move quality result printout to analyze-archive.py

2 files changed, 48 insertions, 31 deletions

diff --git a/bin/analyze-archive.py b/bin/analyze-archive.py
index 81d6f54..ca043c5 100755
--- a/bin/analyze-archive.py
+++ b/bin/analyze-archive.py
@@ -3,6 +3,31 @@
 import sys
 from dfatool import EnergyModel, RawData
 
+def print_model_quality(results):
+    for state_or_tran in results.keys():
+        print()
+        for key, result in results[state_or_tran].items():
+            if 'smape' in result:
+                print('{:20s} {:15s} {:.2f}% / {:.0f}'.format(
+                    state_or_tran, key, result['smape'], result['mae']))
+            else:
+                print('{:20s} {:15s} {:.0f}'.format(
+                    state_or_tran, key, result['mae']))
+
+
+def model_quality_table(result_lists):
+    for state_or_tran in result_lists[0].keys():
+        for key in result_lists[0][state_or_tran].keys():
+            buf = '{:20s} {:15s}'.format(state_or_tran, key)
+            for results in result_lists:
+                buf += '  |||  '
+                result = results[state_or_tran][key]
+                if 'smape' in result:
+                    buf += '{:6.2f}% / {:9.0f}'.format(result['smape'], result['mae'])
+                else:
+                    buf += '{:6}    {:9.0f}'.format('', result['mae'])
+            print(buf)
+
 if __name__ == '__main__':
     filenames = sys.argv[1:]
     raw_data = RawData(filenames)
@@ -10,8 +35,8 @@ if __name__ == '__main__':
     preprocessed_data = raw_data.get_preprocessed_data()
     model = EnergyModel(preprocessed_data)
 
-    #print('--- simple static model ---')
-    #static_model = model.get_static()
+    print('--- simple static model ---')
+    static_model = model.get_static()
     #for state in model.states():
     #    print('{:10s}: {:.0f} µW  ({:.2f})'.format(
     #        state,
@@ -31,21 +56,25 @@ if __name__ == '__main__':
     #        model.generic_param_dependence_ratio(trans, 'rel_energy_prev'),
     #        model.generic_param_dependence_ratio(trans, 'rel_energy_next')))
     #    print('{:10s}: {:.0f} µs'.format(trans, static_model(trans, 'duration')))
-    #model.assess(static_model)
+    static_quality = model.assess(static_model)
 
-    #print('--- LUT ---')
-    #lut_model = model.get_param_lut()
-    #model.assess(lut_model)
+    print('--- LUT ---')
+    lut_model = model.get_param_lut()
+    lut_quality = model.assess(lut_model)
 
     print('--- param model ---')
     param_model, param_info = model.get_fitted()
-    for state in model.states():
-        for attribute in ['power']:
-            if param_info(state, attribute):
-                print('{:10s}: {}'.format(state, param_info(state, attribute)['function']._model_str))
-    for trans in model.transitions():
-        for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']:
-            if param_info(trans, attribute):
-                print('{:10s}: {:10s}: {}'.format(trans, attribute, param_info(trans, attribute)['function']._model_str))
+    #for state in model.states():
+    #    for attribute in ['power']:
+    #        if param_info(state, attribute):
+    #            print('{:10s}: {}'.format(state, param_info(state, attribute)['function']._model_str))
+    #            print('{:10s}  {}'.format('', param_info(state, attribute)['function']._regression_args))
+    #for trans in model.transitions():
+    #    for attribute in ['energy', 'rel_energy_prev', 'rel_energy_next', 'duration', 'timeout']:
+    #        if param_info(trans, attribute):
+    #            print('{:10s}: {:10s}: {}'.format(trans, attribute, param_info(trans, attribute)['function']._model_str))
+    #            print('{:10s}  {:10s}  {}'.format('', '', param_info(trans, attribute)['function']._regression_args))
+    analytic_quality = model.assess(param_model)
+    model_quality_table([static_quality, lut_quality, analytic_quality])
 
     sys.exit(0)
diff --git a/lib/dfatool.py b/lib/dfatool.py
index 3387de0..1699a1d 100755
--- a/lib/dfatool.py
+++ b/lib/dfatool.py
@@ -798,31 +798,19 @@ class EnergyModel:
         return self._parameter_names
 
     def assess(self, model_function):
+        results = {}
         for name, elem in sorted(self.by_name.items()):
-            print('{}:'.format(name))
+            results[name] = {}
             if elem['isa'] == 'state':
                 predicted_data = np.array(list(map(lambda i: model_function(name, 'power', param=elem['param'][i]), range(len(elem['power'])))))
                 measures = regression_measures(predicted_data, elem['power'])
-                if 'smape' in measures:
-                    print('  power: {:.2f}% / {:.0f} µW'.format(
-                        measures['smape'], measures['mae']
-                    ))
-                else:
-                    print('  power: {:.0f} µW'.format(
-                        measures['mae']
-                    ))
+                results[name]['power'] = measures
             else:
                 for key in ['duration', 'energy', 'rel_energy_prev', 'rel_energy_next', 'timeout']:
                     predicted_data = np.array(list(map(lambda i: model_function(name, key, param=elem['param'][i]), range(len(elem[key])))))
                     measures = regression_measures(predicted_data, elem[key])
-                    if 'smape' in measures:
-                        print('  {:15s}: {:.2f}% / {:.0f}'.format(
-                            key, measures['smape'], measures['mae']
-                        ))
-                    else:
-                        print('  {:15s}: {:.0f}'.format(
-                            key, measures['mae']
-                        ))
+                    results[name][key] = measures
+        return results