本文整理汇总了Python中tpot.TPOTClassifier._fitted_pipeline方法的典型用法代码示例。如果您正苦于以下问题：Python TPOTClassifier._fitted_pipeline方法的具体用法？Python TPOTClassifier._fitted_pipeline怎么用？Python TPOTClassifier._fitted_pipeline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._fitted_pipeline方法的5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_2
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import _fitted_pipeline [as 别名]
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
tpot_obj = TPOTClassifier()
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('DecisionTreeClassifier(input_matrix)', 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(testing_features)
assert result.shape == (testing_features.shape[0],)示例2: test_predict_2
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import _fitted_pipeline [as 别名]
def test_predict_2():
"""Assert that the TPOT predict function returns a numpy matrix of shape (num_testing_rows,)"""
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(testing_features)
assert result.shape == (testing_features.shape[0],)示例3: test_score_2
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import _fitted_pipeline [as 别名]
def test_score_2():
"""Assert that the TPOTClassifier score function outputs a known score for a fix pipeline"""
tpot_obj = TPOTClassifier()
known_score = 0.977777777778 # Assumes use of the TPOT balanced_accuracy function
# Reify pipeline with known score
pipeline_string= ('KNeighborsClassifier(input_matrix, KNeighborsClassifier__n_neighbors=10, '
'KNeighborsClassifier__p=1,KNeighborsClassifier__weights=uniform)')
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)
# Get score from TPOT
score = tpot_obj.score(testing_features, testing_classes)
# http://stackoverflow.com/questions/5595425/
def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
assert isclose(known_score, score)示例4: test_score_2
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import _fitted_pipeline [as 别名]
def test_score_2():
"""Assert that the TPOTClassifier score function outputs a known score for a fixed pipeline"""
tpot_obj = TPOTClassifier()
tpot_obj._pbar = tqdm(total=1, disable=True)
known_score = 0.986318199045 # Assumes use of the TPOT balanced_accuracy function
# Reify pipeline with known score
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('RandomForestClassifier(input_matrix)', 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)
# Get score from TPOT
score = tpot_obj.score(testing_features, testing_classes)
# http://stackoverflow.com/questions/5595425/
def isclose(a, b, rel_tol=1e-09, abs_tol=0.0):
return abs(a - b) <= max(rel_tol * max(abs(a), abs(b)), abs_tol)
assert isclose(known_score, score)示例5: test_predict_proba2
# 需要导入模块: from tpot import TPOTClassifier [as 别名]
# 或者: from tpot.TPOTClassifier import _fitted_pipeline [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