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plot_multiple_curves.py
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plot_multiple_curves.py
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from math import sqrt
from statistics import stdev, mean
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
dir = "figures/"
file_name_1 = 'tmp/expert_alg1_online_154K_every10_sparse2_1/test_score_history.csv'
file_name_2 = 'tmp/expert_alg1_online_154K_every10_sparse2_2/test_score_history.csv'
file_name_3 = 'tmp/expert_alg1_online_154K_every10_sparse2_3/test_score_history.csv'
file_name_4 = 'tmp/expert_alg1_offline_154K_every10_sparse2_1/test_score_history.csv'
file_name_5 = 'tmp/expert_alg1_offline_154K_every10_sparse2_2/test_score_history.csv'
file_name_6 = 'tmp/expert_alg1_offline_154K_every10_sparse2_3/test_score_history.csv'
file_name_7 = 'tmp/expert_alg1_online_28K_every10_sparse2_1/test_score_history.csv'
file_name_8 = 'tmp/expert_alg1_online_28K_every10_sparse2_2/test_score_history.csv'
file_name_9 = 'tmp/expert_alg1_online_28K_every10_sparse2_3/test_score_history.csv'
file_name_10 = 'tmp/expert_alg1_offline_28K_every10_sparse2_1/test_score_history.csv'
file_name_11 = 'tmp/expert_alg1_offline_28K_every10_sparse2_2/test_score_history.csv'
file_name_12 = 'tmp/expert_alg1_offline_28K_every10_sparse2_3/test_score_history.csv'
file_name_13 = 'tmp/expert_alg1_online_28K_every10_sparse2_descending_1/test_score_history.csv'
file_name_14 = 'tmp/expert_alg1_online_28K_every10_sparse2_descending_2/test_score_history.csv'
file_name_15 = 'tmp/expert_alg1_online_28K_every10_sparse2_descending_3/test_score_history.csv'
file_name_16 = 'tmp/expert_alg1_offline_28K_every10_sparse2_descending_1/test_score_history.csv'
file_name_17 = 'tmp/expert_alg1_offline_28K_every10_sparse2_descending_2/test_score_history.csv'
file_name_18 = 'tmp/expert_alg1_offline_28K_every10_sparse2_descending_3/test_score_history.csv'
file_name_1t = 'tmp/thanasis/loop1_O_O_a_154K_every10_Sparse_2_normal_thanasis_1/test_score_history.csv'
file_name_2t = 'tmp/thanasis/loop1_O_O_a_154K_every10_Sparse_2_normal_thanasis_2/test_score_history.csv'
file_name_3t = 'tmp/thanasis/loop1_O_O_a_154K_every10_Sparse_2_normal_thanasis_3/test_score_history.csv'
file_name_4t = 'tmp/thanasis/loop1_O_a_154K_every10_Sparse_2_normal_thanasis_1/test_score_history.csv'
file_name_5t = 'tmp/thanasis/loop1_O_a_154K_every10_Sparse_2_normal_thanasis_2/test_score_history.csv'
file_name_6t = 'tmp/thanasis/loop1_O_a_154K_every10_Sparse_2_normal_thanasis_3/test_score_history.csv'
file_name_7t = 'tmp/thanasis/loop1_O_O_a_28K_every10_Sparse_2_normal_thanasis_1/test_score_history.csv'
file_name_8t = 'tmp/thanasis/loop1_O_O_a_28K_every10_Sparse_2_normal_thanasis_2/test_score_history.csv'
file_name_9t = 'tmp/thanasis/loop1_O_O_a_28K_every10_Sparse_2_normal_thanasis_3/test_score_history.csv'
file_name_10t = 'tmp/thanasis/loop1_O_a_28K_every10_Sparse_2_normal_thanasis_1/test_score_history.csv'
file_name_11t = 'tmp/thanasis/loop1_O_a_28K_every10_Sparse_2_normal_thanasis_2/test_score_history.csv'
file_name_12t = 'tmp/thanasis/loop1_O_a_28K_every10_Sparse_2_normal_thanasis_3/test_score_history.csv'
file_name_13t = 'tmp/thanasis/loop1_O_O_a_28K_every10_Sparse_2_descending_thanasis_1/test_score_history.csv'
file_name_14t = 'tmp/thanasis/loop1_O_O_a_28K_every10_Sparse_2_descending_thanasis_2/test_score_history.csv'
file_name_15t = 'tmp/thanasis/loop1_O_O_a_28K_every10_Sparse_2_descending_thanasis_3/test_score_history.csv'
file_name_16t = 'tmp/thanasis/loop1_O_a_28K_every10_Sparse_2_descending_thanasis_1/test_score_history.csv'
file_name_17t = 'tmp/thanasis/loop1_O_a_28K_every10_Sparse_2_descending_thanasis_2/test_score_history.csv'
file_name_18t = 'tmp/thanasis/loop1_O_a_28K_every10_Sparse_2_descending_thanasis_3/test_score_history.csv'
# random_files = [random_file_1]
# legend_names_1 = ["offline_2000_sparse1", "offline_2000_sparse2"]
# legend_names_2 = ["offline_154_every10_sparse1"]
# legend_names = ["offline_28K_every5_sparse2", "online_28K_every5_sparse2", "offline_154K_every10_sparse2", "online_154K_every10_sparse2"]
legend_names = ["O-O-a 154K", "O-a 154K", "O-O-a 28K Descending", "O-a 28K Descending"]
legend_names = ["O-O-a 154K", "O-a 154K", "O-O-a 28K", "O-a 28K"]
# filename_list_1 = [file_name_1, file_name_2]
# filename_list_2 = [file_name_3, file_name_4]
colors = ["g", "b", "r", "m", "navy", "darkorange"]
colors_light = ["limegreen", "cornflowerblue", "lightcoral", "violet", "slateblue", "wheat"]
fill = True
start = 40
end = 205
def plot(filename_list, legend_names, figure_file=None):
fig, ax = plt.subplots()
axes = plt.gca()
axes.set_ylim([start, end])
# colors = sns.color_palette("husl", 5)
with sns.axes_style("darkgrid"):
for c, file_name_sublist in enumerate(filename_list):
merge_lists = []
for file_name in file_name_sublist:
my_data = np.genfromtxt(file_name, delimiter=',')
merge_lists.append(my_data)
# random = np.genfromtxt(random_file, delimiter=',')
# means, stds, x_axis = [mean(random)], [stdev(random) / sqrt(len(random))], [0]
means, stds, x_axis = [], [], []
for i in range(0, len(merge_lists[0]), 10):
data = []
for file_to_merge in merge_lists:
data.extend(file_to_merge[i:i + 10])
means.append(mean(data))
stds.append(stdev(data) / sqrt(len(data)))
x_axis.append(i)
means, stds, x_axis = np.asarray(means), np.asarray(stds), np.asarray(x_axis)
# meanst = np.array(means.ix[i].values[3:-1], dtype=np.float64)
# sdt = np.array(stds.ix[i].values[3:-1], dtype=np.float64)
if fill:
ax.plot(x_axis, means, c=colors[c], label=legend_names[c], marker='*')
ax.fill_between(x_axis, means - stds, means + stds, facecolor=colors_light[c], alpha=0.5)
else:
plt.errorbar(x_axis, means, stds, label=legend_names[c], marker='*')
ax.yaxis.set_ticks(np.arange(start, end, 10))
ax.set_ylabel('Score')
ax.set_xlabel('Trials')
plt.grid()
plt.legend(loc='lower right')
plt.savefig(figure_file)
# plt.show()
plot([[file_name_1, file_name_2, file_name_3, file_name_1t, file_name_2t, file_name_3t],
[file_name_4, file_name_5, file_name_6, file_name_4t, file_name_5t, file_name_6t],
[file_name_7, file_name_8, file_name_9, file_name_7t, file_name_8t, file_name_9t],
[file_name_10, file_name_11, file_name_12, file_name_10t, file_name_11t, file_name_12t]],
legend_names,
figure_file=dir + "154K vs 28K 2 users.png")
# plot([filename_list_sparse1[0]], [legend_names_sparse1[0]], figure_file=dir +"offline_sparse_1")
# plot([filename_list_sparse1[1]], [legend_names_sparse1[1]], figure_file=dir +"online_sparse_1")
# plot([filename_list_sparse2[0]], [legend_names_sparse2[0]], figure_file=dir +"offline_sparse_2")
# plot([filename_list_sparse2[1]], [legend_names_sparse2[1]], figure_file=dir +"online_sparse_2")
#
# plot([filename_list_sparse1[0], filename_list_sparse2[0]], [legend_names_sparse1[0],legend_names_sparse1[0]], figure_file=dir +"offline_all rewards")
# plot([filename_list_sparse1[1], filename_list_sparse2[1]], [legend_names_sparse1[1], legend_names_sparse2[1]], figure_file=dir +"online_all rewards")
#
# plot(filename_list_sparse1, legend_names_sparse1, figure_file=dir +"Learning_Comparison_sparse_1")
# plot(filename_list_sparse2, legend_names_sparse2, figure_file=dir +"Learning_Comparison_sparse_2")
# plot(filename_list_sparse1 + filename_list_sparse2, legend_names_sparse1 + legend_names_sparse2,
# figure_file=dir +"Learning_Comparison_all rewards")