process_shonan_timing_results.py

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"""
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)