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