diff --git a/swh/langdetect/cnn.py b/swh/langdetect/cnn.py index 8ffe778..8bcbced 100644 --- a/swh/langdetect/cnn.py +++ b/swh/langdetect/cnn.py @@ -1,266 +1,265 @@ 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() + 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.train_path: + 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.test_root: + 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') - path = os.path.abspath(__file__) - dir_path = os.path.dirname(path) + 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') 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 e5f3673..8206e93 100644 --- a/swh/langdetect/cnn_w.py +++ b/swh/langdetect/cnn_w.py @@ -1,299 +1,301 @@ 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() + 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.train_path: + 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.test_root: + 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): 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') - path = os.path.abspath(__file__) - dir_path = os.path.dirname(path) + 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._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) + 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() - 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) - indexer = dict((v[0], i + 1) for i, v in enumerate(l)) + earlystop = EarlyStopping(monitor='loss', min_delta=0, patience=1, verbose=0, mode='auto') callbacks = [earlystop] model.fit_generator( - self._generator(self._input_size, self._num_of_classes, indexer, self._batch_size), + self._generator(self._input_size, self._num_of_classes, self._indexer, 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) 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, indexer, batch_size=64): + def _generator(self, length, total_class, batch_size=64): counter = 0 - oov_index = len(indexer) + 1 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 = [indexer.get(x, oov_index) for x in tokenizer(string, 'word')] + tokens = [self._indexer.get(x, 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 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 = [x + 1 for x in tokenizer(string, 'letter')] + 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()