BaTFLED3D (version 0.2.11)

test_results: Get RMSE & explained variance for warm and cold test results

Description

Get RMSE & explained variance for warm and cold test results

Usage

test_results(m, d, test.results = numeric(0), verbose = T,
  warm.resp = numeric(0), test.m1 = numeric(0), test.m2 = numeric(0),
  test.m3 = numeric(0), test.m1m2 = numeric(0), test.m1m3 = numeric(0),
  test.m2m3 = numeric(0), test.m1m2m3 = numeric(0))

Arguments

m

a CP_model or Tucker_model object

d

an input data object created with input_data

test.results

an object generated by this function that will combined with the new results

verbose

Logical indicating whether to print the resulting prediction measures (TRUE)

warm.resp

True responses for warm test data (optional).

test.m1

True responses for mode 1 cold test data (optional).

test.m2

True responses for mode 2 cold test data (optional).

test.m3

True responses for mode 3 cold test data (optional).

test.m1m2

True responses for mode 1/2 combination cold test data (optional).

test.m1m3

True responses for mode 1/3 combination cold test data (optional).

test.m2m3

True responses for mode 2/3 combination cold test data (optional).

test.m1m2m3

True responses for mode 1/2/3 combination cold test data (optional).

Value

list of results TODO: add more here

Examples

# NOT RUN {
data.params <- get_data_params(c('decomp=Tucker'))
toy <- mk_toy(data.params)

# Make training data object excluding the first two samples for modes 1 & 2.
train.data <- input_data$new(mode1.X=toy$mode1.X[-(1:2),-1],
                             mode2.X=toy$mode2.X[-(1:2),-1],
                             mode3.X=toy$mode3.X[,-1],
                             resp=toy$resp)
# Remove some responses for warm prediction
warm.ind <- sample(1:prod(dim(train.data$resp)), 20)
warm.resp <- train.data$resp[warm.ind]
train.data$resp[warm.ind] <- NA

# Make testing objects 
m1.test.data <- input_data$new(mode1.X=toy$mode1.X[1:2,-1],
                               mode2.X=toy$mode2.X[-(1:2),-1],
                               mode3.X=toy$mode3.X[,-1],
                               resp=toy$resp[1:2,-(1:2),])
m2.test.data <- input_data$new(mode1.X=toy$mode1.X[-(1:2),-1],
                               mode2.X=toy$mode2.X[1:2,-1],
                               mode3.X=toy$mode3.X[,-1],
                               resp=toy$resp[-(1:2),1:2,])
m1m2.test.data <- input_data$new(mode1.X=toy$mode1.X[1:2,-1],
                                 mode2.X=toy$mode2.X[1:2,-1],
                                 mode3.X=toy$mode3.X[,-1],
                                 resp=toy$resp[1:2,1:2,])
                               
model.params <- get_model_params(c('decomp=Tucker'))
toy.model <- mk_model(train.data, model.params)
toy.model$rand_init(model.params)
toy.model$iter <- 1

test.results <- numeric(0)
test_results(m=toy.model, d=train.data, warm.resp=warm.resp, 
             test.m1=m1.test.data, test.m2=m2.test.data, 
             test.m1m2=m1m2.test.data)
# }