Adjust main.py for loading of logs

moderation_classifier/main.py

@@ -5,6 +5,7 @@ from src.preprocessing_df import DataProcessor
 import moderation_classifier.split_data as split_data
 import moderation_classifier.train_MNB as train_MNB
 import moderation_classifier.train_BERT as train_BERT
+import moderation_classifier.eval_MNB as eval_MNB
 import moderation_classifier.eval_BERT as eval_BERT
 import moderation_classifier.train_BERT_torch as train_BERT_torch
 
@@ -18,9 +19,9 @@ import os
 @click.option("-tp", "--text_preprocessing", is_flag=True)
 @click.option("-tm", "--train_mnb", is_flag=True)
 @click.option("-tb", "--train_bert", is_flag=True)
+@click.option("-em", "--eval_mnb", is_flag=True)
 @click.option("-eb", "--eval_bert", is_flag=True)
 @click.option("-tbto", "--train_bert_torch", is_flag=True)
-@click.option("-mt", "--model_timestemp")
 @click.argument("input_data")
 def main(
     split: bool,
@@ -28,9 +29,9 @@ def main(
     text_preprocessing: bool,
     train_mnb: bool,
     train_bert: bool,
+    eval_mnb: bool,
     eval_bert: bool,
     train_bert_torch: bool,
-    model_timestemp: Union[str, os.PathLike], 
     input_data: Union[str, os.PathLike],
 ):
     """
@@ -40,6 +41,7 @@ def main(
     :param text_preprocessing: Binary flag to set text preprocessing.
     :param train_mnb: Binary flag to specify whether MNB should be trained.
     :param train_bert: Binary flag to specify whether BERT should be trained.
+    :param eval_mnb: Binary flag to specify whether MNB should be evaluated.
     :param eval_bert: Binary flag to specify whether BERT should be evaluated.
     :param model_timestemp: Path to trained model.
     :param input_data: Path to input dataframe.
@@ -60,8 +62,11 @@ def main(
     if train_bert:
         train_BERT.main(input_data, text_preprocessing)
 
+    if eval_mnb:
+        eval_MNB.main(input_data)
+
     if eval_bert:
-        eval_BERT.main(input_data, model_timestemp)
+        eval_BERT.main(input_data)
 
     if train_bert_torch:
         train_BERT_torch.main(input_data)