naiveBayes giving unexpected result when using nonzero laplace argument (package e1071) - r

Question

I'm trying to use the naiveBayes() function from the e1071 package. When I add a non-zero laplace argument, my resulting probability estimates are not changing and I don't understand why.
Example:
library(e1071)
# Generate data
train.x <- data.frame(x1=c(1,1,0,0), x2=c(1,0,1,0))
train.y <- factor(c("cat", "cat", "dog", "dog"))
test.x <- data.frame(x1=c(1), x2=c(1))
# without laplace smoothing
classifier <- naiveBayes(x=train.x, y=train.y, laplace=0)
predict(classifier, test.x, type="raw") # returns (1, 0.00002507)
# with laplace smoothing
classifier <- naiveBayes(x=train.x, y=train.y, laplace=1)
predict(classifier, test.x, type="raw") # returns (1, 0.00002507)
I would expect the probabilities to change in this case since all the training instances for the "dog" class have 0 for x1. To check this, here's the same thing using Python
Python example:
import numpy as np
from sklearn.naive_bayes import BernoulliNB
train_x = pd.DataFrame({'x1':[1,1,0,0], 'x2':[1,0,1,0]})
train_y = np.array(["cat", "cat", "dog", "dog"])
test_x = pd.DataFrame({'x1':[1,], 'x2':[1,]})
# alpha (i.e. laplace = 0)
classifier = BernoulliNB(alpha=.00000001)
classifier.fit(X=train_x, y=train_y)
classifier.predict_proba(X=test_x) # returns (1, 0)
# alpha (i.e. laplace = 1)
classifier = BernoulliNB(alpha=1)
classifier.fit(X=train_x, y=train_y)
classifier.predict_proba(X=test_x) # returns (.75, .25)
Why am I getting this unexpected result using e1071?

Answer 1

Laplace estimates are only valid for categorical features, not numerical ones. You can find in the source code:
## estimation-function
est <- function(var)
if (is.numeric(var)) {
cbind(tapply(var, y, mean, na.rm = TRUE),
tapply(var, y, sd, na.rm = TRUE))
} else {
tab <- table(y, var)
(tab + laplace) / (rowSums(tab) + laplace * nlevels(var))
}
that for numerical values Gaussian estimates are used. Thus convert your data to factors and you are good to go.
train.x <- data.frame(x1=c("1","1","0","0"), x2=c("1","0","1","0"))
train.y <- factor(c("cat", "cat", "dog", "dog"))
test.x <- data.frame(x1=c("1"), x2=c("1"))
# without laplace smoothing
classifier <- naiveBayes(x=train.x, y=train.y, laplace=0)
predict(classifier, test.x, type="raw") # returns (100% for dog)
# with laplace smoothing
classifier <- naiveBayes(x=train.x, y=train.y, laplace=1)
predict(classifier, test.x, type="raw") # returns (75% for dog)

Answer 2

Major facepalm on this one. The naiveBayes() method was interpreting x1 and x2 as numeric variables and thus trying to use Gaussian conditional probability distributions internally (I think). Encoding my variables as factors solved my problem.
train.x <- data.frame(x1=factor(c(1,1,0,0)), x2=factor(c(1,0,1,0)))
train.y <- factor(c("cat", "cat", "dog", "dog"))
test.x <- data.frame(x1=factor(c(1)), x2=factor(c(1)))