single_variable with neuralnet( ) model about dalex HOT 5 CLOSED

Oh you are fast...! Your proposition is not far from a solution that i was trying since a few hours.

But the plot part seems to not work (the neural network model is flat as the variably "Y" is missing in ex_nn):

set.seed(13)
N <- 250
X1 <- runif(N)
X2 <- runif(N)
X3 <- runif(N)
X4 <- runif(N)
X5 <- runif(N)

f <- function(x1, x2, x3, x4, x5) {
((x1-0.5)2)^2-0.5 + sin(x210) + x3^6 + (x4-0.5)2 + abs(2x5-1)
}
y <- f(X1, X2, X3, X4, X5)

library(randomForest)
library(DALEX)
library(e1071)
library(rms)
library(neuralnet)

df <- data.frame(y, X1, X2, X3, X4, X5)

model_rf<-randomForest(y~., df)
model_svm<-svm(y~., df)
model_lm<-lm(y~., df)
model_nn<-neuralnet(y~X1+X2+X3+X4+X5,df,hidden=1)

dd <- datadist(df)
options(datadist="dd")
model_rms <- ols(y ~ rcs(X1) + rcs(X2) + rcs(X3) + rcs(X4) + rcs(X5), df)

ex_rf<-explain(model_rf)
ex_svm<-explain(model_svm)
ex_lm<-explain(model_lm)
ex_nn<-explain(model_nn,data = df[, -1],y = df[, 1],predict_function = function(x, y) compute(x, y)$net.result)
ex_rms<-explain(model_rms, label = "rms", data = df[, -1], y = df$y)
ex_tr<-explain(model_lm, data = df[,-1],
predict_function = function(m, x) f(x[,1], x[,2], x[,3], x[,4], x[,5]),
label = "True Model")

library(ggplot2)
plot(single_variable(ex_rf, "X1"),
single_variable(ex_svm, "X1"),
single_variable(ex_lm, "X1"),
single_variable(ex_nn, "X1"),
single_variable(ex_rms, "X1"),
single_variable(ex_tr, "X1")) +
ggtitle("Responses for X1. Truth: y ~ (2*x1 - 1)^2")

from dalex.

Try this code:

ex_nn<-explain(model_nn,
               data = df[, -1],
               y = df[, 1],
               predict_function = function(x, y) compute(x, y)$net.result)

Is this okay?

from dalex.

Problem solved... my last post was fucking stupid! Everything works great!

from dalex.

So, what was the problem?

from dalex.

The code you give me made the trick. I was just biased by the result! Stupid human brain!

from dalex.