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test_stack_gen.py
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test_stack_gen.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
Author: Gözde Gül Şahin
Test Stack Generalizer Model
"""
import subprocess
import argparse
from IO.conllWriter import *
from IO.util import *
from loader import *
from scorer import *
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-test_file', type=str, default='data/CoNLL2009-ST-Turkish/CoNLL2009-ST-evaluation-Turkish.txt',
help="test file")
parser.add_argument('-save_dir1', required=False, default=None,
help="directory of the checkpointed models")
parser.add_argument('-save_dir2', required=False, default=None,
help="directory of the checkpointed models")
parser.add_argument('-save_dir3', required=False, default=None,
help="directory of the checkpointed models")
parser.add_argument('-save_dir4', required=False, default=None,
help="directory of the checkpointed models")
parser.add_argument('-save_dir5', required=False, default=None,
help="directory of the checkpointed models")
parser.add_argument('-save_dir6', required=False, default=None,
help="directory of the checkpointed models")
parser.add_argument('-save_dir7', required=False, default=None,
help="directory of the checkpointed models")
parser.add_argument('-ens_model_dir', required=False, default=None,
help="directory of the checkpointed models")
parser.add_argument('-ens_save_dir', required=False, default=None,
help="directory of the checkpointed models")
parser.add_argument('-lang', type=str, default='tur',
help='directory of the checkpointed models')
parser.add_argument('-gpuid', type=int, default=0, help='Id of the GPU to run')
args = parser.parse_args()
test(args)
def test(test_args):
use_cuda = torch.cuda.is_available()
if use_cuda:
torch.cuda.set_device(test_args.gpuid)
# global settings
goldFile = test_args.test_file
experiments = [test_args.save_dir1,test_args.save_dir2,test_args.save_dir3,test_args.save_dir4, \
test_args.save_dir5,test_args.save_dir6,test_args.save_dir7]
predictedSenseSents = None
try:
os.stat(test_args.ens_save_dir)
except:
os.mkdir(test_args.ens_save_dir)
models_lst = []
test_data_lst = []
role_to_ix = {}
ldr_gen = None
for model_dir in experiments:
if model_dir==None:
break
with open(os.path.join(model_dir, 'config.pkl'), 'rb') as f:
args = pickle.load(f)
args.save_dir = model_dir
args.batch_size = 1
ldr = Loader(args, test_file=goldFile, save_dir = model_dir, train=False, test=True)
if len(role_to_ix)==0:
role_to_ix = ldr.role_to_ix
ldr_gen = ldr
# Base learners
test_data = ldr.getData(ldr.test_data, train=False)
model_path, _ = get_last_model_path(model_dir)
mtest = torch.load(model_path)
# Load ensemble
ens_model_path, _ = get_last_model_path(test_args.ens_model_dir)
mensemble = torch.load(ens_model_path)
if args.use_cuda:
mtest = mtest.cuda()
# change all batch sizes to 1
mtest.batch_size = 1
if mtest.subwordModel != None:
mtest.subwordModel.batch_size = 1
models_lst.append(mtest)
test_data_lst.append(test_data)
print("Begin testing...")
plst, glst, num_corr_sr, num_found_sr, num_gold_sr = testRoleLabelsEnsembleLearner(models_lst, mensemble, test_data_lst, role_to_ix,
mode="eval", type="simple")
# Write results
systemFilePath = os.path.join(test_args.ens_save_dir, "system.conll")
conllOut = codecs.open(systemFilePath, "w", encoding='utf-8')
if (test_args.lang=="fin"):
writeCoNLLUD(conllOut, ldr_gen, plst, predictedSenseSents)
else:
writeCoNLL(conllOut, ldr_gen, plst, predictedSenseSents)
# necesary for copula handling in conll09 files
if (test_args.lang in ["tur", "fin"]):
goldFile = os.path.join(test_args.ens_save_dir, "goldTest.conll")
goldConllOut = codecs.open(os.path.join(test_args.ens_save_dir, "goldTest.conll"), "w", encoding='utf-8')
if (test_args.lang=="fin"):
writeCoNLLUD(goldConllOut, ldr_gen, glst)
else:
writeCoNLL(goldConllOut, ldr_gen, glst)
# run eval09 script
scoreOut = codecs.open(os.path.join(test_args.ens_save_dir, "eval09_analysis.out"), "w", encoding='utf-8')
subprocess.call(["perl", "eval09.pl","-g", goldFile,"-s" ,systemFilePath], stdout=scoreOut)
# run self evaluator and write to test.log file
log_out = open(os.path.join(test_args.ens_save_dir, "test_scores.log"), "w")
writeScores(num_corr_sr, num_found_sr, num_gold_sr, log_out)
if __name__ == "__main__":
main()