""" Process timing results from timeShonanAveraging """ import xlrd import numpy as np import matplotlib.pyplot as plt from matplotlib.ticker import FuncFormatter import heapq from collections import Counter def make_combined_plot(name, p_values, times, costs, min_cost_range=10): """ Make a plot that combines timing and SO(3) cost. Arguments: name: string of the plot title p_values: list of p-values (int) times: list of timings (seconds) costs: list of costs (double) Will calculate the range of the costs, default minimum range = 10.0 """ min_cost = min(costs) cost_range = max(max(costs)-min_cost,min_cost_range) fig = plt.figure() ax1 = fig.add_subplot(111) ax1.plot(p_values, times, label="time") ax1.set_ylabel('Time used to optimize \ seconds') ax1.set_xlabel('p_value') ax2 = ax1.twinx() ax2.plot(p_values, costs, 'r', label="cost") ax2.set_ylabel('Cost at SO(3) form') ax2.set_xlabel('p_value') ax2.set_xticks(p_values) ax2.set_ylim(min_cost, min_cost + cost_range) plt.title(name, fontsize=12) ax1.legend(loc="upper left") ax2.legend(loc="upper right") plt.interactive(False) plt.show() def make_convergence_plot(name, p_values, times, costs, iter=10): """ Make a bar that show the success rate for each p_value according to whether the SO(3) cost converges Arguments: name: string of the plot title p_values: list of p-values (int) times: list of timings (seconds) costs: list of costs (double) iter: int of iteration number for each p_value """ max_cost = np.mean(np.array(heapq.nlargest(iter, costs))) # calculate mean costs for each p value p_values = list(dict(Counter(p_values)).keys()) # make sure the iter number iter = int(len(times)/len(p_values)) p_mean_cost = [np.mean(np.array(costs[i*iter:(i+1)*iter])) for i in range(len(p_values))] p_max = p_values[p_mean_cost.index(max(p_mean_cost))] # print(p_mean_cost) # print(p_max) #take mean and make the combined plot mean_times = [] mean_costs = [] for p in p_values: costs_tmp = costs[p_values.index(p)*iter:(p_values.index(p)+1)*iter] mean_cost = sum(costs_tmp)/len(costs_tmp) mean_costs.append(mean_cost) times_tmp = times[p_values.index(p)*iter:(p_values.index(p)+1)*iter] mean_time = sum(times_tmp)/len(times_tmp) mean_times.append(mean_time) make_combined_plot(name, p_values,mean_times, mean_costs) # calculate the convergence rate for each p_value p_success_rates = [] if p_mean_cost[0] >= 0.95*np.mean(np.array(costs)) and p_mean_cost[0] <= 1.05*np.mean(np.array(costs)): p_success_rates = [ 1.0 for p in p_values] else: for p in p_values: if p > p_max: p_costs = costs[p_values.index(p)*iter:(p_values.index(p)+1)*iter] # print(p_costs) converged = [ int(p_cost < 0.3*max_cost) for p_cost in p_costs] success_rate = sum(converged)/len(converged) p_success_rates.append(success_rate) else: p_success_rates.append(0) plt.bar(p_values, p_success_rates, align='center', alpha=0.5) plt.xticks(p_values) plt.yticks(np.arange(0, 1.2, 0.2), ['0%', '20%', '40%', '60%', '80%', '100%']) plt.xlabel("p_value") plt.ylabel("success rate") plt.title(name, fontsize=12) plt.show() def make_eigen_and_bound_plot(name, p_values, times1, costPs, cost3s, times2, min_eigens, subounds): """ Make a plot that combines time for optimizing, time for optimizing and compute min_eigen, min_eigen, subound (subound = (f_R - f_SDP) / f_SDP). Arguments: name: string of the plot title p_values: list of p-values (int) times1: list of timings (seconds) costPs: f_SDP cost3s: f_R times2: list of timings (seconds) min_eigens: list of min_eigen (double) subounds: list of subound (double) """ if dict(Counter(p_values))[5] != 1: p_values = list(dict(Counter(p_values)).keys()) iter = int(len(times1)/len(p_values)) p_mean_times1 = [np.mean(np.array(times1[i*iter:(i+1)*iter])) for i in range(len(p_values))] p_mean_times2 = [np.mean(np.array(times2[i*iter:(i+1)*iter])) for i in range(len(p_values))] print("p_values \n", p_values) print("p_mean_times_opti \n", p_mean_times1) print("p_mean_times_eig \n", p_mean_times2) p_mean_costPs = [np.mean(np.array(costPs[i*iter:(i+1)*iter])) for i in range(len(p_values))] p_mean_cost3s = [np.mean(np.array(cost3s[i*iter:(i+1)*iter])) for i in range(len(p_values))] p_mean_lambdas = [np.mean(np.array(min_eigens[i*iter:(i+1)*iter])) for i in range(len(p_values))] print("p_mean_costPs \n", p_mean_costPs) print("p_mean_cost3s \n", p_mean_cost3s) print("p_mean_lambdas \n", p_mean_lambdas) print("*******************************************************************************************************************") else: plt.figure(1) plt.ylabel('Time used (seconds)') plt.xlabel('p_value') plt.plot(p_values, times1, 'r', label="time for optimizing") plt.plot(p_values, times2, 'blue', label="time for optimizing and check") plt.title(name, fontsize=12) plt.legend(loc="best") plt.interactive(False) plt.show() plt.figure(2) plt.ylabel('Min eigen_value') plt.xlabel('p_value') plt.plot(p_values, min_eigens, 'r', label="min_eigen values") plt.title(name, fontsize=12) plt.legend(loc="best") plt.interactive(False) plt.show() plt.figure(3) plt.ylabel('sub_bounds') plt.xlabel('p_value') plt.plot(p_values, subounds, 'blue', label="sub_bounds") plt.title(name, fontsize=12) plt.legend(loc="best") plt.show() # Process arguments and call plot function import argparse import csv import os parser = argparse.ArgumentParser() parser.add_argument("path") args = parser.parse_args() file_path = [] domain = os.path.abspath(args.path) for info in os.listdir(args.path): file_path.append(os.path.join(domain, info)) file_path.sort() print(file_path) # name of all the plots names = {} names[0] = 'tinyGrid3D vertex = 9, edge = 11' names[1] = 'smallGrid3D vertex = 125, edge = 297' names[2] = 'parking-garage vertex = 1661, edge = 6275' names[3] = 'sphere2500 vertex = 2500, edge = 4949' # names[4] = 'sphere_bignoise vertex = 2200, edge = 8647' names[5] = 'torus3D vertex = 5000, edge = 9048' names[6] = 'cubicle vertex = 5750, edge = 16869' names[7] = 'rim vertex = 10195, edge = 29743' # Parse CSV file for key, name in names.items(): print(key, name) # find according file to process name_file = None for path in file_path: if name[0:3] in path: name_file = path if name_file == None: print("The file %s is not in the path" % name) continue p_values, times1, costPs, cost3s, times2, min_eigens, subounds = [],[],[],[],[],[],[] with open(name_file) as csvfile: reader = csv.reader(csvfile, delimiter='\t') for row in reader: print(row) p_values.append(int(row[0])) times1.append(float(row[1])) costPs.append(float(row[2])) cost3s.append(float(row[3])) if len(row) > 4: times2.append(float(row[4])) min_eigens.append(float(row[5])) subounds.append(float(row[6])) #plot # make_combined_plot(name, p_values, times1, cost3s) # make_convergence_plot(name, p_values, times1, cost3s) make_eigen_and_bound_plot(name, p_values, times1, costPs, cost3s, times2, min_eigens, subounds)