from sklearn.semi_supervised.label_propagation import LabelPropagation as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class LabelPropagationImpl():
def __init__(self, kernel='rbf', gamma=20, n_neighbors=7, alpha=None, max_iter=1000, tol=0.001, n_jobs=None):
self._hyperparams = {
'kernel': kernel,
'gamma': gamma,
'n_neighbors': n_neighbors,
'alpha': alpha,
'max_iter': max_iter,
'tol': tol,
'n_jobs': n_jobs}
[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)
[docs] def predict_proba(self, X):
return self._sklearn_model.predict_proba(X)
_hyperparams_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for LabelPropagation Label Propagation classifier',
'allOf': [{
'type': 'object',
'required': ['kernel', 'gamma', 'n_neighbors', 'alpha', 'max_iter', 'tol', 'n_jobs'],
'relevantToOptimizer': ['kernel', 'gamma', 'n_neighbors', 'max_iter', 'tol'],
'additionalProperties': False,
'properties': {
'kernel': {
'enum': ['knn', 'rbf', 'callable'],
'default': 'rbf',
'description': 'String identifier for kernel function to use or the kernel function'},
'gamma': {
'type': 'number',
'forOptimizer': False,
'default': 20,
'description': 'Parameter for rbf kernel'},
'n_neighbors': {
'XXX TODO XXX': 'integer > 0',
'description': 'Parameter for knn kernel',
'type': 'integer',
'minimumForOptimizer': 5,
'maximumForOptimizer': 20,
'distribution': 'uniform',
'default': 7},
'alpha': {
'anyOf': [{
'type': 'number'}, {
'enum': [None]}],
'default': None,
'description': 'Clamping factor.'},
'max_iter': {
'type': 'integer',
'minimumForOptimizer': 10,
'maximumForOptimizer': 1000,
'distribution': 'uniform',
'default': 1000,
'description': 'Change maximum number of iterations allowed'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.001,
'description': 'Convergence tolerance: threshold to consider the system at steady'},
'n_jobs': {
'anyOf': [{
'type': 'integer'}, {
'enum': [None]}],
'default': None,
'description': 'The number of parallel jobs to run.'},
}}, {
'XXX TODO XXX': "Parameter: kernel > only 'rbf' and 'knn' strings are valid inputs"}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit a semi-supervised label propagation model based',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'A {n_samples by n_samples} size matrix will be created from this'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'n_labeled_samples (unlabeled points are marked as -1)'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Performs inductive inference across the model.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predictions for input data',
'type': 'array',
'items': {
'type': 'number'},
}
_input_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict probability for each possible outcome.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}},
},
}
_output_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Normalized probability distributions across',
'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.',
'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,
'input_predict_proba': _input_predict_proba_schema,
'output_predict_proba': _output_predict_proba_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
LabelPropagation = lale.operators.make_operator(LabelPropagationImpl, _combined_schemas)