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plot_results.py
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#!/usr/bin/env python3
#!/usr/bin/env python2
import sys,argparse
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
sns.set_style("whitegrid")
import matplotlib.axes as ax
from scipy import stats
# Arguments
parser=argparse.ArgumentParser(description='xxx')
parser.add_argument('--experiment',default='',type=str,required=True)
parser.add_argument('--approaches',default='',type=str,required=True)
parser.add_argument('--folders',default='res/',type=str,required=False,help='(default=%(default)s)')
parser.add_argument('--seeds',default='0-9',type=str,required=False,help='(default=%(default)s)')
parser.add_argument('--output',default='',type=str,required=False,help='(default=%(default)s)')
args=parser.parse_args()
args.approaches=args.approaches.split(',')
args.folders=args.folders.split(',')
if '-' in args.seeds:
tmp=args.seeds.split('-')
args.seeds=list(range(int(tmp[0]),int(tmp[1])+1))
else:
args.seeds=args.seeds.split(',')
for i in range(len(args.seeds)): args.seeds[i]=int(args.seeds[i])
print('='*100)
for arg in vars(args):
print(arg+':',getattr(args,arg))
print('='*100)
#aref=['random','sgd-restart']
aref=['random','joint']
for a in aref:
if a not in args.approaches:
print('ERROR: Need',aref,'approaches for normalizing the accuracies across data sets')
sys.exit()
########################################################################################################################
# Viz configs
use_conf_interv=True
use_same_markers=True
#"""
markers=['o','v','^','<','>','s','*','d','x','+','h']
markers_smae=['o','o','o','o','o','o','o','o','o','o','o']
if use_same_markers:
markers=markers_smae
while len(markers)<len(args.approaches): markers+=markers
"""
markers=['o']*len(args.approaches)
"""
jitter=0.08
########################################################################################################################
# Load
print('Load results...')
data={}
ntasks=0
for f in args.folders:
data[f]={}
e=args.experiment
# Load
data[f]={}
for a in args.approaches:
data[f][a]=[]
for s in args.seeds:
fn=f+e+'_'+a+'_'+str(s)+'.txt'
data[f][a].append(np.loadtxt(fn).astype(np.float32))
data[f][a]=100*np.stack(data[f][a],axis=0)
if ntasks==0: ntasks=data[f]['random'].shape[1]
# Normalize
for s in range(len(args.seeds)):
for a in args.approaches:
if a in aref: continue
ref_random=np.repeat(np.reshape(np.diag(data[f]['random'][s]),(1,data[f]['random'][s].shape[1])),data[f]['random'][s].shape[0],axis=0)
#ref_ref=np.repeat(np.reshape(np.diag(data[f]['sgd-restart'][s]),(1,data[f]['sgd-restart'][s].shape[1])),data[f]['sgd-restart'][s].shape[0],axis=0)
ref_ref=data[f]['joint'][s]
data[f][a][s]=(data[f][a][s]-ref_random)/(ref_ref-ref_random)-1
########################################################################################################################
# Plot
print('Plot')
print('-'*100)
fig, ax = plt.subplots()
ax.yaxis.grid(True)
ax.xaxis.grid(False)
#plt.figure()
leg=[]
for f in data.keys():
for a in data[f].keys():
if a in aref: continue
# Get data
acc=np.zeros((data[f][a].shape[0],data[f][a].shape[1]),dtype=np.float32)
for j in range(acc.shape[1]):
acc[:,j]=np.mean(data[f][a][:,j,:j+1],axis=1)
# Prepare things
jit=jitter*2*(len(leg)/(len(args.approaches)-2)-0.5)
ci=np.std(acc,axis=0)
if use_conf_interv:
ci=stats.t._ppf((1+0.95)/2,acc.shape[0]-1)*ci/np.sqrt(acc.shape[0])
mark=markers[len(leg)]
# Do the plot
#plt.errorbar(1+np.arange(acc.shape[1])+jit,np.mean(acc,axis=0),yerr=ci,fmt='-'+mark,markersize=6,capsize=3)
leg.append(a)
acc_mean = np.mean(acc,axis=0)
plt.errorbar(1+np.arange(acc.shape[1])+jit,acc_mean,fmt='-'+mark,markersize=4,capsize=4, linewidth=2.5, antialiased=True)
plt.fill_between(1+np.arange(acc.shape[1]), acc_mean-ci, acc_mean+ci, alpha=0.1, antialiased=True)
#leg.append(f+' : '+a)
# Print
print('{:16s} '.format(a),end='')
for j in range(acc.shape[1]):
print('{:6.3f} ({:5.3f}) '.format(np.mean(acc[:,j]),ci[j]),end='')
print('--> {:6.3f}'.format(np.mean(acc)))
print('-'*100)
#plt.xlim(1-0.2,ntasks+0.2)
plt.xticks(1+np.arange(ntasks).astype(int))
plt.xlabel('Task number')
ax.spines['left'].set_linewidth(2)
ax.spines['bottom'].set_linewidth(2)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_color('black')
ax.spines['bottom'].set_color('black')
plt.margins(0)
#plt.ylim(-0.1,1.1)
plt.ylabel('Forgetting ratio')
leg = [l.upper().replace('XDA', 'XdA').replace('-', ' ', 1) for l in leg]
print(leg)
multitask = 'Multi-Task'
plt.plot([1-jitter,ntasks+jitter],[0,0],'k--',linewidth=3, label=multitask)
leg = [multitask] + leg
lgd = plt.legend(leg, bbox_to_anchor=(0.5, -0.3), loc='lower center', ncol=3, fancybox=True, framealpha=0.0, frameon=False, handleheight=0.2)
lh = lgd.legendHandles
for l in lh:
l.set_alpha(1.0)
if args.output=='':
plt.show()
else:
plt.savefig(args.output,bbox_inches='tight')
plt.close()
########################################################################################################################
print('Done!')