-
Notifications
You must be signed in to change notification settings - Fork 269
/
learner.py
208 lines (167 loc) · 7.11 KB
/
learner.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
from ..imports import *
from .. import utils as U
from ..core import ArrayLearner, GenLearner, _load_model
from .preprocessor import TransformersPreprocessor
class BERTTextClassLearner(ArrayLearner):
"""
```
Main class used to tune and train Keras models for text classification using Array data.
```
"""
def __init__(self, model, train_data=None, val_data=None,
batch_size=U.DEFAULT_BS, eval_batch_size=U.DEFAULT_BS,
workers=1, use_multiprocessing=False):
super().__init__(model, train_data=train_data, val_data=val_data,
batch_size=batch_size, eval_batch_size=eval_batch_size,
workers=workers, use_multiprocessing=use_multiprocessing)
return
def view_top_losses(self, n=4, preproc=None, val_data=None):
"""
```
Views observations with top losses in validation set.
Args:
n(int or tuple): a range to select in form of int or tuple
e.g., n=8 is treated as n=(0,8)
preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
For some data like text data, a preprocessor
is required to undo the pre-processing
to correctly view raw data.
val_data: optional val_data to use instead of self.val_data
Returns:
list of n tuples where first element is either
filepath or id of validation example and second element
is loss.
```
"""
val = self._check_val(val_data)
# get top losses and associated data
tups = self.top_losses(n=n, val_data=val, preproc=preproc)
# get multilabel status and class names
classes = preproc.get_classes() if preproc is not None else None
# iterate through losses
for tup in tups:
# get data
idx = tup[0]
loss = tup[1]
truth = tup[2]
pred = tup[3]
# BERT-style tuple
join_char = ' '
obs = val[0][0][idx]
if preproc is not None:
obs = preproc.undo(obs)
if preproc.is_nospace_lang(): join_char = ''
if type(obs) == str:
obs = join_char.join(obs.split()[:512])
print('----------')
print("id:%s | loss:%s | true:%s | pred:%s)\n" % (idx, round(loss,2), truth, pred))
print(obs)
return
class TransformerTextClassLearner(GenLearner):
"""
```
Main class used to tune and train Keras models for text classification using Array data.
```
"""
def __init__(self, model, train_data=None, val_data=None,
batch_size=U.DEFAULT_BS, eval_batch_size=U.DEFAULT_BS,
workers=1, use_multiprocessing=False):
super().__init__(model, train_data=train_data, val_data=val_data,
batch_size=batch_size, eval_batch_size=eval_batch_size,
workers=workers, use_multiprocessing=use_multiprocessing)
return
def view_top_losses(self, n=4, preproc=None, val_data=None):
"""
```
Views observations with top losses in validation set.
Args:
n(int or tuple): a range to select in form of int or tuple
e.g., n=8 is treated as n=(0,8)
preproc (Preprocessor): A TextPreprocessor or ImagePreprocessor.
For some data like text data, a preprocessor
is required to undo the pre-processing
to correctly view raw data.
val_data: optional val_data to use instead of self.val_data
Returns:
list of n tuples where first element is either
filepath or id of validation example and second element
is loss.
```
"""
val = self._check_val(val_data)
# get top losses and associated data
tups = self.top_losses(n=n, val_data=val, preproc=preproc)
# get multilabel status and class names
classes = preproc.get_classes() if preproc is not None else None
# iterate through losses
for tup in tups:
# get data
idx = tup[0]
loss = tup[1]
truth = tup[2]
pred = tup[3]
join_char = ' '
#obs = val.x[idx][0]
print('----------')
print("id:%s | loss:%s | true:%s | pred:%s)\n" % (idx, round(loss,2), truth, pred))
return
def _prepare(self, data, train=True):
"""
```
prepare data as tf.Dataset
```
"""
# HF_EXCEPTION
# convert arrays to TF dataset (iterator) on-the-fly
# to work around issues with transformers and tf.Datasets
if data is None: return None
return data.to_tfdataset(train=train)
def predict(self, val_data=None):
"""
```
Makes predictions on validation set
```
"""
if val_data is not None:
val = val_data
else:
val = self.val_data
if val is None: raise Exception('val_data must be supplied to get_learner or predict')
if hasattr(val, 'reset'): val.reset()
classification, multilabel = U.is_classifier(self.model)
preds = self.model.predict(self._prepare(val, train=False))
if type(preds).__name__ == 'TFSequenceClassifierOutput': # dep_fix: undocumented breaking change in transformers==4.0.0
preds = preds.logits
# dep_fix: transformers in TF 2.2.0 returns a tuple insead of NumPy array for some reason
if isinstance(preds, tuple) and len(preds) == 1: preds = preds[0]
if classification:
if multilabel:
return activations.sigmoid(tf.convert_to_tensor(preds)).numpy()
else:
return activations.softmax(tf.convert_to_tensor(preds)).numpy()
else:
return preds
def save_model(self, fpath):
"""
```
save Transformers model
```
"""
self._make_model_folder(fpath)
self.model.save_pretrained(fpath)
return
# 2020-07-07: removed, as core.Learner.load_model calls TransformerPreprocessor.load_model_and_configure
#def load_model(self, fpath, preproc=None):
# """
# load Transformers model
# Args:
# fpath(str): path to folder containing model files
# preproc(TransformerPreprocessor): a TransformerPreprocessor instance.
# """
# if preproc is None or not isinstance(preproc, TransformersPreprocessor):
# raise ValueError('preproc arg is required to load Transformer models from disk. ' +\
# 'Supply a TransformersPreprocessor instance. This is ' +\
# 'either the third return value from texts_from* function or '+\
# 'the result of calling ktrain.text.Transformer')
# self.model = _load_model(fpath, preproc=preproc)
# return