changes to support new version

Author: prasankh

ads/opctl/operator/lowcode/anomaly/README.md

@@ -38,6 +38,7 @@ To run anomaly detection locally, create and activate a new conda environment (`
 - datapane
 - cerberus
 - oracle-automlx==23.4.1
+- oracle-automlx[classic]==23.4.1
 - "git+https://github.com/oracle/accelerated-data-science.git@feature/anomaly#egg=oracle-ads"
 ```
 

ads/opctl/operator/lowcode/anomaly/environment.yaml

@@ -8,4 +8,5 @@ dependencies:
       - datapane
       - cerberus
       - oracle-automlx==23.4.1
+      - oracle-automlx[classic]==23.4.1
       - "oracle-ads[anomaly]"

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

@@ -17,20 +17,22 @@ class AutoMLXOperatorModel(AnomalyOperatorBaseModel):
     """Class representing AutoMLX operator model."""
 
     @runtime_dependency(
-        module="automl",
+        module="automlx",
         err_msg=(
-            "Please run `pip3 install oracle-automlx==23.4.1` to "
-            "install the required dependencies for automlx."
+            "Please run `pip3 install oracle-automlx==23.4.1` and "
+            "`pip3 install oracle-automlx[classic]==23.4.1` "
+            "to install the required dependencies for automlx."
         ),
     )
     def _build_model(self) -> pd.DataFrame:
+        from automlx import init
+        init(engine="ray", engine_opts={"ray_setup": {"_temp_dir": "/tmp/ray-temp"}})
         date_column = self.spec.datetime_column.name
         anomaly_output = AnomalyOutput(date_column=date_column)
 
-        time_budget = self.spec.model_kwargs.pop("time_budget", None)
         # Iterate over the full_data_dict items
         for target, df in self.datasets.full_data_dict.items():
-            est = automl.Pipeline(task="anomaly_detection", **self.spec.model_kwargs)
+            est = automlx.Pipeline(task="anomaly_detection", **self.spec.model_kwargs)
             est.fit(
                 X=df,
                 X_valid=self.X_valid_dict[target]
@@ -39,7 +41,6 @@ def _build_model(self) -> pd.DataFrame:
                 y_valid=self.y_valid_dict[target]
                 if self.y_valid_dict is not None
                 else None,
-                time_budget=time_budget,
                 contamination=self.spec.contamination
                 if self.y_valid_dict is not None
                 else None,

ads/opctl/operator/lowcode/forecast/README.md

@@ -40,6 +40,7 @@ To run forecasting locally, create and activate a new conda environment (`ads-fo
 - sktime
 - optuna==2.9.0
 - oracle-automlx==23.4.1
+- oracle-automlx[forecasting]==23.4.1
 - oracle-ads>=2.9.0
 ```
 

ads/opctl/operator/lowcode/forecast/environment.yaml

@@ -17,3 +17,4 @@ dependencies:
       - autots[additional]
       - optuna==2.9.0
       - oracle-automlx==23.4.1
+      - oracle-automlx[forecasting]==23.4.1

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

@@ -57,7 +57,9 @@ def preprocess(self, data, series_id=None):
     @runtime_dependency(
         module="automlx",
         err_msg=(
-            "Please run `pip3 install oracle-automlx==23.4.1` to install the required dependencies for automlx."
+            "Please run `pip3 install oracle-automlx==23.4.1` and "
+            "`pip3 install oracle-automlx[forecasting]==23.4.1` "
+            "to install the required dependencies for automlx."
         ),
     )
     @runtime_dependency(

tests/operators/anomaly/test_anomaly_simple.py

@@ -212,7 +212,7 @@ def test_load_datasets(model, data_dict):
 
         # run(yaml_i, backend="operator.local", debug=False)
 
-        with open(f"{tmpdirname}/anomaly.yaml", "w") as f:
+        with open(anomaly_yaml_filename, "w") as f:
             f.write(yaml.dump(yaml_i))
         sleep(0.5)
         subprocess.run(