From ccfa362ddec1bc942785798dea41c3aac52df60f Mon Sep 17 00:00:00 2001
From: Xinghao <pxinghao@gmail.com>
Date: Sun, 28 Jul 2013 10:33:57 -0700
Subject: [PATCH] Change *_LocalRandomSGD to *LocalRandomSGD
---
.../classification/LogisticRegression.scala | 10 ++++----
.../spark/mllib/classification/SVM.scala | 10 ++++----
.../scala/spark/mllib/regression/Lasso.scala | 10 ++++----
.../LogisticRegressionSuite.scala | 23 +++----------------
.../spark/mllib/classification/SVMSuite.scala | 4 ++--
.../spark/mllib/regression/LassoSuite.scala | 8 +++----
6 files changed, 24 insertions(+), 41 deletions(-)
diff --git a/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala
index 40b96fbe3a..1b093187f2 100644
--- a/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala
+++ b/mllib/src/main/scala/spark/mllib/classification/LogisticRegression.scala
@@ -53,7 +53,7 @@ class LogisticRegressionModel(
}
}
-class LogisticRegression_LocalRandomSGD private (var stepSize: Double, var miniBatchFraction: Double,
+class LogisticRegressionLocalRandomSGD private (var stepSize: Double, var miniBatchFraction: Double,
var numIters: Int)
extends Logging {
@@ -138,7 +138,7 @@ class LogisticRegression_LocalRandomSGD private (var stepSize: Double, var miniB
* NOTE(shivaram): We use multiple train methods instead of default arguments to support
* Java programs.
*/
-object LogisticRegression_LocalRandomSGD {
+object LogisticRegressionLocalRandomSGD {
/**
* Train a logistic regression model given an RDD of (label, features) pairs. We run a fixed number
@@ -163,7 +163,7 @@ object LogisticRegression_LocalRandomSGD {
initialWeights: Array[Double])
: LogisticRegressionModel =
{
- new LogisticRegression_LocalRandomSGD(stepSize, miniBatchFraction, numIterations).train(input, initialWeights)
+ new LogisticRegressionLocalRandomSGD(stepSize, miniBatchFraction, numIterations).train(input, initialWeights)
}
/**
@@ -185,7 +185,7 @@ object LogisticRegression_LocalRandomSGD {
miniBatchFraction: Double)
: LogisticRegressionModel =
{
- new LogisticRegression_LocalRandomSGD(stepSize, miniBatchFraction, numIterations).train(input)
+ new LogisticRegressionLocalRandomSGD(stepSize, miniBatchFraction, numIterations).train(input)
}
/**
@@ -233,7 +233,7 @@ object LogisticRegression_LocalRandomSGD {
}
val sc = new SparkContext(args(0), "LogisticRegression")
val data = MLUtils.loadLabeledData(sc, args(1))
- val model = LogisticRegression_LocalRandomSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
+ val model = LogisticRegressionLocalRandomSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
sc.stop()
}
diff --git a/mllib/src/main/scala/spark/mllib/classification/SVM.scala b/mllib/src/main/scala/spark/mllib/classification/SVM.scala
index 2cd1d668eb..76844f6b9c 100644
--- a/mllib/src/main/scala/spark/mllib/classification/SVM.scala
+++ b/mllib/src/main/scala/spark/mllib/classification/SVM.scala
@@ -53,7 +53,7 @@ class SVMModel(
-class SVM_LocalRandomSGD private (var stepSize: Double, var regParam: Double, var miniBatchFraction: Double,
+class SVMLocalRandomSGD private (var stepSize: Double, var regParam: Double, var miniBatchFraction: Double,
var numIters: Int)
extends Logging {
@@ -138,7 +138,7 @@ class SVM_LocalRandomSGD private (var stepSize: Double, var regParam: Double, va
*/
-object SVM_LocalRandomSGD {
+object SVMLocalRandomSGD {
/**
* Train a SVM model given an RDD of (label, features) pairs. We run a fixed number
@@ -163,7 +163,7 @@ object SVM_LocalRandomSGD {
initialWeights: Array[Double])
: SVMModel =
{
- new SVM_LocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input, initialWeights)
+ new SVMLocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input, initialWeights)
}
/**
@@ -185,7 +185,7 @@ object SVM_LocalRandomSGD {
miniBatchFraction: Double)
: SVMModel =
{
- new SVM_LocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input)
+ new SVMLocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input)
}
/**
@@ -233,7 +233,7 @@ object SVM_LocalRandomSGD {
}
val sc = new SparkContext(args(0), "SVM")
val data = MLUtils.loadLabeledData(sc, args(1))
- val model = SVM_LocalRandomSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
+ val model = SVMLocalRandomSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
sc.stop()
}
diff --git a/mllib/src/main/scala/spark/mllib/regression/Lasso.scala b/mllib/src/main/scala/spark/mllib/regression/Lasso.scala
index 64364323a2..1952658bb2 100644
--- a/mllib/src/main/scala/spark/mllib/regression/Lasso.scala
+++ b/mllib/src/main/scala/spark/mllib/regression/Lasso.scala
@@ -53,7 +53,7 @@ class LassoModel(
}
-class Lasso_LocalRandomSGD private (var stepSize: Double, var regParam: Double, var miniBatchFraction: Double,
+class LassoLocalRandomSGD private (var stepSize: Double, var regParam: Double, var miniBatchFraction: Double,
var numIters: Int)
extends Logging {
@@ -138,7 +138,7 @@ class Lasso_LocalRandomSGD private (var stepSize: Double, var regParam: Double,
*
*
*/
-object Lasso_LocalRandomSGD {
+object LassoLocalRandomSGD {
/**
* Train a Lasso model given an RDD of (label, features) pairs. We run a fixed number
@@ -163,7 +163,7 @@ object Lasso_LocalRandomSGD {
initialWeights: Array[Double])
: LassoModel =
{
- new Lasso_LocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input, initialWeights)
+ new LassoLocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input, initialWeights)
}
/**
@@ -185,7 +185,7 @@ object Lasso_LocalRandomSGD {
miniBatchFraction: Double)
: LassoModel =
{
- new Lasso_LocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input)
+ new LassoLocalRandomSGD(stepSize, regParam, miniBatchFraction, numIterations).train(input)
}
/**
@@ -233,7 +233,7 @@ object Lasso_LocalRandomSGD {
}
val sc = new SparkContext(args(0), "Lasso")
val data = MLUtils.loadLabeledData(sc, args(1))
- val model = Lasso_LocalRandomSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
+ val model = LassoLocalRandomSGD.train(data, args(4).toInt, args(2).toDouble, args(3).toDouble)
sc.stop()
}
diff --git a/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala b/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
index 827ca66330..144b8b1bc7 100644
--- a/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
@@ -1,6 +1,3 @@
-<<<<<<< HEAD:mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
-package spark.mllib.classification
-=======
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
@@ -18,8 +15,7 @@ package spark.mllib.classification
* limitations under the License.
*/
-package spark.mllib.regression
->>>>>>> FETCH_HEAD:mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
+package spark.mllib.classification
import scala.util.Random
@@ -37,13 +33,6 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
System.clearProperty("spark.driver.port")
}
-<<<<<<< HEAD:mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
- // Test if we can correctly learn A, B where Y = logistic(A + B*X)
- test("LogisticRegression_LocalRandomSGD") {
- val nPoints = 10000
- val rnd = new Random(42)
-
-=======
// Generate input of the form Y = logistic(offset + scale*X)
def generateLogisticInput(
offset: Double,
@@ -51,7 +40,6 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
nPoints: Int,
seed: Int): Seq[(Double, Array[Double])] = {
val rnd = new Random(seed)
->>>>>>> FETCH_HEAD:mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
val x1 = Array.fill[Double](nPoints)(rnd.nextGaussian())
// NOTE: if U is uniform[0, 1] then ln(u) - ln(1-u) is Logistic(0,1)
@@ -91,12 +79,7 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
val testRDD = sc.parallelize(testData, 2)
testRDD.cache()
-<<<<<<< HEAD:mllib/src/test/scala/spark/mllib/classification/LogisticRegressionSuite.scala
- val lr = new LogisticRegression_LocalRandomSGD().setStepSize(10.0)
- .setNumIterations(20)
-=======
- val lr = new LogisticRegression().setStepSize(10.0).setNumIterations(20)
->>>>>>> FETCH_HEAD:mllib/src/test/scala/spark/mllib/regression/LogisticRegressionSuite.scala
+ val lr = new LogisticRegressionLocalRandomSGD().setStepSize(10.0).setNumIterations(20)
val model = lr.train(testRDD)
@@ -128,7 +111,7 @@ class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll {
testRDD.cache()
// Use half as many iterations as the previous test.
- val lr = new LogisticRegression().setStepSize(10.0).setNumIterations(10)
+ val lr = new LogisticRegressionLocalRandomSGD().setStepSize(10.0).setNumIterations(10)
val model = lr.train(testRDD, initialWeights)
diff --git a/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala b/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala
index 50cf260f49..0d781c310c 100644
--- a/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/classification/SVMSuite.scala
@@ -19,7 +19,7 @@ class SVMSuite extends FunSuite with BeforeAndAfterAll {
System.clearProperty("spark.driver.port")
}
- test("SVM_LocalRandomSGD") {
+ test("SVMLocalRandomSGD") {
val nPoints = 10000
val rnd = new Random(42)
@@ -46,7 +46,7 @@ class SVMSuite extends FunSuite with BeforeAndAfterAll {
writer_data.write("\n")})
writer_data.close()
- val svm = new SVM_LocalRandomSGD().setStepSize(1.0)
+ val svm = new SVMLocalRandomSGD().setStepSize(1.0)
.setRegParam(1.0)
.setNumIterations(100)
diff --git a/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala b/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala
index 9836ac54c1..0c39e1e09b 100644
--- a/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala
+++ b/mllib/src/test/scala/spark/mllib/regression/LassoSuite.scala
@@ -17,7 +17,7 @@ class LassoSuite extends FunSuite with BeforeAndAfterAll {
System.clearProperty("spark.driver.port")
}
- test("Lasso_LocalRandomSGD") {
+ test("LassoLocalRandomSGD") {
val nPoints = 10000
val rnd = new Random(42)
@@ -36,14 +36,14 @@ class LassoSuite extends FunSuite with BeforeAndAfterAll {
val testRDD = sc.parallelize(testData, 2)
testRDD.cache()
- val ls = new Lasso_LocalRandomSGD().setStepSize(1.0)
+ val ls = new LassoLocalRandomSGD().setStepSize(1.0)
.setRegParam(0.01)
.setNumIterations(20)
val model = ls.train(testRDD)
- val weight0 = model.weights.get(0)
- val weight1 = model.weights.get(1)
+ val weight0 = model.weights(0)
+ val weight1 = model.weights(1)
assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
assert(weight1 >= -1.0e-3 && weight1 <= 1.0e-3, weight1 + " not in [-0.001, 0.001]")
assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
--
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