support forecasting

responsibleai/responsibleai/_internal/_served_model_wrapper.py

@@ -3,9 +3,8 @@
 
 import json
 
-import requests
-
 from responsibleai.serialization_utilities import serialize_json_safe
+from raiutils.webservice import post_with_retries
 
 
 class ServedModelWrapper:
@@ -37,14 +36,13 @@ def forecast(self, X):
         # request formatting according to mlflow docs
         # https://mlflow.org/docs/latest/cli.html#mlflow-models-serve
         # JSON safe serialization takes care of datetime columns
-        response = requests.post(
-            url=f"http://localhost:{self.port}/invocations",
-            headers={"Content-Type": "application/json"},
-            data=json.dumps(
-                {"dataframe_split": X.to_dict(orient='split')},
-                default=serialize_json_safe))
+        uri = f"http://localhost:{self.port}/invocations"
+        input_data = json.dumps(
+            {"dataframe_split": X.to_dict(orient='split')},
+            default=serialize_json_safe)
+        headers = {"Content-Type": "application/json"}
         try:
-            response.raise_for_status()
+            response = post_with_retries(uri, input_data, headers, max_retries=15, retry_delay=30)
         except Exception:
             raise RuntimeError(
                 "Could not retrieve predictions. "

responsibleai/responsibleai/rai_insights/rai_insights.py

@@ -1285,10 +1285,14 @@ def _get_feature_ranges(
                 res_object[_UNIQUE_VALUES] = unique_value.tolist()
             elif datetime_features is not None and col in datetime_features:
                 res_object[_RANGE_TYPE] = "datetime"
+                min_value = test[col].min()
+                min_value = pd.to_datetime(min_value)
                 res_object[_MIN_VALUE] = \
-                    test[col].min().strftime(_STRF_TIME_FORMAT)
+                    min_value.strftime(_STRF_TIME_FORMAT)
+                max_value = test[col].max()
+                max_value = pd.to_datetime(max_value)
                 res_object[_MAX_VALUE] = \
-                    test[col].max().strftime(_STRF_TIME_FORMAT)
+                    max_value.strftime(_STRF_TIME_FORMAT)
             else:
                 col_min = test[col].min()
                 col_max = test[col].max()