from sklearn.decomposition.sparse_pca import MiniBatchSparsePCA as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class MiniBatchSparsePCAImpl():
def __init__(self, n_components=None, alpha=1, ridge_alpha=0.01, n_iter=100, callback=None, batch_size=3, verbose=False, shuffle=True, n_jobs=None, method='lars', random_state=None, normalize_components=False):
self._hyperparams = {
'n_components': n_components,
'alpha': alpha,
'ridge_alpha': ridge_alpha,
'n_iter': n_iter,
'callback': callback,
'batch_size': batch_size,
'verbose': verbose,
'shuffle': shuffle,
'n_jobs': n_jobs,
'method': method,
'random_state': random_state,
'normalize_components': normalize_components}
[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 MiniBatchSparsePCA Mini-batch Sparse Principal Components Analysis',
'allOf': [{
'type': 'object',
'required': ['n_components', 'alpha', 'ridge_alpha', 'n_iter', 'callback', 'batch_size', 'verbose', 'shuffle', 'n_jobs', 'method', 'random_state', 'normalize_components'],
'relevantToOptimizer': ['n_components', 'alpha', 'n_iter', 'batch_size', 'shuffle', 'method'],
'additionalProperties': False,
'properties': {
'n_components': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 256,
'distribution': 'uniform'}, {
'enum': [None]}],
'default': None,
'description': 'number of sparse atoms to extract'},
'alpha': {
'type': 'integer',
'minimumForOptimizer': 1,
'maximumForOptimizer': 2,
'distribution': 'uniform',
'default': 1,
'description': 'Sparsity controlling parameter. Higher values lead to sparser'},
'ridge_alpha': {
'type': 'number',
'default': 0.01,
'description': 'Amount of ridge shrinkage to apply in order to improve'},
'n_iter': {
'type': 'integer',
'minimumForOptimizer': 5,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 100,
'description': 'number of iterations to perform for each mini batch'},
'callback': {
'anyOf': [{
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'callable that gets invoked every five iterations'},
'batch_size': {
'type': 'integer',
'minimumForOptimizer': 3,
'maximumForOptimizer': 128,
'distribution': 'uniform',
'default': 3,
'description': 'the number of features to take in each mini batch'},
'verbose': {
'anyOf': [{
'type': 'integer'}, {
'type': 'boolean'}],
'default': False,
'description': 'Controls the verbosity; the higher, the more messages. Defaults to 0.'},
'shuffle': {
'type': 'boolean',
'default': True,
'description': 'whether to shuffle the data before splitting it in batches'},
'n_jobs': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'Number of parallel jobs to run.'},
'method': {
'enum': ['lars', 'cd'],
'default': 'lars',
'description': 'lars: uses the least angle regression method to solve the lasso problem'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'If int, random_state is the seed used by the random number generator;'},
'normalize_components': {
'type': 'boolean',
'default': False,
'description': '- if False, use a version of Sparse PCA without components'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the model from data in X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training vector, where n_samples in the number of samples'},
'y': {
}},
}
_input_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Least Squares projection of the data onto the sparse components.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Test data to be transformed, must have the same number of'},
'ridge_alpha': {
'type': 'number',
'default': 0.01,
'description': 'Amount of ridge shrinkage to apply in order to improve'},
},
}
_output_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Transformed data.',
'XXX TODO XXX': '',
}
_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)
MiniBatchSparsePCA = lale.operators.make_operator(MiniBatchSparsePCAImpl, _combined_schemas)