from sklearn.cluster.k_means_ import KMeans as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class KMeansImpl():
def __init__(self, n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm='auto'):
self._hyperparams = {
'n_clusters': n_clusters,
'init': init,
'n_init': n_init,
'max_iter': max_iter,
'tol': tol,
'precompute_distances': precompute_distances,
'verbose': verbose,
'random_state': random_state,
'copy_x': copy_x,
'n_jobs': n_jobs,
'algorithm': algorithm}
[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 KMeans K-Means clustering',
'allOf': [{
'type': 'object',
'required': ['n_clusters', 'init', 'n_init', 'max_iter', 'tol', 'precompute_distances', 'verbose', 'random_state', 'copy_x', 'n_jobs', 'algorithm'],
'relevantToOptimizer': ['n_clusters', 'init', 'n_init', 'max_iter', 'tol', 'precompute_distances', 'copy_x', 'algorithm'],
'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++':"},
'n_init': {
'type': 'integer',
'minimumForOptimizer': 3,
'maximumForOptimizer': 10,
'distribution': 'uniform',
'default': 10,
'description': 'Number of time the k-means algorithm will be run with different'},
'max_iter': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 300,
'description': 'Maximum number of iterations of the k-means algorithm for a'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'Relative tolerance with regards to inertia to declare convergence'},
'precompute_distances': {
'enum': ['auto', True, False],
'default': 'auto',
'description': 'Precompute distances (faster but takes more memory).'},
'verbose': {
'type': 'integer',
'default': 0,
'description': 'Verbosity mode.'},
'random_state': {
'anyOf': [{
'type': 'integer'}, {
'type': 'object'}, {
'enum': [None]}],
'default': None,
'description': 'Determines random number generation for centroid initialization. Use'},
'copy_x': {
'type': 'boolean',
'default': True,
'description': 'When pre-computing distances it is more numerically accurate to center'},
'n_jobs': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'The number of jobs to use for the computation. This works by computing'},
'algorithm': {
'XXX TODO XXX': '"auto", "full" or "elkan", default="auto"',
'description': 'K-means algorithm to use. The classical EM-style algorithm is "full".',
'enum': ['auto', 'elkan', 'full'],
'default': 'auto'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Compute k-means clustering.',
'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)
KMeans = lale.operators.make_operator(KMeansImpl, _combined_schemas)