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| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """The CodeEval metric estimates the pass@k metric for code synthesis. | |
| This is an evaluation harness for the HumanEval problem solving dataset | |
| described in the paper "Evaluating Large Language Models Trained on Code" | |
| (https://arxiv.org/abs/2107.03374).""" | |
| import itertools | |
| import os | |
| from collections import Counter, defaultdict | |
| from concurrent.futures import ThreadPoolExecutor, as_completed | |
| import datasets | |
| import numpy as np | |
| import evaluate | |
| from .execute import check_correctness | |
| _CITATION = """\ | |
| @misc{chen2021evaluating, | |
| title={Evaluating Large Language Models Trained on Code}, | |
| author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \ | |
| and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \ | |
| and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \ | |
| and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \ | |
| and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \ | |
| and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \ | |
| and Mohammad Bavarian and Clemens Winter and Philippe Tillet \ | |
| and Felipe Petroski Such and Dave Cummings and Matthias Plappert \ | |
| and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \ | |
| and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \ | |
| and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \ | |
| and William Saunders and Christopher Hesse and Andrew N. Carr \ | |
| and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \ | |
| and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \ | |
| and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \ | |
| and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba}, | |
| year={2021}, | |
| eprint={2107.03374}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.LG} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| This metric implements the evaluation harness for the HumanEval problem solving dataset | |
| described in the paper "Evaluating Large Language Models Trained on Code" | |
| (https://arxiv.org/abs/2107.03374). | |
| """ | |
| _KWARGS_DESCRIPTION = """ | |
| Calculates how good are predictions given some references, using certain scores | |
| Args: | |
| predictions: list of candidates to evaluate. Each candidates should be a list | |
| of strings with several code candidates to solve the problem. | |
| references: a list with a test for each prediction. Each test should evaluate the | |
| correctness of a code candidate. | |
| k: number of code candidates to consider in the evaluation (Default: [1, 10, 100]) | |
| num_workers: number of workers used to evaluate the canidate programs (Default: 4). | |
| timeout: | |
| Returns: | |
| pass_at_k: dict with pass rates for each k | |
| results: dict with granular results of each unittest | |
| Examples: | |
| >>> code_eval = evaluate.load("code_eval") | |
| >>> test_cases = ["assert add(2,3)==5"] | |
| >>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]] | |
| >>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2]) | |
| >>> print(pass_at_k) | |
| {'pass@1': 0.5, 'pass@2': 1.0} | |
| """ | |
| _WARNING = """ | |
| ################################################################################ | |
| !!!WARNING!!! | |
| ################################################################################ | |
| The "code_eval" metric executes untrusted model-generated code in Python. | |
| Although it is highly unlikely that model-generated code will do something | |
| overtly malicious in response to this test suite, model-generated code may act | |
| destructively due to a lack of model capability or alignment. | |
| Users are strongly encouraged to sandbox this evaluation suite so that it | |
| does not perform destructive actions on their host or network. For more | |
| information on how OpenAI sandboxes its code, see the paper "Evaluating Large | |
| Language Models Trained on Code" (https://arxiv.org/abs/2107.03374). | |
| Once you have read this disclaimer and taken appropriate precautions, | |
| set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this | |
| with: | |
| import os | |
| os.environ["HF_ALLOW_CODE_EVAL"] = "1" | |
| ################################################################################\ | |
| """ | |
| _LICENSE = """The MIT License | |
| Copyright (c) OpenAI (https://openai.com) | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is | |
| furnished to do so, subject to the following conditions: | |
| The above copyright notice and this permission notice shall be included in | |
| all copies or substantial portions of the Software. | |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN | |
| THE SOFTWARE.""" | |
| class CodeEval(evaluate.Metric): | |
| def _info(self): | |
| return evaluate.MetricInfo( | |
| # This is the description that will appear on the metrics page. | |
| description=_DESCRIPTION, | |
| citation=_CITATION, | |
| inputs_description=_KWARGS_DESCRIPTION, | |
| # This defines the format of each prediction and reference | |
| features=datasets.Features( | |
| { | |
| "predictions": datasets.Sequence(datasets.Value("string")), | |
| "references": datasets.Value("string"), | |
| } | |
| ), | |
| homepage="https://github.com/openai/human-eval", | |
| codebase_urls=["https://github.com/openai/human-eval"], | |
| reference_urls=["https://github.com/openai/human-eval"], | |
| license=_LICENSE, | |
| ) | |
| def _compute(self, predictions, references, k=[1, 10, 100], num_workers=4, timeout=3.0): | |
| """Returns the scores""" | |
| if os.getenv("HF_ALLOW_CODE_EVAL", 0) != "1": | |
| raise ValueError(_WARNING) | |
| if os.name == "nt": | |
| raise NotImplementedError("This metric is currently not supported on Windows.") | |
| with ThreadPoolExecutor(max_workers=num_workers) as executor: | |
| futures = [] | |
| completion_id = Counter() | |
| n_samples = 0 | |
| results = defaultdict(list) | |
| for task_id, (candidates, test_case) in enumerate(zip(predictions, references)): | |
| for candidate in candidates: | |
| test_program = candidate + "\n" + test_case | |
| args = (test_program, timeout, task_id, completion_id[task_id]) | |
| future = executor.submit(check_correctness, *args) | |
| futures.append(future) | |
| completion_id[task_id] += 1 | |
| n_samples += 1 | |
| for future in as_completed(futures): | |
| result = future.result() | |
| results[result["task_id"]].append((result["completion_id"], result)) | |
| total, correct = [], [] | |
| for result in results.values(): | |
| result.sort() | |
| passed = [r[1]["passed"] for r in result] | |
| total.append(len(passed)) | |
| correct.append(sum(passed)) | |
| total = np.array(total) | |
| correct = np.array(correct) | |
| ks = k | |
| pass_at_k = {f"pass@{k}": estimate_pass_at_k(total, correct, k).mean() for k in ks if (total >= k).all()} | |
| return pass_at_k, results | |
| def estimate_pass_at_k(num_samples, num_correct, k): | |
| """Estimates pass@k of each problem and returns them in an array.""" | |
| def estimator(n: int, c: int, k: int) -> float: | |
| """Calculates 1 - comb(n - c, k) / comb(n, k).""" | |
| if n - c < k: | |
| return 1.0 | |
| return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1)) | |
| if isinstance(num_samples, int): | |
| num_samples_it = itertools.repeat(num_samples, len(num_correct)) | |
| else: | |
| assert len(num_samples) == len(num_correct) | |
| num_samples_it = iter(num_samples) | |
| return np.array([estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)]) | |