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generate_k_shot_data.py
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generate_k_shot_data.py
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"""This script samples K examples randomly without replacement from the original data."""
import argparse
import os
import numpy as np
import pandas as pd
from pandas import DataFrame
def get_label(task, line):
if task in ["MNLI", "MRPC", "QNLI", "QQP", "RTE", "SNLI", "SST-2", "STS-B", "WNLI", "CoLA"]:
# GLUE style
line = line.strip().split('\t')
if task == 'CoLA':
return line[1]
elif task == 'MNLI':
return line[-1]
elif task == 'MRPC':
return line[0]
elif task == 'QNLI':
return line[-1]
elif task == 'QQP':
return line[-1]
elif task == 'RTE':
return line[-1]
elif task == 'SNLI':
return line[-1]
elif task == 'SST-2':
return line[-1]
elif task == 'STS-B':
return 0 if float(line[-1]) < 2.5 else 1
elif task == 'WNLI':
return line[-1]
else:
raise NotImplementedError
else:
return line[0]
def load_datasets(data_dir, tasks):
datasets = {}
for task in tasks:
if task in ["MNLI", "MRPC", "QNLI", "QQP", "RTE", "SNLI", "SST-2", "STS-B", "WNLI", "CoLA"]:
# GLUE style (tsv)
dataset = {}
dirname = os.path.join(data_dir, task)
if task == "MNLI":
splits = ["train", "dev_matched", "dev_mismatched"]
else:
splits = ["train", "dev"]
for split in splits:
filename = os.path.join(dirname, f"{split}.tsv")
with open(filename, "r") as f:
lines = f.readlines()
dataset[split] = lines
datasets[task] = dataset
else:
# Other datasets (csv)
dataset = {}
dirname = os.path.join(data_dir, task)
splits = ["train", "test"]
for split in splits:
filename = os.path.join(dirname, f"{split}.csv")
dataset[split] = pd.read_csv(filename, header=None)
datasets[task] = dataset
return datasets
def split_header(task, lines):
"""
Returns if the task file has a header or not. Only for GLUE tasks.
"""
if task in ["CoLA"]:
return [], lines
elif task in ["MNLI", "MRPC", "QNLI", "QQP", "RTE", "SNLI", "SST-2", "STS-B", "WNLI"]:
return lines[0:1], lines[1:]
else:
raise ValueError("Unknown GLUE task.")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--k", type=int, default=16,
help="Training examples for each class.")
parser.add_argument("--task", type=str, nargs="+",
default=['SST-2', 'sst-5', 'mr', 'cr', 'mpqa', 'subj', 'trec', 'CoLA', 'MRPC', 'QQP', 'STS-B', 'MNLI', 'SNLI', 'QNLI', 'RTE'],
help="Task names")
parser.add_argument("--seed", type=int, nargs="+",
default=[100, 13, 21, 42, 87],
help="Random seeds")
parser.add_argument("--data_dir", type=str, default="data/original", help="Path to original data")
parser.add_argument("--output_dir", type=str, default="data", help="Output path")
parser.add_argument("--mode", type=str, default='k-shot', choices=['k-shot', 'k-shot-10x'], help="k-shot or k-shot-10x (10x dev set)")
args = parser.parse_args()
args.output_dir = os.path.join(args.output_dir, args.mode)
k = args.k
print("K =", k)
datasets = load_datasets(args.data_dir, args.task)
for seed in args.seed:
print("Seed = %d" % (seed))
for task, dataset in datasets.items():
# Set random seed
np.random.seed(seed)
# Shuffle the training set
print("| Task = %s" % (task))
if task in ["MNLI", "MRPC", "QNLI", "QQP", "RTE", "SNLI", "SST-2", "STS-B", "WNLI", "CoLA"]:
# GLUE style
train_header, train_lines = split_header(task, dataset["train"])
np.random.shuffle(train_lines)
else:
# Other datasets
train_lines = dataset['train'].values.tolist()
np.random.shuffle(train_lines)
# Set up dir
task_dir = os.path.join(args.output_dir, task)
setting_dir = os.path.join(task_dir, f"{k}-{seed}")
os.makedirs(setting_dir, exist_ok=True)
# Write test splits
if task in ["MNLI", "MRPC", "QNLI", "QQP", "RTE", "SNLI", "SST-2", "STS-B", "WNLI", "CoLA"]:
# GLUE style
# Use the original development set as the test set (the original test sets are not publicly available)
for split, lines in dataset.items():
if split.startswith("train"):
continue
split = split.replace('dev', 'test')
with open(os.path.join(setting_dir, f"{split}.tsv"), "w") as f:
for line in lines:
f.write(line)
else:
# Other datasets
# Use the original test sets
dataset['test'].to_csv(os.path.join(setting_dir, 'test.csv'), header=False, index=False)
# Get label list for balanced sampling
label_list = {}
for line in train_lines:
label = get_label(task, line)
if label not in label_list:
label_list[label] = [line]
else:
label_list[label].append(line)
if task in ["MNLI", "MRPC", "QNLI", "QQP", "RTE", "SNLI", "SST-2", "STS-B", "WNLI", "CoLA"]:
with open(os.path.join(setting_dir, "train.tsv"), "w") as f:
for line in train_header:
f.write(line)
for label in label_list:
for line in label_list[label][:k]:
f.write(line)
name = "dev.tsv"
if task == 'MNLI':
name = "dev_matched.tsv"
with open(os.path.join(setting_dir, name), "w") as f:
for line in train_header:
f.write(line)
for label in label_list:
dev_rate = 11 if '10x' in args.mode else 2
for line in label_list[label][k:k*dev_rate]:
f.write(line)
else:
new_train = []
for label in label_list:
for line in label_list[label][:k]:
new_train.append(line)
new_train = DataFrame(new_train)
new_train.to_csv(os.path.join(setting_dir, 'train.csv'), header=False, index=False)
new_dev = []
for label in label_list:
dev_rate = 11 if '10x' in args.mode else 2
for line in label_list[label][k:k*dev_rate]:
new_dev.append(line)
new_dev = DataFrame(new_dev)
new_dev.to_csv(os.path.join(setting_dir, 'dev.csv'), header=False, index=False)
if __name__ == "__main__":
main()