def test_selection_changed(self):
"""Test modify selection."""
color_searcher = mock()
view = mock()
color_highlighter = mock()
color_selection_listener = ColorSelectionListener(
color_searcher, view, color_highlighter)
color_region1 = (NormalizedRegion(10, 11), 1)
color_region2 = (NormalizedRegion(11, 12), 2)
color_region3 = (NormalizedRegion(17, 21), 3)
sel_region1 = NormalizedRegion(10, 13)
sel_region2 = NormalizedRegion(16, 20)
sel_line1 = NormalizedRegion(14, 15)
sel_line2 = NormalizedRegion(16, 17)
when(view).lines(sel_region1).thenReturn([sel_line1])
when(view).lines(sel_region2).thenReturn([sel_line2])
when(view).sel().thenReturn([sel_region1, sel_region2])
when(color_searcher).search(view, sel_line1).thenReturn(
[color_region1 + (None, ), color_region2 + (None, )])
when(color_searcher).search(view, sel_line2).thenReturn(
[color_region3 + (None, )])
color_selection_listener.on_selection_modified()
color_regions = captor()
verify(color_highlighter).highlight_regions(color_regions)
self.assertEqual([color_region1, color_region2, color_region3],
[region for region in color_regions.value])
def testShouldReturnLastMatchedValue(self):
c = captor(contains("foo"))
c.matches("foobar")
c.matches("foobam")
c.matches("bambaz")
self.assertListEqual(["foobar", "foobam"], c.all_values)
self.assertEqual("foobam", c.value)
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_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_selection_no_intersections(self):
"""Test modify selection with color searher returning regions with no intersections with the selection."""
color_searcher = mock()
view = mock()
color_highlighter = mock()
color_selection_listener = ColorSelectionListener(color_searcher, view, color_highlighter)
color_region = (NormalizedRegion(4, 5), 1)
sel_region = NormalizedRegion(10, 13)
sel_line = NormalizedRegion(14, 15)
when(view).lines(sel_region).thenReturn([sel_line])
when(view).sel().thenReturn([sel_region])
when(color_searcher).search(view, sel_line).thenReturn([color_region + (None,)])
color_selection_listener.on_selection_modified()
color_regions = captor()
verify(color_highlighter).highlight_regions(color_regions)
self.assertEqual([], [region for region in color_regions.value])
def test_deduplicate_lines(self):
"""Test that having two selections on the same line yields that line only one time ."""
color_searcher = mock()
view = mock()
color_highlighter = mock()
color_selection_listener = ColorSelectionListener(color_searcher, view, color_highlighter)
color_region = (NormalizedRegion(11, 14), 1)
sel_region1 = NormalizedRegion(10, 11)
sel_region2 = NormalizedRegion(12, 13)
sel_line = NormalizedRegion(14, 15)
when(view).lines(sel_region1).thenReturn([sel_line])
when(view).lines(sel_region2).thenReturn([sel_line])
when(view).sel().thenReturn([sel_region1, sel_region2])
when(color_searcher).search(view, sel_line).thenReturn([color_region + (None,)])
color_selection_listener.on_selection_modified()
color_regions = captor()
verify(color_highlighter).highlight_regions(color_regions)
self.assertEqual([color_region], [region for region in color_regions.value])
def test_selection_no_intersections(self):
"""Test modify selection with color searher returning regions with no intersections with the selection."""
color_searcher = mock()
view = mock()
color_highlighter = mock()
color_selection_listener = ColorSelectionListener(
color_searcher, view, color_highlighter)
color_region = (NormalizedRegion(4, 5), 1)
sel_region = NormalizedRegion(10, 13)
sel_line = NormalizedRegion(14, 15)
when(view).lines(sel_region).thenReturn([sel_line])
when(view).sel().thenReturn([sel_region])
when(color_searcher).search(view, sel_line).thenReturn(
[color_region + (None, )])
color_selection_listener.on_selection_modified()
color_regions = captor()
verify(color_highlighter).highlight_regions(color_regions)
self.assertEqual([], [region for region in color_regions.value])
def test_deduplicate_lines(self):
"""Test that having two selections on the same line yields that line only one time ."""
color_searcher = mock()
view = mock()
color_highlighter = mock()
color_selection_listener = ColorSelectionListener(
color_searcher, view, color_highlighter)
color_region = (NormalizedRegion(11, 14), 1)
sel_region1 = NormalizedRegion(10, 11)
sel_region2 = NormalizedRegion(12, 13)
sel_line = NormalizedRegion(14, 15)
when(view).lines(sel_region1).thenReturn([sel_line])
when(view).lines(sel_region2).thenReturn([sel_line])
when(view).sel().thenReturn([sel_region1, sel_region2])
when(color_searcher).search(view, sel_line).thenReturn(
[color_region + (None, )])
color_selection_listener.on_selection_modified()
color_regions = captor()
verify(color_highlighter).highlight_regions(color_regions)
self.assertEqual([color_region],
[region for region in color_regions.value])
def test_selection_changed(self):
"""Test modify selection."""
color_searcher = mock()
view = mock()
color_highlighter = mock()
color_selection_listener = ColorSelectionListener(color_searcher, view, color_highlighter)
color_region1 = (NormalizedRegion(10, 11), 1)
color_region2 = (NormalizedRegion(11, 12), 2)
color_region3 = (NormalizedRegion(17, 21), 3)
sel_region1 = NormalizedRegion(10, 13)
sel_region2 = NormalizedRegion(16, 20)
sel_line1 = NormalizedRegion(14, 15)
sel_line2 = NormalizedRegion(16, 17)
when(view).lines(sel_region1).thenReturn([sel_line1])
when(view).lines(sel_region2).thenReturn([sel_line2])
when(view).sel().thenReturn([sel_region1, sel_region2])
when(color_searcher).search(view, sel_line1).thenReturn([color_region1 + (None,), color_region2 + (None,)])
when(color_searcher).search(view, sel_line2).thenReturn([color_region3 + (None,)])
color_selection_listener.on_selection_modified()
color_regions = captor()
verify(color_highlighter).highlight_regions(color_regions)
self.assertEqual([color_region1, color_region2, color_region3], [region for region in color_regions.value])
def testShouldDefaultMatcherToAny(self):
c = captor()
c.matches("foo")
c.matches(123)
self.assertEqual(123, c.value)
def testShouldReturnLastMatchedValue(self):
c = captor(contains("foo"))
c.matches("foobar")
c.matches("foobam")
c.matches("bambaz")
self.assertEqual("foobam", c.value)
def testShouldReturnNoneValueIfDidntMatch(self):
c = captor(contains("foo"))
c.matches("bar")
self.assertEqual(None, c.value)
def testShouldReturnNoneValueByDefault(self):
c = captor(contains("foo"))
self.assertEqual(None, c.value)
def testShouldNotSatisfyIfInnerMatcherIsNotSatisfied(self):
c = captor(contains("foo"))
self.assertFalse(c.matches("barbam"))
def testShouldSatisfyIfInnerMatcherIsSatisfied(self):
c = captor(contains("foo"))
self.assertTrue(c.matches("foobar"))
def testShouldDefaultMatcherToAny(self):
c = captor()
c.matches("foo")
c.matches(123)
self.assertListEqual(["foo", 123], c.all_values)
self.assertEqual(123, c.value)
def testShouldReturnNoneValueIfDidntMatch(self):
c = captor(contains("foo"))
c.matches("bar")
self.assertListEqual([], c.all_values)
with self.assertRaises(MatcherError):
_ = c.value
def testShouldReturnNoneValueByDefault(self):
c = captor(contains("foo"))
self.assertListEqual([], c.all_values)
with self.assertRaises(MatcherError):
_ = c.value
def testShouldSatisfyIfInnerMatcherIsSatisfied(self):
c = captor(contains("foo"))
self.assertTrue(c.matches("foobar"))
self.assertListEqual([
"foobar",
], c.all_values)
def test_config_is_valid_should_send_predictions_to_database(self):
# valid config
valid_config = {
"selected_value": "default",
"database_options": {
"training": {
"datasource_nilan_dbname": "name",
"datasource_nilan_measurement": "name",
"datasource_weatherdata_dbname": "name",
"datasource_weatherdata_measurement": "name"
},
"prediction": {
"datasource_forecast_dbname": "yalla",
"datasource_forecast_measurement": "name",
"datasource_forecast_register": "name",
"datasink_prediction_dbname": "name",
"datasink_prediction_measurement": "name"
}
},
"prediction_options": {
"default": [
{
"independent": ["outdoor"],
"dependent": ["inlet"],
"test_sample_size": 0.2
},
{
"independent": ["inlet", "outdoor"],
"dependent": ["room"],
"test_sample_size": 0.2
},
{
"independent": ["room"],
"dependent": ["freshAirIntake", "condenser", "evaporator", "outlet"],
"test_sample_size": 0.2
}
]
}
}
# all_prediction_models
example_values = pd.DataFrame.from_dict({
"time": [1.0, 2.0, 3.0],
"outdoor": [1.0, 2.0, 3.0],
"inlet": [2.0, 4.0, 6.0], # outdoor * 2
"room": [3.0, 6.0, 9.0], # outdoor + inlet
"freshAirIntake": [3.0, 3.0, 3.0], # 3
"condenser": [3.0, 3.0, 3.0], # 3
"evaporator": [3.0, 3.0, 3.0], # 3
"outlet": [3.0, 3.0, 3.0] # 3
})
example_values["time"] = pd.to_datetime(example_values["time"])
example_values = example_values.set_index("time")
normal = lm.LinearRegression().fit(example_values[["outdoor"]], example_values[["inlet"]])
multiple = lm.LinearRegression().fit(example_values[["inlet", "outdoor"]], example_values[["room"]])
multivariate = lm.LinearRegression().fit(example_values[["room"]], example_values[["freshAirIntake", "condenser", "evaporator", "outlet"]])
all_prediction_models = {
"average_score": 0.91,
"config": valid_config,
"models": [
{
"dependent": ["inlet"],
"model": normal,
},
{
"dependent": ["room"],
"model": multiple,
},
{
"dependent": ["freshAirIntake", "condenser", "evaporator", "outlet"],
"model": multivariate,
},
]
}
# weather forecast
temperature = {
"time": [1, 2, 3],
"outdoor": [2, 4, 6] # TODO might need to adjust the key of this
}
weather_forecast_dataframe = pd.DataFrame(temperature)
weather_forecast_dataframe["time"] = pd.to_datetime( weather_forecast_dataframe["time"])
weather_forecast_dataframe = weather_forecast_dataframe.set_index("time")
# just say config is valid
when2(cfg_validator.validate_config, ANY).thenReturn(True)
# when the function tries to get the weather forecast from database, return the custom forecast DataFrame above
when2(read_manager.read_data, ANY, measurement=ANY).thenReturn(weather_forecast_dataframe)
# when the function tries to get the persisted models, pass the custom one from above
when2(model_persistor.load).thenReturn(all_prediction_models)
# when the function tries to write the prediction to the database, capture the prediction DataFrame
forecast_captor = captor(any(pd.DataFrame))
when2(write_manager.write_dataframe, ANY, forecast_captor, ANY).thenReturn(None)
# call function under test with custom config above
calculate_prediction(valid_config)
# the actual prediction DataFrame
actual_forecasts = forecast_captor.value
expected_forecasts = pd.DataFrame.from_dict({
"time": [1.0, 2.0, 3.0],
"outdoor": [2.0, 4.0, 6.0],
"inlet": [4.0, 8.0, 12.0], # outdoor * 2
"room": [6.0, 12.0, 18.0], # outdoor + inlet
"freshAirIntake": [3.0, 3.0, 3.0], # 3
"condenser": [3.0, 3.0, 3.0], # 3
"evaporator": [3.0, 3.0, 3.0], # 3
"outlet": [3.0, 3.0, 3.0], # 3
})
expected_forecasts["time"] = pd.to_datetime(expected_forecasts["time"])
expected_forecasts = expected_forecasts.set_index("time")
# verify all interactions (default times is 1)
verify(pred_api).send_classification_request(ANY)
verify(read_manager).read_data(ANY, measurement=ANY)
verify(write_manager).write_dataframe(ANY, ANY, ANY)
verify(cfg_validator).validate_config(ANY)
verify(model_persistor).load()
pd.testing.assert_frame_equal(actual_forecasts, expected_forecasts, check_dtype=False)