pain-imputer 0.1.3

PAIN : A library for imputing missing values

PAIN-Imputer: Precision Adaptive Imputation for Mixed-Type Data

pain-imputer is a Python package built around the values and research-backed Precision Adaptive Imputation Network (PAIN) a tri-step imputation framework designed to handle datasets containing both numerical and categorical features with varying missingness patterns.

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Table of Contents

• What is PAIN?
• Key Features
• Installation
• Quickstart
• API Reference
• How It Works
• Use Cases
• Running Tests
• Contributing
• License
• Contact

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What is PAIN?

PAIN Imputer is a state-of-the-art missing data imputation library that combines statistical methods, machine learning, and neural networks in a tri-step architecture. It is a research-based architecture, proposed in [PAIN paper on arXiv] (https://arxiv.org/abs/2501.10667) consistently outperforms traditional imputation methods while preserving data distributions and relationships.

Unlike traditional imputers that rely on statistical or machine learning methods, PAIN-Imputer combines three complementary strategies:

1. Statistical Imputation
2. Machine Learning-based Estimation
3. Neural Autoencoder with Iterative Refinement

Note: This is a beta release; expect updates as we continue to refine the library.

Key Features

• Handles both categorical and numerical data
• Tri-step imputation strategy for high accuracy and stability
• Works well with high-dimensional datasets
• Automatically detects data types and missingness patterns
• Research-validated on real-world benchmark datasets
• Easy to integrate into any ML pipeline

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Installation

• Install from PyPI:

pip install pain-imputer

Or

• Install from source:

git clone https://github.com/RajEshwariCodes/PAIN_Library.git
cd pain-imputer
pip install -e .

Requirements

• Python ≥ 3.7
• numpy
• pandas
• scikit-learn
• tensorflow

Quickstart

import pandas as pd
import numpy as np
from pain_imputer import PAINImputer

# Load your data with missing values
data = pd.read_csv('your_dataset.csv')

# Initialize PAIN Imputer
imputer = PAINImputer(
    n_iterations=5,
    n_neighbors=5,
    random_state=42
)

# Transform incomplete data to complete
imputed_data = imputer.fit_transform(data)

print(f"Missing values before: {data.isnull().sum().sum()}")
print(f"Missing values after: {imputed_data.isnull().sum().sum()}")

Real-World Example: Forest Fire Data

import pandas as pd
from pain_imputer import PAINImputer

#Load forest fire dataset with missing values
df = pd.read_csv('forestfires.csv')

#Create PAIN imputer with custom settings
imputer = PAINImputer(
    n_iterations=3,          # Number of refinement iterations
    n_neighbors=7,           # KNN neighbors for baseline
    n_estimators=150,        # Random Forest trees
    encoder_layers=[128, 64, 32],  # Autoencoder architecture
    random_state=42
)

#Impute missing values
complete_data = imputer.fit_transform(df)

#Verify data integrity
print("Original data shape:", df.shape)
print("Imputed data shape:", complete_data.shape)
print("Data types preserved:", (df.dtypes == complete_data.dtypes).all())

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API Reference

PAINImputer Class
class PAINImputer:
    def __init__(
        self,
        n_iterations: int = 5,
        n_neighbors: int = 5,
        n_estimators: int = 100,
        encoder_layers: List[int] = [64, 32, 16],
        random_state: int = 42
    )

Parameters

Parameter	Type	Default	Description
n_iterations	int	5	Number of refinement iterations in Layer 2
n_neighbors	int	5	Number of neighbors for KNN imputation
n_estimators	int	100	Number of trees in Random Forest
encoder_layers	List[int]	[64, 32, 16]	Hidden layer sizes for autoencoder
random_state	int	42	Random seed for reproducibility

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Running Tests

To run the test suite and verify the library’s functionality, use pytest. First, ensure you have pytest installed:

pip install pytest

Then, run the tests:

pytest tests/

This will execute all unit tests in the tests/ directory, helping contributors ensure their changes don’t break existing functionality.

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How It Works

(Tri-Step Pipeline)

• Baseline Imputation
  Uses mean/median/KNN weighted by missingness ratio.
• Advanced Modeling
  Uses random forests and autoencoders to refine imputations iteratively.
• Refinement Clips imputed values to avoid outliers, preserving statistical consistency.

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Use Cases

• Medical datasets with missing physiological values
• Mixed survey data (text + numbers)
• High-dimensional research datasets
• ML preprocessing pipelines
• Any situation where missing values exist in structured data

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Contributing

We welcome contributions ! Whether it’s a bug fix, feature request, or new idea, submit pull requests via our GitHub repository.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Maintainer: Rajeshwari Mistri Email: [[e-mail]] (mailto:[e-mail]) Project Repository: GitHub

Ready to make missing data painless? pip install pain-imputer and transform your incomplete datasets today!