File size: 3,456 Bytes
7b82fe6 | 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 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 | import csv
import sys
from pathlib import Path
def load_csv(path):
with open(path, "r", encoding="utf-8") as f:
return list(csv.DictReader(f))
def normalize_binary(value):
if isinstance(value, int):
return value
text = str(value).strip().lower()
if text in {"1", "true", "yes", "positive"}:
return 1
if text in {"0", "false", "no", "negative"}:
return 0
raise ValueError(f"Unrecognized binary value: {value}")
def find_label_column(fieldnames):
matches = [c for c in fieldnames if c.startswith("label_")]
if len(matches) != 1:
raise ValueError(f"Expected exactly one label_ column, found: {matches}")
return matches[0]
def compute_metrics(y_true, y_pred):
tp = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 1)
tn = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 0)
fp = sum(1 for t, p in zip(y_true, y_pred) if t == 0 and p == 1)
fn = sum(1 for t, p in zip(y_true, y_pred) if t == 1 and p == 0)
total = len(y_true)
accuracy = (tp + tn) / total if total else 0.0
precision = tp / (tp + fp) if (tp + fp) else 0.0
recall_successful_stabilization = tp / (tp + fn) if (tp + fn) else 0.0
failed_rescue_rate = fn / (fn + tp) if (fn + tp) else 0.0
f1 = (
2 * precision * recall_successful_stabilization / (precision + recall_successful_stabilization)
if (precision + recall_successful_stabilization)
else 0.0
)
return {
"accuracy": accuracy,
"precision": precision,
"recall_successful_stabilization": recall_successful_stabilization,
"failed_rescue_rate": failed_rescue_rate,
"f1": f1,
"primary_metric": "recall_successful_stabilization",
"secondary_metric": "failed_rescue_rate",
"confusion_matrix": {
"true_positives": tp,
"false_positives": fp,
"true_negatives": tn,
"false_negatives": fn,
},
}
def score(reference_path, prediction_path):
references = load_csv(reference_path)
predictions = load_csv(prediction_path)
if not references:
raise ValueError("Reference file is empty.")
if not predictions:
raise ValueError("Prediction file is empty.")
if len(references) != len(predictions):
raise ValueError("Reference and prediction row counts do not match.")
label_col = find_label_column(references[0].keys())
y_true = [normalize_binary(row[label_col]) for row in references]
pred_col = None
for candidate in ["prediction", "predicted_label", label_col]:
if candidate in predictions[0]:
pred_col = candidate
break
if pred_col is None:
raise ValueError(
"Prediction file must contain one of: prediction, predicted_label, or the label column name."
)
y_pred = [normalize_binary(row[pred_col]) for row in predictions]
return compute_metrics(y_true, y_pred)
if __name__ == "__main__":
if len(sys.argv) != 3:
print("Usage: python scorer.py <reference.csv> <predictions.csv>", file=sys.stderr)
sys.exit(1)
ref_path = Path(sys.argv[1])
pred_path = Path(sys.argv[2])
try:
results = score(ref_path, pred_path)
except Exception as e:
print(f"Scoring error: {e}", file=sys.stderr)
sys.exit(1)
for key, value in results.items():
print(f"{key}: {value}") |