Working with neuralnet in R for the first time: get "requires numeric/complex matrix/vector arguments"
I'm in the process of attempting to learn to work with neural networks in R. As a learning problem, I've been using the following problem over at Kaggle:
Don't worry, this problem is specifically designed for people to learn with, there's no reward tied to it.
I started with a simple logistic regression, which was great for getting my feet wet. Now I'd like to learn to work with neural networks. My training data looks like this (Column:Row):
- survived: 1
- pclass: 3
- sex: male
- age: 22.0
- sibsp: 1
- parch: 0
- ticket: PC 17601
- fare: 7.25
- cabin: C85
- embarked: S
My starting R code looks like this:
> net <- neuralnet(survived ~ pclass + sex + age + sibsp +
parch + ticket + fare + cabin + embarked,
train, hidden=10, threshold=0.01)
When I run this line of code I get the following error:
Error in neurons[[i]] %*% weights[[i]] :
requires numeric/complex matrix/vector arguments
I understand that the problem is in the way I'm presenting my input variables but I'm too much of a novice to understand what I need to do to correct this. Can anyone help?
Thanks!
Answer
Before blindly giving the data to the computer, it is a good idea to look at it:
d <- read.csv("train.csv")
str(d)
# 'data.frame': 891 obs. of 12 variables:
# $ PassengerId: int 1 2 3 4 5 6 7 8 9 10 ...
# $ Survived : int 0 1 1 1 0 0 0 0 1 1 ...
# $ Pclass : int 3 1 3 1 3 3 1 3 3 2 ...
# $ Name : Factor w/ 891 levels "Abbing, Mr. Anthony",..: 109 191 358 277 16 559 520 629 417 581 ...
# $ Sex : Factor w/ 2 levels "female","male": 2 1 1 1 2 2 2 2 1 1 ...
# $ Age : num 22 38 26 35 35 NA 54 2 27 14 ...
# $ SibSp : int 1 1 0 1 0 0 0 3 0 1 ...
# $ Parch : int 0 0 0 0 0 0 0 1 2 0 ...
# $ Ticket : Factor w/ 681 levels "110152","110413",..: 524 597 670 50 473 276 86 396 345 133 ...
# $ Fare : num 7.25 71.28 7.92 53.1 8.05 ...
# $ Cabin : Factor w/ 148 levels "","A10","A14",..: 1 83 1 57 1 1 131 1 1 1 ...
# $ Embarked : Factor w/ 4 levels "","C","Q","S": 4 2 4 4 4 3 4 4 4 2 ...
summary(d)
Some of the variables have too many values to be useful (at least in your first model): you can remove the name, ticket, cabin and passengerId. You may also want to transform some of the numeric variables (say, class), to factors, if it is more meaningful.
Since neuralnet only deals with quantitative variables,
you can convert all the qualitative variables (factors)
to binary ("dummy") variables, with the model.matrix function --
it is one of the very rare situations
in which R does not perform the transformation for you.
m <- model.matrix(
~ Survived + Pclass + Sex + Age + SibSp + Parch + Fare + Embarked,
data = d
)
head(m)
library(neuralnet)
r <- neuralnet(
Survived ~ Pclass + Sexmale + Age + SibSp + Parch + Fare + EmbarkedC + EmbarkedQ + EmbarkedS,
data=m, hidden=10, threshold=0.01
)