pycasso 1.0.1

Picasso Python Package

PICASSO: Penalized Generalized Linear Model Solver - Unleash the Power of Non-convex Penalty

Pycasso provides a Python interface to the PICASSO C++ solver for fitting sparse generalized linear models with L1 (Lasso), MCP, and SCAD penalties via pathwise coordinate optimization.

Features

• Families: Gaussian (linear), Binomial (logistic), Poisson, Sqrt-Lasso

• Penalties: L1 (Lasso), MCP, SCAD

• Standardization: Automatic design matrix standardization with proper coefficient rescaling

• Gaussian solvers: Naive update and covariance update

• Early stopping: dfmax parameter to stop when too many coefficients become nonzero

• Intercept: Optional intercept term with correct initialization for all families

Requirements

• Linux or macOS

• Python 3

• NumPy, SciPy

Installation

Install from PyPI:

pip install pycasso

Install from source:

git clone https://github.com/jasonge27/picasso.git
cd picasso
mkdir build && cd build && cmake .. && make
cd ../python-package
pip install .

Usage

import numpy as np
import pycasso

# Generate example data
n, d = 200, 50
X = np.random.randn(n, d)
beta_true = np.zeros(d)
beta_true[:3] = [1, -0.5, 0.3]
y = X @ beta_true + np.random.randn(n) * 0.5

# Fit sparse linear regression with Lasso
s = pycasso.Solver(X, y, family="gaussian", penalty="l1")
s.train()
result = s.coef()
print(result['beta'])     # coefficient matrix (nlambda x d)
print(result['intercept'])  # intercept for each lambda

# Predict
y_pred = s.predict(X[:5, :])

# Logistic regression with MCP penalty
y_bin = (np.random.rand(n) < 0.5).astype(float)
s2 = pycasso.Solver(X, y_bin, family="binomial", penalty="mcp")
s2.train()

# Early stopping with dfmax
s3 = pycasso.Solver(X, y, family="gaussian", dfmax=10)
s3.train()

Reference

Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, and Tuo Zhao. “Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python.” Journal of Machine Learning Research, 20(44):1-5, 2019.

License

GPL-3.0