Tensorflow with R

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

Parsed with column specification:
cols(
  .default = col_character(),
  SeniorCitizen = col_double(),
  tenure = col_double(),
  MonthlyCharges = col_double(),
  TotalCharges = col_double()
)
See spec(...) for full column specifications.

glimpse(churn_data_raw)

Observations: 7,043
Variables: 21
$ customerID       <chr> "7590-VHVEG", "5575-GNVDE", "3668-QPYBK", "7795-CFOCW", "9237-H...
$ gender           <chr> "Female", "Male", "Male", "Male", "Female", "Female", "Male", "...
$ SeniorCitizen    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...
$ Partner          <chr> "Yes", "No", "No", "No", "No", "No", "No", "No", "Yes", "No", "...
$ Dependents       <chr> "No", "No", "No", "No", "No", "No", "Yes", "No", "No", "Yes", "...
$ tenure           <dbl> 1, 34, 2, 45, 2, 8, 22, 10, 28, 62, 13, 16, 58, 49, 25, 69, 52,...
$ PhoneService     <chr> "No", "Yes", "Yes", "No", "Yes", "Yes", "Yes", "No", "Yes", "Ye...
$ MultipleLines    <chr> "No phone service", "No", "No", "No phone service", "No", "Yes"...
$ InternetService  <chr> "DSL", "DSL", "DSL", "DSL", "Fiber optic", "Fiber optic", "Fibe...
$ OnlineSecurity   <chr> "No", "Yes", "Yes", "Yes", "No", "No", "No", "Yes", "No", "Yes"...
$ OnlineBackup     <chr> "Yes", "No", "Yes", "No", "No", "No", "Yes", "No", "No", "Yes",...
$ DeviceProtection <chr> "No", "Yes", "No", "Yes", "No", "Yes", "No", "No", "Yes", "No",...
$ TechSupport      <chr> "No", "No", "No", "Yes", "No", "No", "No", "No", "Yes", "No", "...
$ StreamingTV      <chr> "No", "No", "No", "No", "No", "Yes", "Yes", "No", "Yes", "No", ...
$ StreamingMovies  <chr> "No", "No", "No", "No", "No", "Yes", "No", "No", "Yes", "No", "...
$ Contract         <chr> "Month-to-month", "One year", "Month-to-month", "One year", "Mo...
$ PaperlessBilling <chr> "Yes", "No", "Yes", "No", "Yes", "Yes", "Yes", "No", "Yes", "No...
$ PaymentMethod    <chr> "Electronic check", "Mailed check", "Mailed check", "Bank trans...
$ MonthlyCharges   <dbl> 29.85, 56.95, 53.85, 42.30, 70.70, 99.65, 89.10, 29.75, 104.80,...
$ TotalCharges     <dbl> 29.85, 1889.50, 108.15, 1840.75, 151.65, 820.50, 1949.40, 301.9...
$ Churn            <chr> "No", "No", "Yes", "No", "Yes", "Yes", "No", "No", "Yes", "No",...

library(rsample)

set.seed(100)

train_test_split <- initial_split(
  churn_data_raw, 
  prop = 0.3)

train_tbl <- training(train_test_split)
test_tbl  <- testing(train_test_split)

library(recipes)

rec_obj <- train_tbl %>%
  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(rec_obj)

save(rec_obj, file = "../shiny-app/rec_obj.RData")

x_train_tbl <- juice(rec_obj, all_predictors(), composition = "matrix") 
y_train_vec <- juice(rec_obj, all_outcomes()) %>% pull()

baked_test <- bake(rec_obj, test_tbl)

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

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

library(tensorflow)
library(keras)

#install_tensorflow()
#install_keras()

model_keras <- keras_model_sequential() %>%
  layer_dense(
    units = 16, 
    kernel_initializer = "uniform", 
    activation = "relu", 
    input_shape = ncol(x_train_tbl)) %>% 
  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
_____________________________________________________________________________________________

# Fit the keras model to the training data
history <- fit(
  object = model_keras, 
  x = x_train_tbl, 
  y = y_train_vec,
  batch_size = 50, 
  epochs = 35,
  validation_split = 0.30,
  verbose = 0
)

2019-03-13 13:43:31.622924: 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: 0.4457
 val_acc: 0.7981
    loss: 0.3909
     acc: 0.816 

theme_set(theme_bw())

# Plot the training/validation history of our Keras model
plot(history) 

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

# Predicted Class Probability
yhat_keras_prob_vec  <- model_keras %>%
  predict_proba(x_test_tbl) %>%
  as.vector()

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

# Format test data and predictions for yardstick metrics
estimates_keras_tbl <- tibble(
  truth      = test_truth,
  estimate   = yhat_keras_class_vec,
  class_prob = yhat_keras_prob_vec
)

estimates_keras_tbl

library(yardstick)

options(yardstick.event_first = FALSE)

# Confusion Table
estimates_keras_tbl %>% 
  conf_mat(truth, estimate)

          Truth
Prediction   no  yes
       no  3247  564
       yes  378  731

# Accuracy
estimates_keras_tbl %>% 
  metrics(truth, estimate)

# AUC
estimates_keras_tbl %>% 
  roc_auc(truth, class_prob)

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

# F1-Statistic
estimates_keras_tbl %>% 
  f_meas(truth, estimate, beta = 1)

library(lime)

model_type.keras.engine.sequential.Sequential <- function(x, ...) {
  "classification"
}
# Setup lime::predict_model() function for keras
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)
}

# Test our predict_model() function
model_keras %>%
  predict_model(x_test_tbl, "raw") %>%
  as_tibble()

library(lime)

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

plot_explanations(explanation) +
  labs(title = "LIME Feature Importance Heatmap",
       subtitle = "Hold Out (Test) Set, First 40 Cases Shown")

library(corrr)

corrr_analysis <- x_train_tbl %>%
  as_tibble() %>%
  mutate(Churn = y_train_vec) %>%
  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 = "Churn correlations", y = "")

NA

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

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

export_savedmodel(model_keras, "tfmodel")

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

library(httr)

baked_numeric <- x_test_tbl %>%
  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: "Tensorflow with R"
output: html_notebook
---

## Install necessary packages

```{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/

The `tidyverse` packages provide an easy way to **import**, **tidy**, **transform** and **visualize** the data.  Some of it's component R packages are:

- `dplyr`
- `tidyr`
- `readr`
- `ggplot2`


```{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"
  ) 
}

churn_data_raw <- read_csv("customer_churn.csv")
```

```{r}
glimpse(churn_data_raw)
```


## rsample

https://tidymodels.github.io/rsample/

`rsample` contains a set of functions that can create different types of resamples and corresponding classes for their analysis. The goal is to have a modular set of methods that can be used across different R packages for:

traditional resampling techniques for estimating the sampling distribution of a statistic and
estimating model performance using a holdout set

```{r}
library(rsample)

set.seed(100)

train_test_split <- initial_split(
  churn_data_raw, 
  prop = 0.3)

train_tbl <- training(train_test_split)
test_tbl  <- testing(train_test_split)
```

## recipes

https://tidymodels.github.io/recipes/

The `recipes` package is an alternative method for creating and preprocessing design matrices that can be used for modeling or visualization. 

```{r}
library(recipes)

rec_obj <- train_tbl %>%
  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(rec_obj)
```

Save the recipe file into the Shiny folder for later use
```{r}
save(rec_obj, file = "../shiny-app/rec_obj.RData")
```

Use `juice()` to extarct the results from the model preparation. The predictor and outcome data are processed separately.  This is because how Keras expects these arguments to be used when fitting a model.

```{r}
x_train_tbl <- juice(rec_obj, all_predictors(), composition = "matrix") 
y_train_vec <- juice(rec_obj, all_outcomes()) %>% pull()
```

The same is done with the testing data so that the two can be compared.

```{r}
baked_test <- bake(rec_obj, test_tbl)

x_test_tbl <- baked_test %>%
  select(-Churn) %>%
  as.matrix()

y_test_vec <- baked_test %>%
  select(Churn) %>%
  pull()
```


## Install 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()
```


### Create Neural Network

```{r}
model_keras <- keras_model_sequential() %>%
  layer_dense(
    units = 16, 
    kernel_initializer = "uniform", 
    activation = "relu", 
    input_shape = ncol(x_train_tbl)) %>% 
  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
```

### Fit model

```{r}
# Fit the keras model to the training data
history <- fit(
  object = model_keras, 
  x = x_train_tbl, 
  y = y_train_vec,
  batch_size = 50, 
  epochs = 35,
  validation_split = 0.30,
  verbose = 0
)

print(history)
```

### Preview results

```{r}
theme_set(theme_bw())

# Plot the training/validation history of our Keras model
plot(history) 
```

```{r}
# Predicted Class
yhat_keras_class_vec <- model_keras %>%
  predict_classes(x_test_tbl) %>%
  as.factor() %>%
  fct_recode(yes = "1", no = "0")

# Predicted Class Probability
yhat_keras_prob_vec  <- model_keras %>%
  predict_proba(x_test_tbl) %>%
  as.vector()

test_truth <- y_test_vec %>% 
  as.factor() %>% 
  fct_recode(yes = "1", no = "0")

# Format test data and predictions for yardstick metrics
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)

# Confusion Table
estimates_keras_tbl %>% 
  conf_mat(truth, estimate)

# Accuracy
estimates_keras_tbl %>% 
  metrics(truth, estimate)

# AUC
estimates_keras_tbl %>% 
  roc_auc(truth, class_prob)

# Precision
estimates_keras_tbl %>%
  precision(truth, estimate) %>%
  bind_rows(
    estimates_keras_tbl %>% 
      recall(truth, estimate) 
  ) 

# F1-Statistic
estimates_keras_tbl %>% 
  f_meas(truth, estimate, beta = 1)
```

## lime

https://github.com/thomasp85/lime

The purpose of `lime` is to explain the predictions of black box classifiers. What this means is that for any given prediction and any given classifier it is able to determine a small set of features in the original data that has driven the outcome of the prediction. 

```{r}
library(lime)

model_type.keras.engine.sequential.Sequential <- function(x, ...) {
  "classification"
}
# Setup lime::predict_model() function for keras
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}
# Test our predict_model() function
model_keras %>%
  predict_model(x_test_tbl, "raw") %>%
  as_tibble()
```


```{r}
library(lime)

# Run lime() on training set
explainer <- x_train_tbl %>%
  as_tibble() %>% 
  lime(model_keras, 
       bin_continuous = FALSE)
  
# Run explain() on explainer
explanation <-  x_train_tbl %>%
  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 = "LIME Feature Importance Heatmap",
       subtitle = "Hold Out (Test) Set, First 40 Cases Shown")
```


## corrr

https://github.com/drsimonj/corrr

`corrr` is a package for exploring correlations in R. It focuses on creating and working with data frames of correlations (instead of matrices) that can be easily explored via corrr functions or by leveraging tools like those in the `tidyverse.` 

```{r}
library(corrr)

corrr_analysis <- x_train_tbl %>%
  as_tibble() %>%
  mutate(Churn = y_train_vec) %>%
  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 = "Churn correlations", y = "")
  
```


## Some more exploration

```{r}
churn_data_raw %>%
  group_by(Contract, Churn) %>%
  tally() %>%
  spread(Churn, n)
```


```{r}
churn_data_raw %>%
  group_by(InternetService, Churn) %>%
  tally() %>%
  spread(Churn, n)
```

## Deploying the model

### Save the Keras model

```{r, eval = FALSE}
export_savedmodel(model_keras, "tfmodel")
```

## Deploy to RStudio Connect

```{r,eval = FALSE}
library(rsconnect)
deployTFModel(
  "tfmodel", 
  server = "colorado.rstudio.com", 
  account = rstudioapi::askForPassword("Enter Connect Username:")
  )
```

### Test the deployed model

```{r}
library(httr)

baked_numeric <- x_test_tbl %>%
  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]
```
