size_categories:n<1K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel arxiv:2406.08464 region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: completion
    dtype: string
  - name: model_name
    dtype: string
  splits:
  - name: train
    num_bytes: 30386
    num_examples: 10
  download_size: 27422
  dataset_size: 30386
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
</p>

# Dataset Card for test-outputs

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## Dataset Summary

This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/dvilasuero/test-outputs/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/dvilasuero/test-outputs/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "completion": "This code correctly calculates the sum of areas of all rectangles within the grid. However, there are a few improvements that can be made:\n\n1. Handling empty grid: The code does not handle the case where the grid is empty. In such a case, the `sum_rectangles` function will throw an error. We can add a check at the beginning of the function to return 0 in case the grid is empty.\n\n2. Using numpy: The code can be optimized by using the `numpy` library. Numpy provides functions to efficiently perform operations on arrays, which can be beneficial when working with large grids.\n\n3. Handling non-rectangle values: The code currently assumes that the values in the grid represent the probability of a cell being a rectangle. However, in a real-world scenario, these values might represent other things. We can modify the code to accept a parameter to specify what these values represent.\n\nHere\u0027s an updated version of the code that incorporates these improvements:\n\n```python\nimport numpy as np\n\ndef sum_rectangles(grid, value_to_check=1):\n    \"\"\"\n    Sum the areas of all rectangles in the grid.\n\n    Args:\n        grid (list or numpy array): 2D list or numpy array representing the grid.\n        value_to_check (int, optional): Value in the grid to check for rectangles. Defaults to 1.\n\n    Returns:\n        int: The sum of areas of all rectangles in the grid.\n    \"\"\"\n    if not grid:\n        return 0\n\n    # Convert the grid to a numpy array for efficient operations\n    grid = np.array(grid)\n\n    def find_rectangles(grid):\n        \"\"\"\n        Find all possible rectangles in the grid.\n\n        Args:\n            grid (numpy array): 2D numpy array representing the grid.\n\n        Returns:\n            list: A list of rectangles where each rectangle is represented by a tuple of (x, y, width, height).\n        \"\"\"\n        rectangles = []\n        for i in range(len(grid)):\n            for j in range(len(grid[0])):\n                for h in range(i + 1, len(grid)):\n                    for w in range(j + 1, len(grid[0])):\n                        # Check if all cells in the rectangle are equal to the value to check\n                        if np.all(grid[i:h, j:w] == value_to_check):\n                            rectangles.append((i, j, w - j, h - i))\n        return rectangles\n\n    def calculate_area(rectangle):\n        \"\"\"\n        Calculate the area of a rectangle.\n\n        Args:\n            rectangle (tuple): A tuple representing the rectangle with (x, y, width, height).\n\n        Returns:\n            int: The area of the rectangle.\n        \"\"\"\n        return rectangle[2] * rectangle[3]\n\n    rectangles = find_rectangles(grid)\n    return sum(calculate_area(rectangle) for rectangle in rectangles)\n\n# Example usage:\ngrid = [\n    [0.8, 0.5, 0.2, 0.9, 0.1],\n    [0.1, 0.8, 0.6, 0.3, 0.9],\n    [0.4, 0.2, 0.9, 0.8, 0.5],\n    [0.6, 0.3, 0.1, 0.2, 0.4],\n    [0.9, 0.6, 0.4, 0.5, 0.3]\n]\n\nprint(sum_rectangles(grid))\n```\n\nThis updated code is more robust and flexible, and it uses the `numpy` library for efficient operations.",
    "model_name": "meta-llama/Meta-Llama-3.1-8B-Instruct",
    "prompt": "# Problem: Calculate the sum of areas of all rectangles within a grid of size m x n\nGiven a grid of size m x n, where each cell contains a number representing the probability of it being a wall or a rectangle, calculate the sum of areas of all rectangles present in the grid.\n\n## Step 1: Define the grid size and the grid itself\nWe will start by defining the grid size m x n and the grid itself as a 2D list in Python.\n\n```python\n# Define the grid size\nm = 5  # number of rows\nn = 5  # number of columns\n\n# Initialize the grid with some random values. In a real scenario, these values would be the probabilities of each cell being a wall or a rectangle\ngrid = [\n    [0.8, 0.5, 0.2, 0.9, 0.1],\n    [0.1, 0.8, 0.6, 0.3, 0.9],\n    [0.4, 0.2, 0.9, 0.8, 0.5],\n    [0.6, 0.3, 0.1, 0.2, 0.4],\n    [0.9, 0.6, 0.4, 0.5, 0.3]\n]\n```\n\n## Step 2: Find all rectangles in the grid\nWe need to find all possible rectangles in the grid. A rectangle can be identified by the top-left cell and the bottom-right cell. We\u0027ll create a function to find all possible rectangles.\n\n```python\ndef find_rectangles(grid):\n    \"\"\"\n    Find all possible rectangles in the grid.\n\n    Args:\n        grid (list): 2D list representing the grid.\n\n    Returns:\n        list: A list of rectangles where each rectangle is represented by a tuple of (x, y, width, height).\n    \"\"\"\n    rectangles = []\n    for i in range(len(grid)):\n        for j in range(len(grid[0])):\n            for h in range(i + 1, len(grid)):\n                for w in range(j + 1, len(grid[0])):\n                    # Check if all cells in the rectangle are 1 (probability of being a rectangle)\n                    if all(grid[x][y] == 1 for x in range(i, h) for y in range(j, w)):\n                        rectangles.append((i, j, w - j, h - i))\n    return rectangles\n```\n\n## Step 3: Calculate the area of each rectangle\nNow that we have all possible rectangles, we need to calculate the area of each rectangle.\n\n```python\ndef calculate_area(rectangle):\n    \"\"\"\n    Calculate the area of a rectangle.\n\n    Args:\n        rectangle (tuple): A tuple representing the rectangle with (x, y, width, height).\n\n    Returns:\n        int: The area of the rectangle.\n    \"\"\"\n    return rectangle[2] * rectangle[3]\n```\n\n## Step 4: Sum the areas of all rectangles\nWe need to sum the areas of all rectangles found in the grid.\n\n```python\ndef sum_rectangles(grid):\n    \"\"\"\n    Sum the areas of all rectangles in the grid.\n\n    Args:\n        grid (list): 2D list representing the grid.\n\n    Returns:\n        int: The sum of areas of all rectangles in the grid.\n    \"\"\"\n    rectangles = find_rectangles(grid)\n    return sum(calculate_area(rectangle) for rectangle in rectangles)\n```\n\n## Step 5: Call the function to sum the areas of all rectangles\nFinally, we\u0027ll call the function to sum the areas of all rectangles.\n\n```python\nprint(sum_rectangles(grid))\n```\n\nHere is the complete code:\n\n```python\ndef sum_rectangles(grid):\n    def find_rectangles(grid):\n        \"\"\"\n        Find all possible rectangles in the grid.\n\n        Args:\n            grid (list): 2D list representing the grid.\n\n        Returns:\n            list: A list of rectangles where each rectangle is represented by a tuple of (x, y, width, height).\n        \"\"\"\n        rectangles = []\n        for i in range(len(grid)):\n            for j in range(len(grid[0])):\n                for h in range(i + 1, len(grid)):\n                    for w in range(j + 1, len(grid[0])):\n                        # Check if all cells in the rectangle are 1 (probability of being a rectangle)\n                        if all(grid[x][y] == 1 for x in range(i, h) for y in range(j, w)):\n                            rectangles.append((i, j, w - j, h - i))\n        return rectangles\n\n    def calculate_area(rectangle):\n        \"\"\"\n        Calculate the area of a rectangle.\n\n        Args:\n            rectangle (tuple): A tuple representing the rectangle with (x, y, width, height).\n\n        Returns:\n            int: The area of the rectangle.\n        \"\"\"\n        return rectangle[2] * rectangle[3]\n\n    rectangles = find_rectangles(grid)\n    return sum(calculate_area(rectangle) for rectangle in rectangles)\n\ngrid = [\n    [0.8, 0.5, 0.2, 0.9, 0.1],\n    [0.1, 0.8, 0.6, 0.3, 0.9],\n    [0.4, 0.2, 0.9, 0.8, 0.5],\n    [0.6, 0.3, 0.1, 0.2, 0.4],\n    [0.9, 0.6, 0.4, 0.5, 0.3]\n]\n\nprint(sum_rectangles(grid))\n```"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/test-outputs", "default")
```

Or simply as it follows, since there's only one configuration and is named `default`: 

```python
from datasets import load_dataset

ds = load_dataset("dvilasuero/test-outputs")
```


</details>





## References


```
@misc{xu2024magpiealignmentdatasynthesis,
    title={Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing},
    author={Zhangchen Xu and Fengqing Jiang and Luyao Niu and Yuntian Deng and Radha Poovendran and Yejin Choi and Bill Yuchen Lin},
    year={2024},
    eprint={2406.08464},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2406.08464},
}
```


