本文整理汇总了Python中sklearn.ensemble.RandomForestClassifier.decision_path方法的典型用法代码示例。如果您正苦于以下问题：Python RandomForestClassifier.decision_path方法的具体用法？Python RandomForestClassifier.decision_path怎么用？Python RandomForestClassifier.decision_path使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.ensemble.RandomForestClassifier的用法示例。
在下文中一共展示了RandomForestClassifier.decision_path方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_drf_classifier_backupsklearn
# 需要导入模块: from sklearn.ensemble import RandomForestClassifier [as 别名]
# 或者: from sklearn.ensemble.RandomForestClassifier import decision_path [as 别名]
def test_drf_classifier_backupsklearn(backend='auto'):
df = pd.read_csv("./open_data/creditcard.csv")
X = np.array(df.iloc[:, :df.shape[1] - 1], dtype='float32', order='C')
y = np.array(df.iloc[:, df.shape[1] - 1], dtype='float32', order='C')
import h2o4gpu
Solver = h2o4gpu.RandomForestClassifier
#Run h2o4gpu version of RandomForest Regression
drf = Solver(backend=backend, random_state=1234, oob_score=True)
print("h2o4gpu fit()")
drf.fit(X, y)
#Run Sklearn version of RandomForest Regression
from sklearn.ensemble import RandomForestClassifier
drf_sk = RandomForestClassifier(random_state=1234, oob_score=True, max_depth=3)
print("Scikit fit()")
drf_sk.fit(X, y)
if backend == "sklearn":
assert (drf.predict(X) == drf_sk.predict(X)).all() == True
assert (drf.predict_log_proba(X) == drf_sk.predict_log_proba(X)).all() == True
assert (drf.predict_proba(X) == drf_sk.predict_proba(X)).all() == True
assert (drf.score(X, y) == drf_sk.score(X, y)).all() == True
assert (drf.decision_path(X)[1] == drf_sk.decision_path(X)[1]).all() == True
assert (drf.apply(X) == drf_sk.apply(X)).all() == True
print("Estimators")
print(drf.estimators_)
print(drf_sk.estimators_)
print("n_features")
print(drf.n_features_)
print(drf_sk.n_features_)
assert drf.n_features_ == drf_sk.n_features_
print("n_classes_")
print(drf.n_classes_)
print(drf_sk.n_classes_)
assert drf.n_classes_ == drf_sk.n_classes_
print("n_features")
print(drf.classes_)
print(drf_sk.classes_)
assert (drf.classes_ == drf_sk.classes_).all() == True
print("n_outputs")
print(drf.n_outputs_)
print(drf_sk.n_outputs_)
assert drf.n_outputs_ == drf_sk.n_outputs_
print("Feature importance")
print(drf.feature_importances_)
print(drf_sk.feature_importances_)
assert (drf.feature_importances_ == drf_sk.feature_importances_).all() == True
print("oob_score")
print(drf.oob_score_)
print(drf_sk.oob_score_)
assert drf.oob_score_ == drf_sk.oob_score_