from sklearn.cluster.k_means_ import MiniBatchKMeans as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class MiniBatchKMeansImpl():
def __init__(self, n_clusters=8, init='k-means++', max_iter=100, batch_size=100, verbose=0, compute_labels=True, random_state=None, tol=0.0, max_no_improvement=10, init_size=None, n_init=3, reassignment_ratio=0.01):
self._hyperparams = {
'n_clusters': n_clusters,
'init': init,
'max_iter': max_iter,
'batch_size': batch_size,
'verbose': verbose,
'compute_labels': compute_labels,
'random_state': random_state,
'tol': tol,
'max_no_improvement': max_no_improvement,
'init_size': init_size,
'n_init': n_init,
'reassignment_ratio': reassignment_ratio}
[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
[docs] def predict(self, X):
return self._sklearn_model.predict(X)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for MiniBatchKMeans Mini-Batch K-Means clustering',
'allOf': [{
'type': 'object',
'required': ['n_clusters', 'init', 'max_iter', 'batch_size', 'verbose', 'compute_labels', 'random_state', 'tol', 'max_no_improvement', 'init_size', 'n_init', 'reassignment_ratio'],
'relevantToOptimizer': ['n_clusters', 'init', 'max_iter', 'batch_size', 'compute_labels', 'tol', 'max_no_improvement', 'n_init'],
'additionalProperties': False,
'properties': {
'n_clusters': {
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 8,
'distribution': 'uniform',
'default': 8,
'description': 'The number of clusters to form as well as the number of'},
'init': {
'enum': ['k-means++', 'random', 'ndarray'],
'default': 'k-means++',
'description': "Method for initialization, defaults to 'k-means++':"},
'max_iter': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 100,
'description': 'Maximum number of iterations over the complete dataset before'},
'batch_size': {
'type': 'integer',
'minimumForOptimizer': 3,
'maximumForOptimizer': 128,
'distribution': 'uniform',
'default': 100,
'description': 'Size of the mini batches.'},
'verbose': {
'anyOf': [{
'type': 'boolean'}, {
'type': 'integer'}],
'default': 0,
'description': 'Verbosity mode.'},
'compute_labels': {
'type': 'boolean',
'default': True,
'description': 'Compute label assignment and inertia for the complete dataset'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'Determines random number generation for centroid initialization and'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.0,
'description': 'Control early stopping based on the relative center changes as'},
'max_no_improvement': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 11,
'distribution': 'uniform',
'default': 10,
'description': 'Control early stopping based on the consecutive number of mini'},
'init_size': {
'XXX TODO XXX': 'int, optional, default: 3 * batch_size',
'description': 'Number of samples to randomly sample for speeding up the',
'enum': [None],
'default': None},
'n_init': {
'type': 'integer',
'minimumForOptimizer': 3,
'maximumForOptimizer': 10,
'distribution': 'uniform',
'default': 3,
'description': 'Number of random initializations that are tried.'},
'reassignment_ratio': {
'type': 'number',
'default': 0.01,
'description': 'Control the fraction of the maximum number of counts for a'},
}}, {
'XXX TODO XXX': 'Parameter: init_size > only algorithm is initialized by running a batch kmeans on a random subset of the data'}, {
'XXX TODO XXX': 'Parameter: n_init > only run once'}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Compute the centroids on X by chunking it into mini-batches.',
'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': 'Training instances to cluster. It must be noted that the data'},
'y': {
'description': 'not used, present here for API consistency by convention.'},
'sample_weight': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'default': None,
'description': 'The weights for each observation in X. If None, all observations'},
},
}
_input_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Transform X to a cluster-distance space.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'New data to transform.'},
},
}
_output_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'X transformed in the new space.',
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict the closest cluster each sample in X belongs to.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'New data to predict.'},
'sample_weight': {
'anyOf': [{
'type': 'array',
'items': {
'type': 'number'},
}, {
'enum': [None]}],
'default': None,
'description': 'The weights for each observation in X. If None, all observations'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Index of the cluster each sample belongs to.',
'type': 'array',
'items': {
'type': 'number'},
}
_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,
'input_predict': _input_predict_schema,
'output_predict': _output_predict_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
MiniBatchKMeans = lale.operators.make_operator(MiniBatchKMeansImpl, _combined_schemas)