How to use test_starting method in autotest
Best Python code snippet using autotest_python
my_utils.py
Source:my_utils.py 
1import os2import numpy as np3from matplotlib import pyplot as plt4from sklearn.model_selection import train_test_split, KFold5def get_cross_validated_subjects(x, y, names, n_folds = 5):6 ''' A function to implement the cross-validation manually '''7 new_person = "ss_ss_ss_ss"8 subjects_pt = np.array([], dtype=str)9 subjects_co = np.array([], dtype=str)10 subjects_pt_idx = np.array([], dtype=int)11 subjects_co_idx = np.array([], dtype=int)12 for i in range(len(names)):13 if new_person.split("_")[0:2] != names[i].split("_")[0:2]:14 new_person = names[i]15 if new_person.__contains__("Pt"):16 subjects_pt = np.append(subjects_pt, [new_person], axis=0)17 subjects_pt_idx = np.append(subjects_pt_idx, [i], axis=0)18 else:19 subjects_co = np.append(subjects_co, [new_person], axis=0)20 subjects_co_idx = np.append(subjects_co_idx, [i], axis=0)21 print("------", subjects_pt.shape, subjects_pt_idx.shape)22 kf = KFold(n_splits=n_folds)23 KFold(n_splits=n_folds, random_state=42, shuffle=True)24 splits = kf.split(subjects_pt, subjects_pt_idx)25 subjects_pt_train_all = np.asarray([])26 subjects_pt_train_idx_all = np.asarray([])27 subjects_pt_test_all = np.asarray([])28 subjects_pt_test_idx_all = np.asarray([])29 n_fold_train_samples = []30 n_fold_test_samples = []31 train_starting = 032 test_starting = 033 for pt_name_train_index, pt_name_test_index in kf.split(subjects_pt, subjects_pt_idx):34 subjects_pt_train_all = np.append(subjects_pt_train_all, subjects_pt[pt_name_train_index])35 subjects_pt_train_idx_all = np.append(subjects_pt_train_idx_all, subjects_pt_idx[pt_name_train_index])36 n_fold_train_samples.append(train_starting)37 train_starting += subjects_pt[pt_name_train_index].shape[0]38 subjects_pt_test_all = np.append(subjects_pt_test_all, subjects_pt[pt_name_test_index])39 subjects_pt_test_idx_all = np.append(subjects_pt_test_idx_all, subjects_pt_idx[pt_name_test_index])40 n_fold_test_samples.append(test_starting)41 test_starting += subjects_pt[pt_name_test_index].shape[0] - 142 patients = [subjects_pt_train_all, subjects_pt_train_idx_all, subjects_pt_test_all, subjects_pt_test_idx_all, n_fold_train_samples, n_fold_test_samples]43 subjects_co_train_all = np.asarray([])44 subjects_co_train_idx_all = np.asarray([])45 subjects_co_test_all = np.asarray([])46 subjects_co_test_idx_all = np.asarray([])47 n_fold_train_samples = []48 n_fold_test_samples = []49 train_starting = 050 test_starting = 051 for co_name_train_index, co_name_test_index in kf.split(subjects_co, subjects_co_idx):52 subjects_co_train_all = np.append(subjects_co_train_all, subjects_co[co_name_train_index])53 subjects_co_train_idx_all = np.append(subjects_co_train_idx_all, subjects_co_idx[co_name_train_index])54 n_fold_train_samples.append(train_starting)55 train_starting += subjects_co[co_name_train_index].shape[0] - 156 subjects_co_test_all = np.append(subjects_co_test_all, subjects_co[co_name_test_index])57 subjects_co_test_idx_all = np.append(subjects_co_test_idx_all, subjects_co_idx[co_name_test_index])58 n_fold_test_samples.append(test_starting)59 test_starting += subjects_co[co_name_test_index].shape[0]60 controls = [subjects_co_train_all, subjects_co_train_idx_all, subjects_co_test_all, subjects_co_test_idx_all,61 n_fold_train_samples, n_fold_test_samples]62 return patients, controls63def split_humanly_v2(x, y, names, pt_name_train_x, pt_name_test_x, pt_name_train_y, pt_name_test_y, co_name_train_x, co_name_test_x, co_name_train_y, co_name_test_y):64 name_train_x = np.append(pt_name_train_x[:co_name_train_x.shape[0]], co_name_train_x, axis=0)65 name_train_x = name_train_x.reshape(name_train_x.shape[0], 1)66 name_train_y = np.append(pt_name_train_y[:co_name_train_y.shape[0]], co_name_train_y, axis=0)67 name_train_y = name_train_y.reshape(name_train_y.shape[0], 1)68 train = np.concatenate((name_train_x, name_train_y), axis=1)69 np.random.shuffle(train)70 name_train_x = train[:, 0]71 name_train_y = np.asarray(train[:, 1], dtype=int)72 name_test_x = np.append(pt_name_test_x[:co_name_test_x.shape[0]], co_name_test_x, axis=0)73 name_test_x = name_test_x.reshape(name_test_x.shape[0], 1)74 name_test_y = np.append(pt_name_test_y[:co_name_test_y.shape[0]], co_name_test_y, axis=0)75 name_test_y = name_test_y.reshape(name_test_y.shape[0], 1)76 test = np.concatenate((name_test_x, name_test_y), axis=1)77 np.random.shuffle(test)78 name_test_x = test[:, 0]79 name_test_y = np.asarray(test[:, 1], dtype=int)80 name_train_x = name_train_x.reshape(name_train_x.shape[0], )81 name_train_y = name_train_y.reshape(name_train_y.shape[0], )82 name_test_x = name_test_x.reshape(name_test_x.shape[0], )83 name_test_y = name_test_y.reshape(name_test_y.shape[0], )84 print("-total train:", len(name_train_x), "-Pt in train:", len([i for i in name_train_x if i.__contains__("Pt")]),85 "-Co in train:", len([i for i in name_train_x if i.__contains__("Co")]))86 print("-total test:", len(name_test_x), "-Pt in test:", len([i for i in name_test_x if i.__contains__("Pt")]),87 "-Co in test:", len([i for i in name_test_x if i.__contains__("Co")]))88 train_x = np.array([])89 train_y = np.array([])90 for i in range(len(name_train_y)):91 unique_name = name_train_x[i]92 if np.size(train_x) == 0:93 train_x = [x[name_train_y[i], :, :]]94 train_y = [y[name_train_y[i], :]]95 else:96 train_x = np.append(train_x, [x[name_train_y[i], :, :]], axis=0)97 train_y = np.append(train_y, [y[name_train_y[i], :]], axis=0)98 next = name_train_y[i] + 199 while next < len(names) and unique_name.split("_")[0:2] == names[next].split("_")[0:2]:100 train_x = np.append(train_x, [x[next, :, :]], axis=0)101 train_y = np.append(train_y, [y[next, :]], axis=0)102 next = next + 1103 test_x = np.array([])104 test_y = np.array([])105 for i in range(len(name_test_y)):106 unique_name = name_test_x[i]107 if np.size(test_x) == 0:108 test_x = [x[name_test_y[i], :, :]]109 test_y = [y[name_test_y[i], :]]110 else:111 test_x = np.append(test_x, [x[name_test_y[i], :, :]], axis=0)112 test_y = np.append(test_y, [y[name_test_y[i], :]], axis=0)113 next = name_test_y[i] + 1114 while next < len(names) and unique_name.split("_")[0:2] == names[next].split("_")[0:2]:115 test_x = np.append(test_x, [x[next, :, :]], axis=0)116 test_y = np.append(test_y, [y[next, :]], axis=0)117 next = next + 1118 return train_x, train_y, test_x, test_y119def split_humanly(x, y, names):120 new_person = "ss_ss_ss_ss"121 subjects_pt = np.array([], dtype=str)122 subjects_co = np.array([], dtype=str)123 subjects_pt_idx = np.array([], dtype=int)124 subjects_co_idx = np.array([], dtype=int)125 # subjects = np.array([new_person], dtype=str)126 # subjects_idx = np.array([0], dtype=int)127 for i in range(len(names)):128 if new_person.split("_")[0:2] != names[i].split("_")[0:2]:129 new_person = names[i]130 if new_person.__contains__("Pt"):131 subjects_pt = np.append(subjects_pt, [new_person], axis=0)132 subjects_pt_idx = np.append(subjects_pt_idx, [i], axis=0)133 else:134 subjects_co = np.append(subjects_co, [new_person], axis=0)135 subjects_co_idx = np.append(subjects_co_idx, [i], axis=0)136 # subjects = np.append(subjects, [new_person], axis=0)137 # subjects_idx = np.append(subjects_idx, [i], axis=0)138 pt_name_train_x, pt_name_test_x, pt_name_train_y, pt_name_test_y = train_test_split(subjects_pt, subjects_pt_idx,139 test_size=0.53,140 random_state=42, shuffle=True)141 pt_name_valid_x, pt_name_test_x, pt_name_valid_y, pt_name_test_y = train_test_split(pt_name_test_x, pt_name_test_y,142 test_size=0.60,143 random_state=42, shuffle=True)144 co_name_train_x, co_name_test_x, co_name_train_y, co_name_test_y = train_test_split(subjects_co, subjects_co_idx,145 test_size=0.14,146 random_state=42, shuffle=True)147 co_name_valid_x, co_name_test_x, co_name_valid_y, co_name_test_y = train_test_split(co_name_test_x, co_name_test_y,148 test_size=0.60,149 random_state=42, shuffle=True)150 name_train_x = np.append(pt_name_train_x[:co_name_train_x.shape[0]], co_name_train_x, axis=0)151 name_train_x = name_train_x.reshape(name_train_x.shape[0], 1)152 name_train_y = np.append(pt_name_train_y[:co_name_train_y.shape[0]], co_name_train_y, axis=0)153 name_train_y = name_train_y.reshape(name_train_y.shape[0], 1)154 train = np.concatenate((name_train_x, name_train_y), axis=1)155 np.random.shuffle(train)156 name_train_x = train[:, 0]157 name_train_y = np.asarray(train[:, 1], dtype=int)158 name_valid_x = np.append(pt_name_valid_x[:co_name_valid_x.shape[0]], co_name_valid_x, axis=0)159 name_valid_x = name_valid_x.reshape(name_valid_x.shape[0], 1)160 name_valid_y = np.append(pt_name_valid_y[:co_name_valid_y.shape[0]], co_name_valid_y, axis=0)161 name_valid_y = name_valid_y.reshape(name_valid_y.shape[0], 1)162 valid = np.concatenate((name_valid_x, name_valid_y), axis=1)163 np.random.shuffle(valid)164 name_valid_x = valid[:, 0]165 name_valid_y = np.asarray(valid[:, 1], dtype=int)166 name_test_x = np.append(pt_name_test_x[:co_name_test_x.shape[0]], co_name_test_x, axis=0)167 name_test_x = name_test_x.reshape(name_test_x.shape[0], 1)168 name_test_y = np.append(pt_name_test_y[:co_name_test_y.shape[0]], co_name_test_y, axis=0)169 name_test_y = name_test_y.reshape(name_test_y.shape[0], 1)170 test = np.concatenate((name_test_x, name_test_y), axis=1)171 np.random.shuffle(test)172 name_test_x = test[:, 0]173 name_test_y = np.asarray(test[:, 1], dtype=int)174 name_train_x = name_train_x.reshape(name_train_x.shape[0], )175 name_train_y = name_train_y.reshape(name_train_y.shape[0], )176 name_valid_x = name_valid_x.reshape(name_valid_x.shape[0], )177 name_valid_y = name_valid_y.reshape(name_valid_y.shape[0], )178 name_test_x = name_test_x.reshape(name_test_x.shape[0], )179 name_test_y = name_test_y.reshape(name_test_y.shape[0], )180 print("-total train:", len(name_train_x), "-Pt in train:", len([i for i in name_train_x if i.__contains__("Pt")]),181 "-Co in train:", len([i for i in name_train_x if i.__contains__("Co")]))182 print("-total valid:", len(name_valid_x), "-Pt in valid:", len([i for i in name_valid_x if i.__contains__("Pt")]),183 "-Co in valid:", len([i for i in name_valid_x if i.__contains__("Co")]))184 print("-total test:", len(name_test_x), "-Pt in test:", len([i for i in name_test_x if i.__contains__("Pt")]),185 "-Co in test:", len([i for i in name_test_x if i.__contains__("Co")]))186 train_x = np.array([])187 train_y = np.array([])188 for i in range(len(name_train_y)):189 unique_name = name_train_x[i]190 if np.size(train_x) == 0:191 train_x = [x[name_train_y[i], :, :]]192 train_y = [y[name_train_y[i], :]]193 else:194 train_x = np.append(train_x, [x[name_train_y[i], :, :]], axis=0)195 train_y = np.append(train_y, [y[name_train_y[i], :]], axis=0)196 next = name_train_y[i] + 1197 while next < len(names) and unique_name.split("_")[0:2] == names[next].split("_")[0:2]:198 train_x = np.append(train_x, [x[next, :, :]], axis=0)199 train_y = np.append(train_y, [y[next, :]], axis=0)200 next = next + 1201 valid_x = np.array([])202 valid_y = np.array([])203 for i in range(len(name_valid_y)):204 unique_name = name_valid_x[i]205 if np.size(valid_x) == 0:206 valid_x = [x[name_valid_y[i], :, :]]207 valid_y = [y[name_valid_y[i], :]]208 else:209 valid_x = np.append(valid_x, [x[name_valid_y[i], :, :]], axis=0)210 valid_y = np.append(valid_y, [y[name_valid_y[i], :]], axis=0)211 next = name_valid_y[i] + 1212 while next < len(names) and unique_name.split("_")[0:2] == names[next].split("_")[0:2]:213 valid_x = np.append(valid_x, [x[next, :, :]], axis=0)214 valid_y = np.append(valid_y, [y[next, :]], axis=0)215 next = next + 1216 test_x = np.array([])217 test_y = np.array([])218 for i in range(len(name_test_y)):219 unique_name = name_test_x[i]220 if np.size(test_x) == 0:221 test_x = [x[name_test_y[i], :, :]]222 test_y = [y[name_test_y[i], :]]223 else:224 test_x = np.append(test_x, [x[name_test_y[i], :, :]], axis=0)225 test_y = np.append(test_y, [y[name_test_y[i], :]], axis=0)226 next = name_test_y[i] + 1227 while next < len(names) and unique_name.split("_")[0:2] == names[next].split("_")[0:2]:228 test_x = np.append(test_x, [x[next, :, :]], axis=0)229 test_y = np.append(test_y, [y[next, :]], axis=0)230 next = next + 1231 return train_x, train_y, valid_x, valid_y, test_x, test_y232def load_data(path="data/", function="None", normalization=False):233 x = np.array([])234 y = np.array([])235 names = np.genfromtxt(path + sorted(os.listdir(path))[1], dtype=str)[1:, -1]236 for filename in sorted(os.listdir(path)):237 if not filename.endswith(".csv"): # if the file is not valid238 continue239 new_x = np.genfromtxt(path + filename, dtype=float)[1:, :-1] # convert each csv file to numpy array240 new_name = np.genfromtxt(path + filename, dtype=str)[1:, -1] # name of each sample ex : JuCo23_01_28_L1241 new_y = np.zeros((np.size(new_name, axis=0), 1))242 for i in range(np.size(new_y, axis=0)):243 if str(new_name[i,]).__contains__("Pt"): # Patient as positive244 new_y[i,] = 1245 else:246 new_y[i,] = 0247 if np.size(x) == 0:248 x = new_x249 y = new_y250 names = new_name251 else:252 x = np.dstack((x, new_x))253 y = np.dstack((y, new_y))254 n_values = x.shape[2] # number of total sensors255 if function == "average":256 n_values = 2257 y = y[:, :, n_values - 1]258 average_left = np.mean(x[:, :, :n_values], axis=2)259 average_right = np.mean(x[:, :, n_values:], axis=2)260 x = np.dstack((average_left, average_right))261 if function == "None": # 16 sensors262 y = y[:, :, n_values - 1]263 if function == "difference": # to reduce the total sensors to half264 n_values = int(n_values / 2)265 x = x[:, :, n_values:] - x[:, :, :n_values]266 y = y[:, :, n_values - 1]267 if normalization:268 for i in range(x.shape[0]):269 for j in range(x.shape[2]):270 m = np.mean(x[i, :, j])271 s = np.std(x[i, :, j]) + np.finfo(np.float32).eps272 x[i, :, j] = (x[i, :, j] - m) / s273 return x, y, names274def plot_data(x, y, type="normall"):275 plt.style.use("seaborn")276 my_dpi = 96277 plt.figure(figsize=(2000 / my_dpi, 1000 / my_dpi), dpi=my_dpi)278 T_x = x.shape[0] # length of the sequence279 n_values = x.shape[1] # number of the values(sensors)280 t = np.arange(0, T_x / 100, 0.01)281 if type == "average":282 y1 = np.ones(t.size)283 y2 = np.ones(t.size) * 2284 z1 = x[:, 0]285 z2 = x[:, 1]286 ax = plt.subplot(projection='3d')287 ax.plot(t, y1, z1, color='r')288 ax.plot(t, y2, z2, color='g')289 ax.add_collection3d(plt.fill_between(t, z1, z1, color='r', alpha=0.3), zs=1, zdir='y')290 ax.add_collection3d(plt.fill_between(t, z2, z2, color='g', alpha=0.3), zs=2, zdir='y')291 ax.set_xlabel('Time(s)', fontsize=20)292 ax.set_zlabel('Average vGRFs(N)', fontsize=20)293 if y == 1:294 subject_type = "Patient Subject"295 elif y == 0:296 subject_type = "Control Subject"297 ax.text2D(0.05, 0.95, subject_type, transform=ax.transAxes, fontsize=20)298 plt.show()299 else:300 y1 = np.ones(t.size)301 y2 = np.ones(t.size) * 2302 y3 = np.ones(t.size) * 3303 y4 = np.ones(t.size) * 4304 y5 = np.ones(t.size) * 5305 y6 = np.ones(t.size) * 6306 y7 = np.ones(t.size) * 7307 y8 = np.ones(t.size) * 8308 z1 = x[:, 0]309 z2 = x[:, 1]310 z3 = x[:, 2]311 z4 = x[:, 3]312 z5 = x[:, 4]313 z6 = x[:, 5]314 z7 = x[:, 6]315 z8 = x[:, 7]316 ax = plt.subplot(projection='3d')317 ax.plot(t, y1, z1, color='r')318 ax.plot(t, y2, z2, color='g')319 ax.plot(t, y3, z3, color='b')320 ax.plot(t, y4, z4, color='c')321 ax.plot(t, y5, z5, color='m')322 ax.plot(t, y6, z6, color='y')323 ax.plot(t, y7, z7, color='w')324 ax.plot(t, y8, z8, color='k')325 ax.add_collection3d(plt.fill_between(t, z1, z1, color='r', alpha=0.3), zs=1, zdir='y')326 ax.add_collection3d(plt.fill_between(t, z2, z2, color='g', alpha=0.3), zs=2, zdir='y')327 ax.add_collection3d(plt.fill_between(t, z3, z3, color='b', alpha=0.3), zs=3, zdir='y')328 ax.add_collection3d(plt.fill_between(t, z4, z4, color='c', alpha=0.3), zs=4, zdir='y')329 ax.add_collection3d(plt.fill_between(t, z5, z5, color='m', alpha=0.3), zs=5, zdir='y')330 ax.add_collection3d(plt.fill_between(t, z6, z6, color='y', alpha=0.3), zs=6, zdir='y')331 ax.add_collection3d(plt.fill_between(t, z7, z7, color='w', alpha=0.3), zs=7, zdir='y')332 ax.add_collection3d(plt.fill_between(t, z8, z8, color='k', alpha=0.3), zs=8, zdir='y')333 ax.set_xlabel('Time(s)', fontsize=20)334 ax.set_zlabel('vGRFs(N)', fontsize=20)335 if y == 1:336 subject_type = "Patient Subject"337 elif y == 0:338 subject_type = "Control Subject"339 ax.text2D(0.05, 0.95, subject_type, transform=ax.transAxes, fontsize=20)...
evaluation.py
Source:evaluation.py
1# python -m pip install tldextract2import tldextract3import time4import requests5import random6import csv7def read_urls(path=None):8 if not path:9 # list of test URLs [URL,title]10 urls = [11 ['https://versicherung.ge-be-in.de/startseite', "Sterbegeldversicherung seit 1923 - GE·BE·IN Versicherungen"], 12 ['https://idsrv.lv1871.de/Login', '"Lebensversicherung von 1871 a. G. München"'],13 ['http://www.rogerebert.com/reviews/reservoir-dogs-1992', 'Reservoir Dogs movie review & film summary (1992) | Roger Ebert'],14 ['http://www.kuselit.de/', ':: Kuselit Verlag :: Rechtsbibliografie'],15 ['http://www.ullapoolholidayhomes.com/', 'Holiday Accommodation Self Catering Cottages | Ullapool Scotland NC500'],16 ['http://www.art-und-tat.de/', 'Veronika Jakob Grafikdesign'],17 ['http://www.tuffies.co.uk/', 'Buy Luxury Dog Beds UK Online | British Handmade Heavy Duty & Chew Proof Dog Beds | Tuffies'],18 # ['http://www.comune.pietraperzia.en.it/', 'Comune di Pietraperzia'],19 ['http://www.traditieialomita.ro/', 'TradiÈie Èi Modernism â Centrul JudeÈean pentru Conservarea Èi Promovarea Culturii TradiÈionale IalomiÈa'],20 ['http://www.theglenlyngorge.co.uk/', '\n']21 #['http://www.kronos.co.uk/', 'Workforce Management and HCM Cloud Solutions | Kronos UK']22 ]23 return urls24 urls = []25 with open(path, newline='') as csvfile:26 reader = csv.reader(csvfile, delimiter=',') # encoding='utf-8'27 next(reader) # skip header 28 for row in reader:29 # print("Reading: " + ', '.join(row))30 url_pair = [row[0],row[1]]31 urls.append(url_pair)32 return urls33def write_sample(fake_urls,benign_urls):34 with open('./' + 'data/fake_urls.csv', 'w', newline='', encoding='utf-8') as file:35 writer = csv.writer(file, delimiter=',')36 writer.writerow(['url','title'])37 writer.writerows(fake_urls)38 with open('./' + 'data/benign_urls.csv', 'w', newline='', encoding='utf-8') as file:39 writer = csv.writer(file, delimiter=',')40 writer.writerow(['url','title'])41 writer.writerows(benign_urls)42def get_starting_url(file):43 f = open(file, "r")44 return f.read()45def set_starting_url(file,index,close=True):46 f = open(file, "w")47 f.write(index)48 if close:49 f.close()50def split_list(a_list):51 half = len(a_list)//252 return a_list[:half], a_list[half:]53def evaluate(urls, index_writer, fake=True, starting_url=0, test=False):54 nof_skipped_samples = 055 i = 0 if not starting_url else int(starting_url)56 # if not starting_url:57 # i = 058 # else:59 # i = int(starting_url)60 # for url in urls:61 for url in urls[i:len(urls)]:62 if not url[1].strip():63 print("Stripped title of " + url[0] + " is empty. Going on.")64 nof_skipped_samples = nof_skipped_samples + 165 i = i + 166 continue67 else:68 url[1] = url[1].strip()69 #strip from quotes70 url[1] = url[1].strip('\"')71 # i = i + 172 # create json:73 if fake: #request for fake phishing site74 current_json = {75 "URL": url[0],76 "uuid": str(client_uuid),77 "pagetitle": url[1],78 # "image64": "",79 "phishURL" : "http://bogusurl" + str(i) + ".co.uk"80 }81 else: #request for benign site82 current_json = {83 "URL": url[0],84 "uuid": str(client_uuid),85 "pagetitle": url[1],86 # "image64": "",87 "phishURL" : ""88 }89 # test_json = {90 # "URL": "https://idsrv.lv1871.de/Login",91 # "uuid": "63054094-01c4-11ed-b939-0242ac120002",92 # "pagetitle": "Lebensversicherung von 1871 a. G. München",93 # "image64": "",94 # "phishURL" : "http://bogusurl1.co.uk"95 # }96 if fake:97 print(str(i) + ': Sent request for ' + url[0] + ' with fakeURL ' + str(current_json["phishURL"]))98 else:99 print(str(i) + ': Sent request for ' + url[0] + ' with benignURL ' + str(current_json["URL"]))100 try:101 res = requests.post('http://192.168.56.100:5000/api/v1/url', json=current_json, timeout = 1200)102 except requests.Timeout as err:103 print(str(url[0]) + " timed out. Going on.")104 print(err)105 nof_skipped_samples = nof_skipped_samples + 1106 i = i +1107 continue108 except requests.RequestException as err:109 print("Error with requets: ")110 print(err)111 print(url[0] + " got error. Going on.")112 nof_skipped_samples = nof_skipped_samples + 1113 i = i + 1 114 continue115 print('Response from antiPhish server: ', res.text)116 #write current processed index to file117 if not test:118 set_starting_url(path_fake_counter,str(i)) if fake else set_starting_url(path_benign_counter,str(i))119 else:120 set_starting_url("data/test_starting.txt",str(i))121 # sleep every x calls to not trigger Google limiting122 if i % 3 == 0:123 sleep_time = random.randint(20,30)124 sleep_time = 1125 print("Sleeping " + str(sleep_time) + " seconds.")126 time.sleep(sleep_time)127 128 # go to next URL129 i = i + 1130 #write last processed index to file again when finished131 if not test:132 set_starting_url(path_fake_counter,str(i)) if fake else set_starting_url(path_benign_counter,str(i))133 else:134 set_starting_url("data/test_starting.txt",str(i))135## global constants ##136test = False137write_samples = False138path_urls = "data/urls.csv" if not test else None139path_fake_urls = "data/fake_urls.csv"# if not test else None140path_benign_urls = "data/benign_urls.csv"# if not test else None141path_fake_counter = "data/last_fake_url.txt"142path_benign_counter = "data/last_benign_url.txt"143fake_starting_url = get_starting_url(path_fake_counter) if not test else 0144benign_starting_url = get_starting_url(path_benign_counter) if not test else 0145# client_uuid = "73054094-01c4-11ed-b939-0242ac120002" #first attempt146# client_uuid = "simulation-" + time.strftime("%H:%M:%S %a, %d-%b-%Y", time.gmtime())147client_uuid = "simulation_1"148# total number of urls to evaluate (split 50/50 for fake/benign)149#urls_to_process = 2274 if not test else None150urls_to_process = None151 152def main():153 startTime = time.time()154 print("Starting time: " + time.strftime("%H:%M:%S %a, %d-%b-%Y", time.gmtime()))155 # get all available urls156 urls = read_urls(path_urls)157 print("Read " + str(len(urls)) + " URLs from " + str(path_urls))158 if not urls_to_process:159 nof_urls = len(urls)160 else:161 nof_urls = urls_to_process162 print("Total number of URLs to evaluate: " + str(nof_urls) + "\n")163 if not test:164 if not write_samples:165 # read fake phish and benign urls166 print("Reading already sampled URLs.")167 fake_urls = read_urls(path_fake_urls)168 print("Read " + str(len(fake_urls)) + " fake URLs from " + str(path_fake_urls))169 benign_urls = read_urls(path_benign_urls)170 print("Read " + str(len(benign_urls)) + " benign URLs from " + str(path_benign_urls))171 else:172 # sample 50% fake_urls and 50% benign_urls173 sampled_urls = random.sample(urls,nof_urls)174 fake_urls, benign_urls = split_list(sampled_urls)175 write_sample(fake_urls,benign_urls)176 # evaluate177 print("Starting fake url: " + str(fake_starting_url))178 print("Beginning to evaluate " + str(len(fake_urls) - int(fake_starting_url)) + " fake URLs.")179 f = open(path_fake_counter, "w")180 evaluate(fake_urls, f, True, fake_starting_url)181 f.close()182 # sleep 5 min before starting the benign urls183 #time.sleep(300)184 print("Starting benign url: " + str(benign_starting_url))185 print("Beginning to evaluate " + str(len(benign_urls) - int(benign_starting_url)) + " benign URLs.\n")186 f = open(path_benign_counter, "w")187 evaluate(benign_urls, f, False, benign_starting_url)188 f.close()189 else:190 test_starting_url = get_starting_url("data/test_starting.txt")191 print("Starting test url: " + str(test_starting_url))192 print("Running test with " + str(nof_urls - int(test_starting_url)) + " URLs \n")193 f = open("data/test_starting.txt", "w")194 evaluate(urls, f, False, test_starting_url, True)195 f.close()196 stopTime = time.time()197 print("Ending time: " + time.strftime("%H:%M:%S %a, %d-%b-%Y", time.gmtime()))198 print(f"Finished. Time elapsed for complete evaluation is {round((stopTime - startTime)/60, 2)} min\n")199if __name__ == '__main__':...
test_schedule.py
Source:test_schedule.py
...89 class TestInterval:90 class TestFinal:91 def test_do(self): # synced92 assert True93 def test_starting(self): # synced94 assert True95 def test_ending(self): # synced96 assert True97 class TestChainableFinal:98 def test_and_(self): # synced99 assert True100 class TestDay:101 def test_at(self): # synced102 assert True103 class TestChainableDay:104 def test_and_(self): # synced105 assert True106 class TestChainableWeekDay:107 def test_and_(self): # synced108 assert True109 class TestMonth:110 def test_on_the(self): # synced111 assert True112 def test_on(self): # synced113 assert True114 class TestChainableMonth:115 def test_and_(self): # synced116 assert True117 class TestYear:118 def test_in_(self): # synced119 assert True120class TestRelative:121 class TestSettings:122 pass123 class TestSelector:124 class TestBase:125 pass126 class TestSecond:127 def test_seconds(self): # synced128 assert True129 class TestMinute:130 def test_minutes(self): # synced131 assert True132 class TestHour:133 def test_hours(self): # synced134 assert True135 class TestDay:136 def test_days(self): # synced137 assert True138 class TestMonth:139 def test_months(self): # synced140 assert True141 class TestYear:142 def test_years(self): # synced143 assert True144 class TestInterval:145 class TestFinal:146 def test_do(self): # synced147 assert True148 def test_starting(self): # synced149 assert True150 def test_ending(self): # synced151 assert True152 class TestMinute:153 def test_and_(self): # synced154 assert True155 class TestHour:156 def test_and_(self): # synced157 assert True158 class TestDay:159 def test_and_(self): # synced160 assert True161 class TestMonth:162 def test_and_(self): # synced...
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