bumping automlx and rc

Author: ahosler

.github/workflows/run-forecast-unit-tests.yml

@@ -56,6 +56,6 @@ jobs:
           $CONDA/bin/conda init
           source /home/runner/.bashrc
           pip install -r test-requirements-operators.txt
-          pip install "oracle-automlx[forecasting]>=24.4.1"
+          pip install "oracle-automlx[forecasting]>=25.1.1"
           pip install pandas>=2.2.0
           python -m pytest -v -p no:warnings --durations=5 tests/operators/forecast

ads/opctl/operator/lowcode/forecast/model/automlx.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-# Copyright (c) 2023, 2024 Oracle and/or its affiliates.
+# Copyright (c) 2023, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 import logging
 import os
@@ -66,8 +66,7 @@ def preprocess(self, data):  # TODO: re-use self.le for explanations
     @runtime_dependency(
         module="automlx",
         err_msg=(
-            "Please run `pip3 install oracle-automlx>=23.4.1` and "
-            "`pip3 install oracle-automlx[forecasting]>=23.4.1` "
+            "Please run `pip3 install oracle-automlx[forecasting]>=25.1.1` "
             "to install the required dependencies for automlx."
         ),
     )
@@ -270,11 +269,15 @@ def _generate_report(self):
                 self.formatted_local_explanation = aggregate_local_explanations
 
                 if not self.target_cat_col:
-                    self.formatted_global_explanation = self.formatted_global_explanation.rename(
-                        {"Series 1": self.original_target_column},
-                        axis=1,
+                    self.formatted_global_explanation = (
+                        self.formatted_global_explanation.rename(
+                            {"Series 1": self.original_target_column},
+                            axis=1,
+                        )
+                    )
+                    self.formatted_local_explanation.drop(
+                        "Series", axis=1, inplace=True
                     )
-                    self.formatted_local_explanation.drop("Series", axis=1, inplace=True)
 
                 # Create a markdown section for the global explainability
                 global_explanation_section = rc.Block(
@@ -423,7 +426,9 @@ def explain_model(self):
                     # Use the MLExplainer class from AutoMLx to generate explanations
                     explainer = automlx.MLExplainer(
                         self.models[s_id],
-                        self.datasets.additional_data.get_data_for_series(series_id=s_id)
+                        self.datasets.additional_data.get_data_for_series(
+                            series_id=s_id
+                        )
                         .drop(self.spec.datetime_column.name, axis=1)
                         .head(-self.spec.horizon)
                         if self.spec.additional_data
@@ -434,7 +439,9 @@ def explain_model(self):
 
                     # Generate explanations for the forecast
                     explanations = explainer.explain_prediction(
-                        X=self.datasets.additional_data.get_data_for_series(series_id=s_id)
+                        X=self.datasets.additional_data.get_data_for_series(
+                            series_id=s_id
+                        )
                         .drop(self.spec.datetime_column.name, axis=1)
                         .tail(self.spec.horizon)
                         if self.spec.additional_data
@@ -446,7 +453,9 @@ def explain_model(self):
                     explanations_df = pd.concat(
                         [exp.to_dataframe() for exp in explanations]
                     )
-                    explanations_df["row"] = explanations_df.groupby("Feature").cumcount()
+                    explanations_df["row"] = explanations_df.groupby(
+                        "Feature"
+                    ).cumcount()
                     explanations_df = explanations_df.pivot(
                         index="row", columns="Feature", values="Attribution"
                     )
@@ -458,5 +467,7 @@ def explain_model(self):
                     # Fall back to the default explanation generation method
                     super().explain_model()
             except Exception as e:
-                logger.warning(f"Failed to generate explanations for series {s_id} with error: {e}.")
+                logger.warning(
+                    f"Failed to generate explanations for series {s_id} with error: {e}."
+                )
                 logger.debug(f"Full Traceback: {traceback.format_exc()}")

pyproject.toml

@@ -172,13 +172,13 @@ forecast = [
   "statsmodels",
   "plotly",
   "oracledb",
-  "report-creator==1.0.32",
+  "report-creator==1.0.37",
 ]
 anomaly  = [
   "oracle_ads[opctl]",
   "autots",
   "oracledb",
-  "report-creator==1.0.32",
+  "report-creator==1.0.37",
   "rrcf==0.4.4",
   "scikit-learn<1.6.0",
   "salesforce-merlion[all]==2.0.4"
@@ -187,7 +187,7 @@ recommender = [
   "oracle_ads[opctl]",
   "scikit-surprise",
   "plotly",
-  "report-creator==1.0.32",
+  "report-creator==1.0.37",
 ]
 feature-store-marketplace = [
     "oracle-ads[opctl]",
@@ -203,7 +203,7 @@ pii = [
   "scrubadub_spacy",
   "spacy-transformers==1.2.5",
   "spacy==3.6.1",
-  "report-creator==1.0.32",
+  "report-creator==1.0.37",
 ]
 llm = ["langchain>=0.2", "langchain-community", "langchain_openai", "pydantic>=2,<3", "evaluate>=0.4.0"]
 aqua = ["jupyter_server"]