Weighted Empirical Risk Minimization for Changepoint Regression.

𐄷 Weighted ERM - fast & accurate change point regression in R

R interface to the weightederm Python package — changepoint regression via Weighted Empirical Risk Minimization (WERM).

Overview

weightederm detects changepoints in ordered regression data by minimizing a Weighted Empirical Risk (WERM). The method is described in:

Inferring Change Points in Regression via Sample Weighting by Gabriel Arpino and Ramji Venkataramanan.

Six estimators are available:

Prerequisites

This package calls Python via reticulate. You need:

1. Python ≥ 3.9 — install from https://www.python.org/ or via conda/pyenv.
2. The weightederm Python package:

pip install git+https://github.com/gabrielarpino/weightederm.git

Installation

From CRAN

install.packages("weightederm")

Development version from GitHub

# install.packages("pak")
pak::pak("gabrielarpino/weightederm-r")

Or with remotes:

# install.packages("remotes")
remotes::install_github("gabrielarpino/weightederm-r")

Pointing to your Python environment

By default, reticulate auto-discovers Python. If it picks the wrong one, call weightederm_configure_python()before your first model fit:

# Option 1 — point to a specific Python binary
weightederm_configure_python(python = "/path/to/your/venv/bin/python")

# Option 2 — point to the Python binary inside a conda environment
weightederm_configure_python("/path/to/miniconda/envs/my_conda_env/bin/python")

You can also set the RETICULATE_PYTHON environment variable in your .Renviron file to avoid calling this every session:

RETICULATE_PYTHON=/path/to/your/venv/bin/python

Quick start

M1 — fixed number of changepoints, sparse linear regression

library(weightederm)
set.seed(0)

n <- 120L; p <- 20L; true_cp <- 60L
X <- matrix(rnorm(n * p), n, p)

beta_left  <- rep(0, p); beta_left[c(1, 4)]  <- c( 2.0, -1.5)
beta_right <- rep(0, p); beta_right[c(1, 4)] <- c(-1.0,  2.5)

y <- numeric(n)
y[1:true_cp]          <- X[1:true_cp, ]          %*% beta_left  + rnorm(true_cp, sd = 0.2)
y[(true_cp+1):n]      <- X[(true_cp+1):n, ]      %*% beta_right + rnorm(n - true_cp, sd = 0.2)

fit <- werm_least_squares(X, y, num_chgpts = 1L, delta = 5L,
                          search_method = "efficient", fit_intercept = FALSE)
fit
#> WERM Changepoint Estimator (werm_least_squares)
#>   Changepoints (1-indexed): 60
#>   num_chgpts: 1 | num_signals: 2 | n_features_in: 20
#>   Objective: ...

M2 — unknown number of changepoints via cross-validation

set.seed(1)
n <- 180L; p <- 10L
X <- matrix(rnorm(n * p), n, p)

beta_1 <- rep(0, p); beta_1[1]      <- 3.5
beta_2 <- rep(0, p); beta_2[1:2]    <- c(-3.0, 3.0)
beta_3 <- rep(0, p); beta_3[1:3]    <- c(2.5, -2.5, 2.5)

y <- numeric(n)
y[1:60]    <- X[1:60, ]    %*% beta_1 + rnorm(60, sd = 0.05)
y[61:120]  <- X[61:120, ]  %*% beta_2 + rnorm(60, sd = 0.05)
y[121:180] <- X[121:180, ] %*% beta_3 + rnorm(60, sd = 0.05)

fit <- werm_least_squares_cv(X, y, max_num_chgpts = 2L, delta = 5L, cv = 3L,
                              search_method = "efficient", fit_intercept = FALSE)
fit$best_num_chgpts  # 2
fit$changepoints     # near c(60, 120)
fit$cv_results       # data.frame of num_chgpts vs mean_test_score

M3 — binary classification with a structural break

set.seed(2)
n <- 160L; p <- 12L; true_cp <- 80L
X <- matrix(rnorm(n * p), n, p)

beta_left  <- rep(0, p); beta_left[c(1, 3)]  <- c( 2.5, -2.0)
beta_right <- rep(0, p); beta_right[c(1, 3)] <- c(-2.5,  2.0)

eta <- numeric(n)
eta[1:true_cp]       <- X[1:true_cp, ]       %*% beta_left
eta[(true_cp+1):n]   <- X[(true_cp+1):n, ]   %*% beta_right
y <- rbinom(n, 1L, 1 / (1 + exp(-eta)))

fit <- werm_logistic(X, y, num_chgpts = 1L, delta = 5L,
                     search_method = "efficient", fit_intercept = FALSE,
                     max_iter = 300L)
fit$changepoints   # near 80
fit$classes        # c("0", "1")

# Predict probabilities on new data
X_new <- matrix(rnorm(10 * p), 10, p)
predict(fit, X_new, type = "prob")   # 10 × 2 matrix
predict(fit, X_new, type = "class")  # character vector of labels

Key arguments

Fitted object

Every werm_*() call returns a named list with class c("werm_<type>", "werm_fit"):

CV estimators additionally expose best_num_chgpts, best_score, cv_results (a data frame), num_chgpts_grid, segment_bounds, segment_coefs, and segment_intercepts.

S3 methods

print(fit)           # compact summary
summary(fit)         # print + coefficient table
coef(fit)            # last_segment_coef vector
predict(fit, X_new)  # numeric predictions (regression) or class labels / probabilities (logistic)

Related resources

• User Guide — algorithm overview, fixed vs CV, penalties, prediction, S3 methods (with tested R examples)
• weightederm Python package — Python API, parameter reference, benchmark notebook

License

Apache License 2.0 — the same license as the underlying Python package.