def plot_perfect_recall_rates_for_dg_weightings_no_err_bars(parsed_data, additional_plot_title):
set_size_buckets = Parser.get_dictionary_list_of_convergence_and_perfect_recall_for_dg_weightings(parsed_data)
# x, y_iters, std_iters, y_ratios, std_ratios
x = range(30)
results_2 = Parser.get_avg_convergence_for_x_and_set_size(2, set_size_buckets, x)
results_3 = Parser.get_avg_convergence_for_x_and_set_size(3, set_size_buckets, x)
results_4 = Parser.get_avg_convergence_for_x_and_set_size(4, set_size_buckets, x)
results_5 = Parser.get_avg_convergence_for_x_and_set_size(5, set_size_buckets, x)
plt.rcParams.update({'font.size': 25})
plt.ylabel('Convergence ratio')
plt.xlabel('Turnover rate')
plt.title('Average convergence rate by DG-weighting, ' + additional_plot_title)
p2 = plt.plot(results_2[0], results_2[3])
p3 = plt.plot(results_3[0], results_3[3])
p4 = plt.plot(results_4[0], results_4[3])
p5 = plt.plot(results_5[0], results_5[3])
plt.legend((p2[0], p3[0], p4[0], p5[0]), ('2x5', '3x5', '4x5', '5x5'))
# bbox_to_anchor=(1, 0.9), ncol=1, fancybox=True, shadow=True)
plt.grid(True)
plt.margins(0.01)
plt.yticks(np.arange(0, 1.1, .1))
plt.show()
def plot_convergence_stats_for_turnover_rates(parsed_data, log_filename):
set_size_buckets = Parser.get_dictionary_list_of_convergence_and_perfect_recall_for_turnover_rates(
Parser.get_data_with_turnover_rates(parsed_data, log_filename))
# x, y_iters, std_iters, y_ratios, std_ratios
x = [x * 0.02 for x in range(30)]
results_2 = Parser.get_avg_convergence_for_x_and_set_size(2, set_size_buckets, x)
results_3 = Parser.get_avg_convergence_for_x_and_set_size(3, set_size_buckets, x)
results_4 = Parser.get_avg_convergence_for_x_and_set_size(4, set_size_buckets, x)
results_5 = Parser.get_avg_convergence_for_x_and_set_size(5, set_size_buckets, x)
plt.rcParams.update({'font.size': 25})
plt.ylabel('Convergence ratio')
plt.xlabel('Turnover rate')
plt.title('Average convergence rate by turnover rate')
# p2 = plt.errorbar(results_2[0], results_2[3], results_2[4])
# p3 = plt.errorbar(results_3[0], results_3[3], results_3[4])
# p4 = plt.errorbar(results_4[0], results_4[3], results_4[4])
# p5 = plt.errorbar(results_5[0], results_5[3], results_5[4])
p2 = plt.plot(results_2[0], results_2[3], linewidth=3.0)
p3 = plt.plot(results_3[0], results_3[3], linewidth=3.0)
p4 = plt.plot(results_4[0], results_4[3], linewidth=3.0)
p5 = plt.plot(results_5[0], results_5[3], linewidth=3.0)
plt.xticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.58])
plt.legend((p2[0], p3[0], p4[0], p5[0]), ('2x5', '3x5', '4x5', '5x5'),
bbox_to_anchor=(1, 1.0155), ncol=4, fancybox=True, shadow=True)
plt.margins(0.09)
plt.grid(True)
plt.show()
def plot_aggregate_figure_for_turnover_rates(parsed_data, log_file):
set_size_buckets = Parser.get_dictionary_list_of_convergence_and_perfect_recall_for_turnover_rates(
Parser.get_data_with_turnover_rates(parsed_data, log_file))
plt.figure(1)
plt.subplot(211)
# x, y_iters, std_iters, y_ratios, std_ratios
x = [x * 0.02 for x in range(30)]
results_2 = Parser.get_avg_convergence_for_x_and_set_size(2, set_size_buckets, x)
results_3 = Parser.get_avg_convergence_for_x_and_set_size(3, set_size_buckets, x)
results_4 = Parser.get_avg_convergence_for_x_and_set_size(4, set_size_buckets, x)
results_5 = Parser.get_avg_convergence_for_x_and_set_size(5, set_size_buckets, x)
plt.rcParams.update({'font.size': 25})
plt.ylabel('Convergence ratio')
# plt.xlabel('Turnover rate')
plt.title('Average convergence and perfect recall rates by turnover rate')
p2 = plt.plot(results_2[0], results_2[3], linewidth=3.0)
p3 = plt.plot(results_3[0], results_3[3], linewidth=3.0)
p4 = plt.plot(results_4[0], results_4[3], linewidth=3.0)
p5 = plt.plot(results_5[0], results_5[3], linewidth=3.0)
plt.xticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.58])
plt.legend((p2[0], p3[0], p4[0], p5[0]), ('2x5', '3x5', '4x5', '5x5'),
bbox_to_anchor=(1.115, 1.0155), ncol=1, fancybox=True, shadow=True)
plt.margins(0.02)
plt.grid(True)
# x, y_iters, std_iters, y_ratios, std_ratios
x = [x * 0.02 for x in range(30)]
results_2 = Parser.get_avg_perfect_recall_for_x_and_set_size(2, set_size_buckets, x)
results_3 = Parser.get_avg_perfect_recall_for_x_and_set_size(3, set_size_buckets, x)
results_4 = Parser.get_avg_perfect_recall_for_x_and_set_size(4, set_size_buckets, x)
results_5 = Parser.get_avg_perfect_recall_for_x_and_set_size(5, set_size_buckets, x)
plt.subplot(212)
# plt.rcParams.update({'font.size': 25})
plt.ylabel('Perfect recall rate')
plt.xlabel('Turnover rate')
# plt.title('Average perfect recall rate by turnover rate')
p2 = plt.plot(results_2[0], results_2[1], linewidth=3.0)
p3 = plt.plot(results_3[0], results_3[1], linewidth=3.0)
p4 = plt.plot(results_4[0], results_4[1], linewidth=3.0)
p5 = plt.plot(results_5[0], results_5[1], linewidth=3.0)
plt.legend((p2[0], p3[0], p4[0], p5[0]), ('2x5', '3x5', '4x5', '5x5'), ncol=1, bbox_to_anchor=(1.115, 1.0155),
fancybox=True, shadow=True)
plt.xticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.58])
plt.margins(0.02)
plt.subplots_adjust(left=0.06)
plt.grid(True)
plt.show()
def plot_aggregate_figure_for_turnover_rates(parsed_data, log_file):
set_size_buckets = Parser.get_dictionary_list_of_convergence_and_perfect_recall_for_turnover_rates(
Parser.get_data_with_turnover_rates(parsed_data, log_file))
values_prrs, values_spurious = ThreeDBarPlot.process_3d_data(parsed_data, iterations_per_config=10,
num_of_configs=30)
plt.figure(1)
plt.subplot(211)
# x, y_iters, std_iters, y_ratios, std_ratios
x = [x * 0.02 for x in range(30)]
results_2 = Parser.get_avg_convergence_for_x_and_set_size(2, set_size_buckets, x)
results_3 = Parser.get_avg_convergence_for_x_and_set_size(3, set_size_buckets, x)
results_4 = Parser.get_avg_convergence_for_x_and_set_size(4, set_size_buckets, x)
results_5 = Parser.get_avg_convergence_for_x_and_set_size(5, set_size_buckets, x)
plt.rcParams.update({'font.size': 25})
plt.ylabel('Convergence rate')
# plt.xlabel('Turnover rate')
plt.title('Average convergence and perfect recall rates by turnover rate')
p2 = plt.plot(results_2[0], results_2[3], linewidth=3.0)
p3 = plt.plot(results_3[0], results_3[3], linewidth=3.0)
p4 = plt.plot(results_4[0], results_4[3], linewidth=3.0)
p5 = plt.plot(results_5[0], results_5[3], linewidth=3.0)
plt.xticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.58])
plt.legend((p2[0], p3[0], p4[0], p5[0]), ('2x5', '3x5', '4x5', '5x5'),
bbox_to_anchor=(1.115, 1.0155), ncol=1, fancybox=True, shadow=True)
plt.margins(0.02)
plt.grid(True)
# x, y_iters, std_iters, y_ratios, std_ratios
x = [x * 0.02 for x in range(30)]
results_2 = Parser.get_avg_perfect_recall_for_x_and_set_size(2, set_size_buckets, x)
results_3 = Parser.get_avg_perfect_recall_for_x_and_set_size(3, set_size_buckets, x)
results_4 = Parser.get_avg_perfect_recall_for_x_and_set_size(4, set_size_buckets, x)
results_5 = Parser.get_avg_perfect_recall_for_x_and_set_size(5, set_size_buckets, x)
plt.subplot(212)
# plt.rcParams.update({'font.size': 25})
plt.ylabel('Recall rate')
plt.xlabel('Turnover rate')
# plt.title('Average perfect recall rate by turnover rate')
p2 = plt.plot(results_2[0], results_2[1], linewidth=3.0)
p3 = plt.plot(results_3[0], results_3[1], linewidth=3.0)
p4 = plt.plot(results_4[0], results_4[1], linewidth=3.0)
p5 = plt.plot(results_5[0], results_5[1], linewidth=3.0)
plt.subplot(212)
s_x, s_y, s_z = ThreeDBarPlot.unwrap_values(values_spurious)
s1, s2, s3, s4 = get_set_size_values_from_aggregate(s_z)
p6 = plt.plot(x, s1, linewidth=3.0, linestyle='--', color='b')
p7 = plt.plot(x, s2, linewidth=3.0, linestyle='--', color='g')
p8 = plt.plot(x, s3, linewidth=3.0, linestyle='--', color='r')
p9 = plt.plot(x, s4, linewidth=3.0, linestyle='--', color='c')
plt.legend((p2[0], p3[0], p4[0], p5[0], p6[0], p7[0], p8[0], p9[0]),
('2x5', '3x5', '4x5', '5x5', '2x5', '3x5', '4x5', '5x5'), ncol=1, bbox_to_anchor=(1.115, 1.0295),
fancybox=True, shadow=True)
plt.xticks([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.58])
plt.margins(0.02)
plt.subplots_adjust(left=0.06)
plt.grid(True)
plt.show()
