# Copyright 2019 IBM Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from sklearn.decomposition import NMF as SKLModel
import lale.helpers
import lale.operators
[docs]class NMFImpl():
def __init__(self, n_components=None, init=None, solver='cd', beta_loss='frobenius', tol=0.0001, max_iter=200, random_state=None, alpha=0.0, l1_ratio=0.0, verbose=0, shuffle=False):
self._hyperparams = {
'n_components': n_components,
'init': init,
'solver': solver,
'beta_loss': beta_loss,
'tol': tol,
'max_iter': max_iter,
'random_state': random_state,
'alpha': alpha,
'l1_ratio': l1_ratio,
'verbose': verbose,
'shuffle': shuffle}
[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': 'Non-Negative Matrix Factorization (NMF)',
'allOf': [{
'type': 'object',
'required': ['n_components', 'init', 'solver', 'beta_loss', 'tol', 'max_iter', 'random_state', 'alpha', 'l1_ratio', 'verbose', 'shuffle'],
'relevantToOptimizer': ['n_components', 'tol', 'max_iter', 'alpha', 'shuffle'],
'additionalProperties': False,
'properties': {
'n_components': {
'anyOf': [
{ 'type': 'integer',
'minimum': 1,
'minimumForOptimizer': 2,
'maximumForOptimizer': 256,
'distribution': 'uniform'},
{ 'enum': [None]}],
'default': None,
'description': 'Number of components, if n_components is not set all features'},
'init': {
'enum': ['custom', 'nndsvd', 'nndsvda', 'nndsvdar', 'random', None],
'default': None,
'description': 'Method used to initialize the procedure.'},
'solver': {
'enum': ['cd', 'mu'],
'default': 'cd',
'description': 'Numerical solver to use:'},
'beta_loss': {
'description': 'Beta divergence to be minimized, measuring the distance between X and the dot product WH.',
'anyOf': [
{ 'type': 'number'},
{ 'enum': ['frobenius', 'kullback-leibler', 'itakura-saito']}],
'default': 'frobenius' },
'tol': {
'type': 'number',
'minimum': 0.0,
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'Tolerance of the stopping condition.'},
'max_iter': {
'type': 'integer',
'minimum': 1,
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 200,
'description': 'Maximum number of iterations before timing out.'},
'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;'},
'alpha': {
'type': 'number',
'minimumForOptimizer': 1e-10,
'maximumForOptimizer': 1.0,
'distribution': 'loguniform',
'default': 0.0,
'description': 'Constant that multiplies the regularization terms. Set it to zero to have no regularization.'},
'l1_ratio': {
'type': 'number',
'default': 0.0,
'minimum': 0.0,
'maximum': 1.0,
'description': 'The regularization mixing parameter.'},
'verbose': {
'anyOf': [{
'type': 'boolean'}, {
'type': 'integer'}],
'default': 0,
'description': 'Whether to be verbose.'},
'shuffle': {
'type': 'boolean',
'default': False,
'description': 'If true, randomize the order of coordinates in the CD solver.'},
}}, {
'description': "beta_loss, only in 'mu' solver",
'anyOf': [{
'type': 'object',
'properties': {
'beta_loss': {
'enum': ['frobenius']},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['mu']},
}}]}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'type': 'object',
'required': ['X'],
'additionalProperties': False,
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number', 'minimum': 0.0},
}},
'y': {}}}
_input_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'type': 'object',
'required': ['X'],
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number', 'minimum': 0.0}}}}}
_output_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Transformed data',
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
}
_combined_schemas = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Combined schema for expected data and hyperparameters.',
'documentation_url': 'https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.NMF.html',
'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)
NMF = lale.operators.make_operator(NMFImpl, _combined_schemas)