GBDTRuLSIFRegressor

Description

GBDTRuLSIFRegressor [1] is an algorithm for the direct density ratio estimation for two samples. It is a Gradient Boosting over Decision Trees model with the RuLSIF [2] loss function. The algorithm takes two samples and learns the ratio of their probability densities without estimation of individual distributions. The density ratios help to calculate of different dissimilarity scores between the samples, that are used in change-point detection and other applications.

• [1] Mikhail Hushchyn and Andrey Ustyuzhanin. “Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation.” J. Comput. Sci. 53 (2021): 101385. [journal] [arxiv]
• [2] Makoto Yamada, Taiji Suzuki, Takafumi Kanamori, Hirotaka Hachiya, Masashi Sugiyama; Relative Density-Ratio Estimation for Robust Distribution Comparison. Neural Comput 2013; 25 (5): 1324–1370. [DOI]

Usage

from roerich.density_ratio import GBDTRuLSIFRegressor
from scipy.stats import norm, uniform
import matplotlib.pyplot as plt
import numpy as np

N = 10000

# generate samples
p0 = uniform(-5, 10)
p1 = norm(0, 1)
X = np.concatenate((p0.rvs((N, 1)), p1.rvs((N, 1))))
y = np.array([0]*N + [1]*N)

# true density ratio
true_ratio = p1.pdf(X) / p0.pdf(X)

# direct density ratio estimation
reg = GBDTRuLSIFRegressor(n_estimators=100, learning_rate=0.2, max_depth=2, alpha=0)
reg.fit(X, y)
pred_ratio = reg.predict_proba_ratio(X)

# visualization
plt.scatter(X, pred_ratio, label='pred')
plt.scatter(X, true_ratio, label='true')
plt.legend()
plt.show()