Feature to label

code/kappa_data/feature_to_label.py

@@ -57,7 +57,8 @@ def add_label(df):
 
 
 if __name__ == "__main__":
-    path = # path to label files for which a class should be estimated
+    path = '.' # path to label files for which a class should be estimated
+    print("Path is current folder")
     # get all csv files in path
     csvs = [x for x in os.listdir(path) if x.endswith('.csv')]
     # open files and add the corresponding labels to each image id
@@ -66,4 +67,4 @@ def add_label(df):
         df = pd.read_csv(full_path, delimiter=';')
         new_df = add_label(df)
         new_file_name = full_path + 'labeltestnew.csv'
-        export_csv = new_df.to_csv(new_file_name)
+        export_csv = new_df.to_csv(new_file_name)

code/pipeline/multiple_models_app.py

@@ -41,7 +41,7 @@ def main():
     # loading the data
     @st.cache
     def load_data():
-        return tm.load_data("../../annotations")
+        return tm.load_data("../../annotations/evaluation_agreement_1")
 
     raw_data, dummy_data = load_data()
     labels = [col for col in dummy_data if col.startswith('Class')]

code/pipeline/train_model.py

@@ -241,6 +241,11 @@ def load_data(folder):
 
             if not infile.is_file():
                 continue
+
+            # disregard the agreement files
+            if infile.name.startswith("agreement"):
+                continue
+
             # extract the class/category name
             infile_name = infile.name[infile.name.index("_"):]
             infile_cat = infile_name[7:-4]