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/ dummy.py
# 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
import numpy as np
import scipy.sparse as sp
from .base import BaseEstimator, ClassifierMixin, RegressorMixin
from .base import MultiOutputMixin
from .utils import check_random_state
from .utils.validation import _num_samples
from .utils.validation import check_array
from .utils.validation import check_consistent_length
from .utils.validation import check_is_fitted, _check_sample_weight
from .utils.random import _random_choice_csc
from .utils.stats import _weighted_percentile
from .utils.multiclass import class_distribution
from .utils import deprecated
class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
"""
DummyClassifier is a classifier that makes predictions using simple rules.
This classifier is useful as a simple baseline to compare with other
(real) classifiers. Do not use it for real problems.
Read more in the :ref:`User Guide <dummy_estimators>`.
.. versionadded:: 0.13
Parameters
----------
strategy : str, default="stratified"
Strategy to use to generate predictions.
* "stratified": generates predictions by respecting the training
set's class distribution.
* "most_frequent": always predicts the most frequent label in the
training set.
* "prior": always predicts the class that maximizes the class prior
(like "most_frequent") and ``predict_proba`` returns the class prior.
* "uniform": generates predictions uniformly at random.
* "constant": always predicts a constant label that is provided by
the user. This is useful for metrics that evaluate a non-majority
class
.. versionchanged:: 0.22
The default value of `strategy` will change to "prior" in version
0.24. Starting from version 0.22, a warning will be raised if
`strategy` is not explicity set.
.. versionadded:: 0.17
Dummy Classifier now supports prior fitting strategy using
parameter *prior*.
random_state : int, RandomState instance or None, optional, default=None
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
constant : int or str or array-like of shape (n_outputs,)
The explicit constant as predicted by the "constant" strategy. This
parameter is useful only for the "constant" strategy.
Attributes
----------
classes_ : array or list of array of shape (n_classes,)
Class labels for each output.
n_classes_ : array or list of array of shape (n_classes,)
Number of label for each output.
class_prior_ : array or list of array of shape (n_classes,)
Probability of each class 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.
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
"""
def __init__(self, strategy="warn", random_state=None,
constant=None):
self.strategy = strategy
self.random_state = random_state
self.constant = constant
def fit(self, X, y, sample_weight=None):
"""Fit the random classifier.
Parameters
----------
X : {array-like, object with finite length or shape}
Training data, requires length = n_samples
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
"""
allowed_strategies = ("most_frequent", "stratified", "uniform",
"constant", "prior")
# TODO: Remove in 0.24
if self.strategy == "warn":
warnings.warn("The default value of strategy will change from "
"stratified to prior in 0.24.", FutureWarning)
self._strategy = "stratified"
elif self.strategy not in allowed_strategies:
raise ValueError("Unknown strategy type: %s, expected one of %s."
% (self.strategy, allowed_strategies))
else:
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, sample_weight)
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, list(self.classes_[k])))
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, object with finite length or shape}
Training data, requires length = n_samples
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, object with finite length or shape}
Training data, requires length = n_samples
Returns
-------
P : array-like or list of array-lke of shape (n_samples, n_classes)
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, requires length = n_samples
Returns
-------
P : array-like or list of array-like of shape (n_samples, n_classes)
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}
def score(self, X, y, sample_weight=None):
"""Returns 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, None}
Test samples with shape = (n_samples, n_features) or
None. 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) wrt. y.
"""
if X is None:
X = np.zeros(shape=(len(y), 1))
return super().score(X, y, sample_weight)
@deprecated(
"The outputs_2d_ attribute is deprecated in version 0.22 "
"and will be removed in version 0.24. It is equivalent to "
"n_outputs_ > 1."
)
@property
def outputs_2d_(self):
return self.n_outputs_ != 1
class DummyRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator):
"""
DummyRegressor is a 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 : str
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,)
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]
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_ : array, 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.
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
"""
def __init__(self, strategy="mean", constant=None, quantile=None):
self.strategy = strategy
self.constant = constant
self.quantile = quantile
def fit(self, X, y, sample_weight=None):
"""Fit the random regressor.
Parameters
----------
X : {array-like, object with finite length or shape}
Training data, requires length = n_samples
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
"""
allowed_strategies = ("mean", "median", "quantile", "constant")
if self.strategy not in allowed_strategies:
raise ValueError("Unknown strategy type: %s, expected one of %s."
% (self.strategy, allowed_strategies))
y = check_array(y, ensure_2d=False)
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.)
for k in range(self.n_outputs_)]
elif self.strategy == "quantile":
if self.quantile is None or not np.isscalar(self.quantile):
raise ValueError("Quantile must be a scalar in the range "
"[0.0, 1.0], but got %s." % self.quantile)
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_ = self.constant
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, object with finite length or shape}
Training data, requires length = n_samples
return_std : boolean, optional
Whether to return the standard deviation of posterior prediction.
All zeros in this case.
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):
"""Returns 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 : {array-like, None}
Test samples with shape = (n_samples, n_features) or None.
For some estimators this may be a
precomputed kernel matrix instead, shape = (n_samples,
n_samples_fitted], where n_samples_fitted is the number of
samples used in the fitting for the estimator.
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) wrt. y.
"""
if X is None:
X = np.zeros(shape=(len(y), 1))
return super().score(X, y, sample_weight)
@deprecated(
"The outputs_2d_ attribute is deprecated in version 0.22 "
"and will be removed in version 0.24. It is equivalent to "
"n_outputs_ > 1."
)
@property
def outputs_2d_(self):
return self.n_outputs_ != 1