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get_explanations_for_human_annotation_esnli.py
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# example:
# --------
# python3 get_explanations_for_human_annotation.py \
# --corpus sst \
# --text-path "../data/human-corpus/sst.txt" \
# --load-model-path "../data/saved-models/test-sst-softmax/" \
# --load-explainer-path "../data/saved-models/communicate-sst-softmax/" \
# --output-path "../data/explanations/sst_tmp.txt" \
# --gpu-id 0
import argparse
import random
from pathlib import Path
import numpy as np
import torch
from sklearn.metrics import accuracy_score
from spec import iterator, models, explainers, laymen
from spec.dataset import dataset, fields
from spec.dataset.corpora import available_corpora
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Get examples for humans")
parser.add_argument("--corpus",
type=str,
choices=list(available_corpora.keys()),
default='sst',
help="corpus type")
parser.add_argument("--text-path",
type=str,
default=None,
help="path to the texts",
required=True)
parser.add_argument("--load-model-path",
type=str,
default=None,
help="path to the saved model",
required=True)
parser.add_argument("--load-explainer-path",
type=str,
default=None,
help="path to the saved explainer",
required=True)
parser.add_argument("--output-path",
type=str,
default=None,
help="path to the output file",
required=True)
parser.add_argument("--lazy-loading",
type=bool,
default=True)
parser.add_argument('--max-length',
type=int,
default=10 ** 12,
help='Maximum sequence length')
parser.add_argument('--min-length',
type=int,
default=0,
help='Minimum sequence length.')
parser.add_argument('--batch-size',
type=int,
default=16,
help='Maximum batch size for evaluating.')
parser.add_argument('--gpu-id',
default=None,
type=int,
help='Use CUDA on the listed devices')
parser.add_argument('--nb-explanations',
type=int,
default=10,
help='The number of explanations (will override the'
'explainer-attn-top-k in communication config)')
parser.add_argument('--random-type',
type=str,
default='shuffle',
help='The type of random expliner. Only useful if the '
'load explainer is random_attn (will override the'
'explainer-attn-top-k in communication config)')
args = parser.parse_args()
args.lazy_loading = True
seed = 42
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
corpus_cls = available_corpora[args.corpus]
fields_tuples = corpus_cls.create_fields_tuples()
text_tuples = list(filter(lambda x: x[0] != 'target', fields_tuples))
print('Reading text file...')
texts = []
texts_b = []
texts_c = []
targets = []
text_file = Path(args.text_path)
with text_file.open('r', encoding='utf8') as f:
for i, line in enumerate(f):
target, words = line.strip().split('\t')
targets.append(target)
words_a, words_b, words_c = words.split('|||')
texts.append(words_a.strip())
texts_b.append(words_b.strip())
if words_c.strip() != '':
idxs = list(map(int, words_c.strip().split()))
texts_c.append([idxs])
else:
texts_c.append([[]])
# if i + 1 == 200:
# break
print('Building dataset...')
texts_abc = [texts, texts_b, texts_c]
text_dataset = dataset.build_pair_texts_with_marks(texts_abc,
text_tuples,
args)
print('Building iterator...')
dataset_iter = iterator.build(
text_dataset, args.gpu_id, args.batch_size, is_train=False, lazy=True
)
print('Loading vocabularies...')
fields.load_vocabs(args.load_model_path, fields_tuples)
print('Loading model...')
classifier = models.load(args.load_model_path, fields_tuples, args.gpu_id)
print('Loading explainer...')
explainer = explainers.load(
args.load_explainer_path, fields_tuples, args.gpu_id
)
explainer.explainer_attn_top_k = args.nb_explanations
print('Loading layman...')
layman = laymen.load(
args.load_explainer_path, fields_tuples, explainer.get_output_size(),
args.gpu_id
)
print('Getting explanations...')
explanations = []
predictions_classifier = []
predictions_layman = []
classifier.eval()
explainer.eval()
def to_labels(preds):
return [classifier.fields_dict['target'].vocab.itos[p] for p in preds]
with torch.no_grad():
for i, batch in enumerate(dataset_iter, start=1):
pred_classes_clf = classifier.predict_classes(batch)
message = explainer(batch, classifier)
pred_classes_layman = layman.predict_classes(batch, message)
for pred_clf, pred_layman in zip(pred_classes_clf,
pred_classes_layman):
if not isinstance(pred_clf, list):
pred_clf = [pred_clf]
if not isinstance(pred_layman, list):
pred_layman = [pred_layman]
predictions_classifier.extend(to_labels(pred_clf))
predictions_layman.extend(to_labels(pred_layman))
for wids in explainer.valid_top_word_ids:
ws = [text_dataset.fields['words'].vocab.itos[w] for w in wids]
explanations.append(ws)
print('Saving explanations...')
output_file = Path(args.output_path)
with output_file.open('w', encoding='utf8') as f:
for true_label, words, pred_l, pred_c in zip(targets,
explanations,
predictions_layman,
predictions_classifier):
line = '{}\t{}\t{}\t{}\n'.format(
true_label, pred_l, pred_c, ' '.join(words)
)
f.write(line)
print('Communication acc for these explanations:')
print('{:.4f}'.format(accuracy_score(predictions_classifier,
predictions_layman)))
print('Layman acc for these explanations:')
print('{:.4f}'.format(accuracy_score(targets, predictions_layman)))
print('Classifier acc for these explanations:')
print('{:.4f}'.format(accuracy_score(targets, predictions_classifier)))