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ai-content-maker/.venv/Lib/site-packages/sklearn/metrics/_regression.py

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"""Metrics to assess performance on regression task.
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better.
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better.
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Arnaud Joly <a.joly@ulg.ac.be>
# Jochen Wersdorfer <jochen@wersdoerfer.de>
# Lars Buitinck
# Joel Nothman <joel.nothman@gmail.com>
# Karan Desai <karandesai281196@gmail.com>
# Noel Dawe <noel@dawe.me>
# Manoj Kumar <manojkumarsivaraj334@gmail.com>
# Michael Eickenberg <michael.eickenberg@gmail.com>
# Konstantin Shmelkov <konstantin.shmelkov@polytechnique.edu>
# Christian Lorentzen <lorentzen.ch@gmail.com>
# Ashutosh Hathidara <ashutoshhathidara98@gmail.com>
# Uttam kumar <bajiraouttamsinha@gmail.com>
# Sylvain Marie <sylvain.marie@se.com>
# Ohad Michel <ohadmich@gmail.com>
# Alejandro Martin Gil <almagil98@gmail.com>
# License: BSD 3 clause
import warnings
from numbers import Real
import numpy as np
from scipy.special import xlogy
from ..exceptions import UndefinedMetricWarning
from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
from ..utils.stats import _weighted_percentile
from ..utils.validation import (
_check_sample_weight,
_num_samples,
check_array,
check_consistent_length,
column_or_1d,
)
__ALL__ = [
"max_error",
"mean_absolute_error",
"mean_squared_error",
"mean_squared_log_error",
"median_absolute_error",
"mean_absolute_percentage_error",
"mean_pinball_loss",
"r2_score",
"root_mean_squared_log_error",
"root_mean_squared_error",
"explained_variance_score",
"mean_tweedie_deviance",
"mean_poisson_deviance",
"mean_gamma_deviance",
"d2_tweedie_score",
"d2_pinball_score",
"d2_absolute_error_score",
]
def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric"):
"""Check that y_true and y_pred belong to the same regression task.
Parameters
----------
y_true : array-like
y_pred : array-like
multioutput : array-like or string in ['raw_values', uniform_average',
'variance_weighted'] or None
None is accepted due to backward compatibility of r2_score().
dtype : str or list, default="numeric"
the dtype argument passed to check_array.
Returns
-------
type_true : one of {'continuous', continuous-multioutput'}
The type of the true target data, as output by
'utils.multiclass.type_of_target'.
y_true : array-like of shape (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples, n_outputs)
Estimated target values.
multioutput : array-like of shape (n_outputs) or string in ['raw_values',
uniform_average', 'variance_weighted'] or None
Custom output weights if ``multioutput`` is array-like or
just the corresponding argument if ``multioutput`` is a
correct keyword.
"""
check_consistent_length(y_true, y_pred)
y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)
if y_true.ndim == 1:
y_true = y_true.reshape((-1, 1))
if y_pred.ndim == 1:
y_pred = y_pred.reshape((-1, 1))
if y_true.shape[1] != y_pred.shape[1]:
raise ValueError(
"y_true and y_pred have different number of output ({0}!={1})".format(
y_true.shape[1], y_pred.shape[1]
)
)
n_outputs = y_true.shape[1]
allowed_multioutput_str = ("raw_values", "uniform_average", "variance_weighted")
if isinstance(multioutput, str):
if multioutput not in allowed_multioutput_str:
raise ValueError(
"Allowed 'multioutput' string values are {}. "
"You provided multioutput={!r}".format(
allowed_multioutput_str, multioutput
)
)
elif multioutput is not None:
multioutput = check_array(multioutput, ensure_2d=False)
if n_outputs == 1:
raise ValueError("Custom weights are useful only in multi-output cases.")
elif n_outputs != len(multioutput):
raise ValueError(
"There must be equally many custom weights (%d) as outputs (%d)."
% (len(multioutput), n_outputs)
)
y_type = "continuous" if n_outputs == 1 else "continuous-multioutput"
return y_type, y_true, y_pred, multioutput
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
},
prefer_skip_nested_validation=True,
)
def mean_absolute_error(
y_true, y_pred, *, sample_weight=None, multioutput="uniform_average"
):
"""Mean absolute error regression loss.
Read more in the :ref:`User Guide <mean_absolute_error>`.
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
(n_outputs,), default='uniform_average'
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
If multioutput is 'raw_values', then mean absolute error is returned
for each output separately.
If multioutput is 'uniform_average' or an ndarray of weights, then the
weighted average of all output errors is returned.
MAE output is non-negative floating point. The best value is 0.0.
Examples
--------
>>> from sklearn.metrics import mean_absolute_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_absolute_error(y_true, y_pred)
0.5
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_error(y_true, y_pred)
0.75
>>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
array([0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.85...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
check_consistent_length(y_true, y_pred, sample_weight)
output_errors = np.average(np.abs(y_pred - y_true), weights=sample_weight, axis=0)
if isinstance(multioutput, str):
if multioutput == "raw_values":
return output_errors
elif multioutput == "uniform_average":
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"alpha": [Interval(Real, 0, 1, closed="both")],
"multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
},
prefer_skip_nested_validation=True,
)
def mean_pinball_loss(
y_true, y_pred, *, sample_weight=None, alpha=0.5, multioutput="uniform_average"
):
"""Pinball loss for quantile regression.
Read more in the :ref:`User Guide <pinball_loss>`.
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
alpha : float, slope of the pinball loss, default=0.5,
This loss is equivalent to :ref:`mean_absolute_error` when `alpha=0.5`,
`alpha=0.95` is minimized by estimators of the 95th percentile.
multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
(n_outputs,), default='uniform_average'
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
If multioutput is 'raw_values', then mean absolute error is returned
for each output separately.
If multioutput is 'uniform_average' or an ndarray of weights, then the
weighted average of all output errors is returned.
The pinball loss output is a non-negative floating point. The best
value is 0.0.
Examples
--------
>>> from sklearn.metrics import mean_pinball_loss
>>> y_true = [1, 2, 3]
>>> mean_pinball_loss(y_true, [0, 2, 3], alpha=0.1)
0.03...
>>> mean_pinball_loss(y_true, [1, 2, 4], alpha=0.1)
0.3...
>>> mean_pinball_loss(y_true, [0, 2, 3], alpha=0.9)
0.3...
>>> mean_pinball_loss(y_true, [1, 2, 4], alpha=0.9)
0.03...
>>> mean_pinball_loss(y_true, y_true, alpha=0.1)
0.0
>>> mean_pinball_loss(y_true, y_true, alpha=0.9)
0.0
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
check_consistent_length(y_true, y_pred, sample_weight)
diff = y_true - y_pred
sign = (diff >= 0).astype(diff.dtype)
loss = alpha * sign * diff - (1 - alpha) * (1 - sign) * diff
output_errors = np.average(loss, weights=sample_weight, axis=0)
if isinstance(multioutput, str) and multioutput == "raw_values":
return output_errors
if isinstance(multioutput, str) and multioutput == "uniform_average":
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
},
prefer_skip_nested_validation=True,
)
def mean_absolute_percentage_error(
y_true, y_pred, *, sample_weight=None, multioutput="uniform_average"
):
"""Mean absolute percentage error (MAPE) regression loss.
Note here that the output is not a percentage in the range [0, 100]
and a value of 100 does not mean 100% but 1e2. Furthermore, the output
can be arbitrarily high when `y_true` is small (which is specific to the
metric) or when `abs(y_true - y_pred)` is large (which is common for most
regression metrics). Read more in the
:ref:`User Guide <mean_absolute_percentage_error>`.
.. versionadded:: 0.24
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average'} or array-like
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
If input is list then the shape must be (n_outputs,).
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
If multioutput is 'raw_values', then mean absolute percentage error
is returned for each output separately.
If multioutput is 'uniform_average' or an ndarray of weights, then the
weighted average of all output errors is returned.
MAPE output is non-negative floating point. The best value is 0.0.
But note that bad predictions can lead to arbitrarily large
MAPE values, especially if some `y_true` values are very close to zero.
Note that we return a large value instead of `inf` when `y_true` is zero.
Examples
--------
>>> from sklearn.metrics import mean_absolute_percentage_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_absolute_percentage_error(y_true, y_pred)
0.3273...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_percentage_error(y_true, y_pred)
0.5515...
>>> mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.6198...
>>> # the value when some element of the y_true is zero is arbitrarily high because
>>> # of the division by epsilon
>>> y_true = [1., 0., 2.4, 7.]
>>> y_pred = [1.2, 0.1, 2.4, 8.]
>>> mean_absolute_percentage_error(y_true, y_pred)
112589990684262.48
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
check_consistent_length(y_true, y_pred, sample_weight)
epsilon = np.finfo(np.float64).eps
mape = np.abs(y_pred - y_true) / np.maximum(np.abs(y_true), epsilon)
output_errors = np.average(mape, weights=sample_weight, axis=0)
if isinstance(multioutput, str):
if multioutput == "raw_values":
return output_errors
elif multioutput == "uniform_average":
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
"squared": [Hidden(StrOptions({"deprecated"})), "boolean"],
},
prefer_skip_nested_validation=True,
)
def mean_squared_error(
y_true,
y_pred,
*,
sample_weight=None,
multioutput="uniform_average",
squared="deprecated",
):
"""Mean squared error regression loss.
Read more in the :ref:`User Guide <mean_squared_error>`.
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
(n_outputs,), default='uniform_average'
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSE value, if False returns RMSE value.
.. deprecated:: 1.4
`squared` is deprecated in 1.4 and will be removed in 1.6.
Use :func:`~sklearn.metrics.root_mean_squared_error`
instead to calculate the root mean squared error.
Returns
-------
loss : float or ndarray of floats
A non-negative floating point value (the best value is 0.0), or an
array of floating point values, one for each individual target.
Examples
--------
>>> from sklearn.metrics import mean_squared_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> mean_squared_error(y_true, y_pred)
0.375
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> mean_squared_error(y_true, y_pred)
0.708...
>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
array([0.41666667, 1. ])
>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.825...
"""
# TODO(1.6): remove
if squared != "deprecated":
warnings.warn(
(
"'squared' is deprecated in version 1.4 and "
"will be removed in 1.6. To calculate the "
"root mean squared error, use the function"
"'root_mean_squared_error'."
),
FutureWarning,
)
if not squared:
return root_mean_squared_error(
y_true, y_pred, sample_weight=sample_weight, multioutput=multioutput
)
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
check_consistent_length(y_true, y_pred, sample_weight)
output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight)
if isinstance(multioutput, str):
if multioutput == "raw_values":
return output_errors
elif multioutput == "uniform_average":
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
},
prefer_skip_nested_validation=True,
)
def root_mean_squared_error(
y_true, y_pred, *, sample_weight=None, multioutput="uniform_average"
):
"""Root mean squared error regression loss.
Read more in the :ref:`User Guide <mean_squared_error>`.
.. versionadded:: 1.4
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
(n_outputs,), default='uniform_average'
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
A non-negative floating point value (the best value is 0.0), or an
array of floating point values, one for each individual target.
Examples
--------
>>> from sklearn.metrics import root_mean_squared_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> root_mean_squared_error(y_true, y_pred)
0.612...
>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
>>> root_mean_squared_error(y_true, y_pred)
0.822...
"""
output_errors = np.sqrt(
mean_squared_error(
y_true, y_pred, sample_weight=sample_weight, multioutput="raw_values"
)
)
if isinstance(multioutput, str):
if multioutput == "raw_values":
return output_errors
elif multioutput == "uniform_average":
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
"squared": [Hidden(StrOptions({"deprecated"})), "boolean"],
},
prefer_skip_nested_validation=True,
)
def mean_squared_log_error(
y_true,
y_pred,
*,
sample_weight=None,
multioutput="uniform_average",
squared="deprecated",
):
"""Mean squared logarithmic error regression loss.
Read more in the :ref:`User Guide <mean_squared_log_error>`.
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
(n_outputs,), default='uniform_average'
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors when the input is of multioutput
format.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
squared : bool, default=True
If True returns MSLE (mean squared log error) value.
If False returns RMSLE (root mean squared log error) value.
.. deprecated:: 1.4
`squared` is deprecated in 1.4 and will be removed in 1.6.
Use :func:`~sklearn.metrics.root_mean_squared_log_error`
instead to calculate the root mean squared logarithmic error.
Returns
-------
loss : float or ndarray of floats
A non-negative floating point value (the best value is 0.0), or an
array of floating point values, one for each individual target.
Examples
--------
>>> from sklearn.metrics import mean_squared_log_error
>>> y_true = [3, 5, 2.5, 7]
>>> y_pred = [2.5, 5, 4, 8]
>>> mean_squared_log_error(y_true, y_pred)
0.039...
>>> y_true = [[0.5, 1], [1, 2], [7, 6]]
>>> y_pred = [[0.5, 2], [1, 2.5], [8, 8]]
>>> mean_squared_log_error(y_true, y_pred)
0.044...
>>> mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
array([0.00462428, 0.08377444])
>>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.060...
"""
# TODO(1.6): remove
if squared != "deprecated":
warnings.warn(
(
"'squared' is deprecated in version 1.4 and "
"will be removed in 1.6. To calculate the "
"root mean squared logarithmic error, use the function"
"'root_mean_squared_log_error'."
),
FutureWarning,
)
if not squared:
return root_mean_squared_log_error(
y_true, y_pred, sample_weight=sample_weight, multioutput=multioutput
)
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
check_consistent_length(y_true, y_pred, sample_weight)
if (y_true < 0).any() or (y_pred < 0).any():
raise ValueError(
"Mean Squared Logarithmic Error cannot be used when "
"targets contain negative values."
)
return mean_squared_error(
np.log1p(y_true),
np.log1p(y_pred),
sample_weight=sample_weight,
multioutput=multioutput,
)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
},
prefer_skip_nested_validation=True,
)
def root_mean_squared_log_error(
y_true, y_pred, *, sample_weight=None, multioutput="uniform_average"
):
"""Root mean squared logarithmic error regression loss.
Read more in the :ref:`User Guide <mean_squared_log_error>`.
.. versionadded:: 1.4
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
(n_outputs,), default='uniform_average'
Defines aggregating of multiple output values.
Array-like value defines weights used to average errors.
'raw_values' :
Returns a full set of errors when the input is of multioutput
format.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
Returns
-------
loss : float or ndarray of floats
A non-negative floating point value (the best value is 0.0), or an
array of floating point values, one for each individual target.
Examples
--------
>>> from sklearn.metrics import root_mean_squared_log_error
>>> y_true = [3, 5, 2.5, 7]
>>> y_pred = [2.5, 5, 4, 8]
>>> root_mean_squared_log_error(y_true, y_pred)
0.199...
"""
_, y_true, y_pred, multioutput = _check_reg_targets(y_true, y_pred, multioutput)
check_consistent_length(y_true, y_pred, sample_weight)
if (y_true < 0).any() or (y_pred < 0).any():
raise ValueError(
"Root Mean Squared Logarithmic Error cannot be used when "
"targets contain negative values."
)
return root_mean_squared_error(
np.log1p(y_true),
np.log1p(y_pred),
sample_weight=sample_weight,
multioutput=multioutput,
)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"multioutput": [StrOptions({"raw_values", "uniform_average"}), "array-like"],
"sample_weight": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def median_absolute_error(
y_true, y_pred, *, multioutput="uniform_average", sample_weight=None
):
"""Median absolute error regression loss.
Median absolute error output is non-negative floating point. The best value
is 0.0. Read more in the :ref:`User Guide <median_absolute_error>`.
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
(n_outputs,), default='uniform_average'
Defines aggregating of multiple output values. Array-like value defines
weights used to average errors.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Errors of all outputs are averaged with uniform weight.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
.. versionadded:: 0.24
Returns
-------
loss : float or ndarray of floats
If multioutput is 'raw_values', then mean absolute error is returned
for each output separately.
If multioutput is 'uniform_average' or an ndarray of weights, then the
weighted average of all output errors is returned.
Examples
--------
>>> from sklearn.metrics import median_absolute_error
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> median_absolute_error(y_true, y_pred)
0.5
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> median_absolute_error(y_true, y_pred)
0.75
>>> median_absolute_error(y_true, y_pred, multioutput='raw_values')
array([0.5, 1. ])
>>> median_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.85
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
if sample_weight is None:
output_errors = np.median(np.abs(y_pred - y_true), axis=0)
else:
sample_weight = _check_sample_weight(sample_weight, y_pred)
output_errors = _weighted_percentile(
np.abs(y_pred - y_true), sample_weight=sample_weight
)
if isinstance(multioutput, str):
if multioutput == "raw_values":
return output_errors
elif multioutput == "uniform_average":
# pass None as weights to np.average: uniform mean
multioutput = None
return np.average(output_errors, weights=multioutput)
def _assemble_r2_explained_variance(
numerator, denominator, n_outputs, multioutput, force_finite
):
"""Common part used by explained variance score and :math:`R^2` score."""
nonzero_denominator = denominator != 0
if not force_finite:
# Standard formula, that may lead to NaN or -Inf
output_scores = 1 - (numerator / denominator)
else:
nonzero_numerator = numerator != 0
# Default = Zero Numerator = perfect predictions. Set to 1.0
# (note: even if denominator is zero, thus avoiding NaN scores)
output_scores = np.ones([n_outputs])
# Non-zero Numerator and Non-zero Denominator: use the formula
valid_score = nonzero_denominator & nonzero_numerator
output_scores[valid_score] = 1 - (
numerator[valid_score] / denominator[valid_score]
)
# Non-zero Numerator and Zero Denominator:
# arbitrary set to 0.0 to avoid -inf scores
output_scores[nonzero_numerator & ~nonzero_denominator] = 0.0
if isinstance(multioutput, str):
if multioutput == "raw_values":
# return scores individually
return output_scores
elif multioutput == "uniform_average":
# Passing None as weights to np.average results is uniform mean
avg_weights = None
elif multioutput == "variance_weighted":
avg_weights = denominator
if not np.any(nonzero_denominator):
# All weights are zero, np.average would raise a ZeroDiv error.
# This only happens when all y are constant (or 1-element long)
# Since weights are all equal, fall back to uniform weights.
avg_weights = None
else:
avg_weights = multioutput
return np.average(output_scores, weights=avg_weights)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"multioutput": [
StrOptions({"raw_values", "uniform_average", "variance_weighted"}),
"array-like",
],
"force_finite": ["boolean"],
},
prefer_skip_nested_validation=True,
)
def explained_variance_score(
y_true,
y_pred,
*,
sample_weight=None,
multioutput="uniform_average",
force_finite=True,
):
"""Explained variance regression score function.
Best possible score is 1.0, lower values are worse.
In the particular case when ``y_true`` is constant, the explained variance
score is not finite: it is either ``NaN`` (perfect predictions) or
``-Inf`` (imperfect predictions). To prevent such non-finite numbers to
pollute higher-level experiments such as a grid search cross-validation,
by default these cases are replaced with 1.0 (perfect predictions) or 0.0
(imperfect predictions) respectively. If ``force_finite``
is set to ``False``, this score falls back on the original :math:`R^2`
definition.
.. note::
The Explained Variance score is similar to the
:func:`R^2 score <r2_score>`, with the notable difference that it
does not account for systematic offsets in the prediction. Most often
the :func:`R^2 score <r2_score>` should be preferred.
Read more in the :ref:`User Guide <explained_variance_score>`.
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average', 'variance_weighted'} or \
array-like of shape (n_outputs,), default='uniform_average'
Defines aggregating of multiple output scores.
Array-like value defines weights used to average scores.
'raw_values' :
Returns a full set of scores in case of multioutput input.
'uniform_average' :
Scores of all outputs are averaged with uniform weight.
'variance_weighted' :
Scores of all outputs are averaged, weighted by the variances
of each individual output.
force_finite : bool, default=True
Flag indicating if ``NaN`` and ``-Inf`` scores resulting from constant
data should be replaced with real numbers (``1.0`` if prediction is
perfect, ``0.0`` otherwise). Default is ``True``, a convenient setting
for hyperparameters' search procedures (e.g. grid search
cross-validation).
.. versionadded:: 1.1
Returns
-------
score : float or ndarray of floats
The explained variance or ndarray if 'multioutput' is 'raw_values'.
See Also
--------
r2_score :
Similar metric, but accounting for systematic offsets in
prediction.
Notes
-----
This is not a symmetric function.
Examples
--------
>>> from sklearn.metrics import explained_variance_score
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> explained_variance_score(y_true, y_pred)
0.957...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> explained_variance_score(y_true, y_pred, multioutput='uniform_average')
0.983...
>>> y_true = [-2, -2, -2]
>>> y_pred = [-2, -2, -2]
>>> explained_variance_score(y_true, y_pred)
1.0
>>> explained_variance_score(y_true, y_pred, force_finite=False)
nan
>>> y_true = [-2, -2, -2]
>>> y_pred = [-2, -2, -2 + 1e-8]
>>> explained_variance_score(y_true, y_pred)
0.0
>>> explained_variance_score(y_true, y_pred, force_finite=False)
-inf
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
check_consistent_length(y_true, y_pred, sample_weight)
y_diff_avg = np.average(y_true - y_pred, weights=sample_weight, axis=0)
numerator = np.average(
(y_true - y_pred - y_diff_avg) ** 2, weights=sample_weight, axis=0
)
y_true_avg = np.average(y_true, weights=sample_weight, axis=0)
denominator = np.average((y_true - y_true_avg) ** 2, weights=sample_weight, axis=0)
return _assemble_r2_explained_variance(
numerator=numerator,
denominator=denominator,
n_outputs=y_true.shape[1],
multioutput=multioutput,
force_finite=force_finite,
)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"multioutput": [
StrOptions({"raw_values", "uniform_average", "variance_weighted"}),
"array-like",
None,
],
"force_finite": ["boolean"],
},
prefer_skip_nested_validation=True,
)
def r2_score(
y_true,
y_pred,
*,
sample_weight=None,
multioutput="uniform_average",
force_finite=True,
):
""":math:`R^2` (coefficient of determination) regression score function.
Best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). In the general case when the true y is
non-constant, a constant model that always predicts the average y
disregarding the input features would get a :math:`R^2` score of 0.0.
In the particular case when ``y_true`` is constant, the :math:`R^2` score
is not finite: it is either ``NaN`` (perfect predictions) or ``-Inf``
(imperfect predictions). To prevent such non-finite numbers to pollute
higher-level experiments such as a grid search cross-validation, by default
these cases are replaced with 1.0 (perfect predictions) or 0.0 (imperfect
predictions) respectively. You can set ``force_finite`` to ``False`` to
prevent this fix from happening.
Note: when the prediction residuals have zero mean, the :math:`R^2` score
is identical to the
:func:`Explained Variance score <explained_variance_score>`.
Read more in the :ref:`User Guide <r2_score>`.
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average', 'variance_weighted'}, \
array-like of shape (n_outputs,) or None, default='uniform_average'
Defines aggregating of multiple output scores.
Array-like value defines weights used to average scores.
Default is "uniform_average".
'raw_values' :
Returns a full set of scores in case of multioutput input.
'uniform_average' :
Scores of all outputs are averaged with uniform weight.
'variance_weighted' :
Scores of all outputs are averaged, weighted by the variances
of each individual output.
.. versionchanged:: 0.19
Default value of multioutput is 'uniform_average'.
force_finite : bool, default=True
Flag indicating if ``NaN`` and ``-Inf`` scores resulting from constant
data should be replaced with real numbers (``1.0`` if prediction is
perfect, ``0.0`` otherwise). Default is ``True``, a convenient setting
for hyperparameters' search procedures (e.g. grid search
cross-validation).
.. versionadded:: 1.1
Returns
-------
z : float or ndarray of floats
The :math:`R^2` score or ndarray of scores if 'multioutput' is
'raw_values'.
Notes
-----
This is not a symmetric function.
Unlike most other scores, :math:`R^2` score may be negative (it need not
actually be the square of a quantity R).
This metric is not well-defined for single samples and will return a NaN
value if n_samples is less than two.
References
----------
.. [1] `Wikipedia entry on the Coefficient of determination
<https://en.wikipedia.org/wiki/Coefficient_of_determination>`_
Examples
--------
>>> from sklearn.metrics import r2_score
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> r2_score(y_true, y_pred)
0.948...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> r2_score(y_true, y_pred,
... multioutput='variance_weighted')
0.938...
>>> y_true = [1, 2, 3]
>>> y_pred = [1, 2, 3]
>>> r2_score(y_true, y_pred)
1.0
>>> y_true = [1, 2, 3]
>>> y_pred = [2, 2, 2]
>>> r2_score(y_true, y_pred)
0.0
>>> y_true = [1, 2, 3]
>>> y_pred = [3, 2, 1]
>>> r2_score(y_true, y_pred)
-3.0
>>> y_true = [-2, -2, -2]
>>> y_pred = [-2, -2, -2]
>>> r2_score(y_true, y_pred)
1.0
>>> r2_score(y_true, y_pred, force_finite=False)
nan
>>> y_true = [-2, -2, -2]
>>> y_pred = [-2, -2, -2 + 1e-8]
>>> r2_score(y_true, y_pred)
0.0
>>> r2_score(y_true, y_pred, force_finite=False)
-inf
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
check_consistent_length(y_true, y_pred, sample_weight)
if _num_samples(y_pred) < 2:
msg = "R^2 score is not well-defined with less than two samples."
warnings.warn(msg, UndefinedMetricWarning)
return float("nan")
if sample_weight is not None:
sample_weight = column_or_1d(sample_weight)
weight = sample_weight[:, np.newaxis]
else:
weight = 1.0
numerator = (weight * (y_true - y_pred) ** 2).sum(axis=0, dtype=np.float64)
denominator = (
weight * (y_true - np.average(y_true, axis=0, weights=sample_weight)) ** 2
).sum(axis=0, dtype=np.float64)
return _assemble_r2_explained_variance(
numerator=numerator,
denominator=denominator,
n_outputs=y_true.shape[1],
multioutput=multioutput,
force_finite=force_finite,
)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
},
prefer_skip_nested_validation=True,
)
def max_error(y_true, y_pred):
"""
The max_error metric calculates the maximum residual error.
Read more in the :ref:`User Guide <max_error>`.
Parameters
----------
y_true : array-like of shape (n_samples,)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,)
Estimated target values.
Returns
-------
max_error : float
A positive floating point value (the best value is 0.0).
Examples
--------
>>> from sklearn.metrics import max_error
>>> y_true = [3, 2, 7, 1]
>>> y_pred = [4, 2, 7, 1]
>>> max_error(y_true, y_pred)
1
"""
y_type, y_true, y_pred, _ = _check_reg_targets(y_true, y_pred, None)
if y_type == "continuous-multioutput":
raise ValueError("Multioutput not supported in max_error")
return np.max(np.abs(y_true - y_pred))
def _mean_tweedie_deviance(y_true, y_pred, sample_weight, power):
"""Mean Tweedie deviance regression loss."""
p = power
if p < 0:
# 'Extreme stable', y any real number, y_pred > 0
dev = 2 * (
np.power(np.maximum(y_true, 0), 2 - p) / ((1 - p) * (2 - p))
- y_true * np.power(y_pred, 1 - p) / (1 - p)
+ np.power(y_pred, 2 - p) / (2 - p)
)
elif p == 0:
# Normal distribution, y and y_pred any real number
dev = (y_true - y_pred) ** 2
elif p == 1:
# Poisson distribution
dev = 2 * (xlogy(y_true, y_true / y_pred) - y_true + y_pred)
elif p == 2:
# Gamma distribution
dev = 2 * (np.log(y_pred / y_true) + y_true / y_pred - 1)
else:
dev = 2 * (
np.power(y_true, 2 - p) / ((1 - p) * (2 - p))
- y_true * np.power(y_pred, 1 - p) / (1 - p)
+ np.power(y_pred, 2 - p) / (2 - p)
)
return np.average(dev, weights=sample_weight)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"power": [
Interval(Real, None, 0, closed="right"),
Interval(Real, 1, None, closed="left"),
],
},
prefer_skip_nested_validation=True,
)
def mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0):
"""Mean Tweedie deviance regression loss.
Read more in the :ref:`User Guide <mean_tweedie_deviance>`.
Parameters
----------
y_true : array-like of shape (n_samples,)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
power : float, default=0
Tweedie power parameter. Either power <= 0 or power >= 1.
The higher `p` the less weight is given to extreme
deviations between true and predicted targets.
- power < 0: Extreme stable distribution. Requires: y_pred > 0.
- power = 0 : Normal distribution, output corresponds to
mean_squared_error. y_true and y_pred can be any real numbers.
- power = 1 : Poisson distribution. Requires: y_true >= 0 and
y_pred > 0.
- 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0
and y_pred > 0.
- power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.
- power = 3 : Inverse Gaussian distribution. Requires: y_true > 0
and y_pred > 0.
- otherwise : Positive stable distribution. Requires: y_true > 0
and y_pred > 0.
Returns
-------
loss : float
A non-negative floating point value (the best value is 0.0).
Examples
--------
>>> from sklearn.metrics import mean_tweedie_deviance
>>> y_true = [2, 0, 1, 4]
>>> y_pred = [0.5, 0.5, 2., 2.]
>>> mean_tweedie_deviance(y_true, y_pred, power=1)
1.4260...
"""
y_type, y_true, y_pred, _ = _check_reg_targets(
y_true, y_pred, None, dtype=[np.float64, np.float32]
)
if y_type == "continuous-multioutput":
raise ValueError("Multioutput not supported in mean_tweedie_deviance")
check_consistent_length(y_true, y_pred, sample_weight)
if sample_weight is not None:
sample_weight = column_or_1d(sample_weight)
sample_weight = sample_weight[:, np.newaxis]
message = f"Mean Tweedie deviance error with power={power} can only be used on "
if power < 0:
# 'Extreme stable', y any real number, y_pred > 0
if (y_pred <= 0).any():
raise ValueError(message + "strictly positive y_pred.")
elif power == 0:
# Normal, y and y_pred can be any real number
pass
elif 1 <= power < 2:
# Poisson and compound Poisson distribution, y >= 0, y_pred > 0
if (y_true < 0).any() or (y_pred <= 0).any():
raise ValueError(message + "non-negative y and strictly positive y_pred.")
elif power >= 2:
# Gamma and Extreme stable distribution, y and y_pred > 0
if (y_true <= 0).any() or (y_pred <= 0).any():
raise ValueError(message + "strictly positive y and y_pred.")
else: # pragma: nocover
# Unreachable statement
raise ValueError
return _mean_tweedie_deviance(
y_true, y_pred, sample_weight=sample_weight, power=power
)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def mean_poisson_deviance(y_true, y_pred, *, sample_weight=None):
"""Mean Poisson deviance regression loss.
Poisson deviance is equivalent to the Tweedie deviance with
the power parameter `power=1`.
Read more in the :ref:`User Guide <mean_tweedie_deviance>`.
Parameters
----------
y_true : array-like of shape (n_samples,)
Ground truth (correct) target values. Requires y_true >= 0.
y_pred : array-like of shape (n_samples,)
Estimated target values. Requires y_pred > 0.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
loss : float
A non-negative floating point value (the best value is 0.0).
Examples
--------
>>> from sklearn.metrics import mean_poisson_deviance
>>> y_true = [2, 0, 1, 4]
>>> y_pred = [0.5, 0.5, 2., 2.]
>>> mean_poisson_deviance(y_true, y_pred)
1.4260...
"""
return mean_tweedie_deviance(y_true, y_pred, sample_weight=sample_weight, power=1)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
},
prefer_skip_nested_validation=True,
)
def mean_gamma_deviance(y_true, y_pred, *, sample_weight=None):
"""Mean Gamma deviance regression loss.
Gamma deviance is equivalent to the Tweedie deviance with
the power parameter `power=2`. It is invariant to scaling of
the target variable, and measures relative errors.
Read more in the :ref:`User Guide <mean_tweedie_deviance>`.
Parameters
----------
y_true : array-like of shape (n_samples,)
Ground truth (correct) target values. Requires y_true > 0.
y_pred : array-like of shape (n_samples,)
Estimated target values. Requires y_pred > 0.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
Returns
-------
loss : float
A non-negative floating point value (the best value is 0.0).
Examples
--------
>>> from sklearn.metrics import mean_gamma_deviance
>>> y_true = [2, 0.5, 1, 4]
>>> y_pred = [0.5, 0.5, 2., 2.]
>>> mean_gamma_deviance(y_true, y_pred)
1.0568...
"""
return mean_tweedie_deviance(y_true, y_pred, sample_weight=sample_weight, power=2)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"power": [
Interval(Real, None, 0, closed="right"),
Interval(Real, 1, None, closed="left"),
],
},
prefer_skip_nested_validation=True,
)
def d2_tweedie_score(y_true, y_pred, *, sample_weight=None, power=0):
"""
:math:`D^2` regression score function, fraction of Tweedie deviance explained.
Best possible score is 1.0 and it can be negative (because the model can be
arbitrarily worse). A model that always uses the empirical mean of `y_true` as
constant prediction, disregarding the input features, gets a D^2 score of 0.0.
Read more in the :ref:`User Guide <d2_score>`.
.. versionadded:: 1.0
Parameters
----------
y_true : array-like of shape (n_samples,)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
power : float, default=0
Tweedie power parameter. Either power <= 0 or power >= 1.
The higher `p` the less weight is given to extreme
deviations between true and predicted targets.
- power < 0: Extreme stable distribution. Requires: y_pred > 0.
- power = 0 : Normal distribution, output corresponds to r2_score.
y_true and y_pred can be any real numbers.
- power = 1 : Poisson distribution. Requires: y_true >= 0 and
y_pred > 0.
- 1 < p < 2 : Compound Poisson distribution. Requires: y_true >= 0
and y_pred > 0.
- power = 2 : Gamma distribution. Requires: y_true > 0 and y_pred > 0.
- power = 3 : Inverse Gaussian distribution. Requires: y_true > 0
and y_pred > 0.
- otherwise : Positive stable distribution. Requires: y_true > 0
and y_pred > 0.
Returns
-------
z : float or ndarray of floats
The D^2 score.
Notes
-----
This is not a symmetric function.
Like R^2, D^2 score may be negative (it need not actually be the square of
a quantity D).
This metric is not well-defined for single samples and will return a NaN
value if n_samples is less than two.
References
----------
.. [1] Eq. (3.11) of Hastie, Trevor J., Robert Tibshirani and Martin J.
Wainwright. "Statistical Learning with Sparsity: The Lasso and
Generalizations." (2015). https://hastie.su.domains/StatLearnSparsity/
Examples
--------
>>> from sklearn.metrics import d2_tweedie_score
>>> y_true = [0.5, 1, 2.5, 7]
>>> y_pred = [1, 1, 5, 3.5]
>>> d2_tweedie_score(y_true, y_pred)
0.285...
>>> d2_tweedie_score(y_true, y_pred, power=1)
0.487...
>>> d2_tweedie_score(y_true, y_pred, power=2)
0.630...
>>> d2_tweedie_score(y_true, y_true, power=2)
1.0
"""
y_type, y_true, y_pred, _ = _check_reg_targets(
y_true, y_pred, None, dtype=[np.float64, np.float32]
)
if y_type == "continuous-multioutput":
raise ValueError("Multioutput not supported in d2_tweedie_score")
if _num_samples(y_pred) < 2:
msg = "D^2 score is not well-defined with less than two samples."
warnings.warn(msg, UndefinedMetricWarning)
return float("nan")
y_true, y_pred = np.squeeze(y_true), np.squeeze(y_pred)
numerator = mean_tweedie_deviance(
y_true, y_pred, sample_weight=sample_weight, power=power
)
y_avg = np.average(y_true, weights=sample_weight)
denominator = _mean_tweedie_deviance(
y_true, y_avg, sample_weight=sample_weight, power=power
)
return 1 - numerator / denominator
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"alpha": [Interval(Real, 0, 1, closed="both")],
"multioutput": [
StrOptions({"raw_values", "uniform_average"}),
"array-like",
],
},
prefer_skip_nested_validation=True,
)
def d2_pinball_score(
y_true, y_pred, *, sample_weight=None, alpha=0.5, multioutput="uniform_average"
):
"""
:math:`D^2` regression score function, fraction of pinball loss explained.
Best possible score is 1.0 and it can be negative (because the model can be
arbitrarily worse). A model that always uses the empirical alpha-quantile of
`y_true` as constant prediction, disregarding the input features,
gets a :math:`D^2` score of 0.0.
Read more in the :ref:`User Guide <d2_score>`.
.. versionadded:: 1.1
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
alpha : float, default=0.5
Slope of the pinball deviance. It determines the quantile level alpha
for which the pinball deviance and also D2 are optimal.
The default `alpha=0.5` is equivalent to `d2_absolute_error_score`.
multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
(n_outputs,), default='uniform_average'
Defines aggregating of multiple output values.
Array-like value defines weights used to average scores.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Scores of all outputs are averaged with uniform weight.
Returns
-------
score : float or ndarray of floats
The :math:`D^2` score with a pinball deviance
or ndarray of scores if `multioutput='raw_values'`.
Notes
-----
Like :math:`R^2`, :math:`D^2` score may be negative
(it need not actually be the square of a quantity D).
This metric is not well-defined for a single point and will return a NaN
value if n_samples is less than two.
References
----------
.. [1] Eq. (7) of `Koenker, Roger; Machado, José A. F. (1999).
"Goodness of Fit and Related Inference Processes for Quantile Regression"
<https://doi.org/10.1080/01621459.1999.10473882>`_
.. [2] Eq. (3.11) of Hastie, Trevor J., Robert Tibshirani and Martin J.
Wainwright. "Statistical Learning with Sparsity: The Lasso and
Generalizations." (2015). https://hastie.su.domains/StatLearnSparsity/
Examples
--------
>>> from sklearn.metrics import d2_pinball_score
>>> y_true = [1, 2, 3]
>>> y_pred = [1, 3, 3]
>>> d2_pinball_score(y_true, y_pred)
0.5
>>> d2_pinball_score(y_true, y_pred, alpha=0.9)
0.772...
>>> d2_pinball_score(y_true, y_pred, alpha=0.1)
-1.045...
>>> d2_pinball_score(y_true, y_true, alpha=0.1)
1.0
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
)
check_consistent_length(y_true, y_pred, sample_weight)
if _num_samples(y_pred) < 2:
msg = "D^2 score is not well-defined with less than two samples."
warnings.warn(msg, UndefinedMetricWarning)
return float("nan")
numerator = mean_pinball_loss(
y_true,
y_pred,
sample_weight=sample_weight,
alpha=alpha,
multioutput="raw_values",
)
if sample_weight is None:
y_quantile = np.tile(
np.percentile(y_true, q=alpha * 100, axis=0), (len(y_true), 1)
)
else:
sample_weight = _check_sample_weight(sample_weight, y_true)
y_quantile = np.tile(
_weighted_percentile(
y_true, sample_weight=sample_weight, percentile=alpha * 100
),
(len(y_true), 1),
)
denominator = mean_pinball_loss(
y_true,
y_quantile,
sample_weight=sample_weight,
alpha=alpha,
multioutput="raw_values",
)
nonzero_numerator = numerator != 0
nonzero_denominator = denominator != 0
valid_score = nonzero_numerator & nonzero_denominator
output_scores = np.ones(y_true.shape[1])
output_scores[valid_score] = 1 - (numerator[valid_score] / denominator[valid_score])
output_scores[nonzero_numerator & ~nonzero_denominator] = 0.0
if isinstance(multioutput, str):
if multioutput == "raw_values":
# return scores individually
return output_scores
else: # multioutput == "uniform_average"
# passing None as weights to np.average results in uniform mean
avg_weights = None
else:
avg_weights = multioutput
return np.average(output_scores, weights=avg_weights)
@validate_params(
{
"y_true": ["array-like"],
"y_pred": ["array-like"],
"sample_weight": ["array-like", None],
"multioutput": [
StrOptions({"raw_values", "uniform_average"}),
"array-like",
],
},
prefer_skip_nested_validation=True,
)
def d2_absolute_error_score(
y_true, y_pred, *, sample_weight=None, multioutput="uniform_average"
):
"""
:math:`D^2` regression score function, fraction of absolute error explained.
Best possible score is 1.0 and it can be negative (because the model can be
arbitrarily worse). A model that always uses the empirical median of `y_true`
as constant prediction, disregarding the input features,
gets a :math:`D^2` score of 0.0.
Read more in the :ref:`User Guide <d2_score>`.
.. versionadded:: 1.1
Parameters
----------
y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weight : array-like of shape (n_samples,), default=None
Sample weights.
multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
(n_outputs,), default='uniform_average'
Defines aggregating of multiple output values.
Array-like value defines weights used to average scores.
'raw_values' :
Returns a full set of errors in case of multioutput input.
'uniform_average' :
Scores of all outputs are averaged with uniform weight.
Returns
-------
score : float or ndarray of floats
The :math:`D^2` score with an absolute error deviance
or ndarray of scores if 'multioutput' is 'raw_values'.
Notes
-----
Like :math:`R^2`, :math:`D^2` score may be negative
(it need not actually be the square of a quantity D).
This metric is not well-defined for single samples and will return a NaN
value if n_samples is less than two.
References
----------
.. [1] Eq. (3.11) of Hastie, Trevor J., Robert Tibshirani and Martin J.
Wainwright. "Statistical Learning with Sparsity: The Lasso and
Generalizations." (2015). https://hastie.su.domains/StatLearnSparsity/
Examples
--------
>>> from sklearn.metrics import d2_absolute_error_score
>>> y_true = [3, -0.5, 2, 7]
>>> y_pred = [2.5, 0.0, 2, 8]
>>> d2_absolute_error_score(y_true, y_pred)
0.764...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> d2_absolute_error_score(y_true, y_pred, multioutput='uniform_average')
0.691...
>>> d2_absolute_error_score(y_true, y_pred, multioutput='raw_values')
array([0.8125 , 0.57142857])
>>> y_true = [1, 2, 3]
>>> y_pred = [1, 2, 3]
>>> d2_absolute_error_score(y_true, y_pred)
1.0
>>> y_true = [1, 2, 3]
>>> y_pred = [2, 2, 2]
>>> d2_absolute_error_score(y_true, y_pred)
0.0
>>> y_true = [1, 2, 3]
>>> y_pred = [3, 2, 1]
>>> d2_absolute_error_score(y_true, y_pred)
-1.0
"""
return d2_pinball_score(
y_true, y_pred, sample_weight=sample_weight, alpha=0.5, multioutput=multioutput
)
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