In order to chose the best set of hyperparameters (leaking rate,
input scaling, spectral radius and ridge penalty) on the train set we
are going to use an accumulate forward procedure. Basically, during the
year 2021, we are going to train the model each 6 months and evaluate
the forecast on the next 3 months. This procedure will be repeated
several times with different sets of hyperparameters each time.
Preparation
First we set the periods of training and evaluation :
# dates of training
vec_train_sets <- as.Date(c("2021-01-01", "2021-06-01"))
# dates of evaluation
vec_test_sets_start <- vec_train_sets+dist_forecast
vec_test_sets_end <- c(vec_test_sets_start[2:length(vec_test_sets_start)], traintest_date-1)
# get everything in a table
dfaccumulateForward = data.frame(train_date = vec_train_sets,
test_start = vec_test_sets_start,
test_end = vec_test_sets_end)
Then we set the objective functions computing the mse for the chosen
of hyperparameters :
fct_objective <- function(ridge,
leaking_rate,
input_scaling,
spectral_radius,
dfaccumulateForward){
##### reservoir architecture
# set reservoir
reservoir <- reservoirnet::createNode(nodeType = "Reservoir",
units = 500,
lr = 0.7,
sr = 1,
input_scaling = 1)
# set readout
readout <- reservoirnet::createNode(nodeType = "Ridge", ridge = 0.1)
# connect them
model <- reservoirnet::link(reservoir, readout)
##### evaluate model
dfPredictions <- apply(dfaccumulateForward,
MARGIN = 1,
FUN = function(row_accumulate_forward){
fct_performance_period(model = model,
dfOutcome = dfOutcome,
train_date = as.Date(row_accumulate_forward["train_date"]),
test_start = as.Date(row_accumulate_forward["test_start"]),
test_end = as.Date(row_accumulate_forward["test_end"]))
}) %>%
bind_rows()
##### get performance
mse = dfPredictions %>%
mutate(squared_error = (pred-outcome)^2) %>%
pull(squared_error) %>%
mean(.)
return(mse)
}
fct_performance_period <- function(model,
train_date,
test_start,
test_end,
dfOutcome){
##### train and test set
# train set
yTrain <- dfOutcome %>% filter(outcomeDate <= train_date) %>% select(outcome)
xTrain <- dfOutcome %>% filter(outcomeDate <= train_date) %>% select(hosp, Positive, Tested)
# test set
xTest <- dfOutcome %>% filter(outcomeDate <= test_end) %>% select(hosp, Positive, Tested)
yTest <- dfOutcome %>%
filter(outcomeDate <= test_end) %>%
select(outcomeDate, outcome) %>%
mutate(eval_period = outcomeDate >= test_start)
##### preprocessing of the data
# standardise based on training set values
ls_fct_stand <- apply(xTrain,
MARGIN = 2,
FUN = function(x) function(feature) return(feature/(max(x))))
xTrainstand <- xTrain
xTeststand <- xTest
lapply(X = names(ls_fct_stand),
FUN = function(x){
xTrainstand[,x] <<- ls_fct_stand[[x]](feature = xTrain[,x])
xTeststand[,x] <<- ls_fct_stand[[x]](feature = xTest[,x])
return()
})
# convert to array
lsdf <- lapply(list(yTrain = yTrain,
xTrain = xTrainstand,
xTest = xTeststand),
function(x) as.array(as.matrix(x)))
##### fit reservoir on train set
fit <- reservoirnet::reservoirR_fit(node = model,
X = lsdf$xTrain,
Y = lsdf$yTrain,
warmup = 30,
reset = TRUE)
##### predict with the reservoir
vec_pred <- reservoirnet::predict_seq(node = fit$fit,
X = lsdf$xTest,
reset = TRUE)
dfPredictions <- yTest %>%
mutate(pred = vec_pred) %>%
filter(eval_period) %>%
select(outcomeDate, outcome, pred)
return(dfPredictions)
}
Generate hyperparameters
To generate the hyperparameters we use log-uniform generations. This
is easily done using the rloguniform and random_search_hyperparam
functions. For instance we have :
random_search_hyperparam(
n = 50,
ls_fct = list(
ridge = function(n)
rloguniform(n = n, min = 1e-10, max = 1e-1),
input_scaling = function(n)
1,
spectral_radius = function(n)
rloguniform(n = n, min = 1e-10, max = 1e5),
leaking_rate = function(n)
0.9
)
) %>%
head()
## # A tibble: 6 × 5
## search_id ridge input_scaling spectral_radius leaking_rate
## <int> <dbl> <dbl> <dbl> <dbl>
## 1 1 0.0000000245 1 1.46e-3 0.9
## 2 2 0.000000223 1 8.28e+2 0.9
## 3 3 0.0000143 1 3.73e-4 0.9
## 4 4 0.0149 1 4.70e-7 0.9
## 5 5 0.00000000653 1 1.15e-9 0.9
## 6 6 0.0122 1 3.10e-9 0.9
