136 lines
4.8 KiB
Python
136 lines
4.8 KiB
Python
'''This example demonstrates the use of fasttext for text classification
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Based on Joulin et al's paper:
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Bags of Tricks for Efficient Text Classification
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https://arxiv.org/abs/1607.01759
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Results on IMDB datasets with uni and bi-gram embeddings:
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Uni-gram: 0.8813 test accuracy after 5 epochs. 8s/epoch on i7 cpu.
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Bi-gram : 0.9056 test accuracy after 5 epochs. 2s/epoch on GTx 980M gpu.
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'''
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from __future__ import print_function
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import numpy as np
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from keras.preprocessing import sequence
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from keras.models import Sequential
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from keras.layers import Dense
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from keras.layers import Embedding
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from keras.layers import GlobalAveragePooling1D
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from keras.datasets import imdb
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def create_ngram_set(input_list, ngram_value=2):
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"""
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Extract a set of n-grams from a list of integers.
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>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)
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{(4, 9), (4, 1), (1, 4), (9, 4)}
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>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)
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[(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)]
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"""
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return set(zip(*[input_list[i:] for i in range(ngram_value)]))
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def add_ngram(sequences, token_indice, ngram_range=2):
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"""
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Augment the input list of list (sequences) by appending n-grams values.
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Example: adding bi-gram
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>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
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>>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017}
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>>> add_ngram(sequences, token_indice, ngram_range=2)
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[[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42]]
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Example: adding tri-gram
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>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
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>>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017, (7, 9, 2): 2018}
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>>> add_ngram(sequences, token_indice, ngram_range=3)
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[[1, 3, 4, 5, 1337], [1, 3, 7, 9, 2, 1337, 2018]]
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"""
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new_sequences = []
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for input_list in sequences:
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new_list = input_list[:]
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for i in range(len(new_list) - ngram_range + 1):
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for ngram_value in range(2, ngram_range + 1):
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ngram = tuple(new_list[i:i + ngram_value])
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if ngram in token_indice:
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new_list.append(token_indice[ngram])
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new_sequences.append(new_list)
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return new_sequences
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# Set parameters:
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# ngram_range = 2 will add bi-grams features
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ngram_range = 1
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max_features = 20000
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maxlen = 400
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batch_size = 32
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embedding_dims = 50
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epochs = 5
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print('Loading data...')
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(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)
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print(len(x_train), 'train sequences')
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print(len(x_test), 'test sequences')
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print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))
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print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))
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if ngram_range > 1:
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print('Adding {}-gram features'.format(ngram_range))
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# Create set of unique n-gram from the training set.
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ngram_set = set()
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for input_list in x_train:
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for i in range(2, ngram_range + 1):
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set_of_ngram = create_ngram_set(input_list, ngram_value=i)
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ngram_set.update(set_of_ngram)
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# Dictionary mapping n-gram token to a unique integer.
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# Integer values are greater than max_features in order
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# to avoid collision with existing features.
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start_index = max_features + 1
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token_indice = {v: k + start_index for k, v in enumerate(ngram_set)}
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indice_token = {token_indice[k]: k for k in token_indice}
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# max_features is the highest integer that could be found in the dataset.
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max_features = np.max(list(indice_token.keys())) + 1
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# Augmenting x_train and x_test with n-grams features
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x_train = add_ngram(x_train, token_indice, ngram_range)
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x_test = add_ngram(x_test, token_indice, ngram_range)
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print('Average train sequence length: {}'.format(np.mean(list(map(len, x_train)), dtype=int)))
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print('Average test sequence length: {}'.format(np.mean(list(map(len, x_test)), dtype=int)))
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print('Pad sequences (samples x time)')
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x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
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x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
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print('x_train shape:', x_train.shape)
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print('x_test shape:', x_test.shape)
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print('Build model...')
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model = Sequential()
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# we start off with an efficient embedding layer which maps
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# our vocab indices into embedding_dims dimensions
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model.add(Embedding(max_features,
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embedding_dims,
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input_length=maxlen))
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# we add a GlobalAveragePooling1D, which will average the embeddings
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# of all words in the document
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model.add(GlobalAveragePooling1D())
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# We project onto a single unit output layer, and squash it with a sigmoid:
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model.add(Dense(1, activation='sigmoid'))
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model.compile(loss='binary_crossentropy',
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optimizer='adam',
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metrics=['accuracy'])
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model.fit(x_train, y_train,
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batch_size=batch_size,
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epochs=epochs,
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validation_data=(x_test, y_test))
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