sample-dataset 0.2.0

A library to create random balanced samples through constraint optimization

sample-dataset

• sample-dataset
  • Features
  • Installation
  • Quick Start
    • Import your data
    • Define the bucket structure + minima
    • Assign buckets
    • Multiple randomized balanced samples
  • How it works
  • Requirements
  • Links

sample-dataset is a small library for generating balanced, constraint-driven samples from tabular data, using:

• pandas for data handling
• Google OR-Tools CP-SAT for constraint optimization

It allows you to divide your dataset into buckets (e.g., train/test × attribute combinations) while respecting arbitrary minimum size requirements provided by the user.

This is useful for:

• Train/test splits with structural constraints
• Balanced dataset construction
• Controlled sampling for linguistic, NLP, or behavioral datasets
• Any application where "random sampling" must satisfy non-trivial rules

Features

• Constraint-based sampling using OR-Tools
• Flexible bucket definitions via a separate minima dataframe
• Supports arbitrary bucket-defining columns (e.g., split, feature_a, feature_b, split1, split2, ...)
• Automatically infers which dataset rows are eligible for which buckets
• Guarantees minimum bucket sizes
• Supports multiple randomized feasible solutions
• Simple API (assign_buckets, assign_buckets_multiple)

Installation

pip install sample-dataset

Quick Start

Import your data

import pandas as pd
from sample_dataset import assign_buckets

Your dataset (df) might look like:

df = pd.DataFrame({
    "ID": [1, 2, 3, 4],
    "feature_a": ["a", "a", "su", "su"],
    "feature_b": ["yes", "no", "yes", "no"],
    "context": ["...", "...", "...", "..."],
})

Define the bucket structure + minima

df_minima = pd.DataFrame({
    "split": ["train", "test", "train", "test"],
    "feature_a": ["a", "a", "su", "su"],
    "feature_b": ["yes", "yes", "no", "no"],
    "min_required": [150, 50, 150, 50],
})

Each row represents a bucket. All columns except min_required define the bucket identity.

Assign buckets

df_out = assign_buckets(df, df_minima, verbose=True)
print(df_out.head())

Output:

   ID feature_a feature_b   context      bucket
0   1        a       yes       ...   train|a|yes
1   2        a        no       ...    test|a|no
2   3       su       yes       ...  train|su|yes
3   4       su        no       ...   test|su|no

Multiple randomized balanced samples

To generate N different feasible assignments, use:

from sample_dataset import assign_buckets_multiple

df_samples = assign_buckets_multiple_wide(df, df_minima, n_samples=3)
print(df_samples.head())

You’ll get:

bucket_0      bucket_1      bucket_2
train|a|yes   test|a|yes    test|a|yes
...

How it works

• The code interprets df_minima as the full set of buckets.
• Matches rows to buckets when all key_cols match
• Enforces minimum bucket sizes
• Forces each row to belong to exactly one bucket
• Uses a randomized objective to obtain diverse feasible assignments
• Solved using OR-Tools’ CP-SAT engine

Requirements

• Python ≥ 3.9
• pandas
• numpy
• ortools

These are installed automatically.

Links

• Source code: [https://github.com/LaboratorioSperimentale/sample-dataset]
• Issues: [https://github.com/LaboratorioSperimentale/sample-dataset/issues]
• PyPI: [https://pypi.org/project/sample-dataset/]