def main():
''' Load data, split data, creates adaboost algorithm
with decision stump, calculates errors, save final file'''
classifier = AdaBoost(DecisionStump)
num_sets = 50
T = 100
percentage = 0.9
all_errors_train = []
all_errors_test = []
aver_error_train = []
aver_error_test = []
# split data in the # of datasets
split_data(percentage, num_sets)
# run for all datasets, for boosting interations = T
for i in range(num_sets):
data_split_train = './data/bupa_train' + str(i) + ".txt"
data_split_test = './data/' + "bupa_test" + str(i) + ".txt"
X_train, Y_train = load_data(data_split_train)
X_test, Y_test = load_data(data_split_test)
score_train, score_test = classifier.run_adaboost(X_train, Y_train, T, X_test)
error_train = calculate_error(T, score_train, Y_train)
error_test = calculate_error(T, score_test, Y_test)
all_errors_train.append(error_train)
all_errors_test.append(error_test)
def main():
classifier = AdaBoost(DecisionStump)
num_sets = 50
T = 100
percentage = 0.9
all_errors_train = []
all_errors_test = []
aver_error_train = []
aver_error_test = []
split_data(percentage, num_sets)
for i in range(num_sets):
data_split_train = './data/bupa_train' + str(i) + ".txt"
data_split_test = './data/' + "bupa_test" + str(i) + ".txt"
X_train, Y_train = load_data(data_split_train)
X_test, Y_test = load_data(data_split_test)
score_train, score_test = classifier.run_adaboost(X_train, Y_train, T, X_test)
error_train = calculate_error(T, score_train, Y_train)
error_test = calculate_error(T, score_test, Y_test)
all_errors_train.append(error_train)
all_errors_test.append(error_test)
# calculates the average errors
for j in range(T):
a_e_train = 0
a_e_test = 0
for i in range(num_sets):
a_e_train += all_errors_train[i][j]
a_e_test += all_errors_test[i][j]
aver_error_train.append(a_e_train/num_sets)
aver_error_test.append(a_e_test/num_sets)
save_result_final(aver_error_train, 'train')
save_result_final(aver_error_test, 'test')
dataset_here = "./data/bupa.data"
X_all, Y_all = load_data(dataset_here)
score_optional = classifier.run_adaboost(X_all, Y_all, T, None, True)
save_result_final(score_optional, 'empirical')
def main():
''' Load data, split data, creates adaboost algorithm
with decision stump, calculates errors, save final file.
Since this is a binary classifier, we will do for each of the
4 networks, one at time'''
classification = []
ada_folder = OUTPUT_FOLDER + 'adaboost/'
if not os.path.exists(ada_folder):
os.makedirs(ada_folder)
output_file = ada_folder + 'results.out'
with open(output_file, "w") as f:
f.write("# ADABOOST RESULTS, TRAIN/TEST FRACTION: " + str(PERCENTAGE) + "\n")
f. write("# Net Norm Set OL? Accu. Train Accu Test\n")
# chose classifier
classifier = AdaBoost(DecisionStump)
# for each normalization:
for norm in NORM:
# for each set
for number in range(1, NUM_SETS+1):
''' with with_outlier '''
with_outlier = True
# get input and output file paths
input_train = get_input_data('train', number, norm, with_outlier)
input_test = get_input_data('test', number, norm, with_outlier)
# for each network type:
for net_name in NET_NAMES:
# get data
X_train, Y_train = one_against_all.load_data(input_train, net_name)
X_test, Y_test = one_against_all.load_data(input_test, net_name)
print 'Calculating adaboost for net ' + net_name + ' with normalization ' + norm + ' and set ' + str(number)
score_train, score_test = classifier.run_adaboost(X_train, Y_train, T, X_test)
error_train = calculate_error(T, score_train, Y_train)
error_test = calculate_error(T, score_test, Y_test)
error_train_total = sum(error_train)/len(error_train)
error_test_total = sum(error_test)/len(error_test)
save_result_final(error_train_total, error_test_total, output_file, net_name, norm, number, with_outlier)
classification.append(str(round(error_test_total,3)) +', ' + str(norm) + ', ' + str(number) + ', ' + str(with_outlier)[0] + '\n')
''' with no outlier '''
with_outlier = False
# get input and output file paths
input_train = get_input_data('train', number, norm, with_outlier)
input_test = get_input_data('test', number, norm, with_outlier)
# for each network type:
for net_name in NET_NAMES:
# get data
X_train, Y_train = one_against_all.load_data(input_train, net_name)
X_test, Y_test = one_against_all.load_data(input_test, net_name)
score_train, score_test = classifier.run_adaboost(X_train, Y_train, T, X_test)
error_train = calculate_error(T, score_train, Y_train)
error_test = calculate_error(T, score_test, Y_test)
error_train_total = sum(error_train)/len(error_train)
error_test_total = sum(error_test)/len(error_test)
save_result_final(error_train_total, error_test_total, output_file, net_name, norm, number, with_outlier)
classification.append(str(round(error_test_total,3)) +', ' + str(norm) + ', ' + str(number) + ', ' + str(with_outlier)[0] + '\n')
#find best classfiers
classification.sort()
with open(output_file + 'good_classification', "w") as f:
f.write("\n\n\nClassification\n\n")
for feat in classification:
f.write(feat + '\n')
f.write("\n")
print 'Results saved at ' + ada_folder
print 'Done!!!'
