def test_sample_weight_init_estimators(): # Smoke test for init estimators with sample weights. rng = check_random_state(13) X = rng.rand(100, 2) sample_weight = np.ones(100) reg_y = rng.rand(100) clf_y = rng.randint(0, 2, size=100) for Loss in LOSS_FUNCTIONS.values(): if Loss is None: continue if issubclass(Loss, RegressionLossFunction): k = 1 y = reg_y else: k = 2 y = clf_y if Loss.is_multi_class: # skip multiclass continue loss = Loss(k) init_est = loss.init_estimator() init_est.fit(X, y) out = loss.get_init_raw_predictions(X, init_est) assert out.shape == (y.shape[0], 1) sw_init_est = loss.init_estimator() sw_init_est.fit(X, y, sample_weight=sample_weight) sw_out = loss.get_init_raw_predictions(X, sw_init_est) assert sw_out.shape == (y.shape[0], 1) # check if predictions match assert_allclose(out, sw_out, rtol=1e-2)
def test_sample_weight_init_estimators(): # Smoke test for init estimators with sample weights. rng = check_random_state(13) X = rng.rand(100, 2) sample_weight = np.ones(100) reg_y = rng.rand(100) clf_y = rng.randint(0, 2, size=100) for Loss in LOSS_FUNCTIONS.values(): if Loss is None: continue if issubclass(Loss, RegressionLossFunction): k = 1 y = reg_y else: k = 2 y = clf_y if Loss.is_multi_class: # skip multiclass continue loss = Loss(k) init_est = loss.init_estimator() init_est.fit(X, y) out = loss.get_init_raw_predictions(X, init_est) assert_equal(out.shape, (y.shape[0], 1)) sw_init_est = loss.init_estimator() sw_init_est.fit(X, y, sample_weight=sample_weight) sw_out = loss.get_init_raw_predictions(X, sw_init_est) assert_equal(sw_out.shape, (y.shape[0], 1)) # check if predictions match assert_allclose(out, sw_out, rtol=1e-2)
def test_sample_weight_deviance(): # Test if deviance supports sample weights. rng = check_random_state(13) sample_weight = np.ones(100) reg_y = rng.rand(100) clf_y = rng.randint(0, 2, size=100) mclf_y = rng.randint(0, 3, size=100) for Loss in LOSS_FUNCTIONS.values(): if Loss is None: continue if issubclass(Loss, RegressionLossFunction): k = 1 y = reg_y p = reg_y else: k = 2 y = clf_y p = clf_y if Loss.is_multi_class: k = 3 y = mclf_y # one-hot encoding p = np.zeros((y.shape[0], k), dtype=np.float64) for i in range(k): p[:, i] = y == i loss = Loss(k) deviance_w_w = loss(y, p, sample_weight) deviance_wo_w = loss(y, p) assert deviance_wo_w == deviance_w_w
def test_sample_weight_deviance(): # Test if deviance supports sample weights. rng = check_random_state(13) sample_weight = np.ones(100) reg_y = rng.rand(100) clf_y = rng.randint(0, 2, size=100) mclf_y = rng.randint(0, 3, size=100) for Loss in LOSS_FUNCTIONS.values(): if Loss is None: continue if issubclass(Loss, RegressionLossFunction): k = 1 y = reg_y p = reg_y else: k = 2 y = clf_y p = clf_y if Loss.is_multi_class: k = 3 y = mclf_y # one-hot encoding p = np.zeros((y.shape[0], k), dtype=np.float64) for i in range(k): p[:, i] = y == i loss = Loss(k) deviance_w_w = loss(y, p, sample_weight) deviance_wo_w = loss(y, p) assert deviance_wo_w == deviance_w_w