context("Logistic Regression")
library(LiblineaR)
# Simple dataset used in the tests
data(testdata)
modelform <- formula(y~.)
classname<-all.vars(modelform)[1]
D <- testdata$D
D_test <- testdata$D_test
X <- testdata$X
X_u <- testdata$X_u
y <- testdata$y
X_test <- testdata$X_test
y_test <- testdata$y_test
test_that("Same result for different input modalities",{
g_mat <- LogisticRegression(X,y)
g_df <- LogisticRegression(modelform,D)
expect_equal(predict(g_mat,X_test),predict(g_df,D_test))
expect_equal(loss(g_mat,X_test,y_test),loss(g_df,D_test))
})
test_that("Centering input does not change predictions", {
g_center <- LogisticRegression(X,y,
x_center=TRUE,lambda=10)
g_noncenter <- LogisticRegression(X,y,
x_center=FALSE,lambda=10)
expect_equal(g_center@w[-1],g_noncenter@w[-1],tolerance=10e-4)
expect_equal(loss(g_center,X_test,y_test),
loss(g_noncenter,X_test,y_test),
tolerance=10e-5)
expect_equal(predict(g_center,X_test),
predict(g_noncenter,X_test))
g_center <- LogisticRegression(X[,1,drop=FALSE],y,x_center=TRUE)
g_noncenter <- LogisticRegression(X[,1,drop=FALSE],y,x_center=FALSE)
expect_equal(loss(g_center,X_test[,1,drop=FALSE],y_test),
loss(g_noncenter,X_test[,1,drop=FALSE],y_test),tolerance=10e-4)
expect_equal(predict(g_center,X_test[,1,drop=FALSE]),
predict(g_noncenter,X_test[,1,drop=FALSE]))
})
test_that("Gradient is superficially correct",{
library("numDeriv")
data(testdata)
X <- cbind(1,testdata$X)
y <- testdata$y
classnames <- levels(y)
for (i in 1:10) {
w <- rnorm(ncol(X))
lambda <- abs(10*rnorm(1))
expect_equal(as.numeric(numDeriv::grad(loss_logisticregression,w,X=X,y=y,classnames=classnames,lambda=lambda, method="simple")),
as.numeric(grad_logisticregression(w,X=X,y=y,classnames=classnames,lambda=lambda)),
tolerance=10e-4)
}
})
test_that("LogisticRegression gives same solution as other implementations",{
# LiblineaR
# Note LiblineaR penalizes the intercept!
g_liblin <- LiblineaR(X,y,cost=0.5,epsilon = 0.000000001,bias=FALSE)
g_lr <- LogisticRegression(X,y,lambda=1,intercept=FALSE)
expect_equal(as.numeric(g_liblin$W), g_lr@w,tolerance=10e-5)
expect_equivalent(predict(g_liblin,X_test)$predictions, predict(g_lr,X_test))
# glmnet
# g_glm <- glmnet(X,y,family="binomial",alpha=0,lambda=0.2,standardize=FALSE)
# g_lr <- LogisticRegression(X,y,lambda=1)
# expect_equal(as.numeric(coef(g_glm)),g_lr@w,tolerance=10e-7)
# glm
g_fast <- LogisticRegressionFast(X[,1,drop=FALSE],y)
g_lr <- LogisticRegression(X[,1,drop=FALSE],y)
expect_equal(as.numeric(g_fast@w), g_lr@w,tolerance=10e-4)
expect_equal(predict(g_fast,X_test[,1,drop=FALSE]),
predict(g_lr,X_test[,1,drop=FALSE]))
})
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