def test_calibrate_uncertainty():
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)
uncertainty_data, uncertainty_calibration_data = train_test_split(
uncertainty_data, test_size=0.5)
ml_predictor = Predictor(type_of_estimator='regressor',
column_descriptions=column_descriptions)
uncertainty_calibration_settings = {
'num_buckets': 3,
'percentiles': [25, 50, 75]
}
ml_predictor.train(
df_boston_train,
perform_feature_selection=True,
train_uncertainty_model=True,
uncertainty_data=uncertainty_data,
calibrate_uncertainty=True,
uncertainty_calibration_settings=uncertainty_calibration_settings,
uncertainty_calibration_data=uncertainty_calibration_data)
uncertainty_score = ml_predictor.predict_uncertainty(df_boston_test)
assert 'percentile_25_delta' in list(uncertainty_score.columns)
assert 'percentile_50_delta' in list(uncertainty_score.columns)
assert 'percentile_75_delta' in list(uncertainty_score.columns)
assert 'bucket_num' in list(uncertainty_score.columns)
def test_predict_uncertainty_returns_pandas_DataFrame_for_more_than_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)
uncertainties = ml_predictor.predict_uncertainty(df_boston_test)
assert isinstance(uncertainties, pd.DataFrame)
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)
