diff --git a/.github/workflows/claude-code-review.yml b/.github/workflows/claude-code-review.yml index b5e8cfd..25f4ad1 100644 --- a/.github/workflows/claude-code-review.yml +++ b/.github/workflows/claude-code-review.yml @@ -41,4 +41,3 @@ jobs: prompt: '/code-review:code-review ${{ github.repository }}/pull/${{ github.event.pull_request.number }}' # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md # or https://code.claude.com/docs/en/cli-reference for available options - diff --git a/.github/workflows/claude.yml b/.github/workflows/claude.yml index d300267..9471a05 100644 --- a/.github/workflows/claude.yml +++ b/.github/workflows/claude.yml @@ -47,4 +47,3 @@ jobs: # See https://github.com/anthropics/claude-code-action/blob/main/docs/usage.md # or https://code.claude.com/docs/en/cli-reference for available options # claude_args: '--allowed-tools Bash(gh pr:*)' - diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 4f776a1..99a1d30 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -19,7 +19,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v5 with: - python-version: 3.11 + python-version: "3.12" - name: Install project with dev dependencies run: pip install .[dev] diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index 0bb4c76..7e6ab7d 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -18,7 +18,7 @@ jobs: - uses: actions/setup-python@v5 with: - python-version: "3.11" + python-version: "3.12" - name: Build run: | diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 76856de..be37436 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -9,7 +9,7 @@ jobs: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: - python-version: 3.11 + python-version: "3.12" - name: Install deps run: | @@ -22,4 +22,4 @@ jobs: - name: Upload coverage to Coveralls env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - run: coveralls --service=github \ No newline at end of file + run: coveralls --service=github diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ea264eb..6b6128f 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -7,28 +7,25 @@ repos: - id: end-of-file-fixer - id: check-yaml - repo: https://github.com/psf/black - rev: 24.3.0 + rev: 23.12.1 hooks: - id: black + language_version: python3.11 - repo: https://github.com/PyCQA/isort rev: 5.12.0 # Known working version (not Poetry-based) hooks: - id: isort language_version: python3.11 - - repo: https://github.com/psf/black - rev: 23.12.1 - hooks: - - id: black - language_version: python3.11 - - repo: https://github.com/pre-commit/mirrors-mypy rev: v1.8.0 hooks: - id: mypy additional_dependencies: [types-requests] # Add any stubs your project needs language_version: python3.11 - exclude: ^docs/ + # Type-check the library strictly; tests are exercised by pytest, not + # type-checked (the suite is only partially annotated). + exclude: ^(docs|tests)/ - repo: https://github.com/myint/autoflake rev: v2.2.1 diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 0000000..b4bcccb --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,58 @@ +# Changelog + +All notable changes to BLayers are documented here. The format follows +[Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and the project aims +to follow semantic versioning (with the usual 0.x caveat that minor releases +may carry breaking changes). + +## [0.3.1] + +### Added +- `FittedModel.to_arviz()` — convert a fit to an ArviZ `InferenceData` for + diagnostics (R-hat, ESS, divergences) and model comparison (PSIS-LOO via + `az.loo`, `az.compare`). MCMC uses `arviz.from_numpyro`; VI builds the + `log_likelihood` group via `numpyro.infer.log_likelihood`. SVGD is + unsupported. Requires the optional `blayers[arviz]` extra (arviz >= 1.0). +- `sample_prior(model, **inputs)` — draw from the prior / prior predictive + before fitting, for prior checks. +- `categorical_link` — Categorical (softmax) likelihood for multiclass + classification (`units = num_classes`). +- `__version__` on the top-level package. + +### Changed +- **`Batched_Trace_ELBO` now raises `ValueError` on models that use + `numpyro.plate`** (previously it emitted a `UserWarning` and continued). + The `num_obs / batch_size` rescaling double-counts plate-subsampled sites, + so the ELBO was silently wrong — better to fail closed. Use the standard + `Trace_ELBO` with plates instead. +- **Requires Python >= 3.12** (was declared `>=3.9`, but the codebase's + `X | None` annotations never actually supported 3.9; arviz 1.x also needs + 3.12). CI, docs, and publish workflows now run on 3.12. +- Documented BLayers' scope in the README: it is a **structured Bayesian + regression** toolkit (GLMs, hierarchical models, factorization machines, + splines, sparse priors) whose layers are *added* into a linear predictor — + not stacked into a deep network. For true Bayesian neural nets, use + NumPyro's `random_flax_module` / `random_haiku_module`. + +### Removed +- **`AttentionLayer`** — removed. It was the one primitive at odds with the + library's additive/interpretable focus, and mean-field VI serves its + weight space poorly. If you need it, pin `blayers==0.3.0`, or use + NumPyro's neural-network module integration. + +### Fixed +- Minibatch VI now **shuffles** the data each epoch (`svi_run_batched` / + `yield_batches`), instead of iterating the same fixed batches in the same + order every pass. Removes a bias in the ELBO gradient estimate, especially + on sorted data. +- `EmbeddingLayer` / `RandomEffectsLayer` / `RandomWalkLayer` index lookups no + longer collapse a single-row batch to a scalar and now accept float-typed + indices (`reshape(-1).astype(int)` instead of `squeeze()`). +- Fixed a duplicated (and mutually conflicting) `black` hook in the + pre-commit config. + +### Documentation +- `Batched_Trace_ELBO` documents that it assumes **all latents are global** + (per-observation latents are unsupported in batched mode). +- `FittedModel.predict` notes that `.mean` / `.std` are not meaningful for + classification / discrete links — work from `.samples` instead. diff --git a/README.md b/README.md index 27b52bd..d7dcca9 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -[![Coverage Status](https://coveralls.io/repos/github/georgeberry/blayers/badge.svg?branch=main)](https://coveralls.io/github/georgeberry/blayers?branch=main) [![License](https://img.shields.io/github/license/georgeberry/blayers)](LICENSE) [![PyPI](https://img.shields.io/pypi/v/blayers)](https://pypi.org/project/blayers/) [![Read - Docs](https://img.shields.io/badge/Read-Docs-2ea44f)](https://georgeberry.github.io/blayers/) [![View - GitHub](https://img.shields.io/badge/View-GitHub-89CFF0)](https://github.com/georgeberry/blayers) [![PyPI Downloads](https://static.pepy.tech/badge/blayers)](https://pepy.tech/projects/blayers) +[![Coverage Status](https://coveralls.io/repos/github/georgeberry/blayers/badge.svg?branch=main)](https://coveralls.io/github/georgeberry/blayers?branch=main) [![License](https://img.shields.io/github/license/georgeberry/blayers)](https://github.com/georgeberry/blayers/blob/main/LICENSE) [![PyPI](https://img.shields.io/pypi/v/blayers)](https://pypi.org/project/blayers/) [![Read - Docs](https://img.shields.io/badge/Read-Docs-2ea44f)](https://georgeberry.github.io/blayers/) [![View - GitHub](https://img.shields.io/badge/View-GitHub-89CFF0)](https://github.com/georgeberry/blayers) [![PyPI Downloads](https://static.pepy.tech/badge/blayers)](https://pepy.tech/projects/blayers) @@ -26,6 +26,18 @@ tweak priors as you wish. Inspiration from Keras and Tensorflow Probability, but made specifically for Numpyro + Jax. +**Scope.** BLayers is for *structured* Bayesian regression — GLMs, hierarchical / +mixed-effects models, factorization machines, splines, and sparse priors. Layers +are meant to be **added together into a linear predictor** (`mu = layer1(...) + +layer2(...) + ...`), the way you'd build a GLM or GAM — not stacked into a deep +network. Each term stays interpretable, and the priors and inference (NUTS / VI / +SVGD) are chosen for honest posteriors over a modest number of meaningful +parameters. If you want a true Bayesian *neural network* (composed nonlinear +layers, weight-space inference), reach for +[`numpyro.contrib.module`](https://num.pyro.ai/en/stable/primitives.html#module)'s +`random_flax_module` / `random_haiku_module` instead — they drop a full Flax or +Haiku net into a NumPyro model with priors on the weights. + BLayers provides tools to - Quickly build Bayesian models from layers which encapsulate useful model parts @@ -161,7 +173,6 @@ The full set of layers included with BLayers: - `RandomWalkLayer` — Gaussian random walk prior over an ordered index (e.g., time). - `HorseshoeLayer` — Horseshoe prior for sparse regression; global-local shrinkage via HalfCauchy. - `SpikeAndSlabLayer` — Spike-and-slab prior; `z ~ Beta(0.5, 0.5)` inclusion weights times a configurable slab. -- `AttentionLayer` — Multi-head self-attention over the feature dimension with FT-Transformer tokenisation ([Gorishniy et al. 2021](https://arxiv.org/abs/2106.11959)). `head_dim` is per-head so total embedding dim is `head_dim * num_heads` — adding heads increases capacity. All layer prior kwargs are validated at construction time — bad kwargs raise `TypeError` immediately. @@ -171,16 +182,18 @@ We provide link helpers in `links.py` to reduce Numpyro boilerplate. Available l - `gaussian_link` — Gaussian likelihood with configurable sigma prior (see below). - `lognormal_link` — LogNormal likelihood with configurable sigma prior. -- `logit_link` — Bernoulli link for logistic regression. +- `student_t_link` — StudentT likelihood for robust regression (default `df=4`). +- `logit_link` — Bernoulli link for binary logistic regression. +- `categorical_link` — Categorical / softmax link for multiclass classification (`units = num_classes`). - `poisson_link` — Poisson link with log-rate input. - `negative_binomial_link` — NegativeBinomial2 for overdispersed counts; learned concentration via `Exponential`. - `ordinal_link` — Cumulative logit / proportional odds for ordinal outcomes. - `zip_link` — Zero-inflated Poisson for count data with excess zeros. - `beta_link` — Beta regression for proportions strictly in (0, 1). -### `gaussian_link` and `lognormal_link` +### `gaussian_link`, `lognormal_link`, and `student_t_link` -Both links are built on a common base and support three scale modes: +All three share a common location-scale base and support three scale modes: ```python from blayers.layers import AdaptiveLayer @@ -277,6 +290,31 @@ summary = result.summary(x=X) Keyword arguments that are JAX arrays are treated as **data** (batched during training). Non-array kwargs are bound as **constants**. +### Diagnostics & model comparison (ArviZ) + +`result.to_arviz()` hands the fit to [ArviZ](https://python.arviz.org) for R-hat, +ESS, divergences, PSIS-LOO, and the full plotting suite — reusing NumPyro's own +ArviZ bridge rather than reinventing diagnostics. Install with `pip install +blayers[arviz]` (arviz ≥ 1.0, Python ≥ 3.12). + +```python +import arviz as az + +# MCMC: divergences, R-hat, ESS, and log-likelihood come through automatically +idata = fit(model, y=y, method="mcmc", num_chains=2, x=X).to_arviz() +az.summary(idata) # R-hat / ESS per latent +az.loo(idata) # PSIS-LOO + +# VI: pass the observed y (and inputs) so the log_likelihood group can be built +idata_vi = fit(model, y=y, num_steps=2000, x=X).to_arviz(y=y, x=X) + +# Compare models on out-of-sample predictive fit +az.compare({"mcmc": idata, "vi": idata_vi}) +``` + +SVGD is not supported by `to_arviz()` (too few particles to be a meaningful +sample for LOO); fit with `method="mcmc"` or `method="vi"` for comparison. + ## Batched loss The default Numpyro way to fit batched VI models is to use `plate`, which confuses @@ -304,17 +342,19 @@ svi_result = svi_run_batched( **⚠️⚠️⚠️ `numpyro.plate` + `Batched_Trace_ELBO` do not mix. ⚠️⚠️⚠️** -`Batched_Trace_ELBO` is known to have issues when your model uses `numpyro.plate`. If your model needs plates, either: +`Batched_Trace_ELBO` does not support `numpyro.plate`: its `N / batch_size` log-likelihood rescaling double-counts plate-subsampled sites and yields an incorrect ELBO. If your model needs plates, either: 1. Batch via `plate` and use the standard `Trace_ELBO`, or 1. Remove plates and use `Batched_Trace_ELBO` + `svi_run_batched`. -`Batched_Trace_ELBO` will warn if your model has plates. +`Batched_Trace_ELBO` **raises `ValueError`** if your model contains a plate. ### Reparameterizing To fit MCMC models well it is crucial to [reparameterize](https://num.pyro.ai/en/latest/reparam.html). BLayers helps you do this via `@autoreparam`, which automatically applies `LocScaleReparam` to all `LocScale` distributions in your model (Normal, LogNormal, StudentT, Cauchy, Laplace, Gumbel). +> **Note:** `fit(method="mcmc")` already applies `@autoreparam` for you (controlled by `autoreparam_model=True`, on by default). You only need to apply the decorator yourself when driving NUTS / HMC manually, as shown below. + ```python from numpyro.infer import MCMC, NUTS from blayers.layers import AdaptiveLayer diff --git a/TODO.md b/TODO.md new file mode 100644 index 0000000..29bc994 --- /dev/null +++ b/TODO.md @@ -0,0 +1,62 @@ +# BLayers roadmap / to-dos + +Positioning: BLayers is a **structured Bayesian regression** toolkit (GLMs, +hierarchical models, factorization machines, splines, sparse priors) — layers are +*added* into a linear predictor, not stacked into a deep net. The list below is +ordered by value/effort for that niche. + +## Done +- [x] Cut `AttentionLayer` from core (off-brand; worst-served by mean-field VI; + interpretability oversold). Removed from `layers.py`, `__init__.py`, README, + and tests. +- [x] README scope note clarifying GLM/GAM focus + pointer to + `random_flax_module` for true Bayesian neural nets. + +## Tier 1 — Bayesian workflow tooling (highest leverage) +Inference exists; evaluation barely does. Mostly plumbing over NumPyro/ArviZ. +- [x] `FittedModel.to_arviz()` — MCMC via `az.from_numpyro` (posterior + + sample_stats + log_likelihood, coerced to NumPy for arviz-stats); VI via + `az.from_dict` + `numpyro.infer.log_likelihood`. Unlocks `az.summary` + (R-hat/ESS), `az.loo` (PSIS-LOO), and `az.compare`. SVGD unsupported. + Optional `blayers[arviz]` extra (arviz >= 1.0, Python >= 3.12). + NOTE: arviz 1.x dropped WAIC — LOO is the comparison metric. +- [ ] MCMC diagnostics surfaced in `summary()` too (R-hat, ESS, divergence + count) for users who don't reach for ArviZ. +- [x] `sample_prior(model, num_samples=...)` prior-predictive helper. Returns + the raw draws dict (latents + prior-predictive `obs`); rejects `y`. + Exported from `blayers`. + +## Tier 2 — Close the GLM likelihood gaps +- [x] `categorical_link` (multiclass softmax) — takes `(n, num_classes)` logits + from a layer's `units=K`; reads K from the trailing dim. Exported. +- [ ] `gamma_link` / `exponential_link` (positive continuous, survival). +- [ ] Censored / Tobit likelihood. +- [ ] `zinb_link` (zero-inflated negative binomial; have ZIP, not ZINB). + +## Tier 3 — Production readiness +- [ ] `FittedModel.save()` / `load()` (params are pytrees — pickle / orbax / + safetensors). +- [ ] Guide shortcuts in `fit()`: `guide="mvn" | "lowrank" | "flow" | "laplace"`, + plus `init_loc_fn` passthrough. (Diagonal-normal VI underestimates the + posterior correlations that hierarchical models produce.) + +## Tier 4 — New marquee layer +- [ ] Hilbert-Space approximate GP layer (HSGP, Riutort-Mayol et al.) — reduces to + a basis-function layer, fast/batchable, sits naturally next to splines and + `RandomWalkLayer`. + +## Correctness / robustness fixes (small, do alongside) +- [ ] `_utils.yield_batches` never shuffles — same fixed batches, same order every + epoch. Add per-epoch permutation (biases minibatch VI, esp. on sorted data). +- [ ] Document that `Batched_Trace_ELBO` assumes **all latents are global** (it + rescales the whole observed log-lik by N/B and never subsamples local + latents). State as a hard constraint, not just a plate warning. +- [ ] `EmbeddingLayer` / `RandomEffectsLayer` use `theta[x.squeeze()]` — `squeeze` + collapses a size-1 batch to a scalar index and misbehaves on multi-column x. + Prefer `x.reshape(-1).astype(int)`. +- [ ] Note that `predict`/`summary` default seeds are constant (1, 2) so identical + reruns aren't mistaken for method determinism. + +## Docs +- [ ] Short "how BLayers composes with `random_flax_module`" note for people who + want to mix structured terms with a neural component. diff --git a/blayers/__init__.py b/blayers/__init__.py index abd9f66..c50dc1e 100644 --- a/blayers/__init__.py +++ b/blayers/__init__.py @@ -1,24 +1,27 @@ +from importlib.metadata import PackageNotFoundError, version + +from blayers.decorators import autoreparam, autoreshape +from blayers.fit import FittedModel, Predictions, fit, sample_prior from blayers.layers import ( AdaptiveLayer, - AttentionLayer, BilinearLayer, EmbeddingLayer, FixedPriorLayer, - FMLayer, FM3Layer, + FMLayer, HorseshoeLayer, InteractionLayer, InterceptLayer, LowRankBilinearLayer, LowRankInteractionLayer, - pairwise_interactions, RandomEffectsLayer, RandomWalkLayer, SpikeAndSlabLayer, + pairwise_interactions, ) - from blayers.links import ( beta_link, + categorical_link, gaussian_link, logit_link, lognormal_link, @@ -29,21 +32,15 @@ zip_link, ) -from blayers.decorators import ( - autoreparam, - autoreshape, -) - -from blayers.fit import ( - fit, - FittedModel, - Predictions, -) +try: + __version__ = version("blayers") +except PackageNotFoundError: # package not installed (e.g. running from source) + __version__ = "0.0.0" __all__ = [ + "__version__", # Layers "AdaptiveLayer", - "AttentionLayer", "BilinearLayer", "EmbeddingLayer", "FixedPriorLayer", @@ -60,6 +57,7 @@ "SpikeAndSlabLayer", # Links "beta_link", + "categorical_link", "gaussian_link", "logit_link", "lognormal_link", @@ -73,6 +71,7 @@ "autoreshape", # Fit "fit", + "sample_prior", "FittedModel", "Predictions", ] diff --git a/blayers/_utils.py b/blayers/_utils.py index 81bdc3c..2c7d570 100644 --- a/blayers/_utils.py +++ b/blayers/_utils.py @@ -1,4 +1,3 @@ -import itertools from typing import Generator import jax @@ -38,15 +37,37 @@ def yield_batches( batch_size: int, num_batches: int, steps_per_epoch: int, + rng_key: jax.Array | None = None, ) -> Generator[dict[str, jax.Array], None, None]: - def batch_iter() -> Generator[dict[str, jax.Array], None, None]: - for i in range(steps_per_epoch): - start = i * batch_size - end = start + batch_size - yield {k: v[start:end] for k, v in data.items()} + """Yield ``num_batches`` minibatches, cycling over the data as needed. + + Each epoch, the row order is re-permuted when ``rng_key`` is supplied so + that minibatch VI sees i.i.d. batches rather than the same fixed slices in + the same order every pass (which biases the ELBO gradient, especially on + sorted data). Pass ``rng_key=None`` for the legacy contiguous ordering. + """ + dataset_size = get_dataset_size(data) - for batch in itertools.islice(itertools.cycle(batch_iter()), num_batches): - yield batch + def epoch_batches( + perm: jax.Array, + ) -> Generator[dict[str, jax.Array], None, None]: + for i in range(steps_per_epoch): + idx = perm[i * batch_size : (i + 1) * batch_size] + yield {k: v[idx] for k, v in data.items()} + + key = rng_key + emitted = 0 + while emitted < num_batches: + if key is not None: + key, subkey = jax.random.split(key) + perm = jax.random.permutation(subkey, dataset_size) + else: + perm = jnp.arange(dataset_size) + for batch in epoch_batches(perm): + if emitted >= num_batches: + break + yield batch + emitted += 1 # ---- Helpers --------------------------------------------------------------- # diff --git a/blayers/decorators.py b/blayers/decorators.py index 3a2c53b..8fef149 100644 --- a/blayers/decorators.py +++ b/blayers/decorators.py @@ -1,16 +1,15 @@ """ Model decorators for blayers. -- `reshape_inputs`: Auto-reshape 1D arrays to (n, 1) -- `autoreparam`: Auto-reparameterize LocScale distributions for MCMC - -Usage: -``` -@reshape_inputs -@autoreparam -def my_model(x, y=None): - ... -``` +- ``autoreshape``: Auto-reshape 1D arrays to (n, 1) +- ``autoreparam``: Auto-reparameterize LocScale distributions for MCMC + +Usage:: + + @autoreshape + @autoreparam + def my_model(x, y=None): + ... """ import logging @@ -83,7 +82,9 @@ def wrapped(*args: Any, **kwargs: Any) -> Any: return wrapped -def autoreparam(model_fn: Callable[..., Any] | None = None, *, centered: float = 0.0) -> Any: +def autoreparam( + model_fn: Callable[..., Any] | None = None, *, centered: float = 0.0 +) -> Any: """Auto-reparameterize LocScale distributions in a model for MCMC. Automatically applies ``LocScaleReparam`` to all LocScale distributions @@ -107,6 +108,7 @@ def model(x, y): ... non-centered (default, best for weak data); 1.0 = fully centered (better when data is informative). """ + def decorator(fn: Any) -> Any: @wraps(fn) def wrapped_model(*args: Any, **kwargs: Any) -> Any: diff --git a/blayers/fit.py b/blayers/fit.py index 238ed2e..241a332 100644 --- a/blayers/fit.py +++ b/blayers/fit.py @@ -52,13 +52,20 @@ def model(x, n_conditions, y=None): import jax import jax.numpy as jnp +import numpy as np import optax -from numpyro.infer import MCMC, NUTS, SVI, Predictive, Trace_ELBO +from numpyro.infer import ( + MCMC, + NUTS, + SVI, + Predictive, + Trace_ELBO, + log_likelihood, +) from numpyro.infer.autoguide import AutoDiagonalNormal, AutoGuide -from blayers.vi_infer import Batched_Trace_ELBO, svi_run_batched from blayers.decorators import autoreparam - +from blayers.vi_infer import Batched_Trace_ELBO, svi_run_batched # --------------------------------------------------------------------------- # # Helpers @@ -89,6 +96,24 @@ def _split_data_and_constants( return data, constants +def _datatree_to_numpy(idata: Any) -> Any: + """Coerce every variable in an ArviZ ``DataTree`` to a NumPy array. + + ``arviz.from_numpyro`` stores JAX-backed arrays; arviz-stats routines such + as PSIS-LOO mutate arrays in place, which raises on immutable JAX arrays. + Re-backing each variable with ``np.asarray`` sidesteps that. + """ + + def _to_numpy(ds: Any) -> Any: + return ds.copy( + data={ + name: np.asarray(da.values) for name, da in ds.data_vars.items() + } + ) + + return idata.map_over_datasets(_to_numpy) + + def _make_schedule( schedule: str, lr: float, @@ -120,21 +145,16 @@ def _make_schedule( @dataclass class Predictions: - """Posterior predictive output from :meth:`FittedModel.predict`. - - Attributes - ---------- - mean : jax.Array - Point predictions averaged over posterior samples. Shape ``(n,)``. - std : jax.Array - Predictive standard deviation over posterior samples. Shape ``(n,)``. - samples : jax.Array - Raw posterior predictive draws. Shape ``(num_samples, n, ...)``. - """ + """Posterior predictive output from :meth:`FittedModel.predict`.""" mean: jax.Array + """Point predictions averaged over posterior samples. Shape ``(n,)``.""" + std: jax.Array + """Predictive standard deviation over posterior samples. Shape ``(n,)``.""" + samples: jax.Array + """Raw posterior predictive draws. Shape ``(num_samples, n, ...)``.""" @dataclass @@ -143,33 +163,36 @@ class FittedModel: Created by :func:`fit`. Provides :meth:`predict` for posterior predictive inference and :meth:`summary` for inspecting latent variable posteriors. - - Attributes - ---------- - model_fn : Callable - The model function with any constants already bound. - method : str - ``"vi"`` or ``"mcmc"``. - params : dict or None - SVI parameters (VI only). - guide : AutoGuide or None - Fitted variational guide (VI only). - losses : jax.Array or None - Per-step ELBO loss curve (VI only). - posterior_samples : dict or None - MCMC posterior samples (MCMC only). """ - model_fn: Callable + model_fn: Callable[..., Any] + """The model function with any constants already bound.""" + method: str + """One of ``"vi"``, ``"mcmc"``, or ``"svgd"``.""" + # VI / SVGD - params: dict | None = None + params: dict[str, Any] | None = None + """SVI / SVGD parameters.""" + guide: Any | None = None + """Fitted variational / Stein guide.""" + losses: jax.Array | None = field(default=None, repr=False) + """Per-step loss curve (VI / SVGD).""" + # MCMC - posterior_samples: dict | None = field(default=None, repr=False) + posterior_samples: dict[str, Any] | None = field(default=None, repr=False) + """MCMC posterior samples (MCMC only).""" + + mcmc: Any | None = field(default=None, repr=False) + """The fitted ``numpyro.infer.MCMC`` object (MCMC only). Retained so + :meth:`to_arviz` can hand it straight to ``arviz.from_numpyro`` for + divergences, R-hat, ESS, and log-likelihood.""" + # SVGD num_particles: int | None = None + """Number of Stein particles (SVGD only).""" def predict( self, @@ -186,7 +209,9 @@ def predict( Number of posterior samples to draw. For VI this controls the guide; for MCMC all posterior samples are used regardless. seed : int - Random seed for the predictive distribution. + Random seed for the predictive distribution. Fixed by default, so + repeated calls return identical draws — vary it to see Monte Carlo + variability (identical reruns are not method determinism). **data Model inputs **excluding** ``y``. Constants that were auto-bound during :func:`fit` should *not* be passed again. @@ -194,6 +219,14 @@ def predict( Returns ------- Predictions + + Notes + ----- + ``.mean`` / ``.std`` assume a continuous outcome. For the + classification / discrete links (``logit_link``, ``categorical_link``, + ``ordinal_link``, count links) the label mean is not meaningful — work + from ``.samples`` instead (e.g. per-observation modal class, or class + probabilities via ``(samples == k).mean(axis=0)``). """ rng_key = jax.random.PRNGKey(seed) @@ -248,7 +281,8 @@ def summary( num_samples : int Samples to draw from the guide (VI only; ignored for MCMC). seed : int - Random seed. + Random seed. Fixed by default, so repeated calls return identical + draws — vary it to see Monte Carlo variability. **data Model inputs (excluding ``y``) needed so the guide can determine parameter shapes. Required for VI; ignored for MCMC. @@ -296,6 +330,117 @@ def summary( } return result + def to_arviz( + self, + *, + y: jax.Array | None = None, + num_samples: int = 1000, + seed: int = 3, + **data: Any, + ) -> Any: + """Convert the fit to an ArviZ ``InferenceData``. + + Reuses NumPyro's own ArviZ bridge rather than reimplementing + diagnostics: + + * **MCMC** — delegates to ``arviz.from_numpyro(mcmc)``, which carries + over posterior draws, sample stats (divergences), and + per-observation log-likelihood. ``y`` / ``**data`` are ignored + (already baked into the completed MCMC run). + * **VI** — draws from the fitted guide, computes per-observation + log-likelihood with ``numpyro.infer.log_likelihood``, and assembles + an ``InferenceData`` via ``arviz.from_dict``. The observed ``y`` + (and any model inputs) are required so the ``log_likelihood`` and + ``observed_data`` groups can be built. + + The result plugs straight into ``arviz.summary`` (R-hat / ESS), + ``arviz.waic``, ``arviz.loo``, and the ArviZ plotting suite — so model + comparison is ``az.compare({"a": fit_a.to_arviz(...), ...})``. + + Parameters + ---------- + y : jax.Array, optional + Observed target. Required for VI (to build the ``log_likelihood`` + and ``observed_data`` groups); ignored for MCMC. + num_samples : int + Posterior draws to take from the guide (VI only). + seed : int + Random seed for guide sampling (VI only). + **data + Model inputs (e.g. ``x``) needed to evaluate the model. Ignored + for MCMC. + + Returns + ------- + arviz.InferenceData + + Notes + ----- + SVGD is not supported: its handful of Stein particles do not form a + meaningful sample for WAIC/LOO, and its params do not map cleanly onto + model sites. Fit with ``method="mcmc"`` or ``method="vi"`` for + ArviZ-based comparison. + """ + try: + import arviz as az + except ImportError as e: # pragma: no cover - optional dependency + raise ImportError( + "to_arviz() requires arviz. Install it with " + "`pip install arviz` or `pip install blayers[arviz]`." + ) from e + + if self.method == "mcmc": + if self.mcmc is None: + raise RuntimeError("MCMC results missing the mcmc object") + # log_likelihood defaults to False in arviz >= 1.0; request it so + # az.loo / az.compare work. R-hat needs >= 2 chains (num_chains). + idata = az.from_numpyro(self.mcmc, log_likelihood=True) + # from_numpyro stores JAX-backed arrays; arviz-stats (PSIS-LOO) + # does in-place assignment, which fails on immutable JAX arrays. + return _datatree_to_numpy(idata) + + if self.method == "svgd": + raise NotImplementedError( + "to_arviz() does not support SVGD; refit with method='mcmc' " + "or method='vi' for ArviZ diagnostics and model comparison." + ) + + if self.method != "vi": + raise ValueError(f"Unknown method {self.method!r}") + + # VI: build InferenceData from guide draws + per-obs log-likelihood. + if y is None: + raise ValueError( + "to_arviz() needs the observed `y` for VI to build the " + "log_likelihood and observed_data groups." + ) + if self.guide is None or self.params is None: + raise RuntimeError("VI results missing guide or params") + + rng_key = jax.random.PRNGKey(seed) + latent = Predictive( + self.guide, params=self.params, num_samples=num_samples + )(rng_key, **data) + # Keep only latent sample sites (a guide should not emit "obs", but be + # defensive in case a custom guide does). + latent = {k: v for k, v in latent.items() if k != "obs"} + + ll = log_likelihood(self.model_fn, latent, y=y, **data) + + # ArviZ expects (chain, draw, *shape); treat the draws as one chain. + # Cast to NumPy: arviz-stats (PSIS-LOO) does in-place assignment, which + # fails on immutable JAX arrays. + posterior = {k: np.asarray(v)[None, ...] for k, v in latent.items()} + log_lik = {k: np.asarray(v)[None, ...] for k, v in ll.items()} + + return az.from_dict( + { + "posterior": posterior, + "log_likelihood": log_lik, + "observed_data": {"obs": np.asarray(y)}, + } + ) + # --------------------------------------------------------------------------- # # Main entry point @@ -303,7 +448,7 @@ def summary( def fit( - model_fn: Callable, + model_fn: Callable[..., Any], *, y: jax.Array, method: Literal["vi", "mcmc", "svgd"] = "vi", @@ -419,7 +564,11 @@ def fit( # ------------------------------------------------------------------ # data, constants = _split_data_and_constants(kwargs) - bound_model = partial(model_fn, **constants) if constants else model_fn + bound_model: Callable[..., Any] + if constants: + bound_model = partial(model_fn, **constants) + else: + bound_model = model_fn data["y"] = y n_obs = y.shape[0] @@ -457,6 +606,7 @@ def fit( "Provide exactly one of num_epochs or num_steps, not both (or neither)." ) total_steps = num_epochs if num_epochs is not None else num_steps + assert total_steps is not None # guaranteed by check above return _fit_svgd( bound_model, @@ -473,13 +623,64 @@ def fit( ) +def sample_prior( + model_fn: Callable[..., Any], + *, + num_samples: int = 500, + seed: int = 0, + **data: Any, +) -> dict[str, jax.Array]: + """Draw from a model's prior (and prior predictive) *before* fitting. + + Runs the model with no observed ``y``, so every latent site and the + ``"obs"`` site are sampled straight from the prior. Use it to sanity-check + that your priors imply sensible outcomes — the core of the "tweak priors as + you wish" workflow — before committing to inference. + + Parameters + ---------- + model_fn : Callable + A blayers / NumPyro model. Pass inputs the same way you would to + :func:`fit`, but **without** ``y`` — supplying ``y`` would condition the + ``"obs"`` site and defeat the purpose. + num_samples : int + Number of prior draws (default 500). + seed : int + Random seed (default 0). + **data + Model inputs (e.g. ``x``) and any constants, used to fix the shapes of + the sampled sites. + + Returns + ------- + dict + ``{site_name: array of shape (num_samples, *site_shape)}``, including + the prior-predictive ``"obs"`` site. + + Examples + -------- + >>> prior = sample_prior(model, x=x_train, num_samples=1000) + >>> prior["obs"].shape # (1000, n) prior-predictive outcomes + >>> prior["AdaptiveLayer_mu_beta"].mean(axis=0) # prior mean of a latent + """ + if "y" in data: + raise ValueError( + "sample_prior() draws from the prior predictive; do not pass `y` " + "(it would condition the obs site). Pass only model inputs." + ) + rng_key = jax.random.PRNGKey(seed) + predictive = Predictive(model_fn, num_samples=num_samples) + samples: dict[str, jax.Array] = predictive(rng_key, **data) + return samples + + # --------------------------------------------------------------------------- # # VI # --------------------------------------------------------------------------- # def _fit_vi( - model_fn: Callable, + model_fn: Callable[..., Any], *, data: dict[str, jax.Array], n_obs: int, @@ -500,17 +701,18 @@ def _fit_vi( ) # ---- Compute total gradient steps (needed for LR schedule) ---- - batched = batch_size is not None - - if batched: + if batch_size is not None: steps_per_epoch = (n_obs + batch_size - 1) // batch_size total_steps = ( - steps_per_epoch * num_epochs if num_epochs is not None else num_steps + steps_per_epoch * num_epochs + if num_epochs is not None + else num_steps ) else: # Without batching each gradient step sees the full dataset, # so one step ≡ one epoch. total_steps = num_epochs if num_epochs is not None else num_steps + assert total_steps is not None # guaranteed by the epoch/steps check above # ---- Guide ---- if guide is None: @@ -529,7 +731,8 @@ def _fit_vi( opt = optimizer # ---- Loss ---- - if batched: + loss: Batched_Trace_ELBO | Trace_ELBO + if batch_size is not None: loss = Batched_Trace_ELBO(num_obs=n_obs, batch_size=batch_size) else: loss = Trace_ELBO() @@ -537,7 +740,7 @@ def _fit_vi( # ---- Run SVI ---- svi = SVI(model_fn, guide_instance, opt, loss=loss) - if batched: + if batch_size is not None: result = svi_run_batched( svi, rng_key, @@ -564,7 +767,7 @@ def _fit_vi( def _fit_mcmc( - model_fn: Callable, + model_fn: Callable[..., Any], *, data: dict[str, jax.Array], num_warmup: int, @@ -591,6 +794,7 @@ def _fit_mcmc( model_fn=model_fn, method="mcmc", posterior_samples=mcmc.get_samples(), + mcmc=mcmc, ) @@ -600,7 +804,7 @@ def _fit_mcmc( def _fit_svgd( - model_fn: Callable, + model_fn: Callable[..., Any], *, data: dict[str, jax.Array], num_steps: int, diff --git a/blayers/layers.py b/blayers/layers.py index ba41806..2c5388c 100644 --- a/blayers/layers.py +++ b/blayers/layers.py @@ -29,7 +29,6 @@ import jax import jax.nn as jnn import jax.numpy as jnp -import numpy as np from numpyro import distributions, sample from blayers._utils import add_trailing_dim @@ -39,14 +38,15 @@ def pairwise_interactions(x: jax.Array, z: jax.Array) -> jax.Array: """ - Compute all pairwise interactions between features in X and Y. + Compute all pairwise interactions between features in ``x`` and ``z``. - Parameters: - X: (n_samples, n_features1) - Y: (n_samples, n_features2) + Args: + x: Input matrix of shape ``(n, d1)``. + z: Input matrix of shape ``(n, d2)``. Returns: - interactions: (n_samples, n_features1 * n_features2) + jax.Array of shape ``(n, d1 * d2)`` containing the flattened outer + product ``x[:, i] * z[:, j]`` for each pair ``(i, j)``. """ n, d1 = x.shape @@ -88,12 +88,17 @@ def _matmul_factorization_machine(x: jax.Array, theta: jax.Array) -> jax.Array: def _matmul_fm3(x: jax.Array, theta: jax.Array) -> jax.Array: - """Apply second-order factorization machine interaction. + """Apply third-order factorization machine interaction. - Based on Rendle (2010). Computes: + Computes all triple-product interactions via Newton's identities + (Blondel et al. 2016). Defining the per-rank power sums + :math:`p_k = \\sum_i x_i^k \\theta_i^k`: .. math:: - 0.5 * sum((xV)^2 - (x^2 V^2)) + \\text{output} = \\sum_l \\frac{p_1^3 - 3 p_2 p_1 + 2 p_3}{6} + + This computes all :math:`\\binom{d}{3}` triplet interactions without + enumerating them. Args: theta: Weight matrix of shape `(d, l, u)`. @@ -124,14 +129,12 @@ def _matmul_uv_decomp( x: jax.Array, z: jax.Array, ) -> jax.Array: - """Implements low rank multiplication. - - According to ChatGPT this is a "factorized bilinear interaction". - Basically, you just need to project x and z down to a common number of - low rank terms and then just multiply those terms. + """Low-rank factorised bilinear interaction between ``x`` and ``z``. - This is equivalent to a UV decomposition where you use n=low_rank_dim - on the columns of the U/V matrices. + Projects each input into a shared ``l``-dimensional space via ``theta1`` + and ``theta2``, then computes the element-wise product summed over the + low-rank axis. Equivalent to a rank-``l`` approximation of the full + bilinear form ``x^T (theta1 theta2^T) z``. Args: theta1: Weight matrix of shape `(d1, l, u)`. @@ -165,7 +168,7 @@ def _matmul_randomwalk( """ theta_cumsum = jnp.cumsum(theta, axis=0) - idx_flat = idx.squeeze().astype(jnp.int32) + idx_flat = idx.reshape(-1).astype(jnp.int32) return theta_cumsum[idx_flat] @@ -174,19 +177,19 @@ def _matmul_interaction( x: jax.Array, z: jax.Array, ) -> jax.Array: - """Full interaction between `x` and `z`. + """Full pairwise interaction between ``x`` and ``z``. + + Builds the flattened outer product of ``x`` and ``z`` and contracts it + against a per-pair weight matrix. Args: - beta: Weight matrix for each interaction between `x` and `z`. - x: First feature matrix. - z: Second feature matrix. + beta: Weight matrix of shape ``(d1 * d2, u)``. + x: Input matrix of shape ``(n, d1)``. + z: Input matrix of shape ``(n, d2)``. Returns: - jax.Array - + jax.Array of shape ``(n, u)``. """ - - # thanks chat GPT interactions = pairwise_interactions(x, z) return jnp.einsum("nd,du->nu", interactions, beta) @@ -195,7 +198,12 @@ def _matmul_interaction( # ---- Classes --------------------------------------------------------------- # -def _validate_prior_kwargs(coef_dist, coef_kwargs, scale_dist=None, scale_kwargs=None): +def _validate_prior_kwargs( + coef_dist: type[distributions.Distribution], + coef_kwargs: dict[str, Any], + scale_dist: type[distributions.Distribution] | None = None, + scale_kwargs: dict[str, Any] | None = None, +) -> None: """Eagerly instantiate distributions at construction time to catch bad kwargs. Raises ``TypeError`` immediately if the supplied kwargs are incompatible @@ -203,6 +211,7 @@ def _validate_prior_kwargs(coef_dist, coef_kwargs, scale_dist=None, scale_kwargs """ try: if scale_dist is not None: + assert scale_kwargs is not None scale_dist(**scale_kwargs) coef_dist(scale=1.0, **coef_kwargs) else: @@ -550,7 +559,7 @@ def __call__( activation: Activation function to apply to output. Returns: - jax.Array: Output array of shape ``(n,)``. + jax.Array: Output array of shape ``(n, u)``. """ # get shapes and reshape if necessary x = add_trailing_dim(x) @@ -603,6 +612,14 @@ def __init__( coef_kwargs: dict[str, float] = {"loc": 0.0}, scale_kwargs: dict[str, float] = {"scale": 1.0}, ): + """ + Args: + scale_dist: NumPyro distribution class for the scale (λ) of the + prior. Each input gets its own scale. + coef_dist: NumPyro distribution class for the coefficient prior. + coef_kwargs: Parameters for the prior distribution. + scale_kwargs: Parameters for the scale distribution. + """ self.scale_dist = scale_dist self.coef_dist = coef_dist self.coef_kwargs = coef_kwargs @@ -619,7 +636,11 @@ def __call__( activation: Callable[[jax.Array], jax.Array] = jnn.identity, ) -> jax.Array: """ - Interaction between feature matrices X and Z in a low rank way. UV decomp. + Low-rank bilinear interaction ``x^T (theta1 theta2^T) z`` between X and Z. + + Projects ``x`` and ``z`` into a shared ``low_rank_dim``-dimensional + space via independent factors ``theta1`` and ``theta2``, then + contracts. Args: name: Variable name scope. @@ -707,7 +728,10 @@ def __call__( activation: Callable[[jax.Array], jax.Array] = jnn.identity, ) -> jax.Array: """ - Interaction between feature matrices X and Z in a low rank way. UV decomp. + Full pairwise interaction between feature matrices X and Z. + + Samples one coefficient per ``(x_i, z_j)`` pair (``d1 * d2`` total) + and returns the weighted sum of all outer-product interactions. Args: name: Variable name scope. @@ -790,7 +814,10 @@ def __call__( activation: Callable[[jax.Array], jax.Array] = jnn.identity, ) -> jax.Array: """ - Interaction between feature matrices X and Z in a low rank way. UV decomp. + Full bilinear form ``x^T W z`` between feature matrices X and Z. + + Samples a dense weight tensor ``W`` of shape ``(d1, d2, units)`` and + contracts it against ``x`` and ``z``. Args: name: Variable name scope. @@ -878,7 +905,10 @@ def __call__( activation: Callable[[jax.Array], jax.Array] = jnn.identity, ) -> jax.Array: """ - Interaction between feature matrices X and Z in a low rank way. UV decomp. + Low-rank bilinear form ``x^T (A B^T) z``. + + Projects ``x`` and ``z`` into a shared ``low_rank_dim``-dimensional + space via shared-scale factors ``A`` and ``B``, then contracts. Args: name: Variable name scope. @@ -999,7 +1029,7 @@ def __call__( ), ) # matmul and return - return theta[x.squeeze()] + return jnp.asarray(theta[x.reshape(-1).astype(jnp.int32)]) class RandomEffectsLayer(BLayer): @@ -1032,10 +1062,11 @@ def __init__( ): """ Args: - num_embeddings: Total number of discrete embedding entries. - embedding_dim: Dimensionality of each embedding vector. - coef_dist: Prior distribution for embedding weights. + scale_dist: NumPyro distribution class for the scale (λ) of the + prior. + coef_dist: NumPyro distribution class for the coefficient prior. coef_kwargs: Parameters for the prior distribution. + scale_kwargs: Parameters for the scale distribution. """ self.scale_dist = scale_dist self.coef_dist = coef_dist @@ -1050,15 +1081,15 @@ def __call__( num_categories: int, ) -> jax.Array: """ - Forward pass through embedding lookup. + Forward pass through scalar random-effect lookup. Args: name: Variable name scope. - x: Integer indicating embeddings to use. - num_categories: The number of distinct things getting an embedding + x: Integer indices indicating which random effect to use. + num_categories: The number of distinct random-effect groups. Returns: - jax.Array: Embedding vectors of shape (n, embedding_dim). + jax.Array: Random-effect values of shape ``(n, 1)``. """ # sampling block @@ -1072,7 +1103,7 @@ def __call__( [num_categories, 1] ), ) - return theta[x.squeeze()] + return jnp.asarray(theta[x.reshape(-1).astype(jnp.int32)]) class RandomWalkLayer(BLayer): @@ -1253,7 +1284,10 @@ def __call__( else: scale = tau * scale # (d, units) - beta = sample(f"{cls}_{name}_beta", self.coef_dist(scale=scale, **self.coef_kwargs)) + beta = sample( + f"{cls}_{name}_beta", + self.coef_dist(scale=scale, **self.coef_kwargs), + ) return activation(_matmul_dot_product(x, beta)) @@ -1335,131 +1369,3 @@ def __call__( # Gate: z≈1 → full slab value; z≈0 → near zero (spike at 0) return activation(_matmul_dot_product(x, z * beta)) - - -# ---- Attention ------------------------------------------------------------- # - - -class AttentionLayer(BLayer): - """Multi-head Bayesian self-attention over the feature dimension. - - Treats the ``d`` input features as tokens using FT-Transformer style - tokenisation (Gorishniy et al. 2021, https://arxiv.org/abs/2106.11959): - each feature gets a per-column bias embedding (identity) plus a - value-scaled embedding, so tokens are distinct even when the feature - value is zero. - - For each observation ``x_i ∈ R^d``: - - 1. Tokenise: ``H_j = x_{i,j} · W_emb_j + W_bias_j`` (``head_dim``-dim each) - 2. Per head: ``Q_m, K_m, V_m = H W_Q_m, H W_K_m, H W_V_m`` - 3. ``Attn_m = softmax(Q_m K_m^T / √h_k)`` - 4. Concatenate heads → mean-pool over features → project to ``units`` - - Requires ``d ≥ 2`` for attention to be non-trivial. - Total embedding dimension is ``head_dim * num_heads`` — adding heads - increases capacity rather than splitting a fixed budget. - """ - - def __init__( - self, - scale_dist: distributions.Distribution = distributions.HalfNormal, - coef_dist: distributions.Distribution = distributions.Normal, - coef_kwargs: dict[str, float] = {"loc": 0.0}, - scale_kwargs: dict[str, float] = {"scale": 1.0}, - ): - self.scale_dist = scale_dist - self.coef_dist = coef_dist - self.coef_kwargs = coef_kwargs - self.scale_kwargs = scale_kwargs - _validate_prior_kwargs(coef_dist, coef_kwargs, scale_dist, scale_kwargs) - - def __call__( - self, - name: str, - x: jax.Array, - head_dim: int = 8, - num_heads: int = 1, - units: int = 1, - activation: Callable[[jax.Array], jax.Array] = jnn.identity, - ) -> jax.Array: - """ - Args: - name: Variable name scope. - x: Input of shape ``(n, d)``. Each column is a feature token. - head_dim: Dimension of each individual head. Total embedding - dimension is ``head_dim * num_heads``, so adding heads - increases capacity. - num_heads: Number of attention heads. - units: Number of output dimensions. - activation: Activation function. - - Returns: - jax.Array of shape ``(n, units)``. - """ - x = add_trailing_dim(x) - n, d = x.shape[0], x.shape[1] - h_k = head_dim # per-head dimension - m = num_heads - h = head_dim * m # total embedding dimension - cls = self.__class__.__name__ - - # FT-Transformer tokenisation: value scaling + per-column bias - # H[i,j] = x[i,j] * W_emb[j] + W_bias[j] → (n, d, h) - scale_emb = sample( - f"{cls}_{name}_scale_emb", - self.scale_dist(**self.scale_kwargs).expand([h]), - ) - W_emb = sample( - f"{cls}_{name}_W_emb", - self.coef_dist(scale=scale_emb, **self.coef_kwargs).expand([d, h]), - ) - W_bias = sample( - f"{cls}_{name}_W_bias", - self.coef_dist(scale=scale_emb, **self.coef_kwargs).expand([d, h]), - ) - H = x[:, :, None] * W_emb[None, :, :] + W_bias[None, :, :] # (n, d, h) - - # Q, K, V projections — one set per head: (m, h, h_k) - # scale_qkv is (m, h_k); unsqueeze to (m, 1, h_k) so it broadcasts to (m, h, h_k) - scale_qkv = sample( - f"{cls}_{name}_scale_qkv", - self.scale_dist(**self.scale_kwargs).expand([m, h_k]), - ) - scale_qkv_bc = scale_qkv[:, None, :] # (m, 1, h_k) - W_Q = sample( - f"{cls}_{name}_W_Q", - self.coef_dist(scale=scale_qkv_bc, **self.coef_kwargs).expand([m, h, h_k]), - ) - W_K = sample( - f"{cls}_{name}_W_K", - self.coef_dist(scale=scale_qkv_bc, **self.coef_kwargs).expand([m, h, h_k]), - ) - W_V = sample( - f"{cls}_{name}_W_V", - self.coef_dist(scale=scale_qkv_bc, **self.coef_kwargs).expand([m, h, h_k]), - ) - - # Project to per-head Q/K/V: (n, d, m, h_k) - Q = jnp.einsum("ndh,mhk->ndmk", H, W_Q) - K = jnp.einsum("ndh,mhk->ndmk", H, W_K) - V = jnp.einsum("ndh,mhk->ndmk", H, W_V) - - # Scaled dot-product attention per head: (n, m, d, d) - scores = jnp.einsum("ndmk,nqmk->nmdq", Q, K) / h_k**0.5 - weights = jax.nn.softmax(scores, axis=-1) - out = jnp.einsum("nmdq,nqmk->ndmk", weights, V) # (n, d, m, h_k) - - # Concatenate heads, mean-pool over features: (n, h) - pooled = out.reshape(n, d, h).mean(axis=1) - - # Output projection - scale_out = sample( - f"{cls}_{name}_scale_out", - self.scale_dist(**self.scale_kwargs).expand([units]), - ) - W_out = sample( - f"{cls}_{name}_W_out", - self.coef_dist(scale=scale_out, **self.coef_kwargs).expand([h, units]), - ) - return activation(pooled @ W_out) diff --git a/blayers/links.py b/blayers/links.py index b3476e4..80cf28e 100644 --- a/blayers/links.py +++ b/blayers/links.py @@ -21,7 +21,8 @@ def model(x, y=None): * ``gaussian_link`` — Normal likelihood, configurable sigma prior * ``lognormal_link`` — LogNormal likelihood, configurable sigma prior * ``student_t_link`` — StudentT likelihood for robust regression (default df=4) -* ``logit_link`` — Bernoulli likelihood +* ``logit_link`` — Bernoulli likelihood (binary) +* ``categorical_link`` — Categorical / softmax likelihood (multiclass) * ``poisson_link`` — Poisson likelihood * ``negative_binomial_link`` — NegativeBinomial2 likelihood, learned concentration * ``ordinal_link`` — Ordinal (cumulative logit / proportional odds) @@ -30,6 +31,7 @@ def model(x, y=None): """ from functools import partial +from typing import Any import jax import jax.nn as jnn @@ -38,13 +40,12 @@ def model(x, y=None): from numpyro import sample - def _loc_scale_link( y_hat: jax.Array, y: jax.Array | None = None, - obs_dist=dists.Normal, - sigma_dist=dists.Exponential, - sigma_kwargs: dict | None = None, + obs_dist: Any = dists.Normal, + sigma_dist: Any = dists.Exponential, + sigma_kwargs: dict[str, Any] | None = None, scale: float | jax.Array | None = None, untransformed_scale: jax.Array | None = None, ) -> jax.Array: @@ -73,13 +74,14 @@ def _loc_scale_link( if sigma_kwargs is None: sigma_kwargs = {"rate": 1.0} + sigma: float | jax.Array if untransformed_scale is not None: sigma = jax.nn.softplus(untransformed_scale) elif scale is not None: sigma = scale else: sigma = sample("sigma", sigma_dist(**sigma_kwargs)) - return sample("obs", obs_dist(loc=y_hat, scale=sigma), obs=y) + return jnp.asarray(sample("obs", obs_dist(loc=y_hat, scale=sigma), obs=y)) gaussian_link = partial(_loc_scale_link, obs_dist=dists.Normal) @@ -135,7 +137,9 @@ def _loc_scale_link( """ -student_t_link = partial(_loc_scale_link, obs_dist=partial(dists.StudentT, df=4.0)) +student_t_link = partial( + _loc_scale_link, obs_dist=partial(dists.StudentT, df=4.0) +) student_t_link.__doc__ = """StudentT likelihood for robust regression. Heavier tails than Gaussian — large residuals are down-weighted rather than @@ -171,7 +175,44 @@ def logit_link( Returns: Sample site ``"obs"``. """ - return sample("obs", dists.Bernoulli(logits=y_hat), obs=y) + return jnp.asarray(sample("obs", dists.Bernoulli(logits=y_hat), obs=y)) + + +def categorical_link( + logits: jax.Array, + y: jax.Array | None = None, +) -> jax.Array: + """Categorical (softmax) likelihood for multiclass classification. + + The multiclass generalisation of :func:`logit_link`. Produce one logit per + class with a layer's ``units`` argument (``units = num_classes``); the + number of classes is read from the trailing dimension of ``logits``. + + .. math:: + P(Y = k \\mid \\text{logits}) = \\mathrm{softmax}(\\text{logits})_k + + Args: + logits: Unnormalised class scores of shape ``(n, num_classes)`` — e.g. + ``AdaptiveLayer()("beta", x, units=K)``. A trailing singleton + (``(n, num_classes, 1)``) is squeezed automatically. + y: Integer class labels in ``{0, ..., num_classes - 1}``, or ``None`` + for prior predictive / inference. + + Returns: + Sample site ``"obs"`` with integer values in ``{0, …, num_classes-1}``. + + Example:: + + from blayers.layers import AdaptiveLayer + from blayers.links import categorical_link + + def model(x, y=None): + logits = AdaptiveLayer()("beta", x, units=4) # 4 classes + return categorical_link(logits, y) + """ + if logits.ndim == 3 and logits.shape[-1] == 1: + logits = logits.squeeze(-1) + return jnp.asarray(sample("obs", dists.Categorical(logits=logits), obs=y)) def poisson_link( @@ -187,7 +228,7 @@ def poisson_link( Returns: Sample site ``"obs"``. """ - return sample("obs", dists.Poisson(rate=jnp.exp(y_hat)), obs=y) + return jnp.asarray(sample("obs", dists.Poisson(rate=jnp.exp(y_hat)), obs=y)) def negative_binomial_link( @@ -206,17 +247,20 @@ def negative_binomial_link( Sample site ``"obs"``. """ concentration = sample("sigma", dists.Exponential(rate=rate)) - return sample( - "obs", - dists.NegativeBinomial2(mean=y_hat, concentration=concentration), - obs=y, + return jnp.asarray( + sample( + "obs", + dists.NegativeBinomial2(mean=y_hat, concentration=concentration), + obs=y, + ) ) def ordinal_link( mu: jax.Array, y: jax.Array | None = None, - num_classes: int = None, + *, + num_classes: int, ) -> jax.Array: """Cumulative logit (proportional odds) link for ordinal outcomes. @@ -255,7 +299,7 @@ def ordinal_link( probs_parts.append(1.0 - cum_probs[:, -1:]) probs = jnp.clip(jnp.concatenate(probs_parts, axis=1), 1e-8, 1.0) - return sample("obs", dists.Categorical(probs=probs), obs=y) + return jnp.asarray(sample("obs", dists.Categorical(probs=probs), obs=y)) def zip_link( @@ -277,7 +321,9 @@ def zip_link( """ rate = jnp.exp(mu.squeeze()) gate = sample("zip_gate", dists.Beta(1.0, 10.0)) - return sample("obs", dists.ZeroInflatedPoisson(gate=gate, rate=rate), obs=y) + return jnp.asarray( + sample("obs", dists.ZeroInflatedPoisson(gate=gate, rate=rate), obs=y) + ) def beta_link( @@ -302,4 +348,6 @@ def beta_link( """ mean = jnn.sigmoid(mu.squeeze()) phi = sample("beta_phi", dists.Exponential(1.0)) - return sample("obs", dists.Beta(mean * phi, (1.0 - mean) * phi), obs=y) + return jnp.asarray( + sample("obs", dists.Beta(mean * phi, (1.0 - mean) * phi), obs=y) + ) diff --git a/blayers/vi_infer.py b/blayers/vi_infer.py index dae511b..b950208 100644 --- a/blayers/vi_infer.py +++ b/blayers/vi_infer.py @@ -1,4 +1,14 @@ -import warnings +""" +Variational-inference utilities for blayers. + +Provides :class:`Batched_Trace_ELBO`, a drop-in ``Trace_ELBO`` replacement +that handles minibatching without requiring the model to use ``numpyro.plate``, +and :func:`svi_run_batched`, an ``svi.run``-style helper that drives it. + +Use ``Batched_Trace_ELBO`` + ``svi_run_batched`` for plate-free batched VI; +fall back to standard ``Trace_ELBO`` if your model already uses plates. +""" + from typing import Any, Callable import jax @@ -13,18 +23,46 @@ from blayers._utils import get_steps_and_steps_per_epoch, yield_batches -def _warn_if_has_plate(model_trace: dict[str, dict[str, Any]]) -> None: +def _raise_if_has_plate(model_trace: dict[str, dict[str, Any]]) -> None: if any(site["type"] == "plate" for site in model_trace.values()): - warnings.warn( - "Model contains plates. Batched_Trace_ELBO is known to have" - " issues with plates. Please batch via plates if you need" - " to use plates for your model.", - UserWarning, - stacklevel=2, # makes the warning point to user code + raise ValueError( + "Batched_Trace_ELBO does not support models that use " + "numpyro.plate: the N/B log-likelihood rescaling double-counts " + "plate-subsampled sites and produces an incorrect ELBO. Either " + "(a) batch via plate and use the standard numpyro Trace_ELBO, or " + "(b) remove the plate and use Batched_Trace_ELBO + " + "svi_run_batched." ) class Batched_Trace_ELBO(ELBO): + """ELBO estimator for minibatched VI without ``numpyro.plate``. + + Behaves like ``Trace_ELBO`` but rescales the per-batch log-likelihood by + ``num_obs / batch_size`` so the gradient is an unbiased estimate of the + full-dataset ELBO. Drive it with :func:`svi_run_batched`. + + **Assumes all latent variables are global.** The whole observed + log-likelihood is scaled by ``num_obs / batch_size`` and the KL over + latents is *not* rescaled, which is only correct when every latent is + shared across observations (the usual case for BLayers: coefficients, + scales, embeddings). Models with **per-observation (local) latents** — + e.g. a latent variable sampled once per row — are **not supported** here; + use ``numpyro.plate`` with the standard ``Trace_ELBO`` instead. + + Args: + num_obs: Total number of observations in the full training set. + num_particles: Number of Monte Carlo samples per gradient step. + batch_size: Minibatch size. If ``None``, inferred from the leading + dimension of the first batched kwarg at loss-evaluation time. + + Warning: + Does not mix with ``numpyro.plate``. A ``ValueError`` is raised if a + plate is detected in the model trace — the ``num_obs / batch_size`` + rescaling double-counts plate-subsampled sites, so the ELBO would be + silently wrong. Use the standard ``Trace_ELBO`` with plates instead. + """ + def __init__( self, num_obs: int, @@ -111,7 +149,7 @@ def elbo_components( **kwargs, ) - _warn_if_has_plate(model_trace) + _raise_if_has_plate(model_trace) # log p(x | z) # upscale here by N / B where N is the nubmer of observations and B @@ -164,7 +202,7 @@ def svi_run_batched( batch_size: int, num_steps: int | None = None, num_epochs: int | None = None, - **data: dict[str, jax.Array], + **data: jax.Array, ) -> SVIRunResult: @jax.jit def update(svi_state: SVIState, **kwargs: Any) -> SVIState: @@ -177,7 +215,8 @@ def update(svi_state: SVIState, **kwargs: Any) -> SVIState: num_epochs, ) - svi_state = svi.init(rng_key, **data) + init_key, batch_key = random.split(rng_key) + svi_state = svi.init(init_key, **data) losses = [] for batch in tqdm.tqdm( yield_batches( @@ -185,6 +224,7 @@ def update(svi_state: SVIState, **kwargs: Any) -> SVIState: batch_size, total_steps_to_run, steps_per_epoch, + rng_key=batch_key, ), total=total_steps_to_run, ): diff --git a/docs/conf.py b/docs/conf.py index a70efcc..e8036fa 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -9,13 +9,18 @@ import os import sys +import tomllib sys.path.insert(0, os.path.abspath("..")) project = "blayers" copyright = "2025, George Berry" author = "George Berry" -release = "0.2.9" + +with open( + os.path.join(os.path.dirname(__file__), "..", "pyproject.toml"), "rb" +) as _f: + release = tomllib.load(_f)["project"]["version"] # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration @@ -31,7 +36,6 @@ autosummary_generate = True -templates_path = ["_templates"] exclude_patterns = [] autodoc_typehints = "description" diff --git a/pyproject.toml b/pyproject.toml index ce38536..40d7702 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,12 +4,12 @@ build-backend = "setuptools.build_meta" [project] name = "blayers" -version = "0.3.0" +version = "0.3.1" description = "Bayesian layers for NumPyro and Jax" authors = [{ name = "George Berry", email = "george.e.berry@gmail.com" }] readme = "README.md" license = { file = "MIT" } -requires-python = ">=3.9" +requires-python = ">=3.12" dependencies = [ "jax", "numpyro", @@ -21,8 +21,13 @@ Homepage = "https://github.com/georgeberry/blayers" Documentation = "https://georgeberry.github.io/blayers/" [project.optional-dependencies] +arviz = [ + # arviz 1.x: nested from_dict API + PSIS-LOO (WAIC dropped upstream). + "arviz>=1.0", +] dev = [ "optax", + "arviz>=1.0", "pytest", "pytest-check", "mypy", @@ -56,7 +61,7 @@ force_grid_wrap = 0 [tool.black] line-length = 80 -target-version = ["py311"] +target-version = ["py312"] [tool.ruff] ignore = ["E731"] diff --git a/tests/arviz_test.py b/tests/arviz_test.py new file mode 100644 index 0000000..bb4be85 --- /dev/null +++ b/tests/arviz_test.py @@ -0,0 +1,143 @@ +"""Tests for FittedModel.to_arviz() — the ArviZ bridge. + +Skipped entirely when arviz is not installed (optional dependency, and +arviz >= 1.0 requires Python >= 3.12). +""" + +import jax.numpy as jnp +import jax.random as random +import pytest + +from blayers.decorators import autoreshape +from blayers.fit import fit +from blayers.layers import AdaptiveLayer, InterceptLayer +from blayers.links import gaussian_link + +az = pytest.importorskip("arviz") + +NUM_OBS = 200 +K = 3 + + +@autoreshape +def _model(x, y=None): + mu = InterceptLayer()("i") + AdaptiveLayer()("b", x) + return gaussian_link(mu, y) + + +@pytest.fixture +def data() -> tuple: + x = random.normal(random.PRNGKey(0), (NUM_OBS, K)) + beta = jnp.array([1.5, -2.0, 0.5]) + y = x @ beta + 0.3 * random.normal(random.PRNGKey(1), (NUM_OBS,)) + return x, y + + +# --------------------------------------------------------------------------- # +# VI +# --------------------------------------------------------------------------- # + + +def test_vi_to_arviz_groups(data) -> None: + x, y = data + result = fit(_model, y=y, num_steps=200, lr=0.05, seed=0, x=x) + idata = result.to_arviz(y=y, x=x, num_samples=200) + children = list(idata.children) + assert "posterior" in children + assert "log_likelihood" in children + assert "observed_data" in children + + +def test_vi_to_arviz_loo_runs(data) -> None: + """The log_likelihood group must be usable by az.loo (PSIS-LOO).""" + x, y = data + result = fit(_model, y=y, num_steps=200, lr=0.05, seed=0, x=x) + idata = result.to_arviz(y=y, x=x, num_samples=200) + loo = az.loo(idata) + assert jnp.isfinite(float(loo.elpd)) + + +def test_vi_to_arviz_requires_y(data) -> None: + x, y = data + result = fit(_model, y=y, num_steps=50, lr=0.05, seed=0, x=x) + with pytest.raises(ValueError, match="needs the observed"): + result.to_arviz(x=x) + + +# --------------------------------------------------------------------------- # +# MCMC +# --------------------------------------------------------------------------- # + + +def test_mcmc_to_arviz_groups(data) -> None: + x, y = data + result = fit( + _model, + y=y, + method="mcmc", + num_warmup=150, + num_mcmc_samples=200, + x=x, + ) + idata = result.to_arviz() + children = list(idata.children) + assert "posterior" in children + assert "sample_stats" in children # divergences etc. + assert "log_likelihood" in children + + +def test_mcmc_to_arviz_loo_and_summary(data) -> None: + x, y = data + result = fit( + _model, + y=y, + method="mcmc", + num_warmup=150, + num_mcmc_samples=200, + x=x, + ) + idata = result.to_arviz() + assert jnp.isfinite(float(az.loo(idata).elpd)) + summ = az.summary(idata, var_names=["AdaptiveLayer_b_beta"]) + assert "ess_bulk" in summ.columns + + +# --------------------------------------------------------------------------- # +# SVGD (unsupported) +# --------------------------------------------------------------------------- # + + +def test_svgd_to_arviz_not_implemented(data) -> None: + x, y = data + result = fit( + _model, + y=y, + method="svgd", + num_steps=100, + num_particles=5, + x=x, + ) + with pytest.raises(NotImplementedError, match="does not support SVGD"): + result.to_arviz(y=y, x=x) + + +# --------------------------------------------------------------------------- # +# Model comparison across methods +# --------------------------------------------------------------------------- # + + +def test_compare_vi_and_mcmc(data) -> None: + x, y = data + vi = fit(_model, y=y, num_steps=200, lr=0.05, seed=0, x=x).to_arviz( + y=y, x=x, num_samples=200 + ) + mc = fit( + _model, + y=y, + method="mcmc", + num_warmup=150, + num_mcmc_samples=200, + x=x, + ).to_arviz() + cmp = az.compare({"vi": vi, "mcmc": mc}) + assert len(cmp) == 2 diff --git a/tests/fit_test.py b/tests/fit_test.py index a061d5a..f9812ea 100644 --- a/tests/fit_test.py +++ b/tests/fit_test.py @@ -1,7 +1,5 @@ """Tests for the high-level ``blayers.fit`` API.""" -from typing import Any - import jax import jax.numpy as jnp import jax.random as random @@ -12,10 +10,16 @@ from numpyro.infer.autoguide import AutoDiagonalNormal, AutoMultivariateNormal from blayers._utils import rmse -from blayers.fit import FittedModel, Predictions, _is_array, _split_data_and_constants, fit +from blayers.decorators import autoreshape +from blayers.fit import ( + FittedModel, + Predictions, + _is_array, + _split_data_and_constants, + fit, +) from blayers.layers import AdaptiveLayer, InterceptLayer from blayers.links import gaussian_link -from blayers.decorators import autoreshape NUM_OBS = 2000 K = 3 @@ -291,7 +295,9 @@ def test_both_epochs_and_steps_raises(sim_data: dict[str, jax.Array]) -> None: ) -def test_neither_epochs_nor_steps_raises(sim_data: dict[str, jax.Array]) -> None: +def test_neither_epochs_nor_steps_raises( + sim_data: dict[str, jax.Array] +) -> None: with pytest.raises(ValueError, match="exactly one"): fit( linear_model, @@ -516,11 +522,14 @@ def test_fit_svgd_num_epochs(sim_data: dict[str, jax.Array]) -> None: def test_fit_learns_coefficients(sim_data: dict[str, jax.Array]) -> None: """Verify that fit() produces predictions that are better than chance.""" + # This DGP has an irreducible noise floor near 0.47 * baseline (SNR ~3.5), + # so we train to convergence rather than testing an under-fit model whose + # loss depends on the exact minibatch trajectory. result = fit( linear_model, y=sim_data["y"], batch_size=512, - num_epochs=50, + num_epochs=200, lr=0.05, seed=0, x=sim_data["x"], @@ -532,7 +541,7 @@ def test_fit_learns_coefficients(sim_data: dict[str, jax.Array]) -> None: prediction_rmse = float(rmse(preds.mean, y)) baseline_rmse = float(rmse(jnp.zeros_like(y), y)) - # Model should do meaningfully better than predicting zero + # Model should do meaningfully better than predicting zero (halve the RMSE). assert prediction_rmse < baseline_rmse * 0.5 @@ -566,7 +575,9 @@ def test_fit_svgd_learns(sim_data: dict[str, jax.Array]) -> None: def test_predict_unknown_method_raises(sim_data: dict[str, jax.Array]) -> None: """predict() raises on an unknown method string.""" - result = fit(linear_model, y=sim_data["y"], num_steps=100, seed=0, x=sim_data["x"]) + result = fit( + linear_model, y=sim_data["y"], num_steps=100, seed=0, x=sim_data["x"] + ) result.method = "unknown" with pytest.raises(ValueError, match="Unknown method"): result.predict(x=sim_data["x"]) @@ -574,20 +585,28 @@ def test_predict_unknown_method_raises(sim_data: dict[str, jax.Array]) -> None: def test_summary_unknown_method_raises(sim_data: dict[str, jax.Array]) -> None: """summary() raises on an unknown method string.""" - result = fit(linear_model, y=sim_data["y"], num_steps=100, seed=0, x=sim_data["x"]) + result = fit( + linear_model, y=sim_data["y"], num_steps=100, seed=0, x=sim_data["x"] + ) result.method = "unknown" with pytest.raises(ValueError, match="Unknown method"): result.summary(x=sim_data["x"]) -def test_summary_vi_missing_guide_raises(sim_data: dict[str, jax.Array]) -> None: - result = fit(linear_model, y=sim_data["y"], num_steps=100, seed=0, x=sim_data["x"]) +def test_summary_vi_missing_guide_raises( + sim_data: dict[str, jax.Array] +) -> None: + result = fit( + linear_model, y=sim_data["y"], num_steps=100, seed=0, x=sim_data["x"] + ) result.guide = None with pytest.raises(RuntimeError, match="guide or params"): result.summary(x=sim_data["x"]) -def test_summary_svgd_missing_params_raises(sim_data: dict[str, jax.Array]) -> None: +def test_summary_svgd_missing_params_raises( + sim_data: dict[str, jax.Array] +) -> None: result = fit( linear_model, y=sim_data["y"], @@ -602,7 +621,9 @@ def test_summary_svgd_missing_params_raises(sim_data: dict[str, jax.Array]) -> N result.summary(x=sim_data["x"]) -def test_summary_mcmc_missing_samples_raises(sim_data: dict[str, jax.Array]) -> None: +def test_summary_mcmc_missing_samples_raises( + sim_data: dict[str, jax.Array] +) -> None: result = fit( linear_model, y=sim_data["y"], @@ -613,11 +634,15 @@ def test_summary_mcmc_missing_samples_raises(sim_data: dict[str, jax.Array]) -> x=sim_data["x"], ) result.posterior_samples = None - with pytest.raises(RuntimeError, match="MCMC results missing posterior_samples"): + with pytest.raises( + RuntimeError, match="MCMC results missing posterior_samples" + ): result.summary(x=sim_data["x"]) -def test_fit_svgd_epochs_steps_conflict_raises(sim_data: dict[str, jax.Array]) -> None: +def test_fit_svgd_epochs_steps_conflict_raises( + sim_data: dict[str, jax.Array] +) -> None: """SVGD raises if both num_epochs and num_steps are given.""" with pytest.raises(ValueError, match="exactly one"): fit( diff --git a/tests/layers_test.py b/tests/layers_test.py index 9be5f7f..e598077 100644 --- a/tests/layers_test.py +++ b/tests/layers_test.py @@ -19,10 +19,10 @@ outer_product_upper_tril_no_diag, rmse, ) -from blayers.vi_infer import Batched_Trace_ELBO, svi_run_batched +from blayers.decorators import autoreparam from blayers.layers import ( - BilinearLayer, AdaptiveLayer, + BilinearLayer, EmbeddingLayer, FixedPriorLayer, FM3Layer, @@ -39,7 +39,7 @@ _matmul_uv_decomp, ) from blayers.links import gaussian_link -from blayers.decorators import autoreparam +from blayers.vi_infer import Batched_Trace_ELBO, svi_run_batched NUM_OBS = 10000 LOW_RANK_DIM = 3 @@ -883,3 +883,42 @@ def model(x, z): predictive = Predictive(model, num_samples=4) samples = predictive(random.PRNGKey(2), x=x, z=z) assert samples["out"].shape == (4, 20, 3) + + +def test_embedding_size_one_batch() -> None: + """Regression: a single-row batch must keep its leading dim (not squeeze to scalar).""" + x = jnp.array([[2]]) # (1, 1) index + + def model(x): + out = EmbeddingLayer()("beta", x, num_categories=5, embedding_dim=3) + return deterministic("out", out) + + predictive = Predictive(model, num_samples=4) + samples = predictive(random.PRNGKey(0), x=x) + assert samples["out"].shape == (4, 1, 3) + + +def test_random_effects_size_one_batch() -> None: + """Regression: single-row batch stays shape (1, 1).""" + x = jnp.array([[3]]) + + def model(x): + out = RandomEffectsLayer()("beta", x, num_categories=5) + return deterministic("out", out) + + predictive = Predictive(model, num_samples=4) + samples = predictive(random.PRNGKey(0), x=x) + assert samples["out"].shape == (4, 1, 1) + + +def test_embedding_float_index_dtype() -> None: + """Float-typed indices should be accepted (cast to int internally).""" + x = jnp.array([[0.0], [2.0], [4.0]]) # float indices + + def model(x): + out = EmbeddingLayer()("beta", x, num_categories=5, embedding_dim=2) + return deterministic("out", out) + + predictive = Predictive(model, num_samples=4) + samples = predictive(random.PRNGKey(0), x=x) + assert samples["out"].shape == (4, 3, 2) diff --git a/tests/new_features_test.py b/tests/new_features_test.py index 1e881ed..9be1419 100644 --- a/tests/new_features_test.py +++ b/tests/new_features_test.py @@ -1,25 +1,25 @@ -"""Tests for HorseshoeLayer, AttentionLayer, ordinal_link, zip_link, beta_link, gaussian_link.""" +"""Tests for HorseshoeLayer, ordinal_link, zip_link, beta_link, gaussian_link, +categorical_link, and sample_prior.""" import jax import jax.numpy as jnp import jax.random as random -import numpyro.distributions as dist import pytest -from numpyro import deterministic, sample +from numpyro import deterministic from numpyro.infer import Predictive from blayers._utils import rmse -from blayers.fit import fit -from blayers.layers import AdaptiveLayer, AttentionLayer, HorseshoeLayer, SpikeAndSlabLayer +from blayers.decorators import autoreshape +from blayers.fit import fit, sample_prior +from blayers.layers import AdaptiveLayer, HorseshoeLayer, SpikeAndSlabLayer from blayers.links import ( beta_link, + categorical_link, gaussian_link, ordinal_link, zip_link, ) -from blayers.decorators import autoreshape - NUM_OBS = 1000 @@ -80,7 +80,9 @@ def model(x): samples = _prior_samples(model, x=x) assert "HorseshoeLayer_beta_c2" in samples - assert samples["HorseshoeLayer_beta_c2"].shape == (4,) # scalar per sample + assert samples["HorseshoeLayer_beta_c2"].shape == ( + 4, + ) # scalar per sample def test_fit_runs(self) -> None: """HorseshoeLayer should work end-to-end with fit().""" @@ -112,99 +114,9 @@ def sparse_model(x, y=None): result = fit(sparse_model, y=y, x=x, num_steps=500, lr=0.01, seed=0) preds = result.predict(x=x, num_samples=100) - assert float(rmse(preds.mean, y)) < float(rmse(jnp.zeros_like(y), y)) * 0.5 - - -# --------------------------------------------------------------------------- # -# AttentionLayer -# --------------------------------------------------------------------------- # - - -class TestAttentionLayer: - def test_output_shape(self) -> None: - x = random.normal(random.PRNGKey(0), (30, 5)) - - def model(x): - out = AttentionLayer()("attn", x, head_dim=4) - return deterministic("out", out) - - samples = _prior_samples(model, x=x) - assert samples["out"].shape == (4, 30, 1) - - def test_output_shape_units(self) -> None: - x = random.normal(random.PRNGKey(0), (30, 5)) - - def model(x): - out = AttentionLayer()("attn", x, head_dim=4, units=2) - return deterministic("out", out) - - samples = _prior_samples(model, x=x) - assert samples["out"].shape == (4, 30, 2) - - def test_sample_sites_present(self) -> None: - x = random.normal(random.PRNGKey(0), (20, 4)) - - def model(x): - return AttentionLayer()("a", x, head_dim=4) - - samples = _prior_samples(model, x=x) - for site in ["W_emb", "W_bias", "W_Q", "W_K", "W_V", "W_out"]: - assert f"AttentionLayer_a_{site}" in samples - - def test_multihead_output_shape(self) -> None: - x = random.normal(random.PRNGKey(0), (30, 5)) - - def model(x): - out = AttentionLayer()("attn", x, head_dim=4, num_heads=2) - return deterministic("out", out) - - samples = _prior_samples(model, x=x) - assert samples["out"].shape == (4, 30, 1) - - def test_multihead_units(self) -> None: - x = random.normal(random.PRNGKey(0), (30, 5)) - - def model(x): - out = AttentionLayer()("attn", x, head_dim=4, num_heads=2, units=3) - return deterministic("out", out) - - samples = _prior_samples(model, x=x) - assert samples["out"].shape == (4, 30, 3) - - def test_bias_site_present(self) -> None: - """W_bias (per-column identity embedding) should be sampled.""" - x = random.normal(random.PRNGKey(0), (20, 4)) - - def model(x): - return AttentionLayer()("a", x, head_dim=4) - - samples = _prior_samples(model, x=x) - assert "AttentionLayer_a_W_bias" in samples - - def test_fit_runs(self) -> None: - """AttentionLayer should run end-to-end with fit().""" - x = random.normal(random.PRNGKey(0), (NUM_OBS, 4)) - y = jnp.sin(x[:, 0]) * x[:, 1] + random.normal(random.PRNGKey(1), (NUM_OBS,)) * 0.2 - - @autoreshape - def attn_model(x, y=None): - mu = AttentionLayer()("attn", x, head_dim=4) - return gaussian_link(mu, y) - - result = fit(attn_model, y=y, x=x, num_steps=200, lr=0.01, seed=0) - assert result.params is not None - - def test_multihead_fit_runs(self) -> None: - x = random.normal(random.PRNGKey(0), (NUM_OBS, 4)) - y = jnp.sin(x[:, 0]) * x[:, 1] + random.normal(random.PRNGKey(1), (NUM_OBS,)) * 0.2 - - @autoreshape - def attn_model(x, y=None): - mu = AttentionLayer()("attn", x, head_dim=4, num_heads=2) - return gaussian_link(mu, y) - - result = fit(attn_model, y=y, x=x, num_steps=200, lr=0.01, seed=0) - assert result.params is not None + assert ( + float(rmse(preds.mean, y)) < float(rmse(jnp.zeros_like(y), y)) * 0.5 + ) # --------------------------------------------------------------------------- # @@ -283,8 +195,8 @@ def _make_count_data(num_obs=NUM_OBS, seed=0): key = random.PRNGKey(seed) k1, k2, k3 = random.split(key, 3) x = random.normal(k1, (num_obs, 2)) - log_rate = x[:, 0] # true log-rate - gate = jnp.full((num_obs,), 0.3) # 30% extra zeros + log_rate = x[:, 0] # true log-rate + gate = jnp.full((num_obs,), 0.3) # 30% extra zeros is_zero = random.bernoulli(k2, gate) counts = random.poisson(k3, jnp.exp(log_rate)) y = jnp.where(is_zero, 0, counts) @@ -332,7 +244,9 @@ def zip_model(x, y=None): mu = AdaptiveLayer()("beta", x) return zip_link(mu, y) - result = fit(zip_model, y=y.astype(float), x=x, num_steps=300, lr=0.02, seed=0) + result = fit( + zip_model, y=y.astype(float), x=x, num_steps=300, lr=0.02, seed=0 + ) assert result.params is not None @@ -576,3 +490,109 @@ def model(x, y=None): result = fit(model, y=y, x=x, num_steps=300, lr=0.01, seed=0) assert result.params is not None + + +# --------------------------------------------------------------------------- # +# categorical_link +# --------------------------------------------------------------------------- # + + +def _make_multiclass_data(num_obs=NUM_OBS, K=4, seed=0): + """Multiclass DGP: argmax of a linear score per class.""" + key = random.PRNGKey(seed) + k1, k2 = random.split(key) + x = random.normal(k1, (num_obs, 3)) + w = random.normal(k2, (3, K)) + logits = x @ w + y = jnp.argmax(logits, axis=1).astype(jnp.int32) + return x, y + + +class TestCategoricalLink: + K = 4 + + def test_prior_obs_shape(self) -> None: + x = random.normal(random.PRNGKey(0), (30, 3)) + + def model(x, y=None): + logits = AdaptiveLayer()("beta", x, units=self.K) + return categorical_link(logits, y) + + samples = _prior_samples(model, x=x) + assert samples["obs"].shape == (4, 30) + + def test_prior_obs_range(self) -> None: + """Prior samples should be integers in {0, ..., K-1}.""" + x = random.normal(random.PRNGKey(0), (50, 3)) + + def model(x, y=None): + logits = AdaptiveLayer()("beta", x, units=self.K) + return categorical_link(logits, y) + + samples = _prior_samples(model, num_samples=10, x=x) + obs = samples["obs"] + assert jnp.all(obs >= 0) + assert jnp.all(obs < self.K) + + def test_fit_learns(self) -> None: + """Categorical fit should beat random-guess accuracy on separable data.""" + x, y = _make_multiclass_data(K=self.K) + + @autoreshape + def model(x, y=None): + logits = AdaptiveLayer()("beta", x, units=self.K) + return categorical_link(logits, y) + + result = fit(model, y=y, x=x, num_steps=500, lr=0.05, seed=0) + preds = result.predict(x=x, num_samples=200) + # preds.mean is the mean class label; recover per-obs modal class from + # samples instead for an accuracy check. + modal = jnp.round( + jnp.median( + preds.samples.reshape(-1, preds.samples.shape[-1]), axis=0 + ) + ) + acc = float(jnp.mean(modal == y)) + assert acc > 1.0 / self.K # better than chance + + +# --------------------------------------------------------------------------- # +# sample_prior +# --------------------------------------------------------------------------- # + + +class TestSamplePrior: + def test_returns_obs_and_latents(self) -> None: + x = random.normal(random.PRNGKey(0), (40, 3)) + + @autoreshape + def model(x, y=None): + mu = AdaptiveLayer()("mu", x) + return gaussian_link(mu, y) + + prior = sample_prior(model, x=x, num_samples=64) + assert prior["obs"].shape[0] == 64 + assert prior["obs"].shape[-2] == 40 + # a latent site from the AdaptiveLayer is present + assert "AdaptiveLayer_mu_beta" in prior + + def test_num_samples_respected(self) -> None: + x = random.normal(random.PRNGKey(0), (20, 2)) + + @autoreshape + def model(x, y=None): + return gaussian_link(AdaptiveLayer()("mu", x), y) + + prior = sample_prior(model, x=x, num_samples=17, seed=3) + assert prior["obs"].shape[0] == 17 + + def test_rejects_y(self) -> None: + x = random.normal(random.PRNGKey(0), (10, 2)) + y = random.normal(random.PRNGKey(1), (10,)) + + @autoreshape + def model(x, y=None): + return gaussian_link(AdaptiveLayer()("mu", x), y) + + with pytest.raises(ValueError, match="do not pass `y`"): + sample_prior(model, x=x, y=y) diff --git a/tests/utils_test.py b/tests/utils_test.py index 5ced35a..778de7b 100644 --- a/tests/utils_test.py +++ b/tests/utils_test.py @@ -1,4 +1,5 @@ import jax.numpy as jnp +import jax.random as random import pytest import pytest_check @@ -6,6 +7,7 @@ add_trailing_dim, get_dataset_size, get_steps_and_steps_per_epoch, + yield_batches, ) @@ -58,3 +60,54 @@ def test_get_steps_per_epoch() -> None: num_epochs=10, ) assert steps == 20 + + +def test_yield_batches_no_shuffle_is_contiguous() -> None: + """rng_key=None reproduces the legacy contiguous ordering.""" + data = {"x": jnp.arange(6)} + batches = list( + yield_batches(data, batch_size=2, num_batches=3, steps_per_epoch=3) + ) + assert [b["x"].tolist() for b in batches] == [[0, 1], [2, 3], [4, 5]] + + +def test_yield_batches_shuffle_covers_all_rows_per_epoch() -> None: + """With a key, one epoch of batches is a permutation of every row.""" + data = {"x": jnp.arange(6)} + key = random.PRNGKey(0) + batches = list( + yield_batches( + data, batch_size=2, num_batches=3, steps_per_epoch=3, rng_key=key + ) + ) + rows = sorted(v for b in batches for v in b["x"].tolist()) + assert rows == [0, 1, 2, 3, 4, 5] + + +def test_yield_batches_shuffle_reorders_across_epochs() -> None: + """Consecutive epochs should not repeat the same fixed ordering.""" + data = {"x": jnp.arange(8)} + key = random.PRNGKey(0) + # two epochs of 4 batches each (batch_size 2) + batches = list( + yield_batches( + data, batch_size=2, num_batches=8, steps_per_epoch=4, rng_key=key + ) + ) + epoch1 = [v for b in batches[:4] for v in b["x"].tolist()] + epoch2 = [v for b in batches[4:] for v in b["x"].tolist()] + assert epoch1 != epoch2 # re-permuted each epoch + assert sorted(epoch1) == sorted(epoch2) == list(range(8)) + + +def test_yield_batches_paired_arrays_stay_aligned() -> None: + """Shuffling must permute every array with the same index order.""" + data = {"x": jnp.arange(6), "y": jnp.arange(6) * 10} + key = random.PRNGKey(1) + batches = list( + yield_batches( + data, batch_size=2, num_batches=3, steps_per_epoch=3, rng_key=key + ) + ) + for b in batches: + assert (b["y"] == b["x"] * 10).all() diff --git a/tests/validation_test.py b/tests/validation_test.py index 13b5e08..6150aa0 100644 --- a/tests/validation_test.py +++ b/tests/validation_test.py @@ -5,21 +5,19 @@ from blayers.layers import ( AdaptiveLayer, - AttentionLayer, BilinearLayer, EmbeddingLayer, - FMLayer, - FM3Layer, FixedPriorLayer, - InterceptLayer, + FM3Layer, + FMLayer, InteractionLayer, + InterceptLayer, LowRankBilinearLayer, LowRankInteractionLayer, RandomEffectsLayer, RandomWalkLayer, ) - # --------------------------------------------------------------------------- # # Adaptive-prior layers (lmbda + coef) # --------------------------------------------------------------------------- # @@ -61,15 +59,6 @@ def test_bad_coef_kwargs(self): FM3Layer(coef_kwargs={"loc": 0.0, "bad_kwarg": 1.0}) -class TestAttentionLayerValidation: - def test_valid_defaults(self): - AttentionLayer() - - def test_bad_scale_kwargs(self): - with pytest.raises(TypeError, match="Invalid distribution kwargs"): - AttentionLayer(scale_kwargs={"oops": 2.0}) - - class TestEmbeddingLayerValidation: def test_valid_defaults(self): EmbeddingLayer() diff --git a/tests/vi_infer_test.py b/tests/vi_infer_test.py index f187dfa..129cddf 100644 --- a/tests/vi_infer_test.py +++ b/tests/vi_infer_test.py @@ -1,5 +1,3 @@ -import warnings - import jax import jax.numpy as jnp import numpyro @@ -11,7 +9,7 @@ from numpyro.infer import SVI, Trace_ELBO from numpyro.infer.autoguide import AutoDiagonalNormal -from blayers.vi_infer import Batched_Trace_ELBO, _warn_if_has_plate +from blayers.vi_infer import Batched_Trace_ELBO, _raise_if_has_plate def test_builtin_vs_batched_elbo_simple() -> None: @@ -125,7 +123,7 @@ def model() -> jax.Array: svi_batched.evaluate(state_batched) -def test_plate_warning() -> None: +def test_plate_raises() -> None: key = jax.random.PRNGKey(0) data = jnp.ones(10) @@ -138,11 +136,11 @@ def model_with_plate(data: jax.Array) -> None: data ) - with pytest.warns(UserWarning, match="Model contains plates"): - _warn_if_has_plate(model_trace) + with pytest.raises(ValueError, match="does not support"): + _raise_if_has_plate(model_trace) -def test_no_plate_no_warning() -> None: +def test_no_plate_does_not_raise() -> None: key = jax.random.PRNGKey(0) data = jnp.ones(10) @@ -154,7 +152,27 @@ def model_no_plate(data: jax.Array) -> None: data ) - with warnings.catch_warnings(record=True) as w: - _warn_if_has_plate(model_trace) + _raise_if_has_plate(model_trace) # should not raise + + +def test_plate_raises_through_elbo() -> None: + """A plate model must fail when the batched ELBO is actually evaluated.""" + key = jax.random.PRNGKey(0) + data = jnp.ones(10) + + def model_with_plate(data: jax.Array) -> None: + mu = sample("mu", dist.Normal(0, 1)) + with plate("data", len(data)): + sample("obs", dist.Normal(mu, 1), obs=data) - assert len(w) == 0 + guide = AutoDiagonalNormal(model_with_plate) + loss = Batched_Trace_ELBO(num_obs=10, batch_size=5) + + with pytest.raises(ValueError, match="does not support"): + loss.loss( + key, + {}, + model_with_plate, + guide, + data, + ) diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..3dbe604 --- /dev/null +++ b/uv.lock @@ -0,0 +1,1589 @@ +version = 1 +revision = 3 +requires-python = 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