22  지도학습-setence (문장 인식)

library(tidyverse)
pkgs = c("tidyverse",  "tidytext", "tensorflow", "keras", "caret", "data.table", 
         "googleLanguageR", "cld2","tidyverse", "datasets", "ggplot2", "tictoc")
for (pkg in pkgs){
  if(!require(pkg, character.only = TRUE)) install.packages(pkg)
  library(pkg, character.only = TRUE)
}

mt2 = readRDS("db/mt2.rds")
mt3 = mt2 %>% 
  # 몇
  mutate(code = code2) %>%
  select(id, code, word = word2)

code_inc = mt3 %>%
  select(id, code) %>% unique() %>%
  group_by(code) %>%
  count() %>%
  arrange(desc(n)) %>% 
  filter(n>1500) %>%     
  pull(code)

length(code_inc)
wordnumber = 2000
wordData = mt3 %>%
  filter(code %in% code_inc)  %>%
  group_by(word) %>%
  count() %>%
  arrange(desc(n)) %>%
  ungroup() %>%
  slice(1:wordnumber) %>%
  mutate(wordid = row_number()) %>%
  select(-n)
wordData
mt4 = mt3 %>% filter(code %in% code_inc) %>%
  left_join(wordData, by = c("word")) %>%
  na.omit() %>%
  ungroup() %>%
  mutate(code = as.numeric(code))

## code lkup 을 통해 code를 1부터 순서대로 만든 code_nums로 트레이닝 시키기 
code_lkup = mt4 %>%
  select(code) %>% unique() %>%
  mutate(code_nums = row_number())


mt5 = mt4 %>%
  filter(id != "W615000") %>%
  left_join(code_lkup, by=c("code"))

# data 만들기
library(parallel)
gg = mt5 %>% select(id, code_nums) %>% unique()
nrow(gg)
gg$code_nums
set.seed(2022)

trainIndex = createDataPartition(gg$code_nums, p =0.7, 
                                 list = FALSE, 
                                 times = 1)
index = trainIndex %>% as.numeric()
training_pre = gg[ index, ]
testing_pre  = gg[-index, ]
train_index = training_pre %>% pull(id) %>% unique()
test_index  = testing_pre %>% pull(id) %>% unique()
training = mt5 %>% filter(id %in% train_index)
testing  = mt5 %>% filter(id %in% test_index)
training$code %>% unique()
training %>%  group_by(code_nums) %>% count() %>%
  left_join(testing  %>%  group_by(code_nums) %>% count(), 
            by= c("code_nums")) %>%
  setNames(c("code_nums", "train", "test")) %>%
  mutate(prob = test/train)
trainDX = training %>% select(id, wordid)
trainDY = training %>% select(id, code_nums) %>% unique()
#trainDY %>%
#  group_by(id) %>%
#  count() %>%
#  arrange(desc(n))
testDX  = testing  %>% select(id, wordid)
testDY  = testing  %>% select(id, code_nums) %>% unique()
trainY = trainDY %>% pull(code_nums)
testY  = testDY %>% pull(code_nums)
trainY %>% unique() %>% length(.)

# list 로 변환
library(parallel)
trainMX = list()
trainF = function(i){trainDX %>% filter(id == i) %>% pull(wordid)}
trainMX =  mclapply(train_index, trainF,  mc.cores = 40)
length(trainMX) == nrow(trainDY)
#saveRDS(trainMX, "db/trainMX.rds")
#trainMX = readRDS("db/trainMX.rds")
trainMX
testMX = list()
testF = function(i){testDX %>% filter(id == i) %>% pull(wordid)}
testMX =  mclapply(test_index, testF,  mc.cores = 40)

sequencingF = function(mx){
  jj = matrix(0, nrow=length(mx), ncol= 2000)
  for (i in 1:length(mx))
    jj[i, mx[[i]]] <-1
  return(jj)
}

# one-hot encode to categories
library(reticulate)
library(tensorflow)
use_virtualenv("/home/sehnr/tensorflow/tensorvenv", required = TRUE)
Xtrain = sequencingF(trainMX)
Xtest  = sequencingF(testMX)
Ytrain = to_categorical(trainY) #

set.seed(2022)
nrow(Xtrain)
val_indices <- sample(c(1:c(Xtrain %>% nrow())), c(Xtrain %>% nrow())/10)
x_val <- Xtrain[val_indices,]
partial_x_train <- Xtrain[-val_indices,]
y_val <- Ytrain[val_indices,]
partial_y_train = Ytrain[-val_indices,]

library(keras)
keras::k_clear_session()
#ref: https://medium.com/@cmukesh8688/activation-functions-sigmoid-tanh-relu-leaky-relu-softmax-50d3778dcea5
model <- keras_model_sequential() %>%
  layer_dense(units = 18, activation = "relu", input_shape = c(wordnumber)) %>%
  layer_dropout(rate = 0.4) %>% #  layers to avoid overfitting.
  layer_dense(units = 72, activation = "relu") %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units = 36, activation = "sigmoid") %>%
  layer_dense(units = 18, activation = "softmax")
model

model %>% compile(
  optimizer = "adam",
  loss = "categorical_crossentropy",
  metrics = c("accuracy"))

history <- model %>% fit(
  partial_x_train,
  partial_y_train,
  epochs = 10,
  batch_size = 50,
  #validation_split=0.2, 
  validation_data = list(x_val, y_val)
)