tests/testthat/test_autoencoder.R

In gdkrmr/dimRed: A Framework for Dimensionality Reduction

skip_if_no_tensorflow <- function() {
  if (!requireNamespace(c("tensorflow", "reticulate"), quietly = TRUE) ||
        (!reticulate::py_module_available("tensorflow") &&
         Sys.getenv("BNET_FORCE_AUTOENCODER_TESTS") != "1"))
    skip("TensorFlow not available for testing")
}
skip_if_no_keras <- function() {
  if (!requireNamespace(c("keras", "reticulate"), quietly = TRUE) ||
        (!keras::is_keras_available() &&
           Sys.getenv("BNET_FORCE_AUTOENCODER_TESTS") != "1"))
    skip("Keras not available for testing")
}

test_that("Check if tensorflow is installed correctly.", {
  skip_if_no_tensorflow()
  requireNamespace("tensorflow", quietly = TRUE)
  tensorflow::tf$compat$v1$disable_v2_behavior()
  # I have not found a way to suppress the warning tf gives on first use.
  sess <- tensorflow::tf$compat$v1$Session()
  hello <- "Hello, TensorFlow!"
  tf_hello <- tensorflow::tf$compat$v1$constant(hello)
  tf_hello_res <- sess$run(tf_hello)

  # in python 3 this returns a `bytes` object $decode() transforms it into a
  # sting, in python 2 this is a simple string
  if(!is.character(tf_hello_res))
    tf_hello_res <- tf_hello_res$decode()

  ## print("tf_hello_res:")
  ## print(str(tf_hello_res))
  ## print(tf_hello_res)

  expect(tf_hello_res == hello, paste("tensorflow does not work:\n",
                                      "hello =", hello, "\n",
                                      "sess$run(tf_hello) =", tf_hello_res))
})

test_that("Check errors when building autoencoder.", {
  skip_if_no_tensorflow()
  iris_data <- as(iris[, 1:4], "dimRedData")
  expect_error(embed(iris_data, "AutoEncoder", activation = "sigmoid"),
               "declare an activation function for each layer")
  expect_error(embed(iris_data, "AutoEncoder", n_hidden = c(1, 2, 2, 1)),
               "the number of layers must be impair")
  expect_error(embed(iris_data, "AutoEncoder", weight_decay = -1),
               "weight decay must be > 0")
  expect_error(embed(iris_data, "AutoEncoder", learning_rate = -1),
               "learning rate must be > 0")
  expect_error(embed(iris_data, "AutoEncoder", n_steps = -1),
               "n_steps must be > 0")
  expect_error(embed(iris_data, "AutoEncoder", n_hidden = c(4, 2, 4), ndim = 3),
               "the middle of n_hidden must be equal to ndim")
})


test_that("using autoencoder with parameters", {
    skip_if_no_tensorflow()
    iris_data <- as(iris[, 1:4], "dimRedData")
    expect_equal(class(iris_data)[1], "dimRedData")

    ae <- lapply(1:2, function(x) embed(iris_data, "AutoEncoder",
                                        n_hidden = c(10, x, 10),
                                        ndim = x,
                                        n_steps = 100))
    aq <- lapply(ae, function(x) quality(x, "reconstruction_rmse"))
    lapply(ae, function(x) expect_s4_class(x, "dimRedResult"))

    ## expect(aq[[1]] > aq[[2]], "the error should decrease with more dimensions")
    ## expect(aq[[2]] > aq[[3]], "the error should decrease with more dimensions")
    ## expect(aq[[3]] > aq[[4]], "the error should decrease with more dimensions")

    lapply(1:length(ae), function(x) expect_equal(x, getNDim(ae[[x]])))


    ae <- lapply(1:2, function(x) embed(iris_data,
                                        "AutoEncoder",
                                        n_hidden = c(10, x, 10),
                                        ndim = x,
                                        weight_decay = 0.1,
                                        n_steps = 100))
    aq <- lapply(ae, function(x) quality(x, "reconstruction_rmse"))
    lapply(ae, function(x) expect_s4_class(x, "dimRedResult"))

    ## expect(aq[[1]] > aq[[2]], "the error should decrease with more dimensions")
    ## expect(aq[[2]] > aq[[3]], "the error should decrease with more dimensions")
    ## expect(aq[[3]] > aq[[4]], "the error should decrease with more dimensions")

    lapply(1:length(ae), function(x) expect_equal(x, getNDim(ae[[x]])))



    ae <- lapply(1:2, function(x) embed(iris_data,
                                        "AutoEncoder",
                                        n_hidden = c(10, x, 10),
                                        ndim = x,
                                        learning_rate = 0.1,
                                        weight_decay = 0.1,
                                        n_steps = 100))
    aq <- lapply(ae, function(x) quality(x, "reconstruction_rmse"))
    lapply(ae, function(x) expect_s4_class(x, "dimRedResult"))

    ## expect(aq[[1]] > aq[[2]], "the error should decrease with more dimensions")
    ## expect(aq[[2]] > aq[[3]], "the error should decrease with more dimensions")
    ## expect(aq[[3]] > aq[[4]], "the error should decrease with more dimensions")

    lapply(1:length(ae), function(x) expect_equal(x, getNDim(ae[[x]])))



    ae <- lapply(1:2, function(x) embed(iris_data,
                                        "AutoEncoder",
                                        n_hidden = c(10, x, 10),
                                        activation = c("sigmoid", "sigmoid", "sigmoid"),
                                        ndim = x,
                                        learning_rate = 0.1,
                                        weight_decay = 0.1,
                                        n_steps = 100))
    aq <- lapply(ae, function(x) quality(x, "reconstruction_rmse"))
    lapply(ae, function(x) expect_s4_class(x, "dimRedResult"))

    aa <- lapply(c("tanh", "sigmoid", "relu", "elu"),
                 function(x) embed(iris_data,
                                   "AutoEncoder",
                                   n_hidden = c(10, 2, 10),
                                   activation = c("sigmoid", "sigmoid", "sigmoid"),
                                   ndim = 2,
                                   learning_rate = 0.1,
                                   weight_decay = 0.1,
                                   n_steps = 100))
    aaq <- lapply(aa, function(x) quality(x, "reconstruction_rmse"))
    lapply(aa, function(x) expect_s4_class(x, "dimRedResult"))
    ## expect(aq[[1]] > aq[[2]], "the error should decrease with more dimensions")
    ## expect(aq[[2]] > aq[[3]], "the error should decrease with more dimensions")
    ## expect(aq[[3]] > aq[[4]], "the error should decrease with more dimensions")

    lapply(1:length(ae), function(x) expect_equal(x, getNDim(ae[[x]])))
})

test_that("using autoencoder with autoencoder results", {
    skip_if_no_tensorflow()

    tensorflow::tf$compat$v1$set_random_seed(2)
    iris_data <- as(iris[, 1:4], "dimRedData")
    expect_equal(class(iris_data)[1], "dimRedData")

    ae1 <- lapply(1:2, function(x) embed(iris_data, "AutoEncoder",
                                         n_hidden = c(10, x, 10),
                                         ndim = x, n_steps = 1))
    aq1 <- lapply(ae1, function(x) quality(x, "reconstruction_rmse"))

    ae2 <- lapply(ae1, function(x) embed(iris_data, "AutoEncoder",
                                         autoencoder = x, n_steps = 1000))
    aq2 <- lapply(ae2, function(x) quality(x, "reconstruction_rmse"))

    lapply(ae1, function(x) expect_s4_class(x, "dimRedResult"))
    lapply(ae2, function(x) expect_s4_class(x, "dimRedResult"))

    expect(aq1[[1]] > aq2[[1]], "the error should decrease with more steps")
    expect(aq1[[2]] > aq2[[2]], "the error should decrease with more steps")
    ## expect(aq1[[3]] > aq2[[3]], "the error should decrease with more steps")
    ## expect(aq1[[4]] > aq2[[4]], "the error should decrease with more steps")

    lapply(1:length(ae1), function(x) expect_equal(x, getNDim(ae1[[x]])))
    lapply(1:length(ae2), function(x) expect_equal(x, getNDim(ae2[[x]])))
})

test_that("using autoencoder with keras", {
  skip_if_no_tensorflow()
  skip_if_no_keras()

  encoder <- function(i) list(keras::layer_dense(units = 10,
                                                 activation = "tanh"),
                              keras::layer_dense(units = i))
  decoder <- function() list(keras::layer_dense(units = 10,
                                                activation = "tanh"),
                             keras::layer_dense(units = 4))

  iris_data <- as(iris[, 1:4], "dimRedData")

  ae1 <- lapply(1:2, function(x) embed(iris_data, "AutoEncoder",
                                       keras_graph = list(encoder = encoder(x),
                                                          decoder = decoder()),
                                       n_steps = 2))
  aq1 <- lapply(ae1, function(x) quality(x, "reconstruction_rmse"))

  ae2 <- lapply(ae1, function(x) embed(iris_data, "AutoEncoder",
                                       autoencoder = x))
  aq2 <- lapply(ae2, function(x) quality(x, "reconstruction_rmse"))

  lapply(ae1, function(x) expect_s4_class(x, "dimRedResult"))
  lapply(ae2, function(x) expect_s4_class(x, "dimRedResult"))

  ## expect(aq1[[1]] > aq2[[1]], "the error should decrease with more steps")
  ## expect(aq1[[2]] > aq2[[2]], "the error should decrease with more steps")
  ## expect(aq1[[3]] > aq2[[3]], "the error should decrease with more steps")
  ## expect(aq1[[4]] > aq2[[4]], "the error should decrease with more steps")

  lapply(1:length(ae1), function(x) expect_equal(x, getNDim(ae1[[x]])))
  lapply(1:length(ae2), function(x) expect_equal(x, getNDim(ae2[[x]])))

})



## test_that("garbage collection", {
##   skip_if_no_tensorflow()
##   tmp <- tf$get_session_handle(environment(ae[[1]]@apply)$dec)

##   tmp <- tf$get_default_session()

##   tmp$close
##   tmp
##   tf$get_session_handle()
##   tf$Session()
## })