def training_round_script(): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); SNRval=0.5; #training_round_list=[]; training_round_list=range(2,15); training_acc=[]; testing_acc=[]; rounds_tillnow=[]; pos_tr_eg=glob_const.no_pos_training_eg; neg_tr_eg=glob_const.no_neg_training_eg; pos_te_eg=glob_const.no_pos_testing_eg; neg_te_eg=glob_const.no_neg_testing_eg; for training_round in training_round_list: boost=adaboost_train_test.AdaBoostTrainTest(SNRval,training_round,\ pos_tr_eg,neg_tr_eg,pos_te_eg,neg_te_eg); boost.train_adaboost(); boost.test_adaboost(); training_acc.append(boost.training_accuracy); testing_acc.append(boost.testing_accuracy); rounds_tillnow.append(training_round); #this now saves the file at every iteration #if you want to quit the program before it finishes vid_utils.savefile(rounds_tillnow,training_acc,'data/adaboost_training_acc.txt') vid_utils.savefile(rounds_tillnow,testing_acc,'data/adaboost_testing_acc.txt') plt.plot(training_round_list,training_acc,'bo-',label='Training Accuracy'); plt.plot(training_round_list,testing_acc,'ro-',label='Testing Accuracy'); plt.legend(); plt.show();
def training_round_script(): glob_const = globalconstants.GlobalConstants() init_set(glob_const) SNRval = 2. #overfitting_e_list=[]; overfitting_e_list = [0.1 * x for x in range(0, 5)] training_acc = [] testing_acc = [] list_till_now = [] for overfitting_e in overfitting_e_list: glob_const.overfitting_e = overfitting_e glob_const.savefile() traintest = train_and_test.TrainAndTest(SNRval) traintest.train_tree() traintest.test_tree() list_till_now.append(overfitting_e) training_acc.append(traintest.training_accuracy) testing_acc.append(traintest.testing_accuracy) vid_utils.savefile(list_till_now, training_acc, 'data/overfittng_e_training_acc.txt') vid_utils.savefile(list_till_now, testing_acc, 'data/overfitting_e_testing_acc.txt') print "Processed e: " + str(overfitting_e) + "...." plt.plot(overfitting_e_list, training_acc, 'bo-', label='Training Accuracy') plt.plot(overfitting_e_list, testing_acc, 'ro-', label='Testing Accuracy') plt.legend() plt.show() pass
def training_round_script(): glob_const = globalconstants.GlobalConstants() init_set(glob_const) SNRval = 0.5 #training_round_list=[]; training_round_list = range(2, 11) training_acc = [] testing_acc = [] for training_round in training_round_list: glob_const.training_rounds = training_round glob_const.savefile() traintest = train_and_test.TrainAndTest(SNRval) traintest.train_tree() traintest.test_tree() training_acc.append(traintest.training_accuracy) testing_acc.append(traintest.testing_accuracy) vid_utils.savefile(training_round_list, training_acc, 'data/training_acc.txt') vid_utils.savefile(training_round_list, testing_acc, 'data/testing_acc.txt') plt.plot(training_round_list, training_acc, 'bo-', label='Training Accuracy') plt.plot(training_round_list, testing_acc, 'ro-', label='Testing Accuracy') plt.legend() plt.show() pass
def compare_script(): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); #SNRlist=[]; SNRlist=[0.25*x for x in range(8,9)]; tree_acc=[]; boost_acc=[]; training_rounds=7; pos_tr_eg=glob_const.no_pos_training_eg; neg_tr_eg=glob_const.no_neg_training_eg; pos_te_eg=glob_const.no_pos_testing_eg; neg_te_eg=glob_const.no_neg_testing_eg; list_till_now=[]; for SNRval in SNRlist: boost=adaboost_train_test.AdaBoostTrainTest(SNRval,training_rounds,\ pos_tr_eg,neg_tr_eg,pos_te_eg,neg_te_eg); traintest=train_and_test.TrainAndTest(SNRval); #Train boost.train_adaboost(); traintest.train_tree(); #Test boost.test_adaboost(); traintest.test_tree(); #traintest.post_prune_tree(); #traintest.test_tree(); #get accuracies boost_acc.append(boost.testing_accuracy); tree_acc.append(traintest.testing_accuracy); list_till_now.append(SNRval); vid_utils.savefile(list_till_now,tree_acc,"data/Tree_accuracies.txt") vid_utils.savefile(list_till_now,boost_acc,"data/Boost_accuracies.txt") print "Processed SNR: "+str(SNRval)+"..." plt.plot(SNRlist,tree_acc,'ro',label="Tree"); plt.plot(SNRlist,boost_acc,'bo',label="Adaboost"); plt.xlim(0,2.5) plt.legend(loc=2); plt.show();
def compare_script(): glob_const = globalconstants.GlobalConstants() init_set(glob_const) #SNRlist=[]; SNRlist = [0.25 * x for x in range(8, 9)] tree_acc = [] boost_acc = [] training_rounds = 7 pos_tr_eg = glob_const.no_pos_training_eg neg_tr_eg = glob_const.no_neg_training_eg pos_te_eg = glob_const.no_pos_testing_eg neg_te_eg = glob_const.no_neg_testing_eg list_till_now = [] for SNRval in SNRlist: boost=adaboost_train_test.AdaBoostTrainTest(SNRval,training_rounds,\ pos_tr_eg,neg_tr_eg,pos_te_eg,neg_te_eg) traintest = train_and_test.TrainAndTest(SNRval) #Train boost.train_adaboost() traintest.train_tree() #Test boost.test_adaboost() traintest.test_tree() #traintest.post_prune_tree(); #traintest.test_tree(); #get accuracies boost_acc.append(boost.testing_accuracy) tree_acc.append(traintest.testing_accuracy) list_till_now.append(SNRval) vid_utils.savefile(list_till_now, tree_acc, "data/Tree_accuracies.txt") vid_utils.savefile(list_till_now, boost_acc, "data/Boost_accuracies.txt") print "Processed SNR: " + str(SNRval) + "..." plt.plot(SNRlist, tree_acc, 'ro', label="Tree") plt.plot(SNRlist, boost_acc, 'bo', label="Adaboost") plt.xlim(0, 2.5) plt.legend(loc=2) plt.show()
def roc_script(SNRval,particle_shape,image_type): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); glob_const.image_type=image_type; glob_const.particle_shape=particle_shape; glob_const.savefile(); traintest=train_and_test.TrainAndTest(SNRval); traintest.train_tree(); traintest.test_tree(); [precision,recall]=traintest.get_precision_recall_curve(); precision_filename="data/precision_"+str(SNRval)+"_"+str(image_type)+\ "_"+str(particle_shape)+".txt"; vid_utils.savefile(precision,recall,precision_filename) [tpr,fpr]=traintest.get_roc_curve(); roc_filename="data/roc_"+str(SNRval)+"_"+str(image_type)+\ "_"+str(particle_shape)+".txt"; vid_utils.savefile(tpr,fpr,roc_filename)
def training_round_script(): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); SNRval=2.; #overfitting_e_list=[]; overfitting_e_list=[0.1*x for x in range(0,5)]; training_acc=[]; testing_acc=[]; list_till_now=[] for overfitting_e in overfitting_e_list: glob_const.overfitting_e=overfitting_e; glob_const.savefile(); traintest=train_and_test.TrainAndTest(SNRval); traintest.train_tree(); traintest.test_tree(); list_till_now.append(overfitting_e) training_acc.append(traintest.training_accuracy); testing_acc.append(traintest.testing_accuracy); vid_utils.savefile(list_till_now,training_acc,'data/overfittng_e_training_acc.txt') vid_utils.savefile(list_till_now,testing_acc,'data/overfitting_e_testing_acc.txt') print "Processed e: "+str(overfitting_e)+"...." plt.plot(overfitting_e_list,training_acc,'bo-',label='Training Accuracy'); plt.plot(overfitting_e_list,testing_acc,'ro-',label='Testing Accuracy'); plt.legend(); plt.show(); pass
def training_round_script(): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); SNRval=0.5; #training_round_list=[]; training_round_list=range(2,11); training_acc=[]; testing_acc=[]; for training_round in training_round_list: glob_const.training_rounds=training_round; glob_const.savefile(); traintest=train_and_test.TrainAndTest(SNRval); traintest.train_tree(); traintest.test_tree(); training_acc.append(traintest.training_accuracy); testing_acc.append(traintest.testing_accuracy); vid_utils.savefile(training_round_list,training_acc,'data/training_acc.txt') vid_utils.savefile(training_round_list,testing_acc,'data/testing_acc.txt') plt.plot(training_round_list,training_acc,'bo-',label='Training Accuracy'); plt.plot(training_round_list,testing_acc,'ro-',label='Testing Accuracy'); plt.legend(); plt.show(); pass
def exampleno_script(): glob_const = globalconstants.GlobalConstants() init_set(glob_const) SNRval = 0.5 #exampleno_list=[]; exampleno_list = range(50, 1000, 50) training_acc = [] testing_acc = [] list_tillnow = [] time_list = [] training_rounds = 15 pos_te_eg = glob_const.no_pos_testing_eg neg_te_eg = glob_const.no_neg_testing_eg for exampleno in exampleno_list: starttime = time.time() glob_const.no_pos_training_eg = exampleno glob_const.no_neg_training_eg = exampleno glob_const.savefile() boost=adaboost_train_test.AdaBoostTrainTest(SNRval,training_rounds,\ exampleno,exampleno,pos_te_eg,neg_te_eg) boost.train_adaboost() boost.test_adaboost() elapsed = time.time() - starttime #append values training_acc.append(boost.training_accuracy) testing_acc.append(boost.testing_accuracy) list_tillnow.append(exampleno) time_list.append(elapsed) vid_utils.savefile(list_tillnow,training_acc,\ 'data/training_acc_eg.txt') vid_utils.savefile(list_tillnow,testing_acc,\ 'data/testing_acc_eg.txt') vid_utils.savefile(list_tillnow,time_list,\ 'data/time_vals.txt') plt.plot(exampleno_list, training_acc, 'bo-', label='Training Accuracy') plt.plot(exampleno_list, testing_acc, 'ro-', label='Testing Accuracy') plt.legend() plt.show()
def training_round_script(): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); SNRval=2.; #exampleno_list=[]; round_list=range(5,30,5); training_acc=[]; testing_acc=[]; list_tillnow=[]; time_list=[]; pos_te_eg=glob_const.no_pos_testing_eg; neg_te_eg=glob_const.no_neg_testing_eg; for training_rounds in round_list: starttime=time.time(); pos_training_eg=glob_const.no_pos_training_eg; neg_training_eg=glob_const.no_neg_training_eg; glob_const.savefile(); boost=adaboost_train_test.AdaBoostTrainTest(SNRval,training_rounds,\ pos_training_eg,neg_training_eg,pos_te_eg,neg_te_eg); boost.train_adaboost(); boost.test_adaboost(); elapsed=time.time()-starttime; #append values training_acc.append(boost.training_accuracy); testing_acc.append(boost.testing_accuracy); list_tillnow.append(training_rounds); time_list.append(elapsed) vid_utils.savefile(list_tillnow,training_acc,\ 'data/training_acc_trainingrounds.txt') vid_utils.savefile(list_tillnow,testing_acc,\ 'data/testing_acc_trainingrounds.txt') vid_utils.savefile(list_tillnow,time_list,\ 'data/time_vals.txt') plt.plot(round_list,training_acc,'bo-',label='Training Accuracy'); plt.plot(round_list,testing_acc,'ro-',label='Testing Accuracy'); plt.legend(loc=2); plt.show();
def exampleno_script(): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); SNRval=0.5; #exampleno_list=[]; exampleno_list=range(50,1000,50); training_acc=[]; testing_acc=[]; list_tillnow=[]; time_list=[]; training_rounds=15; pos_te_eg=glob_const.no_pos_testing_eg; neg_te_eg=glob_const.no_neg_testing_eg; for exampleno in exampleno_list: starttime=time.time(); glob_const.no_pos_training_eg=exampleno; glob_const.no_neg_training_eg=exampleno; glob_const.savefile(); boost=adaboost_train_test.AdaBoostTrainTest(SNRval,training_rounds,\ exampleno,exampleno,pos_te_eg,neg_te_eg); boost.train_adaboost(); boost.test_adaboost(); elapsed=time.time()-starttime; #append values training_acc.append(boost.training_accuracy); testing_acc.append(boost.testing_accuracy); list_tillnow.append(exampleno); time_list.append(elapsed) vid_utils.savefile(list_tillnow,training_acc,\ 'data/training_acc_eg.txt') vid_utils.savefile(list_tillnow,testing_acc,\ 'data/testing_acc_eg.txt') vid_utils.savefile(list_tillnow,time_list,\ 'data/time_vals.txt') plt.plot(exampleno_list,training_acc,'bo-',label='Training Accuracy'); plt.plot(exampleno_list,testing_acc,'ro-',label='Testing Accuracy'); plt.legend(); plt.show();
def training_round_script(): glob_const = globalconstants.GlobalConstants() init_set(glob_const) SNRval = 2. #exampleno_list=[]; round_list = range(5, 30, 5) training_acc = [] testing_acc = [] list_tillnow = [] time_list = [] pos_te_eg = glob_const.no_pos_testing_eg neg_te_eg = glob_const.no_neg_testing_eg for training_rounds in round_list: starttime = time.time() pos_training_eg = glob_const.no_pos_training_eg neg_training_eg = glob_const.no_neg_training_eg glob_const.savefile() boost=adaboost_train_test.AdaBoostTrainTest(SNRval,training_rounds,\ pos_training_eg,neg_training_eg,pos_te_eg,neg_te_eg) boost.train_adaboost() boost.test_adaboost() elapsed = time.time() - starttime #append values training_acc.append(boost.training_accuracy) testing_acc.append(boost.testing_accuracy) list_tillnow.append(training_rounds) time_list.append(elapsed) vid_utils.savefile(list_tillnow,training_acc,\ 'data/training_acc_trainingrounds.txt') vid_utils.savefile(list_tillnow,testing_acc,\ 'data/testing_acc_trainingrounds.txt') vid_utils.savefile(list_tillnow,time_list,\ 'data/time_vals.txt') plt.plot(round_list, training_acc, 'bo-', label='Training Accuracy') plt.plot(round_list, testing_acc, 'ro-', label='Testing Accuracy') plt.legend(loc=2) plt.show()
def depth_script(): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); SNRval=2.; #maxdepth_list=[]; maxdepth_list=range(2,7); training_acc=[]; testing_acc=[]; list_tillnow=[]; time_list=[]; for depth in maxdepth_list: starttime=time.time(); glob_const.maxdepth_tree=depth; glob_const.savefile(); traintest=train_and_test.TrainAndTest(SNRval); traintest.train_tree(); traintest.test_tree(); traintest.post_prune_tree(); traintest.test_tree(); elapsed=time.time()-starttime; #append values training_acc.append(traintest.training_accuracy); testing_acc.append(traintest.testing_accuracy); list_tillnow.append(depth); time_list.append(elapsed) vid_utils.savefile(list_tillnow,training_acc,\ 'data/training_acc_depth.txt') vid_utils.savefile(list_tillnow,testing_acc,\ 'data/testing_acc_depth.txt') vid_utils.savefile(list_tillnow,time_list,\ 'data/time_vals_depth.txt') plt.plot(maxdepth_list,training_acc,'bo-',label='Training'); plt.plot(maxdepth_list,testing_acc,'ro-',label='Testing'); plt.legend(loc=2); plt.show();
def exampleno_script(): glob_const = globalconstants.GlobalConstants() init_set(glob_const) SNRval = 0.5 #exampleno_list=[]; exampleno_list = range(50, 250, 50) training_acc = [] testing_acc = [] list_tillnow = [] time_list = [] for exampleno in exampleno_list: starttime = time.time() glob_const.no_pos_training_eg = exampleno glob_const.no_neg_training_eg = exampleno glob_const.savefile() traintest = train_and_test.TrainAndTest(SNRval) traintest.train_tree() traintest.test_tree() elapsed = time.time() - starttime #append values training_acc.append(traintest.training_accuracy) testing_acc.append(traintest.testing_accuracy) list_tillnow.append(exampleno) time_list.append(elapsed) vid_utils.savefile(list_tillnow,training_acc,\ 'data/training_acc_eg.txt') vid_utils.savefile(list_tillnow,testing_acc,\ 'data/testing_acc_eg.txt') vid_utils.savefile(list_tillnow,time_list,\ 'data/time_vals.txt') plt.plot(exampleno_list, training_acc, 'bo-', label='Training Accuracy') plt.plot(exampleno_list, testing_acc, 'ro-', label='Testing Accuracy') plt.legend() plt.show()
def gamma_script(): glob_const = globalconstants.GlobalConstants() init_set(glob_const) SNRval = 2. #maxdepth_list=[]; #gamma_list=range(1,7); gamma_list = [0] training_acc = [] testing_acc = [] list_tillnow = [] time_list = [] for gamma in gamma_list: starttime = time.time() glob_const.gamma = gamma glob_const.savefile() traintest = train_and_test.TrainAndTest(SNRval) traintest.train_tree() traintest.test_tree() elapsed = time.time() - starttime #append values training_acc.append(traintest.training_accuracy) testing_acc.append(traintest.testing_accuracy) list_tillnow.append(gamma) time_list.append(elapsed) vid_utils.savefile(list_tillnow,training_acc,\ 'data/training_acc_gamma.txt') vid_utils.savefile(list_tillnow,testing_acc,\ 'data/testing_acc_gamma.txt') vid_utils.savefile(list_tillnow,time_list,\ 'data/time_vals_depth.txt') plt.plot(gamma_list, training_acc, 'bo-', label='Training Accuracy') plt.plot(gamma_list, testing_acc, 'ro-', label='Testing Accuracy') plt.legend() plt.show()
def exampleno_script(): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); SNRval=0.5; #exampleno_list=[]; exampleno_list=range(50,250,50); training_acc=[]; testing_acc=[]; list_tillnow=[]; time_list=[]; for exampleno in exampleno_list: starttime=time.time(); glob_const.no_pos_training_eg=exampleno; glob_const.no_neg_training_eg=exampleno; glob_const.savefile(); traintest=train_and_test.TrainAndTest(SNRval); traintest.train_tree(); traintest.test_tree(); elapsed=time.time()-starttime; #append values training_acc.append(traintest.training_accuracy); testing_acc.append(traintest.testing_accuracy); list_tillnow.append(exampleno); time_list.append(elapsed) vid_utils.savefile(list_tillnow,training_acc,\ 'data/training_acc_eg.txt') vid_utils.savefile(list_tillnow,testing_acc,\ 'data/testing_acc_eg.txt') vid_utils.savefile(list_tillnow,time_list,\ 'data/time_vals.txt') plt.plot(exampleno_list,training_acc,'bo-',label='Training Accuracy'); plt.plot(exampleno_list,testing_acc,'ro-',label='Testing Accuracy'); plt.legend(); plt.show();
def gamma_script(): glob_const=globalconstants.GlobalConstants(); init_set(glob_const); SNRval=2.; #maxdepth_list=[]; #gamma_list=range(1,7); gamma_list=[0]; training_acc=[]; testing_acc=[]; list_tillnow=[]; time_list=[]; for gamma in gamma_list: starttime=time.time(); glob_const.gamma=gamma; glob_const.savefile(); traintest=train_and_test.TrainAndTest(SNRval); traintest.train_tree(); traintest.test_tree(); elapsed=time.time()-starttime; #append values training_acc.append(traintest.training_accuracy); testing_acc.append(traintest.testing_accuracy); list_tillnow.append(gamma); time_list.append(elapsed) vid_utils.savefile(list_tillnow,training_acc,\ 'data/training_acc_gamma.txt') vid_utils.savefile(list_tillnow,testing_acc,\ 'data/testing_acc_gamma.txt') vid_utils.savefile(list_tillnow,time_list,\ 'data/time_vals_depth.txt') plt.plot(gamma_list,training_acc,'bo-',label='Training Accuracy'); plt.plot(gamma_list,testing_acc,'ro-',label='Testing Accuracy'); plt.legend(); plt.show();