本文整理汇总了Python中sklearn.feature_selection.base.SelectorMixin方法的典型用法代码示例。如果您正苦于以下问题：Python base.SelectorMixin方法的具体用法？Python base.SelectorMixin怎么用？Python base.SelectorMixin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.feature_selection.base的用法示例。
在下文中一共展示了base.SelectorMixin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_template_1
# 需要导入模块: from sklearn.feature_selection import base [as 别名]
# 或者: from sklearn.feature_selection.base import SelectorMixin [as 别名]
def test_template_1():
"""Assert that TPOT template option generates pipeline when each step is a type of operator."""
tpot_obj = TPOTClassifier(
random_state=42,
verbosity=0,
template='Selector-Transformer-Classifier'
)
tpot_obj._fit_init()
pop = tpot_obj._toolbox.population(n=10)
for deap_pipeline in pop:
operator_count = tpot_obj._operator_count(deap_pipeline)
sklearn_pipeline = tpot_obj._toolbox.compile(expr=deap_pipeline)
assert operator_count == 3
assert issubclass(sklearn_pipeline.steps[0][1].__class__, SelectorMixin)
assert issubclass(sklearn_pipeline.steps[1][1].__class__, TransformerMixin)
assert issubclass(sklearn_pipeline.steps[2][1].__class__, ClassifierMixin) 示例2: test_template_2
# 需要导入模块: from sklearn.feature_selection import base [as 别名]
# 或者: from sklearn.feature_selection.base import SelectorMixin [as 别名]
def test_template_2():
"""Assert that TPOT template option generates pipeline when each step is operator type with a duplicate main type."""
tpot_obj = TPOTClassifier(
random_state=42,
verbosity=0,
template='Selector-Selector-Transformer-Classifier'
)
tpot_obj._fit_init()
pop = tpot_obj._toolbox.population(n=10)
for deap_pipeline in pop:
operator_count = tpot_obj._operator_count(deap_pipeline)
sklearn_pipeline = tpot_obj._toolbox.compile(expr=deap_pipeline)
assert operator_count == 4
assert issubclass(sklearn_pipeline.steps[0][1].__class__, SelectorMixin)
assert issubclass(sklearn_pipeline.steps[1][1].__class__, SelectorMixin)
assert issubclass(sklearn_pipeline.steps[2][1].__class__, TransformerMixin)
assert issubclass(sklearn_pipeline.steps[3][1].__class__, ClassifierMixin) 示例3: test_template_3
# 需要导入模块: from sklearn.feature_selection import base [as 别名]
# 或者: from sklearn.feature_selection.base import SelectorMixin [as 别名]
def test_template_3():
"""Assert that TPOT template option generates pipeline when one of steps is a specific operator."""
tpot_obj = TPOTClassifier(
random_state=42,
verbosity=0,
template='SelectPercentile-Transformer-Classifier'
)
tpot_obj._fit_init()
pop = tpot_obj._toolbox.population(n=10)
for deap_pipeline in pop:
operator_count = tpot_obj._operator_count(deap_pipeline)
sklearn_pipeline = tpot_obj._toolbox.compile(expr=deap_pipeline)
assert operator_count == 3
assert sklearn_pipeline.steps[0][0] == 'SelectPercentile'.lower()
assert issubclass(sklearn_pipeline.steps[0][1].__class__, SelectorMixin)
assert issubclass(sklearn_pipeline.steps[1][1].__class__, TransformerMixin)
assert issubclass(sklearn_pipeline.steps[2][1].__class__, ClassifierMixin) 示例4: test_template_4
# 需要导入模块: from sklearn.feature_selection import base [as 别名]
# 或者: from sklearn.feature_selection.base import SelectorMixin [as 别名]
def test_template_4():
"""Assert that TPOT template option generates pipeline when one of steps is a specific operator."""
tpot_obj = TPOTClassifier(
population_size=5,
generations=2,
random_state=42,
verbosity=0,
config_dict = 'TPOT light',
template='SelectPercentile-Transformer-Classifier'
)
tpot_obj.fit(pretest_X, pretest_y)
assert isinstance(tpot_obj._optimized_pipeline, creator.Individual)
assert not (tpot_obj._start_datetime is None)
sklearn_pipeline = tpot_obj.fitted_pipeline_
operator_count = tpot_obj._operator_count(tpot_obj._optimized_pipeline)
assert operator_count == 3
assert sklearn_pipeline.steps[0][0] == 'SelectPercentile'.lower()
assert issubclass(sklearn_pipeline.steps[0][1].__class__, SelectorMixin)
assert issubclass(sklearn_pipeline.steps[1][1].__class__, TransformerMixin)
assert issubclass(sklearn_pipeline.steps[2][1].__class__, ClassifierMixin) 示例5: _filter
# 需要导入模块: from sklearn.feature_selection import base [as 别名]
# 或者: from sklearn.feature_selection.base import SelectorMixin [as 别名]
def _filter(obj):
if isinstance(obj, DataFrameMapper):
obj.features = _filter_steps(obj.features)
if hasattr(obj, "built_features"):
if obj.built_features is not None:
obj.built_features = _filter_steps(obj.built_features)
elif isinstance(obj, ColumnTransformer):
obj.transformers = _filter_steps(obj.transformers)
obj.remainder = _filter(obj.remainder)
if hasattr(obj, "transformers_"):
obj.transformers_ = _filter_steps(obj.transformers_)
elif isinstance(obj, FeatureUnion):
obj.transformer_list = _filter_steps(obj.transformer_list)
elif isinstance(obj, Pipeline):
obj.steps = _filter_steps(obj.steps)
elif isinstance(obj, SelectorMixin):
return SelectorProxy(obj)
elif isinstance(obj, list):
return [_filter(e) for e in obj]
return obj