Пример #1
0
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
Пример #2
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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();
Пример #3
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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()
Пример #4
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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
Пример #5
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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)
Пример #6
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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()
Пример #7
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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()
Пример #8
0
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();