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library(randomForest) | |
# load data | |
data = read.delim("data/sample.tsv", sep="\t") | |
# create data for k-fold cross validation | |
cv = function(d, k) { | |
n = sample(nrow(d), nrow(d)) | |
d.randomized = data[n,] # randomize data | |
n.residual = k-nrow(d)%%k | |
d.dummy = as.data.frame(matrix(NA, nrow=n.residual, ncol=ncol(d))) | |
names(d.dummy) = names(d) | |
d.randomized = rbind(d.randomized, d.dummy) # append dummy for residuals | |
d.splitted = split(d.randomized, 1:k) | |
for (i in 1:k) { | |
d.splitted[[i]] = na.omit(d.splitted[[i]]) | |
} | |
d.splitted | |
} | |
# training data | |
cv.training = function(d, k) { | |
d.train = as.data.frame(matrix(0, nrow=0, ncol=ncol(d[[1]]))) | |
names(d.train) = names(d[[1]]) | |
for (i in 1:length(d)) { | |
if (i != k) { | |
d.train = rbind(d.train, d[[i]]) | |
} | |
} | |
d.train | |
} | |
# test data | |
cv.test = function(d, k) { | |
d[[k]] | |
} | |
# stacking with glm | |
stacking = function(d, m) { | |
d = cbind(d, predict(m, newdata=d, type="response")) | |
names(d)[length(d)] = "stacking" | |
d | |
} | |
# check | |
score = function(p, r) { | |
s = c(0, 0, 0, 0) | |
for (i in 1:length(p)) { | |
pi = 2-as.integer(p[[i]]) | |
ri = 2-as.integer(r[i]) | |
s[pi*2+ri+1] = s[pi*2+ri+1]+1 | |
} | |
s | |
} | |
# stacking sample | |
k = 10 # cross validation split number | |
result = c() | |
for (i in 1:k) { | |
print(i) | |
data.splitted = cv(data, k) | |
# construct predict model | |
data.train = cv.training(data.splitted, 1) | |
model.glm = glm(y~., data=data.train, family=binomial) | |
data.train = stacking(data.train, model.glm) | |
model.rf = randomForest(y~., data=data.train) | |
# predict with test data | |
data.test = cv.test(data.splitted, 1) | |
data.test = stacking(data.test, model.glm) | |
model.rf.predict = predict(model.rf, newdata=data.test, type="class") | |
result = rbind(result, score(model.rf.predict, data.test$y)) | |
} | |
# show results | |
m = matrix(apply(result, 2, sum), 2, 2) | |
dimnames(m) = list(c("pred$p", "pred$n"), c("res$p", "res$n")) | |
print(m) | |
print(m/nrow(data)) |
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