mgp-imputer 0.1.2

Missing Value Imputation using Deep Gaussian Processes with a scikit-learn compatible API.

MGP-Imputer: Missing Value Imputation with Deep Gaussian Processes

A PyTorch-based implementation of Missing Gaussian Processes (MGP) for missing value imputation, wrapped in a user-friendly scikit-learn compatible API.

This package allows you to seamlessly integrate Deep Gaussian Process models into your data preprocessing pipelines for robust and uncertainty-aware imputation. It is based on the paper "Gaussian processes for missing value imputation".

Chained Deep Gaussian Processes for Missing Value Imputation

Features

• Scikit-learn Compatible: Use fit, predict, and fit_transform methods just like any other scikit-learn transformer.
• Two Imputation Strategies:
  • chained (Default): Builds a separate GP layer for each feature with missing values, modeling dependencies in a chained fashion (MGP).
  • holistic: Builds a single, multi-output Deep GP to model all features simultaneously.
• Probabilistic Imputation: Returns both the imputed values and the standard deviation, giving you a measure of uncertainty for each imputed value.
• GPU Accelerated: Leverages PyTorch to run on CUDA devices for significant speedups.

Installation

You can install mgp-imputer directly from PyPI:

pip install mgp-imputer

Quick Start

Here's how to use MGPImputer to fill in missing values (np.nan) in your dataset.

import numpy as np
import pandas as pd
from mgp import MGPImputer

# 1. Create a synthetic dataset with 20% missing values
np.random.seed(42)
n_samples, n_features = 200, 5
X_true = np.random.rand(n_samples, n_features) * 10
X_missing = X_true.copy()
missing_mask = np.random.rand(n_samples, n_features) < 0.2
X_missing[missing_mask] = np.nan

print(f"Created a dataset with {np.sum(missing_mask)} missing values.")

# 2. Initialize the MGPImputer
# Strategies can be 'chained' (default) or 'holistic'
imputer = MGPImputer(
    imputation_strategy='chained',
    n_inducing_points=100,
    n_iterations=1000, # Use more iterations for real data
    learning_rate=0.01,
    batch_size=64,
    verbose=True,
    seed=42
)

# 3. Fit on the data and transform it to get imputed values
# The imputer returns the imputed data and the standard deviation of the predictions
X_imputed, X_std = imputer.fit_transform(X_missing)

# 4. Evaluate the imputation quality
rmse = np.sqrt(np.mean((X_imputed[missing_mask] - X_true[missing_mask])**2))
print(f"\nImputation complete.")
print(f"RMSE on missing values: {rmse:.4f}")

# The result is a complete numpy array
print("\nImputed data shape:", X_imputed.shape)
print("Number of NaNs in imputed data:", np.isnan(X_imputed).sum())

Configuration Options

You can customize the behavior of MGPImputer by passing parameters during initialization. Here are the available options:

Parameter	Description	Type	Options	Default
imputation_strategy	The core method for building the GP model.	str	'chained', 'holistic'	'chained'
imp_init	The standard imputation method used to create an initial complete dataset before training the GP.	str	'mean', 'median', 'knn', 'mice', 'constant'	'mean'
kernel	The covariance function for the Gaussian Process layers.	str	'matern', 'rbf'	'matern'
n_layers	The number of layers in the Deep GP. Only used when imputation_strategy is 'holistic'.	int	> 0	2
n_inducing_points	The number of inducing points for the sparse GP approximation. A higher number is more accurate but computationally slower.	int	> 0	100
n_iterations	The total number of optimization iterations to run during training.	int	> 0	10000
n_samples	The number of Monte Carlo samples drawn to approximate the model's posterior distribution.	int	> 0	20
learning_rate	The learning rate for the Adam optimizer.	float	> 0	0.01
batch_size	The number of data points in each mini-batch during training.	int	> 0	128
likelihood_var	The initial variance of the Gaussian likelihood function.	float	> 0	0.01
var_noise	The initial variance of the white noise added to the kernel.	float	> 0	0.0001
verbose	If True, prints training progress and other information.	bool	True, False	True
use_cuda	If True, the model will run on a GPU if one is available.	bool	True, False	True
seed	A random seed for reproducibility of results.	int	≥ 0	0

Citation

If you use this work in your research, please cite the original paper:

Jafrasteh, B., Hernández-Lobato, D., Lubián-López, S. P., & Benavente-Fernández, I. (2023). Gaussian processes for missing value imputation. Knowledge-Based Systems, 273, 110603. Missing GPs

License

This project is licensed under the MIT License.