from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as SKLModel
import lale.helpers
import lale.operators
from numpy import nan, inf
[docs]class LinearDiscriminantAnalysisImpl():
def __init__(self, solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001):
self._hyperparams = {
'solver': solver,
'shrinkage': shrinkage,
'priors': priors,
'n_components': n_components,
'store_covariance': store_covariance,
'tol': tol}
[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 = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'inherited docstring for LinearDiscriminantAnalysis Linear Discriminant Analysis',
'allOf': [{
'type': 'object',
'required': ['solver', 'shrinkage', 'priors', 'n_components', 'store_covariance', 'tol'],
'relevantToOptimizer': ['solver', 'n_components', 'tol'],
'additionalProperties': False,
'properties': {
'solver': {
'enum': ['lsqr', 'svd'],
'default': 'svd',
'description': 'Solver to use, possible values:'},
'shrinkage': {
'anyOf': [{
'type': 'string'}, {
'type': 'number'}, {
'enum': [None]}],
'default': None,
'description': 'Shrinkage parameter, possible values:'},
'priors': {
'XXX TODO XXX': 'array, optional, shape (n_classes,)',
'description': 'Class priors.',
'enum': [None],
'default': None},
'n_components': {
'anyOf': [{
'type': 'integer',
'minimumForOptimizer': 2,
'maximumForOptimizer': 256,
'distribution': 'uniform'}, {
'enum': [None]}],
'default': None,
'description': 'Number of components (< n_classes - 1) for dimensionality reduction.'},
'store_covariance': {
'type': 'boolean',
'default': False,
'description': 'Additionally compute class covariance matrix (default False), used'},
'tol': {
'type': 'number',
'minimumForOptimizer': 1e-08,
'maximumForOptimizer': 0.01,
'distribution': 'loguniform',
'default': 0.0001,
'description': 'Threshold used for rank estimation in SVD solver.'},
}}, {
'description': "shrinkage, only with 'lsqr' and 'eigen' solvers",
'anyOf': [{
'type': 'object',
'properties': {
'shrinkage': {
'enum': [None]},
}}, {
'type': 'object',
'properties': {
'solvers': {
'enum': ['lsqr', 'eigen']},
}}]}, {
'description': "store_covariance, only in 'svd' solver",
'anyOf': [{
'type': 'object',
'properties': {
'store_covariance': {
'enum': [False]},
}}, {
'type': 'object',
'properties': {
'solver': {
'enum': ['svd']},
}}]}],
}
_input_fit_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Fit LinearDiscriminantAnalysis model according to the given',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Training data.'},
'y': {
'type': 'array',
'items': {
'type': 'number'},
'description': 'Target values.'},
},
}
_input_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Project data to maximize class separation.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Input data.'},
},
}
_output_transform_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Transformed data.',
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
}
_input_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predict class labels for samples in X.',
'type': 'object',
'properties': {
'X': {
'anyOf': [{
'type': 'array',
'items': {
'XXX TODO XXX': 'item type'},
'XXX TODO XXX': 'array_like or sparse matrix, shape (n_samples, n_features)'}, {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
}}],
'description': 'Samples.'},
},
}
_output_predict_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Predicted class label per sample.',
'type': 'array',
'items': {
'type': 'number'},
}
_input_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Estimate probability.',
'type': 'object',
'properties': {
'X': {
'type': 'array',
'items': {
'type': 'array',
'items': {
'type': 'number'},
},
'description': 'Input data.'},
},
}
_output_predict_proba_schema = {
'$schema': 'http://json-schema.org/draft-04/schema#',
'description': 'Estimated probabilities.',
'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': ['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,
'input_predict_proba': _input_predict_proba_schema,
'output_predict_proba': _output_predict_proba_schema},
}
if (__name__ == '__main__'):
lale.helpers.validate_is_schema(_combined_schemas)
LinearDiscriminantAnalysis = lale.operators.make_operator(LinearDiscriminantAnalysisImpl, _combined_schemas)