jact 0.1.7

JAX-based transition probability computation for multi-state models

jact

JAX-based transition probability and expected cashflow computation for multi-state models with duration-dependent transition intensities.

What is jact?

jact computes transition probabilities and expected cashflows in semi-Markov multi-state models. It takes fitted intensity models — parametric functions, GLMs, neural networks, or any JIT-compatible callable — and produces transition probabilities and cashflow streams for thousands of individuals in a single vectorized pass on GPU. Computations are optimized for JIT-compiled GPU execution.

Quick example

import jax.numpy as jnp
import jact

# Define the state space
state_space = jact.StateSpace(
    states=["healthy", "disabled", "dead"],
    transitions=[
        ("healthy", "disabled"),
        ("healthy", "dead"),
        ("disabled", "dead"),
    ],
)

# Build a model with intensity functions
model = state_space.build(
    transitions={
        ("healthy", "disabled"): onset_fn,
        ("healthy", "dead"): mortality_fn,
        ("disabled", "dead"): disabled_mort_fn,
    }
)

# Compute transition probabilities for 1000 individuals
ages = jnp.linspace(30, 80, 1_000)
result = model.solve(initial="healthy", horizon=30, steps_per_unit=12, age=ages)

Fitted-model intensity wrappers

Use jact.wrappers.bind_intensity() when a fitted model has a separate feature builder and apply function:

def features(t, d, *, age):
    return jnp.stack(
        [
            jnp.broadcast_to(age[:, None] + t, (age.shape[0], d.shape[-1])),
            jnp.broadcast_to(d, (age.shape[0], d.shape[-1])),
        ],
        axis=-1,
    )


def apply(params, x):
    linear = params["intercept"] + jnp.sum(x * params["coef"], axis=-1)
    return jnp.exp(linear)


onset_fn = jact.wrappers.bind_intensity(apply, fitted_params, features)

For fitted models that emit several hazards at once, use jact.wrappers.bind_grouped_intensity(..., output_count=K) or jact.wrappers.bind_exit_intensity(..., output_count=K). The wrappers clamp outputs to non-negative hazards and normalize grouped outputs to (K, batch, D).

Cashflow example

Using the same state_space, model, and ages as above:

import jax.numpy as jnp
import jact


def annual_premium(t, d, *, age):
    return jnp.full((age.shape[0], d.shape[-1]), -1_200.0)


def death_benefit(t, d, *, age):
    return jnp.full((age.shape[0], d.shape[-1]), 100_000.0)


cashflows = state_space.cashflows(
    {
        "premium": jact.cashflows.StateRate({"healthy": annual_premium}),
        "death_benefit": jact.cashflows.TransitionLump(
            {
                ("healthy", "dead"): death_benefit,
                ("disabled", "dead"): death_benefit,
            }
        ),
    }
)

result = model.solve(
    initial="healthy",
    horizon=30,
    steps_per_unit=12,
    record_every=12,
    probability=None,
    cashflows=cashflows,
    cashflow_views={
        "raw": jact.cashflows.Raw(),
        "pv_total": jact.cashflows.Total(
            weight=lambda t, **kwargs: jnp.exp(-0.03 * t),
            terminal=True,
        ),
    },
    age=ages,
)

premium_stream = result.cashflows["raw"]["premium"]
present_value = result.cashflows["pv_total"]

Key features

• Plug in any model: Gompertz, GLM, neural network — anything that's JIT-compatible.
• Swap and compare: Same StateSpace, different intensity models. Experiment easily.
• Probabilities and cashflows together: Emit both in one fused solve, with solve-time cashflow views for grouping and valuation.
• Compute only what's needed: The solver reduces to states reachable from the initial state.
• Exact seeded starts: Initial point masses preserve per-individual starting duration d_0 exactly.
• Batch-first: Designed for 100K+ individuals in a single pass.
• Agent-ready guidance: Ship jact modeling instructions to AI coding agents with the bundled jact-agent-skill helper.

Documentation

See the documentation index for the public documentation set. For the full API contract, use the API specification. For a runnable walkthrough of the main workflow, see the example notebook. For a fitting-to-solver workflow with neural-network intensities, see the fitted neural-network notebook.

AI agent skill

Installed packages include an application-focused AI agent skill for writing jact modeling code. The skill helps coding agents choose the right StateSpace, intensity wrappers, probability reducers, initial distributions, and cashflow views. It is user-facing modeling guidance, separate from repository development guidance such as AGENT.md.

The skill is packaged at jact/agents/jact/SKILL.md and includes YAML frontmatter (name and description) for agent CLIs that auto-discover skills. Use the generic helper to locate, print, or install it into the directory your agent CLI expects:

jact-agent-skill path
jact-agent-skill print
jact-agent-skill install --target ~/.config/my-agent/skills/jact

For example, to install it for a project-local skill-aware agent directory:

jact-agent-skill install --target .github/skills/jact

The same helper is available as a module:

python -m jact.agents install --target ~/.config/my-agent/skills/jact

Namespace

The top-level jact namespace exposes the core types: jact.StateSpace, jact.Model, jact.InitialDistribution, jact.ModelResult, and jact.solve. Domain types and fitted-model helpers live under submodules:

• jact.cashflows for declarations and views (StateRate, TransitionLump, ScheduledEvent, DurationEvent, Raw, Group, Total, ByState, ByKind).
• jact.probability for output reducers (StateProbability, DensityProbability, Density, PointMass, MarginalComponents, Full).
• jact.wrappers for fitted-model intensity helpers (bind_intensity, bind_grouped_intensity, bind_exit_intensity).

Advanced inspection types stay in private modules — for example jact.probability.StateCarry and jact.model.ReducedModel.

Installation

pip install jax jaxlib
pip install jact

For local development from this repository:

pip install -e '.[dev]'
pytest

The package uses a src/ layout, so editable install is the intended local workflow.

To run the example notebook with plotting support from a local checkout:

pip install -e '.[dev,notebook]'

Release checks

Before cutting a PyPI release:

rm -rf build dist src/*.egg-info
python -m build --no-isolation
python -m twine check dist/*
pytest -q

The tag-driven publish flow is documented in RELEASING.md.

Requirements

• Python >= 3.10
• JAX >= 0.4

License

Apache-2.0