How to set class_weight in keras package of R? - r

Question

I am using keras package in R to train a deep learning model. My data set is highly imbalanced. Therefore, I want to set class_weight argument in the fit function. Here is the fit function and its arguments that I used for my model
history <- model %>% fit(
trainData, trainClass,
epochs = 5, batch_size = 1000,
class_weight = ????,
validation_split = 0.2
)
In python I can set class_weight as follow:
class_weight={0:1, 1:30}
But I am not sure how to do it in R. In the help menu of R it describes class_weight as follow:
Optional named list mapping indices (integers) to a weight (float) to
apply to the model's loss for the samples from this class during
training. This can be useful to tell the model to "pay more attention"
to samples from an under-represented class.
Any idea or suggestions?

Answer 1

Class_weight needs to be a list, so
history <- model %>% fit(
trainData, trainClass,
epochs = 5, batch_size = 1000,
class_weight = list("0"=1,"1"=30),
validation_split = 0.2
)
seems to work. Keras internally uses a function called as_class_weights to change the list to a python-dictionary (see https://rdrr.io/cran/keras/src/R/model.R).
class_weight <- dict(list('0'=1,'1'=10))
class_weight
>>> {0: 1.0, 1: 10.0}
Looks just like the python dictionary that you mentioned above.

Answer 2

I found a generic solution in Python solution, so I converted into R:
counter=funModeling::freq(Y_data_aux_tr, plot=F) %>% select(var, frequency)
majority=max(counter$frequency)
counter$weight=ceil(majority/counter$frequency)
l_weights=setNames(as.list(counter$weight), counter$var)
Using it:
fit(..., class_weight = l_weights)
An advice if you are using fit_generator: since the weights are based on frequency, having a different number of training-validation samples may bias the validation results. They should be equally-sized.