I have a table of 5360*51200 size. Here, 5360 are the number of instances and 51200 are the number of features. I need to reduce the dimension of features. I was trying it by the help of stacked autoencoder in H2o, but it did not allow me to train to raise an error as:
Model is a large and large number of parameters
Here is the code:
library(h2o)
h2o.init(nthreads = -1)
check.deeplearning_stacked_autoencoder <- function() {
# this function builds a vector of autoencoder models, one per layer
#library(h2o)
#h2o.init()
get_stacked_ae_array <- function(training_data, layers, args) {
vector <- c()
index = 0
for (i in 1:length(layers)) {
index = index + 1
ae_model <- do.call(h2o.deeplearning,
modifyList(
list(
x = names(training_data),
training_frame = training_data,
autoencoder = T,
hidden = layers[i]
),
args
))
training_data = h2o.deepfeatures(ae_model, training_data, layer =
3)
names(training_data) <-
gsub("DF", paste0("L", index, sep = ""), names(training_data))
vector <- c(vector, ae_model)
}
cat(
length(vector))
}
# this function returns final encoded contents
apply_stacked_ae_array <- function(data, ae) {
index = 0
for (i in 1:length(ae)) {
index = index + 1
data = h2o.deepfeatures(ae[[i]], data, layer = 3)
names(data) <-
gsub("DF", paste0("L", index, sep = ""), names(data))
}
data
}
TRAIN <-
"E:/Chiranjibi file/Geometric features/Lu/Train/d_features.csv"
TEST <-
"E:/Chiranjibi file/Geometric features/Lu/Test/d_features.csv"
response <- 51201
# set to T for RUnit
# set to F for stand-alone demo
if (T) {
train_hex <- h2o.importFile((TRAIN))
test_hex <- h2o.importFile((TEST))
} else
{
library(h2o)
h2o.init()
homedir <-
paste0(path.expand("~"), "/h2o-dev/") #modify if needed
train_hex <-
h2o.importFile(path = paste0(homedir, TRAIN),
header = F,
sep = ',')
test_hex <-
h2o.importFile(path = paste0(homedir, TEST),
header = F,
sep = ',')
}
train <- train_hex[, -response]
test <- test_hex [, -response]
train_hex[, response] <- as.factor(train_hex[, response])
test_hex [, response] <- as.factor(test_hex [, response])
## Build reference model on full dataset and evaluate it on the test set
model_ref <-
h2o.deeplearning(
training_frame = train_hex,
x = 1:(ncol(train_hex) - 1),
y = response,
hidden = c(67),
epochs = 50
)
p_ref <- h2o.performance(model_ref, test_hex)
h2o.logloss(p_ref)
## Now build a stacked autoencoder model with three stacked layer AE models
## First AE model will compress the 717 non-const predictors into 200
## Second AE model will compress 200 into 100
## Third AE model will compress 100 into 50
layers <- c(50000,20000,10000,5000,2000, 1000, 500)
args <- list(activation = "Tanh",
epochs = 1,
l1 = 1e-5)
ae <- get_stacked_ae_array(train, layers, args)
## Now compress the training/testing data with this 3-stage set of AE models
train_compressed <- apply_stacked_ae_array(train, ae)
test_compressed <- apply_stacked_ae_array(test, ae)
## Build a simple model using these new features (compressed training data) and evaluate it on the compressed test set.
train_w_resp <- h2o.cbind(train_compressed, train_hex[, response])
test_w_resp <- h2o.cbind(test_compressed, test_hex[, response])
model_on_compressed_data <-
h2o.deeplearning(
training_frame = train_w_resp,
x = 1:(ncol(train_w_resp) - 1),
y = ncol(train_w_resp),
hidden = c(67),
epochs = 1
)
p <- h2o.performance(model_on_compressed_data, test_w_resp)
h2o.logloss(p)
}
#h2o.describe(train)
#doTest("Deep Learning Stacked Autoencoder", check.deeplearning_stacked_autoencoder)
As Tom says, your autoencoder first layer is too big.
51,200 is a lot of features. How much correlation is there between them? The more correlation you have, the smaller the first layer of your autoencoder can happily be.
Try h2o.prcomp() and seeing how many dimensions cover 99% of the variance, is often a good guide to how big your first layer can/should be.
Or, if you prefer a more experimental approach:
You could then try moving to multiple layers. But not much point using a bigger first layer than the best you can get from trying a single layer.