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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package spark.mllib.classification
import scala.util.Random
import scala.collection.JavaConversions._
import org.scalatest.BeforeAndAfterAll
import org.scalatest.FunSuite
import org.scalatest.matchers.ShouldMatchers
import spark.SparkContext
import spark.mllib.regression._
object LogisticRegressionSuite {
def generateLogisticInputAsList(
offset: Double,
scale: Double,
nPoints: Int,
seed: Int): java.util.List[LabeledPoint] = {
seqAsJavaList(generateLogisticInput(offset, scale, nPoints, seed))
}
// Generate input of the form Y = logistic(offset + scale*X)
def generateLogisticInput(
offset: Double,
scale: Double,
nPoints: Int,
seed: Int): Seq[LabeledPoint] = {
val rnd = new Random(seed)
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)
val unifRand = new scala.util.Random(45)
val rLogis = (0 until nPoints).map { i =>
val u = unifRand.nextDouble()
math.log(u) - math.log(1.0-u)
}
// y <- A + B*x + rLogis()
// y <- as.numeric(y > 0)
val y: Seq[Int] = (0 until nPoints).map { i =>
val yVal = offset + scale * x1(i) + rLogis(i)
if (yVal > 0) 1 else 0
}
val testData = (0 until nPoints).map(i => LabeledPoint(y(i), Array(x1(i))))
testData
}
}
class LogisticRegressionSuite extends FunSuite with BeforeAndAfterAll with ShouldMatchers {
@transient private var sc: SparkContext = _
override def beforeAll() {
sc = new SparkContext("local", "test")
}
override def afterAll() {
sc.stop()
System.clearProperty("spark.driver.port")
}
def validatePrediction(predictions: Seq[Double], input: Seq[LabeledPoint]) {
val numOffPredictions = predictions.zip(input).filter { case (prediction, expected) =>
(prediction != expected.label)
}.size
// At least 83% of the predictions should be on.
((input.length - numOffPredictions).toDouble / input.length) should be > 0.83
}
// Test if we can correctly learn A, B where Y = logistic(A + B*X)
test("logistic regression") {
val nPoints = 10000
val A = 2.0
val B = -1.5
val testData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 42)
val testRDD = sc.parallelize(testData, 2)
testRDD.cache()
val lr = new LogisticRegressionWithSGD()
lr.optimizer.setStepSize(10.0).setNumIterations(20)
val model = lr.run(testRDD)
// Test the weights
val weight0 = model.weights(0)
assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
val validationData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17)
val validationRDD = sc.parallelize(validationData, 2)
// Test prediction on RDD.
validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
// Test prediction on Array.
validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
}
test("logistic regression with initial weights") {
val nPoints = 10000
val A = 2.0
val B = -1.5
val testData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 42)
val initialB = -1.0
val initialWeights = Array(initialB)
val testRDD = sc.parallelize(testData, 2)
testRDD.cache()
// Use half as many iterations as the previous test.
val lr = new LogisticRegressionWithSGD()
lr.optimizer.setStepSize(10.0).setNumIterations(10)
val model = lr.run(testRDD, initialWeights)
val weight0 = model.weights(0)
assert(weight0 >= -1.60 && weight0 <= -1.40, weight0 + " not in [-1.6, -1.4]")
assert(model.intercept >= 1.9 && model.intercept <= 2.1, model.intercept + " not in [1.9, 2.1]")
val validationData = LogisticRegressionSuite.generateLogisticInput(A, B, nPoints, 17)
val validationRDD = sc.parallelize(validationData, 2)
// Test prediction on RDD.
validatePrediction(model.predict(validationRDD.map(_.features)).collect(), validationData)
// Test prediction on Array.
validatePrediction(validationData.map(row => model.predict(row.features)), validationData)
}
}
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