[SPARK-9492] [ML] [R] LogisticRegression in R should provide model statistics

R/pkg/inst/tests/test_mllib.R

@@ -67,3 +67,20 @@ test_that("summary coefficients match with native glm", {
     as.character(stats$features) ==
     c("(Intercept)", "Sepal_Length", "Species_versicolor", "Species_virginica")))
 })
+
+test_that("summary coefficients match with native glm of family 'binomial'", {
+  df <- createDataFrame(sqlContext, iris)
+  training <- filter(df, df$Species != "setosa")
+  stats <- summary(glm(Species ~ Sepal_Length + Sepal_Width, data = training,
+    family = "binomial"))
+  coefs <- as.vector(stats$coefficients)
+
+  rTraining <- iris[iris$Species %in% c("versicolor","virginica"),]
+  rCoefs <- as.vector(coef(glm(Species ~ Sepal.Length + Sepal.Width, data = rTraining,
+    family = binomial(link = "logit"))))
+
+  expect_true(all(abs(rCoefs - coefs) < 1e-4))
+  expect_true(all(
+    as.character(stats$features) ==
+    c("(Intercept)", "Sepal_Length", "Sepal_Width")))
+})

mllib/src/main/scala/org/apache/spark/ml/classification/LogisticRegression.scala

@@ -378,6 +378,7 @@ class LogisticRegression(override val uid: String)
       model.transform(dataset),
       $(probabilityCol),
       $(labelCol),
+      $(featuresCol),
       objectiveHistory)
     model.setSummary(logRegSummary)
   }
@@ -452,7 +453,8 @@ class LogisticRegressionModel private[ml] (
    */
   // TODO: decide on a good name before exposing to public API
   private[classification] def evaluate(dataset: DataFrame): LogisticRegressionSummary = {
-    new BinaryLogisticRegressionSummary(this.transform(dataset), $(probabilityCol), $(labelCol))
+    new BinaryLogisticRegressionSummary(
+      this.transform(dataset), $(probabilityCol), $(labelCol), $(featuresCol))
   }
 
   /**
@@ -614,9 +616,12 @@ sealed trait LogisticRegressionSummary extends Serializable {
   /** Field in "predictions" which gives the calibrated probability of each instance as a vector. */
   def probabilityCol: String
 
-  /** Field in "predictions" which gives the the true label of each instance. */
+  /** Field in "predictions" which gives the true label of each instance. */
   def labelCol: String
 
+  /** Field in "predictions" which gives the features of each instance as a vector. */
+  def featuresCol: String
+
 }
 
 /**
@@ -626,15 +631,17 @@ sealed trait LogisticRegressionSummary extends Serializable {
  * @param probabilityCol field in "predictions" which gives the calibrated probability of
  *                       each instance as a vector.
  * @param labelCol field in "predictions" which gives the true label of each instance.
+ * @param featuresCol field in "predictions" which gives the features of each instance as a vector.
  * @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
  */
 @Experimental
 class BinaryLogisticRegressionTrainingSummary private[classification] (
     predictions: DataFrame,
     probabilityCol: String,
     labelCol: String,
+    featuresCol: String,
     val objectiveHistory: Array[Double])
-  extends BinaryLogisticRegressionSummary(predictions, probabilityCol, labelCol)
+  extends BinaryLogisticRegressionSummary(predictions, probabilityCol, labelCol, featuresCol)
   with LogisticRegressionTrainingSummary {
 
 }
@@ -646,12 +653,14 @@ class BinaryLogisticRegressionTrainingSummary private[classification] (
  * @param probabilityCol field in "predictions" which gives the calibrated probability of
  *                       each instance.
  * @param labelCol field in "predictions" which gives the true label of each instance.
+ * @param featuresCol field in "predictions" which gives the features of each instance as a vector.
  */
 @Experimental
 class BinaryLogisticRegressionSummary private[classification] (
     @transient override val predictions: DataFrame,
     override val probabilityCol: String,
-    override val labelCol: String) extends LogisticRegressionSummary {
+    override val labelCol: String,
+    override val featuresCol: String) extends LogisticRegressionSummary {
 
   private val sqlContext = predictions.sqlContext
   import sqlContext.implicits._

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

@@ -66,9 +66,10 @@ private[r] object SparkRWrappers {
         val attrs = AttributeGroup.fromStructField(
           m.summary.predictions.schema(m.summary.featuresCol))
         Array("(Intercept)") ++ attrs.attributes.get.map(_.name.get)
-      case _: LogisticRegressionModel =>
-        throw new UnsupportedOperationException(
-          "No features names available for LogisticRegressionModel")  // SPARK-9492
+      case m: LogisticRegressionModel =>
+        val attrs = AttributeGroup.fromStructField(
+          m.summary.predictions.schema(m.summary.featuresCol))
+        Array("(Intercept)") ++ attrs.attributes.get.map(_.name.get)
     }
   }
 }

project/MimaExcludes.scala

@@ -59,7 +59,9 @@ object MimaExcludes {
         ProblemFilters.exclude[MissingMethodProblem](
           "org.apache.spark.ml.classification.LogisticAggregator.add"),
         ProblemFilters.exclude[MissingMethodProblem](
-          "org.apache.spark.ml.classification.LogisticAggregator.count")
+          "org.apache.spark.ml.classification.LogisticAggregator.count"),
+        ProblemFilters.exclude[MissingMethodProblem](
+          "org.apache.spark.ml.classification.LogisticRegressionSummary.featuresCol")
       ) ++ Seq(
         // SPARK-10381 Fix types / units in private AskPermissionToCommitOutput RPC message.
         // This class is marked as `private` but MiMa still seems to be confused by the change.