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fixing the context/prior weight settings

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This commit is contained in:
svlandeg 2019-07-03 17:48:09 +02:00
parent 0ea52c86b8
commit b7a0c9bf60
1 changed files with 21 additions and 28 deletions
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31
examples/pipeline/wikidata_entity_linking.py
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@ -154,8 +154,8 @@ def run_pipeline():
optimizer.L2 = L2 optimizer.L2 = L2
# define the size (nr of entities) of training and dev set # define the size (nr of entities) of training and dev set
train_limit = 5 train_limit = 5000
dev_limit = 5 dev_limit = 5000
train_data = training_set_creator.read_training(nlp=nlp_2, train_data = training_set_creator.read_training(nlp=nlp_2,
training_dir=TRAINING_DIR, training_dir=TRAINING_DIR,
@ -198,9 +198,8 @@ def run_pipeline():
print("Error updating batch:", e) print("Error updating batch:", e)
if batchnr > 0: if batchnr > 0:
with el_pipe.model.use_params(optimizer.averages): el_pipe.cfg["context_weight"] = 1
el_pipe.context_weight = 1 el_pipe.cfg["prior_weight"] = 1
el_pipe.prior_weight = 1
dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe) dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
losses['entity_linker'] = losses['entity_linker'] / batchnr losses['entity_linker'] = losses['entity_linker'] / batchnr
print("Epoch, train loss", itn, round(losses['entity_linker'], 2), print("Epoch, train loss", itn, round(losses['entity_linker'], 2),
@ -218,24 +217,19 @@ def run_pipeline():
print("dev acc random:", round(acc_r, 3), [(x, round(y, 3)) for x, y in acc_r_label.items()]) print("dev acc random:", round(acc_r, 3), [(x, round(y, 3)) for x, y in acc_r_label.items()])
print("dev acc prior:", round(acc_p, 3), [(x, round(y, 3)) for x, y in acc_p_label.items()]) print("dev acc prior:", round(acc_p, 3), [(x, round(y, 3)) for x, y in acc_p_label.items()])
with el_pipe.model.use_params(optimizer.averages):
# measuring combined accuracy (prior + context)
el_pipe.context_weight = 1
el_pipe.prior_weight = 1
dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe, error_analysis=False)
print("dev acc combo avg:", round(dev_acc_combo, 3),
[(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
# using only context # using only context
el_pipe.context_weight = 1 el_pipe.cfg["context_weight"] = 1
el_pipe.prior_weight = 0 el_pipe.cfg["prior_weight"] = 0
dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe) dev_acc_context, dev_acc_context_dict = _measure_accuracy(dev_data, el_pipe)
print("dev acc context avg:", round(dev_acc_context, 3), print("dev acc context avg:", round(dev_acc_context, 3),
[(x, round(y, 3)) for x, y in dev_acc_context_dict.items()]) [(x, round(y, 3)) for x, y in dev_acc_context_dict.items()])
# reset for follow-up tests # measuring combined accuracy (prior + context)
el_pipe.context_weight = 1 el_pipe.cfg["context_weight"] = 1
el_pipe.prior_weight = 1 el_pipe.cfg["prior_weight"] = 1
dev_acc_combo, dev_acc_combo_dict = _measure_accuracy(dev_data, el_pipe, error_analysis=False)
print("dev acc combo avg:", round(dev_acc_combo, 3),
[(x, round(y, 3)) for x, y in dev_acc_combo_dict.items()])
# STEP 8: apply the EL pipe on a toy example # STEP 8: apply the EL pipe on a toy example
if to_test_pipeline: if to_test_pipeline:
@ -250,7 +244,6 @@ def run_pipeline():
print("STEP 9: testing NLP IO", datetime.datetime.now()) print("STEP 9: testing NLP IO", datetime.datetime.now())
print() print()
print("writing to", NLP_2_DIR) print("writing to", NLP_2_DIR)
with el_pipe.model.use_params(optimizer.averages) and el_pipe.model.tok2vec.use_params(el_pipe.sgd_context.averages):
nlp_2.to_disk(NLP_2_DIR) nlp_2.to_disk(NLP_2_DIR)
print() print()