CTBoost

CTBoost is a gradient boosting library built around Conditional Inference Trees, with a native C++17 core, pybind11 bindings, optional CUDA support, and optional scikit-learn compatible estimators.

CTBoost is focused on making conditional-inference-tree boosting practical for real datasets and production workflows. Development is centered on data ingestion, preprocessing, metrics, orchestration, serialization, and deployment around the existing learner.

What CTBoost Supports

  • Regression, classification, grouped ranking, and survival training
  • Low-level ctboost.train(...) plus CTBoostClassifier, CTBoostRegressor, and CTBoostRanker
  • NumPy, pandas, and SciPy sparse input without dense conversion
  • Native categorical, text, and embedding preprocessing through FeaturePipeline
  • Row weights, class imbalance controls, missing-value handling, quantization controls, and generic regularization or growth settings
  • Validation watchlists, multiple eval metrics, callable eval metrics, early stopping, per-iteration callbacks, and learning-rate schedules or callback-driven learning-rate changes
  • Stable JSON and pickle persistence, warm start via init_model, snapshot-path resume with config and schema validation, staged prediction, and standalone Python or JSON predictor export
  • Richer Pool schema metadata via feature_names, column_roles, feature_metadata, and categorical_schema
  • Ranking metadata in Pool: group_id, group_weight, subgroup_id, pairs, pairs_weight, and baseline
  • External-memory pool staging plus optional TCP-based distributed training
  • Feature importance, leaf indices, and path-based prediction contributions

See BACKLOG.md for the remaining generic feature roadmap and deferred items.

Demos

The repository ships local Kaggle-oriented demos in demo/ instead of remote kernel automation:

  • demo/kaggle_titanic.py for binary classification on Titanic
  • demo/kaggle_house_prices.py for regression on House Prices

See demo/README.md for expected data layouts and run commands.

Installation

Install from source:

pip install .

Install development dependencies:

pip install -e .[dev]

Install the optional scikit-learn wrappers and ctboost.cv(...) support:

pip install -e .[sklearn]

pip install ctboost uses CPU wheels when a matching wheel exists on PyPI. Tagged GitHub releases also publish CUDA wheel assets for supported Linux and Windows targets.

To force a CPU-only native source build:

CMAKE_ARGS="-DCTBOOST_ENABLE_CUDA=OFF" pip install .

On PowerShell:

$env:CMAKE_ARGS="-DCTBOOST_ENABLE_CUDA=OFF"
pip install .

Quick Start

scikit-learn API

import pandas as pd
from sklearn.datasets import make_classification

from ctboost import CTBoostClassifier

X, y = make_classification(
    n_samples=256,
    n_features=8,
    n_informative=5,
    n_redundant=0,
    random_state=13,
)

frame = pd.DataFrame(X.astype("float32"), columns=[f"f{i}" for i in range(X.shape[1])])
frame["segment"] = pd.Categorical(["a" if i % 2 == 0 else "b" for i in range(len(frame))])

model = CTBoostClassifier(
    iterations=256,
    learning_rate=0.1,
    max_depth=3,
    alpha=1.0,
    lambda_l2=1.0,
    eval_metric="AUC",
)

model.fit(
    frame.iloc[:200],
    y[:200].astype("float32"),
    eval_set=[(frame.iloc[200:], y[200:].astype("float32"))],
    early_stopping_rounds=20,
)

proba = model.predict_proba(frame)
pred = model.predict(frame)
importance = model.feature_importances_

The estimators also accept familiar XGBoost/CatBoost parameter names, so existing model-selection code can usually switch libraries without a parameter rewrite:

model = CTBoostClassifier(
    n_estimators=256,
    depth=3,
    reg_lambda=1.0,       # l2_leaf_reg is accepted too
    random_state=13,
)
model.fit(frame.iloc[:200], y[:200])

leaf_indices = model.apply(frame)
history = model.get_evals_result()
best_iteration = model.get_best_iteration()
native_booster = model.get_booster()

is_fitted(), get_best_score(), evals_result(), and calc_leaf_indexes() are available as convenience aliases. The low-level train(...) API likewise accepts n_estimators/num_trees, eta, depth, reg_lambda/l2_leaf_reg, random_state/seed, and max_bin. Conflicting aliases and unknown parameter names fail early with a useful error instead of being silently ignored.

Low-Level API

import numpy as np
import ctboost

X = np.array([[0.0, 1.0], [1.0, 0.0], [0.5, 0.5]], dtype=np.float32)
y = np.array([0.0, 1.0, 0.5], dtype=np.float32)

pool = ctboost.Pool(X, y)
booster = ctboost.train(
    pool,
    {
        "objective": "RMSE",
        "learning_rate": 0.1,
        "max_depth": 3,
        "alpha": 1.0,
        "lambda_l2": 1.0,
        "eval_metric": "MAE",
    },
    num_boost_round=32,
)

predictions = booster.predict(pool)

For inference-only data, labels are optional: prediction_pool = ctboost.Pool(X_new).

Learning-Rate Schedules And Callbacks

schedule = [0.2, 0.2, 0.1, 0.1, 0.05, 0.05]

booster = ctboost.train(
    pool,
    {
        "objective": "RMSE",
        "learning_rate": schedule[0],
        "max_depth": 3,
        "alpha": 1.0,
        "lambda_l2": 1.0,
    },
    num_boost_round=len(schedule),
    learning_rate_schedule=schedule,
    callbacks=[ctboost.log_evaluation(2)],
)

print(booster.learning_rate_history)

Callbacks receive env.learning_rate and may call env.model.set_learning_rate(...) to change the step size used for later rounds. The scikit-learn estimators accept the same learning_rate_schedule= keyword on fit(...).

Categorical, Text, And Embedding Inputs

import numpy as np
import pandas as pd

from ctboost import CTBoostRegressor

frame = pd.DataFrame(
    {
        "city": ["berlin", "paris", "berlin", "rome"],
        "headline": ["red fox", "blue fox", "red hare", "green fox"],
        "embedding": [
            np.array([0.1, 0.4, 0.2], dtype=np.float32),
            np.array([0.7, 0.1, 0.3], dtype=np.float32),
            np.array([0.2, 0.5, 0.6], dtype=np.float32),
            np.array([0.9, 0.2, 0.4], dtype=np.float32),
        ],
        "value": [1.0, 2.0, 1.5, 3.0],
    }
)
y = np.array([0.5, 1.2, 0.7, 1.6], dtype=np.float32)

model = CTBoostRegressor(
    iterations=64,
    learning_rate=0.1,
    max_depth=3,
    ordered_ctr=True,
    cat_features=["city"],
    text_features=["headline"],
    embedding_features=["embedding"],
)
model.fit(frame, y)

Persistence, Resume, And Export

import ctboost

metric = ctboost.make_eval_metric(
    lambda predictions, label, **_: float(((predictions >= 0.0) == label).mean()),
    name="SignedAccuracy",
    higher_is_better=True,
    allow_early_stopping=True,
)

booster = ctboost.train(
    pool,
    {
        "objective": "Logloss",
        "learning_rate": 0.1,
        "max_depth": 3,
        "alpha": 1.0,
        "lambda_l2": 1.0,
        "eval_metric": [metric, "AUC"],
    },
    num_boost_round=64,
    eval_set=[(X_valid, y_valid)],
    snapshot_path="run_snapshot.ctb",
)

resumed = ctboost.train(
    pool,
    {
        "objective": "Logloss",
        "learning_rate": 0.1,
        "max_depth": 3,
        "alpha": 1.0,
        "lambda_l2": 1.0,
    },
    num_boost_round=128,
    snapshot_path="run_snapshot.ctb",
    resume_from_snapshot=True,
)

booster.export_model("predictor.json", export_format="json_predictor")
predictor = ctboost.load_exported_predictor("predictor.json")
exported_predictions = predictor.predict(X_numeric)

resume_from_snapshot=True validates the saved training configuration and data schema before loading the checkpoint. It remains a warm-start-based convenience flow rather than a blanket exact-equivalence guarantee for every training path. For per-iteration checkpoint emission and logging hooks, use callbacks=[ctboost.log_evaluation(...), ctboost.checkpoint_callback(...)].

Metadata

pool = ctboost.Pool(
    X,
    y,
    feature_names=["score", "ratio", "city_code"],
    column_roles=["numeric", "numeric", "categorical"],
    feature_metadata={"score": {"description": "normalized score"}},
    categorical_schema={"city_code": {"categories": ["berlin", "paris", "rome"]}},
)

booster = ctboost.train(pool, {"objective": "RMSE"}, num_boost_round=16)
print(booster.data_schema)

The scikit-learn estimators expose the same persisted schema through data_schema_.

Build And Test

Run the Python tests:

pytest tests

Build an sdist:

python -m build --sdist

Configure and build the native extension directly with CMake:

python -m pip install pybind11 numpy pandas scikit-learn pytest
cmake -S . -B build -DCTBOOST_ENABLE_CUDA=OFF -Dpybind11_DIR="$(python -m pybind11 --cmakedir)"
cmake --build build --config Release --parallel

Project Layout

ctboost/      Python API surface
demo/         local example workflows, including Kaggle demos
include/      public C++ headers
src/core/     core training, data, objectives, trees, statistics
src/bindings/ pybind11 extension bindings
cuda/         optional CUDA backend
tests/        Python test suite

Acknowledgments

CTBoost draws methodological inspiration from the original conditional inference tree work by Torsten Hothorn, Kurt Hornik, and Achim Zeileis, along with the subsequent partykit work on CRAN by Torsten Hothorn, Achim Zeileis, and Heidi Seibold. If you are using CTBoost in research or want the statistical background behind the learner, start with these references:

  • Hothorn, T., Hornik, K., and Zeileis, A. (2006). Unbiased Recursive Partitioning: A Conditional Inference Framework.
  • Hothorn, T. and Zeileis, A. (2015). partykit: A Modular Toolkit for Recursive Partytioning in R.

License

Apache 2.0. See LICENSE.

Project links

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Newer release available (0.1.56)

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Credits

Author: CTBoost Contributors

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