set.seed(1234)
rmse <- function(theta, theta_star) { sqrt(sum((theta - theta_star)^2)/sum(theta_star^2)) }
## Common parameters
nbNodes <- c(100, 120)
blockProp <- list(row = c(.5, .5), col = c(1/3, 1/3, 1/3)) # group proportions
nbBlocks <- sapply(blockProp, length)
test_that("BipartiteSBM_fit 'Bernoulli' model, undirected, no covariate", {
## BIPARTITE UNDIRECTED BERNOULLI SBM
means <- matrix(c(0.05, 0.95, 0.4, 0.75, 0.15, 0.6), 2, 3) # connectivity matrix
connectParam <- list(mean = means)
## Basic construction - check for wrong specifications
mySampler <- BipartiteSBM$new('bernoulli', nbNodes, blockProp, connectParam)
mySampler$rMemberships(store = TRUE)
mySampler$rEdges(store = TRUE)
## Construction----------------------------------------------------------------
mySBM <- BipartiteSBM_fit$new(mySampler$networkData, 'bernoulli')
expect_error(BipartiteSBM_fit$new(SamplerBernoulli$networkData, 'bernouilli'))
## Checking class
expect_true(inherits(mySBM, "SBM"))
expect_true(inherits(mySBM, "BipartiteSBM"))
expect_true(inherits(mySBM, "BipartiteSBM_fit"))
## Checking field access and format prior to estimation
## parameters
expect_equal(mySBM$modelName, 'bernoulli')
expect_equal(unname(mySBM$nbNodes), nbNodes)
expect_equal(mySBM$nbDyads, nbNodes[1]*nbNodes[2])
expect_true(is.matrix(mySBM$connectParam$mean))
## covariates
expect_equal(mySBM$covarEffect, numeric(0))
expect_equal(mySBM$nbCovariates, 0)
expect_equal(mySBM$covarList, list())
expect_equal(mySBM$covarParam, numeric(0))
## S3 methods
expect_equal(coef(mySBM, 'connectivity'), mySBM$connectParam)
expect_equal(coef(mySBM, 'block') , mySBM$blockProp)
expect_equal(coef(mySBM, 'covariates') , mySBM$covarParam)
## Estimation-----------------------------------------------------------------
BM_out <- mySBM$optimize(estimOptions=list(verbosity = 0))
mySBM$setModel(4)
expect_equal(mySBM$nbConnectParam, unname(nbBlocks[1] * nbBlocks[2]))
expect_equal(mySBM$penalty, nbBlocks[1] * nbBlocks[2] * log(nbNodes[1] * nbNodes[2]) + (nbBlocks[1] - 1) * log(nbNodes[1]) + (nbBlocks[2] - 1) * log(nbNodes[2]))
expect_equal(mySBM$entropy, -sum(mySBM$probMemberships[[1]] * log(mySBM$probMemberships[[1]]))
-sum(mySBM$probMemberships[[2]] * log(mySBM$probMemberships[[2]])))
## Expectation
expect_equal(dim(mySBM$expectation), nbNodes)
expect_true(all(mySBM$expectation >= 0, na.rm = TRUE))
expect_true(all(mySBM$expectation <= 1, na.rm = TRUE))
expect_null(mySBM$connectParam$var)
## blocks
expect_equal(mySBM$nbBlocks, nbBlocks)
expect_equivalent(dim(mySBM$probMemberships[[1]]), c(nbNodes[1], nbBlocks[1]))
expect_equivalent(dim(mySBM$probMemberships[[2]]), c(nbNodes[2], nbBlocks[2]))
expect_equal(sort(unique(mySBM$memberships[[1]])), 1:nbBlocks[1])
expect_equal(sort(unique(mySBM$memberships[[2]])), 1:nbBlocks[2])
## S3 methods
expect_equal(coef(mySBM, 'connectivity'), mySBM$connectParam)
expect_equal(coef(mySBM, 'block') , mySBM$blockProp)
expect_equal(coef(mySBM, 'covariates') , mySBM$covarParam)
expect_equal(mySBM$predict(), predict(mySBM))
expect_equal(fitted(mySBM), predict(mySBM))
## correctness
expect_lt(rmse(sort(mySBM$connectParam$mean), sort(means)), .2)
expect_lt(1 - aricode::ARI(mySBM$memberships[[1]], mySampler$memberships[[1]]), .2)
expect_lt(1 - aricode::ARI(mySBM$memberships[[2]], mySampler$memberships[[2]]), .2)
## prediction wrt BM
for (Q in mySBM$storedModels$indexModel) {
pred_bm <- BM_out$prediction(Q = Q)
mySBM$setModel(Q-1)
pred_sbm <- predict(mySBM)
expect_lt( rmse(pred_bm, pred_sbm), 1e-12)
}
})
test_that("BipartiteSBM_fit 'Poisson' model, undirected, no covariate", {
## SIMPLE DIRECTED POISSON SBM
means <- matrix(c(10, 5, 7, 15, 20, 8), 2, 3) # connectivity matrix
connectParam <- list(mean = means)
## Basic construction - check for wrong specifications
mySampler <- BipartiteSBM$new('poisson', nbNodes, blockProp, connectParam)
mySampler$rMemberships(store = TRUE)
mySampler$rEdges(store = TRUE)
## Construction----------------------------------------------------------------
mySBM <- BipartiteSBM_fit$new(mySampler$networkData, 'poisson')
expect_error(BipartiteSBM_fit$new(SamplerBernoulli$networkData, 'poison'))
## Checking class
expect_true(inherits(mySBM, "SBM"))
expect_true(inherits(mySBM, "BipartiteSBM"))
expect_true(inherits(mySBM, "BipartiteSBM_fit"))
## Checking field access and format prior to estimation
## parameters
expect_equal(mySBM$modelName, 'poisson')
expect_equal(unname(mySBM$nbNodes), nbNodes)
expect_equal(mySBM$nbDyads, nbNodes[1]*nbNodes[2])
expect_true(is.matrix(mySBM$connectParam$mean))
## covariates
expect_equal(mySBM$covarEffect, numeric(0))
expect_equal(mySBM$nbCovariates, 0)
expect_equal(mySBM$covarList, list())
expect_equal(mySBM$covarParam, numeric(0))
## S3 methods
expect_equal(coef(mySBM, 'connectivity'), mySBM$connectParam)
expect_equal(coef(mySBM, 'block') , mySBM$blockProp)
expect_equal(coef(mySBM, 'covariates') , mySBM$covarParam)
## Estimation-----------------------------------------------------------------
BM_out <- mySBM$optimize(estimOptions=list(verbosity = 0))
mySBM$setModel(4)
## Expectation
expect_equal(dim(mySBM$expectation), nbNodes)
expect_true(all(mySBM$expectation >= 0, na.rm = TRUE))
expect_null(mySBM$connectParam$var)
## blocks
expect_equal(mySBM$nbBlocks, nbBlocks)
expect_equivalent(dim(mySBM$probMemberships[[1]]), c(nbNodes[1], nbBlocks[1]))
expect_equivalent(dim(mySBM$probMemberships[[2]]), c(nbNodes[2], nbBlocks[2]))
expect_equal(sort(unique(mySBM$memberships[[1]])), 1:nbBlocks[1])
expect_equal(sort(unique(mySBM$memberships[[2]])), 1:nbBlocks[2])
## S3 methods
expect_equal(coef(mySBM, 'connectivity'), mySBM$connectParam)
expect_equal(coef(mySBM, 'block') , mySBM$blockProp)
expect_equal(coef(mySBM, 'covariates') , mySBM$covarParam)
expect_equal(mySBM$predict(), predict(mySBM))
expect_equal(fitted(mySBM), predict(mySBM))
## correctness
expect_lt(rmse(sort(mySBM$connectParam$mean), sort(means)), 1e-1)
expect_lt(1 - aricode::ARI(mySBM$memberships[[1]], mySampler$memberships[[1]]), 1e-1)
expect_lt(1 - aricode::ARI(mySBM$memberships[[2]], mySampler$memberships[[2]]), 1e-1)
## prediction wrt BM
for (Q in mySBM$storedModels$indexModel) {
pred_bm <- BM_out$prediction(Q = Q)
mySBM$setModel(Q-1)
pred_sbm <- predict(mySBM)
expect_lt( rmse(pred_bm, pred_sbm), 1e-12)
}
})
test_that("BipartiteSBM_fit 'Gaussian' model, undirected, no covariate", {
## SIMPLE UNDIRECTED GAUSSIAN SBM
means <- matrix(c(0.05, 0.95, 0.4, 0.98, 0.15, 0.6), 2, 3) # connectivity matrix
connectParam <- list(mean = means, var = .1)
## Basic construction - check for wrong specifications
mySampler <- BipartiteSBM$new('gaussian', nbNodes, blockProp, connectParam)
mySampler$rMemberships(store = TRUE)
mySampler$rEdges(store = TRUE)
## Construction----------------------------------------------------------------
mySBM <- BipartiteSBM_fit$new(mySampler$networkData, 'gaussian')
expect_error(BipartiteSBM_fit$new(SamplerBernoulli$networkData, 'groß'))
## Checking class
expect_true(inherits(mySBM, "SBM"))
expect_true(inherits(mySBM, "BipartiteSBM"))
expect_true(inherits(mySBM, "BipartiteSBM_fit"))
## Checking field access and format prior to estimation
## parameters
expect_equal(mySBM$modelName, 'gaussian')
expect_equal(unname(mySBM$nbNodes), nbNodes)
expect_equal(mySBM$nbDyads, nbNodes[1]*nbNodes[2])
expect_true(is.matrix(mySBM$connectParam$mean))
## covariates
expect_equal(mySBM$covarEffect, numeric(0))
expect_equal(mySBM$nbCovariates, 0)
expect_equal(mySBM$covarList, list())
expect_equal(mySBM$covarParam, numeric(0))
## S3 methods
expect_equal(coef(mySBM, 'connectivity'), mySBM$connectParam)
expect_equal(coef(mySBM, 'block') , mySBM$blockProp)
expect_equal(coef(mySBM, 'covariates') , mySBM$covarParam)
## Estimation-----------------------------------------------------------------
BM_out <- mySBM$optimize(estimOptions=list(verbosity = 0))
mySBM$setModel(4)
## Expectation
expect_equal(dim(mySBM$expectation), nbNodes)
expect_gt(mySBM$connectParam$var, 0)
## blocks
expect_equal(mySBM$nbBlocks, nbBlocks)
expect_equivalent(dim(mySBM$probMemberships[[1]]), c(nbNodes[1], nbBlocks[1]))
expect_equivalent(dim(mySBM$probMemberships[[2]]), c(nbNodes[2], nbBlocks[2]))
expect_equal(sort(unique(mySBM$memberships[[1]])), 1:nbBlocks[1])
expect_equal(sort(unique(mySBM$memberships[[2]])), 1:nbBlocks[2])
## S3 methods
expect_equal(coef(mySBM, 'connectivity'), mySBM$connectParam)
expect_equal(coef(mySBM, 'block') , mySBM$blockProp)
expect_equal(coef(mySBM, 'covariates') , mySBM$covarParam)
expect_equal(mySBM$predict(), predict(mySBM))
expect_equal(fitted(mySBM), predict(mySBM))
## correctness
expect_lt(rmse(sort(mySBM$connectParam$mean), sort(means)), 1e-1)
expect_lt(1 - aricode::ARI(mySBM$memberships[[1]], mySampler$memberships[[1]]), 1e-1)
expect_lt(1 - aricode::ARI(mySBM$memberships[[2]], mySampler$memberships[[2]]), 1e-1)
## prediction wrt BM
for (Q in mySBM$storedModels$indexModel) {
pred_bm <- BM_out$prediction(Q = Q)
mySBM$setModel(Q-1)
pred_sbm <- predict(mySBM)
expect_lt( rmse(pred_bm, pred_sbm), 1e-12)
}
})
test_that("active bindings are working in the class", {
A <- matrix(rbinom(200,1,.2),20,10)
myBipartite <- BipartiteSBM_fit$new(incidenceMatrix = A,model = "bernoulli",dimLabels = c("Actor","Stuff"))
tau1 <- matrix(runif(20*2),20,2)
tau1 <- tau1 / rowSums(tau1)
tau2 <- matrix(runif(10*3),10,3)
tau2 <- tau2 / rowSums(tau2)
myBipartite$probMemberships <- list(tau1,tau2)
myBipartite$blockProp <- list(colMeans(tau1),colMeans(tau2))
myBipartite$connectParam <- list(mean = matrix(runif(3*2),3,2))
expect_equal(unname(myBipartite$nbNodes),c(20,10))
expect_equal(myBipartite$memberships[[1]], 1+(tau1[,1]<.5)*1)
expect_equal(dim(myBipartite$connectParam$mean),c(3,2))
expect_equal(length(myBipartite$blockProp),2)
})
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