notmiwae-pytorch 0.1.0

Not-MIWAE: Deep Generative Modelling with Missing Not at Random Data

PyTorch Implementation of not-MIWAE

This is a PyTorch implementation of the not-MIWAE model from the paper:

not-MIWAE: Deep Generative Modelling with Missing not at Random Data
Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
ICLR 2021 | Paper

Overview

The not-MIWAE extends the Missing Data Importance Weighted Autoencoder (MIWAE) by explicitly modeling the missing data mechanism. This allows it to handle Missing Not At Random (MNAR) data, where the probability of a value being missing depends on the value itself.

Key Features

• NotMIWAE Model: Full implementation with encoder, decoder, and missing process networks
• MIWAE Model: Standard MIWAE for comparison (assumes MCAR)
• Missing Process Interpretation: Built-in tools to interpret learned missing mechanisms
• Trainer: Complete training loop with TensorBoard logging, early stopping, and checkpointing
• Utilities: Functions for evaluation and data preprocessing

Installation

pip install -r requirements.txt

Quick Start

import torch
from torch.utils.data import DataLoader, TensorDataset
from notmiwae_pytorch import NotMIWAE, Trainer
from notmiwae_pytorch.utils import set_seed, imputation_rmse

# Set seed
set_seed(42)

# Prepare your data (x_filled has 0s for missing, mask is 1=observed, 0=missing)
# DataLoader should return (x_filled, mask, x_original) tuples
train_dataset = TensorDataset(x_filled, mask, x_original)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

# Create model with feature names for interpretation
model = NotMIWAE(
    input_dim=10,
    latent_dim=5,
    hidden_dim=128,
    n_samples=20,
    missing_process='selfmasking_known_signs',
    feature_names=['feat1', 'feat2', ...]  # Optional
)

# Train
trainer = Trainer(model, lr=1e-3)
history = trainer.train(train_loader, val_loader, n_epochs=100)

# Interpret the learned missing process
model.interpret_missing_process()

# Evaluate imputation
rmse, x_imputed = imputation_rmse(model, x_original, x_filled, mask)

Model Architecture

not-MIWAE Objective

The not-MIWAE maximizes a lower bound on the joint log-likelihood:

$$\log p(x_o, s) \geq \mathbb{E}{q(z|x_o)}\left[\log \frac{1}{K}\sum{k=1}^{K} \frac{p(x_o|z_k) \cdot p(s|x_k) \cdot p(z_k)}{q(z_k|x_o)}\right]$$

where:

• $x_o$: observed values
• $s$: missingness indicator (1=observed, 0=missing)
• $z$: latent variables
• $K$: number of importance samples

Missing Process Models

The model supports several missing mechanisms through p(s|x):

1. selfmasking: $\text{logit}(p(s_d=1|x)) = -W_d(x_d - b_d)$
2. selfmasking_known_signs: Same as above but with $W_d > 0$ (known direction)
   • Supports directional control via signs parameter:
     • +1.0: High values more likely to be missing
     • -1.0: Low values more likely to be missing
3. linear: Linear mapping from $x$ to logits
4. nonlinear: MLP mapping from $x$ to logits

Directional Missingness Control (New!)

For selfmasking_known_signs, you can specify the direction of missingness per feature:

import torch

# Define directional patterns for 4 features
signs = torch.tensor([
    +1.0,  # Feature 0: high values → missing (e.g., sensor saturation)
    +1.0,  # Feature 1: high values → missing
    -1.0,  # Feature 2: low values → missing (e.g., below detection threshold)
    -1.0   # Feature 3: low values → missing
])

model = NotMIWAE(
    input_dim=4,
    latent_dim=10,
    missing_process='selfmasking_known_signs',
    signs=signs  # Optional: defaults to all +1.0 (high→missing)
)

See demo_signs.py for a complete demonstration.

Files

notmiwae_pytorch/
├── __init__.py          # Package initialization
├── models/
│   ├── __init__.py
│   ├── base.py          # Encoder, Decoders, MissingProcess
│   ├── notmiwae.py      # NotMIWAE model
│   └── miwae.py         # MIWAE model (baseline)
├── trainer.py           # Training loop with TensorBoard logging
├── utils.py             # Utility functions
├── example.py           # Complete example script
├── requirements.txt     # Dependencies
└── notebooks/
    └── demo_notmiwae.ipynb  # Interactive demo notebook

Note: DataLoaders should return (x_filled, mask, x_original) tuples where:

• x_filled: Data with missing values filled (e.g., with 0)
• mask: Binary mask (1=observed, 0=missing)
• x_original: Original complete data (for evaluation)

Running the Example

cd notmiwae_pytorch
python example.py

This will:

1. Load the UCI Wine Quality dataset
2. Introduce MNAR missing values
3. Train both not-MIWAE and MIWAE models
4. Compare imputation performance

TensorBoard

To view training logs:

tensorboard --logdir=./runs

Then open http://localhost:6006 in your browser.

Notebook Demo

See notebooks/demo_notmiwae.ipynb for an interactive demonstration with visualizations.

Interpreting the Missing Process

After training, you can interpret what the model learned about the missing mechanism:

# For selfmasking models: shows W (strength) and b (threshold) per feature
model.interpret_missing_process()
# Output: "feature_0: Higher values (>0.25) more likely MISSING (W=1.234)"

# For linear/nonlinear models: compute sensitivity matrix
sensitivity = model.compute_missing_sensitivity(x_sample)

Differences from Original TensorFlow Implementation

This PyTorch implementation:

• Uses modern PyTorch conventions (nn.Module, DataLoader, etc.)
• Includes TensorBoard integration via torch.utils.tensorboard
• Provides cleaner separation of concerns (models, trainer)
• Adds type hints and comprehensive docstrings
• Includes missing process interpretation tools

Citation

@inproceedings{ipsen2021notmiwae,
  title={not-MIWAE: Deep Generative Modelling with Missing not at Random Data},
  author={Ipsen, Niels Bruun and Mattei, Pierre-Alexandre and Frellsen, Jes},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

License

This implementation follows the license of the original repository.