### Lab: Deep Learning
### Note: this lab is slightly different from that in the August 2021 printing of ISLRII
### It is based on keras 2.6.0, while the original was based on keras 2.4.0
### The predict_classes() has been deprecated

## A Single Layer Network on the Hitters Data

###
library(ISLR2)
Gitters <- na.omit(Hitters)
n <- nrow(Gitters)
set.seed(13)
ntest <- trunc(n / 3)
testid <- sample(1:n, ntest)
###
lfit <- lm(Salary ~ ., data = Gitters[-testid, ])
lpred <- predict(lfit, Gitters[testid, ])
with(Gitters[testid, ], mean(abs(lpred - Salary)))
###
x <- scale(model.matrix(Salary ~ . - 1, data = Gitters))
y <- Gitters$Salary
###
library(glmnet)
# type.measure: loss to use for cross-validation.
cvfit <- cv.glmnet(x[-testid, ], y[-testid],
    type.measure = "mae")
cpred <- predict(cvfit, x[testid, ], s = "lambda.min")
mean(abs(y[testid] - cpred))
###
library(keras)
modnn <- keras_model_sequential() %>%
  layer_dense(units = 50, activation = "relu",
        input_shape = ncol(x)) %>%
   layer_dropout(rate = 0.4) %>%
   layer_dense(units = 1)
###
x <- scale(model.matrix(Salary ~ . - 1, data = Gitters))
###
x <- model.matrix(Salary ~ . - 1, data = Gitters) %>% scale()
###
modnn %>% compile(loss = "mse",
    optimizer = optimizer_rmsprop(),
    metrics = list("mean_absolute_error")
   )
###
history <- modnn %>% fit(
    x[-testid, ], y[-testid], epochs = 1500, batch_size = 32,
#    x[-testid, ], y[-testid], epochs = 15, batch_size = 32,
    validation_data = list(x[testid, ], y[testid])
  )
###
plot(history)
###
npred <- predict(modnn, x[testid, ])
mean(abs(y[testid] - npred))