How to use bad_init method in pytest-cov

Best Python code snippet using pytest-cov

test_chinese.py

Source:test_chinese.py Github

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1import pickle2from hanziconv import HanziConv3from revscoring.datasources import revision_oriented4from revscoring.languages import chinese5from .util import compare_extraction6bad_init = [7 "王八蛋", # son of a bitch8 "他媽的", # "his mother's"9 "去你媽", # "to your mother"10 "去你的", # "to yours"11 "婊子", "妓女", # prostitute12 "日狗", "日了狗", # lonely dog13 "屁眼", "混蛋", "渾蛋", # asshole14 "混帳", # variant of above15 "王八", # bitch16 "白癡", # idiot17 "腦殘", # brain dead18 "智障", # mentally retarded19 "婊", "妓", # prostitute20 "屎", # shit21 "屌", # dick22 "妈逼", # (this is verbal but definitely bad)23 "艹", "肏", # fuck (in any context)24 "放屁", # fart25 # Variants (homonyms) of the use of "fuck" that use 操 ("operation") and26 # 草 ("grass"), "肏" is the actual character. "艹" is not a real character27 # but it's used this way28 "操你", "草你", "日你", # fuck you29 "操他", "草他", "日他", # fuck his30 "操她", "草她", "日她", # fuck her31 # Discrimination (racial slurs)32 "小日本", # little Japanese33 "台湾狗", # Taiwanese dogs34 "共产中国", # communist Chinese35 "流氓国家", # rogue country36 "人渣", # human slag37 "我去", # this is verbal and bad38 "鬼子" # devil, usually a suffix39]40BAD = [HanziConv.toSimplified(word) for word in bad_init] + \41 [HanziConv.toTraditional(word) for word in bad_init]42INFORMAL = [43 # Hello44 "你好", # nǐ hǎo; The standard "hello" greeting.45 "您好", # nín hǎo; The same "hello" greeting as above46 "你怎么样", # nǐ zěnmeyàng?; "What's up?", "How are you doing?"47 # Good afternoon48 "午安", # wǔ'an; note: seldom used in the Mainland.49 "下午好", # xìawǔ hǎo! Seldom used in the Republic of China50 # Good evening /​ Good night51 "晚安", # wǎn'an; Literally "Peace at night", Good night.52 "晚上好", # wǎnshang hǎo; Good evening!53 # Good-bye54 "再見", # zàijian; Literally "See you again".55 "明天見", # míngtian jiàn; Literally "See you tomorrow".56 "拜拜", # bāibāi/​báibái; From English "Bye-Bye".57 "回頭見", # huítóujiàn: roughly equivalent to "see you soon"58 "回見", # huíjiàn; usually used in Beijing or written Chinese.59 "再會", # zàihuì: Literally "[we'll] hello again".60 "66666666", "666",61 "233", "2333333"62]63WORDS_TO_WATCH = [64 # Advertising language65 "本台", # this channel66 "本公司", # this company67 "代刷", "代练", "代抢", # someone who plays games for you68 "强势回归", # "mightly" return69 "超值", # very cost-effective70 "一条龙", # a proverb? "one line of dragon"71 "一夜情", # selling one's body (advertising)72 "世界一流", "国际一流", # world first-class73 "用户第一", "用户满意", "用户至上", # customer-first74 "核心价值", "核心团队", "核心宗旨", # core value75 "服务小姐", # service lady76 "服务范围", # service area77 "服务项目", # service items78 "服务理念", # service philosophy79]80OTHER = [81 """2005年大西洋颶風季是有纪录以来最活跃的大西洋颶風季，至今仍保持着多项纪录。82 全季对大范围地区造成毁灭性打击，共导致3,913人死亡，损失数额更创下新纪录，高达1592亿美元。83 本季单大型飓风就有7场之多，其中5场在登陆时仍有大型飓风强度，分别是颶風丹尼斯、艾米莉、84 卡特里娜、丽塔和威尔玛，大部分人员伤亡和财产损失都是这5场飓风引起。85 墨西哥的金塔納羅奧州和尤卡坦州，86 以及美国的佛罗里达州和路易斯安那州都曾两度受大型飓风袭击；古巴、巴哈马、海地，87 美国的密西西比州和得克萨斯州，还有墨西哥的塔毛利帕斯州都曾直接受1场大型飓风冲击，88 还有至少1场在附近掠过。美國墨西哥灣沿岸地區是本季受灾最严重的所在，89 飓风卡特里娜产生高达10米的风暴潮，引发毁灭性洪灾，密西西比州沿海地区的大部分建筑物被毁，90 风暴之后又令新奥尔良防洪堤决口，整个城市因此受到重创。此外，飓风斯坦同溫帶氣旋共同影响，91 在中美洲多地引发致命的泥石流，其中又以危地马拉灾情最为严重。"""92]93r_text = revision_oriented.revision.text94def simplified_eq(a, b):95 return len(a) == len(b) and \96 HanziConv.toSimplified(a[0]) == \97 HanziConv.toSimplified(b[0])98def test_badwords():99 compare_extraction(chinese.badwords.revision.datasources.matches,100 BAD, OTHER, eq=simplified_eq)101 assert chinese.badwords == pickle.loads(pickle.dumps(chinese.badwords))102def test_informals():103 compare_extraction(chinese.informals.revision.datasources.matches,104 INFORMAL, OTHER, eq=simplified_eq)105 assert chinese.informals == pickle.loads(pickle.dumps(chinese.informals))106def test_words_to_watch():107 compare_extraction(chinese.words_to_watch.revision.datasources.matches,108 WORDS_TO_WATCH, OTHER, eq=simplified_eq)109 assert chinese.words_to_watch == \...

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learning.py

Source:learning.py Github

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1from model_class import *2from data_creation import *3import pickle4######################################### LEARNING PROCESS #############################################################56all_fpr = []7all_tpr = []8all_auc = []9lorenz = []10all_gini = []1112# loop to train models13for intensitytype in ('f'): # in ('f','h')1415 if intensitytype == 'f':16 hidden_dim = [5, 3]17 else:18 hidden_dim = [30, 15]1920 duan_replic = "" #duan_replic : full duan replic || #exp : exp activation function2122 # plot directory23 directory_roc = 'Results NN for Intensity estimation\\real dataset\\roc\\' + intensitytype + '\\' + str(hidden_dim) + '\\' #add back24 #directory_roc = 'Results NN for Intensity estimation\\real dataset\\roc\\' + intensitytype + '\\' + duan_replic + '\\' #remove25 if not os.path.exists(directory_roc):26 os.makedirs(directory_roc)2728 directory_lorenz = 'Results NN for Intensity estimation\\real dataset\\lorenz\\' + intensitytype + '\\' + str(hidden_dim) + '\\' #add back29 # directory_lorenz = 'Results NN for Intensity estimation\\real dataset\\lorenz\\' + intensitytype + '\\' + duan_replic + '\\' #remove30 if not os.path.exists(directory_lorenz):31 os.makedirs(directory_lorenz)3233 for tau in range(36):34 print('Estimating model ... ' + intensitytype + str(tau))35 deltaT = 1/​1236 learning_rate=0.00137 feature_size = 1238 batch_size = 25639 perc = 0.940 name = intensitytype + str(tau) + '_hidden' + str(hidden_dim[0]) + '_layers' + str(len(hidden_dim)) + '_learning' + str(learning_rate) + '_batch' + \41 str(batch_size) + '_perc' + str(perc) + '_' #add back42 path = intensitytype + str(tau) + '_hidden' + str(hidden_dim[0]) + '_layers' + str(len(hidden_dim)) + '_learning' + str(learning_rate) + '_batch' + \43 str(batch_size) + '_perc' + str(perc) + '\\' #add back44 #name = intensitytype + str(tau) + '_hidden' + str(hidden_dim[0]) + '_layers' + str(len(hidden_dim)) + '_learning' + str(learning_rate) + '_batch' + \45 # str(batch_size) + '_perc' + str(perc) + duan_replic + '_' #remove46 #path = intensitytype + str(tau) + '_hidden' + str(hidden_dim[0]) + '_layers' + str(len(hidden_dim)) + '_learning' + str(learning_rate) + '_batch' + \47 # str(batch_size) + '_perc' + str(perc) + duan_replic + '\\' #remove4849 if intensitytype == 'f':50 x_train,y_train,x_test,y_test,_,_ = RealData_f(tau=tau)51 else:52 x_train,y_train,x_test,y_test,_,_ = RealData_h(tau=tau)535455 model = NeuralNetwork(hidden_dim = hidden_dim, deltaT = deltaT, learning_rate = learning_rate,56 feature_size = feature_size, batch_size = batch_size, perc = perc, path = path,name = name)57 self = model5859 # trick to avoid bad initialization (output of first forward propagation algorithm too close to boundary)60 model.initialise()61 _ , ff = model.pred(x_train,y_train)62 gg = 0.00001*np.ones(ff.shape)63 bad_init = np.allclose(ff,gg) # test if predictions are close to fmin boundary. if yes, initialize again64 if bad_init is True:65 while bad_init is True:66 model.initialise()67 _, ff = model.pred(x_train, y_train)68 gg = 0.00001 * np.ones(ff.shape)69 bad_init = np.allclose(ff, gg)7071 # training process72 for e in range(20):73 model.training(x_train,y_train,e)74 in_loss_value, in_f_value = model.pred(x_train,y_train)75 out_loss_value, out_f_value = model.pred_and_write_summary(x_test,y_test,e)76 print('insample-loss:', in_loss_value, '/​/​ outsample-loss:',out_loss_value)7778 #_,_,auc_score, fpr_val, tpr_val = testmodel(model = model, x = x_test,y = y_test, path = directory_roc, save=True) # computes auc score and save roc curve to directory79 perc, cumy, gini = LorenzCurve(model, x_test, y_test, tau, str(model.hidden_dim), color='green', path=directory_lorenz, save=True)8081 #all_auc.append(auc_score) # store auc_score for this particular model82 #all_fpr.append(fpr_val) # store auc_score for this particular model83 #all_tpr.append(tpr_val) # store auc_score for this particular model84 lorenz.append((perc,cumy))85 all_gini.append(gini)8687 tf.reset_default_graph()88 model.sess.close()8990 with open(directory_lorenz + "gini.txt", "w") as f:91 for s in all_gini:92 f.write(str(s) + "\n")9394 with open(directory_lorenz + 'lorenz.pkl', 'wb') as f:95 pickle.dump(lorenz, f)9697 all_auc = []98 lorenz = []99100101102103104105 ...

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lqrflm_load_play.py

Source:lqrflm_load_play.py Github

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1import numpy as np2import math3import gym4env_name = 'Pendulum-v0'5env = gym.make(env_name)6gains = np.loadtxt('./​save_weights/​kalman_gain.txt', delimiter=" ")7T = gains[-1, 0]8T = np.int(T)9Kt = gains[:, 1:4]10kt = gains[:, -1]11i_ang = 180.0*np.pi/​180.012x0 = np.array([math.cos(i_ang), math.sin(i_ang), 0])13bad_init = True14while bad_init:15 state = env.reset() # shape of observation from gym (3,)16 x0err = state - x017 if np.sqrt(x0err.T.dot(x0err)) < 0.1: # x0=(state_dim,)18 bad_init = False19for time in range(T+1):20 env.render()21 Ktt = np.reshape(Kt[time, :], [1, 3])22 action = Ktt.dot(state) + kt[time]23 action = np.clip(action, -env.action_space.high[0], env.action_space.high[0])24 ang = math.atan2(state[1], state[0])25 print('Time: ', time, ', angle: ', ang * 180.0 /​ np.pi, 'action: ', action)26 state, reward, _, _ = env.step(action)...

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