使用TrueSkill Algo回填熊猫数据库中数据集的最有效方法

Question

[我有一个following gist可以在一个数据集上实现TrueSkill算法，该数据集是一个n玩家免费比赛的集合，那里有赢家，然后是第二，第三，第四名，等等。

基本上我要问的是：

• 我是否正确实施了算法
• 什么是在Pandas数据框中回填数据的最有效方法

Answer 1

这是我想出的，可能可以针对速度进行优化。

# Fetch the data
df_raw = pd.read_csv('http://horse-data-abcd.s3.amazonaws.com/game_results.csv')

# Create a holding DataFrame for our TrueRank
df_truerank_columns = ['game_id', 'player_id', 'position', 'mu', 'sigma', 'post_mu', 'post_sigma']
df_truerank = pd.DataFrame(columns=df_truerank_columns)

# Use a sample of 1000
df = df_raw.head(10000)

# Group by the game_id
games = df.groupby('game_id')

# Now iterate the games
for game_id, game in games:
    # Setup lists so we can zip them back up at the end
    trueskills = []    
    player_ids = []
    game_ids = []  
    mus = []    
    sigmas = []
    post_mus = []
    post_sigmas = []

    # Now iterate over each player in a game
    for index, row in game.iterrows():

        # Create a game_ids arary for zipping up
        game_ids.append(game_id)

        # Now push the player_id onto the player_ids array for zipping up
        player_ids.append(int(row['player_id']))

        # Get the players last game, hence tail(1)
        filter = (df_truerank['game_id'] < game_id) & (df_truerank['player_id'] == row['player_id'])                            
        df_player = df_truerank[filter].tail(1)

        # If there isnt a game then just use the TrueSkill defaults
        if (len(df_player) == 0):
            mu = 25
            sigma = 8.333
        else:
            # Otherwise get the mu and sigma from the players last game
            row = df_player.iloc[0]
            mu = row['post_mu']
            sigma = row['post_sigma']

        # Keep lists of pre mu and sigmas
        mus.append(mu)
        sigmas.append(sigma)

        # Now create a TrueSkull Rating() class and pass it into the trueskills dictionary
        trueskills.append(Rating(mu=mu, sigma=sigma))

    # Use the positions as ranks, they are 0 based so -1 from all of them
    ranks = [x - 1 for x in list(game['position'])]

    # Create tuples out of the trueskills array
    trueskills_tuples = [(x,) for x in trueskills]

    try:
        # Get the results from the TrueSkill rate method
        results = rate(trueskills_tuples, ranks=ranks)

        # Loop the TrueSkill results and get the new mu and sigma for each player
        for result in results:
            post_mus.append(round(result[0].mu, 2))
            post_sigmas.append(round(result[0].sigma, 2))        
    except:
        # If the TrusSkill rate method blows up, just use the previous 
        # games mus and sigmas
        post_mus = mus
        post_sigmas = sigmas

    # Change the positions back to non 0 based
    positions = [x + 1 for x in ranks]

    # Now zip together all our lists 
    data = list(zip(game_ids, player_ids, positions, mus, sigmas, post_mus, post_sigmas))

    # Create a temp DataFrame the same as df_truerank and add data to the DataFrame
    df_temp = pd.DataFrame(data, columns=df_truerank_columns)

    # Add df_temp to our df_truerank
    df_truerank = df_truerank.append(df_temp)