Getting Started

Here is a brief example of how to use the GLM() class.

import numpy as np
import scipy.sparse as sps
from pyglmnet import GLM, simulate_glm

n_samples, n_features = 1000, 100
distr = 'poisson'

# sample a sparse model
beta0 = np.random.normal(0.0, 1.0, 1)
beta = sps.rand(n_features, 1, 0.1)
beta = np.array(beta.todense())

# simulate data
Xtrain = np.random.normal(0.0, 1.0, [n_samples, n_features])
ytrain = simulate_glm('poisson', beta0, beta, Xtrain)[:, 0]
Xtest = np.random.normal(0.0, 1.0, [n_samples, n_features])
ytest = simulate_glm('poisson', beta0, beta, Xtest)[:, 0]

# create an instance of the GLM class
glm = GLM(distr='poisson', score_metric='deviance')

# fit the model on the training data
glm.fit(Xtrain, ytrain)

# predict using fitted model on the test data
yhat = glm.predict(Xtest)

# score the model on test data
deviance = glm.score(Xtest, ytest)