Data Science Problems

Evaluate a natural language code generation model on real data science pedagogical notebooks! Data Science Problems (DSP) includes well-posed data science problems in Markdown along with unit tests to verify correctness and a Docker environment for reproducible execution. About 1/3 of notebooks in this benchmark also include data dependencies, so this benchmark not only can test a model's ability to chain together complex tasks, but also evaluate the solutions on real data! See our paper Training and Evaluating a Jupyter Notebook Data Science Assistant for more details about state of the art results and other properties of the dataset.

Installation

This project requires Python 3.6+ and Docker to run. Assuming you have these, to get started first download and install the Python package:

$ git clone git@github.com:microsoft/DataScienceProblems.git
$ cd DataScienceProblems/src
$ pip install -e .

Usage

Reading the problems

Extract the juice-github-repos.tar.gz file from the DataScienceProblems repository.

$ tar -xvzf juice-github-repos.tar.gz

Data Schema

Here is an example of a notebook context, prompt cell (1 and the markdown), solution cell (2), and unit tests cell (3).

The DSP schema corresponding to this example includes the prompt which is the question to be asked to the student, solution which is the answer to the question and test which is the test case to be run on the student's code.

{
    'notebook_path': '/path/to/the/notebook.ipynb',
    'notebook_problem_index': 0,
    'prompt': '%matplotlib inline\n'
            'import matplotlib.pyplot as plt\n'
            'import numpy as np\n'
            'import scipy.optimize as opt\n'
            '## Hat potential\n'
            'The following potential is often used in Physics and other fields '
            'to describe symmetry breaking and is often known as the "hat '
            'potential":\n'
            '\n'
            '$$ V(x) = -a x^2 + b x^4 $$\n'
            '\n'
            'Write a function `hat(x,a,b)` that returns the value of this '
            'function:\n',
    'solution': 'def hat(x,a=5.0,b=1.0):\n    return -a* x*x + b*x**4',
    'task_id': 'DSP/414',
    'test': 'assert hat(0.0, 1.0, 1.0)==0.0\n'
            'assert hat(0.0, 1.0, 1.0)==0.0\n'
            'assert hat(1.0, 10.0, 1.0)==-9.0'
}

We provide a read_problems function that can be used to read the problems from the jupyter notebooks.

Below is an example of how to use the read_problems function and use your generated code samples to save the samples to a file.

from data_science_problems.read import read_problems
from data_science_problems.utils import write_jsonl

problems = read_problems()

num_samples = 1
samples = [
    dict(task_id=task_id, completion=generate_code(problems[task_id]["prompt"]))
    for task_id in problems
    for _ in range(num_samples)
]
write_jsonl("samples.jsonl", samples)

Executing the problems and unit tests

Once you have saved the generated samples in the samples.jsonl file, you need to build the provided docker container, which would help you safely run the generated samples inside the container.

Use the following command to build the docker container.

$ docker build --pull --rm -f "Dockerfile" -t datascienceproblems:latest "."

Once the Docker container is built, you can execute the generated samples inside the container. You'll need to map the /app/juice-github-repos and /samples/samples.jsonl directory to the host directory where the notebooks are stored.

Use the following command to execute the samples inside the container.

$ docker run -it --rm -v $PWD/juice-github-repos:/app/juice-github-repos -v $PWD/samples.jsonl:/samples/samples.jsonl datascienceproblems /samples/samples.jsonl

The docker run will perform the following things:

• It will read the samples from the samples.jsonl file.
• It will create new notebooks with the generated code samples. The list of new notebooks is saved in the generates-notebooks.txt file.
• It will execute these new notebooks.
• It will compute pass@k for generated samples.

WARNING: Running the docker run command with num_samples = 1 will create with ~1000 new notebooks and save them on your disk. This may take a while.

$ docker run -it --rm -v $PWD/juice-github-repos:/app/juice-github-repos -v $PWD/samples.jsonl:/samples/samples.jsonl datascienceproblems /samples/samples.jsonl
2021-11-02 09:11:11,847 INFO services.py:1164 -- View the Ray dashboard at http://127.0.0.1:8265
Reading the generated samples.
100%|███████████████████████████████████████████████| 305/305 [00:03<00:00, 97.34it/s]
Saving to new notebooks with generated samples.
100%|███████████████████████████████████████████████| 305/305 [00:36<00:00,  8.47it/s]
Execute the new notebooks with generated samples.
100%|███████████████████████████████████████████████| 2192/2192 [05:17<00:40,  9.49it/s]
Complute pass@k for the executed notebooks.
100%|███████████████████████████████████████████████| 2192/2192 [00:28<00:00, 76.73it/s]
{'pass@1': ..., 'pass@10': ...}

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft’s Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

Dataset Metadata

The following table is necessary for this dataset to be indexed by search engines such as Google Dataset Search.

property	value
name	Data Science Problems
url	https://github.com/microsoft/DataScienceProblems
sameAs	https://github.com/microsoft/DataScienceProblems
description	Evaluate a natural language code generation model on real data science pedagogical notebooks! Data Science Problems (DSP) includes well-posed data science problems in Markdown along with unit tests to verify correctness and a Docker environment for reproducible execution. About 1/3 of notebooks in this benchmark also include data dependencies, so this benchmark not only can test a model's ability to chain together complex tasks, but also evaluate the solutions on real data! See our paper Training and Evaluating a Jupyter Notebook Data Science Assistant (https://arxiv.org/abs/2201.12901) for more details about state of the art results and other properties of the dataset.
citation	https://arxiv.org/abs/2201.12901
license	https://github.com/microsoft/DataScienceProblems/blob/main/LICENSE.txt