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();
