Deep Learning

Keras



In [1]:

    
library(keras)



In [2]:

    
df <- read.csv('evasao.csv')

Separando dados de treino e teste:



In [3]:

    
n <- nrow(df)
set.seed(42) # Se não fizer isso, verá valores diferentes dos meus
limite <- sample(1:n, size = round(0.75*n), replace = FALSE)
train_df <- df[limite,]
test_df <- df[-limite,]



In [4]:

    
head(test_df)









    





periodo bolsa repetiu ematraso disciplinas faltas desempenho abandonou

	3  4      0.10    0       1       1        0      8.000000 0       
	4  4      0.20    8       1       1        0      4.000000 1       
	7  9      0.10    6       1       1        1      2.000000 0       
	9  9      0.15    7       1       5       10      2.800000 0       
	11  6      0.25    5       1       3        6      2.666667 0       
	17 10      0.05    0       0       2        0      9.000000 0

Vamos criar o modelo Sequencial:



In [5]:

    
modelo <- keras_model_sequential() 
modelo %>% 
  layer_dense(units = 128, activation = 'relu', input_shape=c(7), kernel_initializer = "normal") %>% 
  layer_dropout(rate = 0.4) %>% 
  layer_dense(units = 512, activation='relu') %>%
  layer_dropout(rate = 0.3) %>%
  layer_dense(units=2,activation='sigmoid')



In [6]:

    
summary(modelo)









    



________________________________________________________________________________
Layer (type)                        Output Shape                    Param #     
================================================================================
dense_1 (Dense)                     (None, 128)                     1024        
________________________________________________________________________________
dropout_1 (Dropout)                 (None, 128)                     0           
________________________________________________________________________________
dense_2 (Dense)                     (None, 512)                     66048       
________________________________________________________________________________
dropout_2 (Dropout)                 (None, 512)                     0           
________________________________________________________________________________
dense_3 (Dense)                     (None, 2)                       1026        
================================================================================
Total params: 68,098
Trainable params: 68,098
Non-trainable params: 0
________________________________________________________________________________

Compilando e treinando o modelo:



In [7]:

    
modelo %>% compile(
  loss = 'categorical_crossentropy',
  optimizer = optimizer_rmsprop(),
  metrics = 'accuracy'
)



In [8]:

    
library(dplyr)
preditores_treino <- data.matrix(select(train_df, c('periodo','bolsa','repetiu','ematraso','disciplinas','faltas','desempenho')))









    



Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union



In [9]:

    
head(preditores_treino)









    





periodo bolsa repetiu ematraso disciplinas faltas desempenho

	275  2  0.15 0   1   1   0   10.0
	281  9  0.00 2   1   5   0    0.8
	86  9  0.10 3   0   1   2    6.0
	247  8  0.15 8   0   0   0    0.0
	190  9  0.25 6   0   3   0    1.0
	154 10  0.20 3   1   2   2    5.0



In [10]:

    
rotulos_treino <- to_categorical(data.matrix(select(train_df,c('abandonou'))))
head(rotulos_treino)



In [11]:

    
historico <- modelo %>% fit(
  preditores_treino, rotulos_treino, 
  epochs = 30, batch_size = 128, 
  validation_split = 0.2
)



In [12]:

    
plot(historico)

Avaliando o modelo:



In [14]:

    
rotulos_teste <- to_categorical(data.matrix(select(test_df,c('abandonou'))))
head(rotulos_teste)
preditores_teste <- data.matrix(select(test_df, c('periodo','bolsa','repetiu','ematraso','disciplinas','faltas','desempenho')))
modelo %>% evaluate(preditores_teste, rotulos_teste)









    






	1 0
	0 1
	1 0
	1 0
	1 0
	1 0









    





	$loss
		0.714963535467784
	$acc
		0.653333331743876



In [15]:

    
modelo









    





Model
________________________________________________________________________________
Layer (type)                        Output Shape                    Param #     
================================================================================
dense_1 (Dense)                     (None, 128)                     1024        
________________________________________________________________________________
dropout_1 (Dropout)                 (None, 128)                     0           
________________________________________________________________________________
dense_2 (Dense)                     (None, 512)                     66048       
________________________________________________________________________________
dropout_2 (Dropout)                 (None, 512)                     0           
________________________________________________________________________________
dense_3 (Dense)                     (None, 2)                       1026        
================================================================================
Total params: 68,098
Trainable params: 68,098
Non-trainable params: 0
________________________________________________________________________________



In [19]:

    
resultado <- modelo %>% predict_classes(preditores_teste)



In [24]:

    
str(resultado)









    



 num [1:75(1d)] 0 1 1 1 1 0 1 1 1 1 ...



In [25]:

    
test_df$abandonou



In [26]:

    
resultado



In [ ]:

	periodo	bolsa	repetiu	ematraso	disciplinas	faltas	desempenho	abandonou
3	4	0.10	0	1	1	0	8.000000	0
4	4	0.20	8	1	1	0	4.000000	1
7	9	0.10	6	1	1	1	2.000000	0
9	9	0.15	7	1	5	10	2.800000	0
11	6	0.25	5	1	3	6	2.666667	0
17	10	0.05	0	0	2	0	9.000000	0

	periodo	bolsa	repetiu	ematraso	disciplinas	faltas	desempenho
275	2	0.15	0	1	1	0	10.0
281	9	0.00	2	1	5	0	0.8
86	9	0.10	3	0	1	2	6.0
247	8	0.15	8	0	0	0	0.0
190	9	0.25	6	0	3	0	1.0
154	10	0.20	3	1	2	2	5.0