from sklearn.neighbors.nearest_centroid import NearestCentroid as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class NearestCentroidImpl():
def __init__(self, metric='euclidean', shrink_threshold=None):
self._hyperparams = {
'metric': metric,
'shrink_threshold': shrink_threshold}
[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 = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for NearestCentroid Nearest centroid classifier.',
'allOf': [{
'type': 'object',
'required': ['metric', 'shrink_threshold'],
'relevantToOptimizer': ['metric'],
'additionalProperties': False,
'properties': {
'metric': {
'anyOf': [{
'type': 'object',
'forOptimizer': False}, {
'enum': ['euclidean', 'manhattan', 'minkowski']}],
'default': 'euclidean',
'description': 'The metric to use when calculating distance between instances in a'},
'shrink_threshold': {
'anyOf': [{
'type': 'number'}, {
'enum': [None]}],
'default': None,
'description': 'Threshold for shrinking centroids to remove features.'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the NearestCentroid model according to the given training data.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training vector, where n_samples is the number of samples and'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Target values (integers)'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Perform classification on an array of test vectors X.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Perform classification on an array of test vectors X.',
'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': ['estimator'],
'post': []},
'properties': {
'hyperparams': _hyperparams_schema,
'input_fit': _input_fit_schema,
'input_predict': _input_predict_schema,
'output_predict': _output_predict_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
NearestCentroid = lale.operators.make_operator(NearestCentroidImpl, _combined_schemas)