File size: 3,380 Bytes
1a6e63a | 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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import os
import pandas as pd
from sklearn.model_selection import train_test_split
# ============================
# INPUT FILES (already created)
# ============================
BASE_ANALYSIS_DIR = r"C:\Users\Sam\OneDrive\AetherMind\AetherMindProject\AetherMind_for_Alzheimers_Research\data\claims\analysis"
ANLI_R1_CSV = os.path.join(BASE_ANALYSIS_DIR, "anli_error_buffer_anli_r1_dev_round14.csv")
ANLI_R2_CSV = os.path.join(BASE_ANALYSIS_DIR, "anli_error_buffer_anli_r2_dev_round14.csv")
ANLI_R3_CSV = os.path.join(BASE_ANALYSIS_DIR, "anli_error_buffer_anli_r3_dev_round14.csv")
# ============================
# OUTPUT FILES (global ANLI buffer)
# ============================
OUT_TRAIN = os.path.join(BASE_ANALYSIS_DIR, "global_error_buffer_anli_round14_train.csv")
OUT_VAL = os.path.join(BASE_ANALYSIS_DIR, "global_error_buffer_anli_round14_val.csv")
RANDOM_SEED = 42
VAL_RATIO = 0.20 # 80% train / 20% val
def main():
print("============================================================")
print("BUILD GLOBAL ANLI ERROR BUFFER (ROUND 1 → SRL SOURCE)")
print("============================================================")
# 1) Load the three ANLI error CSVs
print("\nLoading ANLI error buffers...")
df_r1 = pd.read_csv(ANLI_R1_CSV)
df_r2 = pd.read_csv(ANLI_R2_CSV)
df_r3 = pd.read_csv(ANLI_R3_CSV)
print(f" R1 rows: {len(df_r1)}")
print(f" R2 rows: {len(df_r2)}")
print(f" R3 rows: {len(df_r3)}")
# 2) Concatenate
df_all = pd.concat([df_r1, df_r2, df_r3], ignore_index=True)
print(f"\nTotal ANLI rows (R1+R2+R3): {len(df_all)}")
# Sanity: required columns for SRL pipeline
required_cols = ["premise", "hypothesis", "true_label_id", "is_error"]
missing = [c for c in required_cols if c not in df_all.columns]
if missing:
raise ValueError(f"Missing required columns in ANLI buffers: {missing}")
# 3) Shuffle + split into train/val
df_all = df_all.sample(frac=1.0, random_state=RANDOM_SEED).reset_index(drop=True)
train_df, val_df = train_test_split(
df_all,
test_size=VAL_RATIO,
random_state=RANDOM_SEED,
shuffle=True,
stratify=df_all["true_label_id"], # keep class balance
)
print(f"\nTrain size: {len(train_df)}")
print(f"Val size: {len(val_df)}")
# 4) Show distributions
def show_dist(name, df):
print(f"\n{name} - class distribution:")
total = len(df)
for label_id, label_name in {0: "entailment", 1: "neutral", 2: "contradiction"}.items():
count = (df["true_label_id"] == label_id).sum()
print(f" {label_name}: {count} ({100.0 * count / total:.1f}%)")
errors = df["is_error"].sum()
print(f"{name} - errors: {errors} ({100.0 * errors / total:.1f}%), correct: {total - errors}")
show_dist("TRAIN", train_df)
show_dist("VAL", val_df)
# 5) Save
train_df.to_csv(OUT_TRAIN, index=False, encoding="utf-8")
val_df.to_csv(OUT_VAL, index=False, encoding="utf-8")
print("\nSaved:")
print(f" Train: {OUT_TRAIN}")
print(f" Val : {OUT_VAL}")
print("\n✅ Global ANLI error buffers are ready for SRL.")
print("Use them as input to the SRL buffer rebalance script.")
if __name__ == "__main__":
main()
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