def confirm_base_learner_origin(id):
path = functions.get_path_from_query_string(request)
with functions.DBContextManager(path) as session:
base_learner_origin = session.query(models.BaseLearnerOrigin).filter_by(id=id).first()
if base_learner_origin is None:
raise exceptions.UserError('Base learner origin {} not found'.format(id), 404)
if request.method == 'GET':
if base_learner_origin.final:
raise exceptions.UserError('Base learner origin {} '
'is already final'.format(id))
if not base_learner_origin.validation_results:
raise exceptions.UserError('Base learner origin {} has not yet been '
'verified on a dataset'.format(id))
base_learner = base_learner_origin.return_estimator()
validation_results, hyperparameters = functions.verify_estimator_class(
base_learner,
base_learner_origin.meta_feature_generator,
base_learner_origin.metric_generators,
base_learner_origin.validation_results['dataset']
)
base_learner_origin.validation_results = {
'dataset': base_learner_origin.validation_results['dataset'],
'metrics': validation_results
}
base_learner_origin.hyperparameters = hyperparameters
base_learner_origin.final = True
session.add(base_learner_origin)
session.commit()
return jsonify(base_learner_origin.serialize)
def test_verify_estimator_class(self):
np.random.seed(8)
performance_dict, hyperparameters = functions.verify_estimator_class(
RandomForestClassifier(),
'predict_proba',
dict(Accuracy=self.source),
self.dataset_properties
)
assert round(performance_dict['Accuracy'], 3) == 0.8
assert hyperparameters == {
'warm_start': False,
'oob_score': False,
'n_jobs': 1,
'verbose': 0,
'max_leaf_nodes': None,
'bootstrap': True,
'min_samples_leaf': 1,
'n_estimators': 10,
'min_samples_split': 2,
'min_weight_fraction_leaf': 0.0,
'criterion': 'gini',
'random_state': None,
'min_impurity_split': None,
'min_impurity_decrease': 0.0,
'max_features': 'auto',
'max_depth': None,
'class_weight': None
}
def verify_base_learner_origin(id):
path = functions.get_path_from_query_string(request)
with functions.DBContextManager(path) as session:
base_learner_origin = session.query(
models.BaseLearnerOrigin).filter_by(id=id).first()
if base_learner_origin is None:
raise exceptions.UserError(
'Base learner origin {} not found'.format(id), 404)
if request.method == 'POST':
req_body = request.get_json()
if base_learner_origin.final:
raise exceptions.UserError('Base learner origin {} '
'is already final'.format(id))
base_learner = base_learner_origin.return_estimator()
validation_results, hyperparameters = functions.verify_estimator_class(
base_learner, base_learner_origin.meta_feature_generator,
base_learner_origin.metric_generators, req_body['dataset'])
base_learner_origin.validation_results = {
req_body['dataset']: validation_results
}
base_learner_origin.hyperparameters = hyperparameters
session.add(base_learner_origin)
session.commit()
return jsonify(base_learner_origin.serialize)
def test_non_serializable_parameters(self):
pipeline = Pipeline([('pca', PCA()), ('rf', RandomForestClassifier())])
performance_dict, hyperparameters = functions.verify_estimator_class(
pipeline,
'predict_proba',
dict(Accuracy=self.source),
self.dataset_properties
)
assert functions.is_valid_json(hyperparameters)
