[SPARK-12346][ML] Missing attribute names in GLM for vector-type features

Currently `summary()` fails on a GLM model fitted over a vector feature missing ML attrs, since the output feature attrs will also have no name. We can avoid this situation by forcing `VectorAssembler` to make up suitable names when inputs are missing names.

cc mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #10323 from ericl/spark-12346.

2 files changed, 40 insertions, 2 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
index dc20a5ec21..16e565d8b5 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/RFormulaSuite.scala
@@ -143,6 +143,44 @@ class RFormulaSuite extends SparkFunSuite with MLlibTestSparkContext {
     assert(attrs === expectedAttrs)
   }
 
+  test("vector attribute generation") {
+    val formula = new RFormula().setFormula("id ~ vec")
+    val original = sqlContext.createDataFrame(
+      Seq((1, Vectors.dense(0.0, 1.0)), (2, Vectors.dense(1.0, 2.0)))
+    ).toDF("id", "vec")
+    val model = formula.fit(original)
+    val result = model.transform(original)
+    val attrs = AttributeGroup.fromStructField(result.schema("features"))
+    val expectedAttrs = new AttributeGroup(
+      "features",
+      Array[Attribute](
+        new NumericAttribute(Some("vec_0"), Some(1)),
+        new NumericAttribute(Some("vec_1"), Some(2))))
+    assert(attrs === expectedAttrs)
+  }
+
+  test("vector attribute generation with unnamed input attrs") {
+    val formula = new RFormula().setFormula("id ~ vec2")
+    val base = sqlContext.createDataFrame(
+      Seq((1, Vectors.dense(0.0, 1.0)), (2, Vectors.dense(1.0, 2.0)))
+    ).toDF("id", "vec")
+    val metadata = new AttributeGroup(
+      "vec2",
+      Array[Attribute](
+        NumericAttribute.defaultAttr,
+        NumericAttribute.defaultAttr)).toMetadata
+    val original = base.select(base.col("id"), base.col("vec").as("vec2", metadata))
+    val model = formula.fit(original)
+    val result = model.transform(original)
+    val attrs = AttributeGroup.fromStructField(result.schema("features"))
+    val expectedAttrs = new AttributeGroup(
+      "features",
+      Array[Attribute](
+        new NumericAttribute(Some("vec2_0"), Some(1)),
+        new NumericAttribute(Some("vec2_1"), Some(2))))
+    assert(attrs === expectedAttrs)
+  }
+
   test("numeric interaction") {
     val formula = new RFormula().setFormula("a ~ b:c:d")
     val original = sqlContext.createDataFrame(
diff --git a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
index f7de7c1e93..dce994fdbd 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/feature/VectorAssemblerSuite.scala
@@ -111,8 +111,8 @@ class VectorAssemblerSuite
     assert(userGenderOut === user.getAttr("gender").withName("user_gender").withIndex(3))
     val userSalaryOut = features.getAttr(4)
     assert(userSalaryOut === user.getAttr("salary").withName("user_salary").withIndex(4))
-    assert(features.getAttr(5) === NumericAttribute.defaultAttr.withIndex(5))
-    assert(features.getAttr(6) === NumericAttribute.defaultAttr.withIndex(6))
+    assert(features.getAttr(5) === NumericAttribute.defaultAttr.withIndex(5).withName("ad_0"))
+    assert(features.getAttr(6) === NumericAttribute.defaultAttr.withIndex(6).withName("ad_1"))
   }
 
   test("read/write") {