def eval_proj(xc):
prj_sents = []
sup_sents = []
for inst in xc:
fix_ctn_gloss_line(inst, tag_method=INTENT_POS_PROJ)
# Do the projection comparison
sup = inst.get_pos_tags(GLOSS_WORD_ID, tag_method=INTENT_POS_MANUAL)
prj = inst.get_pos_tags(GLOSS_WORD_ID, tag_method=INTENT_POS_PROJ)
sup_tags = []
prj_tags = []
for s in sup:
sup_tags.append(POSToken(s.value(), label=s.value()))
# If the same tag occurs in the projections...
if not prj:
prj_tags.append(POSToken('UNALIGNED', label='UNALIGNED'))
continue
proj_tag = prj.find(alignment=s.attributes[ALIGNMENT])
if proj_tag:
prj_tags.append(POSToken(proj_tag.value(), label=proj_tag.value()))
else:
prj_tags.append(POSToken('UNALIGNED', label='UNALIGNED'))
sup_sents.append(sup_tags)
prj_sents.append(prj_tags)
poseval(prj_sents, sup_sents, details=True)
def eval_classifier(c, inst_list, context_feats=False, posdict=None):
"""
:param c: The classifier
:param inst_list: A list of Igt instances to test against. Must already have POS tags.
"""
gold_sents = []
eval_sents = []
to_dump = XigtCorpus()
for inst in inst_list:
to_tag = inst.copy()
strip_pos(to_tag)
# Do the classification.
to_tag.classify_gloss_pos(c, lowercase=True,
feat_next_gram=context_feats,
feat_prev_gram=context_feats,
posdict=posdict)
to_dump.append(to_tag)
# Fix the tags...
# fix_ctn_gloss_line(to_tag, tag_method=INTENT_POS_CLASS)
# Now, retrieve eval/gold.
eval_tags = [v.value() for v in to_tag.get_pos_tags(GLOSS_WORD_ID, tag_method=INTENT_POS_CLASS)]
gold_tags = [v.value() for v in inst.get_pos_tags(GLOSS_WORD_ID, tag_method=INTENT_POS_MANUAL)]
tag_tokens = [POSToken('a', label=l) for l in eval_tags]
gold_tokens= [POSToken('a', label=l) for l in gold_tags]
if not len(tag_tokens) == len(gold_tokens):
print("LENGTH OF SEQUENCE IS MISMATCHED")
continue
gold_sents.append(gold_tokens)
eval_sents.append(tag_tokens)
xigtxml.dump(open('./enriched_ctn_dev.xml', 'w'), to_dump)
return poseval(eval_sents, gold_sents, details=True,csv=True, matrix=True)
