"""Base classes for all estimators."""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import copy
import warnings
from collections import defaultdict
import platform
import inspect
import re
import numpy as np
from . import __version__
from ._config import get_config
from .utils import _IS_32BIT
from .utils._set_output import _SetOutputMixin
from .utils._tags import (
_DEFAULT_TAGS,
)
from .utils.validation import check_X_y
from .utils.validation import check_array
from .utils.validation import _check_y
from .utils.validation import _num_features
from .utils.validation import _check_feature_names_in
from .utils.validation import _generate_get_feature_names_out
from .utils.validation import check_is_fitted
from .utils.validation import _get_feature_names
from .utils._estimator_html_repr import estimator_html_repr
from .utils._param_validation import validate_parameter_constraints
def clone(estimator, *, safe=True):
"""Construct a new unfitted estimator with the same parameters.
Clone does a deep copy of the model in an estimator
without actually copying attached data. It returns a new estimator
with the same parameters that has not been fitted on any data.
Parameters
----------
estimator : {list, tuple, set} of estimator instance or a single \
estimator instance
The estimator or group of estimators to be cloned.
safe : bool, default=True
If safe is False, clone will fall back to a deep copy on objects
that are not estimators.
Returns
-------
estimator : object
The deep copy of the input, an estimator if input is an estimator.
Notes
-----
If the estimator's `random_state` parameter is an integer (or if the
estimator doesn't have a `random_state` parameter), an *exact clone* is
returned: the clone and the original estimator will give the exact same
results. Otherwise, *statistical clone* is returned: the clone might
return different results from the original estimator. More details can be
found in :ref:`randomness`.
"""
estimator_type = type(estimator)
# XXX: not handling dictionaries
if estimator_type in (list, tuple, set, frozenset):
return estimator_type([clone(e, safe=safe) for e in estimator])
elif not hasattr(estimator, "get_params") or isinstance(estimator, type):
if not safe:
return copy.deepcopy(estimator)
else:
if isinstance(estimator, type):
raise TypeError(
"Cannot clone object. "
+ "You should provide an instance of "
+ "scikit-learn estimator instead of a class."
)
else:
raise TypeError(
"Cannot clone object '%s' (type %s): "
"it does not seem to be a scikit-learn "
"estimator as it does not implement a "
"'get_params' method." % (repr(estimator), type(estimator))
)
klass = estimator.__class__
new_object_params = estimator.get_params(deep=False)
for name, param in new_object_params.items():
new_object_params[name] = clone(param, safe=False)
new_object = klass(**new_object_params)
params_set = new_object.get_params(deep=False)
# quick sanity check of the parameters of the clone
for name in new_object_params:
param1 = new_object_params[name]
param2 = params_set[name]
if param1 is not param2:
raise RuntimeError(
"Cannot clone object %s, as the constructor "
"either does not set or modifies parameter %s" % (estimator, name)
)
# _sklearn_output_config is used by `set_output` to configure the output
# container of an estimator.
if hasattr(estimator, "_sklearn_output_config"):
new_object._sklearn_output_config = copy.deepcopy(
estimator._sklearn_output_config
)
return new_object
class BaseEstimator:
"""Base class for all estimators in scikit-learn.
Notes
-----
All estimators should specify all the parameters that can be set
at the class level in their ``__init__`` as explicit keyword
arguments (no ``*args`` or ``**kwargs``).
"""
@classmethod
def _get_param_names(cls):
"""Get parameter names for the estimator"""
# fetch the constructor or the original constructor before
# deprecation wrapping if any
init = getattr(cls.__init__, "deprecated_original", cls.__init__)
if init is object.__init__:
# No explicit constructor to introspect
return []
# introspect the constructor arguments to find the model parameters
# to represent
init_signature = inspect.signature(init)
# Consider the constructor parameters excluding 'self'
parameters = [
p
for p in init_signature.parameters.values()
if p.name != "self" and p.kind != p.VAR_KEYWORD
]
for p in parameters:
if p.kind == p.VAR_POSITIONAL:
raise RuntimeError(
"scikit-learn estimators should always "
"specify their parameters in the signature"
" of their __init__ (no varargs)."
" %s with constructor %s doesn't "
" follow this convention." % (cls, init_signature)
)
# Extract and sort argument names excluding 'self'
return sorted([p.name for p in parameters])
def get_params(self, deep=True):
"""
Get parameters for this estimator.
Parameters
----------
deep : bool, default=True
If True, will return the parameters for this estimator and
contained subobjects that are estimators.
Returns
-------
params : dict
Parameter names mapped to their values.
"""
out = dict()
for key in self._get_param_names():
value = getattr(self, key)
if deep and hasattr(value, "get_params") and not isinstance(value, type):
deep_items = value.get_params().items()
out.update((key + "__" + k, val) for k, val in deep_items)
out[key] = value
return out
def set_params(self, **params):
"""Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each component of a nested object.
Parameters
----------
**params : dict
Estimator parameters.
Returns
-------
self : estimator instance
Estimator instance.
"""
if not params:
# Simple optimization to gain speed (inspect is slow)
return self
valid_params = self.get_params(deep=True)
nested_params = defaultdict(dict) # grouped by prefix
for key, value in params.items():
key, delim, sub_key = key.partition("__")
if key not in valid_params:
local_valid_params = self._get_param_names()
raise ValueError(
f"Invalid parameter {key!r} for estimator {self}. "
f"Valid parameters are: {local_valid_params!r}."
)
if delim:
nested_params[key][sub_key] = value
else:
setattr(self, key, value)
valid_params[key] = value
for key, sub_params in nested_params.items():
# TODO(1.4): remove specific handling of "base_estimator".
# The "base_estimator" key is special. It was deprecated and
# renamed to "estimator" for several estimators. This means we
# need to translate it here and set sub-parameters on "estimator",
# but only if the user did not explicitly set a value for
# "base_estimator".
if (
key == "base_estimator"
and valid_params[key] == "deprecated"
and self.__module__.startswith("sklearn.")
):
warnings.warn(
f"Parameter 'base_estimator' of {self.__class__.__name__} is"
" deprecated in favor of 'estimator'. See"
f" {self.__class__.__name__}'s docstring for more details.",
FutureWarning,
stacklevel=2,
)
key = "estimator"
valid_params[key].set_params(**sub_params)
return self
def __repr__(self, N_CHAR_MAX=700):
# N_CHAR_MAX is the (approximate) maximum number of non-blank
# characters to render. We pass it as an optional parameter to ease
# the tests.
from .utils._pprint import _EstimatorPrettyPrinter
N_MAX_ELEMENTS_TO_SHOW = 30 # number of elements to show in sequences
# use ellipsis for sequences with a lot of elements
pp = _EstimatorPrettyPrinter(
compact=True,
indent=1,
indent_at_name=True,
n_max_elements_to_show=N_MAX_ELEMENTS_TO_SHOW,
)
repr_ = pp.pformat(self)
# Use bruteforce ellipsis when there are a lot of non-blank characters
n_nonblank = len("".join(repr_.split()))
if n_nonblank > N_CHAR_MAX:
lim = N_CHAR_MAX // 2 # apprx number of chars to keep on both ends
regex = r"^(\s*\S){%d}" % lim
# The regex '^(\s*\S){%d}' % n
# matches from the start of the string until the nth non-blank
# character:
# - ^ matches the start of string
# - (pattern){n} matches n repetitions of pattern
# - \s*\S matches a non-blank char following zero or more blanks
left_lim = re.match(regex, repr_).end()
right_lim = re.match(regex, repr_[::-1]).end()
if "\n" in repr_[left_lim:-right_lim]:
# The left side and right side aren't on the same line.
# To avoid weird cuts, e.g.:
# categoric...ore',
# we need to start the right side with an appropriate newline
# character so that it renders properly as:
# categoric...
# handle_unknown='ignore',
# so we add [^\n]*\n which matches until the next \n
regex += r"[^\n]*\n"
right_lim = re.match(regex, repr_[::-1]).end()
ellipsis = "..."
if left_lim + len(ellipsis) < len(repr_) - right_lim:
# Only add ellipsis if it results in a shorter repr
repr_ = repr_[:left_lim] + "..." + repr_[-right_lim:]
return repr_
def __getstate__(self):
if getattr(self, "__slots__", None):
raise TypeError(
"You cannot use `__slots__` in objects inheriting from "
"`sklearn.base.BaseEstimator`."
)
try:
state = super().__getstate__()
if state is None:
# For Python 3.11+, empty instance (no `__slots__`,
# and `__dict__`) will return a state equal to `None`.
state = self.__dict__.copy()
except AttributeError:
# Python < 3.11
state = self.__dict__.copy()
if type(self).__module__.startswith("sklearn."):
return dict(state.items(), _sklearn_version=__version__)
else:
return state
def __setstate__(self, state):
if type(self).__module__.startswith("sklearn."):
pickle_version = state.pop("_sklearn_version", "pre-0.18")
if pickle_version != __version__:
warnings.warn(
"Trying to unpickle estimator {0} from version {1} when "
"using version {2}. This might lead to breaking code or "
"invalid results. Use at your own risk. "
"For more info please refer to:\n"
"https://scikit-learn.org/stable/model_persistence.html"
"#security-maintainability-limitations".format(
self.__class__.__name__, pickle_version, __version__
),
UserWarning,
)
try:
super().__setstate__(state)
except AttributeError:
self.__dict__.update(state)
def _more_tags(self):
return _DEFAULT_TAGS
def _get_tags(self):
collected_tags = {}
for base_class in reversed(inspect.getmro(self.__class__)):
if hasattr(base_class, "_more_tags"):
# need the if because mixins might not have _more_tags
# but might do redundant work in estimators
# (i.e. calling more tags on BaseEstimator multiple times)
more_tags = base_class._more_tags(self)
collected_tags.update(more_tags)
return collected_tags
def _check_n_features(self, X, reset):
"""Set the `n_features_in_` attribute, or check against it.
Parameters
----------
X : {ndarray, sparse matrix} of shape (n_samples, n_features)
The input samples.
reset : bool
If True, the `n_features_in_` attribute is set to `X.shape[1]`.
If False and the attribute exists, then check that it is equal to
`X.shape[1]`. If False and the attribute does *not* exist, then
the check is skipped.
.. 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`.
"""
try:
n_features = _num_features(X)
except TypeError as e:
if not reset and hasattr(self, "n_features_in_"):
raise ValueError(
"X does not contain any features, but "
f"{self.__class__.__name__} is expecting "
f"{self.n_features_in_} features"
) from e
# If the number of features is not defined and reset=True,
# then we skip this check
return
if reset:
self.n_features_in_ = n_features
return
if not hasattr(self, "n_features_in_"):
# Skip this check if the expected number of expected input features
# was not recorded by calling fit first. This is typically the case
# for stateless transformers.
return
if n_features != self.n_features_in_:
raise ValueError(
f"X has {n_features} features, but {self.__class__.__name__} "
f"is expecting {self.n_features_in_} features as input."
)
def _check_feature_names(self, X, *, reset):
"""Set or check the `feature_names_in_` attribute.
.. versionadded:: 1.0
Parameters
----------
X : {ndarray, dataframe} of shape (n_samples, n_features)
The input samples.
reset : bool
Whether to reset the `feature_names_in_` attribute.
If False, the input will be checked for consistency with
feature names of 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`.
"""
if reset:
feature_names_in = _get_feature_names(X)
if feature_names_in is not None:
self.feature_names_in_ = feature_names_in
elif hasattr(self, "feature_names_in_"):
# Delete the attribute when the estimator is fitted on a new dataset
# that has no feature names.
delattr(self, "feature_names_in_")
return
fitted_feature_names = getattr(self, "feature_names_in_", None)
X_feature_names = _get_feature_names(X)
if fitted_feature_names is None and X_feature_names is None:
# no feature names seen in fit and in X
return
if X_feature_names is not None and fitted_feature_names is None:
warnings.warn(
f"X has feature names, but {self.__class__.__name__} was fitted without"
" feature names"
)
return
if X_feature_names is None and fitted_feature_names is not None:
warnings.warn(
"X does not have valid feature names, but"
f" {self.__class__.__name__} was fitted with feature names"
)
return
# validate the feature names against the `feature_names_in_` attribute
if len(fitted_feature_names) != len(X_feature_names) or np.any(
fitted_feature_names != X_feature_names
):
message = (
"The feature names should match those that were passed during fit.\n"
)
fitted_feature_names_set = set(fitted_feature_names)
X_feature_names_set = set(X_feature_names)
unexpected_names = sorted(X_feature_names_set - fitted_feature_names_set)
missing_names = sorted(fitted_feature_names_set - X_feature_names_set)
def add_names(names):
output = ""
max_n_names = 5
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,
**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.
**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"
default_check_params = {"estimator": self}
check_params = {**default_check_params, **check_params}
if no_val_X and no_val_y:
raise ValueError("Validation should be done on X, y or both.")
elif not no_val_X and no_val_y:
X = check_array(X, input_name="X", **check_params)
out = X
elif no_val_X and not no_val_y:
y = _check_y(y, **check_params)
out = y
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."""
_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."""
_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 = "clusterer"
def fit_predict(self, X, y=None):
"""
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.
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)
return self.labels_
def _more_tags(self):
return {"preserves_dtype": []}
class BiclusterMixin:
"""Mixin class for all bicluster estimators in scikit-learn."""
@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.
If :term:`get_feature_names_out` is defined, then `BaseEstimator` will
automatically wrap `transform` and `fit_transform` to follow the `set_output`
API. See the :ref:`developer_api_set_output` for details.
:class:`base.OneToOneFeatureMixin` and
:class:`base.ClassNamePrefixFeaturesOutMixin` are helpful mixins for
defining :term:`get_feature_names_out`.
"""
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
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:`~preprocessing.StandardScaler`.
"""
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.
"""
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:`~decomposition.PCA`. For example, if
:class:`~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`.
"""
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 :meth:`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."""
_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."""
_estimator_type = "outlier_detector"
def fit_predict(self, X, y=None):
"""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.
Returns
-------
y : ndarray of shape (n_samples,)
1 for inliers, -1 for outliers.
"""
# override for transductive outlier detectors like LocalOulierFactor
return self.fit(X).predict(X)
class MetaEstimatorMixin:
_required_parameters = ["estimator"]
"""Mixin class for all meta estimators in scikit-learn."""
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.
"""
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.
"""
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"
