Predicting wine quality using a random forest classifier in SparkR

spark_random_forest.R

library(readr)
library(dplyr)

url <- "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv"

df <- 
  read_delim(url, delim = ";") %>% 
  dplyr::mutate(taste = as.factor(ifelse(quality < 6, "bad", ifelse(quality > 6, "good", "average")))) %>% 
  dplyr::select(-quality)

df <- dplyr::mutate(df, id = as.integer(rownames(df)))
  
print(df, n=25)

table(df$taste)

Sys.setenv(SPARK_HOME="/home/andrew/spark-2.1.0-bin-hadoop2.7")
library(SparkR, lib.loc=c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib")))
sparkR.session(master="local[*]")

ddf <- createDataFrame(df)

seed <- 12345

training_ddf <- sample(ddf, withReplacement=FALSE, fraction=0.7, seed=seed)
test_ddf <- except(ddf, training_ddf)

model <- spark.randomForest(training_ddf, taste ~ ., type="classification", seed=seed)
summary(model)

predictions <- predict(model, test_ddf)
prediction_df <- collect(select(predictions, "id", "prediction"))

actual_vs_predicted <- 
  dplyr::inner_join(df, prediction_df, "id") %>% 
  dplyr::select(id, actual = taste, predicted = prediction)

mean(actual_vs_predicted$actual == actual_vs_predicted$predicted)

table(actual_vs_predicted$actual, actual_vs_predicted$predicted)

model_file_path <- "/home/andrew/wine_random_forest_model"
write.ml(model, model_file_path)

new_model <- read.ml(model_file_path)
summary(new_model)