def compare_unsup_indet():
"""
Essaie de faire un lien entre les événements indéterminés mal classés par
LR ou SVM avec classes non-supervisées.
"""
from matplotlib.gridspec import GridSpec
print "### COMPARE UNSUP AND SUP ###"
from results import AnalyseResults
opt = AnalyseResults()
m = opt.man
a = opt.auto
unsup = read_binary_file('../results/Ijen/KMEANS/results_kmeans_3c_11f_ini')
nb_auto = [len(opt.auto[opt.auto.Type==t]) for t in opt.opdict['types']]
NB_class = len(opt.opdict['types'])
for cl in opt.opdict['types']:
#for cl in ['?']:
m = opt.man[opt.man.Type==cl]
a = opt.auto.reindex(index=m.index)
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
opt.data_for_LR()
opt.opdict['channels'] = 'Z'
opt.opdict['stations'] = ['IJEN']
for sta in opt.opdict['stations']:
for comp in opt.opdict['channels']:
u = pd.DataFrame(index=unsup[(sta, comp)][0]['list_ev'],columns=['Type','NumType'])
u['Type'] = unsup[(sta, comp)][0]['StrClass']
u['NumType'] = unsup[(sta, comp)][0]['NumClass']
u = u.reindex(index=m.index)
trad = unsup[(sta, comp)][0]['Equivalence']
fig = plt.figure(figsize=(12,8))
fig.set_facecolor('white')
nb_l, nb_c = 2, NB_class*2
grid = GridSpec(nb_l,nb_c)
ax = fig.add_subplot(grid[0,:nb_c/2])
ax.pie(nb_auto,labels=opt.opdict['types'],autopct='%1.1f%%',colors=colors)
ax.text(.4,-.1,r'# events = %d'%np.sum(nb_auto),transform=ax.transAxes)
ax.axis("equal")
nbs = [len(a[a.Type==t]) for t in opt.opdict['types']]
ax = fig.add_subplot(grid[0,nb_c/2:])
ax.pie(nbs,labels=opt.opdict['types'],autopct='%1.1f%%',colors=colors)
ax.text(.4,-.1,r'# events = %d'%np.sum(nbs),transform=ax.transAxes)
ax.axis("equal")
lab_c = np.array(trad).copy()
for it,t in enumerate(opt.opdict['types']):
i = np.where(np.array(trad)==t)[0][0]
lab_c[it] = i
for it,t in enumerate(opt.opdict['types']):
ared = a[a.Type==t]
ured = u.reindex(index=ared.index)
nbs = [len(ured[ured.Type==ty]) for ty in opt.opdict['types']]
ax = fig.add_subplot(grid[1,2*it:2*it+2])
ax.pie(nbs,labels=lab_c,autopct='%1.1f%%',colors=colors)
ax.text(.3,-.1,r'# %s = %d'%(t,np.sum(nbs)),transform=ax.transAxes)
#ax.set_title(t)
plt.figtext(.1,.92,'(a) %s'%opt.opdict['method'].upper(),fontsize=16)
plt.figtext(.55,.92,'(b) Manual repartition of %s'%cl,fontsize=16)
plt.figtext(.1,.45,r'(c) $K$-means',fontsize=16)
for it, t in enumerate(trad):
plt.figtext(.3+it*.15,.45,r'%s $\approx$ %s'%(it,trad[it]))
plt.savefig('../results/Ijen/KMEANS/figures/unsup_compSVM_%s.png'%cl)
plt.show()
def compare_unsup_indet():
"""
Essaie de faire un lien entre les événements indéterminés mal classés par
LR ou SVM avec classes non-supervisées.
"""
from matplotlib.gridspec import GridSpec
print "### COMPARE UNSUP AND SUP ###"
from results import AnalyseResults
opt = AnalyseResults()
m = opt.man
a = opt.auto
unsup = read_binary_file(
'../results/Ijen/KMEANS/results_kmeans_3c_11f_ini')
nb_auto = [len(opt.auto[opt.auto.Type == t]) for t in opt.opdict['types']]
NB_class = len(opt.opdict['types'])
for cl in opt.opdict['types']:
#for cl in ['?']:
m = opt.man[opt.man.Type == cl]
a = opt.auto.reindex(index=m.index)
colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']
opt.data_for_LR()
opt.opdict['channels'] = 'Z'
opt.opdict['stations'] = ['IJEN']
for sta in opt.opdict['stations']:
for comp in opt.opdict['channels']:
u = pd.DataFrame(index=unsup[(sta, comp)][0]['list_ev'],
columns=['Type', 'NumType'])
u['Type'] = unsup[(sta, comp)][0]['StrClass']
u['NumType'] = unsup[(sta, comp)][0]['NumClass']
u = u.reindex(index=m.index)
trad = unsup[(sta, comp)][0]['Equivalence']
fig = plt.figure(figsize=(12, 8))
fig.set_facecolor('white')
nb_l, nb_c = 2, NB_class * 2
grid = GridSpec(nb_l, nb_c)
ax = fig.add_subplot(grid[0, :nb_c / 2])
ax.pie(nb_auto,
labels=opt.opdict['types'],
autopct='%1.1f%%',
colors=colors)
ax.text(.4,
-.1,
r'# events = %d' % np.sum(nb_auto),
transform=ax.transAxes)
ax.axis("equal")
nbs = [len(a[a.Type == t]) for t in opt.opdict['types']]
ax = fig.add_subplot(grid[0, nb_c / 2:])
ax.pie(nbs,
labels=opt.opdict['types'],
autopct='%1.1f%%',
colors=colors)
ax.text(.4,
-.1,
r'# events = %d' % np.sum(nbs),
transform=ax.transAxes)
ax.axis("equal")
lab_c = np.array(trad).copy()
for it, t in enumerate(opt.opdict['types']):
i = np.where(np.array(trad) == t)[0][0]
lab_c[it] = i
for it, t in enumerate(opt.opdict['types']):
ared = a[a.Type == t]
ured = u.reindex(index=ared.index)
nbs = [
len(ured[ured.Type == ty])
for ty in opt.opdict['types']
]
ax = fig.add_subplot(grid[1, 2 * it:2 * it + 2])
ax.pie(nbs, labels=lab_c, autopct='%1.1f%%', colors=colors)
ax.text(.3,
-.1,
r'# %s = %d' % (t, np.sum(nbs)),
transform=ax.transAxes)
#ax.set_title(t)
plt.figtext(.1,
.92,
'(a) %s' % opt.opdict['method'].upper(),
fontsize=16)
plt.figtext(.55,
.92,
'(b) Manual repartition of %s' % cl,
fontsize=16)
plt.figtext(.1, .45, r'(c) $K$-means', fontsize=16)
for it, t in enumerate(trad):
plt.figtext(.3 + it * .15, .45,
r'%s $\approx$ %s' % (it, trad[it]))
plt.savefig(
'../results/Ijen/KMEANS/figures/unsup_compSVM_%s.png' % cl)
plt.show()
