Bug Fix: Data Diagnosis - Fix bug of failure test and warning of pand…

…as in data diagnosis (#638)

**Description**
Fix bug of failure test and warning of pandas in data diagnosis.

**Major Revision**
- fix warning of pandas in replace and fillna due to type downcast
- fix bug of failure check function only check one matched metric rather
than all matched metrics
- fix bug when converting regex into str of metrics when there're more
than one match group

superbench/analyzer/data_analysis.py

@@ -243,7 +243,7 @@ def aggregate(raw_data_df, pattern=None):
                 match = re.search(pattern, metric)
                 if match:
                     metric_in_list = list(metric)
-                    for i in range(1, len(match.groups()) + 1):
+                    for i in range(len(match.groups()), 0, -1):
                         metric_in_list[match.start(i):match.end(i)] = '*'
                     short = ''.join(metric_in_list)
             if short not in metric_store:

superbench/analyzer/data_diagnosis.py

@@ -262,9 +262,8 @@ def output_all_nodes_results(self, raw_data_df, data_not_accept_df):
                     all_data_df = data_not_accept_df[[
                         append_columns[index]
                     ]].merge(all_data_df, left_index=True, right_index=True, how='right')
-            all_data_df['Accept'] = all_data_df['Accept'].replace(np.nan, True)
-            all_data_df['Number Of Issues'] = all_data_df['Number Of Issues'].replace(np.nan, 0)
-            all_data_df['Number Of Issues'] = all_data_df['Number Of Issues'].astype(int)
+            all_data_df['Accept'] = all_data_df['Accept'].replace(np.nan, 1).astype('bool')
+            all_data_df['Number Of Issues'] = all_data_df['Number Of Issues'].replace(np.nan, 0).astype('int')
 
         return all_data_df
 
@@ -296,7 +295,7 @@ def output_diagnosis_in_jsonl(self, data_not_accept_df, output_path):
             data_not_accept_df (DataFrame): the DataFrame to output
             output_path (str): the path of output jsonl file
         """
-        data_not_accept_df = data_not_accept_df.convert_dtypes().astype('object').fillna(self.na)
+        data_not_accept_df = data_not_accept_df.convert_dtypes().astype('object').infer_objects().fillna(self.na)
         p = Path(output_path)
         try:
             data_not_accept_json = data_not_accept_df.to_json(orient='index')
@@ -327,7 +326,7 @@ def output_diagnosis_in_json(self, data_not_accept_df, output_path):
             data_not_accept_df (DataFrame): the DataFrame to output
             output_path (str): the path of output jsonl file
         """
-        data_not_accept_df = data_not_accept_df.convert_dtypes().astype('object').fillna(self.na)
+        data_not_accept_df = data_not_accept_df.convert_dtypes().astype('object').infer_objects().fillna(self.na)
         data_not_accept_df = data_not_accept_df.reset_index()
         data_not_accept_df = data_not_accept_df.rename(
             columns={
@@ -378,7 +377,7 @@ def generate_md_lines(self, data_not_accept_df, rules, round):
                             data_not_accept_df = data_analysis.round_significant_decimal_places(
                                 data_not_accept_df, round, [metric]
                             )
-        data_not_accept_df = data_not_accept_df.convert_dtypes().astype('object').fillna(self.na)
+        data_not_accept_df = data_not_accept_df.convert_dtypes().astype('object').infer_objects().fillna(self.na)
         lines = file_handler.generate_md_table(data_not_accept_df, header)
         return lines
 

superbench/analyzer/diagnosis_rule_op.py

@@ -239,19 +239,22 @@ def failure_check(data_row, rule, summary_data_row, details, categories, raw_rul
         violated_metric_num = 0
         for metric_regex in raw_rule['metrics']:
             match = False
+            violate = False
             for metric in rule['metrics']:
                 if re.search(metric_regex, metric):
                     match = True
                     # metric not in raw_data or the value is none, miss test
-                    if metric not in data_row or pd.isna(data_row[metric]):
-                        violated_metric_num += 1
-                    break
+                    if RuleOp.miss_test(metric, rule, data_row, details, categories):
+                        violate = True
             # metric_regex written in rules is not matched by any metric, miss test
             if not match:
-                violated_metric_num += 1
+                violate = True
                 RuleOp.add_categories_and_details(metric_regex + '_miss', rule['categories'], details, categories)
+            if violate:
+                violated_metric_num += 1
         # return code != 0, failed test
         violated_metric_num += RuleOp.value(data_row, rule, summary_data_row, details, categories)
+        details[:] = list(dict.fromkeys(details))    # remove duplicate details
         return violated_metric_num