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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.util.MLlibTestSparkContext
import org.apache.spark.sql.{SQLContext, DataFrame}
class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
@transient var sqlContext: SQLContext = _
@transient var dataset: DataFrame = _
override def beforeAll(): Unit = {
super.beforeAll()
sqlContext = new SQLContext(sc)
dataset = sqlContext.createSchemaRDD(
sc.parallelize(generateLogisticInput(1.0, 1.0, 100, 42), 2))
}
test("logistic regression") {
val lr = new LogisticRegression
val model = lr.fit(dataset)
model.transform(dataset)
.select("label", "prediction")
.collect()
}
test("logistic regression with setters") {
val lr = new LogisticRegression()
.setMaxIter(10)
.setRegParam(1.0)
val model = lr.fit(dataset)
model.transform(dataset, model.threshold -> 0.8) // overwrite threshold
.select("label", "score", "prediction")
.collect()
}
test("logistic regression fit and transform with varargs") {
val lr = new LogisticRegression
val model = lr.fit(dataset, lr.maxIter -> 10, lr.regParam -> 1.0)
model.transform(dataset, model.threshold -> 0.8, model.scoreCol -> "probability")
.select("label", "probability", "prediction")
.collect()
}
}
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