Automatic Inference¶

Automatic inference in mixle means the fitting route follows from explicit model structure. It does not mean every decision is hidden. The library tries to expose the chosen shape, confidence gaps, fallback path, and model capabilities so you can decide whether to trust the result or collect better data.

There are five common entry points:

You provide	Call	Result
an estimator	`optimize(data, estimator)`	fit exactly the structure you requested
a prototype distribution	`optimize(data, prototype)`	derive the matching estimator from the prototype shape
raw data only	`optimize(data)` or `get_estimator(data)`	infer a first estimator from observed types and profiles
raw data plus an audit boundary	`create(data, ...)`	fit a model and return an artifact with provenance, certificate, optional calibration, uncertainty, and exchangeability diagnostics
raw data plus an LLM designer	`design_model(data, llm)`	ask for an allowlisted spec, build it, fit-validate it, and fall back on failure

Explicit Estimator¶

Use this when you know the model you want.

from mixle.inference import optimize
from mixle.stats import CompositeEstimator, GammaEstimator, PoissonEstimator

est = CompositeEstimator((PoissonEstimator(), GammaEstimator()))
model = optimize(rows, est, max_its=50, out=None)

The estimator chooses the model family. The structure chooses the inference route: no latent wrapper means ordinary estimation; a mixture or HMM adds EM; a neural child adds gradient training inside its M-step.

Prototype Distribution¶

Use a prototype when the shape is clearer as a model than as an estimator. optimize coerces the prototype to its matching estimator. If the prototype parameters should also be the initialization, pass the same object as prev_estimate.

from mixle.inference import optimize
from mixle.stats import GaussianDistribution, MixtureDistribution

proto = MixtureDistribution(
    [GaussianDistribution(-2.0, 1.0), GaussianDistribution(2.0, 1.0)],
    [0.5, 0.5],
)
model = optimize(reals, proto, prev_estimate=proto, max_its=100, out=None)

This is the pattern to use for latent models where initial component locations matter. Passing only proto still runs, but it uses the prototype for shape rather than guaranteeing those parameter values as the starting point.

Infer an Estimator from Data¶

For a first pass over heterogeneous Python data:

from mixle.inference import optimize
from mixle.utils.automatic import get_estimator

est = get_estimator(rows, pseudo_count=1.0e-4)
model = optimize(rows, est, out=None)

Or use the shorthand:

model = optimize(rows, out=None)

Automatic typing is useful for exploration and baselines. For production, keep the returned estimator or use recommend_model so the decision is visible. Do not treat automatic typing as data cleaning. Missing-value handling, field dropping, and dependency choices should be visible in the returned estimator, profile, recommendation report, or artifact provenance.

Certified Artifact Creation¶

create is the higher-level route when the fit itself should become an auditable artifact. It still uses the same estimator and inference machinery, but it packages the fitted model with provenance and optional validation receipts.

from mixle.inference import create

artifact = create(rows, calibrate=0.2, quantify_uq=True, seed=0)
model = artifact.model

print(artifact.certificate.level)
print(artifact.provenance.get("exchangeability"))

Use optimize when you want the fitted model directly. Use create when a workflow needs to retain how the model was built, what checks were run, and which guarantees were available at creation time.

Model Recommendation¶

recommend_model wraps structural profiling in a report a program can act on.

from mixle.task import recommend_model

rec = recommend_model(rows)
print(rec.estimator)

for field in rec.fields:
    print(field.path, field.family, field.runner_up, field.gap_bits, field.confident)

for line in rec.explain():
    print(line)

model = rec.fit(rows, max_its=30, out=None)

The recommendation includes:

a ready estimator;
per-field family choices;
a runner-up family when there is a real alternative;
a bit-gap showing how clear the choice was;
low-confidence fields where more data would sharpen the model;
pairwise dependency hints that argue for joint modeling.

Use rec.low_confidence_fields() to find the columns where the model choice is still fragile.

Promotion Evidence¶

An automatically proposed model is ready to promote only after it has evidence outside the fitting pass. For a production-facing workflow, keep:

the profile or recommendation report that selected the estimator;
field-level runner-up families and score gaps;
any warnings about ignored fields, missingness, or dependency hints;
a held-out likelihood, calibration, or task metric against a simpler baseline;
the exact estimator or model specification used for the promoted run;
the random seed, split definition, and optional dependency versions needed to repeat the fit.

Small score gaps, dense dependency hints, and fields with high missingness are not failures by themselves. They are review items. A mature workflow either uses domain knowledge to make those choices explicit or collects more data before allowing the automatic recommendation to become the deployed model.

LLM-Designed Models¶

design_model lets an LLM propose a model shape from a compact data profile. The LLM does not get to execute code. It emits JSON from an allowlist, mixle builds the estimator, and then mixle fits a sample before accepting it.

from mixle.task import design_model

designed = design_model(rows, llm)
print(designed.source)  # "llm" or "fallback"
print(designed.spec)

model = designed.fit(rows, max_its=30, out=None)

Allowed specs include scalar families, composites, and mixtures:

{"family": "gamma"}
{"type": "composite", "fields": [{"family": "categorical"}, {"family": "gamma"}]}
{"type": "mixture", "k": 3, "component": {"family": "student_t"}}

If parsing, building, or fit-validation fails, the result falls back to recommend_model when fallback=True.

The returned source and note fields are part of the contract. A model whose source is "fallback" should be documented as an automatic-profile model, not as an LLM-designed model. If the LLM proposal is accepted, keep the allowlisted JSON spec and the fit-validation result with the artifact so the design can be reproduced without replaying an LLM conversation.

PPL Route Selection¶

The PPL lowers formulas to the same estimator/target machinery. how="auto" chooses a route from the lowered model:

from mixle.ppl import Markov, Normal, free

hmm = Markov(Normal(free, free), states=3).fit(sequences, how="auto")

Common route families include conjugate updates, EM, MAP, Laplace, variational inference, MCMC, HMC, NUTS, ensembles, and hierarchical routes. Use explain_fit when available, or mixle.describe on the lowered/fitted object, to inspect what the automatic route selected.

Record the route explanation when a PPL fit becomes an artifact. The important distinction is whether auto produced an analytic update, an EM fixed point, a point-estimate MAP route, or a posterior-bearing route. Those outcomes have different uncertainty and reproducibility implications even when they share the same model expression.

Objectives¶

optimize and fit accept objective=:

Objective	Meaning
`"auto"`	prior/ELBO-aware default; choose MLE, MAP, or VB as appropriate
`"mle"`	maximize observed-data likelihood
`"map"`	maximize penalized likelihood when the estimator carries priors
`"vb"`	use variational evidence lower bound when the model exposes one

The default is usually the right choice. Force an objective when you are testing or comparing routes.

Backends and Engines¶

Automatic inference does not force a local CPU path. The model stays the same:

from mixle.engines import TorchEngine
from mixle.inference import optimize

gpu = optimize(data, est, engine=TorchEngine(device="cuda"), out=None)
mp = optimize(data, est, backend="mp", num_workers=4, out=None)
spark = optimize(data, est, backend="spark", out=None)

engine= controls array/device math. backend= controls where encoded data are folded.

Failure Modes to Watch¶

Symptom	What to do
low recommendation gap	collect more data or choose the family explicitly
mixture fit changes across runs	use `best_of` with validation data
LLM-designed spec falls back	inspect `designed.note` and the allowed-family list
neural fit is slow	reduce model size first, then move to Torch/GPU or streaming
automatic route lacks a capability	call `mixle.describe(model)` and choose a model that supports it

A good workflow is exploratory but auditable: let mixle propose a shape, look at the confidence and capabilities, then make the important choices explicit once the model matters.