diff --git a/swh/langdetect/cnn.py b/swh/langdetect/cnn.py index 8bcbced..b250a02 100644 --- a/swh/langdetect/cnn.py +++ b/swh/langdetect/cnn.py @@ -1,265 +1,263 @@ import os import sys import subprocess import time import random import csv import numpy as np import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") import tensorflow as tf import json import argparse from ast import literal_eval from pickle import dump from pickle import load from numpy import array from .utils.common import tokenizer from .utils.common import file_to_string from keras.preprocessing.sequence import pad_sequences from keras.callbacks import EarlyStopping from keras.models import Model from keras.models import Sequential from keras.models import load_model from keras.layers import Input from keras.layers import Dense from keras.layers import Flatten from keras.layers import Dropout from keras.layers import ThresholdedReLU from keras.layers import Activation from keras.layers import Lambda from keras.layers import Embedding from keras.layers.convolutional import Convolution1D from keras.layers.convolutional import MaxPooling1D from keras.layers.normalization import BatchNormalization from keras.utils import np_utils from keras.optimizers import SGD def main(): parser = argparse.ArgumentParser(description='Training and test tool of charactor-level ConvNet text categorisation.') subparsers = parser.add_subparsers(dest='sub_command') parser_train = subparsers.add_parser('train', help='Training on the dataset, dataset must be a *.csv file. A model will be created in the same directory.') parser_train.add_argument('train_path', metavar='PATH', type=str, help='Path of the training dataset.') parser_train.add_argument('-ms', '--maxsize', metavar='SIZE', dest='train_maxsize', type=int, help='Set maximum input size of ConvNet, default 1024.') parser_train.add_argument('-e', '--epochs', metavar='N', dest='train_epochs', type=int, help='Number of training epochs (iterations), default 50.') parser_test = subparsers.add_parser('test', help='Test on the dataset, dataset must be a directory with *.csv dataset named by corresponding language.') parser_test.add_argument('test_root', metavar='ROOT', type=str, help='Root of the test dataset.') if len(sys.argv[1:]) == 0: parser.print_help() parser.exit() args = parser.parse_args() - - print(args) if args.sub_command == 'train' : if args.train_maxsize: if args.train_epochs: n = CNN(args.train_path, maxsize=args.train_maxsize, epochs=args.train_epochs) n.train() else: n = CNN(args.train_path, maxsize=args.train_maxsize) n.train() else: if args.train_epochs: n = CNN(args.train_path, epochs=args.train_epochs) n.train() else: n = CNN(args.train_path) n.train() elif args.sub_command == 'test': n = CNN(args.test_root) n.test() else: parser.parse_args('-h') class CNN: def __init__(self, path, maxsize=1024, epochs=50): self._path = path # Root of model folder self._root_model = os.path.join(os.path.dirname(path), 'model_cnn') try: os.mkdir(self._root_model) except: pass # Path of result self._path_result = os.path.join(os.path.dirname(path), 'result_cnn') dir_path = os.path.dirname(os.path.abspath(__file__)) with open(os.path.join(dir_path, 'static_data', 'languages.json'), 'r') as f: self._languages = json.load(f) self._path_test_csv = path self._input_size = maxsize self._vocab_size = 256 self._num_of_classes = len(self._languages) self._batch_size = 128 self._epochs = epochs def file_len(self, fname): with open(fname) as f: count = 0 for l in f: count += 1 return count def train(self): model = self._get_model() - earlystop = EarlyStopping(monitor='loss', min_delta=0, patience=1, verbose=0, mode='auto') + earlystop = EarlyStopping(monitor='loss', min_delta=0, patience=2, verbose=0, mode='auto') callbacks = [earlystop] model.fit_generator( self._generator(self._input_size, self._num_of_classes, self._batch_size), steps_per_epoch=self.file_len(self._path) / self._batch_size, epochs=self._epochs, callbacks=callbacks) model.save(os.path.join(self._root_model, 'model.h5')) def _generator(self, length, total_class, batch_size=128): counter = 0 while True: with open(self._path, newline='') as csvfile: r = csv.reader(csvfile, delimiter=' ', quotechar='|') for pair in r: if counter == 0: X = np.empty((0, length)) Y = np.empty((0, total_class)) label, string = pair label = int(label) string = literal_eval(string) tokens = [x + 1 for x in tokenizer(string, 'letter')] X = np.append(X, pad_sequences([tokens], maxlen=length), axis=0) label = array(np_utils.to_categorical([label], total_class)) Y = np.append(Y, label, axis=0) counter += 1 if counter == batch_size: counter = 0 yield(X,Y) def _get_model(self): input_size = self._input_size alphabet_size = self._vocab_size embedding_size = 256 conv_layers = [(256,7,3), (256,7,3), (256,3,-1), (256,3,-1), (256,3,-1), (256,3,3)] threshold = 1e-6 fully_connected_layers = [1024, 1024] dropout_p = 0.2 optimizer = 'adam' loss = 'categorical_crossentropy' num_of_classes = self._num_of_classes # Input layer inputs = Input(shape=(input_size,), name='sent_input', dtype='int64') # Embedding layers x = Embedding(alphabet_size + 1, embedding_size, input_length=input_size)(inputs) # Convolution layers for cl in conv_layers: x = Convolution1D(cl[0], cl[1])(x) x = ThresholdedReLU(threshold)(x) if cl[2] != -1: x = MaxPooling1D(cl[2])(x) x = Flatten()(x) # Fully connected layers for fl in fully_connected_layers: x = Dense(fl)(x) x = ThresholdedReLU(threshold)(x) x = Dropout(dropout_p)(x) # Output layer predictions = Dense(num_of_classes, activation='softmax')(x) # Build and compile model model = Model(inputs=inputs, outputs=predictions) model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy']) print(model.summary()) return model def _max_len(self, texts): return max([len(text) for text in texts]) def test(self): csv.field_size_limit(sys.maxsize) try: r = open(self._path_result, 'rb') test_result = load(r) r.close() except FileNotFoundError: test_result = {} model = self._load_model() for language in [x for x in self._languages if x not in test_result.keys()]: test_result[language] = self.test_class(model, language) with open(self._path_result, 'wb') as f: dump(test_result, f) def _load_model(self): model = load_model(os.path.join(self._root_model, 'model.h5')) return model def _count_size(self, files): size = 0 for f in files: size += os.path.getsize(f) return size def test_class(self, model, language): ok = 0 results = [] count = 0 total_test = self.file_len(os.path.join(self._path_test_csv, language + '.csv')) with open(os.path.join(self._path_test_csv, language + '.csv'), newline='') as csvfile: r = csv.reader(csvfile, delimiter=' ', quotechar='|') for pair in r: label, string = pair label = int(label) string = literal_eval(string) tokens = [x + 1 for x in tokenizer(string, 'letter')] result = self._guess_file_language(model, tokens) count += 1 print('[{0:4d}/{1:4d}] {2}:{3} '.format(count, total_test, result[0][1], result[0][0]),end='\r') results.append(result[0]) if result[0][1] == language: ok += 1 accuracy = ok / total_test print('Tests for {} '.format(language)) print('Total test files : {}'.format(total_test)) print('Correctly classified files : {}'.format(ok)) print('Accuracy : {}%'.format(accuracy * 100)) return (ok, total_test, accuracy, results) def speed_benchmark(self): language = self._languages[10] model = self._load_model() test_set = self._get_test_set(language) total_size = self._count_size(test_set) print('{} kB in total'.format(total_size / 1024)) t_start = time.perf_counter() self.test_class(model, language) t_end = time.perf_counter() print('{} seconds.'.format(t_end - t_start)) print('{} seconds per KiB'.format(((t_end - t_start) / total_size) * 1024)) def _guess_file_language(self, model, tokens): X = pad_sequences([tokens], maxlen=self._input_size) result = list(model.predict(X))[0] result = [(s, self._languages[i]) for i, s in enumerate(result)] return sorted(result, reverse=True) if __name__ == '__main__': main() diff --git a/swh/langdetect/cnn_w.py b/swh/langdetect/cnn_w.py index 8206e93..4dad105 100644 --- a/swh/langdetect/cnn_w.py +++ b/swh/langdetect/cnn_w.py @@ -1,301 +1,300 @@ import os import sys import subprocess import time import random import csv import numpy as np import warnings with warnings.catch_warnings(): warnings.simplefilter("ignore") import tensorflow as tf import json import argparse from ast import literal_eval from pickle import dump from pickle import load from numpy import array from .utils.common import tokenizer from .utils.common import file_to_string from keras.preprocessing.sequence import pad_sequences from keras.callbacks import EarlyStopping from keras.models import Model from keras.models import Sequential from keras.models import load_model from keras.layers import Input from keras.layers import Dense from keras.layers import Flatten from keras.layers import Merge from keras.layers import Dropout from keras.layers import ThresholdedReLU from keras.layers import Activation from keras.layers import Lambda from keras.layers import Embedding from keras.layers.convolutional import Convolution1D from keras.layers.convolutional import MaxPooling1D from keras.layers.normalization import BatchNormalization from keras.layers import Concatenate from keras.utils import np_utils from keras.optimizers import SGD from collections import Counter from keras import backend as K K.set_session(K.tf.Session(config=K.tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1))) def main(): parser = argparse.ArgumentParser(description='Training and test tool of charactor-level ConvNet text categorisation.') subparsers = parser.add_subparsers(dest='sub_command') parser_train = subparsers.add_parser('train', help='Training on the dataset, dataset must be a *.csv file. A model will be created in the same directory.') parser_train.add_argument('train_path', metavar='PATH', type=str, help='Path of the training dataset.') parser_train.add_argument('-ms', '--maxsize', metavar='SIZE', dest='train_maxsize', type=int, help='Set maximum input size of ConvNet, default 1024.') parser_train.add_argument('-e', '--epochs', metavar='N', dest='train_epochs', type=int, help='Number of training epochs (iterations), default 50.') parser_test = subparsers.add_parser('test', help='Test on the dataset, dataset must be a directory with *.csv dataset named by corresponding language.') parser_test.add_argument('test_root', metavar='ROOT', type=str, help='Root of the test dataset.') if len(sys.argv[1:]) == 0: parser.print_help() parser.exit() args = parser.parse_args() - print(args) - + if args.sub_command == "train": if args.train_maxsize: if args.train_epochs: n = CNNword(args.train_path, maxsize=args.train_maxsize, epochs=args.train_epochs) n.train() else: n = CNNword(args.train_path, maxsize=args.train_maxsize) n.train() else: if args.train_epochs: n = CNNword(args.train_path, epochs=args.train_epochs) n.train() else: n = CNNword(args.train_path) n.train() elif args.sub_command == "test": n = CNNword(args.test_root) print(args.test_root) n.test() else: parser.parse_args('-h') class CNNword: - def __init__(self, path, maxsize=300, epochs=30): + def __init__(self, path, maxsize=768, epochs=30): self._path = path # Root of model folder self._root_model = os.path.join(os.path.dirname(path), 'model_cnn_word') try: os.mkdir(self._root_model) except: pass # Path of result self._path_result = os.path.join(os.path.dirname(path), 'result_cnn_word') dir_path = os.path.dirname(os.path.abspath(__file__)) with open(os.path.join(dir_path, 'static_data', 'languages.json'), 'r') as f: self._languages = json.load(f) self._path_test_csv = path self._path_vocab = os.path.join(self._root_model, 'vocab') self._input_size = maxsize - self._vocab_size = 15001 + self._vocab_size = 20001 self._num_of_classes = len(self._languages) self._batch_size = 64 self._epochs = epochs if not os.path.isfile(self._path_vocab): self._learn_vocab(self._input_size, self._num_of_classes) with open(self._path_vocab, 'rb') as f: c = load(f) - l = c.most_common(15000) + l = c.most_common(20000) + print(l) self._indexer = dict((v[0], i + 1) for i, v in enumerate(l)) self._oov_index = len(self._indexer) + 1 def file_len(self, fname): with open(fname) as f: count = 0 for l in f: count += 1 return count def train(self): model = self._get_model() - earlystop = EarlyStopping(monitor='loss', min_delta=0, patience=1, verbose=0, mode='auto') + earlystop = EarlyStopping(monitor='loss', min_delta=0, patience=2, verbose=0, mode='auto') callbacks = [earlystop] model.fit_generator( - self._generator(self._input_size, self._num_of_classes, self._indexer, self._batch_size), + self._generator(self._input_size, self._num_of_classes, self._batch_size), steps_per_epoch=self.file_len(self._path) / self._batch_size, epochs=self._epochs, callbacks=callbacks) model.save(os.path.join(self._root_model, 'model.h5')) def _learn_vocab(self, length, total_class): c = Counter() with open(self._path, newline='') as csvfile: r = csv.reader(csvfile, delimiter=' ', quotechar='|') for pair in r: label, string = pair label = int(label) - print(label) + print(label, end='\r') string = literal_eval(string) tokens = tokenizer(string, 'word') c.update(tokens) with open(self._path_vocab, 'wb') as f: dump(c, f) def _generator(self, length, total_class, batch_size=64): counter = 0 while True: with open(self._path, newline='') as csvfile: r = csv.reader(csvfile, delimiter=' ', quotechar='|') for pair in r: if counter == 0: X = np.empty((0, length)) Y = np.empty((0, total_class)) label, string = pair label = int(label) string = literal_eval(string) - tokens = [self._indexer.get(x, oov_index) for x in tokenizer(string, 'word')] + tokens = [self._indexer.get(x, self._oov_index) for x in tokenizer(string, 'word')] X = np.append(X, pad_sequences([tokens], maxlen=length), axis=0) label = array(np_utils.to_categorical([label], total_class)) Y = np.append(Y, label, axis=0) counter += 1 if counter == batch_size: counter = 0 yield(X,Y) def _get_model(self): input_size = self._input_size vocab_size = self._vocab_size - embedding_size = 256 + embedding_size = 128 optimizer = 'adam' loss = 'categorical_crossentropy' num_of_classes = self._num_of_classes embedding_layer = Embedding(vocab_size + 1, embedding_size, input_length=input_size, # trainable=False, ) # applying a more complex convolutional approach convs = [] filter_sizes = [3,4,5] sequence_input = Input(shape=(input_size,), dtype='int64') embedded_sequences = embedding_layer(sequence_input) for fsz in filter_sizes: l_conv = Convolution1D(filters=32, kernel_size=fsz, activation='relu')(embedded_sequences) l_pool = MaxPooling1D(5)(l_conv) convs.append(l_pool) l_merge = Concatenate(axis=1)(convs) l_cov1= Convolution1D(128, 5, activation='relu')(l_merge) l_pool1 = MaxPooling1D(5)(l_cov1) l_cov2 = Convolution1D(128, 5, activation='relu')(l_pool1) l_pool2 = MaxPooling1D(5)(l_cov2) l_flat = Flatten()(l_pool2) l_dense = Dense(512, activation='relu')(l_flat) preds = Dense(num_of_classes, activation='softmax')(l_dense) model = Model(sequence_input, preds) model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy']) print(model.summary()) return model def _max_len(self, texts): return max([len(text) for text in texts]) def test(self): csv.field_size_limit(sys.maxsize) try: r = open(self._path_result, 'rb') test_result = load(r) r.close() except FileNotFoundError: test_result = {} model = self._load_model() for language in [x for x in self._languages if x not in test_result.keys()]: test_result[language] = self.test_class(model, language) with open(self._path_result, 'wb') as f: dump(test_result, f) def _load_model(self): model = load_model(os.path.join(self._root_model, 'model.h5')) return model def _count_size(self, files): size = 0 for f in files: size += os.path.getsize(f) return size def test_class(self, model, language): ok = 0 results = [] count = 0 total_test = self.file_len(os.path.join(self._path_test_csv, language + '.csv')) - print(self._path_test_csv) with open(os.path.join(self._path_test_csv, language + '.csv'), newline='') as csvfile: r = csv.reader(csvfile, delimiter=' ', quotechar='|') for pair in r: label, string = pair label = int(label) string = literal_eval(string) tokens = [self._indexer.get(x, self._oov_index) for x in tokenizer(string, 'word')] result = self._guess_file_language(model, tokens) count += 1 print('[{0:4d}/{1:4d}] {2}:{3} '.format(count, total_test, result[0][1], result[0][0]),end='\r') results.append(result[0]) if result[0][1] == language: ok += 1 accuracy = ok / total_test print('Tests for {} '.format(language)) print('Total test files : {}'.format(total_test)) print('Correctly classified files : {}'.format(ok)) print('Accuracy : {}%'.format(accuracy * 100)) return (ok, total_test, accuracy, results) def speed_benchmark(self): language = self._languages[10] model = self._load_model() test_set = self._get_test_set(language) total_size = self._count_size(test_set) print('{} kB in total'.format(total_size / 1024)) t_start = time.perf_counter() self.test_class(model, language) t_end = time.perf_counter() print('{} seconds.'.format(t_end - t_start)) print('{} seconds per KiB'.format(((t_end - t_start) / total_size) * 1024)) def _guess_file_language(self, model, tokens): X = pad_sequences([tokens], maxlen=self._input_size) result = list(model.predict(X))[0] result = [(s, self._languages[i]) for i, s in enumerate(result)] return sorted(result, reverse=True) if __name__ == '__main__': main() diff --git a/swh/langdetect/utils/common.py b/swh/langdetect/utils/common.py index 462788a..0b46574 100644 --- a/swh/langdetect/utils/common.py +++ b/swh/langdetect/utils/common.py @@ -1,155 +1,156 @@ """ Here regroup basic preprocessing methods used in learning stage for different approaches. """ -import re, os +import re, os, time -#_re_string = re.compile(b"""("(\\.|[^"\\])*"|'(\\.|[^'\\])*')""") -_re_number = re.compile(b'([\d]+)|([\d]+.[\d]+)[^A-Za-z]') -_re_separator = re.compile(b'([\x20-\x30\x3a-\x40\x5b-\x60\x7b-\x7e\t\n])') +_re_string = re.compile(b"""("(\\\\.|[^"\\\\])*"|'(\\\\.|[^'\\\\])*')""") +_re_number = re.compile(b'\d+(\.\d+)?') +_re_separator = re.compile(b'([\x20-\x30\x3a-\x40\x5b-\x5e\x60\x7b-\x7e\t\n])') _not_start_with_point = lambda x: not x.startswith('.') def tokenizer(text, re_name): ''' Splits text into tokens ''' if re_name == 'letter': return list(text) elif re_name == 'word': - return [word for word in _re_separator.split(text) if word.strip(b'')] + text_replaced = replace_string_and_number(text) + return [word for word in _re_separator.split(text_replaced) if word.strip(b' ')] def file_to_string(filename): """ Read a file to a string. """ with open(filename, 'rb') as f: data = f.read() return replace_string_and_number(data) def count_files(root_language): all_folders = natural_sort(filter (_not_start_with_point, os.listdir(root_language))) files = natural_sort(filter (_not_start_with_point, os.listdir(root_language + '/' + all_folders[-1]))) (max,_) = os.path.splitext(files[-1]) return int(max) def find_file(root_language, n): '''Find the n-th file in language folder''' if n > count_files(root_language): return '' else: start = (n - 1) // 1000 * 1000 + 1 end = start + 999 root_count = root_language + '/' + str(start) + '-' + str(end) files = natural_sort(filter (_not_start_with_point, os.listdir(root_count))) return root_count + '/' + files[n - start] def replace_string_and_number(text): """ Replace strings and numbers in a file by special tokens """ - # str_replaced = re.sub(_re_string, '__str__', text) - # str_num_replaced = re.sub(_re_number, '__num__', str_replaced) - str_num_replaced = text + str_replaced = _re_string.sub(b'"__str__"', text) + str_num_replaced = _re_number.sub(b'__num__', str_replaced) + #str_num_replaced = text return str_num_replaced def natural_sort(l): convert = lambda text: int(text) if text.isdigit() else text.lower() alphanum_key = lambda key: [ convert(c) for c in re.split('([0-9]+)', key) ] return sorted(l, key = alphanum_key) def remove_comment(binary_text, language): splited_text = binary_text.splitlines() text = b'\n'.join(splited_text) regexp = get_regexp(language) if not regexp: return binary_text return regexp.sub(b'\n', text) def get_regexp(language): re_inline = get_inline(language) re_block = get_block(language) rs = [] if re_inline: rs.append(re_inline) if re_block: rs.append(re_block) if rs == []: return None return re.compile(b'|'.join(rs), re.DOTALL) def get_inline(language): r_base = b'[^\\n]*(?:\\n|$)' if language in ['Ada', 'Eiffel', 'VHDL', 'AppleScript', 'Haskell', 'Lua', 'PLSQL']: r = b'(--)' + r_base elif language in ['C', 'C++', 'C#', 'D', 'JavaScript', 'ActionScript', 'Java', 'Rust']: r = b'(//)' + r_base elif language == 'Xojo': r = b'(' + b'|'.join([b'//', b"\'"]) + b')' + r_base elif language in ['R', 'Tcl', 'Awk', 'Perl', 'Perl 6', 'Ruby', 'Python']: r = b'(#)' + r_base elif language in ['COBOL']: r = b'(\\*>)' + r_base elif language in ['Matlab']: r = b'(%)' + r_base else: return None return b'(' + r + b')' def get_block(language): r_base = b'.*?' if language in ['C', 'C++', 'C#', 'JavaScript', 'ActionScript', 'PLSQL', 'PHP', 'Rust']: r = b'(/\\*)' + r_base + b'(\\*/)' elif language in ['OCaml', 'Pascal', 'Modula-2', 'Smarty']: r = b'(\\(\\*)' + r_base + b'(\\*\\))' elif language == 'Python': r = b'(\'\'\')' + r_base + b'(\'\'\')' else: return None return b'(' + r + b')' def purify(text, lang): # TODO: for some language like HTML, remove code other than principal language pass diff --git a/swh/langdetect/utils/training.py b/swh/langdetect/utils/training.py index 1f44ca5..8bdbed7 100644 --- a/swh/langdetect/utils/training.py +++ b/swh/langdetect/utils/training.py @@ -1,109 +1,110 @@ import os import random import csv from .common import count_files, find_file, file_to_string from itertools import islice from shutil import copyfile class Dataset: def __init__(self, root): self.root_code = os.path.join(root, '..', 'code_by_language') self.root_training = os.path.join(root, '..', 'training_set') self.root_training_csv = os.path.join(root, '..', 'training_set_csv') self.root_test = os.path.join(root, '..', 'test_set') self.root_test_csv = os.path.join(root, '..', 'test_set_csv') try: os.mkdir(self.root_training) except FileExistsError: pass try: os.mkdir(self.root_training_csv) except FileExistsError: pass try: os.mkdir(self.root_test) except FileExistsError: pass try: os.mkdir(self.root_test_csv) except FileExistsError: pass def build_training_set(self, languages): for language in languages: # limit defines the size of training set # upper defines the maximum size root_code_language = os.path.join(self.root_code, language) root_training_language = os.path.join(self.root_training, language) total = count_files(root_code_language) try: os.mkdir(root_training_language) except FileExistsError: pass upper = 1000 if total >= upper: limit = upper // 2 else: limit = total // 2 indices = random.sample(range(1, total + 1), limit) files = map(lambda x : find_file(root_code_language, x), indices) for src in files: basename = os.path.basename(src) des = os.path.join(root_training_language, basename) os.symlink(src, des) def build_test_set(self, languages, extension=True): for language in languages: root_language = os.path.join(self.root_code, language) root_test_language = os.path.join(self.root_test, language) try: os.mkdir(root_test_language) except FileExistsError: pass files = self.get_test_set(language) for src in files: if extension: des = os.path.join(root_test_language, os.path.basename(src)) else: des = os.path.join(root_test_language, os.path.splitext(os.path.basename(src))[0]) copyfile(src, des) - def train_files_with_label(self, languages, maxsize): + def train_files_with_label(self, languages): with open(os.path.join(self.root_training_csv, 'training_set.csv'), 'w', newline='') as csvfile: setwriter = csv.writer(csvfile, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL) for language in languages: + print(language) root_training_language = os.path.join(self.root_training, language) index_lang = languages.index(language) for f in [x for x in os.listdir(root_training_language) if not x.startswith('.')]: filename = os.path.join(root_training_language, f) - tokens = file_to_string(filename)[-maxsize:] # 10240 + tokens = file_to_string(filename) # 10240 setwriter.writerow([index_lang, tokens]) def get_test_set(self, language): root_training_language = os.path.join(self.root_training, language) root_language = os.path.join(self.root_code, language) total = count_files(root_language) training_set = [int(os.path.splitext(x)[0]) for x in os.listdir(root_training_language) if not x.startswith('.')] it = (find_file(root_language, x) for x in range(1, total + 1) if x not in training_set and os.path.getsize(find_file(root_language, x)) <= 1048576) test_set = list(islice(it, 1000)) if len(test_set) == 0: it = (find_file(root_language, x) for x in range(1, total + 1) if x not in training_set) test_set = list(islice(it, 1000)) return test_set def test_files_with_label(self, languages): for language in languages: root_test_language = os.path.join(self.root_test, language) index_lang = languages.index(language) with open(os.path.join(self.root_test_csv, language + '.csv'), 'w', newline='') as csvfile: setwriter = csv.writer(csvfile, delimiter=' ', quotechar='|', quoting=csv.QUOTE_MINIMAL) for f in [x for x in os.listdir(root_test_language) if not x.startswith('.')]: filename = os.path.join(root_test_language, f) tokens = file_to_string(filename) setwriter.writerow([index_lang, tokens])