Reformat code to pythonic standards

lottery_predictor/analyze.py

@@ -1,5 +1,6 @@
-from datetime import datetime
+import random
 from collections import Counter
+from datetime import datetime
 
 import numpy as np
 import pandas as pd
@@ -7,12 +8,13 @@ from sklearn.ensemble import RandomForestRegressor
 from sqlalchemy import select
 
 from data.database import Base, dal
-from lottery_predictor.config import RANDOM_SEED, GAME_INFO
+from lottery_predictor.config import GAME_INFO
 
 
 def load_dataframe_by_dates(
-        game: str, start_date: datetime | None = None, end_date: datetime | None = None
-    ) -> pd.DataFrame:
+        game: str, start_date: datetime | None = None,
+        end_date: datetime | None = None,
+) -> pd.DataFrame:
     """
         :param game: the name of the game to get data for
         :param start_date: the start date for the data
@@ -29,17 +31,21 @@ def load_dataframe_by_dates(
         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)
-                .order_by(target_table.columns.draw_date.desc()))
+                             .where(
+                target_table.columns.draw_date >= start_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)
-                .order_by(target_table.columns.draw_date.desc()))
+                             .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)
-                 .order_by(target_table.columns.draw_date.desc()))
+                             .where(target_table.columns.draw_date <= end_date)
+                             .order_by(target_table.columns.draw_date.desc()))
         else:
             sql_statement = select(target_table)
 
@@ -64,16 +70,19 @@ def load_dataframe_most_recent(game: str, limit: int = 10) -> pd.DataFrame:
         target_table = Base.metadata.tables.get(game_dict['db_table_name'])
         if limit is not None and limit > 0:
             sql_statement = (select(target_table)
-                .order_by(target_table.columns.draw_date.desc())
-                .limit(limit))
+                             .order_by(target_table.columns.draw_date.desc())
+                             .limit(limit))
             return pd.read_sql(sql_statement, session.bind)
         else:
-            raise ValueError('Limit must be a positive integer greater than zero.')
+            raise ValueError(
+                'Limit must be a positive integer greater than zero.',
+            )
     else:
         raise ValueError('An invalid table name was provided.')
 
 
-def prepare_split_data(data: np.ndarray, window_size: int = 10) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+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]
 
@@ -81,14 +90,14 @@ def prepare_split_data(data: np.ndarray, window_size: int = 10) -> tuple[np.ndar
     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."
+            f"but window_size requires at least {window_size + 1} rows.",
         )
 
     # Calculate the indices for all windows at once
     indices = np.arange(len(clean_data) - window_size)
 
     # Create X: flattened sliding windows
-    x = np.array([clean_data[i : i + window_size].flatten() for i in indices])
+    x = np.array([clean_data[i: i + window_size].flatten() for i in indices])
 
     # Create y_field: first 5 columns (2D array)
     y_field = clean_data[window_size:, 0:5]
@@ -99,7 +108,11 @@ def prepare_split_data(data: np.ndarray, window_size: int = 10) -> tuple[np.ndar
     return x, y_field, y_game
 
 
-def make_prediction(data_frame: pd.DataFrame, window_size: int = 10) -> tuple[np.ndarray, np.ndarray]:
+def make_prediction(data_frame: pd.DataFrame, window_size: int = 10) -> tuple[
+    np.ndarray, np.ndarray]:
+    # Get a random number
+    state = random.randint(1000, 300_000)
+
     # Convert the data_frame to a numpy array for slicing
     data = data_frame.values
 
@@ -107,26 +120,28 @@ def make_prediction(data_frame: pd.DataFrame, window_size: int = 10) -> tuple[np
     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 = RandomForestRegressor(n_estimators=200, random_state=state)
     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 = RandomForestRegressor(n_estimators=200, random_state=state)
     game_model.fit(x, y_game)
 
     # Predict the next draw
     clean_data = data[:, 1:7]
     current_window = clean_data[-window_size:].flatten().reshape(1, -1)
     # Get predictions and round to the nearest whole number
-    predicted_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),
+    )
     predicted_game = np.round(game_model.predict(current_window)).astype(int)
 
     return predicted_field[0], predicted_game[0]
 
 
 def get_most_common_number(
-        data_frame: pd.DataFrame, columns: list | None = None, top: int = 1
-    ) -> list[int]:
+        data_frame: pd.DataFrame, columns: list | None = None, top: int = 1,
+) -> list[int]:
     """
     :param data_frame: a pandas DataFrame containing the data
     :param columns: a list of column names to use
@@ -135,14 +150,16 @@ def get_most_common_number(
     :returns: a list of the most common numbers
     """
     if columns is None:
-        columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
+        columns = [
+            'main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5',
+        ]
     flat_numbers = data_frame[columns].values.flatten()
     counts = Counter(flat_numbers)
     return [int(num) for num, _ in counts.most_common(top)]
 
 
 def get_least_common_number(
-        data_frame: pd.DataFrame, columns: list | None = None, bottom: int = 1
+        data_frame: pd.DataFrame, columns: list | None = None, bottom: int = 1,
 ) -> list[int]:
     """
     :param data_frame: a pandas DataFrame containing the data
@@ -152,23 +169,30 @@ def get_least_common_number(
     :returns: a list of the least common numbers
     """
     if columns is None:
-        columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
+        columns = [
+            'main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5',
+        ]
     flat_numbers = data_frame[columns].values.flatten()
     counts = Counter(flat_numbers)
-    return list(reversed([int(num) for num, _ in counts.most_common()[-bottom:]]))
+    return list(
+        reversed([int(num) for num, _ in counts.most_common()[-bottom:]]),
+    )
 
 
 def calculate_probabilities(
-        data_frame: pd.DataFrame, max_number: int, columns: list | None = None
-    ) -> dict[int, float]:
+        data_frame: pd.DataFrame, max_number: int, columns: list | None = None,
+) -> dict[int, float]:
     """
-    :param data_frame: A pandas DataFrame containing the data to calculate probabilities for
+    :param data_frame: A pandas DataFrame containing the data to calculate
+    probabilities for
     :param max_number: The maximum number possible in the data_frame
     :param columns: The list of column names to use from the data_frame
     :returns dict: A dictionary containing the probabilities
     """
     if columns is None:
-        columns = ['main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5']
+        columns = [
+            'main_ball1', 'main_ball2', 'main_ball3', 'main_ball4', 'main_ball5',
+        ]
     if data_frame.empty:
         return dict()
 
@@ -177,9 +201,11 @@ def calculate_probabilities(
     # count all the occurrences of each number
     counts = Counter(all_numbers)
     # calculate the basic probability of each number occurring again
-    probabilities = {num: counts.get(num, 0) / (max_number + 1) for num in range(1, max_number + 1)}
+    probabilities = {num: counts.get(num, 0) / (max_number + 1) for num in
+                     range(1, max_number + 1)}
 
-    # if any calculation is greater than one, use what is to the right of the decimal point as the value
+    # if any calculation is greater than one, use what is to the right of the
+    # decimal point as the value
     for key, value in probabilities.items():
         if value > 1:
             probabilities[key] = value - int(str(value).split('.')[0])
@@ -188,16 +214,20 @@ def calculate_probabilities(
     return probabilities
 
 
-def get_hot_numbers(probabilities: dict[int, float], top: int = 5) -> list[tuple[int, float]]:
+def get_hot_numbers(probabilities: dict[int, float], top: int = 5) -> list[
+    tuple[int, float]]:
     """
     :param probabilities: A dictionary containing the probabilities
     :param top: The count of hottest items to return, defaults to 5
     :returns list of tuples: A list of the hot numbers and their raw score
     """
-    return sorted(probabilities.items(), key=lambda item: item[1], reverse=True)[:top]
+    return sorted(
+        probabilities.items(), key=lambda item: item[1], reverse=True,
+    )[:top]
 
 
-def get_cold_numbers(probabilities: dict[int, float], bottom: int = 5) -> list[tuple[int, float]]:
+def get_cold_numbers(probabilities: dict[int, float], bottom: int = 5) -> list[
+    tuple[int, float]]:
     """
     :param probabilities: A dictionary containing the probabilities
     :param bottom: The count of coldest items to return, defaults to 5