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 depth_script():
#important: line 199 in function train_node.py
#which is self._print_histogram in create_children()
#function is uncommented for this script to run
#comment it back and reinstall after the script runs
#this also depends on information from self._print_loginfo
#in tree_node.py
glob_const=globalconstants.GlobalConstants();
init_set(glob_const);
SNRval=0.5;
traintest=train_and_test.TrainAndTest(SNRval);
traintest.train_tree();
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 training_round_script():
glob_const = globalconstants.GlobalConstants()
init_set(glob_const)
examples = glob_const.no_pos_training_eg
init_t = time.time()
SNRval = 0.5
traintest = train_and_test.TrainAndTest(SNRval)
traintest.train_tree()
print "Trained: " + str(time.time() - init_t)
init_t = time.time()
traintest.test_tree()
print "Tested: " + str(time.time() - init_t)
init_t = time.time()
traintest.post_prune_tree()
print "Post Pruned: " + str(time.time() - init_t)
return
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 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()
import pbtspot
from pbtspot import set_default,train_and_test, globalconstants
import matplotlib.pyplot as plt
set_default.set_default_constants();
#set constants
glob_const=globalconstants.GlobalConstants();
glob_const.no_pos_training_eg=50;
glob_const.no_neg_training_eg=50;
glob_const.no_pos_testing_eg=1000;
glob_const.no_neg_testing_eg=1000;
glob_const.savefile();
#main script
SNR=0.5;
train_test=train_and_test.TrainAndTest(SNR);
train_test.load_saved_tree();
#train_test.train_tree();
#train_test.save_tree();
train_test.test_tree();
[tpr,fpr]=train_test.get_roc_curve();
plt.plot(fpr,tpr,'ro-');
plt.xlim(-0.05, 1.05)
plt.ylim(-0.05,1.05)
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
print "tpr: "+str(tpr)
print "fpr: "+str(fpr)
plt.show();