135 lines
4.8 KiB
Markdown
135 lines
4.8 KiB
Markdown
# HumanEvalPlus
|
|
|
|
|
|
## Overview
|
|
|
|
HumanEval Plus is a rigorous extension of OpenAI's HumanEval benchmark, designed to address high false-positive rates in code generation evaluation. It augments the original test cases with tens of thousands of automatically generated inputs to expose edge-case bugs and functional errors.
|
|
|
|
## Task Description
|
|
|
|
- **Task Type**: Code Generation (Python)
|
|
- **Input**: Function signature with docstring describing expected behavior
|
|
- **Output**: Complete Python function implementation
|
|
- **Test Coverage**: Massively expanded compared to original HumanEval
|
|
|
|
## Key Features
|
|
|
|
- 164 problems from original HumanEval with enhanced test suites
|
|
- Tens of thousands of additional test cases per problem
|
|
- LLM-based and mutation-based test input generation
|
|
- Exposes edge cases and bugs missed by original HumanEval
|
|
- Much stricter and more accurate correctness evaluation
|
|
|
|
## Evaluation Notes
|
|
|
|
- Default configuration uses **0-shot** evaluation
|
|
- **Security Warning**: By default, code is executed in the local environment. We strongly recommend using sandbox execution. See the [sandbox documentation](https://evalscope.readthedocs.io/en/latest/user_guides/sandbox.html) for details.
|
|
- Supports `pass@k` metric calculation
|
|
- Default timeout is 300 seconds to accommodate extensive test suites
|
|
- Uses custom Docker image with numpy pre-installed
|
|
|
|
|
|
## Properties
|
|
|
|
| Property | Value |
|
|
|----------|-------|
|
|
| **Benchmark Name** | `humaneval_plus` |
|
|
| **Dataset ID** | [evalscope/humanevalplus](https://modelscope.cn/datasets/evalscope/humanevalplus/summary) |
|
|
| **Paper** | N/A |
|
|
| **Tags** | `Coding` |
|
|
| **Metrics** | `acc` |
|
|
| **Default Shots** | 0-shot |
|
|
| **Evaluation Split** | `test` |
|
|
| **Aggregation** | `mean_and_pass_at_k` |
|
|
|
|
|
|
## Data Statistics
|
|
|
|
| Metric | Value |
|
|
|--------|-------|
|
|
| Total Samples | 164 |
|
|
| Prompt Length (Mean) | 609.57 chars |
|
|
| Prompt Length (Min/Max) | 274 / 1519 chars |
|
|
|
|
## Sample Example
|
|
|
|
**Subset**: `default`
|
|
|
|
```json
|
|
{
|
|
"input": [
|
|
{
|
|
"id": "dcff8346",
|
|
"content": "Read the following function signature and docstring, and fully implement the function described. Your response should only contain the code for this function.\nfrom typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: f ... [TRUNCATED] ... Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n"
|
|
}
|
|
],
|
|
"target": "\n\n sorted_numbers = sorted(numbers)\n for i in range(len(sorted_numbers) - 1):\n if sorted_numbers[i + 1] - sorted_numbers[i] < threshold:\n return True\n return False\n\n",
|
|
"id": 0,
|
|
"group_id": 0,
|
|
"metadata": {
|
|
"task_id": "HumanEval/0",
|
|
"entry_point": "has_close_elements",
|
|
"prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n",
|
|
"test": "\n\nimport numpy as np\n\ndef is_floats(x) -> bool:\n # check if it is float; List[float]; Tuple[float]\n if isinstance(x, float):\n return True\n if isinstance(x, (list, tuple)):\n return all(isinstance(i, float) for i in x)\n ... [TRUNCATED] ... True, True, True, True, True, True, True, True, True, False, True, True, True, True, True, True, True, True, True, True, False, True, True]\n for i, (inp, exp) in enumerate(zip(inputs, results)):\n assertion(candidate(*inp), exp, 0)\n"
|
|
}
|
|
}
|
|
```
|
|
|
|
*Note: Some content was truncated for display.*
|
|
|
|
## Prompt Template
|
|
|
|
**Prompt Template:**
|
|
```text
|
|
Read the following function signature and docstring, and fully implement the function described. Your response should only contain the code for this function.
|
|
{question}
|
|
```
|
|
|
|
## Sandbox Configuration
|
|
|
|
This benchmark requires a sandbox environment for code execution.
|
|
|
|
```json
|
|
{
|
|
"image": "python3.11-numpy",
|
|
"tools_config": {
|
|
"shell_executor": {},
|
|
"python_executor": {}
|
|
}
|
|
}
|
|
```
|
|
|
|
## Usage
|
|
|
|
### Using CLI
|
|
|
|
```bash
|
|
evalscope eval \
|
|
--model YOUR_MODEL \
|
|
--api-url OPENAI_API_COMPAT_URL \
|
|
--api-key EMPTY_TOKEN \
|
|
--datasets humaneval_plus \
|
|
--sandbox '{"enabled": true}' \
|
|
--limit 10 # Remove this line for formal evaluation
|
|
```
|
|
|
|
### Using Python
|
|
|
|
```python
|
|
from evalscope import run_task
|
|
from evalscope.config import TaskConfig
|
|
|
|
task_cfg = TaskConfig(
|
|
model='YOUR_MODEL',
|
|
api_url='OPENAI_API_COMPAT_URL',
|
|
api_key='EMPTY_TOKEN',
|
|
datasets=['humaneval_plus'],
|
|
sandbox={'enabled': True},
|
|
limit=10, # Remove this line for formal evaluation
|
|
)
|
|
|
|
run_task(task_cfg=task_cfg)
|
|
```
|
|
|
|
|