def perform_eval(eid, data):
global word_classifiers
global relation_word_classifiers
global results_outfile
target_dist = Distribution(data['objects'])
landmark_dist = Distribution(data['objects'])
target = data['target']
increment_data = []
relation = None
relation_is_negated = False
inc = 1
prev_rank = len(data['objects'])
for w,tags in [(w['word'],w['tags']) for w in data['speech']]:
utt = {}
word = None
c_rank = None
relation_dist = None
prepare_word(utt, w, tags)
objects = data['objects']
if 't' in utt:
word = utt['t'][0]
target_dist.update(logreg.classify(word, word_classifiers, objects))
if 'l' in utt:
word = utt['l'][0]
landmark_dist.update(logreg.classify(word, word_classifiers, objects))
if 'r' in utt:
word = utt['r'][0]
if relation is not None: relation += '_' + word
else: relation = word
if 'r-' in utt:
word = utt['r-'][0]
if relation is not None: relation += '_' + word
else: relation = word
relation_is_negated = True
if relation is not None: # indent this with above for loop to make it incremental
tdist = target_dist.copy()
ldist = landmark_dist.copy()
relation_dist = apply_relation(tdist, ldist, relation, relation_is_negated, objects)
if relation_dist is not None:
return relation_dist.rank(target)
else:
target_dist.normalise()
return target_dist.rank(target)
