"""Scores predictions on the benchmark: accuracy and expected calibration error (ECE) per task.

predictions/<task>.jsonl: one line per item, {"source_id": ..., "probabilities": [p_option_1, p_option_2, ...]}
in the item's option order.  python evaluate.py predictions/
"""
import json
import sys
from pathlib import Path

CALIBRATION_BIN_EDGES = [0.0, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]


def load(path):
    with path.open(encoding="utf-8") as lines:
        return [json.loads(line) for line in lines if line.strip()]


def expected_calibration_error(confidences, correct):
    error = 0.0
    for low, high in zip(CALIBRATION_BIN_EDGES, CALIBRATION_BIN_EDGES[1:]):
        in_bin = [index for index, confidence in enumerate(confidences) if low <= confidence < high or confidence == high == 1.0]
        if in_bin:
            accuracy = sum(correct[index] for index in in_bin) / len(in_bin)
            confidence = sum(confidences[index] for index in in_bin) / len(in_bin)
            error += len(in_bin) / len(confidences) * abs(accuracy - confidence)
    return error


def main():
    predictions_directory = Path(sys.argv[1])
    print(f"{'task':12s} {'items':>6s} {'accuracy':>9s} {'ECE':>6s}")
    for data_path in sorted((Path(__file__).parent / "data").glob("*.jsonl")):
        predictions_path = predictions_directory / data_path.name
        if not predictions_path.exists():
            continue
        items = load(data_path)
        predictions = {prediction["source_id"]: prediction["probabilities"] for prediction in load(predictions_path)}
        confidences, correct = [], []
        for item in items:
            if item["source_id"] not in predictions:
                raise ValueError(f"{predictions_path}: no prediction for {item['source_id']}")
            probabilities = predictions[item["source_id"]]
            if len(probabilities) != len(item["options"]):
                raise ValueError(f"{item['source_id']}: {len(probabilities)} probabilities for {len(item['options'])} options")
            choice = max(range(len(probabilities)), key=probabilities.__getitem__)
            confidences.append(probabilities[choice])
            correct.append(choice == item["answer_index"])
        print(f"{data_path.stem:12s} {len(items):6d} {sum(correct) / len(items):9.3f} {expected_calibration_error(confidences, correct):6.3f}")


if __name__ == "__main__":
    main()