本文整理汇总了Python中evaluator.Evaluator.tune方法的典型用法代码示例。如果您正苦于以下问题：Python Evaluator.tune方法的具体用法？Python Evaluator.tune怎么用？Python Evaluator.tune使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类evaluator.Evaluator的用法示例。
在下文中一共展示了Evaluator.tune方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from evaluator import Evaluator [as 别名]
# 或者: from evaluator.Evaluator import tune [as 别名]
#.........这里部分代码省略.........
flags.DEFINE_string("hadoop_weights", "", "hadoop weights (formatted specially)")
flags.DEFINE_boolean("add_features", False, "add features to training data")
flags.DEFINE_boolean("prune_train", False, "prune before decoding")
flags.DEFINE_boolean("no_lm", False, "don't use the unigram language model")
flags.DEFINE_boolean("pickleinput", False, "assumed input is pickled")
flags.DEFINE_string("oracle_forests", None, "oracle forests", short_name="o")
flags.DEFINE_string("feature_map_file", None, "file with the integer to feature mapping (for lbfgs)")
flags.DEFINE_boolean("cache_input", False, "cache input sentences (only works for pruned input)")
flags.DEFINE_string("rm_features", None, "list of features to remove")
flags.DEFINE_boolean("just_basic", False, "remove all features but basic")
argv = FLAGS(sys.argv)
if FLAGS.weights:
weights = Model.cmdline_model()
else:
vector = Vector()
assert glob.glob(FLAGS.hadoop_weights)
for file in glob.glob(FLAGS.hadoop_weights):
for l in open(file):
if not l.strip():
continue
f, v = l.strip().split()
vector[f] = float(v)
weights = Model(vector)
rm_features = set()
if FLAGS.rm_features:
for l in open(FLAGS.rm_features):
rm_features.add(l.strip())
lm = Ngram.cmdline_ngram()
if FLAGS.no_lm:
lm = None
if argv[1] == "train":
local_decode = ChiangPerceptronDecoder(weights, lm)
elif argv[1] == "sgd" or argv[1] == "crf":
local_decode = MarginalDecoder(weights, lm)
else:
local_decode = MarginalDecoder(weights, lm)
if FLAGS.add_features:
tdm = local_features.TargetDataManager()
local_decode.feature_adder = FeatureAdder(tdm)
local_decode.prune_train = FLAGS.prune_train
local_decode.use_pickle = FLAGS.pickleinput
local_decode.cache_input = FLAGS.cache_input
print >> logs, "Cache input is %s" % FLAGS.cache_input
if FLAGS.debuglevel > 0:
print >> logs, "beam size = %d" % FLAGS.beam
if argv[1] == "train":
if not FLAGS.dist:
perc = trainer.Perceptron.cmdline_perc(local_decode)
else:
train_files = [FLAGS.prefix + file.strip() for file in sys.stdin]
perc = distributed_trainer.DistributedPerceptron.cmdline_perc(local_decode)
perc.set_training(train_files)
perc.train()
elif argv[1] == "sgd":
crf = sgd.BaseCRF.cmdline_crf(local_decode)
crf.set_oracle_files([FLAGS.oracle_forests])
crf.train()
elif argv[1] == "crf":
if not FLAGS.dist:
crf = CRF.LBFGSCRF.cmdline_crf(local_decode)
crf.set_oracle_files([FLAGS.oracle_forests])
crf.set_feature_mappers(add_features.read_features(FLAGS.feature_map_file))
crf.rm_features(rm_features)
if FLAGS.just_basic:
print "Enforcing Basic"
crf.enforce_just_basic()
crf.train()
else:
# train_files = [FLAGS.prefix+file.strip() for file in sys.stdin]
# oracle_files = [file+".oracle" for file in train_files]
print >> sys.stderr, "DistributedCRF"
crf = distCRF.DistributedCRF.cmdline_distibuted_crf(local_decode)
# os.system("~/.python/bin/dumbo rm train_input -hadoop /home/nlg-03/mt-apps/hadoop/0.20.1+169.89/")
# os.system("~/.python/bin/dumbo put "+crf.trainfiles[0]+" train_input -hadoop /home/nlg-03/mt-apps/hadoop/0.20.1+169.89/")
crf.set_feature_mappers(add_features.read_features(FLAGS.feature_map_file))
crf.rm_features(rm_features)
if FLAGS.just_basic:
print "Enforcing Basic"
crf.enforce_just_basic()
# crf.set_oracle_files(oracle_files)
crf.train()
else:
if not FLAGS.dist:
print "Evaluating"
eval = Evaluator(local_decode, [FLAGS.dev])
eval.tune()
else:
dev_files = [FLAGS.prefix + file.strip() for file in sys.stdin]
eval = Evaluator(local_decode, dev_files)
print eval.eval(verbose=True).compute_score()