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How to use test_negation method in pyshould

Best Python code snippet using pyshould_python

prepro.py

1# Copyright (c) Microsoft. All rights reserved.2# Modified Copyright by Ubiquitous Knowledge Processing (UKP) Lab, Technische UniversitÃ¤t Darmstadt3import argparse4from data_utils.log_wrapper import create_logger5from data_utils.glue_utils import *6import random7LOW_RESOURCE_DATA_RATES = [0.1, 0.3, 0.7]8bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')9logger = create_logger(__name__, to_disk=True, log_file='bert_data_proc_512.log')10def get_random_indices(dump_path, data, rate):11    rdm_indices = []12    if rate < 1.0:13        with open("data_splits/low_resource_splits/"+dump_path.rsplit("/", 1)[1].split("_")[0]+"_train.csv", "r") as split_file_in:14            rdm_indices = [int(idx) for idx in split_file_in.readline().split(",")[:int(len(data)*rate)]]15    return rdm_indices16def build_data(data, dump_path, rate=1.0, MAX_SEQ_LEN=100, is_train=True, tolower=True):17    """Build data of sentence pair tasks18    """19    if os.path.isfile(dump_path):20        print(dump_path + " already existis. Skipping!")21        return22    rdm_indices = get_random_indices(dump_path, data, rate)23    with open(dump_path, 'w', encoding='utf-8') as writer:24        for idx, sample in tqdm(enumerate(data)):25            if rate < 1.0 and idx not in rdm_indices:26                continue27            ids = sample['uid']28            premise = bert_tokenizer.tokenize(sample['premise'])29            hypothesis = bert_tokenizer.tokenize(sample['hypothesis'])30            label = sample['label']31            if "spelling" in dump_path or "negation" in dump_path: # have already been truncated in preprocessing step32                truncate_len = 512-333            else:34                truncate_len = MAX_SEQ_LEN - 335            truncate_seq_pair(premise, hypothesis, truncate_len)36            input_ids =bert_tokenizer.convert_tokens_to_ids(['[CLS]'] + hypothesis + ['[SEP]'] + premise + ['[SEP]'])37            type_ids = [0] * ( len(hypothesis) + 2) + [1] * (len(premise) + 1)38            features = {'uid': ids, 'label': label, 'token_id': input_ids, 'type_id': type_ids, 'hypothesis': sample['hypothesis'], 'premise': sample['premise']}39            writer.write('{}\n'.format(json.dumps(features)))40def build_data_single(data, dump_path, rate=1.0, MAX_SEQ_LEN=100):41    """Build data of single sentence tasks42    """43    if os.path.isfile(dump_path):44        print(dump_path + " already existis. Skipping!")45        return46    rdm_indices = get_random_indices(dump_path, data, rate)47    with open(dump_path, 'w', encoding='utf-8') as writer:48        for idx, sample in tqdm(enumerate(data)):49            if rate < 1.0 and idx not in rdm_indices:50                continue51            ids = sample['uid']52            premise = bert_tokenizer.tokenize(sample['premise'])53            label = sample['label']54            if "spelling" in dump_path or "negation" in dump_path: # have already been truncated in preprocessing step55                premise = premise[:512 - 3]56            elif len(premise) >  MAX_SEQ_LEN - 3:57                premise = premise[:MAX_SEQ_LEN - 3]58            input_ids =bert_tokenizer.convert_tokens_to_ids(['[CLS]'] + premise + ['[SEP]'])59            type_ids = [0] * ( len(premise) + 2)60            features = {'uid': ids, 'label': label, 'token_id': input_ids, 'type_id': type_ids, 'premise': sample['premise']}61            writer.write('{}\n'.format(json.dumps(features)))62def print_status(train_data, dev_data, test_data, test_negation, test_spelling, test_paraphrase, dataset):63    logger.info('Loaded {0} {1} train samples'.format(len(train_data), dataset))64    logger.info('Loaded {0} {1} dev samples'.format(len(dev_data), dataset))65    logger.info('Loaded {0} {1} test samples'.format(len(test_data), dataset))66    if test_negation != None:67        logger.info('Loaded {0} {1} test negation samples'.format(len(test_negation), dataset))68    if test_spelling != None:69        logger.info('Loaded {0} {1} test spelling samples'.format(len(test_spelling), dataset))70    if test_paraphrase != None:71        logger.info('Loaded {0} {1} test paraphras samples'.format(len(test_paraphrase), dataset))72    logger.info('\n')73def build_handler(train_data, dev_data, test_data, test_negation, test_spelling, test_paraphrase,74                  dataset, mt_dnn_root, single=False, MAX_SEQ_LEN=100):75    build_fn = build_data_single if single == True else build_data76    logger.info('Start converting and storing train/dev/test files for the model')77    train_fout = os.path.join(mt_dnn_root, '{0}_train.json'.format(dataset))78    dev_fout = os.path.join(mt_dnn_root, '{0}_dev.json'.format(dataset))79    test_fout = os.path.join(mt_dnn_root, '{0}_test.json'.format(dataset))80    test_negation_fout = os.path.join(mt_dnn_root, '{0}_test_negation.json'.format(dataset))81    test_spelling_fout = os.path.join(mt_dnn_root, '{0}_test_spelling.json'.format(dataset))82    test_paraphrase_fout = os.path.join(mt_dnn_root, '{0}_test_paraphrase.json'.format(dataset))83    for rate in LOW_RESOURCE_DATA_RATES:84        build_fn(train_data, train_fout.split(".json")[0]+str(int(rate*100))+"p.json", rate, MAX_SEQ_LEN=MAX_SEQ_LEN)85    build_fn(train_data, train_fout)86    build_fn(dev_data, dev_fout)87    build_fn(test_data, test_fout)88    if test_negation != None:89        build_fn(test_negation, test_negation_fout)90    if test_spelling != None:91        build_fn(test_spelling, test_spelling_fout)92    if test_paraphrase != None:93        build_fn(test_paraphrase, test_paraphrase_fout)94    logger.info('done with {0}'.format(dataset))95def parse_args():96    parser = argparse.ArgumentParser(description='Preprocessing datasets.')97    parser.add_argument('--seed', type=int, default=13)98    parser.add_argument('--root_dir', type=str, default='data')99    parser.add_argument('--generate_adversarial', type=int, default=0, help="0 if no adversarial samples should be created, otherwise 1")100    parser.add_argument('--max_seq_len', type=int, default=100)101    parser.add_argument('--opennmt_gpu', type=int, default=-1, help="-1 for CPU, else GPU index.")102    parser.add_argument('--opennmt_batch_size', type=int, default=30)103    args = parser.parse_args()104    return args105def main(args):106    root = args.root_dir107    seed = args.seed108    CREATE_ADVERSARIAL = True if args.generate_adversarial == 1 else False109    MAX_SEQ_LEN = args.max_seq_len110    OPENNMT_GPU = args.opennmt_gpu  # Machine translation on GPU111    OPENNMT_BATCH_SIZE = args.opennmt_batch_size  # Machine translation model batch size112    assert os.path.exists(root)113    random.seed(seed)114    np.random.seed(seed)115    mt_dnn_root = os.path.join(root, 'mt_dnn')116    if not os.path.isdir(mt_dnn_root):117        os.mkdir(mt_dnn_root)118    ######################################119    # Stance Detection Tasks120    ######################################121    snopes_path = os.path.join(root, 'Snopes/snopes_corpus_2.csv')122    argmin_path = os.path.join(root, 'ArgMin/')123    fnc1_path = os.path.join(root, 'FNC-1/')124    arc_path = os.path.join(root, 'ARC/')125    ibm_claim_stance_path = os.path.join(root, 'IBM_CLAIM_STANCE/claim_stance_dataset_v1.csv')126    perspectrum_path = os.path.join(root, 'PERSPECTRUM/')127    semeval2016t6_path = os.path.join(root, 'SemEval2016Task6/')128    semeval2019t7_path = os.path.join(root, 'SemEval2019Task7/')129    scd_path = os.path.join(root, 'SCD/')130    iac1_path = os.path.join(root, 'IAC/')131    ######################################132    # Loading / Building DATA133    ######################################134    scd_train_data, scd_dev_data, scd_test_data, scd_test_negation, scd_test_spelling, scd_test_paraphrase = \135        load_scd(scd_path, GLOBAL_MAP['scd'], create_adversarial=CREATE_ADVERSARIAL, dataset='scd', MAX_SEQ_LEN=MAX_SEQ_LEN,136                 OPENNMT_GPU=OPENNMT_GPU, OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)137    print_status(scd_train_data, scd_dev_data, scd_test_data, scd_test_negation, scd_test_spelling,138                 scd_test_paraphrase, 'scd')139    build_handler(scd_train_data, scd_dev_data, scd_test_data, scd_test_negation,140                  scd_test_spelling, scd_test_paraphrase, 'scd', mt_dnn_root, single=True, MAX_SEQ_LEN=MAX_SEQ_LEN)141    semeval2016t6_train_data, semeval2016t6_dev_data, semeval2016t6_test_data, semeval2016t6_test_negation, \142        semeval2016t6_test_spelling, semeval2016t6_test_paraphrase\143        = load_semeval2016t6(semeval2016t6_path, GLOBAL_MAP['semeval2016t6'], create_adversarial=CREATE_ADVERSARIAL,144                             dataset='semeval2016t6', MAX_SEQ_LEN=MAX_SEQ_LEN,145                             OPENNMT_GPU=OPENNMT_GPU, OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)146    print_status(semeval2016t6_train_data, semeval2016t6_dev_data, semeval2016t6_test_data, semeval2016t6_test_negation,147        semeval2016t6_test_spelling, semeval2016t6_test_paraphrase, 'semeval2016t6')148    build_handler(semeval2016t6_train_data, semeval2016t6_dev_data, semeval2016t6_test_data, semeval2016t6_test_negation,149        semeval2016t6_test_spelling, semeval2016t6_test_paraphrase, 'semeval2016t6', mt_dnn_root, MAX_SEQ_LEN=MAX_SEQ_LEN)150    ibmcs_train_data, ibmcs_dev_data, ibmcs_test_data, ibmcs_test_negation, ibmcs_test_spelling,\151        ibmcs_test_paraphrase = load_ibmcs(ibm_claim_stance_path, GLOBAL_MAP['ibmcs'], create_adversarial=CREATE_ADVERSARIAL,152                                           dataset='ibmcs', MAX_SEQ_LEN=MAX_SEQ_LEN,153                                           OPENNMT_GPU=OPENNMT_GPU, OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)154    print_status(ibmcs_train_data, ibmcs_dev_data, ibmcs_test_data, ibmcs_test_negation,155                 ibmcs_test_spelling, ibmcs_test_paraphrase, 'ibmcs')156    build_handler(ibmcs_train_data, ibmcs_dev_data, ibmcs_test_data, ibmcs_test_negation, ibmcs_test_spelling,157        ibmcs_test_paraphrase, 'ibmcs', mt_dnn_root, MAX_SEQ_LEN=MAX_SEQ_LEN)158    iac1_train_data, iac1_dev_data, iac1_test_data, iac1_test_negation, iac1_test_spelling, iac1_test_paraphrase = \159        load_iac1(iac1_path, GLOBAL_MAP['iac1'], create_adversarial=CREATE_ADVERSARIAL, dataset='iac1', MAX_SEQ_LEN=MAX_SEQ_LEN,160                 OPENNMT_GPU=OPENNMT_GPU, OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)161    print_status(iac1_train_data, iac1_dev_data, iac1_test_data, iac1_test_negation, iac1_test_spelling,162                 iac1_test_paraphrase, 'iac1')163    build_handler(iac1_train_data, iac1_dev_data, iac1_test_data, iac1_test_negation, iac1_test_spelling,164                  iac1_test_paraphrase, 'iac1', mt_dnn_root, MAX_SEQ_LEN=MAX_SEQ_LEN)165    snopes_train_data, snopes_dev_data, snopes_test_data, snopes_test_negation,\166        snopes_test_spelling, snopes_test_paraphrase = load_snopes(snopes_path, GLOBAL_MAP['snopes'],167                                                                  create_adversarial=CREATE_ADVERSARIAL,168                                                                  dataset='snopes', MAX_SEQ_LEN=MAX_SEQ_LEN,169                                                                  OPENNMT_GPU=OPENNMT_GPU, OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)170    print_status(snopes_train_data, snopes_dev_data, snopes_test_data, snopes_test_negation,171                 snopes_test_spelling, snopes_test_paraphrase, 'snopes')172    build_handler(snopes_train_data, snopes_dev_data, snopes_test_data, snopes_test_negation,173        snopes_test_spelling, snopes_test_paraphrase, 'snopes', mt_dnn_root, MAX_SEQ_LEN=MAX_SEQ_LEN)174    arc_train_data, arc_dev_data, arc_test_data, arc_test_negation, arc_test_spelling, arc_test_paraphrase = \175        load_fnc1_arc(arc_path, GLOBAL_MAP['arc'], create_adversarial=CREATE_ADVERSARIAL,176                      dataset='arc', MAX_SEQ_LEN=MAX_SEQ_LEN,177                      OPENNMT_GPU=OPENNMT_GPU, OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)178    print_status(arc_train_data, arc_dev_data, arc_test_data, arc_test_negation, arc_test_spelling,179                 arc_test_paraphrase, 'arc')180    build_handler(arc_train_data, arc_dev_data, arc_test_data, arc_test_negation, arc_test_spelling,181                  arc_test_paraphrase, 'arc', mt_dnn_root, MAX_SEQ_LEN=MAX_SEQ_LEN)182    semeval2019t7_train_data, semeval2019t7_dev_data, semeval2019t7_test_data, semeval2019t7_test_negation, \183        semeval2019t7_test_spelling, semeval2019t7_test_paraphrase\184        = load_semeval2019t7(semeval2019t7_path, GLOBAL_MAP['semeval2019t7'], create_adversarial=CREATE_ADVERSARIAL,185                             dataset='semeval2019t7', MAX_SEQ_LEN=MAX_SEQ_LEN,186                             OPENNMT_GPU=OPENNMT_GPU, OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)187    print_status(semeval2019t7_train_data, semeval2019t7_dev_data, semeval2019t7_test_data, semeval2019t7_test_negation,188        semeval2019t7_test_spelling, semeval2019t7_test_paraphrase, 'semeval2019t7')189    build_handler(semeval2019t7_train_data, semeval2019t7_dev_data, semeval2019t7_test_data, semeval2019t7_test_negation,190        semeval2019t7_test_spelling, semeval2019t7_test_paraphrase, 'semeval2019t7', mt_dnn_root, single=True, MAX_SEQ_LEN=MAX_SEQ_LEN)191    perspectrum_train_data, perspectrum_dev_data, perspectrum_test_data, perspectrum_test_negation, \192        perspectrum_test_spelling, perspectrum_test_paraphrase = load_perspectrum(perspectrum_path, GLOBAL_MAP['perspectrum'],193                                                                                 create_adversarial=CREATE_ADVERSARIAL,194                                                                                 dataset='perspectrum', MAX_SEQ_LEN=MAX_SEQ_LEN,195                                                                                 OPENNMT_GPU=OPENNMT_GPU,196                                                                                 OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)197    print_status(perspectrum_train_data, perspectrum_dev_data, perspectrum_test_data, perspectrum_test_negation,198        perspectrum_test_spelling, perspectrum_test_paraphrase, 'perspectrum')199    build_handler(perspectrum_train_data, perspectrum_dev_data, perspectrum_test_data, perspectrum_test_negation,200        perspectrum_test_spelling, perspectrum_test_paraphrase, 'perspectrum', mt_dnn_root, MAX_SEQ_LEN=MAX_SEQ_LEN)201    argmin_train_data, argmin_dev_data, argmin_test_data, argmin_test_negation, \202        argmin_test_spelling, argmin_test_paraphrase = load_argmin(argmin_path, GLOBAL_MAP['argmin'],203                                                                  create_adversarial=CREATE_ADVERSARIAL,204                                                                  dataset='argmin', MAX_SEQ_LEN=MAX_SEQ_LEN,205                                                                  OPENNMT_GPU=OPENNMT_GPU, OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)206    print_status(argmin_train_data, argmin_dev_data, argmin_test_data, argmin_test_negation,207        argmin_test_spelling, argmin_test_paraphrase, 'argmin')208    build_handler(argmin_train_data, argmin_dev_data, argmin_test_data, argmin_test_negation,209        argmin_test_spelling, argmin_test_paraphrase, 'argmin', mt_dnn_root, MAX_SEQ_LEN=MAX_SEQ_LEN)210    fnc1_train_data, fnc1_dev_data, fnc1_test_data, fnc1_test_negation, fnc1_test_spelling,\211        fnc1_test_paraphrase = load_fnc1_arc(fnc1_path, GLOBAL_MAP['fnc1'], create_adversarial=CREATE_ADVERSARIAL,212                                             dataset='fnc1', MAX_SEQ_LEN=MAX_SEQ_LEN,213                                             OPENNMT_GPU=OPENNMT_GPU, OPENNMT_BATCH_SIZE=OPENNMT_BATCH_SIZE)214    print_status(fnc1_train_data, fnc1_dev_data, fnc1_test_data, fnc1_test_negation,215                 fnc1_test_spelling, fnc1_test_paraphrase, 'fnc1')216    build_handler(fnc1_train_data, fnc1_dev_data, fnc1_test_data, fnc1_test_negation, fnc1_test_spelling,217        fnc1_test_paraphrase, 'fnc1', mt_dnn_root, MAX_SEQ_LEN=MAX_SEQ_LEN)218if __name__ == '__main__':219    args = parse_args()...

scanners_improved.py

Source: scanners_improved.py

...39            new_beacon[i] += translations[i]40        beacons[index] = new_beacon41    scanners[unknown_scanner]['position'] = translations42    return scanners43def apply_test_negation(test_negation, control_known_coordinates,44        control_unknown_coordinates, test_coordinates):45        altered_test_coordinates = test_coordinates46        translations = []47        for index, i in enumerate(test_negation):48            if i == '1':49                altered_test_coordinates[index] *= -150                control_unknown_coordinates[index] *= -151            translations.append(control_known_coordinates[index] - control_unknown_coordinates[index])52            altered_test_coordinates[index] = test_coordinates[index] + translations[index]53        return altered_test_coordinates, translations54def apply_movements(movements, coordinates):55    new_coordinates = deque(maxlen=3)56    for index in range(3):57        new_coordinates.append(coordinates[movements[index]])58    59    return new_coordinates60def get_negations_and_translations(scanners, adjustments, known_scanner,61        unknown_scanner, control_known_beacon, control_unknown_beacon,62        test_known_beacon, test_unknown_beacon):63    control_known_beacon_coordinates = scanners[known_scanner]['beacons'][control_known_beacon]64    control_unknown_beacon_coordinates = scanners[unknown_scanner]['beacons'][control_unknown_beacon]65    test_known_beacon_coordinates = scanners[known_scanner]['beacons'][test_known_beacon]66    test_unknown_beacon_coordinates = scanners[unknown_scanner]['beacons'][test_unknown_beacon]67    movements = adjustments['movements']68    moved_control_unknown_beacon_coordinates = apply_movements(movements, control_unknown_beacon_coordinates)69    moved_test_unknown_beacon_coordinates = apply_movements(movements, test_unknown_beacon_coordinates)70    for negation in range(8):71        test_negation = bin(negation)[2:].zfill(3)72        test_coordinates, translations = apply_test_negation(73            test_negation, control_known_beacon_coordinates,74            deepcopy(moved_control_unknown_beacon_coordinates),75            deepcopy(moved_test_unknown_beacon_coordinates))76        if test_coordinates == test_known_beacon_coordinates:77            adjustments['negations'] = test_negation78            adjustments['translations'] = translations79            break80    81    if not adjustments.get('negations', ''):82        print('Did not find negations, please interrupt program')83    return adjustments84def get_movements(scanners, diff_to_evaluate, known_scanner,85        known_scanner_index, unknown_scanner, unknown_scanner_index):86    movements = {}...

test_ldap3_query_classes.py

Source: test_ldap3_query_classes.py

...32		'''33		Test query join with a two elements34		'''35		self.assertEqual(str(self.test_object('|', '(givenName=John)', '(objectClass=*)', '(targetAttribute>=10)', '(cn=*John*Doe*)', '(givenName~=John)')), '(|(givenName=John)(objectClass=*)(targetAttribute>=10)(cn=*John*Doe*)(givenName~=John))')36	def test_negation(self):37		'''38		Test query join negation39		'''40		self.assertEqual(-self.test_object('|', '(givenName=John)', '(objectClass=*)', '(targetAttribute>=10)', '(cn=*John*Doe*)', '(givenName~=John)'), '(!(|(givenName=John)(objectClass=*)(targetAttribute>=10)(cn=*John*Doe*)(givenName~=John)))')41class TestQueryAssertion(unittest.TestCase):42	'''43	Tests for the QueryAssertion class44	'''45	46	def setUp(self):47		self.test_object = ldap3_directories.ldap3_.QueryAssertion48		self.maxDiff = None49	def test_implicit_equality(self):50		'''51		Test query assertion with implicit equality52		'''53		self.assertEqual(self.test_object('givenName', 'John'), '(givenName=John)')54	def test_explicit_equality(self):55		'''56		Test query assertion with explicit equality57		'''58		self.assertEqual(self.test_object('givenName', 'John', '='), '(givenName=John)')59	def test_presence(self):60		'''61		Test query assertion for presence62		'''63		self.assertEqual(self.test_object('objectClass', '*'), '(objectClass=*)')64	def test_greater_or_equal(self):65		'''66		Test query assertion with greater or equal67		'''68		self.assertEqual(self.test_object('targetAttribute', 10, '>='), '(targetAttribute>=10)')69	def test_less_or_equal(self):70		'''71		Test query assertion with less or equal72		'''73		self.assertEqual(self.test_object('targetAttribute', 10, '<='), '(targetAttribute<=10)')74	def test_substring(self):75		'''76		Test query assertion with substring77		'''78		self.assertEqual(self.test_object('cn', '*John*Doe*'), '(cn=*John*Doe*)')79	def test_approximate_match(self):80		'''81		Test query assertion with approximate match82		'''83		self.assertEqual(self.test_object('givenName', 'John', '~='), '(givenName~=John)')84	def test_negation(self):85		'''86		Test query assertion negated87		'''...

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