import xgboost as xgb import testing as tm import numpy as np import pytest rng = np.random.RandomState(1337) class TestEvalMetrics: xgb_params_01 = { 'verbosity': 0, 'nthread': 1, 'eval_metric': 'error' } xgb_params_02 = { 'verbosity': 0, 'nthread': 1, 'eval_metric': ['error'] } xgb_params_03 = { 'verbosity': 0, 'nthread': 1, 'eval_metric': ['rmse', 'error'] } xgb_params_04 = { 'verbosity': 0, 'nthread': 1, 'eval_metric': ['error', 'rmse'] } def evalerror_01(self, preds, dtrain): labels = dtrain.get_label() return 'error', float(sum(labels != (preds > 0.0))) / len(labels) def evalerror_02(self, preds, dtrain): labels = dtrain.get_label() return [('error', float(sum(labels != (preds > 0.0))) / len(labels))] @pytest.mark.skipif(**tm.no_sklearn()) def evalerror_03(self, preds, dtrain): from sklearn.metrics import mean_squared_error labels = dtrain.get_label() return [('rmse', mean_squared_error(labels, preds)), ('error', float(sum(labels != (preds > 0.0))) / len(labels))] @pytest.mark.skipif(**tm.no_sklearn()) def evalerror_04(self, preds, dtrain): from sklearn.metrics import mean_squared_error labels = dtrain.get_label() return [('error', float(sum(labels != (preds > 0.0))) / len(labels)), ('rmse', mean_squared_error(labels, preds))] @pytest.mark.skipif(**tm.no_sklearn()) def test_eval_metrics(self): try: from sklearn.model_selection import train_test_split except ImportError: from sklearn.cross_validation import train_test_split from sklearn.datasets import load_digits digits = load_digits(n_class=2) X = digits['data'] y = digits['target'] Xt, Xv, yt, yv = train_test_split(X, y, test_size=0.2, random_state=0) dtrain = xgb.DMatrix(Xt, label=yt) dvalid = xgb.DMatrix(Xv, label=yv) watchlist = [(dtrain, 'train'), (dvalid, 'val')] gbdt_01 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=10) gbdt_02 = xgb.train(self.xgb_params_02, dtrain, num_boost_round=10) gbdt_03 = xgb.train(self.xgb_params_03, dtrain, num_boost_round=10) assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0] assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0] gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist, early_stopping_rounds=2) gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist, early_stopping_rounds=2) gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist, early_stopping_rounds=2) gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist, early_stopping_rounds=2) assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0] assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0] assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0] gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist, early_stopping_rounds=2, feval=self.evalerror_01) gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist, early_stopping_rounds=2, feval=self.evalerror_02) gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist, early_stopping_rounds=2, feval=self.evalerror_03) gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist, early_stopping_rounds=2, feval=self.evalerror_04) assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0] assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0] assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0] @pytest.mark.skipif(**tm.no_sklearn()) def test_gamma_deviance(self): from sklearn.metrics import mean_gamma_deviance rng = np.random.RandomState(1994) n_samples = 100 n_features = 30 X = rng.randn(n_samples, n_features) y = rng.randn(n_samples) y = y - y.min() * 100 reg = xgb.XGBRegressor(tree_method="hist", objective="reg:gamma", n_estimators=10) reg.fit(X, y, eval_metric="gamma-deviance") booster = reg.get_booster() score = reg.predict(X) gamma_dev = float(booster.eval(xgb.DMatrix(X, y)).split(":")[1].split(":")[0]) skl_gamma_dev = mean_gamma_deviance(y, score) np.testing.assert_allclose(gamma_dev, skl_gamma_dev, rtol=1e-6) def run_roc_auc_binary(self, tree_method, n_samples): import numpy as np from sklearn.datasets import make_classification from sklearn.metrics import roc_auc_score rng = np.random.RandomState(1994) n_samples = n_samples n_features = 10 X, y = make_classification( n_samples, n_features, n_informative=n_features, n_redundant=0, random_state=rng ) Xy = xgb.DMatrix(X, y) booster = xgb.train( { "tree_method": tree_method, "eval_metric": "auc", "objective": "binary:logistic", }, Xy, num_boost_round=8, ) score = booster.predict(Xy) skl_auc = roc_auc_score(y, score) auc = float(booster.eval(Xy).split(":")[1]) np.testing.assert_allclose(skl_auc, auc, rtol=1e-6) X = rng.randn(*X.shape) score = booster.predict(xgb.DMatrix(X)) skl_auc = roc_auc_score(y, score) auc = float(booster.eval(xgb.DMatrix(X, y)).split(":")[1]) np.testing.assert_allclose(skl_auc, auc, rtol=1e-6) @pytest.mark.skipif(**tm.no_sklearn()) @pytest.mark.parametrize("n_samples", [4, 100, 1000]) def test_roc_auc(self, n_samples): self.run_roc_auc_binary("hist", n_samples) def run_roc_auc_multi(self, tree_method, n_samples): import numpy as np from sklearn.datasets import make_classification from sklearn.metrics import roc_auc_score rng = np.random.RandomState(1994) n_samples = n_samples n_features = 10 n_classes = 4 X, y = make_classification( n_samples, n_features, n_informative=n_features, n_redundant=0, n_classes=n_classes, random_state=rng ) Xy = xgb.DMatrix(X, y) booster = xgb.train( { "tree_method": tree_method, "eval_metric": "auc", "objective": "multi:softprob", "num_class": n_classes, }, Xy, num_boost_round=8, ) score = booster.predict(Xy) skl_auc = roc_auc_score(y, score, average="weighted", multi_class="ovr") auc = float(booster.eval(Xy).split(":")[1]) np.testing.assert_allclose(skl_auc, auc, rtol=1e-6) X = rng.randn(*X.shape) score = booster.predict(xgb.DMatrix(X)) skl_auc = roc_auc_score(y, score, average="weighted", multi_class="ovr") auc = float(booster.eval(xgb.DMatrix(X, y)).split(":")[1]) np.testing.assert_allclose(skl_auc, auc, rtol=1e-6) @pytest.mark.parametrize("n_samples", [4, 100, 1000]) def test_roc_auc_multi(self, n_samples): self.run_roc_auc_multi("hist", n_samples)