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 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 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
def tow_outputs():
s1 = SimpleImputer()
s2 = SimpleImputer()(s1)
s3 = StandardScaler()(s1)
return s2
def tow_inputs():
s1 = SimpleImputer()
s2 = SimpleImputer()
s3 = StandardScaler()([s1, s2])
return s3