Nothing
## ---- eval=FALSE, include=TRUE-------------------------------------------
# devtools::install_github("alexioannides/pipeliner")
## ---- eval=FALSE, include=TRUE-------------------------------------------
# transform_features(function(df) {
# data.frame(x1 = log(df$var1))
# })
## ---- eval=FALSE, include=TRUE-------------------------------------------
# transform_response(function(df) {
# data.frame(y = log(df$response))
# })
## ---- eval=FALSE, include=TRUE-------------------------------------------
# estimate_model(function(df) {
# lm(y ~ 1 + x1, df)
# })
## ---- eval=FALSE, include=TRUE-------------------------------------------
# inv_transform_response(function(df) {
# data.frame(pred_response = exp(df$pred_y))
# })
## ------------------------------------------------------------------------
library(pipeliner)
data <- faithful
lm_pipeline <- pipeline(
data,
transform_features(function(df) {
data.frame(x1 = (df$waiting - mean(df$waiting)) / sd(df$waiting))
}),
transform_response(function(df) {
data.frame(y = (df$eruptions - mean(df$eruptions)) / sd(df$eruptions))
}),
estimate_model(function(df) {
lm(y ~ 1 + x1, df)
}),
inv_transform_response(function(df) {
data.frame(pred_eruptions = df$pred_model * sd(df$eruptions) + mean(df$eruptions))
})
)
in_sample_predictions <- predict(lm_pipeline, data, verbose = TRUE)
head(in_sample_predictions)
## ------------------------------------------------------------------------
summary(lm_pipeline$inner_model)
## ------------------------------------------------------------------------
pred_function <- lm_pipeline$predict
predictions <- pred_function(data, verbose = FALSE)
head(predictions)
## ---- warning=FALSE, message=FALSE---------------------------------------
library(tidyverse)
lm_pipeline <- data %>%
pipeline(
transform_features(function(df) {
transmute(df, x1 = (waiting - mean(waiting)) / sd(waiting))
}),
transform_response(function(df) {
transmute(df, y = (eruptions - mean(eruptions)) / sd(eruptions))
}),
estimate_model(function(df) {
lm(y ~ 1 + x1, df)
}),
inv_transform_response(function(df) {
transmute(df, pred_eruptions = pred_model * sd(eruptions) + mean(eruptions))
})
)
head(predict(lm_pipeline, data))
## ------------------------------------------------------------------------
library(modelr)
# define a function that estimates a machine learning pipeline on a single fold of the data
pipeline_func <- function(df) {
pipeline(
df,
transform_features(function(df) {
transmute(df, x1 = (waiting - mean(waiting)) / sd(waiting))
}),
transform_response(function(df) {
transmute(df, y = (eruptions - mean(eruptions)) / sd(eruptions))
}),
estimate_model(function(df) {
lm(y ~ 1 + x1, df)
}),
inv_transform_response(function(df) {
transmute(df, pred_eruptions = pred_model * sd(eruptions) + mean(eruptions))
})
)
}
# 5-fold cross-validation using machine learning pipelines
cv_rmse <- crossv_kfold(data, 5) %>%
mutate(model = map(train, ~ pipeline_func(as.data.frame(.x))),
predictions = map2(model, test, ~ predict(.x, as.data.frame(.y))),
residuals = map2(predictions, test, ~ .x - as.data.frame(.y)$eruptions),
rmse = map_dbl(residuals, ~ sqrt(mean(.x ^ 2)))) %>%
summarise(mean_rmse = mean(rmse), sd_rmse = sd(rmse))
cv_rmse
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.