def get_ad_dataset(noise=0.3):
# Load ad dataset
ad_dataset_file = 'ad-dataset/ad.data'
ad_dataset = np.genfromtxt(ad_dataset_file, delimiter=',', dtype=str)
ad_dataset[ad_dataset == 'ad.'] = 1
ad_dataset[ad_dataset == 'nonad.'] = 0
ads_features = get_ads_features(201239480, 302629605)
ads_features += [np.shape(ad_dataset)[1]-1]
ad_dataset = ad_dataset[:, ads_features].astype(int)
ad_dataset = ad_dataset.tolist()
return get_noisy_folds(ad_dataset, noise)
def get_har_dataset(noise=0.3):
# Load HAR dataset
har_dataset_file = 'UCI HAR Dataset/train/X_train.txt'
har_labels_file = 'UCI HAR Dataset/train/y_train.txt'
har_dataset = np.genfromtxt(har_dataset_file, dtype=float)
har_labels = np.genfromtxt(har_labels_file, dtype=int)
har_labels[har_labels <= 3] = 1 # replace moving labels
har_labels[har_labels >= 4] = 0 # replace resting labels
har_labels.shape = (-1, 1)
har_dataset = np.concatenate((har_dataset, har_labels), axis=1)
har_dataset = har_dataset.tolist()
return get_noisy_folds(har_dataset, noise)
def check():
featureList = get_ads_features(313542516, 208346379)
file = open("ad.data", 'r')
data = []
results = []
for line in file.readlines():
out = line.split(",")
item = [out[index] for index in featureList]
item.append(str(0 if "nonad." in out[-1] else 1))
data.append(item)
file.close()
noisyfolds, folds = get_noisy_folds(data)
output = open("folds.pkl",'wb')
output1 = open("noisyfolds.pkl",'wb')
pickle.dump(folds,output)
pickle.dump(noisyfolds,output1)
output.close()
output1.close()
sumacc=0
for i in range(0,10):
train=[]
for j in range(0,10):
if i != j:
train.extend(noisyfolds[j])
trainFinal=[]
results=[]
for sample in train:
trainFinal.append(sample[:-1])
results.append(sample[-1])
tree=DecisionTreeClassifier(criterion="entropy",splitter="best",min_samples_split=4)
tree=tree.fit(trainFinal,results)
predictSamples=[]
predictResults=[]
predictSamples = [folds[i][index][:-1] for index in range(len(folds[i]))]
predictResults = [folds[i][index][-1] for index in range(len(folds[i]))]
sumacc+=tree.score(predictSamples,predictResults)
return sumacc/10
current_accuracy /= float(len(fold_semi[k]))
accuracy += current_accuracy
accuracy /= float(len(noisy_fold_semi))
print('committee semi-random: subset:{} | size: {} | acc: {}'.format(
'examples' if is_subset_of_examples else 'features', committee_size, accuracy))
def all_semi_random_sub_examples(noisy_fold_semi, fold_semi, features):
for size in sizes:
calculate_semi_random_committee(noisy_fold_semi, fold_semi, features, size, True)
def all_semi_random_sub_features(noisy_fold_semi, fold_semi, features):
for size in sizes:
calculate_semi_random_committee(noisy_fold_semi, fold_semi, features, size, False)
if __name__ == '__main__':
'''this part should be done once'''
x, y = extract_data_from_ads()
x_temp = copy.deepcopy(x)
for i in range(0, len(x_temp)):
x_temp[i].append(y[i])
noisy_folds, folds = noise.get_noisy_folds(x_temp)
'''end of part'''
'''Arye'''
all_semi_random_sub_features(noisy_folds, folds, [i for i in range(0, len(x[0]) - 1)])
'''Max'''
all_semi_random_sub_examples(noisy_folds, folds, [i for i in range(0, len(x[0]) - 1)])
