utils.py

import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.ticker as mticker

def plot_history(point_history, **kwargs):

    lower_limit = 0
    upper_limit = len(point_history)

    window_size = (upper_limit * 10) // 100

    plot_rolling_mean_only = False
    plot_data_only = False

    if kwargs:
        if "window_size" in kwargs:
            window_size = kwargs["window_size"]

        if "lower_limit" in kwargs:
            lower_limit = kwargs["lower_limit"]

        if "upper_limit" in kwargs:
            upper_limit = kwargs["upper_limit"]

        if "plot_rolling_mean_only" in kwargs:
            plot_rolling_mean_only = kwargs["plot_rolling_mean_only"]

        if "plot_data_only" in kwargs:
            plot_data_only = kwargs["plot_data_only"]

    points = point_history[lower_limit:upper_limit]

    # Generate x-axis for plotting.
    episode_num = [x for x in range(lower_limit, upper_limit)]

    # Use Pandas to calculate the rolling mean (moving average).
    rolling_mean = pd.DataFrame(points).rolling(window_size).mean()

    plt.figure(figsize=(10, 7), facecolor="white")

    if plot_data_only:
        plt.plot(episode_num, points, linewidth=1, color="cyan")
    elif plot_rolling_mean_only:
        plt.plot(episode_num, rolling_mean, linewidth=2, color="magenta")
    else:
        plt.plot(episode_num, points, linewidth=1, color="cyan")
        plt.plot(episode_num, rolling_mean, linewidth=2, color="magenta")

    text_color = "black"

    ax = plt.gca()
    ax.set_facecolor("black")
    plt.grid()
    plt.xlabel("Episode", color=text_color, fontsize=30)
    plt.ylabel("Total Points", color=text_color, fontsize=30)
    yNumFmt = mticker.StrMethodFormatter("{x:,}")
    ax.yaxis.set_major_formatter(yNumFmt)
    ax.tick_params(axis="x", colors=text_color)
    ax.tick_params(axis="y", colors=text_color)
    plt.show()