| import os |
| import json |
| import gzip |
| import numpy as np |
| import itertools |
|
|
| from typing import * |
| from tqdm.auto import tqdm |
| from collections import defaultdict |
| from concurrent.futures import ThreadPoolExecutor, as_completed |
| from human_eval.data import stream_jsonl |
| from human_eval.execution import check_correctness |
|
|
| IMPORT_HELPER = { |
| "python": [ |
| "import math", |
| "import re", |
| "import sys", |
| "import copy", |
| "import datetime", |
| "import itertools", |
| "import collections", |
| "import heapq", |
| "import functools", |
| "import hashlib", |
| "import numpy", |
| "import numpy as np", |
| "import string", |
| "from typing import *", |
| "from collections import *", |
| "from functools import *" |
| ], |
| "go" : [ |
| "math", |
| "strings", |
| "fmt", |
| "strconv", |
| "time", |
| "bytes", |
| "regexp", |
| "sort", |
| "math/rand", |
| "crypto/md5", |
| ], |
| "cpp" : [ |
| "#include<stdlib.h>", |
| "#include<algorithm>", |
| "#include<math.h>", |
| "#include<stdio.h>", |
| "#include<vector>", |
| "#include<string>", |
| "#include<climits>", |
| "#include<cstring>", |
| "#include<iostream>", |
| ], |
| } |
|
|
|
|
| LANGUAGE_NAME = { |
| "cpp" : "CPP", |
| "go" : "Go", |
| "java" : "Java", |
| "js" : "JavaScript", |
| "python": "Python", |
| } |
|
|
|
|
| def read_dataset( |
| data_file: str = None, |
| dataset_type: str = "humaneval", |
| num_shot=None, |
| ) -> Dict: |
| if num_shot is not None: |
| print(f"{num_shot}-shot setting...") |
| if "humaneval" in dataset_type.lower(): |
| if data_file is None: |
| current_path = os.path.dirname(os.path.abspath(__file__)) |
| data_file = os.path.join(current_path, "..", "humaneval-x", "python", "data", "humaneval_python.jsonl.gz") |
| dataset = {task["task_id"]: task for task in stream_jsonl(data_file)} |
| else: |
| raise f"Dataset: {dataset_type} not supported." |
|
|
| return dataset |
|
|
| def estimate_pass_at_k( |
| num_samples: Union[int, List[int], np.ndarray], |
| num_correct: Union[List[int], np.ndarray], |
| k: int |
| ) -> np.ndarray: |
| """ |
| 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)]) |
|
|
| def process_humaneval_test(sample, problems, example_test=False, is_mbpp=False, language="python"): |
| task_id = sample["task_id"] |
| |
| if is_mbpp: |
| return sample["generation"] + "\n" + "\n".join(problems[task_id]["test"]) |
| |
|
|
| prompt = sample.get("prompt", "") |
| if example_test and "example_test" in problems[task_id] and problems[task_id]["example_test"] != "": |
| test = problems[task_id]["example_test"] |
| else: |
| test = problems[task_id]["test"] |
| code = sample["generation"] |
|
|
| |
| if language == "python": |
| '''code_ = [] |
| for line in code.split("\n"): |
| if (len(line.strip()) > 0 and line[0] != ' ' and line[0] != '\t'): |
| break |
| code_.append(line) |
| code = "\n".join(code_)''' |
| test_setup = "\n".join(IMPORT_HELPER["python"]) + "\n" |
| test_string = test_setup + code + "\n" + test + "\n" |
| elif language == "cpp": |
| test_set_up = "" |
| for s in IMPORT_HELPER["cpp"]: |
| if s not in prompt: |
| test_set_up += s + "\n" |
| test_string = test_set_up + "\n" + code + "\n" + test |
| elif language == "java": |
| test_string = code + "\n" + test |
| elif language in ["js", "javascript", "ts", "cs", "sh"]: |
| test_string = code + "\n" + test |
| elif language == "go": |
| import_string = problems[task_id]["import"] |
| prompt = prompt.replace(import_string, "") |
| if example_test and "example_test" in problems[task_id]: |
| test = problems[task_id]["example_test"] |
| else: |
| test = problems[task_id]["test"] |
| test_setup = problems[task_id]["test_setup"] |
| other_pkgs = [] |
| for pkg in IMPORT_HELPER["go"]: |
| if pkg not in test_setup: |
| p = pkg.split("/")[-1] |
| if p + "." in code: |
| other_pkgs.append(f"\"{pkg}\"") |
| if other_pkgs: |
| import_other_pkgs = "import (\n" + " ".join([p + "\n" for p in other_pkgs]) + ")" |
| test_string = test_setup + "\n" + import_other_pkgs + "\n" + prompt + code + "\n" + test |
| else: |
| test_string = test_setup + "\n" + prompt + code + "\n" + test |
| elif language == "rust": |
| main = "\nfn main(){ \n } \n" |
| declaration = problems[task_id]["declaration"] |
| test_string = main + declaration + prompt + code + test |
| elif language == "php": |
| test_string = code + "\n" + test + "?>" |
| return test_string |
|
|
|
|
| def stream_jsonl_all(filename: str) -> Iterable[Dict]: |
| results = [] |
| if filename.endswith(".gz"): |
| fp = gzip.open(open(filename, "rb"), "rt") |
| else: |
| fp = open(filename, "r") |
| for line in fp: |
| if any(not x.isspace() for x in line): |
| results.append(json.loads(line)) |
| fp.close() |
|
|
| return results |
|
|
|
|
| def evaluate_functional_correctness( |
| input_file: str = None, |
| tmp_dir: str = "./", |
| n_workers: int = 32, |
| timeout: float = 10.0, |
| problem_file: str = "../data/humaneval_python.jsonl.gz", |
| result_path: str = None, |
| k: List[int] = [1, 10, 100], |
| test_groundtruth: bool = False, |
| example_test: bool = False, |
| is_mbpp: bool = False, |
| language: str = "python", |
| ): |
| if example_test: |
| print("Example test...") |
|
|
| problems = read_dataset(problem_file, dataset_type="humaneval") |
| sample_jsonl = stream_jsonl_all(input_file) |
| with ThreadPoolExecutor(max_workers=n_workers) as executor: |
| futures = [] |
| completion_id = Counter() |
| n_samples = 0 |
| results = defaultdict(list) |
|
|
| if test_groundtruth: |
| print("Testing ground truth...") |
| for sample in tqdm(problems.values()): |
| task_id = sample["task_id"] |
| lang = task_id.split("/")[0].lower() |
| if lang == "javascript": |
| lang = "js" |
| tmp_dir_ = os.path.join(tmp_dir, lang, "evaluation") |
| sample["generation"] = sample["canonical_solution"] |
| sample["test_code"] = process_humaneval_test(sample, problems, example_test, language) |
| if sample["test_code"] is None: |
| continue |
| args = (task_id, sample, lang, timeout, tmp_dir_, completion_id[task_id]) |
| future = executor.submit(check_correctness, *args) |
| futures.append(future) |
| completion_id[task_id] += 1 |
| n_samples += 1 |
| else: |
| print("Reading Samples...") |
| id2samples = {} |
| for sample in tqdm(sample_jsonl): |
| task_id = sample["task_id"] |
|
|
| if not is_mbpp: |
| lang = language |
| if not is_mbpp and lang == "javascript": |
| lang = "js" |
| if is_mbpp: |
| lang = "python" |
| tmp_dir_ = os.path.join(tmp_dir, lang, "evaluation") |
| sample["task_id"] = task_id |
| sample["test_code"] = process_humaneval_test(sample, problems, example_test, is_mbpp, language) |
| if sample["test_code"] is None: |
| continue |
| if "completion_id" in sample: |
| completion_id_ = sample["completion_id"] |
| else: |
| completion_id_ = completion_id[task_id] |
| args = (task_id, sample, lang, timeout, tmp_dir_, completion_id_) |
| id2samples[(task_id, completion_id_)] = sample |
| future = executor.submit(check_correctness, *args) |
| futures.append(future) |
| completion_id[task_id] += 1 |
| n_samples += 1 |
|
|
| if len(completion_id) == len(problems): |
| evaluate_pass_at_k = True |
| else: |
| evaluate_pass_at_k = False |
|
|
| print("Running test suites...") |
| sample_with_results = [] |
| for future in tqdm(as_completed(futures), total=len(futures)): |
| result = future.result() |
| results[result["task_id"]].append((result["completion_id"], result)) |
|
|
| sample = id2samples[(result["task_id"], result["completion_id"])] |
| sample_with_results.append({ |
| 'task_id': result['task_id'], |
| 'completion_id': result["completion_id"], |
| 'passed': result['passed'], |
| 'generation': sample['generation'] |
| }) |
|
|
| for key in sample: |
| if key not in sample_with_results[-1]: |
| sample_with_results[-1][key] = sample[key] |
|
|
| |
| total, correct = [], [] |
| for result in results.values(): |
| passed = [r[1]["passed"] for r in result] |
| total.append(len(passed)) |
| correct.append(sum(passed)) |
|
|
| total = np.array(total) |
| correct = np.array(correct) |
| if evaluate_pass_at_k: |
| ks = k |
| pass_at_k = { |
| f"pass@{k}": estimate_pass_at_k(total, correct, k).mean() |
| for k in ks if (total >= k).all() |
| } |
| print(pass_at_k) |
| else: |
| print("Total:", np.sum(total)) |
| print("Correct:", np.sum(correct)) |
| |
| if result_path is not None: |
| with open(result_path, 'w', encoding='utf-8') as fw: |
| for sample_with_result in sample_with_results: |
| fw.write(json.dumps(sample_with_result) + '\n') |
| print("Save evaluation results to\n{}".format(result_path)) |
|
|
| return pass_at_k |
|
|
