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get_final_scores.py
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import data_ptb as data
import nltk
import sys
sys.path.append("../rjiant/jiant/")
import glob
from src.utils import config
from src.models import build_model
from src.utils.utils import assert_for_log, maybe_make_dir, load_model_state
from src.preprocess import build_tasks
import os
from allennlp.data.token_indexers import \
SingleIdTokenIndexer, ELMoTokenCharactersIndexer, \
TokenCharactersIndexer
from allennlp.data import Vocabulary
from torch.autograd import Variable
import torch.nn as nn
import torch
import numpy
import argparse
import re
import sys
sys.path.append("../rjiant/jiant/")
sys.path.append(os.path.realpath(os.path.dirname(__file__) + "/../../"))
word_tags = [
'CC',
'CD',
'DT',
'EX',
'FW',
'IN',
'JJ',
'JJR',
'JJS',
'LS',
'MD',
'NN',
'NNS',
'NNP',
'NNPS',
'PDT',
'POS',
'PRP',
'PRP$',
'RB',
'RBR',
'RBS',
'RP',
'SYM',
'TO',
'UH',
'VB',
'VBD',
'VBG',
'VBN',
'VBP',
'VBZ',
'WDT',
'WP',
'WP$',
'WRB']
criterion = nn.CrossEntropyLoss()
def evaluate(data_source, batch_size=1):
# Turn on evaluation mode which disables dropout.
model.eval()
total_loss = 0
ntokens = len(corpus.dictionary)
hidden = model.init_hidden(batch_size)
for i in range(0, data_source.size(0) - 1, args.bptt):
data, targets = get_batch(data_source, i, args, evaluation=True)
output, hidden = model(data, hidden)
output = model.decoder(output)
output_flat = output.view(-1, ntokens)
total_loss += len(data) * criterion(output_flat, targets).data
hidden = repackage_hidden(hidden)
return total_loss / len(data_source)
def corpus2idx(sentence):
arr = np.array([data.dictionary.word2idx[c] for c in sentence.split()], dtype=np.int32)
return torch.from_numpy(arr[:, None]).long()
# Test model
def build_tree(depth, sen):
assert len(depth) == len(sen)
if len(depth) == 1:
parse_tree = sen[0]
else:
idx_max = numpy.argmax(depth)
parse_tree = []
if len(sen[:idx_max]) > 0:
tree0 = build_tree(depth[:idx_max], sen[:idx_max])
parse_tree.append(tree0)
tree1 = sen[idx_max]
if len(sen[idx_max + 1:]) > 0:
tree2 = build_tree(depth[idx_max + 1:], sen[idx_max + 1:])
tree1 = [tree1, tree2]
if parse_tree == []:
parse_tree = tree1
else:
parse_tree.append(tree1)
return parse_tree
def get_brackets(tree, idx=0):
brackets = set()
if isinstance(tree, list) or isinstance(tree, nltk.Tree):
for node in tree:
node_brac, next_idx = get_brackets(node, idx)
if next_idx - idx > 1:
brackets.add((idx, next_idx))
brackets.update(node_brac)
idx = next_idx
return brackets, idx
else:
return brackets, idx + 1
def MRG(tr):
if isinstance(tr, str):
# return '(' + tr + ')'
return tr + ' '
else:
s = '( '
for subtr in tr:
s += MRG(subtr)
s += ') '
return s
def MRG_labeled(tr):
if isinstance(tr, nltk.Tree):
if tr.label() in word_tags:
return tr.leaves()[0] + ' '
else:
s = '(%s ' % (re.split(r'[-=]', tr.label())[0])
for subtr in tr:
s += MRG_labeled(subtr)
s += ') '
return s
else:
return ''
def mean(x):
return sum(x) / len(x)
if __name__ == '__main__':
marks = [' ', '-', '=']
numpy.set_printoptions(precision=2, suppress=True, linewidth=5000)
parser = argparse.ArgumentParser(description='PyTorch PTB Language Model')
# Model parameters.
parser.add_argument('--data', type=str, default='data/penn',
help='location of the data corpus')
parser.add_argument('--checkpoint', type=str, default='PTB.pt',
help='onlstmconfig/test1_15_75/model_state_main_epoch_269.best_macro.th')
parser.add_argument('--exp_dir', type=str, default='PTB.pt',
help='model checkpoint to use')
parser.add_argument('--seed', type=int, default=1111,
help='random seed')
parser.add_argument('--cuda', action='store_true',
help='use CUDA')
parser.add_argument('--wsj10', action='store_true',
help='use WSJ10')
parser.add_argument('--eval_every', type=int, default=1,
help='epochs div to pick checkpoints from.')
parser.add_argument('--data_dir', type=str, default="data/",
help='reload tasks')
parser.add_argument('--max_targ_word_v_size', type=int, default=20000,
help='maxt')
parser.add_argument('--config_file', type=str, default='',
help='maxt')
parser.add_argument(
'--ptb_path',
type=str,
default='/Users/anhadmohananey/Downloads/ptb_sec23.jsonl')
parser.add_argument('--use_PP', type=bool, default=False)
args = parser.parse_args()
clargs = config.params_from_file(args.config_file, None)
torch.manual_seed(args.seed)
pretrain_tasks, target_tasks, vocab, word_embs = build_tasks(clargs)
tasks = sorted(set(pretrain_tasks + target_tasks), key=lambda x: x.name)
model = build_model(clargs, vocab, word_embs, tasks)
for j in range(1, 200, args.eval_every):
if os.path.exists(os.path.join(clargs.run_dir, "model_state_main_epoch_" + str(j)+".th")) == False:
continue
else:
print("Epoch " + str(j) + ":\n")
macro_best = glob.glob(os.path.join(clargs.run_dir,
"model_state_main_epoch_"+str(j)+".th"))
load_model_state(model, macro_best[-1], args.cuda)
corpus = data.Corpus(vocab._token_to_index['tokens'])
f1_list = [[], [], []]
prec_list = []
reca_list = []
model.eval()
for i in range(len(corpus.test)):
st = corpus.test[i].reshape(1, -1).cuda()
ta = torch.cat([corpus.test[i][1:].cuda(), corpus.test[i][:1].cuda()]).reshape(1, -1)
inp = {}
tmp = {}
tmp['words'] = st
inp['input'] = tmp
tmp1 = {}
tmp1['words'] = ta
inp['targs'] = tmp1
inp['targs_b'] = tmp1
#sent_encoder(batch['input'], task)
_, _ = model.sent_encoder.forward(tmp, tasks[0])
distances = model.sent_encoder._phrase_layer.distances
for layerID in [0, 1, 2]:
dc = distances[layerID][1:-1]
sen_cut = corpus.test[i][1:-1]
sen_tree = corpus.test_trees[i]
parse_tree = build_tree(dc.cpu().detach(), sen_cut)
model_out, _ = get_brackets(parse_tree)
std_out, _ = get_brackets(sen_tree)
overlap = model_out.intersection(std_out)
prec = float(len(overlap)) / (len(model_out) + 1e-8)
reca = float(len(overlap)) / (len(std_out) + 1e-8)
if len(std_out) == 0:
reca = 1.
if len(model_out) == 0:
prec = 1.
f1 = 2 * prec * reca / (prec + reca + 1e-8)
f1_list[layerID].append(f1)
print("\n")
for layerId in [0, 1, 2]:
print("Layer " + str(layerId))
print(mean(f1_list[layerId]))
print("\n")