diff --git a/swh/langdetect/__init__.py b/swh/langdetect/__init__.py index 5f8841b..947d176 100644 --- a/swh/langdetect/__init__.py +++ b/swh/langdetect/__init__.py @@ -1,5 +1,12 @@ """ -Detectlang detects the programming language of source code file. +Langdetect detects the programming language of source code file. """ +from .cnn import CNN + +__cnn_classifer = CNN(None, 4096, None) + +def classify(path): + __cnn_classifer.classify(path) + diff --git a/swh/langdetect/cnn.py b/swh/langdetect/cnn.py index ad6298a..42faba4 100644 --- a/swh/langdetect/cnn.py +++ b/swh/langdetect/cnn.py @@ -1,346 +1,346 @@ import os import sys import subprocess import time import random import csv import numpy as np import warnings import gzip with warnings.catch_warnings(): warnings.simplefilter("ignore") import tensorflow as tf import json import argparse import magic 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, AlphaDropout from keras.layers import ThresholdedReLU from keras.layers import Activation from keras.layers import Lambda from keras.layers import Embedding from keras.layers import Concatenate, GlobalMaxPooling1D from keras.layers.convolutional import Convolution1D, MaxPooling1D from keras.layers.normalization import BatchNormalization from keras.utils import np_utils from keras.optimizers import SGD #from pyspark import SparkContext, SparkConf #from elephas.spark_model import SparkModel # pip install flask #from elephas import optimizers as elephas_optimizers #from elephas.utils.rdd_utils import to_labeled_point csv.field_size_limit(sys.maxsize) os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 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('-s', '--spark', type=bool, help='Training on cluster.', dest='train_spark') 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.') parser_clf = subparsers.add_parser('clf', help='Test a file.') parser_clf.add_argument('clf_path', metavar='PATH', type=str, help='Path of test file.') if len(sys.argv[1:]) == 0: parser.print_help() parser.exit() args = parser.parse_args() - maxsize = 2048 + maxsize = 4096 epochs = 15 if args.sub_command == 'train' : if args.train_maxsize: maxsize = args.train_maxsize if args.train_epochs: epochs = args.train_epochs n = CNN(args.train_path, maxsize=maxsize, epochs=epochs) if args.train_spark: n.train_on_cluster() else: n.train() elif args.sub_command == 'test': n = CNN(args.test_root, maxsize=maxsize, epochs=epochs) n.test() elif args.sub_command == 'clf': n = CNN(None, maxsize, None) n.classify(args.clf_path) else: parser.parse_args('-h') class CNN: def __init__(self, path, maxsize, epochs): if path != None: 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') self._path_test_csv = 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._input_size = maxsize self._vocab_size = 256 self._num_of_classes = len(self._languages) self._batch_size = 64 self._epochs = epochs self._model = None if path == None and epochs == None: self._model = load_model(os.path.join(dir_path, 'static_data', 'model.h5')) def file_len(self, fname): with open(fname) as f: count = 0 for l in f: count += 1 return count def train(self): self._get_model() earlystop = EarlyStopping(monitor='loss', min_delta=0, patience=3, verbose=0, mode='auto') callbacks = [earlystop] self._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) self._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) if len(string) > self._input_size: len_s = len(string) stop_1 = int(len_s / 3) stop_2 = int(len_s * 2 / 3) part = int(self._input_size / 4) half_part = int(part / 2) string = string[:part] + string[stop_1 - half_part:stop_1 + half_part] + string[stop_2 - half_part:stop_2 + half_part] + string[-part:] tokens = [x + 1 for x in Tokenizer.tokenize(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_zhang(self): input_size = self._input_size alphabet_size = self._vocab_size embedding_size = 128 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()) self._model = model def _get_model(self): input_size = self._input_size alphabet_size = self._vocab_size embedding_size = 64 conv_layers = [(256,10), (256,7), (256,5), (256,3)] threshold = 1e-6 fully_connected_layers = [1024, 1024] dropout_p = 0.1 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_output = [] # Convolution layers for num_filters, filter_width in conv_layers: conv = Convolution1D(filters=num_filters, kernel_size=filter_width, activation='tanh', name='Conv1D_{}_{}'.format(num_filters, filter_width))(x) pool = GlobalMaxPooling1D(name='MaxPoolingOverTime_{}_{}'.format(num_filters, filter_width))(conv) convolution_output.append(pool) x = Concatenate()(convolution_output) # Fully connected layers for fl in fully_connected_layers: x = Dense(fl, activation='selu', kernel_initializer='lecun_normal')(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()) self._model = model def _max_len(self, texts): return max([len(text) for text in texts]) def _load_model(self): self._model = load_model(os.path.join(self._root_model, 'model.h5')) 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 = {} 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(language) with open(self._path_result, 'wb') as f: dump(test_result, f) def _count_size(self, files): size = 0 for f in files: size += os.path.getsize(f) return size def test_class(self, 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) if len(string) > self._input_size: length = len(string) stop_1 = int(length / 3) stop_2 = int(length * 2 / 3) part = int(self._input_size / 4) half_part = int(part / 2) string = string[:part] + string[stop_1 - half_part:stop_1 + half_part] + string[stop_2 - half_part:stop_2 + half_part] + string[-part:] tokens = [x + 1 for x in Tokenizer.tokenize(string, 'letter')] result = self._guess_file_language(tokens) count += 1 - print('[{0:4d}/{1:4d}] {2}:{3} '.format(count, total_test, result[0][1], result[0][0]),end='\r') + print('[{0:4d}/{1:4d}] {2}:\t{3:.3f} '.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] self._model = load_model(os.path.join(self._root_model, 'model.h5')) 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(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, tokens): X = pad_sequences([tokens], maxlen=self._input_size) result = list(self._model.predict(X))[0] result = [(s, self._languages[i]) for i, s in enumerate(result)] return sorted(result, reverse=True) def classify(self, path): with gzip.open(path, 'rb') as f: string = f.read() a = magic.from_buffer(string, mime=True) print(a) tokens = [x + 1 for x in Tokenizer.tokenize(string, 'letter')] res = self._guess_file_language(tokens) print('Filename :\t{}\nLanguage :\t{}\nValue :\t{}'.format(path, res[0][1],res[0][0])) return (res[0][1], res[0][0]) if __name__ == '__main__': main() diff --git a/swh/langdetect/cnn_w.py b/swh/langdetect/cnn_w.py index b622abb..9f44fe7 100644 --- a/swh/langdetect/cnn_w.py +++ b/swh/langdetect/cnn_w.py @@ -1,300 +1,294 @@ 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 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 import GlobalMaxPooling1D 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 csv.field_size_limit(sys.maxsize) 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))) +# 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() 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=1024, epochs=30): + def __init__(self, path, maxsize=400, 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_less.json'), 'r') as f: + 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) - 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=3, 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 _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, end='\r') string = literal_eval(string) tokens = Tokenizer.tokenize(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, self._oov_index) for x in Tokenizer.tokenize(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 = 128 + embedding_size = 50 optimizer = 'adam' loss = 'categorical_crossentropy' num_of_classes = self._num_of_classes embedding_layer = Embedding(vocab_size + 1, embedding_size, input_length=input_size, ) - - # applying a more complex convolutional approach + convs = [] - filter_sizes = [3,4,5] + filter_sizes = [3,4,5,6,7] sequence_input = Input(shape=(input_size,), dtype='int64') embedded_sequences = embedding_layer(sequence_input) + z = Dropout(0.5)(embedded_sequences) for fsz in filter_sizes: - l_conv = Convolution1D(filters=10, kernel_size=fsz, activation='relu')(embedded_sequences) - l_pool = MaxPooling1D(3)(l_conv) - convs.append(l_pool) - - l_merge = Concatenate(axis=1)(convs) - l_conv1= Convolution1D(128, 3, activation='relu')(l_merge) - l_pool1 = MaxPooling1D(5)(l_conv1) - l_conv2 = Convolution1D(128, 3, activation='relu')(l_pool1) - l_pool2 = MaxPooling1D(5)(l_conv2) - l_flat = Flatten()(l_pool2) - l_dense = Dense(512, activation='relu')(l_flat) - preds = Dense(num_of_classes, activation='softmax')(l_dense) + x = Convolution1D(filters=10, kernel_size=fsz, activation='relu')(z) + x = GlobalMaxPooling1D()(x) + convs.append(x) + + x = Concatenate(axis=1)(convs) + x = Dense(1024, activation="relu")(x) + preds = Dense(num_of_classes, activation='softmax')(x) 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')) 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.tokenize(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/hierarchical.py b/swh/langdetect/hierarchical.py index 684e412..86d31fa 100644 --- a/swh/langdetect/hierarchical.py +++ b/swh/langdetect/hierarchical.py @@ -1,238 +1,237 @@ import os import sys import operator import nltk import random import time import numpy as np import csv import argparse import json import matplotlib.pyplot as plt import matplotlib as mpl from ast import literal_eval from itertools import islice from pickle import dump, load from .utils.common import Tokenizer from nltk.util import ngrams from collections import Counter from sklearn.feature_extraction.text import HashingVectorizer, TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity from sklearn.externals import joblib from sklearn.cluster import KMeans, MiniBatchKMeans from sklearn.metrics.pairwise import cosine_similarity, cosine_distances, euclidean_distances from scipy.sparse import vstack from scipy.sparse import csr_matrix from scipy.cluster.hierarchy import ward, dendrogram, centroid, complete, average, weighted, median from sklearn.manifold import MDS csv.field_size_limit(sys.maxsize) def main(): parser = argparse.ArgumentParser(description='Training and test tool of multinumial naive bayesian.') 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('-n', '--ngrams', metavar='N', dest='train_maxsize', type=int, help='Set maximum input size of ConvNet, default 5.') 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() if args.sub_command == 'train' : n = Unsupervised(args.train_path) n.train() n.graph_top_20() elif args.sub_command == 'test': n = Unsupervised(args.test_root) n.test() else: parser.parse_args('-h') class Unsupervised: def __init__(self, path): self._path = path # Root of model folder self._root_model = os.path.join(os.path.dirname(path), 'model_unsupervised') try: os.mkdir(self._root_model) except: pass # Path of result self._path_result = os.path.join(os.path.dirname(path), 'result_unsupervised') 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._num_of_classes = len(self._languages) def train(self): cv = HashingVectorizer(analyzer='char', ngram_range=(1, 5), n_features=2**24, alternate_sign=False) texts = [] label = 0 string = '' - top_20 = ['Python', 'Java', 'JavaScript', 'PHP', 'C#', 'C', 'C++', - 'R', 'Objective-C', 'Swift', 'Matlab', 'Ruby', 'TypeScript', - 'Visual Basic', 'Scala', 'Kotlin', 'Go', 'Perl', 'Lua', - 'Rust', 'Haskell'] + top_20 = ["C", "C#", "C++", "Clojure", "CSS", "Go", + "Haskell", "HTML", "Java", "JavaScript", "Lua", + "Objective-C", "Perl", "PHP", "Python", "R", "Ruby", + "Scala", "Scheme", "XML"] top_20 = [self._languages.index(x) for x in top_20] print(top_20) with open(self._path, newline='') as csvfile: r = csv.reader(csvfile, delimiter=' ', quotechar='|') for pair in r: label_new, string_new = pair print(label_new, end=' \r') if not int(label_new) == label: if not os.path.isfile(os.path.join(self._root_model, 'counts{}.pkl'.format(label))): if label in top_20: counts = cv.fit_transform(texts) self.clustering(counts, 1, label) self.graph(label) texts = [] label = int(label_new) if label in top_20: string = literal_eval(string_new) #tokens = Tokenizer.tokenize(string, 'word') #text = ' '.join([''.join([chr(x) for x in token]) for token in tokens]) tokens = Tokenizer.tokenize(string, 'letter') text = ''.join([chr(token) for token in tokens]) texts.append(text) with open(os.path.join(self._root_model, 'classifier.cv'), 'wb') as f: joblib.dump(cv, f) def clustering(self, counts, num_clusters, label): # km = KMeans(n_clusters=num_clusters) # km.fit(counts) with open(os.path.join(self._root_model, 'counts{}.pkl'.format(label)), 'wb') as f: joblib.dump(counts, f) #with open(os.path.join(self._root_model, 'cluster{}.pkl'.format(label)), 'wb') as f: # joblib.dump(km, f) def graph_top_20(self): - top_20 = ['Python', 'Java', 'JavaScript', 'PHP', 'C#', 'C', 'C++', - 'R', 'Objective-C', 'Swift', 'Matlab', 'Ruby', 'TypeScript', - 'Visual Basic', 'Scala', 'Kotlin', 'Go', 'Perl', 'Lua', - 'Rust', 'Haskell'] + top_20 = ["C", "C#", "C++", "Clojure", "CSS", "Go", "Haskell", + "HTML", "Java", "JavaScript", "Lua", "Objective-C", + "Perl", "PHP", "Python", "R", "Ruby", "Scala", "Scheme", "XML"] top_20 = [self._languages.index(x) for x in top_20] counts = csr_matrix((0, 2 ** 24)) for label in top_20: with open(os.path.join(self._root_model, 'counts{}.pkl'.format(label)), 'rb') as f: counts = vstack((counts, joblib.load(f))) print(counts.shape) if not os.path.isfile(os.path.join(self._root_model, 'linkage_matrix')): dist = euclidean_distances(counts) - linkage_matrix = ward(dist) + linkage_matrix = weighted(dist) with open(os.path.join(self._root_model, 'linkage_matrix'), 'wb') as f: joblib.dump(linkage_matrix, f) else: with open(os.path.join(self._root_model, 'linkage_matrix'), 'rb') as f: linkage_matrix = joblib.load(f) print(linkage_matrix) fig, ax = plt.subplots(figsize=(15, 150)) titles = [self._languages[top_20[x // 500]] for x in list(range(0,counts.shape[0]))] ax = dendrogram(linkage_matrix, orientation="right", labels=titles) plt.tick_params(axis= 'x', which='both', bottom=False, top=False, labelbottom=False) plt.tight_layout() plt.savefig(os.path.join(self._root_model, 'top_20_cluster.pdf')) def graph(self, label): with open(os.path.join(self._root_model, 'counts{}.pkl'.format(label)), 'rb') as f: counts = joblib.load(f) dist = euclidean_distances(counts) linkage_matrix = ward(dist) fig, ax = plt.subplots(figsize=(15, 40)) titles = list(range(1,counts.shape[0]+1)) ax = dendrogram(linkage_matrix, orientation="right", labels=titles) plt.tick_params(axis= 'x', which='both', bottom=False, top=False, labelbottom=False) plt.tight_layout() plt.savefig(os.path.join(self._root_model, '{}_cluster.pdf'.format(self._languages[label]))) def speed_benchmark(self): language = [x for x in os.listdir(self._root_training_set) if not x.startswith('.')][10] models = self._load_models() 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(models, language) t_end = time.perf_counter() print('{} seconds.'.format(t_end - t_start)) print('{} seconds per kB'.format(((t_end - t_start) / total_size) * 1024)) def _count_size(self, files): size = 0 for f in files: size += os.path.getsize(f) return size def file_len(self, fname): with open(fname) as f: count = 0 for l in f: count += 1 return count def _distance(self, model_profile, test_profile): distance = 0 maximum = len(test_profile) for test_ngram in test_profile.keys(): test_rank = test_profile.get(test_ngram) model_rank = model_profile.get(test_ngram, maximum) d = abs(test_rank - model_rank) distance += d return distance ''' def _prob(model, trigrams): print('Checking {} model ...'.format(model)) with open(model, 'rb') as f: kneser_ney = load(f) result = 1 for trigram in trigrams: prob = kneser_ney.prob(trigram) result = result * prob return result ''' if __name__ == '__main__': main() diff --git a/swh/langdetect/naivebayesian.py b/swh/langdetect/naivebayesian.py index d1691e2..94702e2 100644 --- a/swh/langdetect/naivebayesian.py +++ b/swh/langdetect/naivebayesian.py @@ -1,240 +1,256 @@ """ Naive Bayesian """ import os import sys import operator import nltk import random import time import numpy as np import csv import argparse import json from ast import literal_eval from itertools import islice from pickle import dump, load from .utils.common import Tokenizer, file_to_string, find_file, count_files from nltk.util import ngrams from collections import Counter from sklearn.naive_bayes import MultinomialNB from sklearn.feature_extraction.text import HashingVectorizer, TfidfTransformer from sklearn.externals import joblib csv.field_size_limit(sys.maxsize) def main(): parser = argparse.ArgumentParser(description='Training and test tool of multinumial naive bayesian.') 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('-n', '--ngrams', metavar='N', dest='train_maxsize', type=int, help='Set maximum input size of ConvNet, default 5.') 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() if args.sub_command == 'train' : n = NaiveBayesian(args.train_path) n.train() elif args.sub_command == 'test': n = NaiveBayesian(args.test_root) n.test() else: parser.parse_args('-h') class NaiveBayesian: - def __init__(self, path): + def __init__(self, path, token): self._path = path # Root of model folder self._root_model = os.path.join(os.path.dirname(path), 'model_bayesian') try: os.mkdir(self._root_model) except: pass # Path of result self._path_result = os.path.join(os.path.dirname(path), 'result_bayesian') dir_path = os.path.dirname(os.path.abspath(__file__)) - with open(os.path.join(dir_path, 'static_data', 'languages_less.json'), 'r') as f: + 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._num_of_classes = len(self._languages) def train(self): ''' train () generates and stores counted n-grams in '_root_model' folder ''' ''' Calculate frequencies of generated n-grams then store them into a sorted list of (ngram, count) ''' clf = MultinomialNB(alpha=0.001) cv = HashingVectorizer(analyzer='char', ngram_range=(1, 4), n_features=2**16, alternate_sign=False) + + #cv = HashingVectorizer(analyzer='word', ngram_range=(1, 3), n_features=2**18, alternate_sign=False) indices = list(range(len(self._languages))) with open(self._path, newline='') as csvfile: r = csv.reader(csvfile, delimiter=' ', quotechar='|') + labels = [] + texts = [] + label = 0 for pair in r: + label_new, _ = pair + if label != int(label_new): + counts = cv.fit_transform(texts) + tf = TfidfTransformer().fit(counts) + normalised = tf.transform(counts) + clf.partial_fit(normalised, np.array(labels), indices) + texts = [] + labels = [] + label, string = pair label = int(label) print(label, end='\r') string = literal_eval(string) + tokens = Tokenizer.tokenize(string, 'letter') text = ''.join([chr(token) for token in tokens]) - counts = cv.fit_transform([text]) - tf = TfidfTransformer().fit(counts) - normalised = tf.transform(counts) - clf.partial_fit(normalised, np.array([label]), indices) + #tokens = Tokenizer.tokenize(string, 'word') + #textb = b' '.join(tokens) + #text = ''.join([chr(x) for x in list(textb)]) + + texts.append(text) + labels.append(label) + + counts = cv.fit_transform(texts) + tf = TfidfTransformer().fit(counts) + normalised = tf.transform(counts) + clf.partial_fit(normalised, np.array(labels), indices) with open(os.path.join(self._root_model, 'classifier.clf'), 'wb') as f: joblib.dump(clf, f) with open(os.path.join(self._root_model, 'classifier.hv'), 'wb') as f: joblib.dump(cv, f) def test(self): try: r = open(self._path_result, 'rb') test_result = load(r) r.close() except FileNotFoundError: test_result = {} with open(os.path.join(self._root_model, 'classifier.clf'), 'rb') as f: clf = joblib.load(f) with open(os.path.join(self._root_model, 'classifier.hv'), 'rb') as f: cv = joblib.load(f) for language in [x for x in self._languages if x not in test_result.keys()]: test_result[language] = self.test_class((clf, cv), language) with open(self._path_result, 'wb') as f: dump(test_result, f) def speed_benchmark(self): language = [x for x in os.listdir(self._root_training_set) if not x.startswith('.')][10] models = self._load_models() 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(models, language) t_end = time.perf_counter() print('{} seconds.'.format(t_end - t_start)) print('{} seconds per kB'.format(((t_end - t_start) / total_size) * 1024)) def _get_test_set(self, language): root_training_language = os.path.join(self._root_training_set, language) root_language = os.path.join(self._root_language_dataset, 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 _count_size(self, files): size = 0 for f in files: size += os.path.getsize(f) return size def test_class(self, clf, 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) result = self._guess_file_language(clf, string) 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 test_single(self, filename): self._guess_file_language(clf, filename) def file_len(self, fname): with open(fname) as f: count = 0 for l in f: count += 1 return count def _guess_file_language(self, cc, string): clf = cc[0] cv = cc[1] + tokens = Tokenizer.tokenize(string, 'letter') text = ''.join([chr(token) for token in tokens]) + + #tokens = Tokenizer.tokenize(string, 'word') + #textb = b' '.join(tokens) + #text = ''.join([chr(x) for x in list(textb)]) + counts = cv.fit_transform([text]) tf = TfidfTransformer().fit(counts) normalised = tf.transform(counts) result = clf.predict_log_proba(normalised) result = [(val, self._languages[idx]) for idx, val in enumerate(result[0])] return sorted(result, reverse=True) def _distance(self, model_profile, test_profile): distance = 0 maximum = len(test_profile) for test_ngram in test_profile.keys(): test_rank = test_profile.get(test_ngram) model_rank = model_profile.get(test_ngram, maximum) d = abs(test_rank - model_rank) distance += d return distance - ''' - def _prob(model, trigrams): - print('Checking {} model ...'.format(model)) - with open(model, 'rb') as f: - kneser_ney = load(f) - result = 1 - for trigram in trigrams: - prob = kneser_ney.prob(trigram) - result = result * prob - return result - ''' if __name__ == '__main__': main() diff --git a/swh/langdetect/ngramdist.py b/swh/langdetect/ngramdist.py index 004fdd8..962e62f 100644 --- a/swh/langdetect/ngramdist.py +++ b/swh/langdetect/ngramdist.py @@ -1,235 +1,248 @@ import os import sys import time import random import csv import json import argparse import nltk import operator from ast import literal_eval from itertools import islice from pickle import dump, load from nltk.util import ngrams from .utils.common import Tokenizer, file_to_string, find_file, count_files csv.field_size_limit(sys.maxsize) def main(): parser = argparse.ArgumentParser(description='Training and test tool of frequency distance of n-grams.') 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('-n', '--ngrams', metavar='N', dest='train_maxsize', type=int, help='Set maximum input size of ConvNet, default 5.') 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() if args.sub_command == 'train' : n = NGramDist(args.train_path) n.train() elif args.sub_command == 'test': n = NGramDist(args.test_root) n.test() else: parser.parse_args('-h') class NGramDist: def __init__(self, path): self._path = path # Root of model folder self._root_model = os.path.join(os.path.dirname(path), 'model_ngram_dist') try: os.mkdir(self._root_model) except: pass # Path of result self._path_result = os.path.join(os.path.dirname(path), 'result_ngram_dist') dir_path = os.path.dirname(os.path.abspath(__file__)) - with open(os.path.join(dir_path, 'static_data', 'languages_less.json'), 'r') as f: + 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._num_of_classes = len(self._languages) def file_len(self, fname): with open(fname) as f: count = 0 for l in f: count += 1 return count def train(self): statistics = {} + t_start = time.perf_counter() with open(self._path, newline='') as csvfile: r = csv.reader(csvfile, delimiter=' ', quotechar='|') for pair in r: label, string = pair label = int(label) language = self._languages[label] - print(language, end='\r') statistics_lang = statistics.get(language, {}) string = literal_eval(string) tokens = Tokenizer.tokenize(string, 'letter') generated_ngrams = self._generate_ngrams([chr(token) for token in tokens], 3) + + #tokens = Tokenizer.tokenize(string, 'word') + #tokens = [''.join([chr(x) for x in token]) for token in tokens] + #generated_ngrams = self._generate_ngrams(tokens, 3) + self._count_ngrams(statistics_lang, generated_ngrams) statistics[language] = statistics_lang + + t_end = time.perf_counter() + print(str(t_end - t_start) + ' ' + str(label), end='\r') for language in self._languages: with open(os.path.join(self._root_model, language), 'wb') as f: dump(self._sort_by_value(statistics[language]), f) def _generate_ngrams(self, tokens, n): generated_ngrams = [] for i in range(1, n+1): igrams = ngrams(tokens, i, pad_left=True, pad_right=True, left_pad_symbol = '$BOF$', right_pad_symbol = '$EOF$') for igram in igrams: generated_ngrams.append(''.join(igram)) return generated_ngrams def _count_ngrams(self, statistics, ngrams): for ngram in ngrams: statistics[ngram] = statistics.get(ngram, 0) + 1 def test(self): try: r = open(self._path_result, 'rb') test_result = load(r) r.close() except FileNotFoundError: test_result = {} model = self._load_models() 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_models(self): models = {} for model in [model - for model in os.listdir(self._root_model) - if not model.startswith('.')]: + for model in self._languages]: root_model = os.path.join(self._root_model, model) with open(root_model, 'rb') as sorted_file: models[model] = self._list_to_dict(load(sorted_file)) return models def _list_to_dict(self, model): model_ngrams = [x[0] for x in model] model_dict = {} index = 0 for ngram in model_ngrams: index += 1 model_dict[ngram] = index return model_dict 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')) - + + t_start = time.perf_counter() 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) result = self._guess_file_language(model, string) 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 + t_end = time.perf_counter() + print('[{0:4d}/{1:4d}] {2}:{3} {4} '.format(count, total_test, result[0][1], result[0][0], t_end - t_start), end='\r') 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, models, string): tokens = Tokenizer.tokenize(string, 'letter') generated_ngrams = self._generate_ngrams([chr(token) for token in tokens], 3) + + #tokens = Tokenizer.tokenize(string, 'word') + #tokens = [''.join([chr(x) for x in token]) for token in tokens] + #generated_ngrams = self._generate_ngrams(tokens, 3) statistics = {} self._count_ngrams(statistics, generated_ngrams) test_profile = self._list_to_dict(self._sort_by_value(statistics)) result = [] for model in models.keys(): root_model = os.path.join(self._root_model, model) model_profile = models[model] distance = self._distance(model_profile, test_profile) result.append((distance, model)) return sorted(result) def _sort_by_value(self, statistics): statistics_sorted = sorted(statistics.items(), key = operator.itemgetter(1), reverse = True)[:500] return statistics_sorted def _distance(self, model_profile, test_profile): distance = 0 maximum = len(test_profile) for test_ngram in test_profile.keys(): test_rank = test_profile.get(test_ngram) model_rank = model_profile.get(test_ngram, maximum) d = abs(test_rank - model_rank) distance += d return distance if __name__ == '__main__': main() diff --git a/swh/langdetect/ngramprob.py b/swh/langdetect/ngramprob.py index ff2b0ee..e104285 100644 --- a/swh/langdetect/ngramprob.py +++ b/swh/langdetect/ngramprob.py @@ -1,191 +1,211 @@ import os, sys, subprocess, time, csv, argparse, json import kenlm from ast import literal_eval from itertools import islice from pickle import dump, load from .utils.common import Tokenizer, file_to_string, find_file, count_files, remove_comment csv.field_size_limit(sys.maxsize) def main(): parser = argparse.ArgumentParser(description='Training and test tool of n-grams model.') 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('-n', '--ngrams', metavar='N', dest='train_maxsize', type=int, help='Set maximum input size of ConvNet, default 5.') 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() if args.sub_command == 'train' : n = NGramProb(args.train_path) n.train() elif args.sub_command == 'test': n = NGramProb(args.test_root) n.test() else: parser.parse_args('-h') class NGramProb: def __init__(self, path): self._path = path # Root of model folder self._root_model = os.path.join(os.path.dirname(path), 'model_ngram_prob') try: os.mkdir(self._root_model) except: pass + try: + os.mkdir(os.path.join(self._root_model, 'arpa')) + except: + pass + try: + os.mkdir(os.path.join(self._root_model, 'text')) + except: + pass # Path of result self._path_result = os.path.join(os.path.dirname(path), 'result_ngram_prob') dir_path = os.path.dirname(os.path.abspath(__file__)) - with open(os.path.join(dir_path, 'static_data', 'languages_less.json'), 'r') as f: + 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._num_of_classes = len(self._languages) def file_len(self, fname): with open(fname) as f: count = 0 for l in f: count += 1 return count def train(self): command = [os.path.join(os.path.dirname(os.path.abspath(__file__)), '..' , '..', 'bin', 'lmplz'), - '-o', '3', '-T', '/tmp', '--discount_fallback'] + '-o', '3', '--discount_fallback'] with open(self._path, newline='') as csvfile: r = csv.reader(csvfile, delimiter=' ', quotechar='|') label = 0 language = self._languages[label] texts = [] for pair in r: label_new, _ = pair if label != int(label_new): - with open(os.path.join(self._root_model, language), 'wb') as f: + with open(os.path.join(self._root_model, 'arpa', language), 'wb') as f: train_text = ' '.join(texts) - proc = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=f) - proc.communicate(train_text.encode()) + with open(os.path.join(self._root_model, 'text', language), 'w') as t: + t.write(train_text) + with open(os.path.join(self._root_model, 'text', language), 'r') as t: + proc = subprocess.Popen(command, stdin=t, stdout=f) + proc.communicate() texts = [] label, string = pair label = int(label) language = self._languages[label] print(language, end='\r') - text = literal_eval(string) - tokens = Tokenizer.tokenize(text, 'letter') - + string = literal_eval(string) + tokens = Tokenizer.tokenize(string, 'letter') texts.append(' '.join(chr(token) for token in tokens)) + + #tokens = Tokenizer.tokenize(string, 'word') + #textb = b' '.join(tokens) + #text = ''.join([chr(x) for x in list(textb)]) + #text = ' '.join([x for x in text.split(' ') if x.strip('')]) + #texts.append(text) - with open(os.path.join(self._root_model, language), 'wb') as f: + with open(os.path.join(self._root_model, 'arpa', language), 'wb') as f: train_text = ' '.join(texts) - proc = subprocess.Popen(command, stdin=subprocess.PIPE, stdout=f) - proc.communicate(train_text.encode()) + with open(os.path.join(self._root_model, 'text', language), 'w') as t: + t.write(train_text) + with open(os.path.join(self._root_model, 'text', language), 'r') as t: + proc = subprocess.Popen(command, stdin=t, stdout=f) + proc.communicate() + + def test(self): try: r = open(self._path_result, 'rb') test_result = load(r) r.close() except FileNotFoundError: test_result = {} models = self._load_models() for language in [x for x in self._languages if x not in test_result.keys()]: test_result[language] = self.test_class(models, language) with open(self._path_result, 'wb') as f: dump(test_result, f) def _load_models(self): models = {} for model in [model - for model in os.listdir(self._root_model) - if not model.startswith('.')]: - root_model = os.path.join(self._root_model, model) + for model in self._languages]: + root_model = os.path.join(self._root_model, 'arpa', model) models[model] = kenlm.LanguageModel(root_model) return models 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) result = self._guess_file_language(model, string) 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, models, string): tokens = Tokenizer.tokenize(string, 'letter') text = ' '.join(chr(token) for token in tokens) - #text = file_to_string(filename) - #tokens = tokenizer(text, 'word') - #tokens = b' '.join(tokens) - #text = ''.join(chr(token) for token in list(tokens)) + + #tokens = Tokenizer.tokenize(string, 'word') + #textb = b' '.join(tokens) + #text = ''.join([chr(x) for x in list(textb)]) result = [] for model_key in models.keys(): root_model = os.path.join(self._root_model, model_key) model = models[model_key] score = model.score(text) result.append((score, model_key)) return sorted(result, reverse=True) if __name__ == '__main__': main() diff --git a/swh/langdetect/utils/common.py b/swh/langdetect/utils/common.py index 652009e..e586d21 100644 --- a/swh/langdetect/utils/common.py +++ b/swh/langdetect/utils/common.py @@ -1,175 +1,175 @@ """ Here regroup basic preprocessing methods used in learning stage for different approaches. """ import re, os, time _not_start_with_point = lambda x: not x.startswith('.') class Tokenizer(): separator = re.compile( b'([\x20-\x2f\x3a-\x40\x5b-\x5e\x60\x7b-\x7e\s]|\d+\.\d+|\d+|\d+\.\d+[eE][+-]?\d+)') def is_number(n): try: float(n) except ValueError: return False return True def tokenize(text, re_name): ''' Splits text into tokens ''' if re_name == 'letter': return list(text) elif re_name == 'word': - pretokens = [x for x in Tokenizer.separator.split(text) if x and x.strip(b'\n')] + pretokens = [x for x in Tokenizer.separator.split(text) if x ] tokens = [] for x in pretokens : if Tokenizer.is_number(x): tokens.append(b'') - elif x.isspace(): + elif x.isspace() and x != b'\n': tokens.append(b' ') else: tokens.append(x) return tokens def file_to_string(filename): """ Read a file to a string. """ with open(filename, 'rb') as f: data = f.read() return 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_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 09c90c9..ffdf47a 100644 --- a/swh/langdetect/utils/training.py +++ b/swh/langdetect/utils/training.py @@ -1,115 +1,125 @@ import os import random import csv import json 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 dir_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) print(dir_path) - with open(os.path.join(dir_path, 'static_data', 'languages_less.json'), 'r') as f: + with open(os.path.join(dir_path, 'static_data', 'languages_mini.json'), 'r') as f: self._languages = json.load(f) def build_training_set(self): for language in self._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, extension=True): for language in self._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) + os.symlink(src, des) def train_files_with_label(self): 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) + lang_index = {k : v for v, k in enumerate(self._languages)} for language in self._languages: print(language) root_training_language = os.path.join(self.root_training, language) - index_lang = self._languages.index(language) + index_lang = lang_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) # 10240 - setwriter.writerow([index_lang, tokens]) + _, extension = os.path.splitext(f) + text = extension.encode() + b' ' + file_to_string(filename) # 10240 + setwriter.writerow([index_lang, text]) 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)) + + it = [find_file(root_language, x) for x in range(1, total + 1) if x not in training_set] + try: + test_set = random.sample(it, 1000) + except ValueError: + test_set = it + 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)) + it = [find_file(root_language, x) for x in range(1, total + 1) if x not in training_set] + try: + test_set = random.sample(it, 1000) + except ValueError: + test_set = it return test_set def test_files_with_label(self): for language in self._languages: root_test_language = os.path.join(self.root_test, language) index_lang = self._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]) + _, extension = os.path.splitext(f) + text = extension.encode() + b' ' + file_to_string(filename) + setwriter.writerow([index_lang, text])