from sklearn.cluster.birch import Birch as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class BirchImpl():
def __init__(self, threshold=0.5, branching_factor=50, n_clusters=3, compute_labels=True, copy=True):
self._hyperparams = {
'threshold': threshold,
'branching_factor': branching_factor,
'n_clusters': n_clusters,
'compute_labels': compute_labels,
'copy': copy}
[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 = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for Birch Implements the Birch clustering algorithm.',
'allOf': [{
'type': 'object',
'required': ['threshold', 'branching_factor', 'n_clusters', 'compute_labels', 'copy'],
'relevantToOptimizer': ['branching_factor', 'n_clusters', 'compute_labels', 'copy'],
'additionalProperties': False,
'properties': {
'threshold': {
'type': 'number',
'default': 0.5,
'description': 'The radius of the subcluster obtained by merging a new sample and the'},
'branching_factor': {
'type': 'integer',
'minimumForOptimizer': 50,
'maximumForOptimizer': 51,
'distribution': 'uniform',
'default': 50,
'description': 'Maximum number of CF subclusters in each node. If a new samples enters'},
'n_clusters': {
'XXX TODO XXX': 'int, instance of sklearn.cluster model, default 3',
'description': 'Number of clusters after the final clustering step, which treats the',
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 8,
'distribution': 'uniform',
'default': 3},
'compute_labels': {
'type': 'boolean',
'default': True,
'description': 'Whether or not to compute labels for each fit.'},
'copy': {
'type': 'boolean',
'default': True,
'description': 'Whether or not to make a copy of the given data. If set to False,'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Build a CF Tree for the input data.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Input data.'},
'y': {
}},
}
_input_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Transform X into subcluster centroids dimension.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Input data.'},
},
}
_output_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Transformed data.',
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict data using the ``centroids_`` of subclusters.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Input data.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Labelled data.',
'XXX TODO XXX': 'ndarray, shape(n_samples)',
}
_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)
Birch = lale.operators.make_operator(BirchImpl, _combined_schemas)