributes exist.
|
|
"""
|
|
from .utils.validation import check_array
|
|
|
|
data = check_array(data, accept_sparse="csr")
|
|
row_ind, col_ind = self.get_indices(i)
|
|
return data[row_ind[:, np.newaxis], col_ind]
|
|
|
|
|
|
class TransformerMixin(_SetOutputMixin):
|
|
"""Mixin class for all transformers in scikit-learn.
|
|
|
|
This mixin defines the following functionality:
|
|
|
|
- a `fit_transform` method that delegates to `fit` and `transform`;
|
|
- a `set_output` method to output `X` as a specific container type.
|
|
|
|
If :term:`get_feature_names_out` is defined, then :class:`BaseEstimator` will
|
|
automatically wrap `transform` and `fit_transform` to follow the `set_output`
|
|
API. See the :ref:`developer_api_set_output` for details.
|
|
|
|
:class:`OneToOneFeatureMixin` and
|
|
:class:`ClassNamePrefixFeaturesOutMixin` are helpful mixins for
|
|
defining :term:`get_feature_names_out`.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.base import BaseEstimator, TransformerMixin
|
|
>>> class MyTransformer(TransformerMixin, BaseEstimator):
|
|
... def __init__(self, *, param=1):
|
|
... self.param = param
|
|
... def fit(self, X, y=None):
|
|
... return self
|
|
... def transform(self, X):
|
|
... return np.full(shape=len(X), fill_value=self.param)
|
|
>>> transformer = MyTransformer()
|
|
>>> X = [[1, 2], [2, 3], [3, 4]]
|
|
>>> transformer.fit_transform(X)
|
|
array([1, 1, 1])
|
|
"""
|
|
|
|
def fit_transform(self, X, y=None, **fit_params):
|
|
"""
|
|
Fit to data, then transform it.
|
|
|
|
Fits transformer to `X` and `y` with optional parameters `fit_params`
|
|
and returns a transformed version of `X`.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Input samples.
|
|
|
|
y : array-like of shape (n_samples,) or (n_samples, n_outputs), \
|
|
default=None
|
|
Target values (None for unsupervised transformations).
|
|
|
|
**fit_params : dict
|
|
Additional fit parameters.
|
|
|
|
Returns
|
|
-------
|
|
X_new : ndarray array of shape (n_samples, n_features_new)
|
|
Transformed array.
|
|
"""
|
|
# non-optimized default implementation; override when a better
|
|
# method is possible for a given clustering algorithm
|
|
|
|
# we do not route parameters here, since consumers don't route. But
|
|
# since it's possible for a `transform` method to also consume
|
|
# metadata, we check if that's the case, and we raise a warning telling
|
|
# users that they should implement a custom `fit_transform` method
|
|
# to forward metadata to `transform` as well.
|
|
#
|
|
# For that, we calculate routing and check if anything would be routed
|
|
# to `transform` if we were to route them.
|
|
if _routing_enabled():
|
|
transform_params = self.get_metadata_routing().consumes(
|
|
method="transform", params=fit_params.keys()
|
|
)
|
|
if transform_params:
|
|
warnings.warn(
|
|
(
|
|
f"This object ({self.__class__.__name__}) has a `transform`"
|
|
" method which consumes metadata, but `fit_transform` does not"
|
|
" forward metadata to `transform`. Please implement a custom"
|
|
" `fit_transform` method to forward metadata to `transform` as"
|
|
" well. Alternatively, you can explicitly do"
|
|
" `set_transform_request`and set all values to `False` to"
|
|
" disable metadata routed to `transform`, if that's an option."
|
|
),
|
|
UserWarning,
|
|
)
|
|
|
|
if y is None:
|
|
# fit method of arity 1 (unsupervised transformation)
|
|
return self.fit(X, **fit_params).transform(X)
|
|
else:
|
|
# fit method of arity 2 (supervised transformation)
|
|
return self.fit(X, y, **fit_params).transform(X)
|
|
|
|
|
|
class OneToOneFeatureMixin:
|
|
"""Provides `get_feature_names_out` for simple transformers.
|
|
|
|
This mixin assumes there's a 1-to-1 correspondence between input features
|
|
and output features, such as :class:`~sklearn.preprocessing.StandardScaler`.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.base import OneToOneFeatureMixin
|
|
>>> class MyEstimator(OneToOneFeatureMixin):
|
|
... def fit(self, X, y=None):
|
|
... self.n_features_in_ = X.shape[1]
|
|
... return self
|
|
>>> X = np.array([[1, 2], [3, 4]])
|
|
>>> MyEstimator().fit(X).get_feature_names_out()
|
|
array(['x0', 'x1'], dtype=object)
|
|
"""
|
|
|
|
def get_feature_names_out(self, input_features=None):
|
|
"""Get output feature names for transformation.
|
|
|
|
Parameters
|
|
----------
|
|
input_features : array-like of str or None, default=None
|
|
Input features.
|
|
|
|
- If `input_features` is `None`, then `feature_names_in_` is
|
|
used as feature names in. If `feature_names_in_` is not defined,
|
|
then the following input feature names are generated:
|
|
`["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
|
|
- If `input_features` is an array-like, then `input_features` must
|
|
match `feature_names_in_` if `feature_names_in_` is defined.
|
|
|
|
Returns
|
|
-------
|
|
feature_names_out : ndarray of str objects
|
|
Same as input features.
|
|
"""
|
|
check_is_fitted(self, "n_features_in_")
|
|
return _check_feature_names_in(self, input_features)
|
|
|
|
|
|
class ClassNamePrefixFeaturesOutMixin:
|
|
"""Mixin class for transformers that generate their own names by prefixing.
|
|
|
|
This mixin is useful when the transformer needs to generate its own feature
|
|
names out, such as :class:`~sklearn.decomposition.PCA`. For example, if
|
|
:class:`~sklearn.decomposition.PCA` outputs 3 features, then the generated feature
|
|
names out are: `["pca0", "pca1", "pca2"]`.
|
|
|
|
This mixin assumes that a `_n_features_out` attribute is defined when the
|
|
transformer is fitted. `_n_features_out` is the number of output features
|
|
that the transformer will return in `transform` of `fit_transform`.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.base import ClassNamePrefixFeaturesOutMixin
|
|
>>> class MyEstimator(ClassNamePrefixFeaturesOutMixin):
|
|
... def fit(self, X, y=None):
|
|
... self._n_features_out = X.shape[1]
|
|
... return self
|
|
>>> X = np.array([[1, 2], [3, 4]])
|
|
>>> MyEstimator().fit(X).get_feature_names_out()
|
|
array(['myestimator0', 'myestimator1'], dtype=object)
|
|
"""
|
|
|
|
def get_feature_names_out(self, input_features=None):
|
|
"""Get output feature names for transformation.
|
|
|
|
The feature names out will prefixed by the lowercased class name. For
|
|
example, if the transformer outputs 3 features, then the feature names
|
|
out are: `["class_name0", "class_name1", "class_name2"]`.
|
|
|
|
Parameters
|
|
----------
|
|
input_features : array-like of str or None, default=None
|
|
Only used to validate feature names with the names seen in `fit`.
|
|
|
|
Returns
|
|
-------
|
|
feature_names_out : ndarray of str objects
|
|
Transformed feature names.
|
|
"""
|
|
check_is_fitted(self, "_n_features_out")
|
|
return _generate_get_feature_names_out(
|
|
self, self._n_features_out, input_features=input_features
|
|
)
|
|
|
|
|
|
class DensityMixin:
|
|
"""Mixin class for all density estimators in scikit-learn.
|
|
|
|
This mixin defines the following functionality:
|
|
|
|
- `_estimator_type` class attribute defaulting to `"DensityEstimator"`;
|
|
- `score` method that default that do no-op.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.base import DensityMixin
|
|
>>> class MyEstimator(DensityMixin):
|
|
... def fit(self, X, y=None):
|
|
... self.is_fitted_ = True
|
|
... return self
|
|
>>> estimator = MyEstimator()
|
|
>>> hasattr(estimator, "score")
|
|
True
|
|
"""
|
|
|
|
_estimator_type = "DensityEstimator"
|
|
|
|
def score(self, X, y=None):
|
|
"""Return the score of the model on the data `X`.
|
|
|
|
Parameters
|
|
----------
|
|
X : array-like of shape (n_samples, n_features)
|
|
Test samples.
|
|
|
|
y : Ignored
|
|
Not used, present for API consistency by convention.
|
|
|
|
Returns
|
|
-------
|
|
score : float
|
|
"""
|
|
pass
|
|
|
|
|
|
class OutlierMixin:
|
|
"""Mixin class for all outlier detection estimators in scikit-learn.
|
|
|
|
This mixin defines the following functionality:
|
|
|
|
- `_estimator_type` class attribute defaulting to `outlier_detector`;
|
|
- `fit_predict` method that default to `fit` and `predict`.
|
|
|
|
Examples
|
|
--------
|
|
>>> import numpy as np
|
|
>>> from sklearn.base import BaseEstimator, OutlierMixin
|
|
>>> class MyEstimator(OutlierMixin):
|
|
... def fit(self, X, y=None):
|
|
... self.is_fitted_ = True
|
|
... return self
|
|
... def predict(self, X):
|
|
... return np.ones(shape=len(X))
|
|
>>> estimator = MyEstimator()
|
|
>>> X = np.array([[1, 2], [2, 3], [3, 4]])
|
|
>>> estimator.fit_predict(X)
|
|
array([1., 1., 1.])
|
|
"""
|
|
|
|
_estimator_type = "outlier_detector"
|
|
|
|
def fit_predict(self, X, y=None, **kwargs):
|
|
"""Perform fit on X and returns labels for X.
|
|
|
|
Returns -1 for outliers and 1 for inliers.
|
|
|
|
Parameters
|
|
----------
|
|
X : {array-like, sparse matrix} of shape (n_samples, n_features)
|
|
The input samples.
|
|
|
|
y : Ignored
|
|
Not used, present for API consistency by convention.
|
|
|
|
**kwargs : dict
|
|
Arguments to be passed to ``fit``.
|
|
|
|
.. versionadded:: 1.4
|
|
|
|
Returns
|
|
-------
|
|
y : ndarray of shape (n_samples,)
|
|
1 for inliers, -1 for outliers.
|
|
"""
|
|
# we do not route parameters here, since consumers don't route. But
|
|
# since it's possible for a `predict` method to also consume
|
|
# metadata, we check if that's the case, and we raise a warning telling
|
|
# users that they should implement a custom `fit_predict` method
|
|
# to forward metadata to `predict` as well.
|
|
#
|
|
# For that, we calculate routing and check if anything would be routed
|
|
# to `predict` if we were to route them.
|
|
if _routing_enabled():
|
|
transform_params = self.get_metadata_routing().consumes(
|
|
method="predict", params=kwargs.keys()
|
|
)
|
|
if transform_params:
|
|
warnings.warn(
|
|
(
|
|
f"This object ({self.__class__.__name__}) has a `predict` "
|
|
"method which consumes metadata, but `fit_predict` does not "
|
|
"forward metadata to `predict`. Please implement a custom "
|
|
"`fit_predict` method to forward metadata to `predict` as well."
|
|
"Alternatively, you can explicitly do `set_predict_request`"
|
|
"and set all values to `False` to disable metadata routed to "
|
|
"`predict`, if that's an option."
|
|
),
|
|
UserWarning,
|
|
)
|
|
|
|
# override for transductive outlier detectors like LocalOulierFactor
|
|
return self.fit(X, **kwargs).predict(X)
|
|
|
|
|
|
class MetaEstimatorMixin:
|
|
"""Mixin class for all meta estimators in scikit-learn.
|
|
|
|
This mixin defines the following functionality:
|
|
|
|
- define `_required_parameters` that specify the mandatory `estimator` parameter.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.base import MetaEstimatorMixin
|
|
>>> from sklearn.datasets import load_iris
|
|
>>> from sklearn.linear_model import LogisticRegression
|
|
>>> class MyEstimator(MetaEstimatorMixin):
|
|
... def __init__(self, *, estimator=None):
|
|
... self.estimator = estimator
|
|
... def fit(self, X, y=None):
|
|
... if self.estimator is None:
|
|
... self.estimator_ = LogisticRegression()
|
|
... else:
|
|
... self.estimator_ = self.estimator
|
|
... return self
|
|
>>> X, y = load_iris(return_X_y=True)
|
|
>>> estimator = MyEstimator().fit(X, y)
|
|
>>> estimator.estimator_
|
|
LogisticRegression()
|
|
"""
|
|
|
|
_required_parameters = ["estimator"]
|
|
|
|
|
|
class MultiOutputMixin:
|
|
"""Mixin to mark estimators that support multioutput."""
|
|
|
|
def _more_tags(self):
|
|
return {"multioutput": True}
|
|
|
|
|
|
class _UnstableArchMixin:
|
|
"""Mark estimators that are non-determinstic on 32bit or PowerPC"""
|
|
|
|
def _more_tags(self):
|
|
return {
|
|
"non_deterministic": _IS_32BIT
|
|
or platform.machine().startswith(("ppc", "powerpc"))
|
|
}
|
|
|
|
|
|
def is_classifier(estimator):
|
|
"""Return True if the given estimator is (probably) a classifier.
|
|
|
|
Parameters
|
|
----------
|
|
estimator : object
|
|
Estimator object to test.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
True if estimator is a classifier and False otherwise.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.base import is_classifier
|
|
>>> from sklearn.svm import SVC, SVR
|
|
>>> classifier = SVC()
|
|
>>> regressor = SVR()
|
|
>>> is_classifier(classifier)
|
|
True
|
|
>>> is_classifier(regressor)
|
|
False
|
|
"""
|
|
return getattr(estimator, "_estimator_type", None) == "classifier"
|
|
|
|
|
|
def is_regressor(estimator):
|
|
"""Return True if the given estimator is (probably) a regressor.
|
|
|
|
Parameters
|
|
----------
|
|
estimator : estimator instance
|
|
Estimator object to test.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
True if estimator is a regressor and False otherwise.
|
|
|
|
Examples
|
|
--------
|
|
>>> from sklearn.base import is_regressor
|
|
>>> from sklearn.svm import SVC, SVR
|
|
>>> classifier = SVC()
|
|
>>> regressor = SVR()
|
|
>>> is_regressor(classifier)
|
|
False
|
|
>>> is_regressor(regressor)
|
|
True
|
|
"""
|
|
return getattr(estimator, "_estimator_type", None) == "regressor"
|
|
|
|
|
|
def is_outlier_detector(estimator):
|
|
"""Return True if the given estimator is (probably) an outlier detector.
|
|
|
|
Parameters
|
|
----------
|
|
estimator : estimator instance
|
|
Estimator object to test.
|
|
|
|
Returns
|
|
-------
|
|
out : bool
|
|
True if estimator is an outlier detector and False otherwise.
|
|
"""
|
|
return getattr(estimator, "_estimator_type", None) == "outlier_detector"
|
|
|
|
|
|
def _fit_context(*, prefer_skip_nested_validation):
|
|
"""Decorator to run the fit methods of estimators within context managers.
|
|
|
|
Parameters
|
|
----------
|
|
prefer_skip_nested_validation : bool
|
|
If True, the validation of parameters of inner estimators or functions
|
|
called during fit will be skipped.
|
|
|
|
This is useful to avoid validating many times the parameters passed by the
|
|
user from the public facing API. It's also useful to avoid validating
|
|
parameters that we pass internally to inner functions that are guaranteed to
|
|
be valid by the test suite.
|
|
|
|
It should be set to True for most estimators, except for those that receive
|
|
non-validated objects as parameters, such as meta-estimators that are given
|
|
estimator objects.
|
|
|
|
Returns
|
|
-------
|
|
decorated_fit : method
|
|
The decorated fit method.
|
|
"""
|
|
|
|
def decorator(fit_method):
|
|
@functools.wraps(fit_method)
|
|
def wrapper(estimator, *args, **kwargs):
|
|
global_skip_validation = get_config()["skip_parameter_validation"]
|
|
|
|
# we don't want to validate again for each call to partial_fit
|
|
partial_fit_and_fitted = (
|
|
fit_method.__name__ == "partial_fit" and _is_fitted(estimator)
|
|
)
|
|
|
|
if not global_skip_validation and not partial_fit_and_fitted:
|
|
estimator._validate_params()
|
|
|
|
with config_context(
|
|
skip_parameter_validation=(
|
|
prefer_skip_nested_validation or global_skip_validation
|
|
)
|
|
):
|
|
return fit_method(estimator, *args, **kwargs)
|
|
|
|
return wrapper
|
|
|
|
return decorator
|