1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
|
# -*- coding: utf-8 -*-
import numpy as np
import pytest
from tensorflow import keras
from keras_preprocessing import text
from collections import OrderedDict
def test_one_hot():
sample_text = 'The cat sat on the mat.'
encoded = text.one_hot(sample_text, 5)
assert len(encoded) == 6
assert np.max(encoded) <= 4
assert np.min(encoded) >= 0
def test_hashing_trick_hash():
sample_text = 'The cat sat on the mat.'
encoded = text.hashing_trick(sample_text, 5)
assert len(encoded) == 6
assert np.max(encoded) <= 4
assert np.min(encoded) >= 1
def test_hashing_trick_md5():
sample_text = 'The cat sat on the mat.'
encoded = text.hashing_trick(sample_text, 5, hash_function='md5')
assert len(encoded) == 6
assert np.max(encoded) <= 4
assert np.min(encoded) >= 1
def test_tokenizer():
sample_texts = ['The cat sat on the mat.',
'The dog sat on the log.',
'Dogs and cats living together.']
tokenizer = text.Tokenizer(num_words=10)
tokenizer.fit_on_texts(sample_texts)
sequences = []
for seq in tokenizer.texts_to_sequences_generator(sample_texts):
sequences.append(seq)
assert np.max(np.max(sequences)) < 10
assert np.min(np.min(sequences)) == 1
tokenizer.fit_on_sequences(sequences)
for mode in ['binary', 'count', 'tfidf', 'freq']:
tokenizer.texts_to_matrix(sample_texts, mode)
def test_tokenizer_serde_no_fitting():
tokenizer = text.Tokenizer(num_words=100)
tokenizer_json = tokenizer.to_json()
recovered = text.tokenizer_from_json(tokenizer_json)
assert tokenizer.get_config() == recovered.get_config()
assert tokenizer.word_docs == recovered.word_docs
assert tokenizer.word_counts == recovered.word_counts
assert tokenizer.word_index == recovered.word_index
assert tokenizer.index_word == recovered.index_word
assert tokenizer.index_docs == recovered.index_docs
def test_tokenizer_serde_fitting():
sample_texts = [
'There was a time that the pieces fit, but I watched them fall away',
'Mildewed and smoldering, strangled by our coveting',
'I\'ve done the math enough to know the dangers of our second guessing']
tokenizer = text.Tokenizer(num_words=100)
tokenizer.fit_on_texts(sample_texts)
seq_generator = tokenizer.texts_to_sequences_generator(sample_texts)
sequences = [seq for seq in seq_generator]
tokenizer.fit_on_sequences(sequences)
tokenizer_json = tokenizer.to_json()
recovered = text.tokenizer_from_json(tokenizer_json)
assert tokenizer.char_level == recovered.char_level
assert tokenizer.document_count == recovered.document_count
assert tokenizer.filters == recovered.filters
assert tokenizer.lower == recovered.lower
assert tokenizer.num_words == recovered.num_words
assert tokenizer.oov_token == recovered.oov_token
assert tokenizer.word_docs == recovered.word_docs
assert tokenizer.word_counts == recovered.word_counts
assert tokenizer.word_index == recovered.word_index
assert tokenizer.index_word == recovered.index_word
assert tokenizer.index_docs == recovered.index_docs
def test_sequential_fit():
texts = ['The cat sat on the mat.',
'The dog sat on the log.',
'Dogs and cats living together.']
word_sequences = [
['The', 'cat', 'is', 'sitting'],
['The', 'dog', 'is', 'standing']
]
tokenizer = text.Tokenizer()
tokenizer.fit_on_texts(texts)
tokenizer.fit_on_texts(word_sequences)
assert tokenizer.document_count == 5
tokenizer.texts_to_matrix(texts)
tokenizer.texts_to_matrix(word_sequences)
def test_text_to_word_sequence():
sample_text = 'hello! ? world!'
assert text.text_to_word_sequence(sample_text) == ['hello', 'world']
def test_text_to_word_sequence_multichar_split():
sample_text = 'hello!stop?world!'
assert text.text_to_word_sequence(
sample_text, split='stop') == ['hello', 'world']
def test_text_to_word_sequence_unicode():
sample_text = u'ali! veli? kırk dokuz elli'
assert text.text_to_word_sequence(
sample_text) == [u'ali', u'veli', u'kırk', u'dokuz', u'elli']
def test_text_to_word_sequence_unicode_multichar_split():
sample_text = u'ali!stopveli?stopkırkstopdokuzstopelli'
assert text.text_to_word_sequence(
sample_text, split='stop') == [u'ali', u'veli', u'kırk', u'dokuz', u'elli']
def test_tokenizer_unicode():
sample_texts = [u'ali veli kırk dokuz elli',
u'ali veli kırk dokuz elli veli kırk dokuz']
tokenizer = text.Tokenizer(num_words=5)
tokenizer.fit_on_texts(sample_texts)
assert len(tokenizer.word_counts) == 5
def test_tokenizer_oov_flag():
"""Test of Out of Vocabulary (OOV) flag in text.Tokenizer
"""
x_train = ['This text has only known words']
x_test = ['This text has some unknown words'] # 2 OOVs: some, unknown
# Default, without OOV flag
tokenizer = text.Tokenizer()
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
assert len(x_test_seq[0]) == 4 # discards 2 OOVs
# With OOV feature
tokenizer = text.Tokenizer(oov_token='<unk>')
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
assert len(x_test_seq[0]) == 6 # OOVs marked in place
def test_tokenizer_oov_flag_and_num_words():
x_train = ['This text has only known words this text']
x_test = ['This text has some unknown words']
tokenizer = keras.preprocessing.text.Tokenizer(num_words=3,
oov_token='<unk>')
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
trans_text = ' '.join(tokenizer.index_word[t] for t in x_test_seq[0])
assert len(x_test_seq[0]) == 6
assert trans_text == 'this <unk> <unk> <unk> <unk> <unk>'
def test_sequences_to_texts_with_num_words_and_oov_token():
x_train = ['This text has only known words this text']
x_test = ['This text has some unknown words']
tokenizer = keras.preprocessing.text.Tokenizer(num_words=3,
oov_token='<unk>')
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
trans_text = tokenizer.sequences_to_texts(x_test_seq)
assert trans_text == ['this <unk> <unk> <unk> <unk> <unk>']
def test_sequences_to_texts_no_num_words():
x_train = ['This text has only known words this text']
x_test = ['This text has some unknown words']
tokenizer = keras.preprocessing.text.Tokenizer(oov_token='<unk>')
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
trans_text = tokenizer.sequences_to_texts(x_test_seq)
assert trans_text == ['this text has <unk> <unk> words']
def test_sequences_to_texts_no_oov_token():
x_train = ['This text has only known words this text']
x_test = ['This text has some unknown words']
tokenizer = keras.preprocessing.text.Tokenizer(num_words=3)
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
trans_text = tokenizer.sequences_to_texts(x_test_seq)
assert trans_text == ['this text']
def test_sequences_to_texts_no_num_words_no_oov_token():
x_train = ['This text has only known words this text']
x_test = ['This text has some unknown words']
tokenizer = keras.preprocessing.text.Tokenizer()
tokenizer.fit_on_texts(x_train)
x_test_seq = tokenizer.texts_to_sequences(x_test)
trans_text = tokenizer.sequences_to_texts(x_test_seq)
assert trans_text == ['this text has words']
def test_sequences_to_texts():
texts = [
'The cat sat on the mat.',
'The dog sat on the log.',
'Dogs and cats living together.'
]
tokenizer = keras.preprocessing.text.Tokenizer(num_words=10,
oov_token='<unk>')
tokenizer.fit_on_texts(texts)
tokenized_text = tokenizer.texts_to_sequences(texts)
trans_text = tokenizer.sequences_to_texts(tokenized_text)
assert trans_text == ['the cat sat on the mat',
'the dog sat on the log',
'dogs <unk> <unk> <unk> <unk>']
def test_tokenizer_lower_flag():
"""Tests for `lower` flag in text.Tokenizer
"""
# word level tokenizer with sentences as texts
word_tokenizer = text.Tokenizer(lower=True)
texts = ['The cat sat on the mat.',
'The dog sat on the log.',
'Dog and Cat living Together.']
word_tokenizer.fit_on_texts(texts)
expected_word_counts = OrderedDict([('the', 4), ('cat', 2), ('sat', 2),
('on', 2), ('mat', 1), ('dog', 2),
('log', 1), ('and', 1), ('living', 1),
('together', 1)])
assert word_tokenizer.word_counts == expected_word_counts
# word level tokenizer with word_sequences as texts
word_tokenizer = text.Tokenizer(lower=True)
word_sequences = [
['The', 'cat', 'is', 'sitting'],
['The', 'dog', 'is', 'standing']
]
word_tokenizer.fit_on_texts(word_sequences)
expected_word_counts = OrderedDict([('the', 2), ('cat', 1), ('is', 2),
('sitting', 1), ('dog', 1),
('standing', 1)])
assert word_tokenizer.word_counts == expected_word_counts
# char level tokenizer with sentences as texts
char_tokenizer = text.Tokenizer(lower=True, char_level=True)
texts = ['The cat sat on the mat.',
'The dog sat on the log.',
'Dog and Cat living Together.']
char_tokenizer.fit_on_texts(texts)
expected_word_counts = OrderedDict([('t', 11), ('h', 5), ('e', 6), (' ', 14),
('c', 2), ('a', 6), ('s', 2), ('o', 6),
('n', 4), ('m', 1), ('.', 3), ('d', 3),
('g', 5), ('l', 2), ('i', 2), ('v', 1),
('r', 1)])
assert char_tokenizer.word_counts == expected_word_counts
if __name__ == '__main__':
pytest.main([__file__])
|
