[SPARK-10592] [ML] [PySpark] Deprecate weights and use coefficients instead in ML models

Deprecated in `LogisticRegression` and `LinearRegression`

Author: vectorijk <jiangkai@gmail.com>

Closes #9311 from vectorijk/spark-10592.

1 files changed, 3 insertions, 3 deletions

diff --git a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
index 253cabf013..cbabafe1b5 100644
--- a/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
+++ b/mllib/src/test/java/org/apache/spark/ml/classification/JavaOneVsRestSuite.java
@@ -47,16 +47,16 @@ public class JavaOneVsRestSuite implements Serializable {
         jsql = new SQLContext(jsc);
         int nPoints = 3;
 
-        // The following weights and xMean/xVariance are computed from iris dataset with lambda=0.2.
+        // The following coefficients and xMean/xVariance are computed from iris dataset with lambda=0.2.
         // As a result, we are drawing samples from probability distribution of an actual model.
-        double[] weights = {
+        double[] coefficients = {
                 -0.57997, 0.912083, -0.371077, -0.819866, 2.688191,
                 -0.16624, -0.84355, -0.048509, -0.301789, 4.170682 };
 
         double[] xMean = {5.843, 3.057, 3.758, 1.199};
         double[] xVariance = {0.6856, 0.1899, 3.116, 0.581};
         List<LabeledPoint> points = JavaConverters.seqAsJavaListConverter(
-            generateMultinomialLogisticInput(weights, xMean, xVariance, true, nPoints, 42)
+            generateMultinomialLogisticInput(coefficients, xMean, xVariance, true, nPoints, 42)
         ).asJava();
         datasetRDD = jsc.parallelize(points, 2);
         dataset = jsql.createDataFrame(datasetRDD, LabeledPoint.class);