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fixed for R 3.5 Keras 2.2.2 #5

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fixed for R 3.5 Keras 2.2.2 #5

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16 changes: 8 additions & 8 deletions customer_churn.R
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Original file line number Diff line number Diff line change
Expand Up @@ -129,26 +129,26 @@ estimates_keras_tbl %>% roc_auc(truth, class_prob)
options(yardstick.event_first = FALSE)
# Precision
tibble(
precision = estimates_keras_tbl %>% precision(truth, estimate),
recall = estimates_keras_tbl %>% recall(truth, estimate)
precision = estimates_keras_tbl %>% precision(truth, estimate) %>% select(.estimate) %>% pull,
recall = estimates_keras_tbl %>% recall(truth, estimate) %>% select(.estimate) %>% pull
)

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

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

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

# Test our predict_model() function
predictions <- predict_model(x = model_keras, newdata = x_test_tbl, type = 'raw') %>%
predictions <- predict_model(x = model_keras, newdata = as.matrix(x_test_tbl), type = 'raw') %>%
tibble::as_tibble()

test_tbl_with_ids$churn_prob <- predictions$Yes
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