easy-detm 0.1.1

A simple, easy-to-use toolkit for Dynamic Embedded Topic Models on temporal document collections.

easy-detm Package Document

Package Scope

This package provides a Python interface for training and visualizing the Dynamic Embedded Topic Model (DETM).

The current data API is intentionally simple:

documents: List[str]
timestamps: List[int]

Main API

Model

from easy_detm import DETMModel

DETMModel is the high-level class for:

• creating the DETM model,
• fitting it to temporal documents,
• extracting topics,
• inferring document-topic distributions,
• saving and loading checkpoints,
• evaluating topic coherence and topic diversity.

Data

from easy_detm.data import create_dataset_from_list, DocumentCorpus

Use create_dataset_from_list() for most workflows. Use DocumentCorpus only when you need to manually control train/validation/test splits.

Visualization

from easy_detm import (
    configure_cjk_fonts,
    visualize_embeddings,
    visualize_embeddings_over_time,
    visualize_topic_evolution,
)

Visualization functions use the learned model parameters. They do not retrain or modify the model. configure_cjk_fonts() is called automatically by the visualization module and can also be called manually to inspect or reset CJK font support for Korean, Japanese, Chinese, and English labels.

Topic Metrics

diversity = model.get_topic_diversity(num_words=10)
coherence = model.get_topic_coherence(data=train, num_words=10)

get_topic_diversity() uses only the trained topic-word distributions. get_topic_coherence() also needs a reference corpus in DETM format, usually the training split. If you call it on a model restored with load(), pass data=train because checkpoints store model parameters and vocabulary, not the original corpus.

Input Requirements

Documents

Documents should be strings where tokens are separated by whitespace:

documents = [
    "climate carbon emissions",
    "trade market finance",
]

The current package does not perform advanced NLP preprocessing. Recommended preprocessing before calling the package:

• lowercase text,
• remove or normalize punctuation,
• remove domain-specific noise,
• tokenize consistently,
• optionally remove stopwords,
• optionally lemmatize or stem terms.

Timestamps

Timestamps should be integers:

timestamps = [0, 0, 1, 1, 2, 2]

Recommended convention:

• use zero-based indices,
• keep time IDs contiguous,
• make sure every document has one timestamp.

Hyperparameter Notes

Important parameters:

• num_topics: number of topics.
• num_times: number of time periods.
• rho_size: topic embedding dimension.
• emb_size: word embedding dimension.
• t_hidden_size: hidden size for the theta encoder.
• eta_hidden_size: hidden size for the eta LSTM.
• eta_nlayers: number of LSTM layers for eta.
• delta: random-walk prior variance used by the original DETM implementation.
• enc_drop: dropout in the theta encoder.
• batch_size: minibatch size.
• learning_rate: optimizer learning rate.

Output Interpretation

Topics

model.get_topics() returns top words from the learned topic-word distributions. Because DETM is dynamic, each topic can have different top words at different time points.

Document-Topic Matrix

model.get_document_topics() returns an array with shape:

num_documents x num_topics

Each row is a topic-proportion vector for one input document.

Visualizations

• Embedding plots show topics and words in a shared 2D projection.
• Topic evolution plots show word probability changes over time for one topic.

Acknowledgements

The core DETM model implementation is adapted from the original DETM code by Adji Bousso Dieng, Francisco J. R. Ruiz, and David M. Blei: https://github.com/adjidieng/DETM

Please cite the original paper when using the DETM model: "The Dynamic Embedded Topic Model" (Dieng, Ruiz, and Blei, 2019).