def run_test(): from options import TestOptions opt = TestOptions() from do_classification import classifier classifier(opt)
def run_test():
from options import TestOptions
opt = TestOptions()
from do_classification import classifier
classifier(opt)
def run_unsupervised():
from options import MultiOptions
opt = MultiOptions()
opt.opdict['method'] = 'kmean'
from unsupervised import classifier
classifier(opt)
def run_unsupervised(): from options import MultiOptions opt = MultiOptions() opt.opdict['method'] = 'kmean' from unsupervised import classifier classifier(opt)
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 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 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 plot_sep(opt):
opt.set_params()
opt.opdict['fig_path'] = os.path.join(opt.opdict['outdir'], 'figures')
from do_classification import classifier
### LINEAR SVM ###
opt.opdict['method'] = 'svm'
classifier(opt)
#opt.plot_PDFs()
out_svm = opt.out
print "SVM", out_svm['thetas']
x_train = opt.train_x
x_test = opt.x
y_train = opt.train_y
y_test = opt.y
# *** Plot ***
plot_2f_synthetics(out_svm, x_train, x_test, y_test, y_train=y_train)
#plt.savefig('%s/Test_%dc_%s_SVM.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
### LOGISTIC REGRESSION ###
opt.opdict['method'] = 'lr'
out_lr = {}
for b in range(1):
opt.opdict['learn_file'] = os.path.join(opt.opdict['libdir'],
'LR_%d' % b)
#os.remove(opt.opdict['learn_file'])
classifier(opt)
out_lr[b] = opt.out
print "LR", out_lr[0]['thetas']
# *** Plot ***
if b == 0:
print sorted(out_lr[0])
plot_2f_synthetics(out_lr[0], x_train, x_test, y_test, y_train=y_train)
#plt.savefig('%s/Test_%dc_%s_LR.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
else:
plot_2f_synth_var(out_lr, x_train, x_test, y_test, opt.NB_test)
#plt.savefig('%s/Test_%dc_LR_%s.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
if opt.opdict['plot_prec_rec']:
plot_2f_synth_var(out_lr, x_train, x_test, y_test, opt.NB_test)
#plt.savefig('%s/Test_%dc_bad_threshold.png'%(opt.opdict['fig_path'],len(opt.types)))
plt.show()
### NON LINEAR SVM ###
opt.opdict['method'] = 'svm_nl'
classifier(opt)
out_svm_nl = opt.out
plot_2f_nonlinear(out_svm_nl,
x_train,
x_test,
y_test,
y_train=y_train,
synth=True)
#plt.savefig('%s/Test_%dc_%s_SVM_NL.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
### COMPARE ALL 3 METHODS ON THE SAME PLOT ###
plot_2f_synthetics(out_lr[0],
x_train,
x_test,
y_test,
out_comp=out_svm,
y_train=y_train,
map_nl=out_svm_nl)
#plt.savefig('%s/Test_%dc_%s.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
def plot_sep(opt):
opt.set_params()
opt.opdict['fig_path'] = os.path.join(opt.opdict['outdir'],'figures')
from do_classification import classifier
### LINEAR SVM ###
opt.opdict['method'] = 'svm'
classifier(opt)
#opt.plot_PDFs()
out_svm = opt.out
print "SVM", out_svm['thetas']
x_train = opt.train_x
x_test = opt.x
y_train = opt.train_y
y_test = opt.y
# *** Plot ***
plot_2f_synthetics(out_svm,x_train,x_test,y_test,y_train=y_train)
#plt.savefig('%s/Test_%dc_%s_SVM.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
### LOGISTIC REGRESSION ###
opt.opdict['method'] = 'lr'
out_lr = {}
for b in range(1):
opt.opdict['learn_file'] = os.path.join(opt.opdict['libdir'],'LR_%d'%b)
#os.remove(opt.opdict['learn_file'])
classifier(opt)
out_lr[b] = opt.out
print "LR", out_lr[0]['thetas']
# *** Plot ***
if b == 0:
print sorted(out_lr[0])
plot_2f_synthetics(out_lr[0],x_train,x_test,y_test,y_train=y_train)
#plt.savefig('%s/Test_%dc_%s_LR.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
else:
plot_2f_synth_var(out_lr,x_train,x_test,y_test,opt.NB_test)
#plt.savefig('%s/Test_%dc_LR_%s.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
if opt.opdict['plot_prec_rec']:
plot_2f_synth_var(out_lr,x_train,x_test,y_test,opt.NB_test)
#plt.savefig('%s/Test_%dc_bad_threshold.png'%(opt.opdict['fig_path'],len(opt.types)))
plt.show()
### NON LINEAR SVM ###
opt.opdict['method'] = 'svm_nl'
classifier(opt)
out_svm_nl = opt.out
plot_2f_nonlinear(out_svm_nl,x_train,x_test,y_test,y_train=y_train,synth=True)
#plt.savefig('%s/Test_%dc_%s_SVM_NL.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
### COMPARE ALL 3 METHODS ON THE SAME PLOT ###
plot_2f_synthetics(out_lr[0],x_train,x_test,y_test,out_comp=out_svm,y_train=y_train,map_nl=out_svm_nl)
#plt.savefig('%s/Test_%dc_%s.png'%(opt.opdict['fig_path'],len(opt.types),opt.sep))
plt.show()
