def test_logistic_regression_weights(): """[Utils] LogisticRegression: check weights.""" w = np.array([[-0.81643357, 0.76923077], [-0.52156177, 0.51282051], [0.33799534, -0.28205128]]) lr = LogisticRegression() lr.fit(X, z) np.testing.assert_array_almost_equal(lr.coef_, w)
def test_logistic_regression_preds_labels(): """[Utils] LogisticRegression: check label predictions.""" g = np.array([1., 1., 1., 1., 2., 2., 2., 3., 3., 3., 3., 3.]) p = LogisticRegression().fit(X, z).predict(X) np.testing.assert_array_equal(p, g)
def test_logistic_regression_preds_proba(): """[Utils] LogisticRegression: check label predictions.""" g = np.array([[0.68335447, 0.62546744, 0.42995095], [0.66258275, 0.62136312, 0.45756156], [0.64116395, 0.61724135, 0.48543536], [0.61916698, 0.61310265, 0.51340005], [0.59666983, 0.60894755, 0.54128111], [0.57375858, 0.60477659, 0.56890605], [0.55052625, 0.60059033, 0.59610873], [0.5270714, 0.59638931, 0.62273319], [0.50349645, 0.59217411, 0.64863706], [0.47990593, 0.58794531, 0.67369429], [0.45640469, 0.58370348, 0.69779712], [0.43309595, 0.57944923, 0.72085727]]) p = LogisticRegression().fit(X, z).predict_proba(X) np.testing.assert_array_almost_equal(p, g)
def test_logistic_regression_not_fitted(): """[Utils] LogisticRegression: check raises if not fitted.""" np.testing.assert_raises(NotFittedError, LogisticRegression().predict, X)
""" import os import numpy as np from mlens.index import INDEXERS from mlens.testing.dummy import Data, ECM from mlens.utils.dummy import Scale, LogisticRegression from mlens.parallel import make_group, Layer, run from mlens.externals.sklearn.base import clone try: from contextlib import redirect_stdout except ImportError: from mlens.externals.fixes import redirect as redirect_stdout PREPROCESSING_1 = {'no1': [], 'sc1': [('scale', Scale())]} ESTIMATORS_PROBA_1 = {'sc1': [('offs1', LogisticRegression(offset=2)), ('null1', LogisticRegression())], 'no1': [('offs1', LogisticRegression(offset=2)), ('null1', LogisticRegression())]} PREPROCESSING_2 = {'no2': [], 'sc2': [('scale', Scale())]} ESTIMATORS_PROBA_2 = {'sc2': [('offs2', LogisticRegression(offset=2)), ('null2', LogisticRegression())], 'no2': [('offs2', LogisticRegression(offset=2)), ('null2', LogisticRegression())]} def scorer(p, y): return np.mean(p - y) data = Data('stack', True, True)
""" import os import numpy as np from mlens.index import INDEXERS from mlens.testing.dummy import Data, ECM from mlens.utils.dummy import Scale, LogisticRegression from mlens.parallel import make_group, Layer, run from mlens.externals.sklearn.base import clone try: from contextlib import redirect_stdout except ImportError: from mlens.externals.fixes import redirect as redirect_stdout PREPROCESSING_1 = {'no1': [], 'sc1': [('scale', Scale())]} ESTIMATORS_PROBA_1 = { 'sc1': [('offs1', LogisticRegression(offset=2)), ('null1', LogisticRegression())], 'no1': [('offs1', LogisticRegression(offset=2)), ('null1', LogisticRegression())] } PREPROCESSING_2 = {'no2': [], 'sc2': [('scale', Scale())]} ESTIMATORS_PROBA_2 = { 'sc2': [('offs2', LogisticRegression(offset=2)), ('null2', LogisticRegression())], 'no2': [('offs2', LogisticRegression(offset=2)), ('null2', LogisticRegression())] } def scorer(p, y):