aurai-imputer 1.0.5

Universal Adaptive Imputer – A hybrid VAE + latent nearest neighbor imputation model with uncertainty estimation.

AURAI – Adaptive Uncertainty-Regularized Autoencoder Imputer

Author: Abdul Mofique Siddiqui
License: MIT
Install via pip:

pip install aurai-imputer

Import it in your Python code:

from AURAI import AURAIImputer

Overview

AURAI (Adaptive Uncertainty-Regularized Autoencoder Imputer) is an advanced hybrid imputation framework that combines:

• A mask-aware Variational Autoencoder (VAE)
• Latent-space nearest-neighbor refinement
• A feature-wise adaptive gating mechanism
• Monte-Carlo–based uncertainty estimation

AURAI supports both numerical and categorical datasets and performs reliably under:

• MCAR (Missing Completely At Random)
• MAR (Missing At Random)
• MNAR (Missing Not At Random)

The imputer also produces confidence intervals for each filled value, making it suitable for decision-critical applications.

Installation

Install the package via pip:

pip install aurai-imputer

How It Works

• Global VAE Module Learns latent structure and reconstructs both numeric and categorical distributions.
• Latent-Space KNN Module Uses nearest neighbors in latent space to refine local predictions.
• Adaptive Gating Produces a learnable per-feature weight that blends global (VAE) and local (KNN) imputations.
• Uncertainty Estimation Monte-Carlo sampling over latent variables yields:
  • Posterior means
  • 95% confidence intervals
• Mixed Data Support Uses StandardScaler + OrdinalEncoder to handle mixed data seamlessly.

Getting Started

1. Import the package

from AURAI import AURAIImputer

2. Initialize the imputer

imputer = AURAIImputer()

3. Fit the model

imputer.fit(df)

• df: pandas DataFrame containing numerical and/or categorical columns

4. Impute missing values

imputed = imputer.transform(df)

Returns a NumPy array with missing values filled.

5. Impute with uncertainty intervals

mean, lower, upper = imputer.transform(df, return_intervals=True)

API Reference

AURAIImputer()

Initializes the imputer. Supports optional parameters such as latent dimension, Monte Carlo samples, neighbors count, etc.

.fit(df)

Fits the model to training data.

Parameters:

• df: pandas DataFrame with mixed features

.transform(df, return_intervals=False)

Returns imputed values.

Input:

• df: DataFrame or numpy array with missing values

Output:

• A NumPy array with imputed values
• If return_intervals=True: returns (mean, lower, upper)

.save(path)

Saves:

• model weights
• preprocessor
• metadata

.load(path)

Loads a previously saved AURAI model.

Example Usage

Example 1: Basic Imputation

from AURAI import AURAIImputer
import pandas as pd

df = pd.read_csv("data.csv")
imputer = AURAIImputer()
imputer.fit(df)
imputed = imputer.transform(df)

Example 2: Imputation with Uncertainty

mean, lower, upper = imputer.transform(df, return_intervals=True)

Internals

• Variational Autoencoder (VAE) Learns global structure and reconstructs numeric means, variances, and categorical logits.
• Latent-Space Nearest Neighbor Search Provides local refinement to improve imputation accuracy.
• Gating Network Learns per-feature blending weights for global + local fusion.
• Cluster Regularization Encourages structured and stable latent geometry.
• Monte Carlo Sampling Produces mean predictions and confidence intervals.

Notes

• Works with both numeric and categorical data.
• Performs well under MCAR, MAR, and MNAR.
• Provides uncertainty intervals for downstream tasks.
• GPU recommended for training large datasets.

Author

Abdul Mofique Siddiqui

License

This project is licensed under the MIT License.