Fix issue with F1 computing

moderation_classifier/eval_MNB.py

@@ -30,7 +30,7 @@ def main(train_logs: Union[str, os.PathLike]):
 
     # Load logs
     df = pd.read_csv(train_logs, index_col="Unnamed: 0")
-    path_model = df.loc["path"].values[0]
+    path_model = df.loc["path_model"].values[0]
     input_data = df.loc["input_data"].values[0].replace("train", "test")
 
     # Load model
@@ -40,6 +40,7 @@ def main(train_logs: Union[str, os.PathLike]):
     tl = TextLoader(input_data)
     df_test = tl.load_text_csv(
         newspaper="tagesanzeiger",
+        lang='de',
         load_subset=False,
         remove_duplicates=False,
         min_num_words=3,
@@ -51,17 +52,16 @@ def main(train_logs: Union[str, os.PathLike]):
     y_pred = pipe.predict(X_test)
 
     y_pred_t = pipe.predict(X_test)
-    precision, recall, *_ = precision_recall_fscore_support(
+    precision, recall, f1, _ = precision_recall_fscore_support(
         y_test, y_pred, average="weighted"
     )
-    f1 = f1_score(y_test, y_pred)
-    score = pipe.score(X_test, y_test)
+    accuracy = pipe.score(X_test, y_test)
 
     results_all = dict()
     results_all["precision"] = precision
     results_all["recall"] = recall
     results_all["f1"] = f1
-    results_all["score"] = score
+    results_all["accuracy"] = accuracy
 
     #import pdb; pdb.set_trace()
 
@@ -75,10 +75,10 @@ def main(train_logs: Union[str, os.PathLike]):
         y_test_t = df_test[df_test.topic == t].label
 
         y_pred_t = pipe.predict(X_test_t)
-        precision, recall, *_ = precision_recall_fscore_support(
+        precision, recall, f1, _ = precision_recall_fscore_support(
             y_test_t, y_pred_t, average="weighted"
         )
-        f1 = f1_score(y_test_t, y_pred_t)
+        #f1 = f1_score(y_test_t, y_pred_t)
         accuracy = pipe.score(X_test_t, y_test_t)
         results_t[t] = dict()
         results_t[t]["accuracy"] = accuracy