Aprendizaje Automatico con Tensorflow y R

Instalar paquetes

pkgs <- c("keras", "lime", "rsample", "recipes", "yardstick", "corrr")
install.packages(pkgs)

tidyverse

http://tidyverse.org/

Parsed with column specification:
cols(
  .default = col_character(),
  SeniorCitizen = [32mcol_double()[39m,
  tenure = [32mcol_double()[39m,
  MonthlyCharges = [32mcol_double()[39m,
  TotalCharges = [32mcol_double()[39m
)
See spec(...) for full column specifications.

glimpse(datos_perdimiento)

rsample

https://tidymodels.github.io/rsample/

library(rsample)

set.seed(100)

separa_datos <- initial_split(
  datos_perdimiento, 
  prop = 0.3)

tbl_entrenar <- training(separa_datos)
tbl_prueba  <- testing(separa_datos)

recipes

https://tidymodels.github.io/recipes/

library(recipes)

receta <- tbl_entrenar %>%
  recipe(Churn ~ .) %>%
  step_rm(customerID) %>%
  step_naomit(all_outcomes(), all_predictors()) %>%
  step_discretize(tenure, options = list(cuts = 6)) %>%
  step_log(TotalCharges) %>%
  step_mutate(Churn = ifelse(Churn == "Yes", 1, 0)) %>%
  step_dummy(all_nominal(), -all_outcomes()) %>%
  step_center(all_predictors(), -all_outcomes()) %>%
  step_scale(all_predictors(), -all_outcomes()) %>%
  prep()

summary(receta)

save(receta, file = "../aplicacion/receta.RData")

x_tbl_entrenar <- receta %>% 
  juice(all_predictors(), composition = "matrix") 

y_vec_entrenar <- receta %>% 
  juice(all_outcomes()) %>% 
  pull()

baked_test <- bake(receta, tbl_prueba)

x_tbl_prueba <- baked_test %>%
  select(-Churn) %>%
  as.matrix()

y_vec_prueba <- baked_test %>%
  select(Churn) %>%
  pull()

Instalar Tensorflow & Keras

https://tensorflow.rstudio.com/tensorflow/articles/installation.html

https://tensorflow.rstudio.com/keras/#installation

library(tensorflow)
library(keras)

#install_tensorflow()
#install_keras()

Crear una red neural

model_keras <- keras_model_sequential() %>%
  layer_dense(
    units = 16, 
    kernel_initializer = "uniform", 
    activation = "relu", 
    input_shape = ncol(x_tbl_entrenar)) %>% 
  layer_dropout(rate = 0.1) %>%
  layer_dense(
    units = 16, 
    kernel_initializer = "uniform", 
    activation = "relu") %>% 
  layer_dropout(rate = 0.1) %>%
  layer_dense(
    units = 1, 
    kernel_initializer = "uniform", 
    activation = "sigmoid") %>% 
  compile(
    optimizer = 'adam',
    loss = 'binary_crossentropy',
    metrics = c('accuracy')
  )

model_keras

Model
___________________________________________________________________________________________________________
Layer (type)                                    Output Shape                              Param #          
===========================================================================================================
dense_1 (Dense)                                 (None, 16)                                576              
___________________________________________________________________________________________________________
dropout_1 (Dropout)                             (None, 16)                                0                
___________________________________________________________________________________________________________
dense_2 (Dense)                                 (None, 16)                                272              
___________________________________________________________________________________________________________
dropout_2 (Dropout)                             (None, 16)                                0                
___________________________________________________________________________________________________________
dense_3 (Dense)                                 (None, 1)                                 17               
===========================================================================================================
Total params: 865
Trainable params: 865
Non-trainable params: 0
___________________________________________________________________________________________________________

Correr el modelo

history <- fit(
  object = model_keras, 
  x = x_tbl_entrenar, 
  y = y_vec_entrenar,
  batch_size = 50, 
  epochs = 35,
  validation_split = 0.30,
  verbose = 0
)

2019-03-17 11:19:29.569656: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2

print(history)

Trained on 1,478 samples, validated on 634 samples (batch_size=50, epochs=35)
Final epoch (plot to see history):
val_loss: 0.4467
 val_acc: 0.7855
    loss: 0.4049
     acc: 0.8031

Ver los resultados

theme_set(theme_bw())

plot(history)

yhat_keras_class_vec <- model_keras %>%
  predict_classes(x_tbl_prueba) %>%
  as.factor() %>%
  fct_recode(yes = "1", no = "0")

yhat_keras_prob_vec  <- model_keras %>%
  predict_proba(x_tbl_prueba) %>%
  as.vector()

test_truth <- y_vec_prueba %>% 
  as.factor() %>% 
  fct_recode(yes = "1", no = "0")

estimates_keras_tbl <- tibble(
  truth      = test_truth,
  estimate   = yhat_keras_class_vec,
  class_prob = yhat_keras_prob_vec
)

estimates_keras_tbl

yardstick

https://tidymodels.github.io/yardstick/

yardstick is a package to estimate how well models are working using tidy data principals.

library(yardstick)

options(yardstick.event_first = FALSE)

estimates_keras_tbl %>% 
  conf_mat(truth, estimate)

          Truth
Prediction   no  yes
       no  3229  569
       yes  396  726

estimates_keras_tbl %>% 
  metrics(truth, estimate)


estimates_keras_tbl %>% 
  roc_auc(truth, class_prob)


estimates_keras_tbl %>%
  precision(truth, estimate) %>%
  bind_rows(
    estimates_keras_tbl %>% 
      recall(truth, estimate) 
  )


estimates_keras_tbl %>% 
  f_meas(truth, estimate, beta = 1)

lime

https://github.com/thomasp85/lime

library(lime)

model_type.keras.engine.sequential.Sequential <- function(x, ...) {
  "classification"
}

predict_model.keras.engine.sequential.Sequential <- function(x, newdata, type, ...) {
  pred <- predict_proba(object = x, x = as.matrix(newdata))
  data.frame(Yes = pred, No = 1 - pred)
}

model_keras %>%
  predict_model(x_tbl_prueba, "raw") %>%
  as_tibble()

library(lime)

explainer <- x_tbl_entrenar %>%
  as_tibble() %>% 
  lime(model_keras, 
       bin_continuous = FALSE)
  
explanation <-  x_tbl_entrenar %>%
  as.data.frame() %>%
  head(40) %>%
  lime::explain(
    explainer    = explainer, 
    n_labels     = 1, 
    n_features   = 4,
    kernel_width = 0.5
    )

plot_explanations(explanation) +
  labs(
    title = "Importancia de cada variable",
    subtitle = "Usando 40 observaciones de prueba"
    )

corrr

https://github.com/drsimonj/corrr

library(corrr)

corrr_analysis <- x_tbl_entrenar %>%
  as_tibble() %>%
  mutate(Churn = y_vec_entrenar) %>%
  correlate() %>%
  focus(Churn) %>%
  rename(feature = rowname) %>%
  arrange(abs(Churn)) %>%
  mutate(feature = as_factor(feature))


Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'

corrr_analysis

over <- corrr_analysis %>%
  filter(Churn > 0)

under <- corrr_analysis %>%
  filter(Churn < 0)

corrr_analysis %>%
  ggplot(aes(x = Churn, y = fct_reorder(feature, desc(Churn)))) +
    geom_point() +
    geom_segment(aes(xend = 0, yend = feature), data = under, color = "orange") +
    geom_point(data = under, color = "orange") +
    geom_segment(aes(xend = 0, yend = feature), data = over, color = "blue") +
    geom_point(data = over, color = "blue") +
  labs(title = "Corelaciones de perdida de clientes", y = "", x = "")

NA

Mas exploracion

datos_perdimiento %>%
  group_by(Contract, Churn) %>%
  tally() %>%
  spread(Churn, n)

datos_perdimiento %>%
  group_by(InternetService, Churn) %>%
  tally() %>%
  spread(Churn, n)

Desplegar el modelo

export_savedmodel(model_keras, "tfmodel")

library(rsconnect)
deployTFModel(
  "tfmodel", 
  server = "colorado.rstudio.com", 
  account = rstudioapi::askForPassword("Enter Connect Username:")
  )

library(httr)

baked_numeric <- x_tbl_prueba %>%
  as_tibble() %>%
  head(4) %>%
  transpose() %>%
  map(as.numeric)

body <- list(instances = list(baked_numeric))

r <- POST("https://colorado.rstudio.com/rsc/content/2230/serving_default/predict", body = body, encode = "json")

jsonlite::fromJSON(content(r))$predictions[, , 1]

[1] 0.5589350 0.2727856 0.5589350 0.5589350

---
title: "Aprendizaje Automatico con Tensorflow y R"
output: html_notebook
---

## Instalar paquetes

```{r, eval = FALSE}
pkgs <- c("keras", "lime", "rsample", "recipes", "yardstick", "corrr")
install.packages(pkgs)
```

```{r, include = FALSE}
library(keras)
library(lime)
library(tidyverse)
library(rsample)
library(recipes)
library(yardstick)
library(corrr)
library(tensorflow)
```

## tidyverse

http://tidyverse.org/


```{r, echo = FALSE}
library(tidyverse)

if(!file.exists("customer_churn.csv")){
  download.file(
    "https://raw.githubusercontent.com/rstudio/keras-customer-churn/master/data/WA_Fn-UseC_-Telco-Customer-Churn.csv",
    "customer_churn.csv"
  ) 
}

datos_perdimiento <- read_csv("customer_churn.csv")
```

```{r, eval = FALSE}
glimpse(datos_perdimiento)
```


## rsample

https://tidymodels.github.io/rsample/

```{r}
library(rsample)

set.seed(100)

separa_datos <- initial_split(
  datos_perdimiento, 
  prop = 0.3)

tbl_entrenar <- training(separa_datos)
tbl_prueba  <- testing(separa_datos)
```

## recipes

https://tidymodels.github.io/recipes/

```{r}
library(recipes)

receta <- tbl_entrenar %>%
  recipe(Churn ~ .) %>%
  step_rm(customerID) %>%
  step_naomit(all_outcomes(), all_predictors()) %>%
  step_discretize(tenure, options = list(cuts = 6)) %>%
  step_log(TotalCharges) %>%
  step_mutate(Churn = ifelse(Churn == "Yes", 1, 0)) %>%
  step_dummy(all_nominal(), -all_outcomes()) %>%
  step_center(all_predictors(), -all_outcomes()) %>%
  step_scale(all_predictors(), -all_outcomes()) %>%
  prep()

summary(receta)
```

```{r}
save(receta, file = "../aplicacion/receta.RData")
```

```{r}
x_tbl_entrenar <- receta %>% 
  juice(all_predictors(), composition = "matrix") 

y_vec_entrenar <- receta %>% 
  juice(all_outcomes()) %>% 
  pull()
```


```{r}
baked_test <- bake(receta, tbl_prueba)

x_tbl_prueba <- baked_test %>%
  select(-Churn) %>%
  as.matrix()

y_vec_prueba <- baked_test %>%
  select(Churn) %>%
  pull()
```


## Instalar Tensorflow & Keras

https://tensorflow.rstudio.com/tensorflow/articles/installation.html

https://tensorflow.rstudio.com/keras/#installation

```{r, eval = FALSE }
library(tensorflow)
library(keras)

#install_tensorflow()
#install_keras()
```


### Crear una red neural

```{r}
model_keras <- keras_model_sequential() %>%
  layer_dense(
    units = 16, 
    kernel_initializer = "uniform", 
    activation = "relu", 
    input_shape = ncol(x_tbl_entrenar)) %>% 
  layer_dropout(rate = 0.1) %>%
  layer_dense(
    units = 16, 
    kernel_initializer = "uniform", 
    activation = "relu") %>% 
  layer_dropout(rate = 0.1) %>%
  layer_dense(
    units = 1, 
    kernel_initializer = "uniform", 
    activation = "sigmoid") %>% 
  compile(
    optimizer = 'adam',
    loss = 'binary_crossentropy',
    metrics = c('accuracy')
  )

model_keras
```

### Correr el modelo

```{r}
history <- fit(
  object = model_keras, 
  x = x_tbl_entrenar, 
  y = y_vec_entrenar,
  batch_size = 50, 
  epochs = 35,
  validation_split = 0.30,
  verbose = 0
)

print(history)
```

### Ver los resultados

```{r}
theme_set(theme_bw())

plot(history) 
```

```{r}
yhat_keras_class_vec <- model_keras %>%
  predict_classes(x_tbl_prueba) %>%
  as.factor() %>%
  fct_recode(yes = "1", no = "0")

yhat_keras_prob_vec  <- model_keras %>%
  predict_proba(x_tbl_prueba) %>%
  as.vector()

test_truth <- y_vec_prueba %>% 
  as.factor() %>% 
  fct_recode(yes = "1", no = "0")

estimates_keras_tbl <- tibble(
  truth      = test_truth,
  estimate   = yhat_keras_class_vec,
  class_prob = yhat_keras_prob_vec
)

estimates_keras_tbl
```


## yardstick

https://tidymodels.github.io/yardstick/

`yardstick` is a package to estimate how well models are working using tidy data principals.

```{r}
library(yardstick)

options(yardstick.event_first = FALSE)

estimates_keras_tbl %>% 
  conf_mat(truth, estimate)

estimates_keras_tbl %>% 
  metrics(truth, estimate)

estimates_keras_tbl %>% 
  roc_auc(truth, class_prob)

estimates_keras_tbl %>%
  precision(truth, estimate) %>%
  bind_rows(
    estimates_keras_tbl %>% 
      recall(truth, estimate) 
  ) 

estimates_keras_tbl %>% 
  f_meas(truth, estimate, beta = 1)
```

## lime

https://github.com/thomasp85/lime

```{r}
library(lime)

model_type.keras.engine.sequential.Sequential <- function(x, ...) {
  "classification"
}

predict_model.keras.engine.sequential.Sequential <- function(x, newdata, type, ...) {
  pred <- predict_proba(object = x, x = as.matrix(newdata))
  data.frame(Yes = pred, No = 1 - pred)
}
```


```{r}
model_keras %>%
  predict_model(x_tbl_prueba, "raw") %>%
  as_tibble()
```

```{r}
library(lime)

explainer <- x_tbl_entrenar %>%
  as_tibble() %>% 
  lime(model_keras, 
       bin_continuous = FALSE)
  
explanation <-  x_tbl_entrenar %>%
  as.data.frame() %>%
  head(40) %>%
  lime::explain(
    explainer    = explainer, 
    n_labels     = 1, 
    n_features   = 4,
    kernel_width = 0.5
    )
```


```{r, fig.width = 10}
plot_explanations(explanation) +
  labs(
    title = "Importancia de cada variable",
    subtitle = "Usando 40 observaciones de prueba"
    )
```


## corrr

https://github.com/drsimonj/corrr

```{r}
library(corrr)

corrr_analysis <- x_tbl_entrenar %>%
  as_tibble() %>%
  mutate(Churn = y_vec_entrenar) %>%
  correlate() %>%
  focus(Churn) %>%
  rename(feature = rowname) %>%
  arrange(abs(Churn)) %>%
  mutate(feature = as_factor(feature)) 

corrr_analysis
```

```{r, fig.height = 7, fig.width = 7}
over <- corrr_analysis %>%
  filter(Churn > 0)

under <- corrr_analysis %>%
  filter(Churn < 0)

corrr_analysis %>%
  ggplot(aes(x = Churn, y = fct_reorder(feature, desc(Churn)))) +
    geom_point() +
    geom_segment(aes(xend = 0, yend = feature), data = under, color = "orange") +
    geom_point(data = under, color = "orange") +
    geom_segment(aes(xend = 0, yend = feature), data = over, color = "blue") +
    geom_point(data = over, color = "blue") +
  labs(title = "Corelaciones de perdida de clientes", y = "", x = "")
  
```

## Mas exploracion

```{r}
datos_perdimiento %>%
  group_by(Contract, Churn) %>%
  tally() %>%
  spread(Churn, n)
```

```{r}
datos_perdimiento %>%
  group_by(InternetService, Churn) %>%
  tally() %>%
  spread(Churn, n)
```

## Desplegar el modelo


```{r, eval = FALSE}
export_savedmodel(model_keras, "tfmodel")
```

```{r,eval = FALSE}
library(rsconnect)
deployTFModel(
  "tfmodel", 
  server = "colorado.rstudio.com", 
  account = rstudioapi::askForPassword("Enter Connect Username:")
  )
```

```{r}
library(httr)

baked_numeric <- x_tbl_prueba %>%
  as_tibble() %>%
  head(4) %>%
  transpose() %>%
  map(as.numeric)

body <- list(instances = list(baked_numeric))

r <- POST("https://colorado.rstudio.com/rsc/content/2230/serving_default/predict", body = body, encode = "json")

jsonlite::fromJSON(content(r))$predictions[, , 1]
```
