How to use save_file_as method in SeleniumBase

Best Python code snippet using SeleniumBase

util.py

Source:util.py

1from collections import defaultdict2from collections import OrderedDict3from os import path4import logging5import numpy as np6import torch7from properties import *8def init_logger(name='logger'):9    """Initialize and return a logger."""10    logger = logging.getLogger(name)11    logger.setLevel(logging.DEBUG)12    log_fmt = '%(asctime)s/%(name)s[%(levelname)s]: %(message)s'13    logging.basicConfig(format=log_fmt)14    return logger15def to_tensor(numpy_array):16    tensor = torch.from_numpy(numpy_array).float()17    if torch.cuda.is_available():18        tensor = tensor.cuda()19    return tensor20def to_variable(tensor, volatile=False):21    if torch.cuda.is_available():22        tensor = tensor.cuda()23    return torch.autograd.Variable(tensor, volatile)24def get_word_vectors_dicts(filename, dirname=DATA_DIR, save=False,25                           save_file_as='en_dict'):26    word2vec = OrderedDict()27    count = 028    print('Read ' + path.join(dirname, filename))29    with open(path.join(dirname, filename), 'r', encoding='utf-8') as f:30        N, D = f.readline().strip().split()31        D = int(D)32        for count, line in enumerate(f.readlines()):33            row = line.strip().split(' ')34            word = row[0]35            vec = np.array(row[1:]).astype(np.float)36            if len(vec) == D:37                word2vec[word] = vec38            count += 139            if count == top_frequent_words:40                break41    if save:42        np.save(path.join(dirname, save_file_as + '.npy'), word2vec)43    return word2vec44def get_validation_set(filename, dirname=DATA_DIR, save=False, save_file_as='validation'):45    true_dict = OrderedDict()46    print('Read ' + path.join(dirname, filename))47    with open(path.join(dirname, filename), 'r', encoding='utf-8') as f:48        rows = f.readlines()49        for row in rows:50            split_row = row.split(" ")51            key = split_row[0]52            value = split_row[1].rstrip("\n")53            if key not in true_dict.keys():54                true_dict[key] = []55                true_dict[split_row[0]].append(value)56    if save:57        np.save(path.join(dirname, save_file_as + '.npy'), true_dict)58    return true_dict59def get_frequencies():60    src = np.load(path.join(DATA_DIR, 'src.freq.npy'))61    trg = np.load(path.join(DATA_DIR, 'tgt.freq.npy'))62    return src, trg63class WeightedSampler():64    def __init__(self, weights, replacement=True):65        """Initialize a sampler.66        Arguments:67        - weights (list)   : a list of weights, not necessary summing up to one68        - replacement (bool): if ``True``, samples are drawn with replacement.69        """70        self.weights = torch.DoubleTensor(weights)71        self.replacement = replacement72    def get_iterator(self, batch_size):73        """Generate a batch of samples infinitely."""74        while True:75            yield torch.multinomial(self.weights, batch_size, self.replacement)76        77def downsample_frequent_words(counts, thresh=1e-3):78    """Discount frequent words."""79    total_count = counts.sum()80    indices_zero = counts == 081    counts[indices_zero] = 1.0  # avoid a numerical error82    threshold_count = float(thresh * total_count)83    new_weights = (np.sqrt(counts / threshold_count) + 1) * (threshold_count / counts)84    new_weights = np.maximum(new_weights, 1.0)85    new_weights *= counts86    new_weights[indices_zero] = 0.087    return new_weights / new_weights.sum()88class Utils:89    def __init__(self, params):90        self.data_dir = params.data_dir91        self.src_file = params.src_file92        self.tgt_file = params.tgt_file93        self.src_freq_file = params.src_freq_file94        self.tgt_freq_file = params.tgt_freq_file95        self.validation_file = params.validation_file96        self.top_frequent_words = params.top_frequent_words97    def run(self):98        print("Reading source word embeddings...")99        word2vec_src, _ = self.save_word_vectors(self.src_file, self.src_freq_file,100                                                 save=True, save_file_as='src')101        print("Done.")102        print(word2vec_src.shape)103        print("Reading target word embeddings...")104        word2vec_tgt, _ = self.save_word_vectors(self.tgt_file, self.tgt_freq_file,105                                                 save=True, save_file_as='tgt')106        print("Done.")107        print(word2vec_tgt.shape)108        print("Reading validation file...")109        self.save_validation_set(self.validation_file, save=True)110        print("Done.")111        print("Constructing source word-id map...")112        self.save_word_ids_dicts(self.src_file, save=True, save_file_as='src_ids')113        print("Done.")114        print("Constructing target word-id map...")115        self.save_word_ids_dicts(self.tgt_file, save=True, save_file_as='tgt_ids')116        print("Everything Done.")117    def save_word_vectors(self, emb_file, freq_file=None, save=False, save_file_as='src'):118        word2freq = defaultdict(int)119        if freq_file:120            with open(path.join(self.data_dir, freq_file), 'r', encoding='utf-8') as f:121                for line in f:122                    word, freq = line.strip().split(' ')123                    word2freq[word] = int(freq)124        embeddings, freqs = [], []125        print('Read ' + path.join(self.data_dir, emb_file))126        with open(path.join(self.data_dir, emb_file), 'r', encoding='utf-8') as f:127            N, D = f.readline().strip().split()128            D = int(D)129            for count, line in enumerate(f, start=1):130                row = line.strip().split(' ')131                word = row[0]132                vec = np.array(row[1:]).astype(np.float)133                if len(vec) == D:134                    embeddings.append(vec)135                    freqs.append(word2freq[word])136                if count == top_frequent_words:137                    break138            if save:139                np.save(path.join(self.data_dir, save_file_as + '.npy'),140                        np.array(embeddings))141                np.save(path.join(self.data_dir, save_file_as + '.freq.npy'),142                        np.array(freqs))143        return np.array(embeddings), freqs144    def save_word_ids_dicts(self, file, save=False, save_file_as='src_ids'):145        word2id = {}146        count = 0147        with open(path.join(self.data_dir, file), 'r', encoding='utf-8') as f:148            _, dim = f.readline().strip().split()149            dim = int(dim)150            for row in f:151                split_row = row.strip().split(" ")152                vec = np.array(split_row[1:]).astype(np.float)153                if len(vec) == dim:154                    word2id[split_row[0]] = count155                count += 1156                if count == self.top_frequent_words:157                    break158        if save:159            np.save(self.data_dir + save_file_as + '.npy', word2id)160        return word2id161    def save_validation_set(self, file, save=False, save_file_as='validation'):162        true_dict = {}163        with open(self.data_dir + file, 'r', encoding='utf-8') as f:164            rows = f.readlines()165            for row in rows:166                split_row = row.split(" ")167                key = split_row[0]168                value = split_row[1].rstrip("\n")169                if key not in true_dict.keys():170                    true_dict[key] = []171                true_dict[split_row[0]].append(value)172        if save:173            np.save(self.data_dir + save_file_as + '.npy', true_dict)174        return true_dict175def load_npy_one(data_dir, fname):176    return np.load(data_dir + fname).item()177def load_npy_two(data_dir, src_fname, tgt_fname, dict=False):178    if dict:179        x = np.load(data_dir + src_fname).item()180        y = np.load(data_dir + tgt_fname).item()181    else:182        x = np.load(data_dir + src_fname)183        y = np.load(data_dir + tgt_fname)184    return x, y185# Validation set in a dictionary form {src_wrd: [tgt_wrd_1, tgt_wrd_2, ...]}186def get_validation_set_ids(data_dir, validation_fname='validation.npy'):187    val_dict = load_npy_one(data_dir, validation_fname)188    src_ids, tgt_ids = load_npy_two(data_dir, 'src_ids.npy', 'tgt_ids.npy', dict=True)189    val_dict_ids = {}190    for src_wrd, tgt_list in val_dict.items():191        val_dict_ids[src_ids[src_wrd]] = [tgt_ids[tgt_wrd] for tgt_wrd in tgt_list192                                          if tgt_wrd in tgt_ids]193    return val_dict_ids194def convert_to_embeddings(emb_array):195    emb_tensor = to_tensor(emb_array)196    v, d = emb_tensor.size()197    emb = torch.nn.Embedding(v, d)198    if torch.cuda.is_available():199        emb = emb.cuda()200    emb.weight.data.copy_(emb_tensor)201    emb.weight.requires_grad = False...

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