-
Notifications
You must be signed in to change notification settings - Fork 0
/
torch_tree_nn.py
267 lines (220 loc) · 8.33 KB
/
torch_tree_nn.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
import random
import torch
import torch.nn as nn
import torch.utils.data
from torch_model_base import TorchModelBase
from utils import progress_bar
__author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2020"
class TorchTreeNNModel(nn.Module):
def __init__(self, vocab, embed_dim, embedding, output_dim, hidden_activation):
super().__init__()
self.vocab = vocab
self.vocab_size = len(vocab)
self.vocab_lookup = dict(zip(self.vocab, range(self.vocab_size)))
self.embed_dim = embed_dim
self.hidden_dim = embed_dim * 2
self.hidden_activation = hidden_activation
self.output_dim = output_dim
self.tree_layer = nn.Linear(self.hidden_dim, self.embed_dim)
self.embedding = self._define_embedding(embedding)
self.classifier_layer = nn.Linear(self.embed_dim, self.output_dim)
def _define_embedding(self, embedding):
if embedding is None:
return nn.Embedding(self.vocab_size, self.embed_dim)
else:
embedding = torch.tensor(embedding, dtype=torch.float)
return nn.Embedding.from_pretrained(embedding)
def forward(self, tree):
"""Recursively interprets `tree`, applying a classifier layer
to the final representation. The label comes from the root
of the tree itself.
Parameters
----------
tree : nltk.tree.Tree
Returns
-------
torch.LongTensor, label (str)
"""
root = self.interpret(tree)
return self.classifier_layer(root), tree.label()
def interpret(self, subtree):
# Terminal nodes are str:
if isinstance(subtree, str):
i = self.vocab_lookup.get(subtree, self.vocab_lookup['$UNK'])
ind = torch.tensor([i], dtype=torch.long)
return self.embedding(ind)
# Non-branching nodes:
elif len(subtree) == 1:
return self.interpret(subtree[0])
# Branching nodes:
else:
left_subtree, right_subtree = subtree[0], subtree[1]
left_subtree = self.interpret(left_subtree)
right_subtree = self.interpret(right_subtree)
combined = torch.cat((left_subtree, right_subtree), dim=1)
root_rep = self.hidden_activation(self.tree_layer(combined))
return root_rep
class TorchTreeNN(TorchModelBase):
def __init__(self, vocab, embedding=None, embed_dim=50, **kwargs):
self.vocab = vocab
self.embedding = embedding
self.embed_dim = embed_dim
if self.embedding is not None:
self.embed_dim = embedding.shape[1]
super(TorchTreeNN, self).__init__(**kwargs)
self.params += ['embed_dim', 'embedding']
self.device = 'cpu'
def build_graph(self):
self.model = TorchTreeNNModel(
vocab=self.vocab,
embedding=self.embedding,
embed_dim=self.embed_dim,
output_dim=self.n_classes_,
hidden_activation=self.hidden_activation)
def fit(self, X, **kwargs):
"""Fairly standard `fit` method except that the labels `y` are
presumed to come from the root nodes of the trees in `X`.
Parameters
----------
X : list of `nltk.Tree` instances
kwargs : dict
For passing other parameters. If 'X_dev' is included,
then performance is monitored every 10 epochs; use
`dev_iter` to control this number.
Returns
-------
self
"""
# Incremental performance:
X_dev = kwargs.get('X_dev')
if X_dev is not None:
dev_iter = kwargs.get('dev_iter', 10)
# Data prep:
self.classes_ = self.get_classes(X)
self.n_classes_ = len(self.classes_)
self.class2index = dict(zip(self.classes_, range(self.n_classes_)))
# Model:
self.build_graph()
self.model.to(self.device)
self.model.train()
# Optimization:
loss = nn.CrossEntropyLoss()
optimizer = self.optimizer(self.model.parameters(), lr=self.eta)
# Train:
for iteration in range(1, self.max_iter+1):
epoch_error = 0.0
random.shuffle(X)
for tree in X:
pred, label = self.model.forward(tree)
label = self.convert_label(label)
err = loss(pred, label)
epoch_error += err.item()
optimizer.zero_grad()
err.backward()
optimizer.step()
# Incremental predictions where possible:
if X_dev is not None and iteration > 0 and iteration % dev_iter == 0:
self.dev_predictions[iteration] = self.predict(X_dev)
self.errors.append(epoch_error)
progress_bar(
"Finished epoch {} of {}; error is {}".format(
iteration, self.max_iter, epoch_error/len(X)))
return self
@staticmethod
def get_classes(X):
"""Classes as given by the root nodes in `X`.
Parameters
----------
X : list of nltk.tree.Tree
Returns
-------
list
"""
return sorted({t.label() for t in X})
def convert_label(self, label):
"""Convert a class label to a format that PyTorch can handle.
Parameters
----------
label : member of `self.classes_`
Returns
-------
torch.LongTensor of length 1
"""
i = self.class2index[label]
return torch.LongTensor([i])
def predict_proba(self, X):
"""Predicted probabilities for the examples in `X`.
Parameters
----------
X : list of nltk.tree.Tree
Returns
-------
np.array with shape (len(X), self.n_classes_)
"""
self.model.eval()
with torch.no_grad():
preds = []
for tree in X:
pred, _ = self.model.forward(tree)
preds.append(pred.squeeze())
preds = torch.stack(preds)
return torch.softmax(preds, dim=1).numpy()
def predict(self, X):
"""Predicted labels for the examples in `X`. These are converted
from the integers that PyTorch needs back to their original
values in `self.classes_`.
Parameters
----------
X : list of nltk.tree.Tree
Returns
-------
list of length len(X)
"""
probs = self.predict_proba(X)
return [self.classes_[i] for i in probs.argmax(axis=1)]
def simple_example(initial_embedding=False):
from nltk.tree import Tree
train = [
"(odd 1)",
"(even 2)",
"(even (odd 1) (neutral (neutral +) (odd 1)))",
"(odd (odd 1) (neutral (neutral +) (even 2)))",
"(odd (even 2) (neutral (neutral +) (odd 1)))",
"(even (even 2) (neutral (neutral +) (even 2)))",
"(even (odd 1) (neutral (neutral +) (odd (odd 1) (neutral (neutral +) (even 2)))))"]
test = [
"(odd (odd 1))",
"(even (even 2))",
"(odd (odd 1) (neutral (neutral +) (even (odd 1) (neutral (neutral +) (odd 1)))))",
"(even (even 2) (neutral (neutral +) (even (even 2) (neutral (neutral +) (even 2)))))",
"(odd (even 2) (neutral (neutral +) (odd (even 2) (neutral (neutral +) (odd 1)))))",
"(even (odd 1) (neutral (neutral +) (odd (even 2) (neutral (neutral +) (odd 1)))))",
"(odd (even 2) (neutral (neutral +) (odd (odd 1) (neutral (neutral +) (even 2)))))"]
vocab = ["1", "+", "2", "$UNK"]
X_train = [Tree.fromstring(x) for x in train]
X_test = [Tree.fromstring(x) for x in test]
if initial_embedding:
import numpy as np
embedding = np.random.uniform(
low=-1.0, high=1.0, size=(len(vocab), 50))
else:
embedding = None
mod = TorchTreeNN(
vocab,
embed_dim=50,
hidden_dim=50,
max_iter=50,
embedding=embedding)
mod.fit(X_train)
print("\nTest predictions:")
preds = mod.predict(X_test)
y_test = [t.label() for t in X_test]
correct = 0
for tree, label, pred in zip(X_test, y_test, preds):
if pred == label:
correct += 1
print("{}\n\tPredicted: {}\n\tActual: {}".format(tree, pred, label))
print("{}/{} correct".format(correct, len(X_test)))
if __name__ == '__main__':
simple_example()