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import random
from pathlib import Path
from collections import defaultdict, Counter
def build_subsample_then_random_split(
input_json,
output_dir,
samples_per_task=(1, 3),
train_ratio=0.8,
seed=42,
):
random.seed(seed)
input_json = Path(input_json)
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
with open(input_json, "r") as f:
data = json.load(f)
# 1. Group by task
task_to_samples = defaultdict(list)
for item in data:
task_to_samples[item["task"]].append(item)
# 2. Subsample 1-3 samples per task
sub_data = []
task_sample_counts = {}
for task, samples in task_to_samples.items():
k = random.randint(samples_per_task[0], samples_per_task[1])
k = min(k, len(samples))
selected = random.sample(samples, k)
sub_data.extend(selected)
task_sample_counts[task] = k
# 3. Random sample-level train/test split
random.shuffle(sub_data)
n_train = int(len(sub_data) * train_ratio)
train_data = sub_data[:n_train]
test_data = sub_data[n_train:]
train_tasks = [item["task"] for item in train_data]
test_tasks = [item["task"] for item in test_data]
train_task_set = set(train_tasks)
test_task_set = set(test_tasks)
overlap_tasks = sorted(list(train_task_set & test_task_set))
# 4. Save
with open(output_dir / "train.json", "w") as f:
json.dump(train_data, f, indent=2)
with open(output_dir / "test.json", "w") as f:
json.dump(test_data, f, indent=2)
split_info = {
"split_type": "subsample_per_task_then_sample_level_random_split",
"seed": seed,
"samples_per_task": list(samples_per_task),
"train_ratio": train_ratio,
"num_original_samples": len(data),
"num_total_tasks": len(task_to_samples),
"num_subsampled_samples": len(sub_data),
"num_train_samples": len(train_data),
"num_test_samples": len(test_data),
"num_train_tasks": len(train_task_set),
"num_test_tasks": len(test_task_set),
"num_overlap_tasks": len(overlap_tasks),
"overlap_tasks": overlap_tasks,
"task_sample_counts_after_subsample": task_sample_counts,
"train_task_counts": dict(Counter(train_tasks)),
"test_task_counts": dict(Counter(test_tasks)),
}
with open(output_dir / "split_info.json", "w") as f:
json.dump(split_info, f, indent=2)
print(f"Original samples: {len(data)}")
print(f"Total tasks: {len(task_to_samples)}")
print(f"Subsampled samples: {len(sub_data)}")
print(f"Train samples: {len(train_data)}")
print(f"Test samples: {len(test_data)}")
print(f"Train tasks: {len(train_task_set)}")
print(f"Test tasks: {len(test_task_set)}")
print(f"Overlap tasks: {len(overlap_tasks)}")
print(f"Saved to: {output_dir}")
if __name__ == "__main__":
build_subsample_then_random_split(
input_json="./train_metadata.json",
output_dir="./subfolder_exp_split",
samples_per_task=(1, 3),
train_ratio=0.8,
seed=42,
) |