# # Licensed to the Apache Software Foundation (ASF) under one or more # contributor license agreements. See the NOTICE file distributed with # this work for additional information regarding copyright ownership. # The ASF licenses this file to You under the Apache License, Version 2.0 # (the "License"); you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # # To run this example use # ./bin/spark-submit examples/src/main/r/ml/glm.R # Load SparkR library into your R session library(SparkR) # Initialize SparkSession sparkR.session(appName = "SparkR-ML-glm-example") # $example on$ irisDF <- suppressWarnings(createDataFrame(iris)) # Fit a generalized linear model of family "gaussian" with spark.glm gaussianDF <- irisDF gaussianTestDF <- irisDF gaussianGLM <- spark.glm(gaussianDF, Sepal_Length ~ Sepal_Width + Species, family = "gaussian") # Model summary summary(gaussianGLM) # Prediction gaussianPredictions <- predict(gaussianGLM, gaussianTestDF) showDF(gaussianPredictions) # Fit a generalized linear model with glm (R-compliant) gaussianGLM2 <- glm(Sepal_Length ~ Sepal_Width + Species, gaussianDF, family = "gaussian") summary(gaussianGLM2) # Fit a generalized linear model of family "binomial" with spark.glm # Note: Filter out "setosa" from label column (two labels left) to match "binomial" family. binomialDF <- filter(irisDF, irisDF$Species != "setosa") binomialTestDF <- binomialDF binomialGLM <- spark.glm(binomialDF, Species ~ Sepal_Length + Sepal_Width, family = "binomial") # Model summary summary(binomialGLM) # Prediction binomialPredictions <- predict(binomialGLM, binomialTestDF) showDF(binomialPredictions) # $example off$