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| import itertools | |
| import numpy as np | |
| from typing import Dict | |
| from datasets import load_dataset | |
| import testing_util as test_util | |
| DATASET = "codeparrot/apps" | |
| def evaluate_generations(generations: list, level: str = "all", debug: bool = False): | |
| """We take the list of code generations and try to compile them | |
| and the run their corresponding unit tests which are retrieved from the APPS dataset. | |
| Args: | |
| generations: list of code generations (same order as samples in APPS dataset) | |
| level: difficulty level used in the generation, can be "all", "introductory", "interview" or "competition" | |
| Returns: | |
| results: dictionary of results, key is the problem index, value is a list of results for each generation | |
| [-2] = compile error, [-1] = runtime error [False] = failed test case [True] = passed test case | |
| """ | |
| # generations are code generations in the same order of the dataset | |
| apps_eval = load_dataset(DATASET, split="test", difficulties=[level]) | |
| results = {} | |
| for index in range(len(generations)): | |
| # code generations for problem (index) | |
| problem_generations = generations[index] | |
| # get corresponding samples from APPS dataset | |
| sample = apps_eval[index] | |
| res = [] | |
| # loop over the generations | |
| for o_idx, o in enumerate(problem_generations): | |
| curr_res = [-2] | |
| try: | |
| curr_res = test_util.run_test(sample, test=o, debug=debug) | |
| #if debug: | |
| print(f"\nSuccessful compilation of task {index}!") | |
| fixed = [] | |
| for e in curr_res: | |
| if isinstance(e, np.ndarray): | |
| e = e.item(0) | |
| if isinstance(e, np.bool_): | |
| e = bool(e) | |
| fixed.append(e) | |
| curr_res = fixed | |
| if not np.all(curr_res): | |
| #if debug: | |
| print(f"Results were not True for all test cases") | |
| except Exception as e: | |
| if debug: | |
| print(f"Compilation failed, test framework exception = {repr(e)}{e}\n") | |
| break | |
| finally: | |
| assert isinstance(curr_res, list) | |
| res.append(curr_res) | |
| results[index] = res | |
| return 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)]) | |
| def get_results(results: Dict[int, list], count_errors: bool = False, k_list: list = [1, 10, 100]): | |
| """ | |
| Given the results evaluated against the testcases we output some statistics. | |
| For single generations: | |
| >>> example_results = {0: [[-2]], 1: [[False,False]], 2: [[True,True]], 3: [[False,True,False,True]], 4: [[-1,-1]]} | |
| >>> get_results(example_results, count_errors=True) | |
| Computing accuracy metrics... | |
| number of compile errors = 1 avg = 0.2 | |
| number of runtime errors = 1 avg = 0.2 | |
| number of problems evaluated = 5 | |
| Average Accuracy : 0.3 | |
| Strict Accuracy : 0.2 | |
| {'avg_accuracy': 0.3, 'strict_accuracy': 0.2, 'pass_at_k': None} | |
| For multiple generations: | |
| >>> example_results = {0: [[-2], [True, True, True]], 1: [[-1,-1, -1], [True, False, True]]} | |
| >>> get_results(example_results, k_list=[1, 2]) | |
| Computing pass@k metric for multiple generations... | |
| {'pass@1': 0.25, 'pass@2': 0.5} | |
| {'avg_accuracy': None, 'strict_accuracy': None, 'pass_at_k': {'pass@1': 0.25, 'pass@2': 0.5}} | |
| """ | |
| metrics = {"avg_accuracy": None, "strict_accuracy": None, "pass_at_k": None} | |
| if len(results[0]) == 1: | |
| # for single generations we compute average accuracy and stric accuracy: original APPS metrics | |
| print("Computing accuracy metrics...") | |
| res = [] | |
| per_prob_res = [] | |
| all_correct = [] | |
| for index in results: | |
| problem_results = np.asarray(results[index]) | |
| res.extend(problem_results) | |
| per_prob_res.append(np.mean(problem_results > 0)) | |
| all_correct.append(np.all(problem_results > 0)) | |
| # we count campilation and runtime errors once per pronlem | |
| compile_errors = len([e for e in res if -2 in e]) | |
| runtime_errors = len([e for e in res if -1 in e]) | |
| total_testcases = len(res) | |
| if count_errors: | |
| print(f"number of compile errors = {compile_errors} avg = {compile_errors / total_testcases}") | |
| print(f"number of runtime errors = {runtime_errors} avg = {runtime_errors / total_testcases}") | |
| print(f"number of problems evaluated = {total_testcases}") | |
| print(f"Average Accuracy : {np.mean(per_prob_res)}") | |
| print(f"Strict Accuracy : {np.mean(all_correct)}") | |
| metrics["avg_accuracy"] = np.mean(per_prob_res) | |
| metrics["strict_accuracy"] = np.mean(all_correct) | |
| else: | |
| # for multiple generations we use pass@k metric used in the HumanEval benchmark | |
| # we use strict accuracy, a generation is valid if it has to pass all the tests | |
| print("Computing pass@k metric for multiple generations...") | |
| # total is list with nb generations per task (task=index) | |
| # correct is number of generations that passed all tests per task | |
| total = [] | |
| correct = [] | |
| for index in results: | |
| all_correct = [] | |
| for generation in results[index]: | |
| gen = np.array(generation) | |
| all_correct.append(np.all(gen>0)) | |
| total.append(len(all_correct)) | |
| correct.append(sum(all_correct)) | |
| total = np.array(total) | |
| correct = np.array(correct) | |
| ks = k_list | |
| 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) | |
| metrics["pass_at_k"] = pass_at_k | |
| return metrics | |
| def compute_metrics(generations, level="all", k_list=[1, 10, 100], count_errors=True, debug=False): | |
| """Return metrics for the given generations. | |
| Args: | |
| generations: list of code generations for each problem (each generation is a list of generations) | |
| k_list: list of k values to compute pass@k when using multiple generations | |
| count_errors: whether to count compilation and runtime errors when using single generations | |
| level: difficulty level in APPS dataset that was used for the given generations (from: "all", "introductory", "interview", "competition") | |
| Returns: | |
| metrics: dict of metrics | |
| Examples: | |
| >>> import json | |
| >>> # lists of solutions to the two first APPS problems (note not all solutions pass all tests) | |
| >>> solution_sample1 = json.load(open("test_examples/solutions_problem_1.json", "r")) | |
| >>> solution_sample2 = json.load(open("test_examples/solutions_problem_2.json", "r")) | |
| >>> single_solutions = [solution_sample1[:1], solution_sample2[:1]] | |
| >>> compute_metrics(single_solutions, level="all") | |
| Computing accuracy metrics... | |
| number of compile errors = 0 avg = 0.0 | |
| number of runtime errors = 0 avg = 0.0 | |
| number of problems evaluated = 2 | |
| Average Accuracy : 1.0 | |
| Strict Accuracy : 1.0 | |
| {'avg_accuracy': 1.0, 'strict_accuracy': 1.0, 'pass_at_k': None} | |
| >>> multiple_solutions = [solution_sample1[:3], solution_sample2[:3]] | |
| >>> compute_metrics(multiple_solutions, level="all", k_list=[1, 2, 3]) | |
| Computing pass@k metric for multiple generations... | |
| {'pass@1': 1.0, 'pass@2': 1.0, 'pass@3': 1.0} | |
| {'avg_accuracy': None, 'strict_accuracy': None, 'pass_at_k': {'pass@1': 1.0, 'pass@2': 1.0, 'pass@3': 1.0}} | |
| """ | |
| results = evaluate_generations(generations, level=level, debug=debug) | |
| metrics = get_results(results, count_errors=count_errors, k_list=k_list) | |
| return metrics | |
| #import doctest | |
| #doctest.testmod() |