File size: 1,162 Bytes
24a410c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 |
import os
from collections import Counter
import pandas as pd
dfs = []
base_path = "dataframes"
for file_path in os.listdir(base_path):
dfs.append(pd.read_json(os.path.join(base_path, file_path), lines=True))
raw_df = pd.concat(dfs, ignore_index=True)
print(f"{raw_df=}")
clue_counts = Counter(clue for clues in raw_df['clues'] for clue in clues)
# take 20% of data with unique stories for a test set
unique_stories = set(c for c in raw_df['story'])
test_stories = set(x for i, x in enumerate(unique_stories) if i % 5 == 0)
test_df0 = raw_df[raw_df['story'].apply(lambda story: story in test_stories)]
test_df = test_df0[test_df0['clues'].apply(lambda clues: sum(clue_counts[clue] > 1 for clue in clues) <= 1)]
test_df.to_json("test.jsonl", orient='records', lines=True)
print(f"{test_df=}")
train_val_df = raw_df[raw_df['story'].apply(lambda story: story not in test_stories)]
val_df = train_val_df[train_val_df.index % 5 == 0]
val_df.to_json("validation.jsonl", orient='records', lines=True)
print(f"{val_df=}")
train_df = train_val_df[train_val_df.index % 5 != 0]
train_df.to_json("train.jsonl", orient='records', lines=True)
print(f"{train_df=}")
|