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graph_results.py
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graph_results.py
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import os, argparse
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import matplotlib.patches as mpatches
import matplotlib as mp
from matplotlib.colors import LinearSegmentedColormap
from src.graph_globals import global_params
from src.graphs import graph, boxplot, multi_line, multi_line_with_CI, get_cmap, scatter, save_graph
from src.picklefuncs import load_data
from src.helper_funcs import check_and_make_dir
def main():
global_params()
#you must have the same number of colours as labels
colours = ['b', 'c', 'orange', 'y', 'm', 'gray']
labels = {'ddpg':'DDPG', 'dqn':'DQN', 'sotl':'SOTL', 'maxpressure':'Max-pressure', 'websters':'Webster\'s', 'uniform':'Uniform'}
if len(colours) != len(labels):
assert 0, 'Error: the number of colours '+str(len(colours))+' does not equal the number of labels'+str(len(labels))
#make dict of labels to colours
colours = { l:c for c, l in zip(colours, labels)}
args = parse_cl_args()
check_and_make_dir(args.save_dir)
if args.type == 'moe':
fp = 'metrics/'
graph_travel_time(labels, colours, fp, args.save_dir)
metrics = ['queue', 'delay']
graph_individual_intersections(labels, colours, fp, metrics, args.save_dir)
elif args.type == 'hp':
fp = 'hp/'
graph_hyper_params(labels, colours, fp, args.save_dir)
else:
assert 0, print('Error, supplied graph type argument '+str(args.type)+' does not exist')
def parse_cl_args():
parser = argparse.ArgumentParser()
##sumo params
parser.add_argument("-type", type=str, default='moe', dest='type', help='Data to be graphed, default: moe, options: moe, hp')
parser.add_argument("-save_dir", type=str, default='figures/', dest='save_dir', help='Directory to save figures, default: figures/')
args = parser.parse_args()
return args
def graph_hyper_params(labels, colours, fp, save_dir):
tsc = os.listdir(fp)
tsc_hp = {}
#get data
for t in tsc:
tsc_fp = fp+t+'/'
data = [ load_data(tsc_fp+f) for f in os.listdir(tsc_fp)]
tsc_hp[t] = np.stack([ [np.mean(d), np.std(d)] for d in data]).T
#create appropriate graph
n = len(tsc)
if n == 1:
f, axes = plt.subplots()
axes = [axes]
else:
nrows = 2
ncols = int(n/nrows) if n%nrows == 0 else int((n+1)/nrows)
f, axes = plt.subplots(nrows=nrows,ncols=ncols)
axes = axes.flat
if n%nrows != 0:
f.delaxes(axes[-1])
XTITLE = 'Mean\nTravel Time '+r"$(s)$"
YTITLE = ('Standard\nDeviation\nTravel Time '+r"$(s)$", 80)
#graph each tsc hyperparemeter
for ax, t, i in zip(axes, tsc, range(len(tsc))):
#order hp performance from low to high
#w.r.t mean+std
mean_data = tsc_hp[t][0]
std_data = tsc_hp[t][1]
data = sorted([ (m+s, m, s) for m,s in zip(mean_data, std_data) ], key = lambda x:x[0] )
data = np.stack([ [d[1], d[2]] for d in data]).T
mean_data = data[0]
std_data = data[1]
#rainbow_colours = mp.cm.rainbow(np.linspace(0, 1, len(mean_data)))
rg_colours = mp.cm.brg(np.linspace(1.0, 0.5, len(mean_data)))
if i%ncols == 0 and i >= len(tsc)/2:
xtitle = XTITLE
ytitle = YTITLE
elif i%ncols == 0:
xtitle = ''
ytitle = YTITLE
elif i >= len(tsc)/2:
xtitle = XTITLE
ytitle = ''
else:
xtitle = ''
ytitle = ''
#graph each tsc hp performance
graph( ax, mean_data, scatter( ax, mean_data, std_data, rg_colours, ['']*len(mean_data)),
xtitle=xtitle,
ytitle_pad = ytitle,
title=str(labels[t]),
xlim = [0.0, max(mean_data)*1.05],
ylim= [0.0, max(std_data)*1.05],
grid=True)
#axis colourbar
cax = f.add_axes([0.915, 0.1, 0.05, 0.85])
cmap = mp.cm.brg
cm = LinearSegmentedColormap.from_list('rg', rg_colours, N=rg_colours.shape[0])
norm = mp.colors.Normalize(vmin=0.5, vmax=1.0)
cb = mp.colorbar.ColorbarBase(cax, cmap=cm,
norm=norm,
orientation='vertical')
#color bar axis text
#print([ l._text for l in cb.ax.get_yticklabels()])
#cb_labels = ['']*rg_colours.shape[0]
cb_labels = [ l._text for l in cb.ax.get_yticklabels()]
cb_labels[0] = 'Best'
cb_labels[-1] = 'Worst'
cb.ax.set_yticklabels(cb_labels)
f.suptitle('Hyperparameter Performance')
save_graph(f, save_dir+'tsc_hp.pdf', 600, 14, 24.9)
plt.show()
#now compare all tsc hp sets together in one graph
#prepare data
data_order = sorted(tsc_hp.keys())
#tsc_color = colours[:len(data_order)]
mean_data, std_data, colors, tsc_labels = [], [], [], []
for d in data_order:
n = len(tsc_hp[d][0])
mean_data.extend(tsc_hp[d][0])
std_data.extend(tsc_hp[d][1])
tsc_labels.extend(labels[d])
#colors.extend([c]*len(tsc_hp[d][0]))
colors.extend( [colours[d]]*n )
#graph all hp data all together
f, ax = plt.subplots(1,1)
graph( ax, mean_data, scatter( ax, mean_data, std_data, colors, ['']*len(mean_data)),
xtitle=XTITLE,
ytitle_pad = YTITLE,
title='Traffic Signal Control\nHyperparameter Comparison',
xlim = [0.0, 200.0],
ylim= [0.0, 200.0],
#xlim = [0.0, max(mean_data)*1.05],
#ylim= [0.0, max(std_data)*1.05],
#legend=(0.82, 0.72),
#colours=colours,
grid=True)
#colorbar
#add legend manually because we only
#want one for each tsc
patches = []
for d in data_order:
c = colours[d]
patches.append( mpatches.Patch(color=c, label=labels[d]) )
plt.legend(handles=patches, framealpha=1.0)
save_graph(f, save_dir+'hp.pdf', 600, 14, 24.9)
plt.show()
def graph_travel_time(labels, colours, fp, save_dir):
#read metric data for all tsc types
data = get_data(fp, 'traveltime', get_folder_data)
#prepare data for graph
data_order = sorted(data.keys())
data = [ data[d] for d in data_order]
labels = [ labels[d] for d in data_order]
c = [ colours[d] for d in data_order]
#graph data
f, ax = plt.subplots(1,1)
t = 'Travel Time '+r"$(s)$"+'\n('+r"$\mu,\sigma,$"+'median'+r"$)$"
graph( ax, data, boxplot( ax, data, c, labels),
xtitle='Traffic Signal Controller',
#ytitle_pad = ('Travel Time (s)\n('+r"/mu,/sigma,"+"median)", 60),
ytitle_pad = (t, 60),
title='Traffic Signal Controller\nTravel Time) ',
legend=(0.82, 0.72),
grid=True)
for i, d in enumerate(data_order):
text = '('+str(int(np.mean(data[i])))+', '+str(int( np.std(data[i]) ) )+', '+str(int( np.median(data[i]) ) )+r"$)$"
ax.text(i+1.1, 300, text, color= c[i])
#f.suptitle('Travel Time')
#display graph
save_graph(f, save_dir+'travel_time.pdf', 600, 14, 24.9)
plt.show()
def graph_conf_interval(labels, colours, fp, metric):
#read metric data for all tsc types
data = get_data(fp, metric, get_metric_data)
#prepare data for graph
data_order = sorted(data.keys())
data = [ data[d] for d in data_order]
labels = [ labels[d] for d in data_order]
#graph data
f, ax = plt.subplots(1,1)
metric_title = metric.capitalize()
graph( ax, data, multi_line_with_CI( ax, data, colours, labels),
xtitle='Time (s)',
ytitle_pad = (metric_title, 60),
title=metric_title+' by\nTraffic Signal Controller',
legend=(0.72, 0.72),
grid=True)
#f.suptitle(metric_title)
#display graph
plt.show()
def get_data(fp, metric, read_data_func):
tsc = os.listdir(fp)
tsc_data = { t:read_data_func(fp+t+'/'+metric) for t in tsc}
return tsc_data
def get_metric_data(fp):
#for use with queue and delay data
#sort all metric data from same tsc_id
if not os.path.exists(fp):
assert 0, 'Supplied path '+str(fp)+' does not exist.'
tsc_data = {tsc_id:sorted(os.listdir(fp+'/'+tsc_id))
for tsc_id in os.listdir(fp)}
sim_runs_data = []
#until all data has been popped
k = list(tsc_data.keys())[0]
while len(tsc_data[k]) > 0:
#get file path for each intersection from same sim run
same_run_data = [ fp+'/'+tsc_id+'/'+tsc_data[tsc_id].pop(0)
for tsc_id in tsc_data ]
same_run_data = [ load_data(f) for f in same_run_data ]
#sum across time axis, each element of array
#represents the sum of all tsc_id metric
sim_runs_data.append( np.sum(same_run_data, axis=0) )
return np.stack(sim_runs_data)
def get_folder_data(fp):
#all the travel times can be
#grouped together by extending list
if not os.path.exists(fp):
assert 0, 'Supplied path '+str(fp)+' does not exist.'
data = []
for f in os.listdir(fp):
data.extend(load_data(fp+'/'+f))
return np.array(data)
def stack_folder_files(fp):
data = [ load_data(fp+f) for f in os.listdir(fp)]
return np.stack(data)
def graph_individual_intersections(labels, colours, fp, metrics, save_dir):
#rows are metrics
#columns are intersections
tsc = os.listdir(fp)
tsc.remove('sotl')
intersections = os.listdir(fp+tsc[0]+'/'+metrics[0]+'/')
ncols = len(intersections)
nrows = len(metrics)
f, ax = plt.subplots(nrows=nrows,ncols=ncols)
if ax.ndim == 1:
ax = ax[...,np.newaxis]
for m, r in zip(metrics, range(nrows)):
#get metric data for each intersection
data = {}
for t in tsc:
data[t] = {}
for i in intersections:
alias_p = 60
data[t][i] = alias( stack_folder_files(fp+'/'+t+'/'+m+'/'+i+'/'), alias_p)
xtitle = 'Time '+r" $(min)$" if r == nrows-1 else ''
#graph same metric for each intersection
for I, c in zip(intersections, range(ncols)):
title = I if r == 0 else ''
if m == 'queue':
ytitle = m.capitalize()+r" $(veh)$" if c == 0 else ''
else:
ytitle = m.capitalize()+r" $(s)$" if c == 0 else ''
legend = (0.59, 0.6)
data_order = sorted(data.keys())
alias_p = 60
order_data = [ data[d][I] for d in data_order]
order_labels = [ labels[d] for d in data_order]
order_colors = [ colours[d] for d in data_order]
label_c = { labels[d]:colours[d] for d in data_order}
graph( ax[r,c], order_data, multi_line_with_CI( ax[r,c], order_data, order_colors, order_labels),
xtitle=xtitle,
ytitle_pad = (ytitle , 60),
title=title,
legend=legend,
colours = label_c,
grid=True)
ax[r,c].set_xlim(left=0)
ax[r,c].set_ylim(bottom=0)
f.suptitle('Intersection Measures of Effectiveness')
save_graph(f, save_dir+'intersection_moe.pdf', 600, 14, 24.9)
plt.show()
def alias(data, a):
n = data.shape[-1]
if n % a != 0:
#error?
a = 1
stop = n-a
m = int(n/a)
alias_data = []
for d in data:
#alias data timeseries
alias_data.append( np.array([np.sum(d[i*a:(i+1)*a]) for i in range(m) ]) )
return np.stack(alias_data)
#return np.stack([np.sum(data[i*a:(i+1)*a]) for i in range(stop) ])
#return np.array([ np.sum(data[i*a:(i+1)*a]) for i in range(stop)])
if __name__ == '__main__':
main()