701 lines
24 KiB
Python
701 lines
24 KiB
Python
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# Author: Mathieu Blondel <mathieu@mblondel.org>
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# Arnaud Joly <a.joly@ulg.ac.be>
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# Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
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# License: BSD 3 clause
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import warnings
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from numbers import Integral, Real
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import numpy as np
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import scipy.sparse as sp
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from .base import (
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BaseEstimator,
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ClassifierMixin,
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MultiOutputMixin,
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RegressorMixin,
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_fit_context,
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)
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from .utils import check_random_state
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from .utils._param_validation import Interval, StrOptions
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from .utils.multiclass import class_distribution
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from .utils.random import _random_choice_csc
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from .utils.stats import _weighted_percentile
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from .utils.validation import (
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_check_sample_weight,
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_num_samples,
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check_array,
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check_consistent_length,
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check_is_fitted,
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)
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class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
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"""DummyClassifier makes predictions that ignore the input features.
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This classifier serves as a simple baseline to compare against other more
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complex classifiers.
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The specific behavior of the baseline is selected with the `strategy`
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parameter.
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All strategies make predictions that ignore the input feature values passed
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as the `X` argument to `fit` and `predict`. The predictions, however,
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typically depend on values observed in the `y` parameter passed to `fit`.
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Note that the "stratified" and "uniform" strategies lead to
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non-deterministic predictions that can be rendered deterministic by setting
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the `random_state` parameter if needed. The other strategies are naturally
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deterministic and, once fit, always return the same constant prediction
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for any value of `X`.
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Read more in the :ref:`User Guide <dummy_estimators>`.
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.. versionadded:: 0.13
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Parameters
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----------
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strategy : {"most_frequent", "prior", "stratified", "uniform", \
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"constant"}, default="prior"
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Strategy to use to generate predictions.
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* "most_frequent": the `predict` method always returns the most
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frequent class label in the observed `y` argument passed to `fit`.
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The `predict_proba` method returns the matching one-hot encoded
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vector.
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* "prior": the `predict` method always returns the most frequent
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class label in the observed `y` argument passed to `fit` (like
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"most_frequent"). ``predict_proba`` always returns the empirical
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class distribution of `y` also known as the empirical class prior
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distribution.
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* "stratified": the `predict_proba` method randomly samples one-hot
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vectors from a multinomial distribution parametrized by the empirical
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class prior probabilities.
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The `predict` method returns the class label which got probability
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one in the one-hot vector of `predict_proba`.
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Each sampled row of both methods is therefore independent and
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identically distributed.
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* "uniform": generates predictions uniformly at random from the list
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of unique classes observed in `y`, i.e. each class has equal
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probability.
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* "constant": always predicts a constant label that is provided by
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the user. This is useful for metrics that evaluate a non-majority
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class.
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.. versionchanged:: 0.24
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The default value of `strategy` has changed to "prior" in version
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0.24.
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random_state : int, RandomState instance or None, default=None
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Controls the randomness to generate the predictions when
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``strategy='stratified'`` or ``strategy='uniform'``.
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Pass an int for reproducible output across multiple function calls.
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See :term:`Glossary <random_state>`.
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constant : int or str or array-like of shape (n_outputs,), default=None
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The explicit constant as predicted by the "constant" strategy. This
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parameter is useful only for the "constant" strategy.
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Attributes
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----------
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classes_ : ndarray of shape (n_classes,) or list of such arrays
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Unique class labels observed in `y`. For multi-output classification
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problems, this attribute is a list of arrays as each output has an
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independent set of possible classes.
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n_classes_ : int or list of int
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Number of label for each output.
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class_prior_ : ndarray of shape (n_classes,) or list of such arrays
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Frequency of each class observed in `y`. For multioutput classification
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problems, this is computed independently for each output.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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feature_names_in_ : ndarray of shape (`n_features_in_`,)
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Names of features seen during :term:`fit`. Defined only when `X` has
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feature names that are all strings.
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n_outputs_ : int
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Number of outputs.
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sparse_output_ : bool
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True if the array returned from predict is to be in sparse CSC format.
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Is automatically set to True if the input `y` is passed in sparse
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format.
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See Also
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--------
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DummyRegressor : Regressor that makes predictions using simple rules.
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.dummy import DummyClassifier
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>>> X = np.array([-1, 1, 1, 1])
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>>> y = np.array([0, 1, 1, 1])
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>>> dummy_clf = DummyClassifier(strategy="most_frequent")
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>>> dummy_clf.fit(X, y)
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DummyClassifier(strategy='most_frequent')
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>>> dummy_clf.predict(X)
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array([1, 1, 1, 1])
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>>> dummy_clf.score(X, y)
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0.75
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"""
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_parameter_constraints: dict = {
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"strategy": [
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StrOptions({"most_frequent", "prior", "stratified", "uniform", "constant"})
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],
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"random_state": ["random_state"],
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"constant": [Integral, str, "array-like", None],
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}
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def __init__(self, *, strategy="prior", random_state=None, constant=None):
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self.strategy = strategy
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self.random_state = random_state
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self.constant = constant
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@_fit_context(prefer_skip_nested_validation=True)
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def fit(self, X, y, sample_weight=None):
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"""Fit the baseline classifier.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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Training data.
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y : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Target values.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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Returns
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-------
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self : object
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Returns the instance itself.
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"""
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self._validate_data(X, cast_to_ndarray=False)
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self._strategy = self.strategy
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if self._strategy == "uniform" and sp.issparse(y):
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y = y.toarray()
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warnings.warn(
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(
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"A local copy of the target data has been converted "
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"to a numpy array. Predicting on sparse target data "
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"with the uniform strategy would not save memory "
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"and would be slower."
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),
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UserWarning,
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)
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self.sparse_output_ = sp.issparse(y)
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if not self.sparse_output_:
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y = np.asarray(y)
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y = np.atleast_1d(y)
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if y.ndim == 1:
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y = np.reshape(y, (-1, 1))
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self.n_outputs_ = y.shape[1]
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check_consistent_length(X, y)
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if sample_weight is not None:
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sample_weight = _check_sample_weight(sample_weight, X)
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if self._strategy == "constant":
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if self.constant is None:
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raise ValueError(
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"Constant target value has to be specified "
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"when the constant strategy is used."
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)
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else:
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constant = np.reshape(np.atleast_1d(self.constant), (-1, 1))
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if constant.shape[0] != self.n_outputs_:
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raise ValueError(
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"Constant target value should have shape (%d, 1)."
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% self.n_outputs_
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)
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(self.classes_, self.n_classes_, self.class_prior_) = class_distribution(
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y, sample_weight
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)
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if self._strategy == "constant":
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for k in range(self.n_outputs_):
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if not any(constant[k][0] == c for c in self.classes_[k]):
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# Checking in case of constant strategy if the constant
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# provided by the user is in y.
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err_msg = (
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"The constant target value must be present in "
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"the training data. You provided constant={}. "
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"Possible values are: {}.".format(
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self.constant, self.classes_[k].tolist()
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)
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)
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raise ValueError(err_msg)
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if self.n_outputs_ == 1:
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self.n_classes_ = self.n_classes_[0]
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self.classes_ = self.classes_[0]
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self.class_prior_ = self.class_prior_[0]
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return self
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def predict(self, X):
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"""Perform classification on test vectors X.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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Test data.
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Returns
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-------
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y : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Predicted target values for X.
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"""
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check_is_fitted(self)
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# numpy random_state expects Python int and not long as size argument
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# under Windows
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n_samples = _num_samples(X)
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rs = check_random_state(self.random_state)
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n_classes_ = self.n_classes_
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classes_ = self.classes_
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class_prior_ = self.class_prior_
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constant = self.constant
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if self.n_outputs_ == 1:
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# Get same type even for self.n_outputs_ == 1
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n_classes_ = [n_classes_]
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classes_ = [classes_]
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class_prior_ = [class_prior_]
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constant = [constant]
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# Compute probability only once
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if self._strategy == "stratified":
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proba = self.predict_proba(X)
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if self.n_outputs_ == 1:
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proba = [proba]
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if self.sparse_output_:
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class_prob = None
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if self._strategy in ("most_frequent", "prior"):
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classes_ = [np.array([cp.argmax()]) for cp in class_prior_]
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elif self._strategy == "stratified":
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class_prob = class_prior_
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elif self._strategy == "uniform":
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raise ValueError(
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"Sparse target prediction is not "
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"supported with the uniform strategy"
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)
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elif self._strategy == "constant":
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classes_ = [np.array([c]) for c in constant]
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y = _random_choice_csc(n_samples, classes_, class_prob, self.random_state)
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else:
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if self._strategy in ("most_frequent", "prior"):
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y = np.tile(
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[
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classes_[k][class_prior_[k].argmax()]
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for k in range(self.n_outputs_)
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],
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[n_samples, 1],
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)
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elif self._strategy == "stratified":
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y = np.vstack(
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[
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classes_[k][proba[k].argmax(axis=1)]
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for k in range(self.n_outputs_)
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]
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).T
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elif self._strategy == "uniform":
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ret = [
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classes_[k][rs.randint(n_classes_[k], size=n_samples)]
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for k in range(self.n_outputs_)
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]
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y = np.vstack(ret).T
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elif self._strategy == "constant":
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y = np.tile(self.constant, (n_samples, 1))
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if self.n_outputs_ == 1:
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y = np.ravel(y)
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return y
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def predict_proba(self, X):
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"""
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Return probability estimates for the test vectors X.
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Parameters
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----------
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X : array-like of shape (n_samples, n_features)
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Test data.
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Returns
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-------
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P : ndarray of shape (n_samples, n_classes) or list of such arrays
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Returns the probability of the sample for each class in
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the model, where classes are ordered arithmetically, for each
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output.
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"""
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check_is_fitted(self)
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# numpy random_state expects Python int and not long as size argument
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# under Windows
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n_samples = _num_samples(X)
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rs = check_random_state(self.random_state)
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n_classes_ = self.n_classes_
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classes_ = self.classes_
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class_prior_ = self.class_prior_
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constant = self.constant
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if self.n_outputs_ == 1:
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# Get same type even for self.n_outputs_ == 1
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n_classes_ = [n_classes_]
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classes_ = [classes_]
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class_prior_ = [class_prior_]
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constant = [constant]
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P = []
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for k in range(self.n_outputs_):
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if self._strategy == "most_frequent":
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ind = class_prior_[k].argmax()
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out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)
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out[:, ind] = 1.0
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elif self._strategy == "prior":
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out = np.ones((n_samples, 1)) * class_prior_[k]
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elif self._strategy == "stratified":
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out = rs.multinomial(1, class_prior_[k], size=n_samples)
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out = out.astype(np.float64)
|
||
|
|
|
||
|
|
elif self._strategy == "uniform":
|
||
|
|
out = np.ones((n_samples, n_classes_[k]), dtype=np.float64)
|
||
|
|
out /= n_classes_[k]
|
||
|
|
|
||
|
|
elif self._strategy == "constant":
|
||
|
|
ind = np.where(classes_[k] == constant[k])
|
||
|
|
out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)
|
||
|
|
out[:, ind] = 1.0
|
||
|
|
|
||
|
|
P.append(out)
|
||
|
|
|
||
|
|
if self.n_outputs_ == 1:
|
||
|
|
P = P[0]
|
||
|
|
|
||
|
|
return P
|
||
|
|
|
||
|
|
def predict_log_proba(self, X):
|
||
|
|
"""
|
||
|
|
Return log probability estimates for the test vectors X.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : {array-like, object with finite length or shape}
|
||
|
|
Training data.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
P : ndarray of shape (n_samples, n_classes) or list of such arrays
|
||
|
|
Returns the log probability of the sample for each class in
|
||
|
|
the model, where classes are ordered arithmetically for each
|
||
|
|
output.
|
||
|
|
"""
|
||
|
|
proba = self.predict_proba(X)
|
||
|
|
if self.n_outputs_ == 1:
|
||
|
|
return np.log(proba)
|
||
|
|
else:
|
||
|
|
return [np.log(p) for p in proba]
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
return {
|
||
|
|
"poor_score": True,
|
||
|
|
"no_validation": True,
|
||
|
|
"_xfail_checks": {
|
||
|
|
"check_methods_subset_invariance": "fails for the predict method",
|
||
|
|
"check_methods_sample_order_invariance": "fails for the predict method",
|
||
|
|
},
|
||
|
|
}
|
||
|
|
|
||
|
|
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 : None or array-like of shape (n_samples, n_features)
|
||
|
|
Test samples. Passing None as test samples gives the same result
|
||
|
|
as passing real test samples, since DummyClassifier
|
||
|
|
operates independently of the sampled observations.
|
||
|
|
|
||
|
|
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.
|
||
|
|
"""
|
||
|
|
if X is None:
|
||
|
|
X = np.zeros(shape=(len(y), 1))
|
||
|
|
return super().score(X, y, sample_weight)
|
||
|
|
|
||
|
|
|
||
|
|
class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
|
||
|
|
"""Regressor that makes predictions using simple rules.
|
||
|
|
|
||
|
|
This regressor is useful as a simple baseline to compare with other
|
||
|
|
(real) regressors. Do not use it for real problems.
|
||
|
|
|
||
|
|
Read more in the :ref:`User Guide <dummy_estimators>`.
|
||
|
|
|
||
|
|
.. versionadded:: 0.13
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
strategy : {"mean", "median", "quantile", "constant"}, default="mean"
|
||
|
|
Strategy to use to generate predictions.
|
||
|
|
|
||
|
|
* "mean": always predicts the mean of the training set
|
||
|
|
* "median": always predicts the median of the training set
|
||
|
|
* "quantile": always predicts a specified quantile of the training set,
|
||
|
|
provided with the quantile parameter.
|
||
|
|
* "constant": always predicts a constant value that is provided by
|
||
|
|
the user.
|
||
|
|
|
||
|
|
constant : int or float or array-like of shape (n_outputs,), default=None
|
||
|
|
The explicit constant as predicted by the "constant" strategy. This
|
||
|
|
parameter is useful only for the "constant" strategy.
|
||
|
|
|
||
|
|
quantile : float in [0.0, 1.0], default=None
|
||
|
|
The quantile to predict using the "quantile" strategy. A quantile of
|
||
|
|
0.5 corresponds to the median, while 0.0 to the minimum and 1.0 to the
|
||
|
|
maximum.
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
constant_ : ndarray of shape (1, n_outputs)
|
||
|
|
Mean or median or quantile of the training targets or constant value
|
||
|
|
given by the user.
|
||
|
|
|
||
|
|
n_features_in_ : int
|
||
|
|
Number of features seen during :term:`fit`.
|
||
|
|
|
||
|
|
feature_names_in_ : ndarray of shape (`n_features_in_`,)
|
||
|
|
Names of features seen during :term:`fit`. Defined only when `X` has
|
||
|
|
feature names that are all strings.
|
||
|
|
|
||
|
|
n_outputs_ : int
|
||
|
|
Number of outputs.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
DummyClassifier: Classifier that makes predictions using simple rules.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> import numpy as np
|
||
|
|
>>> from sklearn.dummy import DummyRegressor
|
||
|
|
>>> X = np.array([1.0, 2.0, 3.0, 4.0])
|
||
|
|
>>> y = np.array([2.0, 3.0, 5.0, 10.0])
|
||
|
|
>>> dummy_regr = DummyRegressor(strategy="mean")
|
||
|
|
>>> dummy_regr.fit(X, y)
|
||
|
|
DummyRegressor()
|
||
|
|
>>> dummy_regr.predict(X)
|
||
|
|
array([5., 5., 5., 5.])
|
||
|
|
>>> dummy_regr.score(X, y)
|
||
|
|
0.0
|
||
|
|
"""
|
||
|
|
|
||
|
|
_parameter_constraints: dict = {
|
||
|
|
"strategy": [StrOptions({"mean", "median", "quantile", "constant"})],
|
||
|
|
"quantile": [Interval(Real, 0.0, 1.0, closed="both"), None],
|
||
|
|
"constant": [
|
||
|
|
Interval(Real, None, None, closed="neither"),
|
||
|
|
"array-like",
|
||
|
|
None,
|
||
|
|
],
|
||
|
|
}
|
||
|
|
|
||
|
|
def __init__(self, *, strategy="mean", constant=None, quantile=None):
|
||
|
|
self.strategy = strategy
|
||
|
|
self.constant = constant
|
||
|
|
self.quantile = quantile
|
||
|
|
|
||
|
|
@_fit_context(prefer_skip_nested_validation=True)
|
||
|
|
def fit(self, X, y, sample_weight=None):
|
||
|
|
"""Fit the random regressor.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like of shape (n_samples, n_features)
|
||
|
|
Training data.
|
||
|
|
|
||
|
|
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
|
||
|
|
Target values.
|
||
|
|
|
||
|
|
sample_weight : array-like of shape (n_samples,), default=None
|
||
|
|
Sample weights.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
self : object
|
||
|
|
Fitted estimator.
|
||
|
|
"""
|
||
|
|
self._validate_data(X, cast_to_ndarray=False)
|
||
|
|
|
||
|
|
y = check_array(y, ensure_2d=False, input_name="y")
|
||
|
|
if len(y) == 0:
|
||
|
|
raise ValueError("y must not be empty.")
|
||
|
|
|
||
|
|
if y.ndim == 1:
|
||
|
|
y = np.reshape(y, (-1, 1))
|
||
|
|
self.n_outputs_ = y.shape[1]
|
||
|
|
|
||
|
|
check_consistent_length(X, y, sample_weight)
|
||
|
|
|
||
|
|
if sample_weight is not None:
|
||
|
|
sample_weight = _check_sample_weight(sample_weight, X)
|
||
|
|
|
||
|
|
if self.strategy == "mean":
|
||
|
|
self.constant_ = np.average(y, axis=0, weights=sample_weight)
|
||
|
|
|
||
|
|
elif self.strategy == "median":
|
||
|
|
if sample_weight is None:
|
||
|
|
self.constant_ = np.median(y, axis=0)
|
||
|
|
else:
|
||
|
|
self.constant_ = [
|
||
|
|
_weighted_percentile(y[:, k], sample_weight, percentile=50.0)
|
||
|
|
for k in range(self.n_outputs_)
|
||
|
|
]
|
||
|
|
|
||
|
|
elif self.strategy == "quantile":
|
||
|
|
if self.quantile is None:
|
||
|
|
raise ValueError(
|
||
|
|
"When using `strategy='quantile', you have to specify the desired "
|
||
|
|
"quantile in the range [0, 1]."
|
||
|
|
)
|
||
|
|
percentile = self.quantile * 100.0
|
||
|
|
if sample_weight is None:
|
||
|
|
self.constant_ = np.percentile(y, axis=0, q=percentile)
|
||
|
|
else:
|
||
|
|
self.constant_ = [
|
||
|
|
_weighted_percentile(y[:, k], sample_weight, percentile=percentile)
|
||
|
|
for k in range(self.n_outputs_)
|
||
|
|
]
|
||
|
|
|
||
|
|
elif self.strategy == "constant":
|
||
|
|
if self.constant is None:
|
||
|
|
raise TypeError(
|
||
|
|
"Constant target value has to be specified "
|
||
|
|
"when the constant strategy is used."
|
||
|
|
)
|
||
|
|
|
||
|
|
self.constant_ = check_array(
|
||
|
|
self.constant,
|
||
|
|
accept_sparse=["csr", "csc", "coo"],
|
||
|
|
ensure_2d=False,
|
||
|
|
ensure_min_samples=0,
|
||
|
|
)
|
||
|
|
|
||
|
|
if self.n_outputs_ != 1 and self.constant_.shape[0] != y.shape[1]:
|
||
|
|
raise ValueError(
|
||
|
|
"Constant target value should have shape (%d, 1)." % y.shape[1]
|
||
|
|
)
|
||
|
|
|
||
|
|
self.constant_ = np.reshape(self.constant_, (1, -1))
|
||
|
|
return self
|
||
|
|
|
||
|
|
def predict(self, X, return_std=False):
|
||
|
|
"""Perform classification on test vectors X.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like of shape (n_samples, n_features)
|
||
|
|
Test data.
|
||
|
|
|
||
|
|
return_std : bool, default=False
|
||
|
|
Whether to return the standard deviation of posterior prediction.
|
||
|
|
All zeros in this case.
|
||
|
|
|
||
|
|
.. versionadded:: 0.20
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
|
||
|
|
Predicted target values for X.
|
||
|
|
|
||
|
|
y_std : array-like of shape (n_samples,) or (n_samples, n_outputs)
|
||
|
|
Standard deviation of predictive distribution of query points.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
n_samples = _num_samples(X)
|
||
|
|
|
||
|
|
y = np.full(
|
||
|
|
(n_samples, self.n_outputs_),
|
||
|
|
self.constant_,
|
||
|
|
dtype=np.array(self.constant_).dtype,
|
||
|
|
)
|
||
|
|
y_std = np.zeros((n_samples, self.n_outputs_))
|
||
|
|
|
||
|
|
if self.n_outputs_ == 1:
|
||
|
|
y = np.ravel(y)
|
||
|
|
y_std = np.ravel(y_std)
|
||
|
|
|
||
|
|
return (y, y_std) if return_std else y
|
||
|
|
|
||
|
|
def _more_tags(self):
|
||
|
|
return {"poor_score": True, "no_validation": True}
|
||
|
|
|
||
|
|
def score(self, X, y, sample_weight=None):
|
||
|
|
"""Return the coefficient of determination R^2 of the prediction.
|
||
|
|
|
||
|
|
The coefficient R^2 is defined as `(1 - u/v)`, where `u` is the
|
||
|
|
residual sum of squares `((y_true - y_pred) ** 2).sum()` and `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 R^2 score of
|
||
|
|
0.0.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : None or array-like of shape (n_samples, n_features)
|
||
|
|
Test samples. Passing None as test samples gives the same result
|
||
|
|
as passing real test samples, since `DummyRegressor`
|
||
|
|
operates independently of the sampled observations.
|
||
|
|
|
||
|
|
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
|
||
|
|
R^2 of `self.predict(X)` w.r.t. y.
|
||
|
|
"""
|
||
|
|
if X is None:
|
||
|
|
X = np.zeros(shape=(len(y), 1))
|
||
|
|
return super().score(X, y, sample_weight)
|