class Hyperparams(hyperparams.Hyperparams):
d = hyperparams.Hyperparameter(
default=0,
description=
'The reduced dimension of matrix factorization. It is usually smaller than the sizes of the matrix. When setting to 0, d will be automatically roughly estimated.',
semantic_types=[
'https://metadata.datadrivendiscovery.org/types/TuningParameter'
])
tol = hyperparams.Hyperparameter(
default=1e-5,
description=
'The tolerance of the relative changes of the variables in optimization. It will be utilized to check the convergence.',
semantic_types=[
'https://metadata.datadrivendiscovery.org/types/TuningParameter'
])
maxiter = hyperparams.Hyperparameter(
default=200,
description='The maximum number of iterations.',
semantic_types=[
'https://metadata.datadrivendiscovery.org/types/TuningParameter'
])
alpha = hyperparams.Hyperparameter(
default=0.1,
description=
'The regularization parameter for the factorized dense matrix A.',
semantic_types=[
'https://metadata.datadrivendiscovery.org/types/TuningParameter'
])
class Hyperparams(hyperparams.Hyperparams):
convert = hyperparams.Hyperparameter(
default=True,
description='Whether to convert non-numerical features to numerical.',
semantic_types=[
'https://metadata.datadrivendiscovery.org/types/ControlParameter'
])
to_type = hyperparams.Hyperparameter(
default=float,
description='The data type we convert features to.',
semantic_types=[
'https://metadata.datadrivendiscovery.org/types/ControlParameter'
])
class Hyperparams(hyperparams.Hyperparams):
period = hyperparams.Hyperparameter(default=1,semantic_types=[
'https://metadata.datadrivendiscovery.org/types/TuningParameter',
],description="Window Size for decomposition")
model = hyperparams.Hyperparameter(default='additive', semantic_types=[
'https://metadata.datadrivendiscovery.org/types/ControlParameter',
], description="Window Size for decomposition")
use_columns = hyperparams.Set(
elements=hyperparams.Hyperparameter[int](-1),
default=(),
semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
description="A set of column indices to force primitive to operate on. If any specified column cannot be parsed, it is skipped.",
)
exclude_columns = hyperparams.Set(
elements=hyperparams.Hyperparameter[int](-1),
default=(),
semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
description="A set of column indices to not operate on. Applicable only if \"use_columns\" is not provided.",
)
return_result = hyperparams.Enumeration(
values=['append', 'replace', 'new'],
default='append',
semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
description="Should parsed columns be appended, should they replace original columns, or should only parsed columns be returned? This hyperparam is ignored if use_semantic_types is set to false.",
)
use_semantic_types = hyperparams.UniformBool(
default=False,
semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
description="Controls whether semantic_types metadata will be used for filtering columns in input dataframe. Setting this to false makes the code ignore return_result and will produce only the output dataframe"
)
add_index_columns = hyperparams.UniformBool(
default=False,
semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
description="Also include primary index columns if input data has them. Applicable only if \"return_result\" is set to \"new\".",
)
error_on_no_input = hyperparams.UniformBool(
default=True,
semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter'],
description="Throw an exception if no input column is selected/provided. Defaults to true to behave like sklearn. To prevent pipelines from breaking set this to False.",
)
return_semantic_type = hyperparams.Enumeration[str](
values=['https://metadata.datadrivendiscovery.org/types/Attribute',
'https://metadata.datadrivendiscovery.org/types/ConstructedAttribute'],
default='https://metadata.datadrivendiscovery.org/types/Attribute',
description='Decides what semantic type to attach to generated attributes',
semantic_types=['https://metadata.datadrivendiscovery.org/types/ControlParameter']
)
class SubclassTestHyperparams(TestHyperparams):
b = hyperparams.Hyperparameter(
default=1,
semantic_types=[
'https://metadata.datadrivendiscovery.org/types/TuningParameter'
],
)