def test_datasource_classifier_unpicklable(self):
wrong_config = config.copy()
wrong_config[
'datasource_classifier'] = "test_models/unpicklable_model.txt"
with self.assertRaises(ex.InvalidConfigValueException):
mp.load_classifier(wrong_config, self.current_model_type_std[0])
def test_correct_execution_with_right_parameter(self):
test_config = config.copy()
test_config['selected_event'] = 'standard'
enriched_df = pd.DataFrame.from_dict({
"time": [1.0, 2.0, 3.0],
"outdoor": [14.2, 4.1, 1.32],
"inlet": [99.2, 91.2, 192.2],
"room": [13.2, 61.2, 02.3],
"freshAirIntake": [13.0, 34.2, 34.0],
"condenser": [17.0, 17.0, 17.0],
"evaporator": [17.0, 17.0, 17.0],
"outlet": [17.0, 17.0, 17.0],
'evaporator_deriv': [17.0, 17.0, 17.0],
'evaporator_pct_ch': [17.0, 17.0, 17.0],
'evaporator_ch_abs': [17.0, 17.0, 17.0],
'evaporator_diff': [0.0, 0.0, 0.0],
'condenser_deriv': [17.0, 17.0, 17.0],
'condenser_pct_ch': [17.0, 17.0, 17.0],
'condenser_ch_abs': [17.0, 17.0, 17.0],
'condenser_diff': [0.0, 0.0, 0.0]
})
enriched_df["time"] = pd.to_datetime(enriched_df["time"])
enriched_df = enriched_df.set_index("time")
when2(read_manager.read_query, ANY, ANY).thenReturn(enriched_df)
when2(write_manager.write_dataframe, ANY, ANY, ANY)
self.assertEqual(trainingsdata.mark_data(test_config), 0)
def test_datasource_classifier_is_wrong(self):
wrong_config = config.copy()
wrong_config[
'datasource_classifier'] = "i_am_not_an_existing_file.yeah"
with self.assertRaises(ex.InvalidConfigValueException):
mp.load_classifier(wrong_config, self.current_model_type_std[0])
def test_for_UnpicklingError(self):
test_config = config.copy()
test_config[
'datasource_classifier'] = "test_models/unpicklable_model.txt"
with self.assertRaises(ex.InvalidConfigValueException):
mp.persist_classifier(self.model, test_config,
self.current_model_type_std[0])
def test_model_at_event_is_empty_returns_KNC(self):
test_config = config.copy()
test_config[
'datasource_classifier'] = "test_models/model_test_returns_not_existing_classifier.txt"
tested_model = mp.load_classifier(test_config,
self.current_model_type_std[0])
self.assertTrue(isinstance(tested_model, type(self.model)))
def test_returns_existing_classifier(self):
classification_config = config.copy()
classification_config[
'datasource_classifier'] = "test_models/model_test_returns_existing_classifier.txt"
self.assertTrue(
isinstance(
mp.load_classifier(classification_config,
self.current_model_type_std[0]),
type(self.model)))
def test_new_classifer_method_is_not_empty(self):
test_config = config.copy()
test_config[
'datasource_classifier'] = "test_models/model_returns_code_0.txt"
test_config['new_classifier_method'] = "kNN"
test_model = mp.load_classifier(test_config,
self.current_model_type_std[0])
self.assertTrue(isinstance(test_model, type(self.model)))
def test_for_existing_models_in_dictionary(self):
test_config = config.copy()
test_config[
'datasource_classifier'] = "test_models/model_returns_code_0.txt"
test_config['selected_event'] = "warmwasseraufbereitung"
mp.persist_classifier(self.model, test_config,
self.current_model_type_std[1])
existing_model = mp.load_classifier(test_config,
self.current_model_type_std[1])
self.assertTrue(isinstance(existing_model, type(self.model)))
def test_marked_dataframe_has_abtauzyklus_column(self):
test_config = config.copy()
test_config['selected_event'] = 'standard'
enriched_df = pd.DataFrame.from_dict({
"time": [1.0, 2.0, 3.0],
"outdoor": [14.2, 4.1, 1.32],
"inlet": [99.2, 91.2, 192.2],
"room": [13.2, 61.2, 02.3],
"freshAirIntake": [13.0, 34.2, 34.0],
"condenser": [17.0, 17.0, 17.0],
"evaporator": [17.0, 17.0, 17.0],
"outlet": [17.0, 17.0, 17.0],
'evaporator_deriv': [17.0, 17.0, 17.0],
'evaporator_pct_ch': [17.0, 17.0, 17.0],
'evaporator_ch_abs': [17.0, 17.0, 17.0],
'evaporator_diff': [0.0, 0.0, 0.0],
'condenser_deriv': [17.0, 17.0, 17.0],
'condenser_pct_ch': [17.0, 17.0, 17.0],
'condenser_ch_abs': [17.0, 17.0, 17.0],
'condenser_diff': [0.0, 0.0, 0.0]
})
enriched_df["time"] = pd.to_datetime(enriched_df["time"])
enriched_df = enriched_df.set_index("time")
marked_df = pd.DataFrame.from_dict({
"time": [1.0, 2.0, 3.0],
"outdoor": [14.2, 4.1, 1.32],
"inlet": [99.2, 91.2, 192.2],
"room": [13.2, 61.2, 02.3],
"freshAirIntake": [13.0, 34.2, 34.0],
"condenser": [17.0, 17.0, 17.0],
"evaporator": [17.0, 17.0, 17.0],
"outlet": [17.0, 17.0, 17.0],
'evaporator_deriv': [17.0, 17.0, 17.0],
'evaporator_pct_ch': [17.0, 17.0, 17.0],
'evaporator_ch_abs': [17.0, 17.0, 17.0],
'evaporator_diff': [17.0, 17.0, 17.0],
'condenser_deriv': [17.0, 17.0, 17.0],
'condenser_pct_ch': [17.0, 17.0, 17.0],
'condenser_ch_abs': [17.0, 17.0, 17.0],
'condenser_diff': [17.0, 17.0, 17.0],
'abtauzyklus_marker': [1.0, 0.0, 0.0],
'abtauzyklus': [True, True, False],
'warmwasseraufbereitung_marker': [True, True, False],
'warmwasseraufbereitung': [True, True, False]
})
marked_df["time"] = pd.to_datetime(marked_df["time"])
marked_df = marked_df.set_index("time")
when2(read_manager.read_query, ANY, ANY).thenReturn(enriched_df)
df_captor = captor(any(pd.DataFrame))
when2(write_manager.write_dataframe, ANY, df_captor, ANY)
trainingsdata.mark_data(test_config)
df = df_captor.value
self.assertListEqual(df.columns.tolist(), marked_df.columns.tolist())
def test_model_for_event_is_not_instance_of_sklearn(self):
path_to_wrong_dictionary = "test_models/model_returns_code_0_contains_wrong_model_at_abauzyklus.txt"
classifier_dictionary = mp.load_dictionary(
"test_models/model_returns_code_0.txt")
classifier_dictionary['abtauzyklus'] = "Ich bin kein richtiges Model"
mp.save_dictionary(classifier_dictionary, path_to_wrong_dictionary)
wrong_config = config.copy()
wrong_config['datasource_classifier'] = path_to_wrong_dictionary
test_model = mp.load_classifier(wrong_config,
self.current_model_type_std[0])
self.assertFalse(isinstance(test_model, type(self.model)))
def test_enriched_dataframe_has_correct_format(self):
test_config = config.copy()
test_config['selected_event'] = 'standard'
example_df = pd.DataFrame.from_dict({
"time": [1.0, 2.0, 3.0],
"outdoor": [14.2, 4.1, 1.32],
"inlet": [99.2, 91.2, 192.2],
"room": [13.2, 61.2, 02.3],
"freshAirIntake": [13.0, 34.2, 34.0],
"condenser": [17.0, 17.0, 17.0],
"evaporator": [17.0, 17.0, 17.0],
"outlet": [17.0, 17.0, 17.0]
})
example_df["time"] = pd.to_datetime(example_df["time"])
example_df = example_df.set_index("time")
enriched_df = pd.DataFrame.from_dict({
"time": [1.0, 2.0, 3.0],
"outdoor": [14.2, 4.1, 1.32],
"inlet": [99.2, 91.2, 192.2],
"room": [13.2, 61.2, 02.3],
"freshAirIntake": [13.0, 34.2, 34.0],
"condenser": [17.0, 17.0, 17.0],
"evaporator": [17.0, 17.0, 17.0],
"outlet": [17.0, 17.0, 17.0],
'evaporator_deriv': [17.0, 17.0, 17.0],
'evaporator_pct_ch': [17.0, 17.0, 17.0],
'evaporator_ch_abs': [17.0, 17.0, 17.0],
'evaporator_diff': [17.0, 17.0, 17.0],
'condenser_deriv': [17.0, 17.0, 17.0],
'condenser_pct_ch': [17.0, 17.0, 17.0],
'condenser_ch_abs': [17.0, 17.0, 17.0],
'condenser_diff': [17.0, 17.0, 17.0],
'room_deriv': [17.0, 17.0, 17.0],
'room_pct_ch': [17.0, 17.0, 17.0],
'room_ch_abs': [17.0, 17.0, 17.0],
'room_diff': [17.0, 17.0, 17.0],
'inlet_deriv': [17.0, 17.0, 17.0],
'inlet_pct_ch': [17.0, 17.0, 17.0],
'inlet_ch_abs': [17.0, 17.0, 17.0],
'inlet_diff': [17.0, 17.0, 17.0]
})
enriched_df["time"] = pd.to_datetime(enriched_df["time"])
enriched_df = enriched_df.set_index("time")
when2(read_manager.read_query, ANY, ANY).thenReturn(example_df)
df_captor = captor(any(pd.DataFrame))
when2(write_manager.write_dataframe, ANY, df_captor, ANY)
trainingsdata.enrich_data(test_config)
df = df_captor.value
self.assertListEqual(df.columns.tolist(), enriched_df.columns.tolist())
def test_datasource_classifier_none(self):
wrong_config = config.copy()
wrong_config["datasource_classifier"] = None
with self.assertRaises(ex.InvalidConfigValueException):
mp.load_classifier(wrong_config, self.current_model_type_std[0])
