parameterize rise time series test synthetic data

introduce failing test

tests/methods/test_rise_timeseries.py

@@ -1,5 +1,5 @@
 import numpy as np
-import pandas
+import pytest
 import dianna
 from dianna.methods.rise_timeseries import RISETimeseries
 from tests.methods.time_series_test_case import average_temperature_timeseries_with_1_cold_and_1_hot_day
@@ -24,42 +24,34 @@ def test_rise_timeseries_correct_output_shape():
     assert heatmaps.shape == (len(labels), *input_data.shape)
 
 
-def test_rise_timeseries_with_expert_model_for_correct_max_and_min():
+@pytest.mark.parametrize('series_length', [
+    10,
+    3,
+])
+def test_rise_timeseries_with_expert_model_for_correct_max_and_min(
+        series_length):
     """Test if RISE highlights the correct areas for this artificial example."""
     hot_day_index = 1
     cold_day_index = 2
-    series_length = 4
     temperature_timeseries = average_temperature_timeseries_with_1_cold_and_1_hot_day(
         cold_day_index, hot_day_index, series_length=series_length)
 
-    # summer_explanation, winter_explanation = dianna.explain_timeseries(
-    #     # run_expert_model,
-    #     run_continuous_expert_model,
-    #     timeseries_data=temperature_timeseries,
-    #     method='rise',
-    #     labels=[0, 1],
-    #     p_keep=0.1,
-    #     n_masks=50,
-    #     feature_res=series_length,
-    #     mask_type=input_train_mean)
-
     explainer = RISETimeseries(n_masks=100000,
                                p_keep=0.5,
                                feature_res=series_length)
     summer_explanation, winter_explanation = explainer.explain(
         run_expert_model_3_step, temperature_timeseries, labels=[0, 1])
-    print('\n')
-    print(pandas.DataFrame(temperature_timeseries))
-    dot = (explainer.masks[:, :, 0].T * explainer.predictions[:, 0]).T
-    log = pandas.DataFrame(
-        np.column_stack((explainer.masks[:, :, 0], explainer.masked[:, :, 0],
-                         explainer.predictions, dot)),
-        columns=[f'm{i}' for i in range(len(summer_explanation))] +
-        [f'd{i}' for i in range(len(summer_explanation))] + ['P(S)', 'P(W)'] +
-        [f'S{i}' for i in range(len(summer_explanation))])
-    print(log)
-    print(log.sum())
 
+    _visualize_explainer_output(hot_day_index, cold_day_index,
+                                summer_explanation, winter_explanation)
+    assert np.argmin(winter_explanation) == hot_day_index
+    assert np.argmax(summer_explanation) == hot_day_index
+    assert np.argmin(summer_explanation) == cold_day_index
+    assert np.argmax(winter_explanation) == cold_day_index
+
+
+def _visualize_explainer_output(hot_day_index, cold_day_index,
+                                summer_explanation, winter_explanation):
     print('\n')
     length = len(summer_explanation)
     margin = '      '
@@ -74,11 +66,6 @@ def test_rise_timeseries_with_expert_model_for_correct_max_and_min():
     _print_series('summer', summer_explanation)
     _print_series('winter', winter_explanation)
 
-    assert np.argmin(winter_explanation) == hot_day_index
-    assert np.argmax(summer_explanation) == hot_day_index
-    assert np.argmin(summer_explanation) == cold_day_index
-    assert np.argmax(winter_explanation) == cold_day_index
-
 
 def _print_series(title, series):
     mini = np.min(series)