Nothing
context("Test Gaussian")
test_that("Predict", {
y <- as.factor(c("Ham", "Ham", "Spam", "Spam", "Spam"))
x <- matrix(
c(2, 3, 2, 1, 2,
5, 3, 4, 2, 4,
0, 1, 3, 1, 0,
3, 4, 4, 3, 5),
nrow = 5,
ncol = 4,
dimnames = list(NULL, c("wo", "mo", "bo", "so")))
df <- as.data.frame(x)
# Data frame casting
mod <- fnb.gaussian(x, y)
df_mod <- fnb.gaussian(df, y)
expect_error(fnb.gaussian(x[1:3,], y[1:3]))
predictions <- predict(mod, x, type = "raw")
risky_predictions <- predict(mod, x, type = "raw", check=FALSE)
df_predictions <- predict(df_mod, df, type = "raw")
expect_equal(sum(round(abs(predictions-df_predictions), digits = 12)), 0)
expect_equal(sum(round(abs(predictions-risky_predictions), digits = 12)), 0)
classification <- predict(mod, x, type = "class")
expect_equal(as.factor(mod$levels[max.col(predictions)]), classification)
# Column padding
expect_warning(predict(mod, x[,1:3], type = "raw"))
dropped_predictions <- predict(mod, x[,1:3], type = "raw", silent = TRUE)
dropped_x <- x[,1:3]
mod <- fnb.gaussian(dropped_x, y)
alt_predictions <- predict(mod, x, type = "raw", silent=TRUE)
expect_equal(sum(round(abs(dropped_predictions-alt_predictions), digits = 12)), 0)
# Ignore new column
mod <- fnb.gaussian(x, y)
predictions <- predict(mod, x, type = "raw")
x <- cbind(x, x[,1, drop=FALSE])
colnames(x)[5] <- "womo"
new_predictions <- predict(mod, x, type = "raw", silent=TRUE)
expect_equal(sum(round(abs(predictions-new_predictions), digits = 12)), 0)
# All new columns is same as all 0
all_new_columns_predictions <- predict(mod, x[,5,drop=FALSE], type="raw", silent = TRUE)
predictions <- matrix(
c(2/5,2/5,2/5,2/5,2/5,
3/5,3/5,3/5,3/5,3/5),
nrow = 5,
ncol = 2
)
expect_equal(sum(round(abs(predictions-all_new_columns_predictions), digits = 12)), 0)
})
test_that("Standard 3 classes", {
y <- as.factor(c("Ham", "Ham", "Spam", "Spam", "Spam"))
x <- matrix(
c(2, 3, 2, 1, 2,
5, 3, 4, 2, 4,
0, 1, 3, 1, 0,
3, 4, 4, 3, 5),
nrow = 5,
ncol = 4,
dimnames = list(NULL, c("wo", "mo", "bo", "so")))
actuals <- matrix(
c(
0.668632030, 0.3313680,
0.793288288, 0.2067117,
0.002007792, 0.9979922,
0.092781300, 0.9072187,
0.127527893, 0.8724721
),
nrow = 5,
ncol = 2,
byrow=TRUE,
dimnames = list(NULL, c("Ham", "Spam"))
)
mod <- fnb.gaussian(x, y, priors = c(1/4, 3/4))
predictions <- predict(mod, x, type="raw")
expect_equal(sum(round(abs(predictions-actuals), digits = 7)), 0)
# Test Sparse Matrices
sparse_mod <- fnb.gaussian(Matrix(x, sparse = TRUE), y, priors = c(1/4, 3/4))
sparse_cast_mod <- fnb.gaussian(x, y, sparse = TRUE, priors = c(1/4, 3/4))
sparse_predictions <- predict(sparse_mod, x, type = "raw")
sparse_cast_predictions <- predict(sparse_cast_mod, x, type = "raw")
expect_equal(sum(round(abs(predictions-sparse_predictions), digits = 12)), 0)
expect_equal(sum(round(abs(predictions-sparse_cast_predictions), digits = 12)), 0)
})
test_that("Single column",{
y <- as.factor(c( "Spam", "Spam", "Ham", "Ham", "Ham"))
x <- matrix(
c(2, 3, 2, 1, 2),
nrow = 5,
ncol = 1,
dimnames = list(NULL, c("ho"))
)
actuals_ho_only <- matrix(
c(
0.6663074, 0.3336926,
0.1408233, 0.8591767,
0.6663074, 0.3336926,
0.8994864, 0.1005136,
0.6663074, 0.3336926
),
nrow = 5,
ncol = 2,
byrow = TRUE,
dimnames = list(NULL, c("Ham", "Spam"))
)
mod <- fnb.gaussian(x[, 1, drop=FALSE], y)
predictions <- predict(mod, x[, 1, drop=FALSE], type="raw")
expect_equal(sum(round(abs(predictions-actuals_ho_only), digits = 7)), 0)
# Test Sparse Matrices
sparse_mod <- fnb.gaussian(Matrix(x[, 1, drop=FALSE], sparse = TRUE), y)
sparse_cast_mod <- fnb.gaussian(x[, 1, drop=FALSE], y, sparse = TRUE)
sparse_predictions <- predict(sparse_mod, x[, 1, drop=FALSE], type = "raw")
sparse_cast_predictions <- predict(sparse_cast_mod, x[, 1, drop=FALSE], type = "raw")
expect_equal(sum(round(abs(predictions-sparse_predictions), digits = 12)), 0)
expect_equal(sum(round(abs(predictions-sparse_cast_predictions), digits = 12)), 0)
})
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.