File size: 2,965 Bytes
a88e206 | 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 | import pandas as pd
from sklearn.metrics import accuracy_score
import argparse
import os
def process_tsv_files(parent_dir, output_csv, is_qa=None):
results = []
# Walk through all subdirectories
for root, _, files in os.walk(parent_dir):
for file in files:
if file.endswith(".tsv"): # Check for TSV files
file_path = os.path.join(root, file)
try:
df = pd.read_csv(file_path, sep="\t")
# Evaluate non-qa inference results
if is_qa is None:
if "gold_label" in df.columns and "prediction" in df.columns:
y_gold = df["gold_label"].tolist()
y_pred = df["prediction"].tolist()
accuracy = accuracy_score(y_gold, y_pred)
results.append([file_path, accuracy])
else:
print(f"Skipping {file_path}: Required columns not found.")
# Evaluate qa inference results
elif is_qa == "y":
for neg_value in df["prediction"].unique():
if neg_value.startswith("not_"):
evaluating_label = neg_value.replace("not_", "")
# Replace all other labels in gold_label with the negated version
df["gold_label"] = df["gold_label"].apply(
lambda x: x if x == evaluating_label else neg_value
)
if "gold_label" in df.columns and "prediction" in df.columns:
y_gold = df["gold_label"].tolist()
y_pred = df["prediction"].tolist()
accuracy = accuracy_score(y_gold, y_pred)
results.append([file_path, accuracy])
else:
print(f"Skipping {file_path}: Required columns not found.")
except Exception as e:
print(f"Error processing {file_path}: {e}")
# Save results to a CSV
results_df = pd.DataFrame(results, columns=["File Path", "Accuracy"])
results_df.to_csv(output_csv, index=False)
print(f"Results saved to {output_csv}")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--parent_dir", type=str, required=True, help="Path to the parent folder")
parser.add_argument("--output_csv", type=str, required=True, help="Path to the output CSV file")
parser.add_argument("--is_qa", type=str, help="Are we evaluating qa inference?")
args = parser.parse_args()
process_tsv_files(args.parent_dir, args.output_csv, args.is_qa)
if __name__ == "__main__":
main() |