plot for cartpole and reacher complete
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@ -7,8 +7,8 @@ def plot_csv(paths, x_axis, y_axis):
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for path_ in paths:
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data = np.genfromtxt(path_, delimiter=',', skip_header=0, dtype=float)
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mean = np.mean(data, axis=1)
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std = np.std(data, axis=1)
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mean = np.mean(data, axis=0)
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std = np.std(data, axis=0)
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x = np.linspace(0, mean.shape[0], mean.shape[0])
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@ -35,13 +35,10 @@ def plot_csv(paths, x_axis, y_axis):
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if __name__ == '__main__':
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filenames = ['cp-e150r10-bf15-base/cp-ei-random-1_0-15-1690282051_2959082.csv',
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'cp-e150r10-bf15-noshaping/cp-pei-random-0_95-15-1690276946_1944933.csv',
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'cp-e150r10-bf15-noshaping/cp-pei-regular-25_0-15-1690290021_6843266.csv',
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'cp-e150r10-bf15-noshaping/cp-pei-improvement-0_1-15-1690292664_0382216.csv',
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filenames = ['franka-pei-random-1_0-6-1694787936_385925.csv',
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]
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home_dir = os.path.expanduser('~')
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file_path = os.path.join(home_dir, 'Documents/IntRLResults/CP-Results')
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file_path = os.path.join(home_dir, 'Documents/IntRLResults/Franka-Results')
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paths = [os.path.join(file_path, filename) for filename in filenames]
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plot_csv(paths, 'Episodes', 'Reward')
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#
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@ -1,26 +1,37 @@
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.gridspec as gridspec
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import matplotlib.ticker as ticker
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import os
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def modified_plot_v7(paths_dict, x_axis, y_axis, subplot_titles, ylims=(0, 200),offset=0):
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def fixed_width_formatter(x, _):
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return "{:>7}".format(x)
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# Create a figure with 4 subplots
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# fig, axs = plt.subplots(1, 4, figsize=(20, 6))
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fig = plt.figure(figsize=(20, 6))
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gs = gridspec.GridSpec(1, 4, width_ratios=[1, 1, 1, 1])
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axs=[]
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axs.append(fig.add_subplot(gs[0, 0]))
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axs.append(fig.add_subplot(gs[0, 1]))
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axs.append(fig.add_subplot(gs[0, 2]))
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axs.append(fig.add_subplot(gs[0, 3]))
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def plot_csv_with_titles(paths_dict, x_axis, y_axis, subplot_titles, y_limits=(0, 200)):
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# Adjustments for dynamic subplot creation based on the dictionary size
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num_subplots = len(paths_dict)
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fig, axs = plt.subplots(1, num_subplots, figsize=(4*num_subplots, 4))
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plt.rcParams['font.size'] = 16
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label_font_size = 14
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plt.tick_params(axis='y', labelsize=14)
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# If only one subplot, axs is not an array, so we need to convert it
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if num_subplots == 1:
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axs = [axs]
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for idx, (_, file_list) in enumerate(paths_dict.items()):
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for path_, color in file_list:
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# Plotting mean and std for the first 3 subplots
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for idx, key in enumerate(['subplot1', 'subplot2', 'subplot3']):
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for path_, color, plot_line in paths_dict[key]:
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if plot_line:
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data = np.genfromtxt(path_, delimiter=',', skip_header=0, dtype=float)
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if data.shape[0] > data.shape[1]:
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mean = np.mean(data, axis=1)
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mean = np.mean(data, axis=1) + offset
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std = np.std(data, axis=1)
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else:
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mean = np.mean(data, axis=0)
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mean = np.mean(data, axis=0) +offset
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std = np.std(data, axis=0)
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x = np.linspace(0, mean.shape[0], mean.shape[0])
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@ -33,24 +44,86 @@ def plot_csv_with_titles(paths_dict, x_axis, y_axis, subplot_titles, y_limits=(0
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color=color,
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alpha=0.5
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)
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axs[idx].set_title(subplot_titles[idx])
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axs[idx].set_xlabel(x_axis)
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axs[idx].set_xlim([0, mean.shape[0]])
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axs[idx].grid(True)
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axs[idx].set_ylim(y_limits)
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# Only label the y-axis for the leftmost plot
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if idx == 0:
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axs[idx].set_ylabel(y_axis)
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else:
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axs[idx].tick_params(axis='x', labelsize=14)
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axs[idx].tick_params(axis='y', labelsize=14)
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axs[idx].set_xlabel(x_axis, fontsize=label_font_size)
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axs[idx].text(0.5, -0.15, subplot_titles[idx], horizontalalignment='center', verticalalignment='center',
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transform=axs[idx].transAxes, fontsize=label_font_size)
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axs[idx].set_xlim([0, mean.shape[0]])
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axs[idx].set_ylim(ylims)
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axs[idx].spines['right'].set_visible(False)
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axs[idx].spines['top'].set_visible(False)
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axs[idx].set_xlabel(x_axis, fontsize=label_font_size, position=(0.5, -0.2))
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if idx != 0:
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axs[idx].set_ylabel('')
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axs[idx].set_yticklabels([])
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else:
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axs[0].set_ylabel(y_axis, fontsize=label_font_size)
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axs[0].yaxis.set_tick_params(size=14, pad=0)
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axs[idx].yaxis.set_major_formatter(ticker.FuncFormatter(fixed_width_formatter))
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# Preparing data for the grouped bar chart in the fourth subplot
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groups = [
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[paths_dict['subplot1'][0]],
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[paths_dict['subplot1'][1], paths_dict['subplot1'][2], paths_dict['subplot1'][3]], # Adjusted order
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[paths_dict['subplot2'][1], paths_dict['subplot2'][2], paths_dict['subplot2'][3]], # Adjusted order
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[paths_dict['subplot3'][1], paths_dict['subplot3'][2], paths_dict['subplot3'][3]] # Adjusted order
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]
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last_means = []
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last_stds = []
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colors = []
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for group in groups:
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for path_, color, _ in group:
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data = np.genfromtxt(path_, delimiter=',', skip_header=0, dtype=float)
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if data.shape[0] > data.shape[1]:
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mean = np.mean(data, axis=1) + offset
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std = np.std(data, axis=1)
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else:
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mean = np.mean(data, axis=0) + offset
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std = np.std(data, axis=0)
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last_means.append(mean[-1])
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last_stds.append(std[-1])
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colors.append(color)
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# Plotting the grouped bar chart in the fourth subplot
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bar_width = 0.2
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r = [0] + [x * bar_width + 0.5 for x in np.arange(len(groups[1]))] + \
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[x * bar_width + 1.5 for x in np.arange(len(groups[2]))] + \
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[x * bar_width + 2.5 for x in np.arange(len(groups[3]))]
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labels = ['BO', 'IBO-Random', 'IBO-Regular', 'IBO-Improvement', None, None, None, None, None, None]
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for i in range(len(r)):
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axs[3].bar(r[i], last_means[i]-ylims[0], yerr=last_stds[i], width=bar_width,
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color=colors[i], capsize=5, bottom=ylims[0], label=labels[i])
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axs[3].set_xticks([r[0], r[2], r[5], r[8]])
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axs[3].set_xticklabels(['BO', 'IBO Preference', 'IBO Shaping', 'IBO Mixture'], fontsize=label_font_size, rotation=15)
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axs[3].set_xlabel('')
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axs[3].spines['right'].set_visible(False)
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axs[3].spines['top'].set_visible(False)
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axs[3].set_ylim(ylims)
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axs[3].set_ylabel('')
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axs[3].set_yticklabels([])
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axs[3].text(0.5, -0.15, subplot_titles[3], horizontalalignment='center',
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verticalalignment='center', transform=axs[3].transAxes, fontsize=label_font_size)
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# Display legend for the first and second groups
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handles, labels = axs[3].get_legend_handles_labels()
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filtered_handles = [h for h, l in zip(handles, labels) if l]
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filtered_labels = [l for l in labels if l]
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axs[3].legend(filtered_handles, filtered_labels, loc='upper left', frameon=False, fontsize=12)
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plt.tight_layout()
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plt.show()
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if __name__ == '__main__':
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home_dir = os.path.expanduser('~')
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file_path = os.path.join(home_dir, 'Documents/IntRLResults/CP-Results')
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@ -69,31 +142,58 @@ if __name__ == '__main__':
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filepaths = [os.path.join(file_path, filename) for filename in filenames]
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# Demonstrating the adjusted function with subplot titles
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titles = ["Preference", "Shaping", "Combination", "Regular"]
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data_dict_colored = {
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'subplot1': [(filepaths[0], 'C0'), (filepaths[1], 'C1'), (filepaths[2], 'C2'), (filepaths[3], 'C3')],
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'subplot2': [(filepaths[0], 'C0'), (filepaths[4], 'C1'), (filepaths[5], 'C2'), (filepaths[6], 'C3')],
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'subplot3': [(filepaths[0], 'C0'), (filepaths[7], 'C1'), (filepaths[8], 'C2'), (filepaths[9], 'C3')],
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'subplot4': [(filepaths[0], 'C0'), (filepaths[2], 'C6'), (filepaths[5], 'C8'), (filepaths[8], 'C5')],
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}
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plot_csv_with_titles(data_dict_colored, 'Episodes', 'Reward', titles)
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titles = ["a) Preference", "b) Shaping", "c) Combination"]
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file_path_reacher = os.path.join(home_dir, 'Documents/IntRLResults/RE-Results')
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filenames_reacher = ['base_line/re-ei-random-1_0-5-1694370994_0363934.csv',
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'shaping/re-ei-random-1_0-10-1694359559_616903.csv',
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'shaping/re-ei-regular-10_0-5-1694371946_5364418.csv'
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'preference/re-pei-random-0_95-5-1694419627_105307.csv',
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'preference/re-pei-regular_outliner_free.csv',
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'preference/re-pei-improvement-0_1-5-1694416723_645404.csv',
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'shaping/re-ei-random-0_95-5-1694611010_6658554.csv',
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'shaping/re-ei-regular-10_0-5-1694371946_5364418.csv',
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'shaping/re-ei-improvement-0_1-5-1694415947_8134162.csv',
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'combination/re-pei-random-0_95-5-1694423273_9474058.csv',
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'combination/re-pei-regular_outliner_free.csv',
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'combination/re-pei-improvement-0_1-5-1694430822_181309.csv',
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]
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filepaths_reacher = [os.path.join(file_path_reacher, filename) for filename in filenames_reacher]
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titles_reacher = ["Shaping"]
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titles_reacher = ["(a)", "(b)", "(c)", "(d)"]
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data_dict_reacher = {
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'subplot1': [(filepaths_reacher[0], 'C0'), (filepaths_reacher[1], 'C1'), (filepaths_reacher[2], 'C2')]
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data_dict_colored = {
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'subplot1': [(filepaths[0], 'C0', True), (filepaths[1], 'C3', True), (filepaths[2], 'C1', False),
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(filepaths[3], 'C2', False)],
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'subplot2': [(filepaths[0], 'C0', True), (filepaths[4], 'C3', False), (filepaths[5], 'C1', True),
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(filepaths[6], 'C2', False)],
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'subplot3': [(filepaths[0], 'C0', True), (filepaths[7], 'C3', False), (filepaths[8], 'C1', True),
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(filepaths[9], 'C2', False)],
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# 'subplot4': [(filepaths[0], 'C0'), (filepaths[2], 'C6'), (filepaths[5], 'C8'), (filepaths[8], 'C5')],
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}
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plot_csv_with_titles(data_dict_reacher, 'Episodes', 'Reward', titles_reacher, y_limits=(-150, 50))
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# plot_csv_with_titles(data_dict_colored, 'Episodes', 'Reward', titles)
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data_dict_reacher = {
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'subplot1': [(filepaths_reacher[0], 'C0', True), (filepaths_reacher[1], 'C3', False),
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(filepaths_reacher[2], 'C1', True), (filepaths_reacher[3], 'C2', False)],
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'subplot2': [(filepaths_reacher[0], 'C0', True), (filepaths_reacher[4], 'C3', True),
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(filepaths_reacher[5], 'C1', False), (filepaths_reacher[6], 'C2', False)],
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'subplot3': [(filepaths_reacher[0], 'C0', True), (filepaths_reacher[7], 'C3', False),
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(filepaths_reacher[8], 'C1', True), (filepaths_reacher[9], 'C2', False)],
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}
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# plot_csv_with_titles(data_dict_reacher, 'Episodes', 'Reward', titles_reacher, y_limits=(-150, 50))
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# data_dict_combined = {**data_dict_colored, **data_dict_reacher}
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# combined_titles = titles + titles_reacher
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#
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# plot_csv_with_titles(
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# paths_dict=data_dict_combined,
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# x_axis='Episodes',
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# y_axis='Rewards',
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# subplot_titles=combined_titles
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# )
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modified_plot_v7(paths_dict=data_dict_colored, x_axis='Episodes', y_axis='Rewards', subplot_titles=titles_reacher)
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modified_plot_v7(paths_dict=data_dict_reacher, x_axis='Episodes', y_axis='Rewards with +80 offset', subplot_titles=titles_reacher,
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ylims=(-80, 150), offset=80)
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