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name: pomegranate description: "Guides pomegranate probabilistic modeling workflows: distributions, mixture models, Bayesian classifiers, Bayesian networks, factor graphs, HMMs, Markov chains, KMeans, and PyTorch-backed training features." disable-model-invocation: true metadata: disco-role: operating license: MIT

pomegranate

Use this skill when

Use this repo skill when a task needs package-specific guidance for pomegranate v1.x probabilistic modeling: distribution fitting/scoring, mixture models, Bayesian classifiers, Bayesian networks, factor graphs, hidden Markov models, Markov chains, KMeans initialization/clustering, missing values, priors, out-of-core updates, or PyTorch device/dtype behavior.

Pomegranate v1.x is a PyTorch-backed rewrite. Prefer current imports such as from pomegranate.distributions import Normal and from pomegranate.hmm import DenseHMM; do not rely on pre-v1 Cython-era names such as NormalDistribution, HiddenMarkovModel, State, Node, or bake.

Install and import check

For a published install, use:

python -m pip install pomegranate

For a local checkout of this package, use:

python -m pip install -e .

Then verify imports with the bundled helper:

python scripts/check_env.py
# optional CUDA allocation check when a CUDA torch build is installed:
python scripts/check_env.py --cuda

Core dependencies are numpy, scipy, scikit-learn, torch, apricot-select, and networkx. There are no repository-defined console entry points; use the Python APIs directly.

Route by task

User task or signal	Read next
Fit, score, sample, weight, freeze, or compose individual distributions such as Normal, Categorical, ConditionalCategorical, Gamma, Poisson, IndependentComponents, or ZeroInflated	sub-skills/distributions/SKILL.md
Build unsupervised mixture models, Gaussian or heterogeneous mixtures, Bayes classifiers, posterior probabilities, or prior-weighted component inference	sub-skills/mixtures-and-classifiers/SKILL.md
Build Bayesian networks, run factor-graph inference, learn categorical network structures, or impute discrete missing values from graph evidence	sub-skills/graph-models/SKILL.md
Work with Markov chains, dense/sparse HMMs, sequence likelihoods, variable-length sequence batches, Baum-Welch fitting, posterior decoding, or Viterbi	sub-skills/sequence-models/SKILL.md
Use pomegranate KMeans as a standalone clusterer or as an initialization helper for probabilistic models	sub-skills/clustering/SKILL.md
Need a compact class-to-subskill map before routing	references/model-catalog.md
Need missing-value, GPU, mixed precision, priors, out-of-core, serialization, or torch.compile behavior	references/feature-guide.md
Encounter install, import, legacy API, shape, dtype, masked tensor, or backend errors	references/troubleshooting.md and the nearest sub-skill troubleshooting file
Need to check whether this skill matches a repository version	references/repo-provenance.md

Common API pattern

Most pomegranate models either subclass torch.nn.Module directly or subclass the pomegranate Distribution abstraction. Expect these recurring methods where implemented:

• fit(...) performs one-shot training, often by calling summarize(...) followed by from_summaries().
• summarize(...) accumulates sufficient statistics for mini-batch or out-of-core updates.
• from_summaries() applies accumulated statistics and resets caches.
• probability(...) and log_probability(...) score complete examples.
• Composite models add predict(...), predict_proba(...), and predict_log_proba(...) for posterior assignments or missing-value inference.

Use check_data=False only when inputs are already known to satisfy the documented shapes and ranges, or when following torch.compile guidance. Otherwise leave validation enabled while debugging.

Guardrails

• Keep current v1.x import paths and class names in generated code.
• Move both model and tensors to the same PyTorch device before using CUDA.
• Use torch.masked.MaskedTensor for missing values where supported; read the feature guide first because not every distribution supports missingness.
• Treat optional GPU and mixed-precision workflows as PyTorch-device workflows, not pomegranate-specific installs.
• Do not tell users to run original repository notebooks or tests as runtime steps. Use the bundled smoke scripts and references instead.