Merge pull request #64 from RWTH-EBC/63-add-option-for-different-kpis…

…-for-different-variables

#63 added capability for different kpis for different goal variables

.gitignore

@@ -32,3 +32,5 @@ venv
 .pytest_cache
 htmlcov
 .coverage
+.vscode/settings.json
+tests/testzone

aixcalibuha/data_types.py

@@ -4,7 +4,7 @@
 """
 import warnings
 import logging
-from typing import Union, Callable
+from typing import Union, Callable, List
 from copy import deepcopy
 import pandas as pd
 import numpy as np
@@ -68,7 +68,7 @@ class Goals:
     def __init__(self,
                  meas_target_data: Union[TimeSeriesData, pd.DataFrame],
                  variable_names: dict,
-                 statistical_measure: str,
+                 statistical_measure: Union[str, List[str]],
                  weightings: list = None):
         """Initialize class-objects and check correct input."""
 
@@ -170,18 +170,34 @@ def statistical_measure(self):
         return self._stat_meas
 
     @statistical_measure.setter
-    def statistical_measure(self, statistical_measure: Union[str, Callable]):
+    def statistical_measure(self, 
+                            statistical_measure: Union[str, Callable, List[Union[str, Callable]]]):
         """
         Set the new statistical measure. The value must be
         supported by the method argument in the
         ``StatisticsAnalyzer`` class of ``ebcpy``.
         """
-        self._stat_analyzer = StatisticsAnalyzer(method=statistical_measure)
-        if callable(statistical_measure):
-            self._stat_meas = statistical_measure.__name__
-        else:
-            self._stat_meas = statistical_measure
-
+        def _get_stat_meas(statistical_measure):
+            if callable(statistical_measure):
+                return statistical_measure.__name__
+            return statistical_measure
+
+        self._stat_meas = None
+        if not isinstance(statistical_measure, list):
+            statistical_measure = [statistical_measure] * len(self.variable_names)
+            self._stat_meas = _get_stat_meas(statistical_measure[0])
+
+        if len(statistical_measure) != len(self.variable_names):
+            raise ValueError("The number of statistical measures does not match the number of goals.")
+
+        if self._stat_meas is None:
+            self._stat_meas = '_'.join([_get_stat_meas(i) for i in statistical_measure])
+
+        self._stat_analyzer = {}
+        for n, goal_name in enumerate(self.variable_names.keys()):
+            self._stat_analyzer[goal_name] = StatisticsAnalyzer(method=statistical_measure[n])
+
+
     def eval_difference(self, verbose=False, penaltyfactor=1):
         """
         Evaluate the difference of the measurement and simulated data based on the
@@ -208,7 +224,7 @@ def eval_difference(self, verbose=False, penaltyfactor=1):
                                  "interval of measured and simulated data "
                                  "are not equal. \nPlease check the frequencies "
                                  "in the toml file (output_interval & frequency).")
-            _diff = self._stat_analyzer.calc(
+            _diff = self._stat_analyzer[goal_name].calc(
                 meas=self._tsd[(goal_name, self.meas_tag_str)],
                 sim=self._tsd[(goal_name, self.sim_tag_str)]
             )

tests/test_data_types.py

@@ -57,7 +57,6 @@ def test_goals(self):
         goals = Goals(meas_target_data=meas_target_data,
                       variable_names=var_names,
                       statistical_measure="RMSE")
-
         # Check set_sim_target_data:
         goals.set_sim_target_data(sim_target_data)
 
@@ -83,6 +82,32 @@ def test_goals(self):
                   statistical_measure="RMSE",
                   weightings=weightings)
 
+        # Check different KPIs for different goals:
+        goals = Goals(meas_target_data=meas_target_data,
+                      variable_names=var_names,
+                      statistical_measure=["RMSE", "MAE"])
+        # Check set_sim_target_data:
+        goals.set_sim_target_data(sim_target_data)
+
+        # Set relevant time interval test:
+        goals.set_relevant_time_intervals([(0, 100)])
+
+        # Check the eval_difference function:
+        self.assertIsInstance(goals.eval_difference(), float)
+
+        with self.assertRaises(ValueError):
+            # Test if wrong statistical_measure raises an error.
+            Goals(meas_target_data=meas_target_data,
+                  variable_names=var_names,
+                  statistical_measure="not a valid KPI")
+
+        with self.assertRaises(ValueError):
+            # Test that the length of the statistical_measure list is equal to the number of variables.
+            goals = Goals(meas_target_data=meas_target_data,
+                        variable_names=var_names,
+                        statistical_measure=["RMSE", "MAE", "MSE"])
+
+
     def test_tuner_paras(self):
         """Test the class TunerParas"""
         dim = np.random.randint(1, 100)