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# -*- coding: utf-8 -*-
from datetime import datetime
from parser import Model
from parser.cmds.cmd import CMD
from parser.utils.corpus import Corpus
from parser.utils.data import TextDataset, batchify
class Evaluate(CMD):
def add_subparser(self, name, parser):
subparser = parser.add_parser(
name, help='Evaluate the specified model and dataset.'
)
subparser.add_argument('--batch-size', default=1000, type=int,
help='batch size')
subparser.add_argument('--buckets', default=10, type=int,
help='max num of buckets to use')
subparser.add_argument('--punct', action='store_true',
help='whether to include punctuation')
subparser.add_argument('--fdata', default='data/ptb/tamtest.conllx',
help='path to dataset')
return subparser
def __call__(self, args):
super(Evaluate, self).__call__(args)
print("Load the dataset")
corpus = Corpus.load(args.fdata, self.fields)
dataset = TextDataset(corpus, self.fields, args.buckets)
# set the data loader
dataset.loader = batchify(dataset, args.batch_size)
print(f"{len(dataset)} sentences, "
f"{len(dataset.loader)} batches, "
f"{len(dataset.buckets)} buckets")
print("Load the model")
self.model = Model.load(args.model)
print(f"{self.model}\n")
print("Evaluate the dataset")
start = datetime.now()
loss, metric = self.evaluate(dataset.loader)
total_time = datetime.now() - start
print(f"Loss: {loss:.4f} {metric}")
print(f"{total_time}s elapsed, "
f"{len(dataset) / total_time.total_seconds():.2f} Sents/s")
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