def get_space_p_in_p():
space = HyperSpace()
with space.as_default():
p1 = Pipeline([SimpleImputer(name='imputer1'), StandardScaler(name='scaler1')], name='p1')
p2 = Pipeline([SimpleImputer(name='imputer2'), StandardScaler(name='scaler2')], name='p2')
input = HyperInput(name='input1')
p3 = Pipeline([p1, p2], name='p3')(input)
space.set_inputs(input)
return space
def get_space_column_transformer():
space = HyperSpace()
with space.as_default():
input = HyperInput(name='input1')
p1 = Pipeline([SimpleImputer(name='imputer1'), StandardScaler(name='scaler1')], columns=['a', 'b', 'c'],
name='p1')(input)
p2 = Pipeline([SimpleImputer(name='imputer2'), StandardScaler(name='scaler2')], columns=['c', 'd'], name='p2')(
input)
p3 = ColumnTransformer()([p1, p2])
space.set_inputs(input)
return space
def numeric_pipeline_complex(impute_strategy=None, seq_no=0):
if impute_strategy is None:
impute_strategy = Choice(
['mean', 'median', 'constant', 'most_frequent'])
elif isinstance(impute_strategy, list):
impute_strategy = Choice(impute_strategy)
# reduce_skewness_kurtosis = SkewnessKurtosisTransformer(transform_fn=Choice([np.log, np.log10, np.log1p]))
# reduce_skewness_kurtosis_optional = Optional(reduce_skewness_kurtosis, keep_link=True,
# name=f'numeric_reduce_skewness_kurtosis_optional_{seq_no}')
imputer = SimpleImputer(missing_values=np.nan,
strategy=impute_strategy,
name=f'numeric_imputer_{seq_no}')
scaler_options = ModuleChoice([
StandardScaler(name=f'numeric_standard_scaler_{seq_no}'),
MinMaxScaler(name=f'numeric_minmax_scaler_{seq_no}'),
MaxAbsScaler(name=f'numeric_maxabs_scaler_{seq_no}'),
RobustScaler(name=f'numeric_robust_scaler_{seq_no}')
],
name=f'numeric_or_scaler_{seq_no}')
scaler_optional = Optional(scaler_options,
keep_link=True,
name=f'numeric_scaler_optional_{seq_no}')
pipeline = Pipeline([imputer, scaler_optional],
name=f'numeric_pipeline_complex_{seq_no}',
columns=column_number_exclude_timedelta)
return pipeline
def categorical_pipeline_complex(impute_strategy=None,
svd_components=3,
seq_no=0):
if impute_strategy is None:
impute_strategy = Choice(['constant', 'most_frequent'])
elif isinstance(impute_strategy, list):
impute_strategy = Choice(impute_strategy)
if isinstance(svd_components, list):
svd_components = Choice(svd_components)
def onehot_svd():
onehot = OneHotEncoder(name=f'categorical_onehot_{seq_no}')
optional_svd = Optional(TruncatedSVD(n_components=svd_components,
name=f'categorical_svd_{seq_no}'),
name=f'categorical_optional_svd_{seq_no}',
keep_link=True)(onehot)
return optional_svd
imputer = SimpleImputer(missing_values=np.nan,
strategy=impute_strategy,
name=f'categorical_imputer_{seq_no}')
label_encoder = MultiLabelEncoder(
name=f'categorical_label_encoder_{seq_no}')
onehot = onehot_svd()
le_or_onehot_pca = ModuleChoice(
[label_encoder, onehot], name=f'categorical_le_or_onehot_pca_{seq_no}')
pipeline = Pipeline([imputer, le_or_onehot_pca],
name=f'categorical_pipeline_complex_{seq_no}',
columns=column_object_category_bool)
return pipeline
def numeric_pipeline_simple(impute_strategy='mean', seq_no=0):
pipeline = Pipeline(
[
SimpleImputer(missing_values=np.nan,
strategy=impute_strategy,
name=f'numeric_imputer_{seq_no}'),
StandardScaler(name=f'numeric_standard_scaler_{seq_no}')
],
columns=column_number_exclude_timedelta,
name=f'numeric_pipeline_simple_{seq_no}',
)
return pipeline
def categorical_pipeline_simple(impute_strategy='constant', seq_no=0):
pipeline = Pipeline(
[
SimpleImputer(missing_values=np.nan,
strategy=impute_strategy,
name=f'categorical_imputer_{seq_no}'),
MultiLabelEncoder(name=f'categorical_label_encoder_{seq_no}')
],
columns=column_object_category_bool,
name=f'categorical_pipeline_simple_{seq_no}',
)
return pipeline
def get_space_2outputs():
space = HyperSpace()
with space.as_default():
Pipeline([tow_outputs()])
return space
def get_space_2inputs():
space = HyperSpace()
with space.as_default():
Pipeline([tow_inputs(), StandardScaler()])
return space
def get_space():
space = HyperSpace()
with space.as_default():
Pipeline([SimpleImputer(), StandardScaler()])
return space