from sklearn.preprocessing.data import PolynomialFeatures as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class PolynomialFeaturesImpl():
def __init__(self, degree=2, interaction_only=False, include_bias=True):
self._hyperparams = {
'degree': degree,
'interaction_only': interaction_only,
'include_bias': include_bias}
[docs] def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for PolynomialFeatures Generate polynomial and interaction features.',
'allOf': [{
'type': 'object',
'required': ['degree', 'interaction_only', 'include_bias'],
'relevantToOptimizer': ['degree', 'interaction_only'],
'additionalProperties': False,
'properties': {
'degree': {
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 3,
'distribution': 'uniform',
'default': 2,
'description': 'The degree of the polynomial features. Default = 2.'},
'interaction_only': {
'type': 'boolean',
'default': False,
'description': 'If true, only interaction features are produced: features that are'},
'include_bias': {
'type': 'boolean',
'default': True,
'description': 'If True (default), then include a bias column, the feature in which'},
}}, {
'XXX TODO XXX': 'Parameter: interaction_only > only interaction features are produced: features that are products of at most degree *distinct* input features (so not x[1] ** 2'}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Compute number of output features.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'The data.'},
},
}
_input_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Transform data to polynomial features',
'type': 'object',
'properties': {
'X': {
'anyOf': [{
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': 'array-like or sparse matrix, shape [n_samples, n_features]'}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'The data to transform, row by row.'},
},
}
_output_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'The matrix of features, where NP is the number of polynomial',
'XXX TODO XXX': 'np.ndarray or CSC sparse matrix, shape [n_samples, NP]',
}
_combined_schemas = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Combined schema for expected data and hyperparameters.',
'type': 'object',
'tags': {
'pre': [],
'op': ['transformer'],
'post': []},
'properties': {
'hyperparams': _hyperparams_schema,
'input_fit': _input_fit_schema,
'input_transform': _input_transform_schema,
'output_transform': _output_transform_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
PolynomialFeatures = lale.operators.make_operator(PolynomialFeaturesImpl, _combined_schemas)