remove classification specific steps

tsfm_public/toolkit/util.py

@@ -1205,52 +1205,56 @@ def join_list_without_repeat(*lists: List[List[Any]]) -> List[Any]:
         final_set = set(final)
     return final
 
-def convert_tsfile(filename: str, classification=False) -> pd.DataFrame:
+
+def convert_tsfile(filename: str) -> pd.DataFrame:
     """Converts a .ts file into a pandas dataframe.
     Returns the result in canonical multi-time series format, with an ID column, and timestamp.
 
     Args:
         filename (str): Input file name.
-        classification (bool): classification dataset
 
     Returns:
         pd.DataFrame: Converted time series
     """
-
-    final_df = pd.DataFrame()
 
+    dfs = []
     df = convert_tsfile_to_dataframe(filename, return_separate_X_and_y=False)
 
     rows, columns = df.shape
 
     for i in range(rows):
         temp_df = pd.DataFrame()
         for j in range(columns):
-            if j!=columns-1:
+            if j != columns - 1:
                 series_to_df = df.iloc[i].iloc[j].to_frame().reset_index()
-                if j==0:
+                if j == 0:
                     repeat = len(series_to_df)
-                    if type(series_to_df['index'][0])==pd.Timestamp: ## include timestamp columns if data includes timestamps
-                        temp_df['timestamp'] = series_to_df['index']
-                    temp_df['id'] = [i]*repeat
-                temp_df[f'value_{j}'] = series_to_df[0]
+                    if (
+                        type(series_to_df["index"][0]) == pd.Timestamp
+                    ):  ## include timestamp columns if data includes timestamps
+                        temp_df["timestamp"] = series_to_df["index"]
+                    temp_df["id"] = [i] * repeat
+                temp_df[f"value_{j}"] = series_to_df[0]
             else:
                 target = df.iloc[i].iloc[j]
-                temp_df['target'] = [target]*repeat
-
-        final_df = pd.concat([final_df, temp_df],ignore_index=True)
-
-    ## convert targets to floats or integers
-    ## non-numeric classification labels will be converted to integers as well
-    try:
-        final_df['target'] = pd.to_numeric(final_df['target'])
-    except:
-        string_labels = final_df['target'].unique()
-        label_to_int_map = {str_label: num for num, str_label in enumerate(string_labels)}
-        final_df['target'] = final_df['target'].map(label_to_int_map) 
-
-    ## make sure labels are 0 indexed if classification
-    if classification and final_df['target'].min() != 0:
-         final_df['target'] = final_df['target'] - 1 
+                temp_df["target"] = [target] * repeat
+
+        dfs.append(temp_df)
+
+    final_df = pd.concat(dfs, ignore_index=True)
+
+    # to be moved to a preprocessor
+    # ## convert targets to floats or integers
+    # ## non-numeric classification labels will be converted to integers as well
+    # try:
+    #     final_df["target"] = pd.to_numeric(final_df["target"])
+    # except KeyError:
+    #     string_labels = final_df["target"].unique()
+    #     label_to_int_map = {str_label: num for num, str_label in enumerate(string_labels)}
+    #     final_df["target"] = final_df["target"].map(label_to_int_map)
+
+    # ## make sure labels are 0 indexed if classification
+    # if classification and final_df["target"].min() != 0:
+    #     final_df["target"] = final_df["target"] - 1
 
     return final_df