Update prep and predict functions

Change to new config dict

lottery_predictor/analyze.py

@@ -7,16 +7,14 @@ from sklearn.ensemble import RandomForestRegressor
 from sqlalchemy import select
 
 from data.database import Base, dal
-import util.drawing as drawutil
+from lottery_predictor.config import RANDOM_SEED, GAME_INFO
-
-RANDOM_SEED = 42
 
 
 def load_dataframe_by_dates(
-        table_name: str, start_date: datetime | None = None, end_date: datetime | None = None
+        game: str, start_date: datetime | None = None, end_date: datetime | None = None
     ) -> pd.DataFrame:
     """
-        :param table_name: the name of the table to load data from
+        :param game: the name of the game to get data for
         :param start_date: the start date for the data
         :param end_date: the end date for the data
 
@@ -26,17 +24,22 @@ def load_dataframe_by_dates(
     dal.connect()
     session = dal.Session()
 
-    game_dict = drawutil.check_table_name(table_name=table_name)
+    game_dict = GAME_INFO[game]
     if game_dict:
         target_table = Base.metadata.tables.get(game_dict['db_table_name'])
         if start_date is not None and end_date is not None:
             sql_statement = (select(target_table)
                 .where(target_table.columns.draw_date >= start_date)
-                .where(target_table.columns.draw_date <= end_date))
+                .where(target_table.columns.draw_date <= end_date)
+                .order_by(target_table.columns.draw_date.desc()))
         elif start_date is not None and end_date is None:
-            sql_statement = (select(target_table).where(target_table.columns.draw_date >= start_date))
+            sql_statement = (select(target_table)
+                .where(target_table.columns.draw_date >= start_date)
+                .order_by(target_table.columns.draw_date.desc()))
         elif start_date is None and end_date is not None:
-            sql_statement = (select(target_table).where(target_table.columns.draw_date <= end_date))
+            sql_statement = (select(target_table)
+                 .where(target_table.columns.draw_date <= end_date)
+                 .order_by(target_table.columns.draw_date.desc()))
         else:
             sql_statement = select(target_table)
 
@@ -45,9 +48,9 @@ def load_dataframe_by_dates(
         raise ValueError('An invalid table name was provided')
 
 
-def load_dataframe_most_recent(table_name: str, limit: int = 10) -> pd.DataFrame:
+def load_dataframe_most_recent(game: str, limit: int = 10) -> pd.DataFrame:
     """
-        :param table_name: the name of the table to load data from
+        :param game: the name of the game to get data for
         :param limit: The N most recent draws (default: 10)
 
         :returns: a pandas DataFrame containing the data
@@ -56,7 +59,7 @@ def load_dataframe_most_recent(table_name: str, limit: int = 10) -> pd.DataFrame
     dal.connect()
     session = dal.Session()
 
-    game_dict = drawutil.check_table_name(table_name=table_name)
+    game_dict = GAME_INFO[game]
     if game_dict:
         target_table = Base.metadata.tables.get(game_dict['db_table_name'])
         if limit is not None and limit > 0:
@@ -71,21 +74,29 @@ def load_dataframe_most_recent(table_name: str, limit: int = 10) -> pd.DataFrame
 
 
 def prepare_split_data(data: np.ndarray, window_size: int = 10) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    # Clean the data by removing the draw_date and multiplier columns
+    clean_data = data[:, 1:7]
+
+    # Check to be sure there is enough data for the window_size
+    if len(clean_data) <= window_size:
+        raise ValueError(
+            f"Not enough data! Dataset has {len(clean_data)} rows, "
+            f"but window_size requires at least {window_size + 1} rows."
+        )
 
     # Calculate the indices for all windows at once
-    indices = np.arange(len(data) - window_size)
+    indices = np.arange(len(clean_data) - window_size)
 
     # Create X: flattened sliding windows
-    # Reshapes into (samples, window_size * columns)
+    x = np.array([clean_data[i : i + window_size].flatten() for i in indices])
-    X = np.array([data[i : i + window_size]. flatten() for i in indices])
 
-    # Create y_field: first 5 columns of the row which are the field balls
+    # Create y_field: first 5 columns (2D array)
-    y_field = data[window_size:, 0:5]
+    y_field = clean_data[window_size:, 0:5]
 
-    # Create y_game: 6th column (index 5) of the row which is the game ball
+    # Create y_game on the last column ensuring it is a 2D array
-    y_game = data[window_size:, 5]
+    y_game = clean_data[window_size:, 5].ravel()
 
-    return X, y_field, y_game
+    return x, y_field, y_game
 
 
 def make_prediction(data_frame: pd.DataFrame, window_size: int = 10) -> tuple[np.ndarray, np.ndarray]:
@@ -93,23 +104,24 @@ def make_prediction(data_frame: pd.DataFrame, window_size: int = 10) -> tuple[np
     data = data_frame.values
 
     # Prepare and split the data
-    X, y_field, y_game = prepare_split_data(data, window_size=window_size)
+    x, y_field, y_game = prepare_split_data(data, window_size=window_size)
 
     # Train the model for the field balls
     field_model = RandomForestRegressor(n_estimators=200, random_state=RANDOM_SEED)
-    field_model.fit(X, y_field)
+    field_model.fit(x, y_field)
 
     # Train the model for the game ball
     game_model = RandomForestRegressor(n_estimators=200, random_state=RANDOM_SEED)
-    game_model.fit(X, y_game)
+    game_model.fit(x, y_game)
 
     # Predict the next draw
-    current_window = data[-window_size:].flatten().reshape(1, -1)
+    clean_data = data[:, 1:7]
+    current_window = clean_data[-window_size:].flatten().reshape(1, -1)
     # Get predictions and round to the nearest whole number
-    pred_field = np.sort(np.round(field_model.predict(current_window)).astype(int))
+    predicted_field = np.sort(np.round(field_model.predict(current_window)).astype(int))
-    pred_game = np.round(game_model.predict(current_window)).astype(int)
+    predicted_game = np.round(game_model.predict(current_window)).astype(int)
 
-    return pred_field[0], pred_game[0]
+    return predicted_field[0], predicted_game[0]
 
 
 def get_most_common_number(