Hello!

I need to create a dataset encoded with autoenceders.
The data set has 500 records as follows:

I have installed and loaded the following libraries:

library("rminer")
library("keras")
library("tensorflow")
library("reticulate")
I have divided my data set into two parts, training and test, with the holdout method:
Division <- holdout(y=Datos$recid)
where Datos is my dataset

I get the training and test data, removing the column that is the data that you want to predict:

x_train <- subset(Datos[Division$tr,], select = -recid)
x_test <- subset(Datos[Division$ts,], select = -recid)
Then I convert the data set to matrix (I do not know if this is necessary)

x_train_matrix = data.matrix(x_train)
x_test_matrix = data.matrix(x_test)
At this moment, what I want to do is the following:

Define a coding model, with an input layer and the coding layer.
Extract the encoded data, for later take this reduction of characteristics to use them in other training models
original_dim <- 7L #334L
encoding_dim <- 4L #32L
input_img <- layer_input(shape = c(original_dim))
encoded<- layer_dense(input_img,encoding_dim , activation = "relu")
autoencoder <- keras_model(input_img, encoded)
autoencoder %>% compile(optimizer = 'adadelta', loss = 'binary_crossentropy')

Am I on the right path?

I need help to achieve the goal I have tried to explain, I hope you help me.
Thank you very much

Am I correct that what you intend to do is in the line of what @max describes in his book

https://bookdown.org/max/FES/engineering-numeric-predictors.html#autoencoders

?

that is - some kind of semi-supervised feature extraction to use on structurally identical datasets?
(This scenario assumes you have other datasets of the same structure that you can then just run through the encoder and extract the hidden "features". Another scenario would be where you were interested in encoding individual features - in that case, you might want to look into embeddings instead).

In general, a good introduction to how autoencoders work (with code in Python, but it should look pretty similar in R) is

https://blog.keras.io/building-autoencoders-in-keras.html

(In your case, probably the first two models - simple and sparse autoencoder) would apply.)

Regarding the code snippets, some comments

You will want to have a "bottleneck layer" "in the middle" and then an output layer of the same dimensionality as the input
The categorical data needs to be one-hot encoded
With a mix of categorical and continuous variables, it can be difficult to find an adequate loss function - mean squared error will probably work best
For better performance, standardize the numerical variables
Hope this gets you started

Thanks, I'm trying to do the following, but I do not know if I'm right

```
# read de dataset
Datos <- read.table(file="datos_campus_virtual.txt",header=TRUE)
# conver to categorical data
for(unique_value in unique(Datos$feno)){
Datos[paste("feno", unique_value, sep = ".")] <- ifelse(Datos$feno == unique_value, 1, 0)
}
for(unique_value in unique(Datos$grado)){
Datos[paste("grado", unique_value, sep = ".")] <- ifelse(Datos$grado == unique_value, 1, 0)
}
Datos$quim <- ifelse(Datos$quim=="No",0,1)
Datos$horm <- ifelse(Datos$horm=="No",0,1)
Datos$recid <- ifelse(Datos$recid=="No",0,1)
# Two set: training (334) y test (166) with holdout
Division <- holdout(y=Datos$recid)
# prepara de data training and test
train <- Datos[Division$tr,]
x_train <- subset.data.frame(train,select = -grado)
x_train <- subset.data.frame(x_train,select = -feno)
test <- Datos[Division$ts,]
x_test <- subset.data.frame(test,select = -grado)
x_test <- subset.data.frame(x_test,select = -feno)
x_train_matrix = data.matrix(x_train)
x_test_matrix = data.matrix(x_test)
# Autoencoder and encoder
original_dim <- 15 #7L
encoding_dim <- 7 #4L
latent_dim <-15 #7L
# **********************
# Model definition
# **********************
# this is our input placeholder
input <- layer_input(shape = c(original_dim))
# "encoded" is the encoded representation of the input
encoded<- layer_dense(input,encoding_dim , activation = "relu")
#decode layer
decoded<- layer_dense(encoded, latent_dim , activation = "sigmoid")
model_enconded <- keras_model(input, encoded)
model_autoencoder <- keras_model(input, decoded)
# **********************
# Compile
# **********************
model_autoencoder %>% compile(optimizer = 'adadelta', loss = 'binary_crossentropy')
# **********************
# training
# **********************
history <- model_autoencoder %>% fit(x_train_matrix, x_train_matrix,epochs=50,batch_size=256)
#predict
feature <- predict(object = model_enconded,x = x_test_matrix)
# Save de new data
write.csv(feature, file = "dataencoded.csv",row.names = FALSE)
```

Max
July 18, 2018, 7:38pm
4
There's a better workflow example given as an example here . I would avoid using `data.table`

if you are going to put the data into a model; most model functions don't work with them based on how they store the data.

Also, having a small reproducible example is the main way for people to check/verify your code. Otherwise, it's all guesswork on our part.