1 
data 

normalize 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51  # 1. classification
library(FMwR)
data("airquality")
airquality < airquality[complete.cases(airquality),]
airquality < airquality[sample(1:nrow(airquality), 1000, TRUE),]
airquality$Ozone < ifelse(airquality$Ozone > 60, 1, 1)
idx < sample(1:nrow(airquality), ceiling(nrow(airquality)*0.6))
train < fm.matrix(airquality[idx, 2:6], airquality[idx, 1])
test < fm.matrix(airquality[idx, 2:6], airquality[idx, 1])
fm_fit < fm.train(train, control = list(track.control(step_size = 1000),
solver.control(solver = TDAP.solver(random_step = 10, gamma = 1e5), max_iter = 12000),
model.control(L2.w1 = 0.1)))
# or
# fm_fit < fm.train(train, control = list(track.control(step_size = 1),
# solver.control(solver = MCMC.solver(), max_iter = 20)))
fm_track < fm.track(fm_fit, train, test, evaluate.metric = "ACC")
plot(fm_track)
fm_pred < predict(fm_fit, test)
table(test$labels, fm_pred > 0.5)
fm_fit_update < fm.update(fm_fit, train, control = list(track.control(step_size = 1000), solver.control(max_iter = 5000)))
fm_track_update < fm.track(fm_fit_update, train, test, evaluate.metric = "LL")
fm_best < fm.select(fm_fit_update, fm_track_update, drop.trace = TRUE)
# 2. regression
data("airquality")
airquality < airquality[complete.cases(airquality),]
airquality < airquality[sample(1:nrow(airquality), 1000, TRUE),]
idx < sample(1:nrow(airquality), ceiling(nrow(airquality)*0.6))
train < fm.matrix(airquality[idx, 2:6], airquality[idx, 1])
test < fm.matrix(airquality[idx, 2:6], airquality[idx, 1])
fm_fit < fm.train(train, normalize = T, control = list(track.control(step_size = 1000, evaluate.metric = "RMSE", convergence = 0),
solver.control(solver = SGD.solver(random_step = 10, learn_rate = 0.0001), max_iter = 12000),
model.control(task = "RE", factor.number = 3, L2.v = 0.5)))
# or
# fm_fit < fm.train(train, normalize = T, control = list(track.control(step_size = 1, evaluate.metric = "RMSE"),
# solver.control(solver = ALS.solver(), max_iter = 20),
# model.control(task = "RE", factor.number = 2)))
fm_track < fm.track(fm_fit, train, test, evaluate.metric = "MAE")
plot(fm_track)
fm_pred < predict(fm_fit, test)

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