hapc 2.6.0

Highly Adaptive Principal Components

HAPC: Highly Adaptive Prinicipal Components

A fast and flexible machine learning library for nonparametric high-dimensional regression and classification with guarantees.

Documentation

• Python API (rendered from docstrings): https://hapc.readthedocs.io — configured via .readthedocs.yaml and docs/ (Sphinx + autodoc). Build locally with pip install -e ".[docs]" && sphinx-build -b html docs docs/_build/html.
• R API (rendered from roxygen): a pkgdown site built by .github/workflows/pkgdown.yaml (config in _pkgdown.yml). Build locally with Rscript -e 'pkgdown::build_site()'.

Installation

Prerequisites

• Python 3.8+
• C++ compiler (g++, clang, or MSVC)
• CMake 3.15+
• Eigen3

Quick Install

pip install hapc

Prebuilt wheels are published for Linux (manylinux2014, x86_64), macOS (Intel + Apple Silicon) and Windows, for CPython 3.8–3.12. No compiler, CMake or Eigen is needed when a wheel is available.

Linux / HPC clusters

The Linux wheels use the manylinux2014 baseline (glibc 2.17), so pip install hapc works out of the box on HPC login/compute nodes — no conda toolchain, devtoolset, or sysroot setup required:

pip install hapc

If you must build from the source distribution (niche architecture, very old Python, or an air-gapped node), provide a C++17 compiler and either let CMake fetch Eigen automatically (needs network) or install Eigen and let find_package(Eigen3) find it:

# with conda compilers (recommended on HPC)
conda install -c conda-forge cxx-compiler cmake eigen
pip install hapc --no-binary hapc

Install from GitHub (latest development version)

pip install git+https://github.com/meixide/hapc.git

Or with editable install for development:

git clone https://github.com/meixide/hapc.git
cd hapc
pip install -e .

Install build dependencies

If installation fails, you may need to install build dependencies:

macOS:

brew install cmake eigen

Ubuntu/Debian:

sudo apt-get install cmake libeigen3-dev build-essential

Windows:

pip install cmake
# Install Visual Studio Build Tools or use conda
conda install -c conda-forge eigen

Quick Start

import numpy as np
from hapc.single import single_pcghal
from hapc.cv import pcghal_cv

# Generate sample data
X = np.random.randn(100, 5)
Y = X[:, 0] + 0.5 * X[:, 1] + np.random.randn(100) * 0.1

# Single fit with fixed lambda
result = single_pcghal(X, Y, maxdeg=2, npc=5, single_lambda=0.01)
print(f"Risk: {result.optimizer_output.risk:.6f}")

# Cross-validation to select lambda
lambdas = np.logspace(-4, 0, 10)
cv_result = pcghal_cv(X, Y, maxdeg=2, npc=5, lambdas=lambdas, nfolds=5)
print(f"Best lambda: {cv_result.best_lambda:.6f}")

# Make predictions
X_test = np.random.randn(20, 5)
result = single_pcghal(X, Y, maxdeg=2, npc=5, single_lambda=0.01, predict=X_test)
print(f"Predictions: {result.predictions}")

Usage

Regression

from hapc.single import single_pcghal

result = single_pcghal(
    X, Y,
    maxdeg=2,        # Maximum degree of interactions
    npc=10,          # Number of principal components
    single_lambda=0.01,
    predict=X_test   # Optional: test data for predictions
)

Classification

from hapc.single import single_pcghal

result = single_pcghal(
    X, Y_binary,
    maxdeg=2,
    npc=10,
    single_lambda=0.01,
    predict=X_test
)

Cross-Validation

from hapc.cv import pcghal_cv

cv_result = pcghal_cv(
    X, Y,
    maxdeg=2,
    npc=10,
    lambdas=np.logspace(-4, 0, 20),
    nfolds=5
)
print(cv_result.best_lambda)

Average Treatment Effect (ATE)

Estimate the ATE E[Y(1)] − E[Y(0)] with HAPC nuisance models and a doubly-robust (AIPW) efficient influence function. ate_hapc returns a point estimate and a (1 − alpha) Wald confidence interval.

from hapc import ate_hapc

# W: covariates (n, p); A: binary treatment in {0,1} or {-1,+1}; Y: outcome
res = ate_hapc(W, Y, A, alpha=0.05, method="undersmooth")
print(res.estimate, res.lower, res.upper)

Two bias-control strategies are available through method:

• method="undersmooth" (default) — single-sample estimator. The outcome model is undersmoothed (λ pushed below the CV-optimal value) until the empirical influence function is within σ / (√n · log n). This requires the full PC basis (npcs = n, the default) and a λ grid that reaches small λ (defaults log_lambda_out_min = -10); otherwise the gate never reaches the low-bias regime and ate_hapc emits a warning. Pass report_undersmoothing=True to print the |mean(EIF)|-vs-λ path.
• method="crossfit" — DML-style K-fold cross-fitting (cf_folds, default 5, stratified by treatment). Both nuisances are fit on the training folds and the influence function is evaluated out-of-fold, giving honest point estimates and coverage without undersmoothing. Recommended under good overlap.

Discrete-time survival (family = "logit-hazard")

Fit a discrete-time logistic hazard model with HAPC. You supply only the observed right-censored data — baseline covariates X, the observed time T = min(T_event, C), and the event indicator Delta = 1(T_event <= C) — and the wrapper performs the person-period expansion (one row per subject-per-interval-at-risk, hazard label = 1 at the event interval), prepends the visit time as the first HAL covariate, and cross-validates the binomial fit.

Model. The discrete hazard is the conditional event probability in interval t given survival up to t, modelled on the logit scale by a HAPC fit f of the augmented covariate (t, x):

lambda(t | x) = P(T_event = t | T_event >= t, X = x)
logit lambda(t | x) = f(t, x)

Person-period likelihood. Under independent right-censoring the observed-data likelihood factorises over the at-risk intervals,

prod_i prod_{t <= T_i}  lambda(t|x_i)^Y_it * (1 - lambda(t|x_i))^(1 - Y_it),
with  Y_it = 1(T_event_i = t),

which is exactly the Bernoulli (logistic) likelihood of the expanded person-period table — so a binomial HAPC fit of Y_it on (t, x_i) estimates the discrete hazard (Cox 1972; Brown 1975; Allison 1982).

Survival. The conditional survival function follows by the product-limit relation S(t | x) = prod_{s <= t} (1 - lambda(s | x)), returned for new subjects when predict= is supplied.

from hapc import hazard_hapc
import numpy as np

# X: baseline covariates (n, p); T: observed times; Delta: 0/1 event indicator
fit = hazard_hapc(X, T, Delta, norm="1", max_degree=2, time_grid=np.arange(1, 7))
fit.hazard        # estimated hazard per person-period row (CV predictions)
fit.best_lambda, fit.interior   # CV-selected lambda; is it interior to the grid?

# survival curves S(t|x) for new subjects
fit = hazard_hapc(X, T, Delta, norm="1", predict=X_new)
fit.predict_survival            # (m, K) survival probabilities over the grid

library(hapc)
# equivalent to cv.hapc(X, T, family = "logit-hazard", Delta = Delta, norm = "1")
fit <- hazard.hapc(X, T, Delta, norm = "1", max_degree = 2, time_grid = 1:6)
fit$hazard; fit$best_lambda; fit$interior

norm must be "1" (logistic LASSO) or "2" (logistic ridge); norm = "sv" is not implemented for this family and is flagged.

Returns (Python HazardResult / R hapc_hazard):

• hazard — cross-validated discrete hazard for each person-period row
• lambdas, risk, best_lambda — CV grid, mean logistic deviance, selected λ
• interior — whether best_lambda is strictly inside the grid (sanity check)
• time_grid, ids/id, Y — the discrete grid and person-period bookkeeping
• predict_hazard, predict_survival — hazard surface and survival curves for new subjects (only when predict= is given)
• cv — the underlying cross-validation result

Worked end-to-end examples (five hazard data-generating processes, with true-vs-estimated hazard scatters and CV risk-vs-λ curves verifying an interior optimum) are in examples/hazard_logit_hazard_examples.R and examples/hazard_logit_hazard_examples.py.

References. Cox (1972, JRSS B); Brown (1975, Biometrics); Allison (1982, Sociological Methodology); Singer & Willett (2003, Applied Longitudinal Data Analysis); Benkeser & van der Laan (2016, IEEE DSAA).

API Reference

hapc.single.single_pcghal()

Fit PC-GHAL with a single lambda value.

Parameters:

• X (ndarray, shape (n, p)): Input features
• Y (ndarray, shape (n,)): Response variable
• maxdeg (int): Maximum degree of interactions
• npc (int): Number of principal components
• single_lambda (float): Regularization parameter
• max_iter (int, default=100): Maximum iterations
• tol (float, default=1e-6): Convergence tolerance
• verbose (bool, default=False): Print progress
• predict (ndarray, optional): Test data for predictions
• center (bool, default=True): Center the design matrix

Returns:

• result.optimizer_output.alpha: Coefficients
• result.optimizer_output.risk: Final risk
• result.optimizer_output.iter: Iterations until convergence
• result.predictions: Predictions on test data (if provided)

hapc.cv.pcghal_cv()

Cross-validation to select lambda.

Parameters:

• lambdas (ndarray): Grid of lambda values to test
• nfolds (int, default=5): Number of CV folds
• ...other parameters same as single_pcghal

Returns:

• cv_result.best_lambda: Optimal lambda
• cv_result.mses: CV errors for each lambda
• cv_result.best_model: Fitted model with best lambda
• cv_result.predictions: Predictions on test data (if provided)

Contributing

Contributions welcome! The C++ core is shared between R and Python packages.

git clone https://github.com/meixide/hapc.git
cd hapc
pip install -e .
pytest

License

MIT License - see LICENSE file