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Added assertions for classification tests

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This commit is contained in:
terrytangyuan 2015-10-04 23:01:07 -05:00
parent 3dbd4af263
commit d20bfb12e4
1 changed files with 8 additions and 7 deletions
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15
tests/python/test_with_sklearn.py
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@ -16,9 +16,10 @@ def test_binary_classification():
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])
predictions = xgb_model.predict(X[test_index])
actuals = y[test_index]
print(confusion_matrix(actuals, predictions))
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():
iris = load_iris()
@ -27,10 +28,10 @@ def test_multiclass_classification():
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])
predictions = xgb_model.predict(X[test_index])
actuals = y[test_index]
print(confusion_matrix(actuals, predictions))
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 < 0.3