Enable model_list filtering for AUTO_SELECT_SERIES

ads/opctl/operator/lowcode/forecast/meta_selector.py

@@ -13,7 +13,7 @@ class MetaSelector:
     The rules are based on the meta-features calculated by the FFORMS approach.
     """
 
-    def __init__(self):
+    def __init__(self, allowed_models=None):
         """Initialize the MetaSelector with pre-learned meta rules"""
         # Pre-learned rules based on meta-features
         self._meta_rules = {
@@ -216,6 +216,22 @@ def __init__(self):
             },
         }
 
+        # Normalize and apply allowed_models filter if provided
+        self._allowed_set = None
+        if allowed_models:
+            known = {"prophet", "arima", "neuralprophet", "automlx", "autots"}
+            if isinstance(allowed_models, (list, tuple, set)):
+                self._allowed_set = {str(m).lower() for m in allowed_models}
+            else:
+                self._allowed_set = {str(allowed_models).lower()}
+            self._allowed_set = {m for m in self._allowed_set if m in known}
+            if self._allowed_set:
+                self._meta_rules = {
+                    name: rule
+                    for name, rule in self._meta_rules.items()
+                    if rule.get("model") in self._allowed_set
+                }
+
     def _evaluate_condition(self, value, operator, threshold):
         """Evaluate a single condition based on pre-defined operators"""
         if pd.isna(value):
@@ -288,7 +304,13 @@ def select_best_model(self, meta_features_df):
                 series_info["matched_features"] = matched_features[best_rule]
             else:
                 best_rule = "default"
-                best_model = "prophet"  # Default to prophet if no rules match
+                if getattr(self, "_allowed_set", None):
+                    if "prophet" in self._allowed_set:
+                        best_model = "prophet"
+                    else:
+                        best_model = sorted(self._allowed_set)[0]
+                else:
+                    best_model = "prophet"  # Default to prophet if no rules match
                 series_info["matched_features"] = []
 
             series_info["selected_model"] = best_model

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

@@ -47,11 +47,11 @@
     AUTO_SELECT,
     BACKTEST_REPORT_NAME,
     SUMMARY_METRICS_HORIZON_LIMIT,
+    TROUBLESHOOTING_GUIDE,
     ForecastOutputColumns,
     SpeedAccuracyMode,
     SupportedMetrics,
     SupportedModels,
-    TROUBLESHOOTING_GUIDE,
 )
 from ..operator_config import ForecastOperatorConfig, ForecastOperatorSpec
 from .forecast_datasets import ForecastDatasets, ForecastResults

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

@@ -76,7 +76,8 @@ def get_model(
 
         if model_type == AUTO_SELECT_SERIES:
             # Initialize MetaSelector for series-specific model selection
-            selector = MetaSelector()
+            allowed = operator_config.spec.model_kwargs.get("model_list", None) if hasattr(operator_config.spec, "model_kwargs") and operator_config.spec.model_kwargs else None
+            selector = MetaSelector(allowed_models=allowed)
             # Create a Transformations instance
             transformer = Transformations(dataset_info=datasets.historical_data.spec)
 
@@ -89,7 +90,15 @@ def get_model(
                 )
             )
             # Get the most common model as default
-            model_type = meta_features['selected_model'].mode().iloc[0]
+            selected_str = str(meta_features['selected_model'].mode().iloc[0]).lower()
+            str_to_enum = {
+                "prophet": SupportedModels.Prophet,
+                "arima": SupportedModels.Arima,
+                "neuralprophet": SupportedModels.NeuralProphet,
+                "automlx": SupportedModels.AutoMLX,
+                "autots": SupportedModels.AutoTS,
+            }
+            model_type = str_to_enum.get(selected_str, SupportedModels.Prophet)
             # Store the series-specific model selections in the config for later use
             operator_config.spec.meta_features = meta_features
             operator_config.spec.model_kwargs = {}

tests/operators/forecast/test_datasets.py

@@ -413,5 +413,81 @@ def run_operator(
 #         generate_train_metrics = True
 
 
+@pytest.mark.parametrize("allowed", [["prophet", "arima"], ["prophet"], ["arima"], ["automlx"], ["neuralprophet"]])
+def test_auto_select_series_model_list_filter(allowed):
+    # Skip neuralprophet when running with NumPy 2.x due to upstream np.NaN usage
+    if "neuralprophet" in allowed:
+        try:
+            import numpy as np  # local import to avoid unused import in other tests
+            major = int(str(np.__version__).split(".")[0])
+        except Exception:
+            major = 0
+        if major >= 2:
+            pytest.skip("Skipping neuralprophet with NumPy >= 2.0 due to upstream incompatibility (uses np.NaN).")
+
+    # Skip pure-arima case if pmdarima cannot be imported (e.g., binary incompatibility with current NumPy)
+    if [str(m).lower() for m in allowed] == ["arima"]:
+        try:
+            import pmdarima as pm  # noqa: F401
+        except Exception as e:
+            pytest.skip(f"Skipping arima due to pmdarima import error: {e}")
+
+    dataset_name = f"{DATASET_PREFIX}dataset1.csv"
+    dataset_i = pd.read_csv(dataset_name)
+    target = "Y"
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        historical_data_path = f"{tmpdirname}/primary_data.csv"
+        test_data_path = f"{tmpdirname}/test_data.csv"
+        output_data_path = f"{tmpdirname}/results"
+        yaml_i = deepcopy(TEMPLATE_YAML)
+
+        # Train/Test split
+        dataset_i[[DATETIME_COL, target]][:-PERIODS].to_csv(
+            historical_data_path, index=False
+        )
+        dataset_i[[DATETIME_COL, target]][-PERIODS:].to_csv(test_data_path, index=False)
+
+        # Prepare YAML
+        yaml_i["spec"]["historical_data"]["url"] = historical_data_path
+        yaml_i["spec"]["test_data"] = {"url": test_data_path}
+        yaml_i["spec"]["output_directory"]["url"] = output_data_path
+        yaml_i["spec"]["model"] = "auto-select-series"
+        yaml_i["spec"]["target_column"] = target
+        yaml_i["spec"]["datetime_column"]["name"] = DATETIME_COL
+        yaml_i["spec"]["horizon"] = PERIODS
+        yaml_i["spec"]["generate_metrics"] = True
+        yaml_i["spec"]["model_kwargs"] = {"model_list": allowed}
+
+        # Run operator
+        run(yaml_i, backend="operator.local", debug=False)
+
+        # Collect per-model metrics produced by auto-select-series
+        result_files = os.listdir(output_data_path)
+        train_metrics_files = [
+            f for f in result_files if f.startswith("metrics_") and f.endswith(".csv")
+        ]
+        test_metrics_files = [
+            f
+            for f in result_files
+            if f.startswith("test_metrics_") and f.endswith(".csv")
+        ]
+
+        # Extract model names from filenames
+        found_models = set()
+        for f in train_metrics_files:
+            found_models.add(f[len("metrics_") : -len(".csv")])
+        for f in test_metrics_files:
+            found_models.add(f[len("test_metrics_") : -len(".csv")])
+
+        assert found_models, "No per-model metrics files were generated."
+        # Ensure only allowed models are present
+        assert found_models.issubset(set(allowed)), f"Found disallowed models in outputs: {found_models - set(allowed)}"
+
+        # Ensure disallowed models are absent
+        known_models = {"prophet", "arima", "neuralprophet", "automlx", "autots"}
+        disallowed = known_models - set(allowed)
+        assert found_models.isdisjoint(disallowed), f"Disallowed models present: {found_models & disallowed}"
+
 if __name__ == "__main__":
     pass