Source code for lale.lib.autogen.additive_chi2_sampler

from sklearn.kernel_approximation import AdditiveChi2Sampler as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf

class AdditiveChi2SamplerImpl():

    def __init__(self, sample_steps=2, sample_interval=None):
        self._hyperparams = {
            'sample_steps': sample_steps,
            'sample_interval': sample_interval}

    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

    def transform(self, X):
        return self._sklearn_model.transform(X)
_hyperparams_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'inherited docstring for AdditiveChi2Sampler    Approximate feature map for additive chi2 kernel.',
    'allOf': [{
        'type': 'object',
        'required': ['sample_steps', 'sample_interval'],
        'relevantToOptimizer': ['sample_steps'],
        'additionalProperties': False,
        'properties': {
            'sample_steps': {
                'type': 'integer',
                'minimumForOptimizer': 2,
                'maximumForOptimizer': 3,
                'distribution': 'uniform',
                'default': 2,
                'description': 'Gives the number of (complex) sampling points.'},
            'sample_interval': {
                'anyOf': [{
                    'type': 'number'}, {
                    'enum': [None]}],
                'default': None,
                'description': 'Sampling interval. Must be specified when sample_steps not in {1,2,3}.'},
        }}],
}
_input_fit_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'Set the parameters',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            },
            'description': 'Training data, where n_samples in the number of samples'},
    },
}
_input_transform_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'Apply approximate feature map to X.',
    'type': 'object',
    'properties': {
        'X': {
            'type': 'array',
            'items': {
                'type': 'array',
                'items': {
                    'type': 'number'},
            }},
    },
}
_output_transform_schema = {
    '$schema': 'http://json-schema.org/draft-04/schema#',
    'description': 'Whether the return value is an array of sparse matrix depends on',
    'XXX TODO XXX': '{array, sparse matrix},                shape = (n_samples, n_features * (2*sample_steps + 1))',
}
_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)
AdditiveChi2Sampler = lale.operators.make_operator(AdditiveChi2SamplerImpl, _combined_schemas)