ODSC-60854: pass model params via kwargs, ruff formatting

Author: codeloop

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

@@ -1,18 +1,18 @@
 #!/usr/bin/env python
-# -*- coding: utf-8 -*--
 
 # Copyright (c) 2024 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
-import pandas as pd
 import numpy as np
+import pandas as pd
 
-from ads.opctl import logger
 from ads.common.decorator import runtime_dependency
+from ads.opctl import logger
 from ads.opctl.operator.lowcode.forecast.utils import _select_plot_list
+
+from ..const import ForecastOutputColumns, SupportedModels
+from ..operator_config import ForecastOperatorConfig
 from .base_model import ForecastOperatorBaseModel
 from .forecast_datasets import ForecastDatasets, ForecastOutput
-from ..operator_config import ForecastOperatorConfig
-from ..const import ForecastOutputColumns, SupportedModels
 
 
 class MLForecastOperatorModel(ForecastOperatorBaseModel):
@@ -58,18 +58,25 @@ def _train_model(self, data_train, data_test, model_kwargs):
             from mlforecast.target_transforms import Differences
 
             lgb_params = {
-                "verbosity": -1,
-                "num_leaves": 512,
+                "verbosity": model_kwargs.get("verbosity", -1),
+                "num_leaves": model_kwargs.get("num_leaves", 512),
             }
             additional_data_params = {}
             if len(self.datasets.get_additional_data_column_names()) > 0:
                 additional_data_params = {
-                    "target_transforms": [Differences([12])],
+                    "target_transforms": [
+                        Differences([model_kwargs.get("Differences", 12)])
+                    ],
                     "lags": model_kwargs.get("lags", [1, 6, 12]),
                     "lag_transforms": (
                         {
                             1: [ExpandingMean()],
-                            12: [RollingMean(window_size=24)],
+                            12: [
+                                RollingMean(
+                                    window_size=model_kwargs.get("RollingMean", 24),
+                                    min_samples=1,
+                                )
+                            ],
                         }
                     ),
                 }