def test_predict_uncertainty_returns_dict_for_one_value():
np.random.seed(0)
df_boston_train, df_boston_test = utils.get_boston_regression_dataset()
column_descriptions = {
'MEDV': 'output'
, 'CHAS': 'categorical'
}
df_boston_train, uncertainty_data = train_test_split(df_boston_train, test_size=0.5)
ml_predictor = Predictor(type_of_estimator='regressor', column_descriptions=column_descriptions)
ml_predictor.train(df_boston_train, perform_feature_selection=True, train_uncertainty_model=True, uncertainty_data=uncertainty_data)
test_list = df_boston_test.to_dict('records')
for item in test_list:
prediction = ml_predictor.predict_uncertainty(item)
assert isinstance(prediction, dict)
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