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_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()