本文整理汇总了Python中tpot.TPOTClassifier.predict_proba方法的典型用法代码示例。如果您正苦于以下问题：Python TPOTClassifier.predict_proba方法的具体用法？Python TPOTClassifier.predict_proba怎么用？Python TPOTClassifier.predict_proba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tpot.TPOTClassifier的用法示例。
在下文中一共展示了TPOTClassifier.predict_proba方法的2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_predict_proba
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import predict_proba [as 别名]
def test_predict_proba():
"""Assert that the TPOT predict_proba function returns a numpy matrix of shape (num_testing_rows, num_testing_classes)"""
tpot_obj = TPOTClassifier()
pipeline_string= ('DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini'
', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,'
'DecisionTreeClassifier__min_samples_split=5)')
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(expr=tpot_obj._optimized_pipeline)
tpot_obj._fitted_pipeline.fit(training_features, training_classes)
result = tpot_obj.predict_proba(testing_features)
num_labels = np.amax(testing_classes) + 1
assert result.shape == (testing_features.shape[0], num_labels)示例2: test_predict_proba2
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import predict_proba [as 别名]
def test_predict_proba2():
"""Assert that the TPOT predict_proba function returns a numpy matrix filled with probabilities (float)"""
tpot_obj = TPOTClassifier()
pipeline_string= ('DecisionTreeClassifier(input_matrix, DecisionTreeClassifier__criterion=gini'
', DecisionTreeClassifier__max_depth=8,DecisionTreeClassifier__min_samples_leaf=5,'
'DecisionTreeClassifier__min_samples_split=5)')
tpot_obj._optimized_pipeline = creator.Individual.from_string(pipeline_string, tpot_obj._pset)
tpot_obj._fitted_pipeline = tpot_obj._toolbox.compile(expr=tpot_obj._optimized_pipeline)
tpot_obj._fitted_pipeline.fit(training_features, training_classes)
result = tpot_obj.predict_proba(testing_features)
rows = result.shape[0]
columns = result.shape[1]
try:
for i in range(rows):
for j in range(columns):
float_range(result[i][j])
assert True
except Exception:
assert False