| import os
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| import json
|
| import gzip
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| import numpy as np
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| import itertools
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|
|
| from typing import *
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| from tqdm.auto import tqdm
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| from collections import defaultdict
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| from concurrent.futures import ThreadPoolExecutor, as_completed
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| from human_eval.data import stream_jsonl
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| from human_eval.execution import check_correctness
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|
|
| IMPORT_HELPER = {
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| "python": [
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| "import math",
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| "import re",
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| "import sys",
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| "import copy",
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| "import datetime",
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| "import itertools",
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| "import collections",
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| "import heapq",
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| "import functools",
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| "import hashlib",
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| "import numpy",
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| "import numpy as np",
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| "import string",
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| "from typing import *",
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| "from collections import *",
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| "from functools import *"
|
| ],
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| "go" : [
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| "math",
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| "strings",
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| "fmt",
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| "strconv",
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| "time",
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| "bytes",
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| "regexp",
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| "sort",
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| "math/rand",
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| "crypto/md5",
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| ],
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| "cpp" : [
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| "#include<stdlib.h>",
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| "#include<algorithm>",
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| "#include<math.h>",
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| "#include<stdio.h>",
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| "#include<vector>",
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| "#include<string>",
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| "#include<climits>",
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| "#include<cstring>",
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| "#include<iostream>",
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| ],
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| }
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|
|
|
|
| LANGUAGE_NAME = {
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| "cpp" : "CPP",
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| "go" : "Go",
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| "java" : "Java",
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| "js" : "JavaScript",
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| "python": "Python",
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| }
|
|
|
|
|
| def read_dataset(
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| data_file: str = None,
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| dataset_type: str = "humaneval",
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| num_shot=None,
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| ) -> Dict:
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| if num_shot is not None:
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| print(f"{num_shot}-shot setting...")
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| if "humaneval" in dataset_type.lower():
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| if data_file is None:
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| current_path = os.path.dirname(os.path.abspath(__file__))
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| data_file = os.path.join(current_path, "..", "humaneval-x", "python", "data", "humaneval_python.jsonl.gz")
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| dataset = {task["task_id"]: task for task in stream_jsonl(data_file)}
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| else:
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| raise f"Dataset: {dataset_type} not supported."
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|
|
| return dataset
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|
|
| def estimate_pass_at_k(
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| num_samples: Union[int, List[int], np.ndarray],
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| num_correct: Union[List[int], np.ndarray],
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| k: int
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| ) -> 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:
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| return 1.0
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| return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1, n + 1))
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|
|
| if isinstance(num_samples, int):
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| num_samples_it = itertools.repeat(num_samples, len(num_correct))
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| else:
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| assert len(num_samples) == len(num_correct)
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| num_samples_it = iter(num_samples)
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|
|
| return np.array([estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)])
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|
|
| def process_humaneval_test(sample, problems, example_test=False, is_mbpp=False, language="python"):
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| task_id = sample["task_id"]
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|
|
| if is_mbpp:
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| return sample["generation"] + "\n" + "\n".join(problems[task_id]["test"])
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|
|
|
|
| prompt = sample.get("prompt", "")
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| if example_test and "example_test" in problems[task_id] and problems[task_id]["example_test"] != "":
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| test = problems[task_id]["example_test"]
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| else:
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| test = problems[task_id]["test"]
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| code = sample["generation"]
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|
|
|
|
| if language == "python":
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| '''code_ = []
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| for line in code.split("\n"):
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| if (len(line.strip()) > 0 and line[0] != ' ' and line[0] != '\t'):
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| break
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| code_.append(line)
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| code = "\n".join(code_)'''
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| test_setup = "\n".join(IMPORT_HELPER["python"]) + "\n"
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| test_string = test_setup + code + "\n" + test + "\n"
|
| elif language == "cpp":
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| test_set_up = ""
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| for s in IMPORT_HELPER["cpp"]:
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| if s not in prompt:
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| test_set_up += s + "\n"
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| test_string = test_set_up + "\n" + code + "\n" + test
|
| elif language == "java":
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| test_string = code + "\n" + test
|
| elif language in ["js", "javascript", "ts", "cs", "sh"]:
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| test_string = code + "\n" + test
|
| elif language == "go":
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| import_string = problems[task_id]["import"]
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| prompt = prompt.replace(import_string, "")
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| if example_test and "example_test" in problems[task_id]:
|
| test = problems[task_id]["example_test"]
|
| else:
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| test = problems[task_id]["test"]
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| test_setup = problems[task_id]["test_setup"]
|
| other_pkgs = []
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| for pkg in IMPORT_HELPER["go"]:
|
| if pkg not in test_setup:
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| p = pkg.split("/")[-1]
|
| if p + "." in code:
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| other_pkgs.append(f"\"{pkg}\"")
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| if other_pkgs:
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| import_other_pkgs = "import (\n" + " ".join([p + "\n" for p in other_pkgs]) + ")"
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| 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":
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| main = "\nfn main(){ \n } \n"
|
| declaration = problems[task_id]["declaration"]
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| test_string = main + declaration + prompt + code + test
|
| elif language == "php":
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| test_string = code + "\n" + test + "?>"
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| return test_string
|
|
|
|
|
| def stream_jsonl_all(filename: str) -> Iterable[Dict]:
|
| results = []
|
| if filename.endswith(".gz"):
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| fp = gzip.open(open(filename, "rb"), "rt")
|
| else:
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| fp = open(filename, "r")
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| for line in fp:
|
| if any(not x.isspace() for x in line):
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| results.append(json.loads(line))
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| fp.close()
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|
|
| return results
|
|
|
|
|
| def evaluate_functional_correctness(
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| input_file: str = None,
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| tmp_dir: str = "./",
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| n_workers: int = 32,
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| timeout: float = 10.0,
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| problem_file: str = "../data/humaneval_python.jsonl.gz",
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| result_path: str = None,
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| k: List[int] = [1, 10, 100],
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| test_groundtruth: bool = False,
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| example_test: bool = False,
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| is_mbpp: bool = False,
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| 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:
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| futures = []
|
| completion_id = Counter()
|
| n_samples = 0
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| results = defaultdict(list)
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|
|
| if test_groundtruth:
|
| print("Testing ground truth...")
|
| for sample in tqdm(problems.values()):
|
| task_id = sample["task_id"]
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| lang = task_id.split("/")[0].lower()
|
| if lang == "javascript":
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| lang = "js"
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| tmp_dir_ = os.path.join(tmp_dir, lang, "evaluation")
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| 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)
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| 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
|
|
|
