def evaluate(self, sentences, ex_arcs):
"""LAS on either training or test sets"""
act_arcs = minibatch_parse(sentences, self, self.config.batch_size)
ex_arcs = tuple([(a[0], a[1], self.transducer.id2deprel[a[2]])
for a in pp] for pp in ex_arcs)
stdout.flush()
return score_arcs(act_arcs, ex_arcs)
def eval(self, sentences, ex_arcs):
'''LAS on either training or test sets'''
act_arcs = minibatch_parse(sentences, self, self.config.batch_size)
ex_arcs = tuple([(a[0], a[1], self.transducer.id2deprel[a[2]]) for a in pp] for pp in ex_arcs)
if FLAGS.output:
import json
with open(FLAGS.output, 'w+') as f:
for row in act_arcs:
f.write('%s\n' % json.dumps(row))
return score_arcs(act_arcs, ex_arcs)
def eval(self, sentences, ex_arcs):
'''LAS on either training or test sets'''
act_arcs = minibatch_parse(sentences, self, self.config.batch_size)
ex_arcs = tuple([(a[0], a[1], self.transducer.id2deprel[a[2]])
for a in pp] for pp in ex_arcs)
# code to print a list of arcs. obtained from 2017 bb
import json
with open('q2btest.txt', 'w+') as f:
for row in act_arcs:
f.write('%s\n' % json.dumps(row))
return score_arcs(act_arcs, ex_arcs)
def eval(self, sentences, ex_arcs, isTest=False):
'''LAS on either training or test sets'''
act_arcs = minibatch_parse(sentences, self, self.config.batch_size)
ex_arcs = tuple([(a[0], a[1], self.transducer.id2deprel[a[2]])
for a in pp] for pp in ex_arcs)
if isTest:
f = open('q2btest.txt', 'w')
for x in act_arcs:
f.write("%s\n" % str(x))
f.close()
return score_arcs(act_arcs, ex_arcs)
def eval(self, sentences, ex_arcs):
'''LAS on either training or test sets'''
act_arcs = minibatch_parse(sentences, self, self.config.batch_size)
ex_arcs = tuple([(a[0], a[1], self.transducer.id2deprel[a[2]])
for a in pp] for pp in ex_arcs)
# TA modification to generate q2b.txt
import json
with open('q2btest.txt', 'w+') as f:
for row in act_arcs:
f.write('%s\n' % json.dumps(row))
return score_arcs(act_arcs, ex_arcs)
