[SPARK-18476][SPARKR][ML] SparkR Logistic Regression should should support output original label.

## What changes were proposed in this pull request?

Similar to SPARK-18401, as a classification algorithm, logistic regression should support output original label instead of supporting index label.

In this PR, original label output is supported and test cases are modified and added. Document is also modified.

## How was this patch tested?

Unit tests.

Author: wm624@hotmail.com <wm624@hotmail.com>

Closes apache#15910 from wangmiao1981/audit.

Author: wangmiao1981

R/pkg/R/mllib.R

@@ -712,7 +712,6 @@ setMethod("predict", signature(object = "KMeansModel"),
 #'                        of L1 and L2. Default is 0.0 which is an L2 penalty.
 #' @param maxIter maximum iteration number.
 #' @param tol convergence tolerance of iterations.
-#' @param fitIntercept whether to fit an intercept term.
 #' @param family the name of family which is a description of the label distribution to be used in the model.
 #'               Supported options:
 #'                 \itemize{
@@ -747,11 +746,11 @@ setMethod("predict", signature(object = "KMeansModel"),
 #' \dontrun{
 #' sparkR.session()
 #' # binary logistic regression
-#' label <- c(1.0, 1.0, 1.0, 0.0, 0.0)
-#' feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
-#' binary_data <- as.data.frame(cbind(label, feature))
+#' label <- c(0.0, 0.0, 0.0, 1.0, 1.0)
+#' features <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
+#' binary_data <- as.data.frame(cbind(label, features))
 #' binary_df <- createDataFrame(binary_data)
-#' blr_model <- spark.logit(binary_df, label ~ feature, thresholds = 1.0)
+#' blr_model <- spark.logit(binary_df, label ~ features, thresholds = 1.0)
 #' blr_predict <- collect(select(predict(blr_model, binary_df), "prediction"))
 #'
 #' # summary of binary logistic regression
@@ -783,7 +782,7 @@ setMethod("predict", signature(object = "KMeansModel"),
 #' @note spark.logit since 2.1.0
 setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula"),
           function(data, formula, regParam = 0.0, elasticNetParam = 0.0, maxIter = 100,
-                   tol = 1E-6, fitIntercept = TRUE, family = "auto", standardization = TRUE,
+                   tol = 1E-6, family = "auto", standardization = TRUE,
                    thresholds = 0.5, weightCol = NULL, aggregationDepth = 2,
                    probabilityCol = "probability") {
             formula <- paste(deparse(formula), collapse = "")
@@ -795,10 +794,10 @@ setMethod("spark.logit", signature(data = "SparkDataFrame", formula = "formula")
             jobj <- callJStatic("org.apache.spark.ml.r.LogisticRegressionWrapper", "fit",
                                 data@sdf, formula, as.numeric(regParam),
                                 as.numeric(elasticNetParam), as.integer(maxIter),
-                                as.numeric(tol), as.logical(fitIntercept),
-                                as.character(family), as.logical(standardization),
-                                as.array(thresholds), as.character(weightCol),
-                                as.integer(aggregationDepth), as.character(probabilityCol))
+                                as.numeric(tol), as.character(family),
+                                as.logical(standardization), as.array(thresholds),
+                                as.character(weightCol), as.integer(aggregationDepth),
+                                as.character(probabilityCol))
             new("LogisticRegressionModel", jobj = jobj)
           })
 

R/pkg/inst/tests/testthat/test_mllib.R

@@ -646,30 +646,30 @@ test_that("spark.isotonicRegression", {
 
 test_that("spark.logit", {
   # test binary logistic regression
-  label <- c(1.0, 1.0, 1.0, 0.0, 0.0)
+  label <- c(0.0, 0.0, 0.0, 1.0, 1.0)
   feature <- c(1.1419053, 0.9194079, -0.9498666, -1.1069903, 0.2809776)
   binary_data <- as.data.frame(cbind(label, feature))
   binary_df <- createDataFrame(binary_data)
 
   blr_model <- spark.logit(binary_df, label ~ feature, thresholds = 1.0)
   blr_predict <- collect(select(predict(blr_model, binary_df), "prediction"))
-  expect_equal(blr_predict$prediction, c(0, 0, 0, 0, 0))
+  expect_equal(blr_predict$prediction, c("0.0", "0.0", "0.0", "0.0", "0.0"))
   blr_model1 <- spark.logit(binary_df, label ~ feature, thresholds = 0.0)
   blr_predict1 <- collect(select(predict(blr_model1, binary_df), "prediction"))
-  expect_equal(blr_predict1$prediction, c(1, 1, 1, 1, 1))
+  expect_equal(blr_predict1$prediction, c("1.0", "1.0", "1.0", "1.0", "1.0"))
 
   # test summary of binary logistic regression
   blr_summary <- summary(blr_model)
   blr_fmeasure <- collect(select(blr_summary$fMeasureByThreshold, "threshold", "F-Measure"))
-  expect_equal(blr_fmeasure$threshold, c(0.8221347, 0.7884005, 0.6674709, 0.3785437, 0.3434487),
+  expect_equal(blr_fmeasure$threshold, c(0.6565513, 0.6214563, 0.3325291, 0.2115995, 0.1778653),
                tolerance = 1e-4)
-  expect_equal(blr_fmeasure$"F-Measure", c(0.5000000, 0.8000000, 0.6666667, 0.8571429, 0.7500000),
+  expect_equal(blr_fmeasure$"F-Measure", c(0.6666667, 0.5000000, 0.8000000, 0.6666667, 0.5714286),
                tolerance = 1e-4)
   blr_precision <- collect(select(blr_summary$precisionByThreshold, "threshold", "precision"))
-  expect_equal(blr_precision$precision, c(1.0000000, 1.0000000, 0.6666667, 0.7500000, 0.6000000),
+  expect_equal(blr_precision$precision, c(1.0000000, 0.5000000, 0.6666667, 0.5000000, 0.4000000),
                tolerance = 1e-4)
   blr_recall <- collect(select(blr_summary$recallByThreshold, "threshold", "recall"))
-  expect_equal(blr_recall$recall, c(0.3333333, 0.6666667, 0.6666667, 1.0000000, 1.0000000),
+  expect_equal(blr_recall$recall, c(0.5000000, 0.5000000, 1.0000000, 1.0000000, 1.0000000),
                tolerance = 1e-4)
 
   # test model save and read
@@ -683,6 +683,16 @@ test_that("spark.logit", {
   expect_error(summary(blr_model2))
   unlink(modelPath)
 
+  # test prediction label as text
+  training <- suppressWarnings(createDataFrame(iris))
+  binomial_training <- training[training$Species %in% c("versicolor", "virginica"), ]
+  binomial_model <- spark.logit(binomial_training, Species ~ Sepal_Length + Sepal_Width)
+  prediction <- predict(binomial_model, binomial_training)
+  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "character")
+  expected <- c("virginica", "virginica", "virginica", "versicolor", "virginica",
+                "versicolor", "virginica", "versicolor", "virginica", "versicolor")
+  expect_equal(as.list(take(select(prediction, "prediction"), 10))[[1]], expected)
+
   # test multinomial logistic regression
   label <- c(0.0, 1.0, 2.0, 0.0, 0.0)
   feature1 <- c(4.845940, 5.64480, 7.430381, 6.464263, 5.555667)
@@ -694,7 +704,7 @@ test_that("spark.logit", {
 
   model <- spark.logit(df, label ~., family = "multinomial", thresholds = c(0, 1, 1))
   predict1 <- collect(select(predict(model, df), "prediction"))
-  expect_equal(predict1$prediction, c(0, 0, 0, 0, 0))
+  expect_equal(predict1$prediction, c("0.0", "0.0", "0.0", "0.0", "0.0"))
   # Summary of multinomial logistic regression is not implemented yet
   expect_error(summary(model))
 })

core/src/main/scala/org/apache/spark/SparkContext.scala

@@ -19,7 +19,7 @@ package org.apache.spark
 
 import java.io._
 import java.lang.reflect.Constructor
-import java.net.{MalformedURLException, URI}
+import java.net.{URI}
 import java.util.{Arrays, Locale, Properties, ServiceLoader, UUID}
 import java.util.concurrent.{ConcurrentHashMap, ConcurrentMap}
 import java.util.concurrent.atomic.{AtomicBoolean, AtomicInteger, AtomicReference}

mllib/src/main/scala/org/apache/spark/ml/r/LogisticRegressionWrapper.scala

@@ -23,9 +23,9 @@ import org.json4s.JsonDSL._
 import org.json4s.jackson.JsonMethods._
 
 import org.apache.spark.ml.{Pipeline, PipelineModel}
-import org.apache.spark.ml.attribute.AttributeGroup
 import org.apache.spark.ml.classification.{BinaryLogisticRegressionSummary, LogisticRegression, LogisticRegressionModel}
-import org.apache.spark.ml.feature.RFormula
+import org.apache.spark.ml.feature.{IndexToString, RFormula}
+import org.apache.spark.ml.r.RWrapperUtils._
 import org.apache.spark.ml.util._
 import org.apache.spark.sql.{DataFrame, Dataset}
 
@@ -34,6 +34,8 @@ private[r] class LogisticRegressionWrapper private (
     val features: Array[String],
     val isLoaded: Boolean = false) extends MLWritable {
 
+  import LogisticRegressionWrapper._
+
   private val logisticRegressionModel: LogisticRegressionModel =
     pipeline.stages(1).asInstanceOf[LogisticRegressionModel]
 
@@ -57,7 +59,11 @@ private[r] class LogisticRegressionWrapper private (
   lazy val recallByThreshold: DataFrame = blrSummary.recallByThreshold
 
   def transform(dataset: Dataset[_]): DataFrame = {
-    pipeline.transform(dataset).drop(logisticRegressionModel.getFeaturesCol)
+    pipeline.transform(dataset)
+      .drop(PREDICTED_LABEL_INDEX_COL)
+      .drop(logisticRegressionModel.getFeaturesCol)
+      .drop(logisticRegressionModel.getLabelCol)
+
   }
 
   override def write: MLWriter = new LogisticRegressionWrapper.LogisticRegressionWrapperWriter(this)
@@ -66,14 +72,16 @@ private[r] class LogisticRegressionWrapper private (
 private[r] object LogisticRegressionWrapper
     extends MLReadable[LogisticRegressionWrapper] {
 
+  val PREDICTED_LABEL_INDEX_COL = "pred_label_idx"
+  val PREDICTED_LABEL_COL = "prediction"
+
   def fit( // scalastyle:ignore
       data: DataFrame,
       formula: String,
       regParam: Double,
       elasticNetParam: Double,
       maxIter: Int,
       tol: Double,
-      fitIntercept: Boolean,
       family: String,
       standardization: Boolean,
       thresholds: Array[Double],
@@ -84,14 +92,14 @@ private[r] object LogisticRegressionWrapper
 
     val rFormula = new RFormula()
       .setFormula(formula)
-    RWrapperUtils.checkDataColumns(rFormula, data)
+      .setForceIndexLabel(true)
+    checkDataColumns(rFormula, data)
     val rFormulaModel = rFormula.fit(data)
 
-    // get feature names from output schema
-    val schema = rFormulaModel.transform(data).schema
-    val featureAttrs = AttributeGroup.fromStructField(schema(rFormulaModel.getFeaturesCol))
-      .attributes.get
-    val features = featureAttrs.map(_.name.get)
+    val fitIntercept = rFormula.hasIntercept
+
+    // get labels and feature names from output schema
+    val (features, labels) = getFeaturesAndLabels(rFormulaModel, data)
 
     // assemble and fit the pipeline
     val logisticRegression = new LogisticRegression()
@@ -105,16 +113,23 @@ private[r] object LogisticRegressionWrapper
       .setWeightCol(weightCol)
       .setAggregationDepth(aggregationDepth)
       .setFeaturesCol(rFormula.getFeaturesCol)
+      .setLabelCol(rFormula.getLabelCol)
       .setProbabilityCol(probability)
+      .setPredictionCol(PREDICTED_LABEL_INDEX_COL)
 
     if (thresholds.length > 1) {
       logisticRegression.setThresholds(thresholds)
     } else {
       logisticRegression.setThreshold(thresholds(0))
     }
 
+    val idxToStr = new IndexToString()
+      .setInputCol(PREDICTED_LABEL_INDEX_COL)
+      .setOutputCol(PREDICTED_LABEL_COL)
+      .setLabels(labels)
+
     val pipeline = new Pipeline()
-      .setStages(Array(rFormulaModel, logisticRegression))
+      .setStages(Array(rFormulaModel, logisticRegression, idxToStr))
       .fit(data)
 
     new LogisticRegressionWrapper(pipeline, features)