support forecasting

erroranalysis/requirements-dev.txt

@@ -4,4 +4,5 @@ pytest-mock==3.6.1
 
 requirements-parser==0.2.0
 rai_test_utils[object_detection]
+scikit-learn<=1.3.2
 interpret-core[required]<=0.3.2

responsibleai/responsibleai/_internal/_served_model_wrapper.py

@@ -3,8 +3,7 @@
 
 import json
 
-import requests
-
+from raiutils.webservice import post_with_retries
 from responsibleai.serialization_utilities import serialize_json_safe
 
 
@@ -37,14 +36,14 @@ 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()

responsibleai/tests/rai_insights/test_served_model.py

@@ -6,7 +6,6 @@
 from unittest import mock
 
 import pytest
-import requests
 from tests.common_utils import (RandomForecastingModel,
                                 create_tiny_forecasting_dataset)
 
@@ -41,7 +40,7 @@ def rai_forecasting_insights_for_served_model():
 
 
 @mock.patch("requests.post")
-@mock.patch.dict("os.environ", {"RAI_MODEL_SERVING_PORT": "5123"})
+@mock.patch.dict("os.environ", {"RAI_MODEL_SERVING_PORT": "5432"})
 def test_served_model(
         mock_post,
         rai_forecasting_insights_for_served_model):
@@ -58,25 +57,3 @@ def test_served_model(
     forecasts = rai_insights.model.forecast(X_test)
     assert len(forecasts) == len(X_test)
     assert mock_post.call_count == 1
-
-
-@mock.patch("requests.post")
-@mock.patch.dict("os.environ", {"RAI_MODEL_SERVING_PORT": "5123"})
-def test_served_model_failed(
-        mock_post,
-        rai_forecasting_insights_for_served_model):
-    _, X_test, _, _ = create_tiny_forecasting_dataset()
-
-    response = requests.Response()
-    response.status_code = 400
-    response._content = b"Could not connect to host since it actively " \
-        b"refuses the connection."
-    mock_post.return_value = response
-
-    rai_insights = RAIInsights.load(RAI_INSIGHTS_DIR_NAME)
-    with pytest.raises(
-            Exception,
-            match="Could not retrieve predictions. "
-                  "Model server returned status code 400 "
-                  f"and the following response: {response.content}"):
-        rai_insights.model.forecast(X_test)