How to get performance matrices in sparkR classification, e.g., F1 score, Precision, Recall, Confusion Matrix
# Load training data
df <- read.df("data/mllib/sample_libsvm_data.txt", source = "libsvm")
training <- df
testing <- df
# Fit a random forest classification model with spark.randomForest
model <- spark.randomForest(training, label ~ features, "classification", numTrees = 10)
# Model summary
summary(model)
# Prediction
predictions <- predict(model, testing)
head(predictions)
# Performance evaluation
I've tried caret::confusionMatrix(testing$label,testing$prediction) it shows error:
Error in unique.default(x, nmax = nmax) : unique() applies only to vectors
Caret's confusionMatrix will not work, since it needs R dataframes while your data are in Spark dataframes.
One not recommended way for getting your metrics is to "collect" locally your Spark dataframes to R using as.data.frame, and then use caret etc.; but this means that your data can fit in the main memory of your driver machine, in which case of course you have absolutely no reason to use Spark...
So, here is a way to get the accuracy in a distributed manner (i.e. without collecting data locally), using the iris data as an example:
sparkR.version()
# "2.1.1"
df <- as.DataFrame(iris)
model <- spark.randomForest(df, Species ~ ., "classification", numTrees = 10)
predictions <- predict(model, df)
summary(predictions)
# SparkDataFrame[summary:string, Sepal_Length:string, Sepal_Width:string, Petal_Length:string, Petal_Width:string, Species:string, prediction:string]
createOrReplaceTempView(predictions, "predictions")
correct <- sql("SELECT prediction, Species FROM predictions WHERE prediction=Species")
count(correct)
# 149
acc = count(correct)/count(predictions)
acc
# 0.9933333
(Regarding the 149 correct predictions out of 150 samples, if you do a showDF(predictions, numRows=150) you will see indeed that there is a single virginica sample misclassified as versicolor).
