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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 org.apache.spark.ml.classification
import org.scalatest.FunSuite
import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
import org.apache.spark.mllib.linalg.Vector
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.mllib.util.TestingUtils._
import org.apache.spark.sql.{DataFrame, Row, SQLContext}
class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
@transient var sqlContext: SQLContext = _
@transient var dataset: DataFrame = _
private val eps: Double = 1e-5
override def beforeAll(): Unit = {
super.beforeAll()
sqlContext = new SQLContext(sc)
dataset = sqlContext.createDataFrame(
sc.parallelize(generateLogisticInput(1.0, 1.0, nPoints = 100, seed = 42), 2))
}
test("logistic regression: default params") {
val lr = new LogisticRegression
assert(lr.getLabelCol == "label")
assert(lr.getFeaturesCol == "features")
assert(lr.getPredictionCol == "prediction")
assert(lr.getRawPredictionCol == "rawPrediction")
assert(lr.getProbabilityCol == "probability")
val model = lr.fit(dataset)
model.transform(dataset)
.select("label", "probability", "prediction", "rawPrediction")
.collect()
assert(model.getThreshold === 0.5)
assert(model.getFeaturesCol == "features")
assert(model.getPredictionCol == "prediction")
assert(model.getRawPredictionCol == "rawPrediction")
assert(model.getProbabilityCol == "probability")
}
test("logistic regression with setters") {
// Set params, train, and check as many params as we can.
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(1.0)
.setThreshold(0.6)
.setProbabilityCol("myProbability")
val model = lr.fit(dataset)
assert(model.fittingParamMap.get(lr.maxIter) === Some(10))
assert(model.fittingParamMap.get(lr.regParam) === Some(1.0))
assert(model.fittingParamMap.get(lr.threshold) === Some(0.6))
assert(model.getThreshold === 0.6)
// Modify model params, and check that the params worked.
model.setThreshold(1.0)
val predAllZero = model.transform(dataset)
.select("prediction", "myProbability")
.collect()
.map { case Row(pred: Double, prob: Vector) => pred }
assert(predAllZero.forall(_ === 0),
s"With threshold=1.0, expected predictions to be all 0, but only" +
s" ${predAllZero.count(_ === 0)} of ${dataset.count()} were 0.")
// Call transform with params, and check that the params worked.
val predNotAllZero =
model.transform(dataset, model.threshold -> 0.0, model.probabilityCol -> "myProb")
.select("prediction", "myProb")
.collect()
.map { case Row(pred: Double, prob: Vector) => pred }
assert(predNotAllZero.exists(_ !== 0.0))
// Call fit() with new params, and check as many params as we can.
val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4,
lr.probabilityCol -> "theProb")
assert(model2.fittingParamMap.get(lr.maxIter).get === 5)
assert(model2.fittingParamMap.get(lr.regParam).get === 0.1)
assert(model2.fittingParamMap.get(lr.threshold).get === 0.4)
assert(model2.getThreshold === 0.4)
assert(model2.getProbabilityCol == "theProb")
}
test("logistic regression: Predictor, Classifier methods") {
val sqlContext = this.sqlContext
val lr = new LogisticRegression
val model = lr.fit(dataset)
assert(model.numClasses === 2)
val threshold = model.getThreshold
val results = model.transform(dataset)
// Compare rawPrediction with probability
results.select("rawPrediction", "probability").collect().map {
case Row(raw: Vector, prob: Vector) =>
assert(raw.size === 2)
assert(prob.size === 2)
val probFromRaw1 = 1.0 / (1.0 + math.exp(-raw(1)))
assert(prob(1) ~== probFromRaw1 relTol eps)
assert(prob(0) ~== 1.0 - probFromRaw1 relTol eps)
}
// Compare prediction with probability
results.select("prediction", "probability").collect().map {
case Row(pred: Double, prob: Vector) =>
val predFromProb = prob.toArray.zipWithIndex.maxBy(_._1)._2
assert(pred == predFromProb)
}
}
}
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