diff --git a/Ordinal/build_Amazon_datasets.py b/Ordinal/build_Amazon_datasets.py
index cf29eb9..ebf6069 100644
--- a/Ordinal/build_Amazon_datasets.py
+++ b/Ordinal/build_Amazon_datasets.py
@@ -9,7 +9,7 @@ from pathlib import Path
import numpy as np
-datadir = '/mnt/1T/Datasets/Amazon/reviews'
+datadir = '/media/moreo/Volume/Datasets/Amazon/reviews'
outdir = './data/'
real_prev_path = './data/Books-real-prevalence-by-product_votes1_reviews100.csv'
domain = 'Books'
@@ -22,7 +22,7 @@ nval = 1000
nte = 5000
-def from_gz_text(path, encoding='utf-8', class2int=True):
+def from_text(path, encoding='utf-8', class2int=True):
"""
Reads a labelled colletion of documents.
File fomart <0-4>\t<document>\n
@@ -32,7 +32,7 @@ def from_gz_text(path, encoding='utf-8', class2int=True):
:return: a list of sentences, and a list of labels
"""
all_sentences, all_labels = [], []
- file = gzip.open(path, 'rt', encoding=encoding).readlines()
+ file = open(path, 'rt', encoding=encoding).readlines()
for line in file:
line = line.strip()
if line:
@@ -40,7 +40,7 @@ def from_gz_text(path, encoding='utf-8', class2int=True):
label, sentence = line.split('\t')
sentence = sentence.strip()
if class2int:
- label = int(label) - 1
+ label = int(label)
if label >= 0:
if sentence:
all_sentences.append(sentence)
@@ -66,6 +66,7 @@ def gen_samples_APP(pool: LabelledCollection, nsamples, sample_size, outdir, pre
write_txt_sample(sample, join(outdir, f'{i}.txt'))
prevfile.write(f'{i},' + ','.join(f'{p:.3f}' for p in sample.prevalence()) + '\n')
+
def gen_samples_NPP(pool: LabelledCollection, nsamples, sample_size, outdir, prevpath):
os.makedirs(outdir, exist_ok=True)
with open(prevpath, 'wt') as prevfile:
@@ -85,36 +86,39 @@ def gen_samples_real_prevalences(real_prevalences, pool: LabelledCollection, sam
prevfile.write(f'{i},' + ','.join(f'{p:.3f}' for p in sample.prevalence()) + '\n')
-# fullpath = join(datadir,domain)+'.txt.gz'
-#
+# fullpath = join(datadir,domain)+'.txt.gz' <- deprecated; there were duplicates
# data = LabelledCollection.load(fullpath, from_gz_text)
-# print(len(data))
-# print(data.classes_)
-# print(data.prevalence())
+
+fullpath = './data/Books/Books.txt'
+data = LabelledCollection.load(fullpath, from_text)
+
+print(len(data))
+print(data.classes_)
+print(data.prevalence())
with qp.util.temp_seed(seed):
- # train, rest = data.split_stratified(train_prop=tr_size)
- #
- # devel, test = rest.split_stratified(train_prop=0.5)
- # print(len(train))
- # print(len(devel))
- # print(len(test))
- #
+ train, rest = data.split_stratified(train_prop=tr_size)
+
+ devel, test = rest.split_stratified(train_prop=0.5)
+ print(len(train))
+ print(len(devel))
+ print(len(test))
+
domaindir = join(outdir, domain)
- # write_txt_sample(train, join(domaindir, 'training_data.txt'))
- # write_txt_sample(devel, join(domaindir, 'development_data.txt'))
- # write_txt_sample(test, join(domaindir, 'test_data.txt'))
+ write_txt_sample(train, join(domaindir, 'training_data.txt'))
+ write_txt_sample(devel, join(domaindir, 'development_data.txt'))
+ write_txt_sample(test, join(domaindir, 'test_data.txt'))
# this part is to be used when the partitions have already been created, in order to avoid re-generating them
- train = load_simple_sample_raw(domaindir, 'training_data')
- devel = load_simple_sample_raw(domaindir, 'development_data')
- test = load_simple_sample_raw(domaindir, 'test_data')
+ #train = load_simple_sample_raw(domaindir, 'training_data')
+ #devel = load_simple_sample_raw(domaindir, 'development_data')
+ #test = load_simple_sample_raw(domaindir, 'test_data')
- # gen_samples_APP(devel, nsamples=nval, sample_size=val_size, outdir=join(domaindir, 'app', 'dev_samples'),
- # prevpath=join(domaindir, 'app', 'dev_prevalences.txt'))
- # gen_samples_APP(test, nsamples=nte, sample_size=te_size, outdir=join(domaindir, 'app', 'test_samples'),
- # prevpath=join(domaindir, 'app', 'test_prevalences.txt'))
+ gen_samples_APP(devel, nsamples=nval, sample_size=val_size, outdir=join(domaindir, 'app', 'dev_samples'),
+ prevpath=join(domaindir, 'app', 'dev_prevalences.txt'))
+ gen_samples_APP(test, nsamples=nte, sample_size=te_size, outdir=join(domaindir, 'app', 'test_samples'),
+ prevpath=join(domaindir, 'app', 'test_prevalences.txt'))
# gen_samples_NPP(devel, nsamples=nval, sample_size=val_size, outdir=join(domaindir, 'npp', 'dev_samples'),
# prevpath=join(domaindir, 'npp', 'dev_prevalences.txt'))
diff --git a/Ordinal/finetune_bert.py b/Ordinal/finetune_bert.py
index 3ff5870..c079f9b 100644
--- a/Ordinal/finetune_bert.py
+++ b/Ordinal/finetune_bert.py
@@ -49,7 +49,7 @@ if __name__ == '__main__':
datapath = sys.argv[1] # './data/Books/training_data.txt'
checkpoint = sys.argv[2] #e.g., 'bert-base-uncased' or 'distilbert-base-uncased' or 'roberta-base'
- modelout = checkpoint+'-val-finetuned'
+ modelout = checkpoint+'-finetuned-new'
# load the training set, and extract a held-out validation split of 1000 documents (stratified)
df = pd.read_csv(datapath, sep='\t', names=['labels', 'review'], quoting=csv.QUOTE_NONE)
diff --git a/Ordinal/generate_bert_vectors_npytxt.py b/Ordinal/generate_bert_vectors_npytxt.py
index 2e83ae4..257824c 100644
--- a/Ordinal/generate_bert_vectors_npytxt.py
+++ b/Ordinal/generate_bert_vectors_npytxt.py
@@ -98,14 +98,14 @@ if __name__ == '__main__':
assert torch.cuda.is_available(), 'cuda is not available'
#checkpoint='roberta-base-val-finetuned'
- #generation_mode = 'ave'
+ #generation_mode = 'average' #ave seemed to work slightly better
n_args = len(sys.argv)
assert n_args==3, 'wrong arguments, expected: <checkpoint> <generation-mode>\n' \
'\tgeneration-mode: last (last layer), ave (average pooling), or posteriors (posterior probabilities)'
checkpoint = sys.argv[1] #e.g., 'bert-base-uncased'
- generation_mode = sys.argv[2] # e.g., 'last'
+ generation_mode = sys.argv[2] # e.g., 'average' # ave seemed to work slightly better
assert 'finetuned' in checkpoint, 'looks like this model is not finetuned'
@@ -115,7 +115,7 @@ if __name__ == '__main__':
datapath = './data'
domain = 'Books'
- protocols = ['real'] # ['app', 'npp']
+ protocols = ['real', 'app'] # ['app', 'npp']
assert generation_mode in ['last', 'average', 'posteriors'], 'unknown generation_model'
outname = domain + f'-{checkpoint}-{generation_mode}'