How to use bad_init method in pytest-cov

Best Python code snippet using pytest-cov

test_chinese.py

Source:test_chinese.py

1import pickle2from hanziconv import HanziConv3from revscoring.datasources import revision_oriented4from revscoring.languages import chinese5from .util import compare_extraction6bad_init = [7    "çå«è",  # son of a bitch8    "ä»åª½ç",  # "his mother's"9    "å»ä½ åª½",  # "to your mother"10    "å»ä½ ç",  # "to yours"11    "å©å", "å¦å¥³",  # prostitute12    "æ¥ç", "æ¥äºç",  # lonely dog13    "å±ç¼", "æ··è", "æ¸¾è",  # asshole14    "æ··å¸³",  # variant of above15    "çå«",  # bitch16    "ç½ç¡",  # idiot17    "è¦æ®",  # brain dead18    "æºé",  # mentally retarded19    "å©", "å¦",  # prostitute20    "å±",  # shit21    "å±",  # dick22    "å¦é¼",  # (this is verbal but definitely bad)23    "è¹", "è",  # fuck (in any context)24    "æ¾å±",  # fart25    # Variants (homonyms) of the use of "fuck" that use æ ("operation") and26    # è ("grass"), "è" is the actual character.  "è¹" is not a real character27    # but it's used this way28    "æä½ ", "èä½ ", "æ¥ä½ ",  # fuck you29    "æä»", "èä»", "æ¥ä»",  # fuck his30    "æå¥¹", "èå¥¹", "æ¥å¥¹",  # fuck her31    # Discrimination (racial slurs)32    "å°æ¥æ¬",  # little Japanese33    "å°æ¹¾ç",  # Taiwanese dogs34    "å±äº§ä¸å½",  # communist Chinese35    "æµæ°å½å®¶",  # rogue country36    "äººæ¸£",  # human slag37    "æå»",  # this is verbal and bad38    "é¬¼å"  # 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    "ä½ å¥½",  # nÇ hÇo; The standard "hello" greeting.45    "æ¨å¥½",  # nÃn hÇo; The same "hello" greeting as above46    "ä½ æä¹æ ·",  # nÇ zÄnmeyÃ ng?; "What's up?", "How are you doing?"47    # Good afternoon48    "åå®",  # wÇ'an; note: seldom used in the Mainland.49    "ä¸åå¥½",  # xÃ¬awÇ hÇo! Seldom used in the Republic of China50    # Good evening / Good night51    "æå®",  # wÇn'an; Literally "Peace at night", Good night.52    "æä¸å¥½",  # wÇnshang hÇo; Good evening!53    # Good-bye54    "åè¦",  # zÃ ijian; Literally "See you again".55    "æå¤©è¦",  # mÃngtian jiÃ n; Literally "See you tomorrow".56    "ææ",  # bÄibÄi/bÃ¡ibÃ¡i; From English "Bye-Bye".57    "åé è¦",  # huÃtÃ³ujiÃ n: roughly equivalent to "see you soon"58    "åè¦",  # huÃjiÃ n; usually used in Beijing or written Chinese.59    "åæ",  # zÃ ihuÃ¬: Literally "[we'll] hello again".60    "66666666", "666",61    "233", "2333333"62]63WORDS_TO_WATCH = [64    # Advertising language65    "æ¬å°",  # this channel66    "æ¬å¬å¸",  # this company67    "ä»£å·", "ä»£ç»", "ä»£æ¢",  # someone who plays games for you68    "å¼ºå¿åå½",  # "mightly" return69    "è¶å¼",  # very cost-effective70    "ä¸æ¡é¾",  # a proverb? "one line of dragon"71    "ä¸å¤æ",  # selling one's body (advertising)72    "ä¸çä¸æµ", "å½éä¸æµ",  # world first-class73    "ç¨æ·ç¬¬ä¸", "ç¨æ·æ»¡æ", "ç¨æ·è³ä¸",  # customer-first74    "æ ¸å¿ä»·å¼", "æ ¸å¿å¢é", "æ ¸å¿å®æ¨",  # core value75    "æå¡å°å§",  # service lady76    "æå¡èå´",  # service area77    "æå¡é¡¹ç®",  # service items78    "æå¡çå¿µ",  # service philosophy79]80OTHER = [81    """2005å¹´å¤§è¥¿æ´é¢¶é¢¨å£æ¯æçºªå½ä»¥æ¥ææ´»è·çå¤§è¥¿æ´é¢¶é¢¨å£ï¼è³ä»ä»ä¿æçå¤é¡¹çºªå½ã82    å¨å£å¯¹å¤§èå´å°åºé ææ¯çæ§æå»ï¼å±å¯¼è´3,913äººæ»äº¡ï¼æå¤±æ°é¢æ´åä¸æ°çºªå½ï¼é«è¾¾1592äº¿ç¾åã83    æ¬å£åå¤§åé£é£å°±æ7åºä¹å¤ï¼å¶ä¸5åºå¨ç»éæ¶ä»æå¤§åé£é£å¼ºåº¦ï¼åå«æ¯é¢¶é¢¨ä¸¹å°¼æ¯ãè¾ç±³èã84    å¡ç¹éå¨ãä¸½å¡åå¨å°çï¼å¤§é¨åäººåä¼¤äº¡åè´¢äº§æå¤±é½æ¯è¿5åºé£é£å¼èµ·ã85    å¢¨è¥¿å¥çéå¡ç´ç¾å¥§å·åå°¤å¡å¦å·ï¼86    ä»¥åç¾å½çä½ç½éè¾¾å·åè·¯ææ¯å®é£å·é½æ¾ä¸¤åº¦åå¤§åé£é£è¢å»ï¼å¤å·´ãå·´åé©¬ãæµ·å°ï¼87    ç¾å½çå¯è¥¿è¥¿æ¯å·åå¾åè¨æ¯å·ï¼è¿æå¢¨è¥¿å¥çå¡æ¯å©å¸æ¯å·é½æ¾ç´æ¥å1åºå¤§åé£é£å²å»ï¼88    è¿æè³å°1åºå¨éè¿æ è¿ãç¾åå¢¨è¥¿å¥ç£æ²¿å²¸å°åæ¯æ¬å£åç¾æä¸¥éçæå¨ï¼89    é£é£å¡ç¹éå¨äº§çé«è¾¾10ç±³çé£æ´æ½®ï¼å¼åæ¯çæ§æ´ªç¾ï¼å¯è¥¿è¥¿æ¯å·æ²¿æµ·å°åºçå¤§é¨åå»ºçç©è¢«æ¯ï¼90    é£æ´ä¹ååä»¤æ°å¥¥å°è¯é²æ´ªå ¤å³å£ï¼æ´ä¸ªåå¸å æ¤åå°éåãæ¤å¤ï¼é£é£æ¯å¦åæº«å¸¶æ°£æå±åå½±åï¼91    å¨ä¸ç¾æ´²å¤å°å¼åè´å½çæ³¥ç³æµï¼å¶ä¸åä»¥å±å°é©¬æç¾ææä¸ºä¸¥éã"""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 == \...

learning.py

Source:learning.py

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
...

lqrflm_load_play.py

Source:lqrflm_load_play.py

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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