683 lines
23 KiB
Python
683 lines
23 KiB
Python
|
# Author: Mathieu Blondel <mathieu@mblondel.org>
|
||
|
|
# Arnaud Joly <a.joly@ulg.ac.be>
|
||
|
|
# Maheshakya Wijewardena <maheshakya.10@cse.mrt.ac.lk>
|
||
|
|
# License: BSD 3 clause
|
||
|
|
|
||
|
|
import warnings
|
||
|
|
from numbers import Integral, Real
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
import scipy.sparse as sp
|
||
|
|
|
||
|
|
from .base import (
|
||
|
|
BaseEstimator,
|
||
|
|
ClassifierMixin,
|
||
|
|
MultiOutputMixin,
|
||
|
|
RegressorMixin,
|
||
|
|
_fit_context,
|
||
|
|
)
|
||
|
|
from .utils import check_random_state
|
||
|
|
from .utils._param_validation import Interval, StrOptions
|
||
|
|
from .utils.multiclass import class_distribution
|
||
|
|
from .utils.random import _random_choice_csc
|
||
|
|
from .utils.stats import _weighted_percentile
|
||
|
|
from .utils.validation import (
|
||
|
|
_check_sample_weight,
|
||
|
|
_num_samples,
|
||
|
|
check_array,
|
||
|
|
check_consistent_length,
|
||
|
|
check_is_fitted,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
|
||
|
|
"""DummyClassifier makes predictions that ignore the input features.
|
||
|
|
|
||
|
|
This classifier serves as a simple baseline to compare against other more
|
||
|
|
complex classifiers.
|
||
|
|
|
||
|
|
The specific behavior of the baseline is selected with the `strategy`
|
||
|
|
parameter.
|
||
|
|
|
||
|
|
All strategies make predictions that ignore the input feature values passed
|
||
|
|
as the `X` argument to `fit` and `predict`. The predictions, however,
|
||
|
|
typically depend on values observed in the `y` parameter passed to `fit`.
|
||
|
|
|
||
|
|
Note that the "stratified" and "uniform" strategies lead to
|
||
|
|
non-deterministic predictions that can be rendered deterministic by setting
|
||
|
|
the `random_state` parameter if needed. The other strategies are naturally
|
||
|
|
deterministic and, once fit, always return the same constant prediction
|
||
|
|
for any value of `X`.
|
||
|
|
|
||
|
|
Read more in the :ref:`User Guide <dummy_estimators>`.
|
||
|
|
|
||
|
|
.. versionadded:: 0.13
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
strategy : {"most_frequent", "prior", "stratified", "uniform", \
|
||
|
|
"constant"}, default="prior"
|
||
|
|
Strategy to use to generate predictions.
|
||
|
|
|
||
|
|
* "most_frequent": the `predict` method always returns the most
|
||
|
|
frequent class label in the observed `y` argument passed to `fit`.
|
||
|
|
The `predict_proba` method returns the matching one-hot encoded
|
||
|
|
vector.
|
||
|
|
* "prior": the `predict` method always returns the most frequent
|
||
|
|
class label in the observed `y` argument passed to `fit` (like
|
||
|
|
"most_frequent"). ``predict_proba`` always returns the empirical
|
||
|
|
class distribution of `y` also known as the empirical class prior
|
||
|
|
distribution.
|
||
|
|
* "stratified": the `predict_proba` method randomly samples one-hot
|
||
|
|
vectors from a multinomial distribution parametrized by the empirical
|
||
|
|
class prior probabilities.
|
||
|
|
The `predict` method returns the class label which got probability
|
||
|
|
one in the one-hot vector of `predict_proba`.
|
||
|
|
Each sampled row of both methods is therefore independent and
|
||
|
|
identically distributed.
|
||
|
|
* "uniform": generates predictions uniformly at random from the list
|
||
|
|
of unique classes observed in `y`, i.e. each class has equal
|
||
|
|
probability.
|
||
|
|
* "constant": always predicts a constant label that is provided by
|
||
|
|
the user. This is useful for metrics that evaluate a non-majority
|
||
|
|
class.
|
||
|
|
|
||
|
|
.. versionchanged:: 0.24
|
||
|
|
The default value of `strategy` has changed to "prior" in version
|
||
|
|
0.24.
|
||
|
|
|
||
|
|
random_state : int, RandomState instance or None, default=None
|
||
|
|
Controls the randomness to generate the predictions when
|
||
|
|
``strategy='stratified'`` or ``strategy='uniform'``.
|
||
|
|
Pass an int for reproducible output across multiple function calls.
|
||
|
|
See :term:`Glossary <random_state>`.
|
||
|
|
|
||
|
|
constant : int or str 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.
|
||
|
|
|
||
|
|
Attributes
|
||
|
|
----------
|
||
|
|
classes_ : ndarray of shape (n_classes,) or list of such arrays
|
||
|
|
Unique class labels observed in `y`. For multi-output classification
|
||
|
|
problems, this attribute is a list of arrays as each output has an
|
||
|
|
independent set of possible classes.
|
||
|
|
|
||
|
|
n_classes_ : int or list of int
|
||
|
|
Number of label for each output.
|
||
|
|
|
||
|
|
class_prior_ : ndarray of shape (n_classes,) or list of such arrays
|
||
|
|
Frequency of each class observed in `y`. For multioutput classification
|
||
|
|
problems, this is computed independently for each output.
|
||
|
|
|
||
|
|
n_outputs_ : int
|
||
|
|
Number of outputs.
|
||
|
|
|
||
|
|
sparse_output_ : bool
|
||
|
|
True if the array returned from predict is to be in sparse CSC format.
|
||
|
|
Is automatically set to True if the input `y` is passed in sparse
|
||
|
|
format.
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
DummyRegressor : Regressor that makes predictions using simple rules.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> import numpy as np
|
||
|
|
>>> from sklearn.dummy import DummyClassifier
|
||
|
|
>>> X = np.array([-1, 1, 1, 1])
|
||
|
|
>>> y = np.array([0, 1, 1, 1])
|
||
|
|
>>> dummy_clf = DummyClassifier(strategy="most_frequent")
|
||
|
|
>>> dummy_clf.fit(X, y)
|
||
|
|
DummyClassifier(strategy='most_frequent')
|
||
|
|
>>> dummy_clf.predict(X)
|
||
|
|
array([1, 1, 1, 1])
|
||
|
|
>>> dummy_clf.score(X, y)
|
||
|
|
0.75
|
||
|
|
"""
|
||
|
|
|
||
|
|
_parameter_constraints: dict = {
|
||
|
|
"strategy": [
|
||
|
|
StrOptions({"most_frequent", "prior", "stratified", "uniform", "constant"})
|
||
|
|
],
|
||
|
|
"random_state": ["random_state"],
|
||
|
|
"constant": [Integral, str, "array-like", None],
|
||
|
|
}
|
||
|
|
|
||
|
|
def __init__(self, *, strategy="prior", random_state=None, constant=None):
|
||
|
|
self.strategy = strategy
|
||
|
|
self.random_state = random_state
|
||
|
|
self.constant = constant
|
||
|
|
|
||
|
|
@_fit_context(prefer_skip_nested_validation=True)
|
||
|
|
def fit(self, X, y, sample_weight=None):
|
||
|
|
"""Fit the baseline classifier.
|
||
|
|
|
||
|
|
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
|
||
|
|
Returns the instance itself.
|
||
|
|
"""
|
||
|
|
self._strategy = self.strategy
|
||
|
|
|
||
|
|
if self._strategy == "uniform" and sp.issparse(y):
|
||
|
|
y = y.toarray()
|
||
|
|
warnings.warn(
|
||
|
|
(
|
||
|
|
"A local copy of the target data has been converted "
|
||
|
|
"to a numpy array. Predicting on sparse target data "
|
||
|
|
"with the uniform strategy would not save memory "
|
||
|
|
"and would be slower."
|
||
|
|
),
|
||
|
|
UserWarning,
|
||
|
|
)
|
||
|
|
|
||
|
|
self.sparse_output_ = sp.issparse(y)
|
||
|
|
|
||
|
|
if not self.sparse_output_:
|
||
|
|
y = np.asarray(y)
|
||
|
|
y = np.atleast_1d(y)
|
||
|
|
|
||
|
|
if y.ndim == 1:
|
||
|
|
y = np.reshape(y, (-1, 1))
|
||
|
|
|
||
|
|
self.n_outputs_ = y.shape[1]
|
||
|
|
|
||
|
|
check_consistent_length(X, y)
|
||
|
|
|
||
|
|
if sample_weight is not None:
|
||
|
|
sample_weight = _check_sample_weight(sample_weight, X)
|
||
|
|
|
||
|
|
if self._strategy == "constant":
|
||
|
|
if self.constant is None:
|
||
|
|
raise ValueError(
|
||
|
|
"Constant target value has to be specified "
|
||
|
|
"when the constant strategy is used."
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
constant = np.reshape(np.atleast_1d(self.constant), (-1, 1))
|
||
|
|
if constant.shape[0] != self.n_outputs_:
|
||
|
|
raise ValueError(
|
||
|
|
"Constant target value should have shape (%d, 1)."
|
||
|
|
% self.n_outputs_
|
||
|
|
)
|
||
|
|
|
||
|
|
(self.classes_, self.n_classes_, self.class_prior_) = class_distribution(
|
||
|
|
y, sample_weight
|
||
|
|
)
|
||
|
|
|
||
|
|
if self._strategy == "constant":
|
||
|
|
for k in range(self.n_outputs_):
|
||
|
|
if not any(constant[k][0] == c for c in self.classes_[k]):
|
||
|
|
# Checking in case of constant strategy if the constant
|
||
|
|
# provided by the user is in y.
|
||
|
|
err_msg = (
|
||
|
|
"The constant target value must be present in "
|
||
|
|
"the training data. You provided constant={}. "
|
||
|
|
"Possible values are: {}.".format(
|
||
|
|
self.constant, self.classes_[k].tolist()
|
||
|
|
)
|
||
|
|
)
|
||
|
|
raise ValueError(err_msg)
|
||
|
|
|
||
|
|
if self.n_outputs_ == 1:
|
||
|
|
self.n_classes_ = self.n_classes_[0]
|
||
|
|
self.classes_ = self.classes_[0]
|
||
|
|
self.class_prior_ = self.class_prior_[0]
|
||
|
|
|
||
|
|
return self
|
||
|
|
|
||
|
|
def predict(self, X):
|
||
|
|
"""Perform classification on test vectors X.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like of shape (n_samples, n_features)
|
||
|
|
Test data.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
y : array-like of shape (n_samples,) or (n_samples, n_outputs)
|
||
|
|
Predicted target values for X.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
|
||
|
|
# numpy random_state expects Python int and not long as size argument
|
||
|
|
# under Windows
|
||
|
|
n_samples = _num_samples(X)
|
||
|
|
rs = check_random_state(self.random_state)
|
||
|
|
|
||
|
|
n_classes_ = self.n_classes_
|
||
|
|
classes_ = self.classes_
|
||
|
|
class_prior_ = self.class_prior_
|
||
|
|
constant = self.constant
|
||
|
|
if self.n_outputs_ == 1:
|
||
|
|
# Get same type even for self.n_outputs_ == 1
|
||
|
|
n_classes_ = [n_classes_]
|
||
|
|
classes_ = [classes_]
|
||
|
|
class_prior_ = [class_prior_]
|
||
|
|
constant = [constant]
|
||
|
|
# Compute probability only once
|
||
|
|
if self._strategy == "stratified":
|
||
|
|
proba = self.predict_proba(X)
|
||
|
|
if self.n_outputs_ == 1:
|
||
|
|
proba = [proba]
|
||
|
|
|
||
|
|
if self.sparse_output_:
|
||
|
|
class_prob = None
|
||
|
|
if self._strategy in ("most_frequent", "prior"):
|
||
|
|
classes_ = [np.array([cp.argmax()]) for cp in class_prior_]
|
||
|
|
|
||
|
|
elif self._strategy == "stratified":
|
||
|
|
class_prob = class_prior_
|
||
|
|
|
||
|
|
elif self._strategy == "uniform":
|
||
|
|
raise ValueError(
|
||
|
|
"Sparse target prediction is not "
|
||
|
|
"supported with the uniform strategy"
|
||
|
|
)
|
||
|
|
|
||
|
|
elif self._strategy == "constant":
|
||
|
|
classes_ = [np.array([c]) for c in constant]
|
||
|
|
|
||
|
|
y = _random_choice_csc(n_samples, classes_, class_prob, self.random_state)
|
||
|
|
else:
|
||
|
|
if self._strategy in ("most_frequent", "prior"):
|
||
|
|
y = np.tile(
|
||
|
|
[
|
||
|
|
classes_[k][class_prior_[k].argmax()]
|
||
|
|
for k in range(self.n_outputs_)
|
||
|
|
],
|
||
|
|
[n_samples, 1],
|
||
|
|
)
|
||
|
|
|
||
|
|
elif self._strategy == "stratified":
|
||
|
|
y = np.vstack(
|
||
|
|
[
|
||
|
|
classes_[k][proba[k].argmax(axis=1)]
|
||
|
|
for k in range(self.n_outputs_)
|
||
|
|
]
|
||
|
|
).T
|
||
|
|
|
||
|
|
elif self._strategy == "uniform":
|
||
|
|
ret = [
|
||
|
|
classes_[k][rs.randint(n_classes_[k], size=n_samples)]
|
||
|
|
for k in range(self.n_outputs_)
|
||
|
|
]
|
||
|
|
y = np.vstack(ret).T
|
||
|
|
|
||
|
|
elif self._strategy == "constant":
|
||
|
|
y = np.tile(self.constant, (n_samples, 1))
|
||
|
|
|
||
|
|
if self.n_outputs_ == 1:
|
||
|
|
y = np.ravel(y)
|
||
|
|
|
||
|
|
return y
|
||
|
|
|
||
|
|
def predict_proba(self, X):
|
||
|
|
"""
|
||
|
|
Return probability estimates for the test vectors X.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
X : array-like of shape (n_samples, n_features)
|
||
|
|
Test data.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
P : ndarray of shape (n_samples, n_classes) or list of such arrays
|
||
|
|
Returns the probability of the sample for each class in
|
||
|
|
the model, where classes are ordered arithmetically, for each
|
||
|
|
output.
|
||
|
|
"""
|
||
|
|
check_is_fitted(self)
|
||
|
|
|
||
|
|
# numpy random_state expects Python int and not long as size argument
|
||
|
|
# under Windows
|
||
|
|
n_samples = _num_samples(X)
|
||
|
|
rs = check_random_state(self.random_state)
|
||
|
|
|
||
|
|
n_classes_ = self.n_classes_
|
||
|
|
classes_ = self.classes_
|
||
|
|
class_prior_ = self.class_prior_
|
||
|
|
constant = self.constant
|
||
|
|
if self.n_outputs_ == 1:
|
||
|
|
# Get same type even for self.n_outputs_ == 1
|
||
|
|
n_classes_ = [n_classes_]
|
||
|
|
classes_ = [classes_]
|
||
|
|
class_prior_ = [class_prior_]
|
||
|
|
constant = [constant]
|
||
|
|
|
||
|
|
P = []
|
||
|
|
for k in range(self.n_outputs_):
|
||
|
|
if self._strategy == "most_frequent":
|
||
|
|
ind = class_prior_[k].argmax()
|
||
|
|
out = np.zeros((n_samples, n_classes_[k]), dtype=np.float64)
|
||
|
|
out[:, ind] = 1.0
|
||
|
|
elif self._strategy == "prior":
|
||
|
|
out = np.ones((n_samples, 1)) * class_prior_[k]
|
||
|
|
|
||
|
|
elif self._strategy == "stratified":
|
||
|
|
out = rs.multinomial(1, class_prior_[k], size=n_samples)
|
||
|
|
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_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.
|
||
|
|
"""
|
||
|
|
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)
|