forked from napsternxg/DeepSequenceClassification
/
model.py
509 lines (478 loc) · 34.5 KB
/
model.py
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
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
# coding: utf-8
import logging
logger = logging.getLogger("DeepSequenceClassification_Model")
logger.setLevel(logging.INFO)
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = logging.Formatter('%(name)s %(levelname)s %(asctime)s:%(message)s')
ch.setFormatter(formatter)
logger.addHandler(ch)
logger.info("Started Logger")
import theano, keras
logger.info("Using Keras version %s" % keras.__version__)
logger.info("Using Theano version %s" % theano.__version__)
from keras.models import Sequential, Graph
from keras.layers.core import Dense, Dropout, Activation, TimeDistributedDense, Flatten, Merge, Permute, Reshape, TimeDistributedMerge
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM, GRU
from keras.layers.convolutional import Convolution1D, MaxPooling1D, Convolution2D, MaxPooling2D
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import accuracy
import numpy as np
import glob
import json
import time
import os, sys
import preprocess as pp
import vector_utils as vtu
def vectorize_data(filenames, maxlen=100, max_charlen=20, output_label_size=6, output_label_dict=None, output_type="boundary", return_chars=False):
assert output_label_dict is not None, "The output label dictionary should be specified before vectorizing data"
X = []
X_char = []
Y = []
for i, filename in enumerate(filenames):
for docid, doc in pp.get_documents(filename):
for seq in pp.get_sequences(doc):
x = []
x_char = []
y = []
for token in seq:
x.append(1 + token.word_index) # Add 1 to include token for padding
if return_chars:
x_char.append((1 + np.array(token.char_seq)).tolist()) # Add 1 to include token for padding
if output_type == "category":
y_idx = 1 + output_label_dict.get(token.c_label, -1) # Add 1 to include token for padding
else:
y_idx = 1 + output_label_dict.get(token.b_label, -1) # Add 1 to include token for padding
y.append(y_idx) # Add 1 to include token for padding
X.append(x)
if return_chars:
padded_sequence = pad_sequences([[] for k in xrange(maxlen - len(x_char))], maxlen=max_charlen).tolist() +\
pad_sequences(x_char[:maxlen], maxlen=max_charlen).tolist()
X_char.append(padded_sequence)
Y.append(y)
X = pad_sequences(X, maxlen=maxlen)
Y = pad_sequences(Y, maxlen=maxlen)
X = np.array(X)
Y = vtu.to_onehot(Y, output_label_size)
if return_chars:
return X, Y, np.array(X_char)
return X, Y
def gen_model(vocab_size=100, embedding_size=128, maxlen=100, output_size=6, hidden_layer_size=100, num_hidden_layers = 1, RNN_LAYER_TYPE="LSTM"):
RNN_CLASS = LSTM
if RNN_LAYER_TYPE == "GRU":
RNN_CLASS = GRU
logger.info("Parameters: vocab_size = %s, embedding_size = %s, maxlen = %s, output_size = %s, hidden_layer_size = %s, " %\
(vocab_size, embedding_size, maxlen, output_size, hidden_layer_size))
logger.info("Building Model")
model = Sequential()
logger.info("Init Model with vocab_size = %s, embedding_size = %s, maxlen = %s" % (vocab_size, embedding_size, maxlen))
model.add(Embedding(vocab_size, embedding_size, input_length=maxlen))
logger.info("Added Embedding Layer")
model.add(Dropout(0.5))
logger.info("Added Dropout Layer")
for i in xrange(num_hidden_layers):
model.add(RNN_CLASS(output_dim=hidden_layer_size, activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True))
logger.info("Added %s Layer" % RNN_LAYER_TYPE)
model.add(Dropout(0.5))
logger.info("Added Dropout Layer")
model.add(RNN_CLASS(output_dim=output_size, activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True))
logger.info("Added %s Layer" % RNN_LAYER_TYPE)
model.add(Dropout(0.5))
logger.info("Added Dropout Layer")
model.add(TimeDistributedDense(output_size, activation="softmax"))
logger.info("Added Dropout Layer")
logger.info("Created model with following config:\n%s" % json.dumps(model.get_config(), indent=4))
logger.info("Compiling model with optimizer %s" % optimizer)
start_time = time.time()
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
total_time = time.time() - start_time
logger.info("Model compiled in %.4f seconds." % total_time)
return model
def gen_model_brnn(vocab_size=100, embedding_size=128, maxlen=100, output_size=6, hidden_layer_size=100, num_hidden_layers = 1, RNN_LAYER_TYPE="LSTM"):
RNN_CLASS = LSTM
if RNN_LAYER_TYPE == "GRU":
RNN_CLASS = GRU
logger.info("Parameters: vocab_size = %s, embedding_size = %s, maxlen = %s, output_size = %s, hidden_layer_size = %s, " %\
(vocab_size, embedding_size, maxlen, output_size, hidden_layer_size))
logger.info("Building Graph model for Bidirectional RNN")
model = Graph()
model.add_input(name='input', input_shape=(maxlen,), dtype=int)
logger.info("Added Input node")
logger.info("Init Model with vocab_size = %s, embedding_size = %s, maxlen = %s" % (vocab_size, embedding_size, maxlen))
model.add_node(Embedding(vocab_size, embedding_size, input_length=maxlen), name='embedding', input='input')
logger.info("Added Embedding node")
model.add_node(Dropout(0.5), name="dropout_0", input="embedding")
logger.info("Added Dropout Node")
for i in xrange(num_hidden_layers):
last_dropout_name = "dropout_%s" % i
forward_name, backward_name, dropout_name = ["%s_%s" % (k, i + 1) for k in ["forward", "backward", "dropout"]]
model.add_node(RNN_CLASS(output_dim=hidden_layer_size, activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True), name=forward_name, input=last_dropout_name)
logger.info("Added %s forward node[%s]" % (RNN_LAYER_TYPE, i+1))
model.add_node(RNN_CLASS(output_dim=hidden_layer_size, activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True, go_backwards=True), name=backward_name, input=last_dropout_name)
logger.info("Added %s backward node[%s]" % (RNN_LAYER_TYPE, i+1))
model.add_node(Dropout(0.5), name=dropout_name, inputs=[forward_name, backward_name])
logger.info("Added Dropout node[%s]" % (i+1))
model.add_node(TimeDistributedDense(output_size, activation="softmax"), name="tdd", input=dropout_name)
logger.info("Added TimeDistributedDense node")
model.add_output(name="output", input="tdd")
logger.info("Added Output node")
logger.info("Created model with following config:\n%s" % model.get_config())
logger.info("Compiling model with optimizer %s" % optimizer)
start_time = time.time()
model.compile(optimizer, {"output": 'categorical_crossentropy'})
total_time = time.time() - start_time
logger.info("Model compiled in %.4f seconds." % total_time)
return model
def gen_model_brnn_multitask(vocab_size=100, embedding_size=128, maxlen=100, output_size=[6, 96], hidden_layer_size=100, num_hidden_layers = 1, RNN_LAYER_TYPE="LSTM"):
RNN_CLASS = LSTM
if RNN_LAYER_TYPE == "GRU":
RNN_CLASS = GRU
logger.info("Parameters: vocab_size = %s, embedding_size = %s, maxlen = %s, output_size = %s, hidden_layer_size = %s, " %\
(vocab_size, embedding_size, maxlen, output_size, hidden_layer_size))
logger.info("Building Graph model for Bidirectional RNN")
model = Graph()
model.add_input(name='input', input_shape=(maxlen,), dtype=int)
logger.info("Added Input node")
logger.info("Init Model with vocab_size = %s, embedding_size = %s, maxlen = %s" % (vocab_size, embedding_size, maxlen))
model.add_node(Embedding(vocab_size, embedding_size, input_length=maxlen, mask_zero=True), name='embedding', input='input')
logger.info("Added Embedding node")
model.add_node(Dropout(0.5), name="dropout_0", input="embedding")
logger.info("Added Dropout Node")
for i in xrange(num_hidden_layers):
last_dropout_name = "dropout_%s" % i
forward_name, backward_name, dropout_name = ["%s_%s" % (k, i + 1) for k in ["forward", "backward", "dropout"]]
model.add_node(RNN_CLASS(output_dim=hidden_layer_size, activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True), name=forward_name, input=last_dropout_name)
logger.info("Added %s forward node[%s]" % (RNN_LAYER_TYPE, i+1))
model.add_node(RNN_CLASS(output_dim=hidden_layer_size, activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True, go_backwards=True), name=backward_name, input=last_dropout_name)
logger.info("Added %s backward node[%s]" % (RNN_LAYER_TYPE, i+1))
model.add_node(Dropout(0.5), name=dropout_name, inputs=[forward_name, backward_name])
logger.info("Added Dropout node[%s]" % (i+1))
output_names = []
for i, output_task_size in enumerate(output_size):
tdd_name, output_name = "tdd_%s" % i, "output_%s" % i
model.add_node(TimeDistributedDense(output_task_size, activation="softmax"), name=tdd_name, input=dropout_name)
logger.info("Added TimeDistributedDense node %s with output_size %s" % (i, output_task_size))
model.add_output(name=output_name, input=tdd_name)
output_names.append(output_name)
logger.info("Added Output node")
logger.info("Created model with following config:\n%s" % model.get_config())
logger.info("Compiling model with optimizer %s" % optimizer)
start_time = time.time()
model.compile(optimizer, {k: 'categorical_crossentropy' for k in output_names})
total_time = time.time() - start_time
logger.info("Model compiled in %.4f seconds." % total_time)
return model, output_names
def gen_model_brnn_cnn_multitask(vocab_size=100, char_vocab_size = 100, embedding_size=128, char_embedding_size = 50, nb_filters = 10,\
maxlen=100, max_charlen=20, output_size=[6, 96], hidden_layer_size=100, num_hidden_layers = 1, RNN_LAYER_TYPE="LSTM"):
RNN_CLASS = LSTM
if RNN_LAYER_TYPE == "GRU":
RNN_CLASS = GRU
logger.info("Parameters: vocab_size = %s, embedding_size = %s, maxlen = %s, output_size = %s, hidden_layer_size = %s, " %\
(vocab_size, embedding_size, maxlen, output_size, hidden_layer_size))
logger.info("CNN Parameters: char_vocab_size = %s, char_embedding_size = %s, max_charlen = %s, nb_filters = %s" %\
(char_vocab_size, char_embedding_size, max_charlen, nb_filters))
logger.info("Building sequential CNN model for char based word embeddings")
model_cnn = Sequential()
model_cnn.add(Embedding(char_vocab_size, char_embedding_size, input_length=maxlen*max_charlen))
model_cnn.add(Reshape((maxlen, max_charlen, char_embedding_size)))
model_cnn.add(Permute((3,1,2)))
model_cnn.add(Convolution2D(nb_filters, 1, 2, border_mode='same'))
model_cnn.add(Permute((2,1,3)))
model_cnn.add(MaxPooling2D((2, 2)))
model_cnn.add(Reshape((maxlen, 50)))
logger.info("Building embedding model for word embeddings")
model_word = Sequential()
model_word.add(Embedding(vocab_size, embedding_size, input_length=maxlen))
logger.info("Building Graph model for Bidirectional RNN")
model = Graph()
model.add_input(name='input1', input_shape=(maxlen,), dtype=int)
logger.info("Added Input node 1")
model.add_node(model_word, name="embed_word", input="input1")
model.add_input(name='input2', input_shape=(maxlen*max_charlen,), dtype=int)
logger.info("Added Input node 2")
model.add_node(model_cnn, name="cnn_feature", input="input2")
logger.info("Init Model with vocab_size = %s, embedding_size = %s, maxlen = %s" % (vocab_size, embedding_size, maxlen))
model.add_node(Dropout(0.5), name="dropout_0", inputs=["embed_word", "cnn_feature"])
logger.info("Added Dropout Node")
for i in xrange(num_hidden_layers):
last_dropout_name = "dropout_%s" % i
forward_name, backward_name, dropout_name = ["%s_%s" % (k, i + 1) for k in ["forward", "backward", "dropout"]]
model.add_node(RNN_CLASS(output_dim=hidden_layer_size, activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True), name=forward_name, input=last_dropout_name)
logger.info("Added %s forward node[%s]" % (RNN_LAYER_TYPE, i+1))
model.add_node(RNN_CLASS(output_dim=hidden_layer_size, activation='sigmoid', inner_activation='hard_sigmoid', return_sequences=True, go_backwards=True), name=backward_name, input=last_dropout_name)
logger.info("Added %s backward node[%s]" % (RNN_LAYER_TYPE, i+1))
model.add_node(Dropout(0.5), name=dropout_name, inputs=[forward_name, backward_name])
logger.info("Added Dropout node[%s]" % (i+1))
output_names = []
for i, output_task_size in enumerate(output_size):
tdd_name, output_name = "tdd_%s" % i, "output_%s" % i
model.add_node(TimeDistributedDense(output_task_size, activation="softmax"), name=tdd_name, input=dropout_name)
logger.info("Added TimeDistributedDense node %s with output_size %s" % (i, output_task_size))
model.add_output(name=output_name, input=tdd_name)
output_names.append(output_name)
logger.info("Added Output node")
logger.info("Created model with following config:\n%s" % model.get_config())
logger.info("Compiling model with optimizer %s" % optimizer)
start_time = time.time()
model.compile(optimizer, {k: 'categorical_crossentropy' for k in output_names})
total_time = time.time() - start_time
logger.info("Model compiled in %.4f seconds." % total_time)
return model, output_names, (model_word, model_cnn)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--config", help="Path to config file", default="config.json")
parser.add_argument("--verbose", help="Verbosity level in training.", default=2, type=int)
parser.add_argument("-w", "--weights", help="Path to weights file", default=None)
parser.add_argument("-e", "--base_epochs", help="Resume training from number of epochs", default=0, type=int)
args = parser.parse_args()
config_file = args.config
verbosity = args.verbose
weights_file = args.weights
base_epochs = args.base_epochs
if weights_file is None and base_epochs != 0:
logger.warn("base_epochs should only be set when loading weights. Continuing with base_epochs = 0")
else:
logger.info("Will load model weights from %s and train using base_epochs = %s" % (weights_file, base_epochs))
logger.info("Using config file: %s and verbosity: %s" % (config_file, verbosity))
CONFIG = json.load(open(config_file))
BASE_DATA_DIR = CONFIG["BASE_DATA_DIR"]
DATA_DIR = "%s/%s" % (BASE_DATA_DIR, CONFIG["DATA_DIR"])
vocab_file = "%s/%s" % (BASE_DATA_DIR, CONFIG["vocab_file"])
char_vocab_file = "%s/%s" % (BASE_DATA_DIR, CONFIG.get("char_vocab_file", None))
labels_file = "%s/%s" % (BASE_DATA_DIR, CONFIG["labels_file"])
boundary_file = "%s/%s" % (BASE_DATA_DIR, CONFIG["boundary_file"])
category_file = "%s/%s" % (BASE_DATA_DIR, CONFIG["category_file"])
BASE_OUT_DIR = CONFIG["BASE_OUT_DIR"]
SAVE_MODEL_DIR = "%s/%s" % (BASE_OUT_DIR, CONFIG["SAVE_MODEL_DIR"])
label_type = CONFIG.get("label_type", "boundary")
MODEL_PREFIX = CONFIG.get("MODEL_PREFIX", "model")
maxlen = CONFIG["maxlen"]
max_charlen = CONFIG.get("max_charlen", 20)
num_hidden_layers = CONFIG["num_hidden_layers"]
embedding_size = CONFIG["embedding_size"]
char_embedding_size = CONFIG.get("char_embedding_size", 100)
nb_filters = CONFIG.get("nb_filters", 10)
hidden_layer_size = CONFIG["hidden_layer_size"]
RNN_LAYER_TYPE = CONFIG.get("RNN_LAYER_TYPE", "LSTM")
optimizer = CONFIG["optimizer"]
n_epochs = CONFIG["n_epochs"] + base_epochs
save_every = CONFIG["save_every"]
model_type = CONFIG.get("model_type", "rnn") # rnn, brnn
RNN_CLASS = LSTM
if RNN_LAYER_TYPE == "GRU":
RNN_CLASS = GRU
index_word, word_dict = pp.load_vocab(vocab_file)
char_dict = {}
if char_vocab_file is not None:
index_char, char_dict = pp.load_vocab(char_vocab_file)
char_vocab_size = len(index_char) + 2 # Add offset for OOV and padding
pp.WordToken.set_vocab(word_dict = word_dict, char_dict = char_dict)
index_labels, labels_dict = pp.load_vocab(labels_file)
index_boundary, boundary_dict = pp.load_vocab(boundary_file)
index_category, category_dict = pp.load_vocab(category_file)
vocab_size = len(index_word) + pp.WordToken.VOCAB + 1 # Add offset of VOCAB and then extra token for padding
labels_size = len(index_labels) + 1 # Add extra token for padding
boundary_size = len(index_boundary) + 1 # Add extra token for padding
category_size = len(index_category) + 1 # Add extra token for padding
logger.info("Parameters: vocab_size = %s, label_type = %s, labels_size = %s, embedding_size = %s, maxlen = %s, boundary_size = %s, category_size = %s, embedding_size = %s, hidden_layer_size = %s" %\
(vocab_size, label_type, labels_size, embedding_size, maxlen, boundary_size, category_size, embedding_size, hidden_layer_size))
# Read the data
if sum([os.path.isfile("%s/%s" % (BASE_DATA_DIR, k)) for k in CONFIG["data_vectors"]]) < len(CONFIG["data_vectors"]):
logger.info("Preprocessed vectors don't exist. Generating again.")
CV_filenames = [glob.glob("%s/%s/*.xml" % (DATA_DIR, i)) for i in range(1,6)]
train_files = reduce(lambda x, y: x + y, CV_filenames[0:4])
test_files = reduce(lambda x, y: x + y, CV_filenames[4:])
if model_type == "brnn_cnn_multitask":
assert char_vocab_file is not None, "In order to use char CNN one must set a char_vocab_file to the character vocab file in %s" % config_file
Y_train = []
Y_test = []
X_train, Y_train_t = vectorize_data(train_files, maxlen=maxlen, output_label_size=boundary_size, output_label_dict=boundary_dict, output_type="boundary")
X_test, Y_test_t = vectorize_data(test_files, maxlen=maxlen, output_label_size=boundary_size, output_label_dict=boundary_dict, output_type="boundary")
Y_train.append(Y_train_t)
Y_test.append(Y_test_t)
X_train, Y_train_t, X_char_train = vectorize_data(train_files, maxlen=maxlen, max_charlen = max_charlen, output_label_size=category_size, output_label_dict=category_dict, output_type="category", return_chars=True) # Only get the chars the 2nd time to imporve computation
X_test, Y_test_t, X_char_test = vectorize_data(test_files, maxlen=maxlen, max_charlen = max_charlen, output_label_size=category_size, output_label_dict=category_dict, output_type="category", return_chars=True)
Y_train.append(np.array(Y_train_t))
Y_test.append(np.array(Y_test_t))
logger.info("Saving preprocessed vectors for faster computation next time in %s files." % ["%s/%s" % (BASE_DATA_DIR, k) for k in CONFIG["data_vectors"]])
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][0]), X_train)
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][1]), X_char_train)
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][2]), vtu.onehot_to_idxarr(Y_train[0]))
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][3]), vtu.onehot_to_idxarr(Y_train[1]))
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][4]), X_test)
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][5]), X_char_test)
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][6]), vtu.onehot_to_idxarr(Y_test[0]))
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][7]), vtu.onehot_to_idxarr(Y_test[1]))
# Reshape arrays after saving
X_char_train = X_char_train.reshape((X_char_train.shape[0], X_char_train.shape[1]*X_char_train.shape[2]))
X_char_test = X_char_test.reshape((X_char_test.shape[0], X_char_test.shape[1]*X_char_test.shape[2]))
logger.info("Loaded X_char_train: %s, X_char_test: %s" % (X_char_train.shape, X_char_test.shape))
elif model_type == "brnn_multitask":
Y_train = []
Y_test = []
X_train, Y_train_t = vectorize_data(train_files, maxlen=maxlen, output_label_size=boundary_size, output_label_dict=boundary_dict, output_type="boundary")
X_test, Y_test_t = vectorize_data(test_files, maxlen=maxlen, output_label_size=boundary_size, output_label_dict=boundary_dict, output_type="boundary")
Y_train.append(Y_train_t)
Y_test.append(Y_test_t)
X_train, Y_train_t = vectorize_data(train_files, maxlen=maxlen, output_label_size=category_size, output_label_dict=category_dict, output_type="category")
X_test, Y_test_t = vectorize_data(test_files, maxlen=maxlen, output_label_size=category_size, output_label_dict=category_dict, output_type="category")
Y_train.append(np.array(Y_train_t))
Y_test.append(np.array(Y_test_t))
logger.info("Saving preprocessed vectors for faster computation next time in %s files." % ["%s/%s" % (BASE_DATA_DIR, k) for k in CONFIG["data_vectors"]])
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][0]), X_train)
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][1]), vtu.onehot_to_idxarr(Y_train[0]))
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][2]), vtu.onehot_to_idxarr(Y_train[1]))
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][3]), X_test)
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][4]), vtu.onehot_to_idxarr(Y_test[0]))
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][5]), vtu.onehot_to_idxarr(Y_test[1]))
else:
X_train, Y_train = vectorize_data(train_files, maxlen=maxlen, output_label_size=labels_size, output_label_dict=labels_dict, output_type=label_type)
X_test, Y_test = vectorize_data(test_files, maxlen=maxlen, output_label_size=labels_size, output_label_dict=labels_dict, output_type=label_type)
logger.info("Saving preprocessed vectors for faster computation next time in %s files." % ["%s/%s" % (BASE_DATA_DIR, k) for k in CONFIG["data_vectors"]])
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][0]), X_train)
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][1]), vtu.onehot_to_idxarr(Y_train))
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][2]), X_test)
np.save("%s/%s" % (BASE_DATA_DIR, CONFIG["data_vectors"][3]), vtu.onehot_to_idxarr(Y_test))
else:
logger.info("Preprocessed vectors exist. Loading from files %s." % ["%s/%s" % (BASE_DATA_DIR, k) for k in CONFIG["data_vectors"]])
if model_type == "brnn_multitask":
X_train, X_test = [np.load("%s/%s" % (BASE_DATA_DIR, k)) for k in CONFIG["data_vectors"][::3]]
Y_train = [vtu.to_onehot(np.load("%s/%s" % (BASE_DATA_DIR, k[0])), k[1]) for k in zip(CONFIG["data_vectors"][1:3], [boundary_size, category_size])]
Y_test = [vtu.to_onehot(np.load("%s/%s" % (BASE_DATA_DIR, k[0])), k[1]) for k in zip(CONFIG["data_vectors"][4:6], [boundary_size, category_size])]
elif model_type == "brnn_cnn_multitask":
X_train, X_test = [np.load("%s/%s" % (BASE_DATA_DIR, k)) for k in CONFIG["data_vectors"][::4]]
X_char_train, X_char_test = [np.load("%s/%s" % (BASE_DATA_DIR, k)) for k in CONFIG["data_vectors"][1::4]]
logger.info("Loaded X_char_train: %s, X_char_test: %s" % (X_char_train.shape, X_char_test.shape))
X_char_train = X_char_train.reshape((X_char_train.shape[0], X_char_train.shape[1]*X_char_train.shape[2]))
X_char_test = X_char_test.reshape((X_char_test.shape[0], X_char_test.shape[1]*X_char_test.shape[2]))
Y_train = [vtu.to_onehot(np.load("%s/%s" % (BASE_DATA_DIR, k[0])), k[1]) for k in zip(CONFIG["data_vectors"][2:4], [boundary_size, category_size])]
Y_test = [vtu.to_onehot(np.load("%s/%s" % (BASE_DATA_DIR, k[0])), k[1]) for k in zip(CONFIG["data_vectors"][6:8], [boundary_size, category_size])]
else:
X_train, X_test = [np.load("%s/%s" % (BASE_DATA_DIR, k)) for k in CONFIG["data_vectors"][::2]]
Y_train, Y_test = [vtu.to_onehot(np.load("%s/%s" % (BASE_DATA_DIR, k)), labels_size) for k in CONFIG["data_vectors"][1::2]]
if model_type == "brnn_multitask":
logger.info("Loaded data shapes:\nX_train: %s, Y_train: %s\nX_test: %s, Y_test: %s" % (X_train.shape, [k.shape for k in Y_train], X_test.shape, [k.shape for k in Y_train]))
elif model_type == "brnn_cnn_multitask":
logger.info("Loaded data shapes:\nX_train: %s, X_char_train: %s, Y_train: %s\nX_test: %s, X_char_test: %s, Y_test: %s" % (X_train.shape, X_char_train.shape, [k.shape for k in Y_train], X_test.shape, X_char_test.shape, [k.shape for k in Y_train]))
else:
logger.info("Loaded data shapes:\nX_train: %s, Y_train: %s\nX_test: %s, Y_test: %s" % (X_train.shape, Y_train.shape, X_test.shape, Y_test.shape))
if model_type == "brnn":
model = gen_model_brnn(vocab_size=vocab_size, embedding_size=embedding_size, maxlen=maxlen, output_size=labels_size, hidden_layer_size=hidden_layer_size, num_hidden_layers = num_hidden_layers, RNN_LAYER_TYPE=RNN_LAYER_TYPE)
elif model_type == "brnn_multitask":
model, output_names = gen_model_brnn_multitask(vocab_size=vocab_size, embedding_size=embedding_size, maxlen=maxlen, output_size=[boundary_size, category_size], hidden_layer_size=hidden_layer_size, num_hidden_layers = num_hidden_layers, RNN_LAYER_TYPE=RNN_LAYER_TYPE)
elif model_type == "brnn_cnn_multitask":
model, output_names, _temp_models = gen_model_brnn_cnn_multitask(vocab_size=vocab_size, char_vocab_size = char_vocab_size, embedding_size=embedding_size, char_embedding_size = char_embedding_size, nb_filters = nb_filters, maxlen=maxlen, max_charlen=max_charlen, output_size=[boundary_size, category_size], hidden_layer_size=hidden_layer_size, num_hidden_layers = num_hidden_layers, RNN_LAYER_TYPE=RNN_LAYER_TYPE)
logger.error("Feature under development.")
else:
model = gen_model(vocab_size=vocab_size, embedding_size=embedding_size, maxlen=maxlen, output_size=labels_size, hidden_layer_size=hidden_layer_size, num_hidden_layers = num_hidden_layers, RNN_LAYER_TYPE=RNN_LAYER_TYPE)
if weights_file is not None:
logger.info("Loading model weights from %s. Will continue training model from %s epochs." % (weights_file, base_epochs))
model.load_weights(weights_file)
if base_epochs > 0:
if model_type == "brnn_cnn_multitask":
Y_out = model.predict({'input1': X_test, "input2": X_char_test})
#Y_idx = (Y_test[0][:,:,0] == 0) & (Y_test[0][:,:,5] == 0) # Get indexes of only those tokens which correspond to entitites
Y_idx = Y_test[0][:,:,0] >= 0 # Get all indexes
# Calculate accuracy only based on correct entity identity
logger.info("Evaluation scores on test data:")
scores = {}
Y_pred = []
Y_true = []
score_keys = ["accuracy", "micro_precision", "micro_recall", "micro_f1", "macro_f1", "c_mf1", "c_mp", "c_mr"]
for i, k in enumerate(output_names):
labels = range(Y_out[k].shape[-1])
Y_pred.append(np.argmax(np.array(Y_out[k]), axis=-1)[Y_idx])
Y_true.append(np.argmax(Y_test[i], axis=-1)[Y_idx])
scores[k] = vtu.get_eval_scores(Y_pred[-1], Y_true[-1], labels = labels)
scores[k]["accuracy"] = accuracy(Y_pred[-1], Y_true[-1])
TP, FP, FN = (scores[k][_k][1:-1] for _k in ["TP", "FP", "FN"])
micro_precision = np.sum(TP) * 1. / np.sum(TP + FP)
micro_recall = np.sum(TP) * 1. / np.sum(TP + FN)
micro_f1 = 2*micro_precision*micro_recall / (micro_precision+micro_recall)
scores[k]["c_mf1"] = micro_f1
scores[k]["c_mp"] = micro_precision
scores[k]["c_mr"] = micro_recall
logger.info("%s: %s" % (k, dict((_k, scores[k][_k]) for _k in score_keys)))
all_labels = dict((k, i) for i, k in enumerate((b_i, c_i) for b_i in range(Y_test[0].shape[-1]) for c_i in range(Y_test[1].shape[-1])))
all_true = [all_labels.get(k) for k in zip(Y_true[0], Y_true[1])]
all_pred = [all_labels.get(k) for k in zip(Y_pred[0], Y_pred[1])]
scores_all = vtu.get_eval_scores(all_pred, all_true, labels=range(len(all_labels)))
scores_all["accuracy"] = accuracy(all_pred, all_true)
valid_idx = map(lambda x: x[1], filter(lambda k: (k[0][0] > 0 and k[0][0] < 5 and k[0][1] > 0 and k[0][1] < 95), all_labels.iteritems()))
TP, FP, FN = (scores_all[_k][valid_idx] for _k in ["TP", "FP", "FN"])
micro_precision = np.sum(TP) * 1. / np.sum(TP + FP)
micro_recall = np.sum(TP) * 1. / np.sum(TP + FN)
micro_f1 = 2*micro_precision*micro_recall / (micro_precision+micro_recall)
scores_all["c_mf1"] = micro_f1
scores_all["c_mp"] = micro_precision
scores_all["c_mr"] = micro_recall
logger.info("%s: %s" % (k, dict((_k, scores_all[_k]) for _k in score_keys)))
for epoch in xrange(base_epochs, n_epochs, save_every):
logger.info("Starting Epochs %s to %s" % (epoch, epoch + save_every))
start_time = time.time()
if model_type == "brnn":
model.fit({"input": X_train,"output": Y_train}, validation_data={"input": X_test, "output": Y_test}, nb_epoch=save_every, verbose=verbosity)
Y_out = model.predict({'input': X_test})['output']
Y_idx = (Y_test[:,:,0] == 0) & (Y_test[:,:,5] == 0) # Get indexes of only those tokens which correspond to entitites
# Calculate accuracy only based on correct entity identity
acc = accuracy(np.argmax(np.array(Y_out), axis=-1)[Y_idx], np.argmax(Y_test, axis=-1)[Y_idx])
logger.info("Output test accuracy: %.3f" % acc*100)
elif model_type == "brnn_multitask":
model.fit({"input": X_train, output_names[0]: Y_train[0], output_names[1]: Y_train[1]},\
validation_data={"input": X_test, output_names[0]: Y_test[0], output_names[1]: Y_test[1]}, nb_epoch=save_every, verbose=verbosity)
Y_out = model.predict({'input': X_test})
Y_idx = (Y_test[0][:,:,0] == 0) & (Y_test[0][:,:,5] == 0) # Get indexes of only those tokens which correspond to entitites
# Calculate accuracy only based on correct entity identity
acc1 = accuracy(np.argmax(np.array(Y_out[output_names[0]]), axis=-1)[Y_idx], np.argmax(Y_test[0], axis=-1)[Y_idx])
acc2 = accuracy(np.argmax(np.array(Y_out[output_names[1]]), axis=-1)[Y_idx], np.argmax(Y_test[1], axis=-1)[Y_idx])
logger.info("Test accuracy: %.3f[%s], %.3f[%s]" % (acc1* 100, output_names[0], acc2 * 100, output_names[1]))
elif model_type == "brnn_cnn_multitask":
model.fit({"input1": X_train, "input2": X_char_train, output_names[0]: Y_train[0], output_names[1]: Y_train[1]},\
validation_data={"input1": X_test, "input2": X_char_test, output_names[0]: Y_test[0], output_names[1]: Y_test[1]}, nb_epoch=save_every, verbose=verbosity)
Y_out = model.predict({'input1': X_test, "input2": X_char_test})
#Y_idx = (Y_test[0][:,:,0] == 0) & (Y_test[0][:,:,5] == 0) # Get indexes of only those tokens which correspond to entitites
Y_idx = Y_test[0][:,:,0] >= 0 # Get all indexes
# Calculate accuracy only based on correct entity identity
logger.info("Evaluation scores on test data:")
scores = {}
Y_pred = []
Y_true = []
score_keys = ["accuracy", "micro_precision", "micro_recall", "micro_f1", "macro_f1", "c_mf1", "c_mp", "c_mr"]
for i, k in enumerate(output_names):
labels = range(Y_out[k].shape[-1])
Y_pred.append(np.argmax(np.array(Y_out[k]), axis=-1)[Y_idx])
Y_true.append(np.argmax(Y_test[i], axis=-1)[Y_idx])
scores[k] = vtu.get_eval_scores(Y_pred[-1], Y_true[-1], labels = labels)
scores[k]["accuracy"] = accuracy(Y_pred[-1], Y_true[-1])
TP, FP, FN = (scores[k][_k][1:-1] for _k in ["TP", "FP", "FN"])
micro_precision = np.sum(TP) * 1. / np.sum(TP + FP)
micro_recall = np.sum(TP) * 1. / np.sum(TP + FN)
micro_f1 = 2*micro_precision*micro_recall / (micro_precision+micro_recall)
scores[k]["c_mf1"] = micro_f1
scores[k]["c_mp"] = micro_precision
scores[k]["c_mr"] = micro_recall
logger.info("%s: %s" % (k, dict((_k, scores[k][_k]) for _k in score_keys)))
all_labels = dict((k, i) for i, k in enumerate((b_i, c_i) for b_i in range(Y_test[0].shape[-1]) for c_i in range(Y_test[1].shape[-1])))
all_true = [all_labels.get(k) for k in zip(Y_true[0], Y_true[1])]
all_pred = [all_labels.get(k) for k in zip(Y_pred[0], Y_pred[1])]
scores_all = vtu.get_eval_scores(all_pred, all_true, labels=range(len(all_labels)))
scores_all["accuracy"] = accuracy(all_pred, all_true)
valid_idx = map(lambda x: x[1], filter(lambda k: (k[0][0] > 0 and k[0][0] < 5 and k[0][1] > 0 and k[0][1] < 95), all_labels.iteritems()))
TP, FP, FN = (scores_all[_k][valid_idx] for _k in ["TP", "FP", "FN"])
micro_precision = np.sum(TP) * 1. / np.sum(TP + FP)
micro_recall = np.sum(TP) * 1. / np.sum(TP + FN)
micro_f1 = 2*micro_precision*micro_recall / (micro_precision+micro_recall)
scores_all["c_mf1"] = micro_f1
scores_all["c_mp"] = micro_precision
scores_all["c_mr"] = micro_recall
logger.info("%s: %s" % (k, dict((_k, scores_all[_k]) for _k in score_keys)))
#logger.info("Test accuracy: %.3f[%s], %.3f[%s]" % (acc1* 100, output_names[0], acc2 * 100, output_names[1]))
logger.error("Feature under development.")
#sys.exit(1)
else:
model.fit(X_train,Y_train, validation_data=(X_test, Y_test), nb_epoch=save_every, verbose=verbosity, show_accuracy=True)
total_time = time.time() - start_time
logger.info("Finished training %.3f epochs in %s seconds with %.5f seconds/epoch" % (save_every, total_time, total_time * 1.0/ save_every))
model.save_weights("%s/%s_%s_h%s-%s.h5" % (SAVE_MODEL_DIR, MODEL_PREFIX, model_type, num_hidden_layers, epoch), overwrite=True)