implement usage of Dataframe in eval

mu_map/eval/measures.py

@@ -94,7 +94,7 @@ if __name__ == "__main__":
     )
 
     measures = {"NMAE": nmae, "MSE": mse}
-    values = dict(map(lambda x: (x, []), measures.keys()))
+    values = pd.Dataframe(map(lambda x: (x, []), measures.keys()))
     for i, (recon, mu_map) in enumerate(dataset):
         print(
             f"Process input {str(i):>{len(str(len(dataset)))}}/{len(dataset)}", end="\r"
@@ -104,12 +104,12 @@ if __name__ == "__main__":
         prediction = prediction.squeeze().cpu().numpy()
         mu_map = mu_map.squeeze().cpu().numpy()
 
-        for key, measure in measures.items():
-            values[key].append(measure(prediction, mu_map))
+        row = dict(
+            map(lambda item: (item[0], item[1](prediction, mu_map)), measures.items)
+        )
+        values = values.append(row, ignore_index=True)
     print(f" " * 100, end="\r")
 
-    values = dict(map(lambda x: (x[0], np.array(x[1])), values.items()))
-
     print("Scores:")
     for measure_name, measure_values in values.items():
         mean = measure_values.mean()