"""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 # Mathieu Blondel # Olivier Grisel # Arnaud Joly # Jochen Wersdorfer # Lars Buitinck # Joel Nothman # Noel Dawe # Manoj Kumar # Michael Eickenberg # Konstantin Shmelkov # License: BSD 3 clause from __future__ import division import numpy as np from ..utils.validation import check_array, check_consistent_length from ..utils.validation import column_or_1d from ..externals.six import string_types import warnings __ALL__ = [ "mean_absolute_error", "mean_squared_error", "median_absolute_error", "r2_score", "explained_variance_score" ] def _check_reg_targets(y_true, y_pred, multioutput): """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(). 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) y_pred = check_array(y_pred, ensure_2d=False) 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] multioutput_options = (None, 'raw_values', 'uniform_average', 'variance_weighted') if multioutput not in multioutput_options: 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 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 `. 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), optional Sample weights. multioutput : string in ['raw_values', 'uniform_average'] or array-like of shape (n_outputs) 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]) ... # doctest: +ELLIPSIS 0.849... """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput) output_errors = np.average(np.abs(y_pred - y_true), weights=sample_weight, axis=0) if isinstance(multioutput, string_types): 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 mean_squared_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average'): """Mean squared error regression loss Read more in the :ref:`User Guide `. 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), optional Sample weights. multioutput : string in ['raw_values', 'uniform_average'] or array-like of shape (n_outputs) 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 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) # doctest: +ELLIPSIS 0.708... >>> mean_squared_error(y_true, y_pred, multioutput='raw_values') ... # doctest: +ELLIPSIS array([ 0.416..., 1. ]) >>> mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7]) ... # doctest: +ELLIPSIS 0.824... """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput) output_errors = np.average((y_true - y_pred) ** 2, axis=0, weights=sample_weight) if isinstance(multioutput, string_types): 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 median_absolute_error(y_true, y_pred): """Median absolute error regression loss Read more in the :ref:`User Guide `. 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 ------- loss : float A positive floating point value (the best value is 0.0). 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_type, y_true, y_pred, _ = _check_reg_targets(y_true, y_pred, 'uniform_average') if y_type == 'continuous-multioutput': raise ValueError("Multioutput not supported in median_absolute_error") return np.median(np.abs(y_pred - y_true)) 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 `. 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), optional Sample weights. multioutput : string in ['raw_values', 'uniform_average', \ 'variance_weighted'] or array-like of shape (n_outputs) 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) # doctest: +ELLIPSIS 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') ... # doctest: +ELLIPSIS 0.983... """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput) 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, string_types): 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) def r2_score(y_true, y_pred, sample_weight=None, multioutput=None): """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 R^2 score of 0.0. Read more in the :ref:`User Guide `. 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), optional Sample weights. multioutput : string in ['raw_values', 'uniform_average', \ 'variance_weighted'] or None or array-like of shape (n_outputs) Defines aggregating of multiple output scores. Array-like value defines weights used to average scores. Default value corresponds to 'variance_weighted', this behaviour is deprecated since version 0.17 and will be changed to 'uniform_average' starting from 0.19. '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 ------- z : float or ndarray of floats The R^2 score or ndarray of scores if 'multioutput' is 'raw_values'. Notes ----- This is not a symmetric function. Unlike most other scores, R^2 score may be negative (it need not actually be the square of a quantity R). References ---------- .. [1] `Wikipedia entry on the 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) # doctest: +ELLIPSIS 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') # doctest: +ELLIPSIS 0.938... """ y_type, y_true, y_pred, multioutput = _check_reg_targets( y_true, y_pred, multioutput) 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 multioutput is None and y_true.shape[1] != 1: warnings.warn("Default 'multioutput' behavior now corresponds to " "'variance_weighted' value which is deprecated since " "0.17, it will be changed to 'uniform_average' " "starting from 0.19.", DeprecationWarning) multioutput = 'variance_weighted' if isinstance(multioutput, string_types): 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)