changes to support new version

Author: prasankh

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

@@ -55,7 +55,7 @@ def preprocess(self, data, series_id=None):
         return data.set_index(self.spec.datetime_column.name)
 
     @runtime_dependency(
-        module="automl",
+        module="automlx",
         err_msg=(
             "Please run `pip3 install oracle-automlx==23.4.1` to install the required dependencies for automlx."
         ),
@@ -67,15 +67,10 @@ def preprocess(self, data, series_id=None):
         ),
     )
     def _build_model(self) -> pd.DataFrame:
-        from automl import init
+        from automlx import init
         from sktime.forecasting.model_selection import temporal_train_test_split
 
-        init(
-            engine="local",
-            engine_opts={"n_jobs": -1, "model_n_jobs": -1},
-            check_deprecation_warnings=False,
-            logger=50,
-        )
+        init(engine="ray", engine_opts={"ray_setup": {"_temp_dir": "/tmp/ray-temp"}})
 
         full_data_dict = self.datasets.get_data_by_series()
 
@@ -95,7 +90,7 @@ def _build_model(self) -> pd.DataFrame:
 
         for i, (s_id, df) in enumerate(full_data_dict.items()):
             try:
-                logger.debug(f"Running automl on series {s_id}")
+                logger.debug(f"Running automlx on series {s_id}")
                 model_kwargs = model_kwargs_cleaned.copy()
                 target = self.original_target_column
                 self.forecast_output.init_series_output(
@@ -110,14 +105,13 @@ def _build_model(self) -> pd.DataFrame:
                 if self.loaded_models is not None:
                     model = self.loaded_models[s_id]
                 else:
-                    model = automl.Pipeline(
+                    model = automlx.Pipeline(
                         task="forecasting",
                         **model_kwargs,
                     )
                     model.fit(
                         X=data_i.drop(target, axis=1),
-                        y=data_i[[target]],
-                        time_budget=time_budget,
+                        y=data_i[[target]]
                     )
                 logger.debug(f"Selected model: {model.selected_model_}")
                 logger.debug(f"Selected model params: {model.selected_model_params_}")
@@ -149,18 +143,7 @@ def _build_model(self) -> pd.DataFrame:
 
                 self.model_parameters[s_id] = {
                     "framework": SupportedModels.AutoMLX,
-                    "score_metric": model.score_metric,
-                    "random_state": model.random_state,
-                    "model_list": model.model_list,
-                    "n_algos_tuned": model.n_algos_tuned,
-                    "adaptive_sampling": model.adaptive_sampling,
-                    "min_features": model.min_features,
-                    "optimization": model.optimization,
-                    "preprocessing": model.preprocessing,
-                    "search_space": model.search_space,
                     "time_series_period": model.time_series_period,
-                    "min_class_instances": model.min_class_instances,
-                    "max_tuning_trials": model.max_tuning_trials,
                     "selected_model": model.selected_model_,
                     "selected_model_params": model.selected_model_params_,
                 }