BuiltInOp¶
- class hana_ml.algorithms.pal.pipeline.BuiltInOp(op_name, **kwargs)¶
Pipeline built-in operators.
- Parameters
- op_name{'OneHotEncoder', 'LabelEncoder', 'CBEncoder', 'PolynomialFeatures', 'TargetEncoder'}
- **kwargskeywords arguments
Arbitrary keywords arguments passed to the specified built-in operator specified by
op_name.Valid operator-parameter setting is displayed as follows:
OneHotEncoder
ignore_unknown: type int, with valid values {0, 1} and default value 1, where 0 means does not ignore the any unknown categorical value in predict data(i.e. throw-error if encountered), and 1 means encoding the unknown categories from -n to -1(n is the number of unknown categories in predict data).minimum_fraction: type float with default value 0.0, which specifies the minimum fraction of uniques values in a feature for it to be considered categorical.
LabelEncoder
ignore_unknown: type int with valid values {0, 1} and default value 1, where 0 means does not ignore the any unknown categorical value in predict data(i.e. throw-error if encountered), and 1 means encoding the unknown categories from -n to -1(n is the number of unknown categories in predict data).
PolynomialFeatures
min_degree: type int with default value 1. It specifies the minimum polynomial degree of generated features.max_degree: type int with default value 2. It specifies the maximum polynomial degree of generated features.interaction_only: type bool with default value False. If set as true, only interaction features are produced.include_bias: type bool with default value True. It specifies whether or not to include a column of constant 1 in the generated polynomial features.
CBEncoder
None
Methods
fit(data)Dummy function.
fit_transform(data)Dummy function.
- fit(data)¶
Dummy function.
- fit_transform(data)¶
Dummy function.