Float output precision issue - failed test for abc315_f problem despite correct solution #129
Description
I have recently evaluated llm generated solution for abc315_f problem using lcb_runner.runner.custom_evaluator.
Using the following file as input:
[
{
"question_id": "abc315_f",
"code_list": [
'import math\n\ndef main():\n import sys\n input = sys.stdin.read().split()\n idx = 0\n N = int(input[idx])\n idx += 1\n points = []\n for _ in range(N):\n x = int(input[idx])\n y = int(input[idx+1])\n points.append((x, y))\n idx += 2\n \n INF = float(\'inf\')\n max_c = 20 # Maximum number of skips considered\n dp = [[INF] * (max_c + 1) for _ in range(N)]\n dp[0][0] = 0.0 # Starting at checkpoint 0 with 0 skips\n\n for i in range(1, N):\n start_j = max(0, i - (max_c + 1))\n for j in range(start_j, i):\n for c_prev in range(0, max_c + 1):\n if dp[j][c_prev] == INF:\n continue\n skips = i - j - 1\n new_c = c_prev + skips\n if new_c > max_c:\n continue\n x1, y1 = points[j]\n x2, y2 = points[i]\n dist = math.hypot(x2 - x1, y2 - y1)\n if dp[i][new_c] > dp[j][c_prev] + dist:\n dp[i][new_c] = dp[j][c_prev] + dist\n\n min_total = INF\n for c in range(0, max_c + 1):\n if dp[N-1][c] == INF:\n continue\n total = dp[N-1][c]\n if c == 0:\n total += 0.0\n else:\n total += (2 ** (c - 1))\n if total < min_total:\n min_total = total\n\n print("{0:.20f}".format(min_total))\n\nif __name__ == "__main__":\n main()'
]
}
]I have noticed that the first public test fails with the following error_message:
Wrong answer at output_line_idx=0: 5.82842712474618984686 != 5.82842712474619009753
However the question_content states:
Your output is considered correct if the absolute or relative error from the true value is at most 10^{-5}.
It looks like the expected answer is of such high precision that the build-in python's float that the solution uses is not enough to exactly match it (as I understood from the codebase the decimal.Decimal is used for performing this exact match).
I run an additional simple test:
import numpy as np
print(
float("5.82842712474618984686") == float("5.82842712474619009753"),
np.float64("5.82842712474618984686") == np.float64("5.82842712474619009753"),
)
# Outputs: True Truewhich confirmed that the solution is correct even when we ignore the statement from the question and use 64-bit float for evaluation.
Most straightforward solution seems to be lowering the precision of the expected output for abc315_f problem.
It is probably worth checking if other problems does not face the same issue (I have evaluated on a small subset and found only this one).