SIENTIAPDE-1255: Add thread-safety documentation and in-place modification warnings to LinearRegressionModel and DataPreprocessor classes.

model_manager/sientia/models.py

@@ -10,6 +10,19 @@ from sklearn.preprocessing import StandardScaler
 
 
 class LinearRegressionModel(BaseEstimator, TransformerMixin):
+    """
+    Linear Regression Model for Time Series Analysis.
+
+    Thread-safety: This class is NOT thread-safe during fit() operations.
+    Do not call fit() on the same instance from multiple threads simultaneously.
+    After fitting, predict() is thread-safe for read-only operations.
+
+    For multi-threaded environments:
+    - Fit the model in a single thread
+    - Share the fitted instance across threads for prediction only
+    - Or create separate instances per thread
+    """
+
     def __init__(
         self,
         target_variable: str = '',
@@ -99,6 +112,21 @@ class LinearRegressionModel(BaseEstimator, TransformerMixin):
 
 
 class DataPreprocessor(BaseEstimator, TransformerMixin):
+    """
+    Data Preprocessor for Time Series Analysis.
+
+    Thread-safety: This class is NOT thread-safe during fit() operations.
+    Do not call fit() on the same instance from multiple threads simultaneously.
+    After fitting, transform() is thread-safe for read-only operations IF the
+    input DataFrames are not shared between threads.
+
+    For multi-threaded environments:
+    - Fit the preprocessor in a single thread
+    - Share the fitted instance across threads for transform() only
+    - Ensure each thread passes its own DataFrame copy to transform()
+    - Or create separate instances per thread
+    """
+
     def __init__(
         self,
         date_column: str = '',
@@ -176,9 +204,11 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
         self.static_threshold = static_threshold
         self.low_lim = low_lim
         self.upp_lim = upp_lim
-        # self.window = window
+        # self.window = window  # Not implemented yet
         self.scaler_name = scaler_name
         self.scaler_params = scaler_params
+        self.feature_names_order: list[str] = []  # Initialize to avoid AttributeError
+
         if self.scaler_name == 'Standard Scaler':
             self.scaler = StandardScaler()
         elif self.scaler_name == 'None':
@@ -299,11 +329,16 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
 
         Returns:
             pandas.DataFrame: The treated data
+
+        Note:
+            This method modifies input_data in-place. Ensure the caller passes
+            a copy if the original DataFrame needs to be preserved.
         """
         if lag_dict:
             for var, lag in lag_dict.items():
                 if lag > 0:
                     input_data[var] = input_data[var].shift(lag)
+            # WARNING: Modifies DataFrame in-place
             input_data.dropna(inplace=True)
         return input_data
 
@@ -366,9 +401,13 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
 
         Returns:
             pandas.DataFrame: The treated data
+
+        Note:
+            This method modifies input_data in-place.
         """
         if self.ar_var:
             input_data[self.ar_var] = input_data[self.target_variable].shift(1)
+            # WARNING: Modifies DataFrame in-place
             input_data.dropna(inplace=True)
         return input_data
 
@@ -381,12 +420,16 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
 
         Returns:
             pandas.DataFrame: The treated data
+
+        Note:
+            This method modifies input_data in-place.
         """
         if self.created_lags:
             for var, lag in self.created_lags.items():
                 if lag > 0 and var in input_data.columns:
                     new_col = f'{var}_lag{lag}'
                     input_data[new_col] = input_data[var].shift(lag)
+            # WARNING: Modifies DataFrame in-place
             input_data.dropna(inplace=True)
         return input_data