65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
import xgboost as xgb
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import numpy as np
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from sklearn.cross_validation import KFold, train_test_split
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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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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)) 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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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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err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
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assert err < 0.4
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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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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,
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{'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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