hessboost for Python

Fast, deterministic gradient boosting in Rust. The core API (DMatrix, train, cv, Booster, and scikit-learn estimators) takes the parameter names XGBoost users know, and models move to and from XGBoost as JSON or UBJSON.

• Strict. An unknown parameter, a value of the wrong type or range, or a combination hessboost does not implement raises an error; nothing is silently ignored.
• Deterministic. The same parameters, data, and seed give the same model at any nthread.
• Typed (py.typed, complete type information), with the GIL released while training and predicting, and free-threaded CPython supported.
• Modern modeling (opt-in). Conformal prediction intervals, confidence intervals for the regression function (Boulevard boosting), distributional boosting (a predictive distribution per row), LightGBM/CatBoost tree options, class-balanced binary bagging, and XE-NDCG ranking (objective="rank:xendcg").

Installation

uv add hessboost        # or: pip install hessboost

Extras: hessboost[pandas], hessboost[polars], and hessboost[scikit-learn]. numpy is the only required dependency.

Prebuilt wheels are published for Linux x86_64 and aarch64 (glibc manylinux and musl/Alpine musllinux), macOS arm64, and Windows x86_64: one abi3 wheel per platform for CPython 3.11 and newer, plus a wheel for free-threaded CPython 3.14t. Every wheel trains and predicts on a GPU through wgpu (Vulkan, Metal, DirectX 12), and the macOS wheels also through native Metal (see GPU training and prediction). Elsewhere the installer builds from the source distribution, which needs Rust 1.93 or newer and a C compiler (for libzstd).

Quick start

Train on a DMatrix, evaluating a holdout set every round and stopping once it stops improving:

import numpy as np
import hessboost

rng = np.random.default_rng(0)
X = rng.normal(size=(1000, 5))
y = (X[:, 0] + X[:, 1] ** 2 > 1).astype(int)

dtrain = hessboost.DMatrix(X[:800], label=y[:800])
dvalid = hessboost.DMatrix(X[800:], label=y[800:])

booster = hessboost.train(
    {"objective": "binary:logistic", "max_depth": 4, "eta": 0.1, "eval_metric": "auc"},
    dtrain,
    num_boost_round=500,
    evals=[(dvalid, "valid")],
    early_stopping_rounds=20,
    verbose_eval=False,
)

predict takes a DMatrix or anything its constructor accepts. The default range is the iterations through best_iteration (pass iteration_range=(0, 0) for every iteration), except pred_leaf, which defaults to all of the model's trees, not just through best_iteration. The flag arguments are exclusive:

probabilities = booster.predict(X[800:])  # through best_iteration
margins = booster.predict(X[800:], output_margin=True)
shap = booster.predict(X[800:], pred_contribs=True)  # (rows, features + 1)
leaves = booster.predict(X[800:], pred_leaf=True)  # (rows, trees) int32, all trees
print(booster.best_iteration, booster.get_score(importance_type="gain"))

Input data

DMatrix takes numpy arrays of any numeric dtype and memory layout (a C-contiguous float32 array is used without a copy), pandas and polars DataFrames, polars LazyFrames, scipy sparse matrices, and anything numpy.asarray accepts. NaN, and a frame's null, is missing (or pass missing=); ranking data takes group= sizes or qid=, and survival:aft takes label_lower_bound=/label_upper_bound=. Booster.predict accepts the same inputs directly.

With a frame, the per-row metadata can name its columns instead of arriving as separate arrays: label, weight, base_margin, qid, label_lower_bound and label_upper_bound (label and base_margin also a list of names, for a label matrix or per-output margins). The named columns leave the features:

import polars as pl

dtrain = hessboost.DMatrix(
    pl.scan_parquet("train.parquet").filter(pl.col("split") == "train").drop("split"),
    label="price",
    weight="exposure",
)

A LazyFrame is collected once, by polars' default engine, which since polars 2.0 is the streaming engine (spilling to disk when the frame outgrows memory). Because that engine keeps no row order after a join or group_by, a LazyFrame takes labels and the other per-row metadata by column name only, so that they come out of the same collect as the features; an array alongside it (or group sizes) is refused. Predictions on a LazyFrame align with its collected rows, so to attach them to a frame, collect it yourself and predict on the DataFrame. A polars frame converts through a select of Float32 expressions that polars evaluates in parallel, in row blocks of 64 MiB of output; Decimal and all-null columns are numeric, and a column of another dtype (Datetime, String, polars 2.0's Extension for Arrow extension types the frame was read with) is refused with the conversion that would make it numeric. polars 1.x and 2.x are both supported (polars>=1.0).

Training controls

train supports XGBoost's everyday arguments: evals, evals_result, early_stopping_rounds, verbose_eval (printed live, every round or every n-th), xgb_model (continued training, or tree refresh with process_type="update"), a custom objective obj, a custom_metric, and callbacks. Ctrl-C stops training at the end of the current round and raises KeyboardInterrupt. hessboost.cv cross-validates over shuffled folds, explicit folds, or a scikit-learn splitter; on ranking data each fold must hold whole query groups (e.g. GroupKFold over the query ids). hessboost.train_with_budget(params, dtrain, budget) trains with one fitting budget in place of eta, tree limits, and a round count (PerpetualBooster's algorithm).

Subclass TrainingCallback to stop on your own condition; after_iteration sees the round and the evaluation history, and returning True stops training with the rounds so far:

class StopAtTarget(hessboost.TrainingCallback):
    def after_iteration(self, iteration, evals_log):
        return evals_log["valid"]["auc"][-1] > 0.99  # True stops training


hessboost.train(
    {"objective": "binary:logistic", "eval_metric": "auc", "max_depth": 4},
    dtrain,
    1000,
    evals=[(dvalid, "valid")],
    callbacks=[StopAtTarget()],
)

DataFrames and categorical features

pandas and polars column names become feature names, and categorical columns (pandas category; polars Enum and Categorical) become native categorical features. A category's code is its position in the column's categories: a pandas column's or an Enum's declared categories, or a polars Categorical's values, sorted (as pandas infers them). The booster remembers each column's categories and re-codes a frame passed to predict (and the other prediction and calibration methods) whose categories are ordered differently or include unseen values (which count as missing), whichever library the frame is from:

import pandas as pd

df = pd.DataFrame({"color": pd.Categorical(["red", "blue", "red"] * 100), "size": np.arange(300.0)})
booster = hessboost.train({}, hessboost.DMatrix(df, label=np.arange(300.0)), 20)
booster.predict(df)

A DMatrix is coded once, when it is built, so it must have the features of every model or matrix it meets: predict and the calibrators check it against each model reading it, and train checks every evals matrix against dtrain and against xgb_model, and dtrain against xgb_model (continued training or refresh). Feature names (where both have them; predict(validate_features=False) skips them), which features are categorical, and each categorical feature's categories, in order, must match; a mismatch raises HessboostError naming the eval set and the feature. Build eval frames with the training frame's categories (for example valid["color"].cat.set_categories(train["color"].cat.categories) in pandas; in polars, an Enum of the training categories, since a Categorical missing some of its values has other categories). Codes without recorded categories (numpy data with feature_types=["c", ...]) are taken to be the other side's codes; only which features are categorical is compared, and a model continued on them keeps the earlier model's categories. ConformalizedQuantile.calibrate likewise needs its two models to share their features, and re-codes frames to whichever model records categories. The scikit-learn estimators re-code every eval_set frame to the training frame's categories and, with xgb_model, the training frame to the earlier model's.

scikit-learn

The estimators take XGBoost's scikit-learn parameter names, plus callbacks. Parameters left at None keep hessboost's defaults, and params={...} passes any other training parameter; fit(..., verbose=True) (or a period) prints rounds live:

from hessboost.sklearn import HessboostClassifier

model = HessboostClassifier(
    n_estimators=300, max_depth=4, learning_rate=0.1, early_stopping_rounds=20
)
model.fit(X_train, y_train, eval_set=[(X_valid, y_valid)])
model.predict_proba(X_test)
model.feature_importances_

HessboostRegressor (multi-target with a 2-D y), HessboostClassifier (any class labels), HessboostRanker (group= or qid=), and HessboostDistributionRegressor (dist:* objectives; predict returns means, predict_distribution the distributions) work with pipelines, clone, grid search, and pickling, and pass scikit-learn's estimator checks (except three documented deviations). import hessboost does not import scikit-learn; hessboost.sklearn needs it.

Model files

The native binary format is compressed, checksummed, and lossless; files load in subsequent releases. A LightGBM model (lightgbm.Booster.save_model) predicts LightGBM's values for inputs with missing values as NaN and categorical features as non-negative codes; models with no exact equivalent raise ModelFormatError:

booster.save_model("model.ubj")  # a file XGBoost loads; .json for native JSON
loaded = hessboost.Booster("model.ubj")  # format detected from the content
early = booster[:10]  # the first 10 boosting iterations, as a Booster

Modern modeling

Conformal intervals

SplitConformal wraps one single-output model in f(x) ± Q, where Q is the calibration set's quantile of |y - f(x)| — finite-sample 1 - alpha coverage for exchangeable rows:

from hessboost.conformal import SplitConformal

cal = SplitConformal.calibrate(booster, X_cal, y_cal, alpha=0.1)
lower, upper = cal.predict_interval(X_test).T

ConformalizedQuantile instead adjusts a quantile band [q_lo(x), q_hi(x)] by its calibration quantile, so it also tightens a band that over-covers. The band comes from two single-quantile models (calibrate), two outputs of one multi-quantile model (calibrate_outputs), or a dist:* model's central quantiles (calibrate_distribution):

from hessboost.conformal import ConformalizedQuantile

band = hessboost.train(
    {"objective": "reg:quantileerror", "quantile_alpha": [0.05, 0.95]},
    hessboost.DMatrix(X_train, y_train),
    200,
)
cqr = ConformalizedQuantile.calibrate_outputs(band, X_cal, y_cal, alpha=0.1)
lower, upper = cqr.predict_interval(X_test).T  # >= 90% coverage, finite-sample

Distributional boosting

A dist:* objective fits a predictive distribution per row instead of a point. predict_distribution returns a Distributions object whose summaries are vectorized over rows:

dist = hessboost.train({"objective": "dist:normal"}, hessboost.DMatrix(X_train, y_train), 300)
d = dist.predict_distribution(X_test)
means, stds = d.mean(), d.std()
lower, upper = d.interval(0.9).T
ll, score = d.log_prob(y_test), d.crps(y_test)

Virtual ensembles

A model trained with posterior_sampling (SGLB: langevin noise plus model_shrink_rate) carries its own posterior ensemble: count members rebuilt exactly from its model shrinkage, member-major ((count, rows) for single-output models), with each member's iteration count alongside:

sglb = hessboost.train({"posterior_sampling": True}, hessboost.DMatrix(X_train, y_train), 1000)
members, iterations = sglb.predict_virtual_ensembles(X_test, 10)
u = sglb.predict_uncertainty(X_test, 10)  # u.mean, u.knowledge, u.data, u.total

predict_uncertainty decomposes the ensemble à la CatBoost: knowledge (epistemic) uncertainty rises off the training data. Plain regression has only mean and knowledge (data/total are None); dist:* and classification models get the full decomposition.

Online updates

OnlineModel keeps a trained model with its training data and updates both in place as rows arrive or must be forgotten. Exact() retrains, so every update equals hessboost.train on the updated data bit for bit; Approximate(tolerance) (default, 0.1) keeps splits that still rank near the top and is faster for small changes. A refused, stopped, or interrupted (Ctrl-C) update changes nothing:

from hessboost.online import Approximate, OnlineModel

online = OnlineModel.train(
    {"tree_method": "hist", "max_depth": 6},
    hessboost.DMatrix(X_train, y_train),
    100,
    Approximate(0.1),
)
report = online.update(hessboost.DMatrix(X_new, y_new), deletions=[3, 17])
online.model.predict(X_test)  # online.data: the updated training rows

The report counts kept nodes, regrown subtrees, and refreshed rows (UpdateReport); OnlineModel.from_model resumes from a saved Booster and its training data. Updates need hist depth-wise trees without sampling or constraints and unweighted data.

Explainable boosting machines

{"booster": "ebm"} trains a cyclic GA2M with outer bags, FAST pairs, and per-bag early stopping. Every term's piecewise-constant shape function is readable from the model (NumericAxis edges or CategoricalAxis codes, plus a missing cell per axis); intercept plus shapes is the margin:

from hessboost import ebm

model = hessboost.train({"booster": "ebm"}, hessboost.DMatrix(X_train, y_train), 50)
shapes = ebm.shape_functions(model)
shapes.intercept, shapes.terms[0].values

A Boulevard EBM ({"booster": "ebm", "ebm_boulevard": True}) additionally supports confidence bands on its shapes via hessboost.inference.EbmInference.term_bands.

Boulevard inference

Boulevard boosting ({"booster": "boulevard"}) samples trees with dropout so the ensemble converges to a kernel ridge posterior, whose leaf kernel gives asymptotic confidence, prediction, and reproduction intervals for f(x). Refit the leaves on independent rows first (honest_refit), fit the kernel over those rows, then query any rows:

from hessboost.inference import BoulevardInference, honest_refit

trained = hessboost.train(
    {"booster": "boulevard", "eta": 0.8, "boulevard_dropout": 0.5, "subsample": 0.8},
    hessboost.DMatrix(X_struct, y_struct),
    200,
)
model = honest_refit(trained, X_values, y_values)  # leaves from independent rows
inference = BoulevardInference.fit(model, X_values, y_values, holdout=X_cal, holdout_label=y_cal)
lower, upper = inference.confidence_intervals(X_test, alpha=0.05).T  # for f(x)

The intervals are conditional on the tree structures: nominal for low-dimensional smooth signals after an honest refit, under-covering elsewhere (see the crate's inference docs). Booster.boulevard records how the model was trained.

Diffusion models

hessboost.diffusion fits nonparametric p(y | x) for scalar or vector labels (multimodal, skewed, heavy-tailed) by conditional diffusion or flow matching with GBDT score models, after Treeffuser and DiffGBM. sample returns (rows, n_samples, outputs) draws, deterministic per seed and step count (n_steps= overrides the model's); mean, quantiles, and crps summarize them:

from hessboost.diffusion import DiffusionModel, DiffusionParams, crps, quantiles

flow = DiffusionModel.fit(DiffusionParams.flow_matching(), X_train, y_train)
draws = flow.sample(X_test, 200, seed=0)  # (rows, 200, outputs) float32
bands = quantiles(draws, [0.05, 0.5, 0.95])  # (rows, 3, outputs)
scores = crps(draws, y_test)  # (rows, outputs)

DiffusionParams is a frozen dataclass tree with presets default(), treeffuser(), and flow_matching(); its training mappings are XGBoost parameters, as train reads them. Models save with to_bytes(format="binary") / save(path, format="binary") ("binary" or "json") and load with from_bytes(data, format="auto") / load(path, format="auto"), or pickle (which also keeps feature names and categories). fit releases the GIL but cannot be interrupted: Ctrl-C takes effect once it returns.

Synthetic tabular data

hessboost.diffusion.forest fits ForestFlow / ForestDiffusion models of whole rows (optionally per class of a label) with per-noise-level GBDTs. sample draws synthetic rows as a ForestSamples (values, plus labels for a class-conditional model); sample_for_labels draws one row per given label:

from hessboost.diffusion.forest import ForestModel, ForestParams

forest = ForestModel.fit(ForestParams.forest_diffusion(), X_with_nans)
synthetic = forest.sample(1000, seed=0)  # .values (1000, columns), .labels None

The diffusion variant also imputes missing values, keeping the observed entries (flow models refuse). Values are in the data's own coding:

filled = forest.impute(X_with_nans, n_imputations=5)  # (5, rows, columns)

ForestParams has presets forest_flow() (the defaults) and forest_diffusion(); its training mappings are XGBoost parameters, and models save and load like DiffusionModels.

GPU training and prediction

Two GPU backends reproduce the CPU's results bit for bit: native Metal ("metal", macOS) and wgpu ("wgpu": Vulkan on Linux and Windows, Metal on macOS, DirectX 12 on Windows). Booster.to_gpu() lays a model out for batch prediction (on Metal on macOS and on wgpu elsewhere; to_gpu("wgpu") asks for wgpu): the forest uploads once, and each call predicts bit-identically to Booster.predict (values or raw margins). GpuModel.available() says whether the device can predict here:

from hessboost import GpuModel

if GpuModel.available():
    gpu = booster.to_gpu()
    probabilities = gpu.predict(X_test)

Training with device="metal" or device="wgpu" builds the larger nodes' histograms on the GPU and gives the CPU's model bit for bit:

booster = hessboost.train({"device": "wgpu", "max_depth": 6}, dtrain, 100)

Metal prediction is faster than the CPU from roughly a few thousand row-trees upward. wgpu needs an adapter with 64-bit shader integers: desktop Vulkan drivers, Apple GPUs, or DirectX 12 with dxcompiler.dll where Windows finds DLLs. It uses a software adapter such as Mesa's lavapipe (correct, but slower than the CPU) only when there is no other; GpuModel.device_name("wgpu") names the adapter it picked, and the WGPU_ADAPTER_NAME environment variable picks one by name.

Compact models

Booster.to_compact() packs the trees default prediction uses into hessboost's bit-packed HBTD format (Boosted Trees on a Diet), which predicts bit-identical values and margins in a fraction of the size; training with toad_penalty_feature/toad_penalty_threshold shrinks it further. Booster.size_report() compares the two formats:

from hessboost import CompactModel

compact = booster.to_compact()
compact.save_model("model.hbtd")
predictions = CompactModel("model.hbtd").predict(X_test)
print(booster.size_report().compression_ratio)

Validation folds

hessboost.folds builds (train_rows, test_rows) splits for cv or custom loops: shuffled k_fold (what cv uses by default), forward_chaining expanding windows with a purged gap, and purged_forward (timestamped rows, purged by each row's own label window, for overlapping or irregular horizons):

from hessboost import folds

splits = folds.forward_chaining(dtrain.num_row(), 4, gap=24)
result = hessboost.cv({"max_depth": 4}, dtrain, 100, folds=splits)

Ordered target statistics

hessboost.target_stats.OrderedTargetEncoder replaces categorical columns with CatBoost-style ordered target means: a training row's encoding never sees its own label. label= supplies the target for a multi-target matrix or a class's 0/1 indicator. In cv, target_stats= fits the encoder on each fold's training rows only, so no held-out label reaches an encoding:

from hessboost.target_stats import OrderedTargetEncoder

encoder = OrderedTargetEncoder(seed=7)
dtrain_encoded, stats = encoder.fit_transform(dtrain, ["city"])
booster = hessboost.train({"max_depth": 4}, dtrain_encoded, 100)
predictions = booster.predict(stats.transform(X_test))
result = hessboost.cv({"max_depth": 4}, dtrain, 100, target_stats=["city"], target_encoder=encoder)

Extra training options

Every hessboost training option (path_smooth, extra_trees, linear_tree, grow_policy="symmetric", use_quantized_grad, pos_bagging_fraction, neg_bagging_fraction, bagging_by_query, the dist:* objectives and their dist_gradient, rank:xendcg, ...) is a params key. XE-NDCG's keyed per-round random stream differs from LightGBM's rank_xendcg stream.

Differences from XGBoost's Python package

• Errors: hessboost.HessboostError (a ValueError) for refused inputs, with subclasses InvalidDataError (data content: labels outside the objective's domain, negative weights, bad groups or bounds; the message names the input and any eval set), IncompatibleModelError (a model the parameters or data do not match: continued training, refresh, slicing) and ModelFormatError (model files); invalid or conflicting parameters raise HessboostError itself. TypeError for wrong types. Unsupported parameters are refused, including verbosity; missing belongs to DMatrix.
• TrainingCallback.after_iteration(iteration, evals_log) -> bool sees the round and the evaluation history, not the model (XGBoost's also gets the booster and has before_training/after_training hooks); returning True stops training with the rounds so far. hessboost.cv has no callbacks and finishes its folds before Ctrl-C takes effect.
• custom_metric(predictions, labels, weights) returns a float and is named by the function's __name__ (XGBoost passes a DMatrix and returns (name, value)). obj(margins, dtrain) matches XGBoost; the model then predicts margins from a zero intercept (or base_score). obj replaces objective, which params must then not set, and num_class is the custom objective's output count (XGBoost's custom-softmax convention; default: one per label column). Unlike XGBoost, which accepts both, hessboost refuses objective alongside obj: drop objective from ported params; the callback needs no change.
• cv returns a dict of numpy arrays (test-<metric>-mean/-std) with held-out metrics only; there is no stratified or as_pandas.
• predict defaults to the iterations through best_iteration (XGBoost's scikit-learn behavior); pass iteration_range=(0, 0) for all. SHAP and leaf ranges start at iteration 0; pred_leaf defaults to every iteration instead, and returns int32.
• GPUs: device="metal" (macOS) or device="wgpu" trains on one instead of device="cuda", and Booster.to_gpu() lays the model out for GPU batch prediction (GpuModel.predict, bit-identical to Booster.predict) instead of predicting on the training device.
• Model files do not store feature names or categories (pickles do).
• Not available: DMatrix from files or QuantileDMatrix, inplace_predict (predict takes arrays directly), Booster.get_dump/trees_to_dataframe /dump_model, attributes (set_attr), plotting, distributed (Dask/Spark) and CUDA training, approx_contribs, and strict_shape.

Development

From python/ in the repository, with uv:

uv sync --locked              # build the extension and install dev tools
uv run --locked pytest
uv run --locked pyright --verifytypes hessboost --ignoreexternal

Lint, format, and type-check all of the repository's Python from its root (configuration: ruff.toml, ty.toml):

uv run --project python --locked ruff check
uv run --project python --locked ruff format --check
uv run --project python --locked ty check

License

Apache-2.0.