899 lines
31 KiB
Python
899 lines
31 KiB
Python
"""Metrics to assess performance on regression task.
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Functions named as ``*_score`` return a scalar value to maximize: the higher
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the better.
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Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
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the lower the better.
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"""
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# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
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# Mathieu Blondel <mathieu@mblondel.org>
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# Olivier Grisel <olivier.grisel@ensta.org>
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# Arnaud Joly <a.joly@ulg.ac.be>
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# Jochen Wersdorfer <jochen@wersdoerfer.de>
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# Lars Buitinck
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# Joel Nothman <joel.nothman@gmail.com>
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# Karan Desai <karandesai281196@gmail.com>
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# Noel Dawe <noel@dawe.me>
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# Manoj Kumar <manojkumarsivaraj334@gmail.com>
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# Michael Eickenberg <michael.eickenberg@gmail.com>
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# Konstantin Shmelkov <konstantin.shmelkov@polytechnique.edu>
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# Christian Lorentzen <lorentzen.ch@googlemail.com>
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# Ashutosh Hathidara <ashutoshhathidara98@gmail.com>
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# License: BSD 3 clause
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import numpy as np
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import warnings
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from .._loss.glm_distribution import TweedieDistribution
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from ..utils.validation import (check_array, check_consistent_length,
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_num_samples)
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from ..utils.validation import column_or_1d
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from ..utils.validation import _deprecate_positional_args
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from ..utils.validation import _check_sample_weight
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from ..utils.stats import _weighted_percentile
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from ..exceptions import UndefinedMetricWarning
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__ALL__ = [
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"max_error",
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"mean_absolute_error",
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"mean_squared_error",
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"mean_squared_log_error",
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"median_absolute_error",
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"mean_absolute_percentage_error",
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"r2_score",
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"explained_variance_score",
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"mean_tweedie_deviance",
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"mean_poisson_deviance",
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"mean_gamma_deviance",
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]
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def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric"):
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"""Check that y_true and y_pred belong to the same regression task.
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Parameters
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----------
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y_true : array-like
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y_pred : array-like
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multioutput : array-like or string in ['raw_values', uniform_average',
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'variance_weighted'] or None
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None is accepted due to backward compatibility of r2_score().
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Returns
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-------
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type_true : one of {'continuous', continuous-multioutput'}
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The type of the true target data, as output by
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'utils.multiclass.type_of_target'.
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y_true : array-like of shape (n_samples, n_outputs)
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Ground truth (correct) target values.
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y_pred : array-like of shape (n_samples, n_outputs)
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Estimated target values.
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multioutput : array-like of shape (n_outputs) or string in ['raw_values',
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uniform_average', 'variance_weighted'] or None
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Custom output weights if ``multioutput`` is array-like or
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just the corresponding argument if ``multioutput`` is a
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correct keyword.
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dtype : str or list, default="numeric"
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the dtype argument passed to check_array.
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"""
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check_consistent_length(y_true, y_pred)
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y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
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y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)
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if y_true.ndim == 1:
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y_true = y_true.reshape((-1, 1))
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if y_pred.ndim == 1:
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y_pred = y_pred.reshape((-1, 1))
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if y_true.shape[1] != y_pred.shape[1]:
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raise ValueError("y_true and y_pred have different number of output "
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"({0}!={1})".format(y_true.shape[1], y_pred.shape[1]))
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n_outputs = y_true.shape[1]
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allowed_multioutput_str = ('raw_values', 'uniform_average',
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'variance_weighted')
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if isinstance(multioutput, str):
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if multioutput not in allowed_multioutput_str:
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raise ValueError("Allowed 'multioutput' string values are {}. "
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"You provided multioutput={!r}".format(
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allowed_multioutput_str,
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multioutput))
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elif multioutput is not None:
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multioutput = check_array(multioutput, ensure_2d=False)
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if n_outputs == 1:
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raise ValueError("Custom weights are useful only in "
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"multi-output cases.")
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elif n_outputs != len(multioutput):
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raise ValueError(("There must be equally many custom weights "
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"(%d) as outputs (%d).") %
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(len(multioutput), n_outputs))
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y_type = 'continuous' if n_outputs == 1 else 'continuous-multioutput'
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return y_type, y_true, y_pred, multioutput
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@_deprecate_positional_args
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def mean_absolute_error(y_true, y_pred, *,
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sample_weight=None,
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multioutput='uniform_average'):
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"""Mean absolute error regression loss.
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Read more in the :ref:`User Guide <mean_absolute_error>`.
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Parameters
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----------
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y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Ground truth (correct) target values.
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y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Estimated target values.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
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(n_outputs,), default='uniform_average'
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Defines aggregating of multiple output values.
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Array-like value defines weights used to average errors.
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'raw_values' :
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Returns a full set of errors in case of multioutput input.
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'uniform_average' :
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Errors of all outputs are averaged with uniform weight.
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Returns
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-------
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loss : float or ndarray of floats
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If multioutput is 'raw_values', then mean absolute error is returned
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for each output separately.
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If multioutput is 'uniform_average' or an ndarray of weights, then the
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weighted average of all output errors is returned.
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MAE output is non-negative floating point. The best value is 0.0.
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Examples
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--------
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>>> from sklearn.metrics import mean_absolute_error
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>>> y_true = [3, -0.5, 2, 7]
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>>> y_pred = [2.5, 0.0, 2, 8]
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>>> mean_absolute_error(y_true, y_pred)
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0.5
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>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
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>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
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>>> mean_absolute_error(y_true, y_pred)
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0.75
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>>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
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array([0.5, 1. ])
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>>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
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0.85...
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"""
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y_type, y_true, y_pred, multioutput = _check_reg_targets(
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y_true, y_pred, multioutput)
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check_consistent_length(y_true, y_pred, sample_weight)
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output_errors = np.average(np.abs(y_pred - y_true),
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weights=sample_weight, axis=0)
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if isinstance(multioutput, str):
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if multioutput == 'raw_values':
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return output_errors
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elif multioutput == 'uniform_average':
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# pass None as weights to np.average: uniform mean
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multioutput = None
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return np.average(output_errors, weights=multioutput)
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def mean_absolute_percentage_error(y_true, y_pred,
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sample_weight=None,
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multioutput='uniform_average'):
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"""Mean absolute percentage error regression loss.
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Note here that we do not represent the output as a percentage in range
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[0, 100]. Instead, we represent it in range [0, 1/eps]. Read more in the
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:ref:`User Guide <mean_absolute_percentage_error>`.
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.. versionadded:: 0.24
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Parameters
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----------
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y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Ground truth (correct) target values.
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y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Estimated target values.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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multioutput : {'raw_values', 'uniform_average'} or array-like
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Defines aggregating of multiple output values.
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Array-like value defines weights used to average errors.
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If input is list then the shape must be (n_outputs,).
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'raw_values' :
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Returns a full set of errors in case of multioutput input.
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'uniform_average' :
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Errors of all outputs are averaged with uniform weight.
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Returns
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-------
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loss : float or ndarray of floats in the range [0, 1/eps]
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If multioutput is 'raw_values', then mean absolute percentage error
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is returned for each output separately.
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If multioutput is 'uniform_average' or an ndarray of weights, then the
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weighted average of all output errors is returned.
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MAPE output is non-negative floating point. The best value is 0.0.
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But note the fact that bad predictions can lead to arbitarily large
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MAPE values, especially if some y_true values are very close to zero.
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Note that we return a large value instead of `inf` when y_true is zero.
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Examples
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--------
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>>> from sklearn.metrics import mean_absolute_percentage_error
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>>> y_true = [3, -0.5, 2, 7]
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>>> y_pred = [2.5, 0.0, 2, 8]
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>>> mean_absolute_percentage_error(y_true, y_pred)
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0.3273...
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>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
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>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
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>>> mean_absolute_percentage_error(y_true, y_pred)
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0.5515...
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>>> mean_absolute_percentage_error(y_true, y_pred, multioutput=[0.3, 0.7])
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0.6198...
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"""
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y_type, y_true, y_pred, multioutput = _check_reg_targets(
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y_true, y_pred, multioutput)
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check_consistent_length(y_true, y_pred, sample_weight)
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epsilon = np.finfo(np.float64).eps
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mape = np.abs(y_pred - y_true) / np.maximum(np.abs(y_true), epsilon)
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output_errors = np.average(mape,
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weights=sample_weight, axis=0)
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if isinstance(multioutput, str):
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if multioutput == 'raw_values':
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return output_errors
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elif multioutput == 'uniform_average':
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# pass None as weights to np.average: uniform mean
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multioutput = None
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return np.average(output_errors, weights=multioutput)
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@_deprecate_positional_args
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def mean_squared_error(y_true, y_pred, *,
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sample_weight=None,
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multioutput='uniform_average', squared=True):
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"""Mean squared error regression loss.
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Read more in the :ref:`User Guide <mean_squared_error>`.
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Parameters
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----------
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y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Ground truth (correct) target values.
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y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Estimated target values.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
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(n_outputs,), default='uniform_average'
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Defines aggregating of multiple output values.
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Array-like value defines weights used to average errors.
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'raw_values' :
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Returns a full set of errors in case of multioutput input.
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'uniform_average' :
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Errors of all outputs are averaged with uniform weight.
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squared : bool, default=True
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If True returns MSE value, if False returns RMSE value.
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Returns
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-------
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loss : float or ndarray of floats
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A non-negative floating point value (the best value is 0.0), or an
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array of floating point values, one for each individual target.
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Examples
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--------
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>>> from sklearn.metrics import mean_squared_error
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>>> y_true = [3, -0.5, 2, 7]
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>>> y_pred = [2.5, 0.0, 2, 8]
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>>> mean_squared_error(y_true, y_pred)
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0.375
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>>> y_true = [3, -0.5, 2, 7]
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>>> y_pred = [2.5, 0.0, 2, 8]
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>>> mean_squared_error(y_true, y_pred, squared=False)
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0.612...
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>>> y_true = [[0.5, 1],[-1, 1],[7, -6]]
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>>> y_pred = [[0, 2],[-1, 2],[8, -5]]
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>>> mean_squared_error(y_true, y_pred)
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0.708...
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>>> mean_squared_error(y_true, y_pred, squared=False)
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0.822...
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>>> mean_squared_error(y_true, y_pred, multioutput='raw_values')
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array([0.41666667, 1. ])
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>>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7])
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0.825...
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"""
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y_type, y_true, y_pred, multioutput = _check_reg_targets(
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y_true, y_pred, multioutput)
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check_consistent_length(y_true, y_pred, sample_weight)
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output_errors = np.average((y_true - y_pred) ** 2, axis=0,
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weights=sample_weight)
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if not squared:
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output_errors = np.sqrt(output_errors)
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if isinstance(multioutput, str):
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if multioutput == 'raw_values':
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return output_errors
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elif multioutput == 'uniform_average':
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# pass None as weights to np.average: uniform mean
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multioutput = None
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return np.average(output_errors, weights=multioutput)
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@_deprecate_positional_args
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def mean_squared_log_error(y_true, y_pred, *,
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sample_weight=None,
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multioutput='uniform_average'):
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"""Mean squared logarithmic error regression loss.
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Read more in the :ref:`User Guide <mean_squared_log_error>`.
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Parameters
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----------
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y_true : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Ground truth (correct) target values.
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y_pred : array-like of shape (n_samples,) or (n_samples, n_outputs)
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Estimated target values.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
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(n_outputs,), default='uniform_average'
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Defines aggregating of multiple output values.
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Array-like value defines weights used to average errors.
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'raw_values' :
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Returns a full set of errors when the input is of multioutput
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format.
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'uniform_average' :
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Errors of all outputs are averaged with uniform weight.
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Returns
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-------
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loss : float or ndarray of floats
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A non-negative floating point value (the best value is 0.0), or an
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array of floating point values, one for each individual target.
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Examples
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--------
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>>> from sklearn.metrics import mean_squared_log_error
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>>> y_true = [3, 5, 2.5, 7]
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>>> y_pred = [2.5, 5, 4, 8]
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>>> mean_squared_log_error(y_true, y_pred)
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0.039...
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>>> y_true = [[0.5, 1], [1, 2], [7, 6]]
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>>> y_pred = [[0.5, 2], [1, 2.5], [8, 8]]
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>>> mean_squared_log_error(y_true, y_pred)
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0.044...
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>>> mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
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array([0.00462428, 0.08377444])
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>>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
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0.060...
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"""
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y_type, y_true, y_pred, multioutput = _check_reg_targets(
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y_true, y_pred, multioutput)
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check_consistent_length(y_true, y_pred, sample_weight)
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if (y_true < 0).any() or (y_pred < 0).any():
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raise ValueError("Mean Squared Logarithmic Error cannot be used when "
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"targets contain negative values.")
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return mean_squared_error(np.log1p(y_true), np.log1p(y_pred),
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sample_weight=sample_weight,
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multioutput=multioutput)
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@_deprecate_positional_args
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def median_absolute_error(y_true, y_pred, *, multioutput='uniform_average',
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sample_weight=None):
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"""Median absolute error regression loss.
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Median absolute error output is non-negative floating point. The best value
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is 0.0. Read more in the :ref:`User Guide <median_absolute_error>`.
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Parameters
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----------
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y_true : array-like of shape = (n_samples) or (n_samples, n_outputs)
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Ground truth (correct) target values.
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y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
|
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Estimated target values.
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multioutput : {'raw_values', 'uniform_average'} or array-like of shape \
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(n_outputs,), default='uniform_average'
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Defines aggregating of multiple output values. Array-like value defines
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weights used to average errors.
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'raw_values' :
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Returns a full set of errors in case of multioutput input.
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'uniform_average' :
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Errors of all outputs are averaged with uniform weight.
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sample_weight : array-like of shape (n_samples,), default=None
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Sample weights.
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.. versionadded:: 0.24
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Returns
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-------
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loss : float or ndarray of floats
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If multioutput is 'raw_values', then mean absolute error is returned
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for each output separately.
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If multioutput is 'uniform_average' or an ndarray of weights, then the
|
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weighted average of all output errors is returned.
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Examples
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--------
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>>> from sklearn.metrics import median_absolute_error
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>>> y_true = [3, -0.5, 2, 7]
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>>> y_pred = [2.5, 0.0, 2, 8]
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>>> median_absolute_error(y_true, y_pred)
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0.5
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>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
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>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
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>>> median_absolute_error(y_true, y_pred)
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0.75
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>>> median_absolute_error(y_true, y_pred, multioutput='raw_values')
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array([0.5, 1. ])
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>>> median_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
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0.85
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"""
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y_type, y_true, y_pred, multioutput = _check_reg_targets(
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y_true, y_pred, multioutput)
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if sample_weight is None:
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output_errors = np.median(np.abs(y_pred - y_true), axis=0)
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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)
|
|
|
|
|
|
@_deprecate_positional_args
|
|
def explained_variance_score(y_true, y_pred, *,
|
|
sample_weight=None,
|
|
multioutput='uniform_average'):
|
|
"""Explained variance regression score function.
|
|
|
|
Best possible score is 1.0, lower values are worse.
|
|
|
|
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.
|
|
|
|
Returns
|
|
-------
|
|
score : float or ndarray of floats
|
|
The explained variance or ndarray if 'multioutput' is 'raw_values'.
|
|
|
|
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_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)
|
|
|
|
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.
|
|
if isinstance(multioutput, str):
|
|
if multioutput == 'raw_values':
|
|
# return scores individually
|
|
return output_scores
|
|
elif multioutput == 'uniform_average':
|
|
# passing to np.average() None as weights results is uniform mean
|
|
avg_weights = None
|
|
elif multioutput == 'variance_weighted':
|
|
avg_weights = denominator
|
|
else:
|
|
avg_weights = multioutput
|
|
|
|
return np.average(output_scores, weights=avg_weights)
|
|
|
|
|
|
@_deprecate_positional_args
|
|
def r2_score(y_true, y_pred, *, sample_weight=None,
|
|
multioutput="uniform_average"):
|
|
""":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). A constant model that always
|
|
predicts the expected value of y, disregarding the input features,
|
|
would get a :math:`R^2` score of 0.0.
|
|
|
|
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'.
|
|
|
|
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_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.
|
|
|
|
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)
|
|
nonzero_denominator = denominator != 0
|
|
nonzero_numerator = numerator != 0
|
|
valid_score = nonzero_denominator & nonzero_numerator
|
|
output_scores = np.ones([y_true.shape[1]])
|
|
output_scores[valid_score] = 1 - (numerator[valid_score] /
|
|
denominator[valid_score])
|
|
# arbitrary set to zero to avoid -inf scores, having a constant
|
|
# y_true is not interesting for scoring a regression anyway
|
|
output_scores[nonzero_numerator & ~nonzero_denominator] = 0.
|
|
if isinstance(multioutput, str):
|
|
if multioutput == 'raw_values':
|
|
# return scores individually
|
|
return output_scores
|
|
elif multioutput == 'uniform_average':
|
|
# passing None as weights results is uniform mean
|
|
avg_weights = None
|
|
elif multioutput == 'variance_weighted':
|
|
avg_weights = denominator
|
|
# avoid fail on constant y or one-element arrays
|
|
if not np.any(nonzero_denominator):
|
|
if not np.any(nonzero_numerator):
|
|
return 1.0
|
|
else:
|
|
return 0.0
|
|
else:
|
|
avg_weights = multioutput
|
|
|
|
return np.average(output_scores, weights=avg_weights)
|
|
|
|
|
|
def max_error(y_true, y_pred):
|
|
"""
|
|
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))
|
|
|
|
|
|
@_deprecate_positional_args
|
|
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]
|
|
|
|
dist = TweedieDistribution(power=power)
|
|
dev = dist.unit_deviance(y_true, y_pred, check_input=True)
|
|
|
|
return np.average(dev, weights=sample_weight)
|
|
|
|
|
|
@_deprecate_positional_args
|
|
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
|
|
)
|
|
|
|
|
|
@_deprecate_positional_args
|
|
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
|
|
)
|