CartoBoost

CartoBoost is a Python toolkit for regression, classification, grouped ranking, and forecasting problems where place, time, route structure, or repeated identifiers matter. It is aimed at scientific and applied modeling workflows such as mobility, logistics, demand forecasting, route ranking, and other structured prediction problems.

Choose CartoBoost when a standard tabular booster is a serious baseline, but the study also needs model structure for:

cyclic time such as hour-of-day, weekday, or seasonal demand;
2D spatial patterns such as corridors, neighborhoods, hotspots, and service boundaries;
list-valued memberships such as zones, route cells, H3 cells, or S2 cells;
directed movement such as source to target flow;
high-cardinality place or route ids that may benefit from learned embeddings;
leakage-aware validation and reproducible benchmark comparisons.

CartoBoost keeps a familiar estimator workflow, but the main goal is not to hide the modeling choices. It helps you state them clearly, test them against simpler baselines, and preserve the fitted artifacts that produced the result.

When It Fits

CartoBoost is most useful when the scientific question is about structured temporal-spatial signal:

Does hour-of-day interact with location context when estimating duration?
Do zone memberships change fare estimates after distance and calendar features are included?
Does preserving route direction change source-target predictions compared with unordered identifiers?
How do rolling-origin demand forecasts compare with naive, seasonal naive, theta, ETS, or supervised lag baselines on the same split?
Do spatial splitters recover zone or corridor signal that an axis-only model approximates poorly?

It is less useful when place/time structure is irrelevant, the dataset is too small to support structured validation, or a simple interpretable model already answers the study question.

Modeling Primitives

CartoBoost supports:

L2 and quantile regression objectives.
Constant and linear residual leaves.
Axis, histogram-axis, diagonal 2D, Gaussian/radial 2D, periodic, sparse-set, and fuzzy split behavior.
Dense numeric arrays plus list-valued sparse-set features.
Feature schemas for numeric, periodic, sparse-set, and model-contract validation.
JSON model artifacts and portable weights artifacts.
Optional SHAP explanations, Optuna tuning, Polars input support, and ONNX export for the supported dense axis-tree subset.
Standalone neural embedding regressors and optional neural feature-generation workflows for high-cardinality IDs.
node2vec, GraphSAGE, heterogeneous GraphSAGE, and typed-schema HinSAGE graph regressors, link predictors, and graph feature encoders.
Forecasting APIs for geographic and temporal single-series or panel demand, including rolling-origin backtests, naive/seasonal naive/theta/optimized-theta/ETS/AutoARIMA models, supervised CartoBoost lag forecasting, weighted ensembles, CLI runs, and portable forecast artifacts.
General utilities outside the forecasting API, including single-series forecast helpers, local-level/local-linear Kalman filters, Croston/SBA/TSB intermittent demand, and ordinary kriging.

Install

Install the released package from PyPI:

uv add cartoboost

CartoBoost 0.3 keeps the root import surface intentionally small. Read the 0.3 migration guide when upgrading imports from 0.2; forecasting and validation use named modules, while advanced model families are explicitly under cartoboost.supported.

CartoBoost does not publish package extras. The base install contains only the runtime dependencies needed by the core Python API. Install each third-party integration directly when your workflow needs it:

uv add shap          # SHAP support
uv add h3            # H3 point and decoded-route encoder
uv add s2sphere      # S2 point and decoded-route encoder
uv add duckdb        # DuckDB relation inputs
uv add optuna        # Optuna tuning
uv add polars        # Polars inputs
uv add onnx          # ONNX export subset

Verify the install:

python -c "import cartoboost; print(cartoboost.__version__)"
python examples/quickstart.py

The five-minute NumPy quickstart creates a complete place/time dataset, performs an out-of-time split, compares axis-only and structured models, and checks save/load prediction parity. Install pandas to enable ForecastFrame.from_pandas and other pandas adapters:

uv add pandas

Structured Regression Workflow

Start with the scientific design:

Define the target, such as transformed duration, fare amount, or demand.
Hold out data in a way that matches deployment, usually out-of-time for tabular rows or rolling-origin for demand forecasts.
Compare against serious baselines on the same rows, such as LightGBM or XGBoost for tabular regression.
Add CartoBoost structure only when it maps to a real place, time, or relationship hypothesis.

Then fit the estimator:

from cartoboost import CartoBoostRegressor

model = CartoBoostRegressor(
    n_estimators=200,
    learning_rate=0.04,
    max_depth=5,
    min_samples_leaf=30,
    split_policy="structured",
)

model.fit(X_train, y_train)
predictions = model.predict(X_validation)

For structured mobility or operations data, dense columns might include trip distance, hour, weekday, coordinates, route context, or category flags. Add sparse-set columns when each row has route-cell, zone, or similar memberships. Decoded OSRM or Valhalla routes can be converted into H3/S2 sparse rows with build_h3_route_sparse_sets or build_s2_route_sparse_sets.

schema = {
    "dense": [
        {"name": "trip_distance", "kind": "numeric"},
        {"name": "pickup_hour", "kind": "periodic", "period": 24},
        {"name": "pickup_x", "kind": "numeric"},
        {"name": "pickup_y", "kind": "numeric"},
    ],
    "sparse_sets": [
        {"name": "zone_ids", "kind": "sparse_set"},
    ],
}

model = CartoBoostRegressor(
    n_estimators=200,
    learning_rate=0.04,
    max_depth=5,
    min_samples_leaf=30,
    split_policy="structured",
)

model.fit(
    X_train_dense,
    y_train,
    sparse_sets={"zone_ids": zone_ids_train},
    feature_schema=schema,
)

Why these choices can matter:

periodic:24 treats midnight-adjacent pickup hours as neighbors.
diagonal_2d can represent oblique spatial boundaries more directly than axis-only trees.
gaussian_2d can isolate radial neighborhoods around hotspots or airports.
sparse_set splits on list-valued route or cell membership without a wide one-hot matrix.
fuzzy routing can reduce hard jumps near spatial or temporal boundaries.

Forecast Regular Series

Use forecasting APIs when the target is future demand, counts, or other regular series.

from cartoboost.forecasting import ForecastFrame, ThetaForecaster

frame = ForecastFrame.from_pandas(
    lane_demand,
    timestamp_col="timestamp",
    target_col="demand",
    series_id_col="series_id",
    freq="D",
)

model = ThetaForecaster(season_length=7)
model.fit(frame)
forecast = model.predict(horizon=14)

Forecast outputs use deterministic columns: series_id, timestamp, horizon, model, and mean. Use rolling-origin backtests before making quality claims, and compare against naive, seasonal, local, or external forecasting baselines on the same series and cutoff dates.

Graph And Learned-ID Structure

Use graph models when relationships are part of the observation process: directed flows, zone hierarchies, route networks, or metapaths. Direction is explicit, so A -> B and B -> A can be different facts, features, and embeddings.

Use neural embedding models when high-cardinality ids, such as locations or route ids, carry stable residual signal. Treat these as hypotheses to validate, not automatic upgrades.

from cartoboost import NeuralEmbeddingRegressor

model = NeuralEmbeddingRegressor(
    dim=16,
    base_model_kwargs={"n_estimators": 80, "split_policy": "axis_only"},
    final_model_kwargs={"n_estimators": 120, "split_policy": "structured"},
)

model.fit(X_train, y_train, ids=location_ids_train)
predictions = model.predict(X_validation, ids=location_ids_validation)

Benchmarks And Claims

Benchmark reports should identify the dataset, target, feature set, split design, comparison models, metrics, and meaning of the result. In this repo, benchmarks track structured regression and forecasting tasks over real data families.

Quality claims should come from real runs with fixed comparable settings. Record RMSE, MAE, R2, training time, prediction time, model settings, sample size, task names, and split names.

Do not publish a benchmark claim unless the CartoBoost row satisfies the primary metric threshold under the same split, comparable feature access, comparable tuning budget, and complete baseline set. If a required baseline fails or interval coverage is not actually computed, the benchmark is incomplete for that claim.

Save, Load, And Explain

model.save("duration.cartoboost.json")
loaded = CartoBoostRegressor.load("duration.cartoboost.json")

# LightGBM-compatible native TreeSHAP: feature columns, then base value.
contributions = loaded.predict(X_validation_dense, pred_contrib=True)
reconstructed = contributions.sum(axis=1)

# A standard shap.Explanation, still without serving-time background data.
explanation = loaded.explain_shap(X_validation_dense)

# Explicit background data selects the existing background-based explanation.
explanation = loaded.explain_shap(
    X_validation_dense,
    background=X_train_dense,
    sparse_sets={"zone_ids": zone_ids_validation},
    background_sparse_sets={"zone_ids": zone_ids_train},
)

For a fast exact decomposition that scales with the number of rows and fitted trees, rather than SHAP permutation samples, request tree-component weights:

explainer = loaded.make_shap_explainer(
    X_train_dense,
    decomposition="weights",
)
explanation = explainer(X_validation_dense)

This attributes each prediction to its initial value and each fitted tree. It does not attribute original input columns; use the default feature decomposition for that. See SHAP support for baseline semantics, sparse IDs, performance guidance, and the TreeExplainer compatibility limit.

Model artifacts are versioned JSON and include optional metadata, feature schema, and training configuration fields. Graph and neural standalone artifacts are complete model artifacts. Feature-generation artifacts should be persisted with whichever downstream model consumes their generated columns.

Source-checkout CLI

The Rust CLI is a source-checkout tool while the Python wheel remains focused on the estimator and forecasting APIs. Run it from this repository with cargo run -p cartoboost-cli -- --help; it is not installed by uv add cartoboost.

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