from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class QuadraticDiscriminantAnalysisImpl():
def __init__(self, priors=None, reg_param=0.0, store_covariance=False, tol=0.0001, store_covariances=None):
self._hyperparams = {
'priors': priors,
'reg_param': reg_param,
'store_covariance': store_covariance,
'tol': tol,
'store_covariances': store_covariances}
[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 = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for QuadraticDiscriminantAnalysis Quadratic Discriminant Analysis',
'allOf': [{
'type': 'object',
'required': ['priors', 'reg_param', 'store_covariance', 'tol', 'store_covariances'],
'relevantToOptimizer': ['tol'],
'additionalProperties': False,
'properties': {
'priors': {
'XXX TODO XXX': 'array, optional, shape = [n_classes]',
'description': 'Priors on classes',
'enum': [None],
'default': None},
'reg_param': {
'type': 'number',
'default': 0.0,
'description': 'Regularizes the covariance estimate as'},
'store_covariance': {
'type': 'boolean',
'default': False,
'description': 'If True the covariance matrices are computed and stored in the'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'Threshold used for rank estimation.'},
'store_covariances': {
'anyOf': [{
'type': 'boolean'}, {
'enum': [None]}],
'default': None,
'description': 'Deprecated, use `store_covariance`.'},
}}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit the model according to the given training data and parameters.',
'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'},
}
_input_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Return posterior probabilities of classification.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Array of samples/test vectors.'},
},
}
_output_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Posterior probabilities of classification per class.',
'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)
QuadraticDiscriminantAnalysis = lale.operators.make_operator(QuadraticDiscriminantAnalysisImpl, _combined_schemas)