autopreprocessor 0.1.1

Automatic preprocessing for tabular datasets using heuristics and RAG-assisted encoding detection.

AutoPreprocessor

An experimental AutoML preprocessing package that combines rule-based preprocessing with a lightweight Retrieval-Augmented Generation (RAG) system to automatically suggest encoding strategies for tabular datasets.

The project uses Sentence Transformers and ChromaDB locally. It does not rely on external LLM APIs.

Features

• Mean, median, and mode imputation
• Label encoding for binary columns
• One-hot encoding
• RAG-assisted ordinal encoding
• Standard scaling and robust scaling
• Automatic dropping of columns with excessive missing values

Installation

Install locally from the project root:

pip install .

For development, you can also install dependencies directly:

pip install -r requirements.txt

Usage

import pandas as pd
from autopreprocessor import AutoPreprocessor

data = pd.read_csv("sample.csv")

auto = AutoPreprocessor()
X_processed = auto.fit_transform(data)

print(auto.report_)

RAG And ChromaDB

The package preserves the existing RAG behavior and ChromaDB usage. AutoPreprocessor loads the persistent ChromaDB collection named rag_collection from:

autopreprocessor/chroma_db/

The ChromaDB path is resolved with pathlib relative to the installed package location, so it can work across machines and operating systems.

Project Structure

AutoPreprocessor/
|
|-- autopreprocessor/
|   |-- __init__.py
|   |-- auto_preprocessor.py
|   |-- chroma_db/
|
|-- requirements.txt
|-- setup.py
|-- pyproject.toml
|-- README.md
|-- .gitignore

API

from autopreprocessor import AutoPreprocessor

AutoPreprocessor exposes:

• fit(data)
• transform(data)
• fit_transform(data)
• report_

The preprocessing logic is preserved from the original notebook implementation.