import xgboost as xgb import numpy as np from sklearn.metrics import mean_squared_error from sklearn.grid_search import GridSearchCV from sklearn.datasets import load_iris, load_digits, load_boston from sklearn.cross_validation import KFold, StratifiedKFold, train_test_split rng = np.random.RandomState(1994) def test_binary_classification(): digits = load_digits(2) y = digits['target'] X = digits['data'] kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng) for train_index, test_index in kf: xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index]) preds = xgb_model.predict(X[test_index]) labels = y[test_index] err = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds)) assert err < 0.1 def test_multiclass_classification(): def check_pred(preds, labels): err = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds)) assert err < 0.4 iris = load_iris() y = iris['target'] X = iris['data'] kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng) for train_index, test_index in kf: xgb_model = xgb.XGBClassifier().fit(X[train_index], y[train_index]) preds = xgb_model.predict(X[test_index]) # test other params in XGBClassifier().fit preds2 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=3) preds3 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=0) preds4 = xgb_model.predict(X[test_index], output_margin=False, ntree_limit=3) labels = y[test_index] check_pred(preds, labels) check_pred(preds2, labels) check_pred(preds3, labels) check_pred(preds4, labels) def test_boston_housing_regression(): boston = load_boston() y = boston['target'] X = boston['data'] kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng) for train_index, test_index in kf: xgb_model = xgb.XGBRegressor().fit(X[train_index], y[train_index]) preds = xgb_model.predict(X[test_index]) # test other params in XGBRegressor().fit preds2 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=3) preds3 = xgb_model.predict(X[test_index], output_margin=True, ntree_limit=0) preds4 = xgb_model.predict(X[test_index], output_margin=False, ntree_limit=3) labels = y[test_index] assert mean_squared_error(preds, labels) < 25 assert mean_squared_error(preds2, labels) < 350 assert mean_squared_error(preds3, labels) < 25 assert mean_squared_error(preds4, labels) < 350 def test_parameter_tuning(): boston = load_boston() y = boston['target'] X = boston['data'] xgb_model = xgb.XGBRegressor() clf = GridSearchCV(xgb_model, {'max_depth': [2, 4, 6], 'n_estimators': [50, 100, 200]}, verbose=1) clf.fit(X, y) assert clf.best_score_ < 0.7 assert clf.best_params_ == {'n_estimators': 100, 'max_depth': 4} def test_regression_with_custom_objective(): def objective_ls(y_true, y_pred): grad = (y_pred - y_true) hess = np.ones(len(y_true)) return grad, hess boston = load_boston() y = boston['target'] X = boston['data'] kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng) for train_index, test_index in kf: xgb_model = xgb.XGBRegressor(objective=objective_ls).fit( X[train_index], y[train_index] ) preds = xgb_model.predict(X[test_index]) labels = y[test_index] assert mean_squared_error(preds, labels) < 25 # Test that the custom objective function is actually used class XGBCustomObjectiveException(Exception): pass def dummy_objective(y_true, y_pred): raise XGBCustomObjectiveException() xgb_model = xgb.XGBRegressor(objective=dummy_objective) np.testing.assert_raises(XGBCustomObjectiveException, xgb_model.fit, X, y) def test_classification_with_custom_objective(): def logregobj(y_true, y_pred): y_pred = 1.0 / (1.0 + np.exp(-y_pred)) grad = y_pred - y_true hess = y_pred * (1.0 - y_pred) return grad, hess digits = load_digits(2) y = digits['target'] X = digits['data'] kf = KFold(y.shape[0], n_folds=2, shuffle=True, random_state=rng) for train_index, test_index in kf: xgb_model = xgb.XGBClassifier(objective=logregobj) xgb_model.fit(X[train_index], y[train_index]) preds = xgb_model.predict(X[test_index]) labels = y[test_index] err = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds)) assert err < 0.1 # Test that the custom objective function is actually used class XGBCustomObjectiveException(Exception): pass def dummy_objective(y_true, y_preds): raise XGBCustomObjectiveException() xgb_model = xgb.XGBClassifier(objective=dummy_objective) np.testing.assert_raises( XGBCustomObjectiveException, xgb_model.fit, X, y ) def test_sklearn_api(): iris = load_iris() tr_d, te_d, tr_l, te_l = train_test_split(iris.data, iris.target, train_size=120) classifier = xgb.XGBClassifier() classifier.fit(tr_d, tr_l) preds = classifier.predict(te_d) labels = te_l err = sum([1 for p, l in zip(preds, labels) if p != l]) / len(te_l) assert err < 0.2 def test_sklearn_plotting(): iris = load_iris() classifier = xgb.XGBClassifier() classifier.fit(iris.data, iris.target) import matplotlib matplotlib.use('Agg') from matplotlib.axes import Axes from graphviz import Digraph ax = xgb.plot_importance(classifier) assert isinstance(ax, Axes) assert ax.get_title() == 'Feature importance' assert ax.get_xlabel() == 'F score' assert ax.get_ylabel() == 'Features' assert len(ax.patches) == 4 g = xgb.to_graphviz(classifier, num_trees=0) assert isinstance(g, Digraph) ax = xgb.plot_tree(classifier, num_trees=0) assert isinstance(ax, Axes) def test_sklearn_nfolds_cv(): digits = load_digits(3) X = digits['data'] y = digits['target'] dm = xgb.DMatrix(X, label=y) params = { 'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'multi:softprob', 'num_class': 3 } seed = 2016 nfolds = 5 skf = StratifiedKFold(y, n_folds=nfolds, shuffle=True, random_state=seed) cv1 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, seed=seed) cv2 = xgb.cv(params, dm, num_boost_round=10, folds=skf, seed=seed) cv3 = xgb.cv(params, dm, num_boost_round=10, nfold=nfolds, stratified=True, seed=seed) assert cv1.shape[0] == cv2.shape[0] and cv2.shape[0] == cv3.shape[0] assert cv2.iloc[-1, 0] == cv3.iloc[-1, 0]