preprocess.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')


def extract_games(df):
    all_games = []
    for games in df:
        all_games.append(games)
    return all_games

def extract_moves(games):
    all_white_moves = []
    all_black_moves = []
    for game in games:
        game = game.split(",")
        for move in game:
            move = move.split(" ")
            all_black_moves.append(move[1::2])
            all_white_moves.append(move[0::2])
    return all_white_moves, all_black_moves
    
def number_of_takes(player_games):
    all_takes = []
    for game in player_games:
        takes = 0  
        for moves in game:
            takes = takes + moves.count("x")
        all_takes.append(takes)
    return all_takes

def mate_games(chess_data):
    chess_data = chess_data[chess_data.rated != False]
    chess_data = chess_data[chess_data.victory_status == "mate"]
    chess_data = chess_data[chess_data.turns > 4]
    chess_data.drop_duplicates(subset=['id'], inplace=True)
    chess_data[['whites_opening','blacks_opening']] = chess_data.opening_name.str.split(": ", 1, expand=True)
    chess_data[['time_limit','increment']] = chess_data.increment_code.str.split("+", 1, expand=True).astype('int')
    chess_data.drop(['id', 'rated', 'white_id', 'black_id','opening_name', 'increment_code', 'victory_status'],axis=1,inplace=True)
    from sklearn.preprocessing import LabelEncoder
    le = LabelEncoder()
    for column_name in ['winner','whites_opening','blacks_opening', 'opening_eco']:
        chess_data[column_name] = le.fit_transform(chess_data[column_name])
    games_df = extract_games(chess_data.moves)
    white_moves, black_moves = extract_moves(games_df)
    white_took = number_of_takes(white_moves)
    wtdf = pd.DataFrame(data = white_took, columns=["white_took"])
    black_took = number_of_takes(black_moves)
    btdf = pd.DataFrame(data = black_took, columns=["black_took"])
    chess_data = pd.concat([chess_data,wtdf],axis=1)
    chess_data = pd.concat([chess_data,btdf],axis=1)
    chess_data.drop(['moves', 'last_move_at', 'created_at'], axis=1, inplace=True)
    chess_data = chess_data[chess_data.turns < 200]
    chess_data.reset_index(inplace = True)
    chess_data.drop(['index'],axis=1,inplace=True)
    x = chess_data.iloc[:, chess_data.columns != 'winner']
    y = chess_data.iloc[:, 2]
    return x, y

def draw_and_mate_games(chess_data):
    chess_data = chess_data[chess_data.rated != False]
    chess_data = chess_data[chess_data.victory_status != "outoftime"]
    chess_data = chess_data[chess_data.victory_status != "resign"]
    chess_data = chess_data[chess_data.turns > 4]
    chess_data.drop_duplicates(subset=['id'], inplace=True)
    chess_data[['whites_opening','blacks_opening']] = chess_data.opening_name.str.split(": ", 1, expand=True)
    chess_data[['time_limit','increment']] = chess_data.increment_code.str.split("+", 1, expand=True).astype('int')
    chess_data.drop(['id', 'rated', 'white_id', 'black_id','opening_name', 'increment_code'],axis=1,inplace=True)
    from sklearn.preprocessing import LabelEncoder
    le = LabelEncoder()
    for column_name in ['winner','whites_opening','blacks_opening', 'opening_eco', 'victory_status']:
        chess_data[column_name] = le.fit_transform(chess_data[column_name])
    games_df = extract_games(chess_data.moves)
    white_moves, black_moves = extract_moves(games_df)
    white_took = number_of_takes(white_moves)
    wtdf = pd.DataFrame(data = white_took, columns=["white_took"])
    black_took = number_of_takes(black_moves)
    btdf = pd.DataFrame(data = black_took, columns=["black_took"])
    chess_data = pd.concat([chess_data,wtdf],axis=1)
    chess_data = pd.concat([chess_data,btdf],axis=1)
    chess_data.drop(['moves', 'last_move_at', 'created_at'], axis=1, inplace=True)
    chess_data = chess_data[chess_data.turns < 200]
    chess_data.reset_index(inplace = True)
    chess_data.drop(['index'],axis=1,inplace=True)
    x = chess_data.iloc[:, chess_data.columns != 'winner']
    y = chess_data.iloc[:, 2]
    return x, y