from __future__ import division, print_function import numpy as np from itertools import product from sklearn.utils.testing import assert_raises, assert_raises_regex from sklearn.utils.testing import assert_equal from sklearn.utils.testing import assert_almost_equal from sklearn.utils.testing import assert_array_equal from sklearn.utils.testing import assert_array_almost_equal from sklearn.metrics import explained_variance_score from sklearn.metrics import mean_absolute_error from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_squared_log_error from sklearn.metrics import median_absolute_error from sklearn.metrics import r2_score from sklearn.metrics.regression import _check_reg_targets def test_regression_metrics(n_samples=50): y_true = np.arange(n_samples) y_pred = y_true + 1 assert_almost_equal(mean_squared_error(y_true, y_pred), 1.) assert_almost_equal(mean_squared_log_error(y_true, y_pred), mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred))) assert_almost_equal(mean_absolute_error(y_true, y_pred), 1.) assert_almost_equal(median_absolute_error(y_true, y_pred), 1.) assert_almost_equal(r2_score(y_true, y_pred), 0.995, 2) assert_almost_equal(explained_variance_score(y_true, y_pred), 1.) def test_multioutput_regression(): y_true = np.array([[1, 0, 0, 1], [0, 1, 1, 1], [1, 1, 0, 1]]) y_pred = np.array([[0, 0, 0, 1], [1, 0, 1, 1], [0, 0, 0, 1]]) error = mean_squared_error(y_true, y_pred) assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.) error = mean_squared_log_error(y_true, y_pred) assert_almost_equal(error, 0.200, decimal=2) # mean_absolute_error and mean_squared_error are equal because # it is a binary problem. error = mean_absolute_error(y_true, y_pred) assert_almost_equal(error, (1. / 3 + 2. / 3 + 2. / 3) / 4.) error = r2_score(y_true, y_pred, multioutput='variance_weighted') assert_almost_equal(error, 1. - 5. / 2) error = r2_score(y_true, y_pred, multioutput='uniform_average') assert_almost_equal(error, -.875) def test_regression_metrics_at_limits(): assert_almost_equal(mean_squared_error([0.], [0.]), 0.00, 2) assert_almost_equal(mean_squared_log_error([0.], [0.]), 0.00, 2) assert_almost_equal(mean_absolute_error([0.], [0.]), 0.00, 2) assert_almost_equal(median_absolute_error([0.], [0.]), 0.00, 2) assert_almost_equal(explained_variance_score([0.], [0.]), 1.00, 2) assert_almost_equal(r2_score([0., 1], [0., 1]), 1.00, 2) assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be " "used when targets contain negative values.", mean_squared_log_error, [-1.], [-1.]) assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be " "used when targets contain negative values.", mean_squared_log_error, [1., 2., 3.], [1., -2., 3.]) assert_raises_regex(ValueError, "Mean Squared Logarithmic Error cannot be " "used when targets contain negative values.", mean_squared_log_error, [1., -2., 3.], [1., 2., 3.]) def test__check_reg_targets(): # All of length 3 EXAMPLES = [ ("continuous", [1, 2, 3], 1), ("continuous", [[1], [2], [3]], 1), ("continuous-multioutput", [[1, 1], [2, 2], [3, 1]], 2), ("continuous-multioutput", [[5, 1], [4, 2], [3, 1]], 2), ("continuous-multioutput", [[1, 3, 4], [2, 2, 2], [3, 1, 1]], 3), ] for (type1, y1, n_out1), (type2, y2, n_out2) in product(EXAMPLES, repeat=2): if type1 == type2 and n_out1 == n_out2: y_type, y_check1, y_check2, multioutput = _check_reg_targets( y1, y2, None) assert_equal(type1, y_type) if type1 == 'continuous': assert_array_equal(y_check1, np.reshape(y1, (-1, 1))) assert_array_equal(y_check2, np.reshape(y2, (-1, 1))) else: assert_array_equal(y_check1, y1) assert_array_equal(y_check2, y2) else: assert_raises(ValueError, _check_reg_targets, y1, y2, None) def test__check_reg_targets_exception(): invalid_multioutput = 'this_value_is_not_valid' expected_message = ("Allowed 'multioutput' string values are.+" "You provided multioutput={!r}".format( invalid_multioutput)) assert_raises_regex(ValueError, expected_message, _check_reg_targets, [1, 2, 3], [[1], [2], [3]], invalid_multioutput) def test_regression_multioutput_array(): y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]] y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]] mse = mean_squared_error(y_true, y_pred, multioutput='raw_values') mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values') r = r2_score(y_true, y_pred, multioutput='raw_values') evs = explained_variance_score(y_true, y_pred, multioutput='raw_values') assert_array_almost_equal(mse, [0.125, 0.5625], decimal=2) assert_array_almost_equal(mae, [0.25, 0.625], decimal=2) assert_array_almost_equal(r, [0.95, 0.93], decimal=2) assert_array_almost_equal(evs, [0.95, 0.93], decimal=2) # mean_absolute_error and mean_squared_error are equal because # it is a binary problem. y_true = [[0, 0]]*4 y_pred = [[1, 1]]*4 mse = mean_squared_error(y_true, y_pred, multioutput='raw_values') mae = mean_absolute_error(y_true, y_pred, multioutput='raw_values') r = r2_score(y_true, y_pred, multioutput='raw_values') assert_array_almost_equal(mse, [1., 1.], decimal=2) assert_array_almost_equal(mae, [1., 1.], decimal=2) assert_array_almost_equal(r, [0., 0.], decimal=2) r = r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput='raw_values') assert_array_almost_equal(r, [0, -3.5], decimal=2) assert_equal(np.mean(r), r2_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput='uniform_average')) evs = explained_variance_score([[0, -1], [0, 1]], [[2, 2], [1, 1]], multioutput='raw_values') assert_array_almost_equal(evs, [0, -1.25], decimal=2) # Checking for the condition in which both numerator and denominator is # zero. y_true = [[1, 3], [-1, 2]] y_pred = [[1, 4], [-1, 1]] r2 = r2_score(y_true, y_pred, multioutput='raw_values') assert_array_almost_equal(r2, [1., -3.], decimal=2) assert_equal(np.mean(r2), r2_score(y_true, y_pred, multioutput='uniform_average')) evs = explained_variance_score(y_true, y_pred, multioutput='raw_values') assert_array_almost_equal(evs, [1., -3.], decimal=2) assert_equal(np.mean(evs), explained_variance_score(y_true, y_pred)) # Handling msle separately as it does not accept negative inputs. y_true = np.array([[0.5, 1], [1, 2], [7, 6]]) y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]]) msle = mean_squared_log_error(y_true, y_pred, multioutput='raw_values') msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred), multioutput='raw_values') assert_array_almost_equal(msle, msle2, decimal=2) def test_regression_custom_weights(): y_true = [[1, 2], [2.5, -1], [4.5, 3], [5, 7]] y_pred = [[1, 1], [2, -1], [5, 4], [5, 6.5]] msew = mean_squared_error(y_true, y_pred, multioutput=[0.4, 0.6]) maew = mean_absolute_error(y_true, y_pred, multioutput=[0.4, 0.6]) rw = r2_score(y_true, y_pred, multioutput=[0.4, 0.6]) evsw = explained_variance_score(y_true, y_pred, multioutput=[0.4, 0.6]) assert_almost_equal(msew, 0.39, decimal=2) assert_almost_equal(maew, 0.475, decimal=3) assert_almost_equal(rw, 0.94, decimal=2) assert_almost_equal(evsw, 0.94, decimal=2) # Handling msle separately as it does not accept negative inputs. y_true = np.array([[0.5, 1], [1, 2], [7, 6]]) y_pred = np.array([[0.5, 2], [1, 2.5], [8, 8]]) msle = mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7]) msle2 = mean_squared_error(np.log(1 + y_true), np.log(1 + y_pred), multioutput=[0.3, 0.7]) assert_almost_equal(msle, msle2, decimal=2)