deeplatent 0.1.3

A unified latent variable modeling framework for analyzing large multimodal and multilingual datasets

DeepLatent

DeepLatent is a unified latent variable modeling framework for analyzing large multimodal and multilingual datasets. It relies on variational inference using deep neural networks for estimation.

The package currently supports:

• Generic latent factor models
• Topic models: The latent variables are a mixture of topics within documents.
• Ideal point models: The latent variables are interpreted as ideological dimensions.

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🌟 Key Features

• Multilingual and multimodal support

  • Learn topics / ideal points across multiple modalities (e.g., texts and images, texts and votes, etc.)
  • Learn the weight of each modality in determining the latent variables per observation

• Flexible metadata handling:

  • prevalence: covariates that influence the latent variables
  • content: covariates that influence the response variables conditional on the latent variables (e.g., topic-word distributions)
  • labels: outcomes for classification or regression tasks
  • prediction: additional predictors for the labels

• Flexible input/output representations:

  • Document embeddings (for texts, images, audio-visual data)
  • Word frequencies (BoW)
  • Raw images
  • Discrete choice data
  • Voting records

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📦 Models

GTM (Generalized Topic Model)

• Learns topics on the simplex
• Supports dirichlet or logistic_normal priors (optionally conditioned on covariates)

IdealPointNN

• Learns unconstrained latent variables (ℝ️ⁿ) for ideal point modeling
• Designed for political texts, images, audio and video recordings, surveys, and votes
• Uses a gaussian prior (optionally conditioned on covariates)

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Installation

From PyPI (Recommended)

pip install deeplatent

From Source

git clone https://github.com/PinchOfData/DeepLatent.git  
cd deeplatent
pip install -e .

Development Installation

git clone https://github.com/PinchOfData/DeepLatent.git 
cd deeplatent
python setup_dev.py

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🚀 Getting Started

1. Prepare Your Data with Corpus()

Supports text, embeddings, votes, and survey questions:

import sys
sys.path.append('../src/')

from corpus import Corpus

modalities = {
    "text": {
        "column": "doc_clean",
        "views": {
            "bow": {
                "type": "bow",
                "vectorizer": CountVectorizer()
            }
        }
    },
    "image": {
        "column": "image_path",
        "views": {
            "embedding": {
                "type": "embedding",
                "embed_fn": my_image_embedder
            }
        }
    }
}

my_dataset = Corpus(df, modalities=modalities)

Optionally include metadata:

• prevalence, content, labels, prediction

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2. Train a Model

For Topic Models:

from models import GTM

model = GTM(
    n_topics=20, 
    doc_topic_prior="logistic_normal",
    ae_type="wae"
)

For Ideal Point Models:

from models import IdealPointNN

model = IdealPointNN(
    n_ideal_points=1, # one-dimensional ideal point model
    ae_type="vae"
)

🔧 Common Options

Argument	Description
ae_type	"wae" (Wasserstein autoencoder) or "vae" (variational autoencoder) or "ae" (plain autoencoder)
fusion	"poe" (Product of Experts), "moe_gating" (Mixture of Experts), or "moe_average" (Simple averaging across modalities)
update_prior	Learn a structured prior conditioned on prevalence covariates
w_prior	Strength of prior alignment for wae
w_pred_loss	Weight of supervised loss predicting label
kl_annealing_*	Strength of prior alignment for vae. Helps preventing posterior collapse.

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🔍 Analysis and Utilities

📚 Topic Models (GTM)

• get_topic_words() – top words per topic
• get_covariate_words() – word shifts by content covariates
• get_top_docs() – representative documents
• get_topic_word_distribution() – topic-word matrix
• get_covariate_word_distribution() – word shift matrix
• plot_topic_word_distribution() – word clouds / bar plots
• visualize_docs() – document embeddings (UMAP, t-SNE, PCA)
• visualize_words() – word embeddings
• visualize_topics() – topic embeddings

👤 Ideal Point Models (IdealPointNN)

• get_ideal_points() – ℝ️ⁿ latent space
• get_predictions() – supervised output
• get_modality_weights() – fusion weights (PoE or gating)

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📁 Tutorials

Check out the example notebooks to get started.

Download sample data to run some notebooks: Congressional Speeches CSV

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📖 References

• Deep Latent Variable Models for Unstructured Data , Germain Gauthier, Philine Widmer, Elliott Ash (2025)

• The Neural Ideal Point Model , Germain Gauthier, Hugo Subtil, Philine Widmer (2025)

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⚠️ Disclaimer

This package is under active development 🚧 — feedback and contributions welcome!