[SPARK-8963][ML] cleanup tests in linear regression suite

Simplify model weight assertions to use vector comparision, switch to using absTol when comparing with 0.0 intercepts

Author: Holden Karau <holden@pigscanfly.ca>

Closes #7327 from holdenk/SPARK-8913-cleanup-tests-from-SPARK-8700-logistic-regression and squashes the following commits:

5bac185 [Holden Karau] Simplify model weight assertions to use vector comparision, switch to using absTol when comparing with 0.0 intercepts

1 files changed, 24 insertions, 33 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
index 5f39d44f37..4f6a577395 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/regression/LinearRegressionSuite.scala
@@ -18,7 +18,7 @@
 package org.apache.spark.ml.regression
 
 import org.apache.spark.SparkFunSuite
-import org.apache.spark.mllib.linalg.DenseVector
+import org.apache.spark.mllib.linalg.{DenseVector, Vectors}
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
 import org.apache.spark.mllib.util.TestingUtils._
 import org.apache.spark.sql.{DataFrame, Row}
@@ -75,11 +75,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 7.198257
      */
     val interceptR = 6.298698
-    val weightsR = Array(4.700706, 7.199082)
+    val weightsR = Vectors.dense(4.700706, 7.199082)
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -104,11 +103,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V2. 6.995908
        as.numeric.data.V3. 5.275131
      */
-    val weightsR = Array(6.995908, 5.275131)
+    val weightsR = Vectors.dense(6.995908, 5.275131)
 
-    assert(model.intercept ~== 0 relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.intercept ~== 0 absTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
     /*
        Then again with the data with no intercept:
        > weightsWithoutIntercept
@@ -118,11 +116,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data3.V2. 4.70011
        as.numeric.data3.V3. 7.19943
      */
-    val weightsWithoutInterceptR = Array(4.70011, 7.19943)
+    val weightsWithoutInterceptR = Vectors.dense(4.70011, 7.19943)
 
-    assert(modelWithoutIntercept.intercept ~== 0 relTol 1E-3)
-    assert(modelWithoutIntercept.weights(0) ~== weightsWithoutInterceptR(0) relTol 1E-3)
-    assert(modelWithoutIntercept.weights(1) ~== weightsWithoutInterceptR(1) relTol 1E-3)
+    assert(modelWithoutIntercept.intercept ~== 0 absTol 1E-3)
+    assert(modelWithoutIntercept.weights ~= weightsWithoutInterceptR relTol 1E-3)
   }
 
   test("linear regression with intercept with L1 regularization") {
@@ -139,11 +136,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 6.679841
      */
     val interceptR = 6.24300
-    val weightsR = Array(4.024821, 6.679841)
+    val weightsR = Vectors.dense(4.024821, 6.679841)
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -169,11 +165,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 4.772913
      */
     val interceptR = 0.0
-    val weightsR = Array(6.299752, 4.772913)
+    val weightsR = Vectors.dense(6.299752, 4.772913)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.intercept ~== interceptR absTol 1E-5)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -197,11 +192,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 4.926260
      */
     val interceptR = 5.269376
-    val weightsR = Array(3.736216, 5.712356)
+    val weightsR = Vectors.dense(3.736216, 5.712356)
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -227,11 +221,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 4.214502
      */
     val interceptR = 0.0
-    val weightsR = Array(5.522875, 4.214502)
+    val weightsR = Vectors.dense(5.522875, 4.214502)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.intercept ~== interceptR absTol 1E-3)
+    assert(model.weights ~== weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -255,11 +248,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.data.V3. 5.200403
      */
     val interceptR = 5.696056
-    val weightsR = Array(3.670489, 6.001122)
+    val weightsR = Vectors.dense(3.670489, 6.001122)
 
     assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.weights ~== weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>
@@ -285,11 +277,10 @@ class LinearRegressionSuite extends SparkFunSuite with MLlibTestSparkContext {
        as.numeric.dataM.V3. 4.322251
      */
     val interceptR = 0.0
-    val weightsR = Array(5.673348, 4.322251)
+    val weightsR = Vectors.dense(5.673348, 4.322251)
 
-    assert(model.intercept ~== interceptR relTol 1E-3)
-    assert(model.weights(0) ~== weightsR(0) relTol 1E-3)
-    assert(model.weights(1) ~== weightsR(1) relTol 1E-3)
+    assert(model.intercept ~== interceptR absTol 1E-3)
+    assert(model.weights ~= weightsR relTol 1E-3)
 
     model.transform(dataset).select("features", "prediction").collect().foreach {
       case Row(features: DenseVector, prediction1: Double) =>