fix: _adjust_min_data_points

pfund/models/model_base.py

@@ -41,6 +41,7 @@
         TalibFunction,
         Any,
     ]
+
 from pfund.models.model_meta import MetaModel
 from pfund.products.product_base import BaseProduct
 from pfund.utils.utils import short_path, get_engine_class, load_yaml_file, convert_ts_to_dt
@@ -124,7 +125,6 @@ def __getattr__(self, attr):
     def df(self):
         return self._data_tool.df
 
-    @property
     def get_data_tool(self):
         return self._data_tool
 
@@ -342,8 +342,8 @@ def add_consumer_datas_if_no_data(self) -> list[BaseData]:
     # IMPROVE: current issue is in next(), when the df has multiple products and resolutions,
     # don't know how to determine the exact minimum amount of data points for predict()
     def _adjust_min_data_points(self):
-        num_products = len(self._data_tool.get_df_products())
-        num_resolutions = len(self._data_tool.get_df_resolutions())
+        num_products = len(self._data_tool.get_index_values(self.df, 'product'))
+        num_resolutions = len(self._data_tool.get_index_values(self.df, 'resolution'))
         assert num_products and num_resolutions, f"{num_products=} and/or {num_resolutions=} are invalid, please check your dataframe"
         adj_min_data_points = self.min_data_points * num_products * num_resolutions
         adj_max_data_points = self.max_data_points * num_products * num_resolutions