本文整理汇总了Python中tpot.TPOT.pbar方法的典型用法代码示例。如果您正苦于以下问题：Python TPOT.pbar方法的具体用法？Python TPOT.pbar怎么用？Python TPOT.pbar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.TPOT的用法示例。
在下文中一共展示了TPOT.pbar方法的3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_gp_new_generation
# 需要导入模块: from tpot import TPOT [as 别名]
# 或者: from tpot.TPOT import pbar [as 别名]
def test_gp_new_generation():
"""Assert that the gp_generation count gets incremented when _gp_new_generation is called"""
tpot_obj = TPOT()
tpot_obj.pbar = tqdm(total=1, disable=True)
assert(tpot_obj.gp_generation == 0)
# Since _gp_new_generation is a decorator, and we dont want to run a full
# fit(), decorate a dummy function and then call the dummy function.
@_gp_new_generation
def dummy_function(self, foo):
pass
dummy_function(tpot_obj, None)
assert(tpot_obj.gp_generation == 1)示例2: test_score_2
# 需要导入模块: from tpot import TPOT [as 别名]
# 或者: from tpot.TPOT import pbar [as 别名]
def test_score_2():
"""Ensure that the TPOT score function outputs a known score for a fixed pipeline"""
tpot_obj = TPOT()
tpot_obj._training_classes = training_classes
tpot_obj._training_features = training_features
tpot_obj.pbar = tqdm(total=1, disable=True)
known_score = 0.981993770448 # Assumes use of the TPOT balanced_accuracy function
# Reify pipeline with known score
tpot_obj._optimized_pipeline = creator.Individual.\
from_string('_logistic_regression(input_df, 1.0, 0, True)', tpot_obj._pset)
# 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3: test_score_2
# 需要导入模块: from tpot import TPOT [as 别名]
# 或者: from tpot.TPOT import pbar [as 别名]
def test_score_2():
"""Assert that the TPOT score function outputs a known score for a fixed pipeline"""
tpot_obj = TPOT()
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)