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debugging

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svlandeg 2019-05-17 17:44:11 +02:00
parent 400b19353d
commit dd691d0053
3 changed files with 98 additions and 46 deletions
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96
examples/pipeline/wiki_entity_linking/train_el.py
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@ -28,13 +28,16 @@ class EL_Model:
PRINT_LOSS = False PRINT_LOSS = False
PRINT_F = True PRINT_F = True
PRINT_TRAIN = True
EPS = 0.0000000005 EPS = 0.0000000005
CUTOFF = 0.5 CUTOFF = 0.5
INPUT_DIM = 300 INPUT_DIM = 300
ENTITY_WIDTH = 64 ENTITY_WIDTH = 4 # 64
ARTICLE_WIDTH = 128 ARTICLE_WIDTH = 8 # 128
HIDDEN_WIDTH = 64 HIDDEN_WIDTH = 6 # 64
DROP = 0.00
name = "entity_linker" name = "entity_linker"
@ -78,10 +81,19 @@ class EL_Model:
print() print()
print("Training on", len(train_inst.values()), "articles") print("Training on", len(train_inst.values()), "articles")
print("Dev test on", len(dev_inst.values()), "articles") print("Dev test on", len(dev_inst.values()), "articles")
print()
print(" CUTOFF", self.CUTOFF)
print(" INPUT_DIM", self.INPUT_DIM)
print(" ENTITY_WIDTH", self.ENTITY_WIDTH)
print(" ARTICLE_WIDTH", self.ARTICLE_WIDTH)
print(" HIDDEN_WIDTH", self.ARTICLE_WIDTH)
print(" DROP", self.DROP)
print()
# TODO: proper batches. Currently 1 article at the time # TODO: proper batches. Currently 1 article at the time
article_count = 0 article_count = 0
for article_id, inst_cluster_set in train_inst.items(): for article_id, inst_cluster_set in train_inst.items():
try:
# if to_print: # if to_print:
# print() # print()
# print(article_count, "Training on article", article_id) # print(article_count, "Training on article", article_id)
@ -90,6 +102,7 @@ class EL_Model:
entities = list() entities = list()
golds = list() golds = list()
for inst_cluster in inst_cluster_set: for inst_cluster in inst_cluster_set:
if instance_pos_count < 2: # TODO remove
article_docs.append(train_doc[article_id]) article_docs.append(train_doc[article_id])
entities.append(train_pos.get(inst_cluster)) entities.append(train_pos.get(inst_cluster))
golds.append(float(1.0)) golds.append(float(1.0))
@ -100,18 +113,31 @@ class EL_Model:
golds.append(float(0.0)) golds.append(float(0.0))
instance_neg_count += 1 instance_neg_count += 1
for k in range(5):
print()
print("update", k)
print()
# print("article docs", article_docs)
print("entities", entities)
print("golds", golds)
print()
self.update(article_docs=article_docs, entities=entities, golds=golds) self.update(article_docs=article_docs, entities=entities, golds=golds)
# dev eval # dev eval
self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_inter", avg=False) self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_inter", avg=False)
self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_inter_avg", avg=True)
except ValueError as e:
print("Error in article id", article_id)
if to_print: if to_print:
print() print()
print("Trained on", instance_pos_count, "/", instance_neg_count, "instances pos/neg") print("Trained on", instance_pos_count, "/", instance_neg_count, "instances pos/neg")
print() print()
self._test_dev(train_inst, train_pos, train_neg, train_doc, print_string="train_post", calc_random=False) self._test_dev(train_inst, train_pos, train_neg, train_doc, print_string="train_post", avg=False)
self._test_dev(train_inst, train_pos, train_neg, train_doc, print_string="train_post_avg", avg=True)
self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_post", avg=False) self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_post", avg=False)
self._test_dev(dev_inst, dev_pos, dev_neg, dev_doc, print_string="dev_post_avg", avg=True)
def _test_dev(self, instances, pos, neg, doc, print_string, avg=False, calc_random=False): def _test_dev(self, instances, pos, neg, doc, print_string, avg=False, calc_random=False):
predictions = list() predictions = list()
@ -155,16 +181,24 @@ class EL_Model:
def _predict(self, article_doc, entity, avg=False, apply_threshold=True): def _predict(self, article_doc, entity, avg=False, apply_threshold=True):
if avg: if avg:
with self.sgd.use_params(self.model.averages): with self.article_encoder.use_params(self.sgd_article.averages) \
doc_encoding = self.article_encoder([article_doc]) and self.entity_encoder.use_params(self.sgd_article.averages):
entity_encoding = self.entity_encoder([entity])
return self.model(np.append(entity_encoding, doc_encoding)) # TODO list
doc_encoding = self.article_encoder([article_doc])[0] doc_encoding = self.article_encoder([article_doc])[0]
entity_encoding = self.entity_encoder([entity])[0] entity_encoding = self.entity_encoder([entity])[0]
else:
doc_encoding = self.article_encoder([article_doc])[0]
entity_encoding = self.entity_encoder([entity])[0]
concat_encoding = list(entity_encoding) + list(doc_encoding) concat_encoding = list(entity_encoding) + list(doc_encoding)
np_array = np.asarray([concat_encoding]) np_array = np.asarray([concat_encoding])
if avg:
with self.model.use_params(self.sgd.averages):
prediction = self.model(np_array) prediction = self.model(np_array)
else:
prediction = self.model(np_array)
if not apply_threshold: if not apply_threshold:
return float(prediction) return float(prediction)
if prediction > self.CUTOFF: if prediction > self.CUTOFF:
@ -199,14 +233,17 @@ class EL_Model:
>> flatten_add_lengths \ >> flatten_add_lengths \
>> ParametricAttention(in_width)\ >> ParametricAttention(in_width)\
>> Pooling(mean_pool) \ >> Pooling(mean_pool) \
>> Residual((ExtractWindow(nW=1) >> LN(Maxout(in_width, in_width * 3)))) \ >> (ExtractWindow(nW=1) >> LN(Maxout(in_width, in_width * 3))) \
>> zero_init(Affine(hidden_width, in_width, drop_factor=0.0)) >> zero_init(Affine(hidden_width, in_width, drop_factor=0.0))
# TODO: ReLu instead of LN(Maxout) ? # TODO: ReLu instead of LN(Maxout) ?
# TODO: more convolutions ?
return encoder return encoder
def _begin_training(self): def _begin_training(self):
self.sgd_article = create_default_optimizer(self.article_encoder.ops)
self.sgd_entity = create_default_optimizer(self.entity_encoder.ops)
self.sgd = create_default_optimizer(self.model.ops) self.sgd = create_default_optimizer(self.model.ops)
@staticmethod @staticmethod
@ -216,34 +253,49 @@ class EL_Model:
loss = (d_scores ** 2).sum() loss = (d_scores ** 2).sum()
return loss, d_scores return loss, d_scores
def update(self, article_docs, entities, golds, drop=0., apply_threshold=True): def update(self, article_docs, entities, golds, apply_threshold=True):
doc_encodings, bp_doc = self.article_encoder.begin_update(article_docs, drop=drop) print("article_docs", len(article_docs))
entity_encodings, bp_encoding = self.entity_encoder.begin_update(entities, drop=drop) for a in article_docs:
print(a[0:10], a[-10:])
doc_encoding, bp_doc = self.article_encoder.begin_update([a], drop=self.DROP)
print(doc_encoding)
doc_encodings, bp_doc = self.article_encoder.begin_update(article_docs, drop=self.DROP)
entity_encodings, bp_encoding = self.entity_encoder.begin_update(entities, drop=self.DROP)
concat_encodings = [list(entity_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))] concat_encodings = [list(entity_encodings[i]) + list(doc_encodings[i]) for i in range(len(entities))]
predictions, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=drop) print("doc_encodings", len(doc_encodings), doc_encodings)
print("entity_encodings", len(entity_encodings), entity_encodings)
print("concat_encodings", len(concat_encodings), concat_encodings)
predictions, bp_model = self.model.begin_update(np.asarray(concat_encodings), drop=self.DROP)
print("predictions", predictions)
predictions = self.model.ops.flatten(predictions) predictions = self.model.ops.flatten(predictions)
golds = self.model.ops.asarray(golds) golds = self.model.ops.asarray(golds)
loss, d_scores = self.get_loss(predictions, golds) loss, d_scores = self.get_loss(predictions, golds)
# if self.PRINT_LOSS: if self.PRINT_LOSS and self.PRINT_TRAIN:
# print("loss train", round(loss, 5)) print("loss train", round(loss, 5))
# if self.PRINT_F: if self.PRINT_F and self.PRINT_TRAIN:
# predictions_f = [x for x in predictions] predictions_f = [x for x in predictions]
# if apply_threshold: if apply_threshold:
# predictions_f = [1.0 if x > self.CUTOFF else 0.0 for x in predictions_f] predictions_f = [1.0 if x > self.CUTOFF else 0.0 for x in predictions_f]
# p, r, f = run_el.evaluate(predictions_f, golds, to_print=False) p, r, f = run_el.evaluate(predictions_f, golds, to_print=False)
# print("p/r/F train", round(p, 1), round(r, 1), round(f, 1)) print("p/r/F train", round(p, 1), round(r, 1), round(f, 1))
d_scores = d_scores.reshape((-1, 1)) d_scores = d_scores.reshape((-1, 1))
d_scores = d_scores.astype(np.float32) d_scores = d_scores.astype(np.float32)
print("d_scores", d_scores)
model_gradient = bp_model(d_scores, sgd=self.sgd) model_gradient = bp_model(d_scores, sgd=self.sgd)
print("model_gradient", model_gradient)
doc_gradient = [x[0:self.ARTICLE_WIDTH] for x in model_gradient] doc_gradient = [x[0:self.ARTICLE_WIDTH] for x in model_gradient]
print("doc_gradient", doc_gradient)
entity_gradient = [x[self.ARTICLE_WIDTH:] for x in model_gradient] entity_gradient = [x[self.ARTICLE_WIDTH:] for x in model_gradient]
print("entity_gradient", entity_gradient)
bp_doc(doc_gradient) bp_doc(doc_gradient)
bp_encoding(entity_gradient) bp_encoding(entity_gradient)

2
examples/pipeline/wiki_entity_linking/wiki_nel_pipeline.py
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@ -111,7 +111,7 @@ if __name__ == "__main__":
print("STEP 6: training", datetime.datetime.now()) print("STEP 6: training", datetime.datetime.now())
my_nlp = spacy.load('en_core_web_md') my_nlp = spacy.load('en_core_web_md')
trainer = EL_Model(kb=my_kb, nlp=my_nlp) trainer = EL_Model(kb=my_kb, nlp=my_nlp)
trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=2000, devlimit=200) trainer.train_model(training_dir=TRAINING_DIR, entity_descr_output=ENTITY_DESCR, trainlimit=1, devlimit=10)
print() print()
# STEP 7: apply the EL algorithm on the dev dataset # STEP 7: apply the EL algorithm on the dev dataset

2
spacy/pipeline/pipes.pyx
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@ -293,7 +293,7 @@ class Tensorizer(Pipe):
docs (iterable): A batch of `Doc` objects. docs (iterable): A batch of `Doc` objects.
golds (iterable): A batch of `GoldParse` objects. golds (iterable): A batch of `GoldParse` objects.
drop (float): The droput rate. drop (float): The dropout rate.
sgd (callable): An optimizer. sgd (callable): An optimizer.
RETURNS (dict): Results from the update. RETURNS (dict): Results from the update.
""" """