parser.py
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import random
from copy import deepcopy
import numpy as np
from sparse import COO
from sklearn.base import BaseEstimator, TransformerMixin
from keras.utils.np_utils import to_categorical
from keras.preprocessing.sequence import pad_sequences
from utils import Tree
from models import (
KerasModel,
ParserModel,
)
from encoders import (
FeaturesFactory,
TargetsFactory,
)
class Parser(BaseEstimator, TransformerMixin, KerasModel):
def __init__(self, params):
self.params = params
self.features_factory = FeaturesFactory(self.params)
self.targets_factory = TargetsFactory(self.params)
self.model = None
def create(self):
return ParserModel(
self.params,
self.features_factory,
self.targets_factory,
).model
def create_generator(self, batches, multiple):
if not multiple:
for batch in batches:
yield batch
else:
n_batches = len(batches)
batch_idx = 0
while True:
yield batches[batch_idx]
batch_idx = (batch_idx + 1) % n_batches
def batchify_X(self, trees):
raw = self.features_factory.transform(trees)
output = []
words_batch = 0
n_cols = len(raw)
batch = [[] for _ in range(n_cols)]
for row_idx, tree in enumerate(trees):
words_batch += len(tree.tokens)
for col_idx in range(n_cols):
batch[col_idx].append(raw[col_idx][row_idx])
if words_batch > self.params.batch_size:
output_batch = [pad_sequences(x, padding='post') for x in batch]
batch = [[] for _ in range(n_cols)]
output.append(output_batch)
words_batch = 0
if words_batch > 0:
output_batch = [pad_sequences(x, padding='post') for x in batch]
output.append(output_batch)
return output
def batchify_y(self, trees):
raw = self.targets_factory.transform(trees)
output = []
words_batch = 0
n_cols = len(raw)
batch = [[] for _ in range(n_cols)]
for row_idx, tree in enumerate(trees):
words_batch += len(tree.tokens)
for col_idx in range(n_cols):
batch[col_idx].append(raw[col_idx][row_idx])
if words_batch > self.params.batch_size:
padded_batch = [pad_sequences(x, padding='post') for x in batch]
output_batch = []
for target, padded_target in zip(self.params.targets, padded_batch):
if target == 'head':
output_batch.append(to_categorical(padded_target, num_classes=padded_target.shape[1]))
elif target in ['feats', 'sent']:
output_batch.append(padded_target)
else:
output_batch.append(to_categorical(padded_target, num_classes=self.targets_factory.encoders[target].vocab_size))
batch = [[] for _ in range(n_cols)]
output.append([COO.from_numpy(a) for a in output_batch])
words_batch = 0
if words_batch > 0:
padded_batch = [pad_sequences(x, padding='post') for x in batch]
output_batch = []
for target, padded_target in zip(self.params.targets, padded_batch):
if target == 'head':
output_batch.append(to_categorical(padded_target, num_classes=padded_target.shape[1]))
elif target in ['feats', 'sent']:
output_batch.append(padded_target)
else:
output_batch.append(to_categorical(padded_target, num_classes=self.targets_factory.encoders[target].vocab_size))
batch = [[] for _ in range(n_cols)]
output.append([COO.from_numpy(a) for a in output_batch])
return output
def batchify_weights(self, trees):
output = []
words_batch = 0
targets = [t for t in self.params.targets if t not in ['sent']]
n_cols = len(targets)
batch = [[] for _ in range(n_cols)]
for row_idx, tree in enumerate(trees):
words_batch += len(tree.tokens)
sample_weight = np.log(len(tree.tokens))
if not self.params.train_partial or self.params.full_tree in tree.comments:
nonzero_targets = {
'head',
'deprel',
'lemma',
'upostag',
'xpostag',
'feats',
'semrel',
}
elif self.params.partial_tree in tree.comments:
nonzero_targets = {
'lemma',
'upostag',
'xpostag',
'feats',
}
else:
nonzero_targets = set()
for col_idx, target in enumerate(targets):
batch[col_idx].append(sample_weight if target in nonzero_targets else 1e-9)
if words_batch > self.params.batch_size:
output.append(batch)
batch = [[] for _ in range(n_cols)]
words_batch = 0
if words_batch > 0:
output.append(batch)
batch = [[] for _ in range(n_cols)]
return output
def fit(self, trees, shuffle=True):
trees = sorted(trees, key=lambda x: len(x.tokens))
if self.model is None:
self.features_factory = self.features_factory.fit(trees)
self.targets_factory = self.targets_factory.fit(trees)
self.model = self.create()
batches = list(zip(
self.batchify_X(trees),
self.batchify_y(trees),
self.batchify_weights(trees),
),
)
try:
for epoch_idx in range(self.params.epochs):
if shuffle:
random.shuffle(batches)
for batch_idx, batch in enumerate(batches):
losses = self.model.train_on_batch(
x=batch[0],
y=[a.todense() for a in batch[1]],
sample_weight=[np.array(w) for w in batch[2]],
# class_weight=['auto']*len(self.params.targets),
)
if not isinstance(losses, list):
losses = [losses]
print(epoch_idx, batch_idx, list(zip(self.model.metrics_names, losses)))
except KeyboardInterrupt:
pass
def predict(self, trees):
trees = sorted(trees, key=lambda x: len(x.tokens))
output_trees = []
tree_idx = 0
for batch in self.batchify_X(trees):
batch_trees = trees[tree_idx:(tree_idx + batch[0].shape[0])]
batch_probs = self.model.predict_on_batch(batch)
if not isinstance(batch_probs, list):
batch_probs = [batch_probs]
batch_preds = self.targets_factory.inverse_transform(batch_probs, batch_trees)
for row_idx, old_tree in enumerate(batch_trees):
row_probs = [p[row_idx] for p in batch_probs]
row_preds = [p[row_idx] for p in batch_preds]
emb = None
new_tokens = []
for token_idx, token in enumerate(old_tree.tokens):
new_token = deepcopy(token)
for field, pred in zip(self.params.targets, row_preds):
if field == 'sent':
emb = pred
else:
new_token.fields[field] = pred[token_idx]
new_tokens.append(new_token)
output_trees.append(Tree(
tree_id=old_tree.id,
tokens=new_tokens,
words=old_tree.words,
comments=old_tree.comments,
probs=row_probs if self.params.save_probs else None,
emb=emb,
))
tree_idx += 1
output_trees = sorted(output_trees, key=lambda x: x.id)
return output_trees