def inference_run(self):
print("Test accuracy starting ... ")
print(self.reader.typeOfReader)
assert self.reader.typeOfReader == "inference"
# assert self.reader.batch_size == 1
self.reader.reset_test()
numInstances = 0
# For types: List contains numpy matrices of row_size = BatchSize
predLabelScoresnumpymat_list = []
# For EL : Lists contain one list per mention
condProbs_list = [] # Crosswikis conditional priors
widIdxs_list = [] # Candidate WID IDXs (First is true)
contextProbs_list = [] # Predicted Entity prob using context
while self.reader.epochs < 1:
(left_batch, left_lengths, right_batch, right_lengths,
coherence_batch, wid_idxs_batch,
wid_cprobs_batch) = self.reader.next_test_batch()
# Candidates for entity linking
# feed_dict = {self.sampled_entity_ids: wid_idxs_batch,
# self.entity_priors: wid_cprobs_batch}
feed_dict = {self.sampled_entity_ids: wid_idxs_batch}
# Required Context
if self.textcontext:
if not self.pretrain_word_embed:
context_dict = {
self.left_batch: left_batch,
self.right_batch: right_batch,
self.left_lengths: left_lengths,
self.right_lengths: right_lengths
}
else:
context_dict = {
self.left_context_embeddings: left_batch,
self.right_context_embeddings: right_batch,
self.left_lengths: left_lengths,
self.right_lengths: right_lengths
}
feed_dict.update(context_dict)
if self.coherence:
coherence_dict = {
self.coherence_indices: coherence_batch[0],
self.coherence_values: coherence_batch[1],
self.coherence_matshape: coherence_batch[2]
}
feed_dict.update(coherence_dict)
fetch_tensors = [
self.labeling_model.label_probs,
self.posterior_model.entity_posteriors
]
fetches = self.sess.run(fetch_tensors, feed_dict=feed_dict)
[label_sigms, context_probs] = fetches
predLabelScoresnumpymat_list.append(label_sigms)
condProbs_list.extend(wid_cprobs_batch)
widIdxs_list.extend(wid_idxs_batch)
contextProbs_list.extend(context_probs.tolist())
numInstances += self.reader.batch_size
print("Num of instances {}".format(numInstances))
# print("Starting Type and EL Evaluations ... ")
pred_TypeSetsList = evaluate_types.evaluate(
predLabelScoresnumpymat_list, self.reader.idx2label)
(evWTs, sortedContextWTs) = evaluate_inference.evaluateEL(
condProbs_list,
widIdxs_list,
contextProbs_list,
self.reader.idx2knwid,
self.reader.wid2WikiTitle,
verbose=False)
return (predLabelScoresnumpymat_list, widIdxs_list, condProbs_list,
contextProbs_list, evWTs, pred_TypeSetsList)
def inference_run(self):
assert self.reader.typeOfReader == "inference"
# assert self.reader.batch_size == 1
self.reader.reset_test()
numInstances = 0
# For types: List contains numpy matrices with row_size = BatchSize
predLabelScoresnumpymat_list = []
# For EL : Lists contain one list per mention
widIdxs_list = [] # Candidate WID IDXs (First is true)
condProbs_list = [] # Crosswikis conditional priors
contextProbs_list = [] # Predicted Entity prob using context
while self.reader.epochs < 1:
(
left_batch,
left_lengths,
right_batch,
right_lengths,
coherence_batch,
wid_idxs_batch,
wid_cprobs_batch,
) = self.reader.next_test_batch()
if left_batch is None:
return (None, None, None, None, None, None, None)
# Candidates for entity linking
# feed_dict = {self.sampled_entity_ids: wid_idxs_batch,
# self.entity_priors: wid_cprobs_batch}
feed_dict = {self.sampled_entity_ids: wid_idxs_batch}
# Required Context
if self.textcontext:
if not self.pretrain_word_embed:
context_dict = {
self.left_batch: left_batch,
self.right_batch: right_batch,
self.left_lengths: left_lengths,
self.right_lengths: right_lengths,
}
else:
context_dict = {
self.left_context_embeddings: left_batch,
self.right_context_embeddings: right_batch,
self.left_lengths: left_lengths,
self.right_lengths: right_lengths,
}
feed_dict.update(context_dict)
if self.coherence:
coherence_dict = {
self.coherence_indices: coherence_batch[0],
self.coherence_values: coherence_batch[1],
self.coherence_matshape: coherence_batch[2],
}
feed_dict.update(coherence_dict)
fetch_tensors = [
self.labeling_model.label_probs,
self.posterior_model.entity_posteriors,
]
fetches = self.sess.run(fetch_tensors, feed_dict=feed_dict)
[label_sigms, context_probs] = fetches
predLabelScoresnumpymat_list.append(label_sigms)
condProbs_list.extend(wid_cprobs_batch)
widIdxs_list.extend(wid_idxs_batch)
contextProbs_list.extend(context_probs.tolist())
numInstances += self.reader.batch_size
# print("Num of instances {}".format(numInstances))
# print("Starting Type and EL Evaluations ... ")
# pred_TypeSetsList: [B, Types], For each mention, list of pred types
pred_TypeSetsList = evaluate_types.evaluate(
predLabelScoresnumpymat_list, self.reader.idx2label)
# evWTs:For each mention: Contains a list of [WTs, WIDs, Probs]
# Each element above has (MaxPrior, MaxContext, MaxJoint)
# sortedContextWTs: Titles sorted in decreasing context prob
(jointProbs_list, evWTs,
sortedContextWTs) = evaluate_inference.evaluateEL(
condProbs_list,
widIdxs_list,
contextProbs_list,
self.reader.idx2knwid,
self.reader.wid2WikiTitle,
verbose=False,
)
return (
predLabelScoresnumpymat_list,
widIdxs_list,
condProbs_list,
contextProbs_list,
jointProbs_list,
evWTs,
pred_TypeSetsList,
)