def run_all():
from options import MultiOptions
opt = MultiOptions()
#opt.count_number_of_events()
### UNSUPERVISED METHOD ###
if opt.opdict['method'] == 'kmeans':
from unsupervised import classifier
classifier(opt)
### SUPERVISED METHODS ###
elif opt.opdict['method'] in ['lr','svm','svm_nl','lrsk']:
from do_classification import classifier
classifier(opt)
from results import AnalyseResults
res = AnalyseResults()
if res.opdict['plot_confusion']:
res.plot_confusion()
elif opt.opdict['method'] in ['ova','1b1']:
from do_classification import classifier
classifier(opt)
from results import AnalyseResultsExtraction
res = AnalyseResultsExtraction()
def compare_pdfs_reclass():
"""
Affiche et compare les pdfs avant et après reclassification automatique.
"""
from results import AnalyseResults
opt = AnalyseResults()
opt.opdict['stations'] = ['IJEN']
opt.opdict['channels'] = ['Z']
opt.opdict['Types'] = ['Tremor', 'VulkanikB', '?']
opt.opdict['feat_list'] = [
'Centroid_time', 'Dur', 'Ene0-5', 'F_up', 'Growth', 'Kurto',
'RappMaxMean', 'RappMaxMeanTF', 'Skewness', 'TimeMaxSpec', 'Width'
]
for sta in opt.opdict['stations']:
for comp in opt.opdict['channels']:
opt.opdict[
'label_filename'] = '%s/Ijen_3class_all.csv' % opt.opdict[
'libdir']
opt.x, opt.y = opt.features_onesta(sta, comp)
opt.classname2number()
opt.compute_pdfs()
g1 = opt.gaussians
opt.opdict[
'label_filename'] = '%s/Ijen_3class_all_SVM.csv' % opt.opdict[
'libdir']
opt.x, opt.y = opt.features_onesta(sta, comp)
opt.classname2number()
opt.compute_pdfs()
g2 = opt.gaussians
c = ['r', 'b', 'g']
for feat in opt.opdict['feat_list']:
fig = plt.figure()
fig.set_facecolor('white')
for it, t in enumerate(opt.types):
plt.plot(g1[feat]['vec'],
g1[feat][t],
ls='-',
color=c[it],
label=t)
plt.plot(g2[feat]['vec'],
g2[feat][t],
ls='--',
color=c[it])
plt.title(feat)
plt.legend()
#plt.savefig('../results/Ijen/comp_BrutReclass_%s.png'%feat)
plt.show()
def run_all():
from options import MultiOptions
opt = MultiOptions()
#opt.count_number_of_events()
from do_classification import classifier
classifier(opt)
if opt.opdict['method'] == 'lr' or opt.opdict[
'method'] == 'svm' or opt.opdict['method'] == 'lrsk':
from results import AnalyseResults
res = AnalyseResults()
if res.opdict['plot_confusion']:
res.plot_confusion()
else:
from results import AnalyseResultsExtraction
res = AnalyseResultsExtraction()
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()
plt.plot(p_tr,
p_test,
marker=markers[k],
color=colors[k],
lw=0,
label=labels[k])
k = k + 1
plt.legend(numpoints=1, loc='upper left')
plt.plot([0, 100], [0, 100], 'k--')
plt.xlim([60, 100])
plt.ylim([60, 100])
plt.figtext(.7, .15, 'Sur-apprentissage')
plt.xlabel('% training set')
plt.ylabel('% test set')
#plt.savefig('../results/Ijen/figures/SVM_training.png')
plt.show()
if __name__ == '__main__':
#plot_test_vs_train()
from results import AnalyseResults
res = AnalyseResults()
#new_catalogue(res)
plot_on_pdf(res)
#plot_waveforms(res)
#compare_pdfs_reclass()
#compare_pdfs_train()