BlackRubby Regressor

BlackRubby is a numeric regression estimator that fuses two complementary learners:

• Rubby — a symbolic search that evolves closed-form candidate expressions (e.g. sqrt(x0^3 / (x1 + x2))) on a 3×3×3 cube-walk genome.
• Black — a random-Fourier-feature ridge regression that fits a smooth kernel model over the inputs.

The final model is a single ridge over [Fourier features | standardized inputs | symbolic columns], where the bandwidth, ridge strength, and whether to trust the symbolic columns are all chosen by exact leave-one-out cross-validation. It handles multiple numeric input features and one numeric target, with no Python-side dependencies beyond NumPy.

import numpy as np
from blackrubby import BlackRubbyRegressor

rng = np.random.default_rng(7)
X = rng.uniform(-1, 1, size=(512, 3))
y = np.sin(X[:, 0] * X[:, 1]) + 0.2 * X[:, 2]

model = BlackRubbyRegressor(expansion_dim=256, random_state=7)
model.fit(X[:400], y[:400])
print("held-out R2:", model.score(X[400:], y[400:]))
print("champion:", model.champion_formula_)

Install

pip install blackrubby

Pre-built wheels are published for CPython 3.10–3.15 on Linux (x86_64), Windows (x64), and macOS (Intel and Apple Silicon). On a supported platform this installs in seconds with no compiler.

Building from source requires only a C++17 compiler and CMake. Eigen 3.4.0 is vendored under vendor/eigen, so no network access is needed; set EIGEN3_INCLUDE_DIR to use a different Eigen installation.

How it works

Given training pairs (x_i, y_i) with x_i ∈ R^d, BlackRubby fits a linear model in a lifted feature space:

ŷ(x) = wᵀ Φ(x) + b

The feature map Φ(x) has three blocks:

1. Random Fourier features. D cos/sin pairs approximating a stationary kernel: Φ_fourier(x) = (1/√D) [cos(W x̂ / γ), sin(W x̂ / γ)], where x̂ is the standardized input, W ∈ R^{D×d} has standard-normal entries, and γ is the bandwidth. This is the classic Rahimi–Recht approximation — as D → ∞, the inner product Φ(x)ᵀΦ(x') converges to the Gaussian kernel k(x, x') = exp(-‖x̂ - x̂'‖² / 2γ²).

2. Standardized inputs. The raw (x - μ) / σ block, which lets the model represent exactly-linear components without spending Fourier capacity on them.

3. Symbolic columns (Rubby). Up to sym_top_k expressions found by the search, each standardized to zero mean and unit variance. These give the ridge a direct handle on non-smooth or strongly multiplicative structure (ratios, square roots, powers) that Fourier features approximate only slowly.

The ridge weights minimize the regularized squared error:

w* = argmin_w  Σ_i (y_i - wᵀΦ(x_i) - b)² + α ‖w‖²

Hyperparameters by exact leave-one-out CV

bandwidth γ, ridge strength α, and the symbolic on/off decision are chosen on a select_rows-row subset using the exact leave-one-out formula for ridge regression. One eigendecomposition of the centered Gram matrix gives the LOO residual for every α in the grid simultaneously:

e_i^LOO = (y_i - ŷ_i) / (1 - h_i),   h = U diag(λ_j / (λ_j + nα)) Uᵀ

so the model only leans on the symbolic columns when they demonstrably reduce out-of-sample error. A 2-fold holdout at full feature width re-checks the symbolic decision, because the reduced-width selection screen can mis-rank those columns.

The Rubby symbolic search

Candidate expressions are encoded as a walk on a 3×3×3 cube whose 27 cells each hold one operation or operand (const, x_k, +, -, *, /, log, exp, sqrt, abs, x², x³). Starting from the corner cell, the genome's path (sym_turns moves, each ±x/±y/±z with wraparound) visits cells in order, and the visited nodes form a stack program evaluated column-wise over the data. Each candidate gets a least-squares scale and offset (a·f(x) + b), so the search only has to find the right shape.

A generation consists of tournament selection (with a path-distance diversity check), uniform crossover over cube cells, point mutation, occasional MCTS refinement of the walk itself (UCT over path prefixes), and a periodic Levenberg–Marquardt polish of the constants on the active path. Fitness is normalized mean squared error, NMSE = MSE / Var(y). The best distinct expressions form a hall of fame that feeds the ridge.

The search runs on a strided sym_rows-row subsample for sym_generations generations and is fully seeded by random_state.

Final solve

The full model is solved in primal form over up to solve_rows rows (auto-capped at max(50000, 20 × n_features)), accumulating the centered Gram matrix G = ΦᵀΦ in batches and applying a Cholesky solve with an LDLT fallback.

Reproducibility

With the default sym_time_limit=0, the symbolic search is generation-bounded and fully deterministic: the same random_state, data, and parameters produce a bit-identical model on every run, regardless of thread count or machine load. Setting sym_time_limit > 0 switches to a wall-clock cap, which can stop at different generations on different runs and therefore trades reproducibility for a hard time budget.

API

BlackRubbyRegressor(
    expansion_dim=1024,    # Fourier cos/sin pairs (D)
    alpha=0.0,             # ridge strength; 0 = auto by LOO-CV
    bandwidth=0.0,         # Fourier bandwidth γ; 0 = auto by LOO-CV
    random_state=42,       # seed for projections and symbolic search
    use_symbolic=True,     # run the Rubby search
    sym_population=128,    # GA population size
    sym_generations=100,   # GA generations (when time limit is 0)
    sym_rows=1024,         # rows used by the symbolic search
    sym_top_k=6,           # symbolic columns offered to the ridge
    sym_time_limit=0.0,    # seconds; 0 = generation-bounded (reproducible)
    select_rows=2048,      # rows used for hyperparameter selection
    select_dim=128,        # Fourier pairs used during selection
    batch_size=2048,       # rows per block in feature assembly
    solve_rows=-1,         # rows in the final solve; -1 = auto cap, 0 = all
    max_frequency=54.598,  # largest 1D frequency (standardized units); 1D inputs only
)

Methods: fit(X, y), predict(X), score(X, y) (R²), get_params(), set_params(**params).

After fitting, the following attributes are available:

Usage notes

• X must be a finite 1D or 2D numeric array; y must be one finite numeric target per row. Multi-output regression, categorical encoding, and missing-value imputation are not built in — encode and impute before calling fit.
• Fit on training data only, tune on validation data, and report on an untouched test set. The internal LOO-CV protects hyperparameter selection, but it is not a substitute for a held-out evaluation.
• For 1D inputs the Fourier projection is a deterministic log-spaced frequency grid up to max_frequency, which suits signal-like data; for d ≥ 2 the projection is a seeded random Gaussian matrix.
• score(X, y) returns the coefficient of determination R² (1.0 is perfect, 0.0 is no better than predicting the mean).

Build from source

git clone --recurse-submodules https://github.com/zeusnotjupiter/BlackRubby.git
cd BlackRubby
python -m pip install build
python -m build

(If you cloned without --recurse-submodules, run git submodule update --init first — the Eigen headers under vendor/eigen are required.)

For an editable development build:

python -m pip install scikit-build-core pybind11 cmake numpy
python -m pip install -e . --no-build-isolation

Run the tests with python -m pip install pytest then python -m pytest tests.

License

Apache License 2.0 — see LICENSE.