"""Apply a declared decision-cost contract to ID-aligned predictions."""
import math
from baseline import LABELS, evaluate


def validate_costs(costs):
    if not isinstance(costs, dict) or set(costs) != set(LABELS):
        raise ValueError('cost matrix needs exactly the known actual labels')
    for actual, row in costs.items():
        if not isinstance(row, dict) or set(row) != set(LABELS):
            raise ValueError('each cost row needs exactly the known predictions')
        for predicted, value in row.items():
            if type(value) not in (int, float) or not math.isfinite(value) or value < 0:
                raise ValueError('costs must be finite nonnegative numbers')
            if actual == predicted and value != 0:
                raise ValueError('this error-cost contract requires zero diagonal costs')
    return costs


def score(cases, predictions, costs):
    costs = validate_costs(costs)
    if not cases:
        raise ValueError('cannot score an empty reference set')
    expected = {}
    for case in cases:
        if not isinstance(case['id'], str) or not case['id'] or case['id'] in expected:
            raise ValueError('reference IDs must be nonempty and unique')
        if case['label'] not in LABELS:
            raise ValueError('unknown reference label')
        expected[case['id']] = case
    received = {}
    for prediction in predictions:
        id, label = prediction['id'], prediction['label']
        if not isinstance(id, str) or not id:
            raise ValueError('prediction IDs must be nonempty strings')
        if id in received:
            raise ValueError('duplicate prediction ID')
        if id not in expected or label not in LABELS:
            raise ValueError('unknown prediction ID or label')
        received[id] = label
    if set(received) != set(expected):
        raise ValueError('every reference case needs one prediction')
    # Reuse the baseline's evaluator, but align by ID before its ordered input.
    ordered = [received[case['id']] for case in cases]
    metrics = evaluate(ordered, cases)
    errors = [{'id': case['id'], 'actual': case['label'], 'predicted': predicted,
               'cost': costs[case['label']][predicted]}
              for case, predicted in zip(cases, ordered) if predicted != case['label']]
    total_cost = math.fsum(error['cost'] for error in errors)
    return {'metrics': metrics, 'errors': errors, 'total_cost': total_cost,
            'cost_per_case': round(total_cost / len(cases), 6)}