To implement pipeline architecture for classification in spark with R using sparklyR package
spark_connect(master = “local”) – To create a spark connection
sdf_copy_to(spark_connection,R object,name) – To copy data to spark environment
sdf_partition(spark_dataframe,partitions with weights,seed) – To partition spark dataframe into multiple groups
ml_pipeline(spark connection) – To create a Spark ML pipeline
ft_r_formula(formula) – To implement the transforms required for fititng a dataset against an R model formula
ml_logistic_regression() – To build a linear regression model
ml_fit(pipeline_model,train_data) – To fit the model
ml_transform(fitted_pipeline,test_data) – To predict the test data
ml_binary_classification_evaluator(predict,label_col,prediction_col) – To evaluate the metrics AUC(Area Under Curve)
#Load the sparklyr library
library(sparklyr)
library(caret)
#Create a spark connection
sc %
ft_r_formula(Gender~.) %>%
ml_logistic_regression()
#Split the data for train and test
partitions=sdf_partition(data_s,training=0.8,test=0.2,seed=111)
train_data=partitions$training
test_data=partitions$test
#Fit the pipeline model
fitted_pipeline fitted_pipeline
#Predict using the test data
predictions predictions
#Evaluate the metrics AUC
cat(“Area Under Curve : “,ml_binary_classification_evaluator(predictions, label_col =”label”,prediction_col = “prediction”))
cat(“\nF1 : “,ml_multiclass_classification_evaluator(predictions, label_col = “label”,prediction_col = “prediction”))
cat(“\nAccuracy : “,ml_multiclass_classification_evaluator(predictions, label_col = “label”,prediction_col = “prediction”,metric_name=”accuracy”))
cat(“\nPrecision : “,ml_multiclass_classification_evaluator(predictions, label_col = “label”,prediction_col = “prediction”,metric_name=”weightedPrecision”))
cat(“\nRecall : “,ml_multiclass_classification_evaluator(predictions, label_col = “label”,prediction_col = “prediction”,metric_name=”weightedRecall”))
