SIENTIAPDE-1430: Refactor DataPreprocessor date filtering, make rce_train radius optional, and introduce constants for model names.

model_manager/sientia/models.py

@@ -442,6 +442,36 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
             input_data = treat_nan(input_data, treatment)
         return input_data
 
+    def _parse_datetime(self, date_str: str | None) -> pd.Timestamp | None:
+        """Parse a date string to Timestamp, returning None on failure."""
+        if not date_str:
+            return None
+        try:
+            return pd.to_datetime(date_str)
+        except (ValueError, TypeError):
+            return None
+
+    def _filter_by_date_range(
+        self, input_data: pd.DataFrame, start: pd.Timestamp | None, end: pd.Timestamp | None
+    ) -> pd.DataFrame:
+        """Filter DataFrame by start and end dates."""
+        if start is not None:
+            input_data = input_data[input_data.index >= start]
+        if end is not None:
+            input_data = input_data[input_data.index <= end]
+        return input_data
+
+    def _remove_interval(self, input_data: pd.DataFrame, interval: tuple | list) -> pd.DataFrame:
+        """Remove a single interval from the DataFrame."""
+        if len(interval) < 2:
+            return input_data
+        interval_start = self._parse_datetime(interval[0])
+        interval_end = self._parse_datetime(interval[1])
+        if interval_start is None or interval_end is None:
+            return input_data
+        mask = ~((input_data.index >= interval_start) & (input_data.index <= interval_end))
+        return input_data[mask]
+
     def range_selection(self, input_data: pd.DataFrame) -> pd.DataFrame:
         """
         Filter data by date range and remove specified intervals.
@@ -452,35 +482,13 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
         Returns:
             pandas.DataFrame: The filtered data
         """
-        # Filter by start_date and end_date
-        if self.start_date:
-            try:
-                start = pd.to_datetime(self.start_date)
-                input_data = input_data[input_data.index >= start]
-            except (ValueError, TypeError):
-                pass  # Invalid date format, skip filtering
+        start = self._parse_datetime(self.start_date)
+        end = self._parse_datetime(self.end_date)
+        input_data = self._filter_by_date_range(input_data, start, end)
 
-        if self.end_date:
-            try:
-                end = pd.to_datetime(self.end_date)
-                input_data = input_data[input_data.index <= end]
-            except (ValueError, TypeError):
-                pass  # Invalid date format, skip filtering
-
-        # Remove specified intervals
         if self.removed_intervals:
             for interval in self.removed_intervals:
-                if len(interval) >= 2:
-                    try:
-                        interval_start = pd.to_datetime(interval[0])
-                        interval_end = pd.to_datetime(interval[1])
-                        mask = ~(
-                            (input_data.index >= interval_start)
-                            & (input_data.index <= interval_end)
-                        )
-                        input_data = input_data[mask]
-                    except (ValueError, TypeError):
-                        pass  # Invalid date format, skip this interval
+                input_data = self._remove_interval(input_data, interval)
 
         return input_data