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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.util
import java.io.{File, IOException}
import org.scalatest.Suite
import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.{Estimator, Model}
import org.apache.spark.ml.param._
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.sql.{DataFrame, Dataset}
trait DefaultReadWriteTest extends TempDirectory { self: Suite =>
/**
* Checks "overwrite" option and params.
* This saves to and loads from [[tempDir]], but creates a subdirectory with a random name
* in order to avoid conflicts from multiple calls to this method.
*
* @param instance ML instance to test saving/loading
* @param testParams If true, then test values of Params. Otherwise, just test overwrite option.
* @tparam T ML instance type
* @return Instance loaded from file
*/
def testDefaultReadWrite[T <: Params with MLWritable](
instance: T,
testParams: Boolean = true): T = {
val uid = instance.uid
val subdirName = Identifiable.randomUID("test")
val subdir = new File(tempDir, subdirName)
val path = new File(subdir, uid).getPath
instance.save(path)
intercept[IOException] {
instance.save(path)
}
instance.write.overwrite().save(path)
val loader = instance.getClass.getMethod("read").invoke(null).asInstanceOf[MLReader[T]]
val newInstance = loader.load(path)
assert(newInstance.uid === instance.uid)
if (testParams) {
instance.params.foreach { p =>
if (instance.isDefined(p)) {
(instance.getOrDefault(p), newInstance.getOrDefault(p)) match {
case (Array(values), Array(newValues)) =>
assert(values === newValues, s"Values do not match on param ${p.name}.")
case (value, newValue) =>
assert(value === newValue, s"Values do not match on param ${p.name}.")
}
} else {
assert(!newInstance.isDefined(p), s"Param ${p.name} shouldn't be defined.")
}
}
}
val load = instance.getClass.getMethod("load", classOf[String])
val another = load.invoke(instance, path).asInstanceOf[T]
assert(another.uid === instance.uid)
another
}
/**
* Default test for Estimator, Model pairs:
* - Explicitly set Params, and train model
* - Test save/load using [[testDefaultReadWrite()]] on Estimator and Model
* - Check Params on Estimator and Model
* - Compare model data
*
* This requires that the [[Estimator]] and [[Model]] share the same set of [[Param]]s.
*
* @param estimator Estimator to test
* @param dataset Dataset to pass to [[Estimator.fit()]]
* @param testParams Set of [[Param]] values to set in estimator
* @param checkModelData Method which takes the original and loaded [[Model]] and compares their
* data. This method does not need to check [[Param]] values.
* @tparam E Type of [[Estimator]]
* @tparam M Type of [[Model]] produced by estimator
*/
def testEstimatorAndModelReadWrite[
E <: Estimator[M] with MLWritable, M <: Model[M] with MLWritable](
estimator: E,
dataset: Dataset[_],
testParams: Map[String, Any],
checkModelData: (M, M) => Unit): Unit = {
// Set some Params to make sure set Params are serialized.
testParams.foreach { case (p, v) =>
estimator.set(estimator.getParam(p), v)
}
val model = estimator.fit(dataset)
// Test Estimator save/load
val estimator2 = testDefaultReadWrite(estimator)
testParams.foreach { case (p, v) =>
val param = estimator.getParam(p)
assert(estimator.get(param).get === estimator2.get(param).get)
}
// Test Model save/load
val model2 = testDefaultReadWrite(model)
testParams.foreach { case (p, v) =>
val param = model.getParam(p)
assert(model.get(param).get === model2.get(param).get)
}
checkModelData(model, model2)
}
}
class MyParams(override val uid: String) extends Params with MLWritable {
final val intParamWithDefault: IntParam = new IntParam(this, "intParamWithDefault", "doc")
final val intParam: IntParam = new IntParam(this, "intParam", "doc")
final val floatParam: FloatParam = new FloatParam(this, "floatParam", "doc")
final val doubleParam: DoubleParam = new DoubleParam(this, "doubleParam", "doc")
final val longParam: LongParam = new LongParam(this, "longParam", "doc")
final val stringParam: Param[String] = new Param[String](this, "stringParam", "doc")
final val intArrayParam: IntArrayParam = new IntArrayParam(this, "intArrayParam", "doc")
final val doubleArrayParam: DoubleArrayParam =
new DoubleArrayParam(this, "doubleArrayParam", "doc")
final val stringArrayParam: StringArrayParam =
new StringArrayParam(this, "stringArrayParam", "doc")
setDefault(intParamWithDefault -> 0)
set(intParam -> 1)
set(floatParam -> 2.0f)
set(doubleParam -> 3.0)
set(longParam -> 4L)
set(stringParam -> "5")
set(intArrayParam -> Array(6, 7))
set(doubleArrayParam -> Array(8.0, 9.0))
set(stringArrayParam -> Array("10", "11"))
override def copy(extra: ParamMap): Params = defaultCopy(extra)
override def write: MLWriter = new DefaultParamsWriter(this)
}
object MyParams extends MLReadable[MyParams] {
override def read: MLReader[MyParams] = new DefaultParamsReader[MyParams]
override def load(path: String): MyParams = super.load(path)
}
class DefaultReadWriteSuite extends SparkFunSuite with MLlibTestSparkContext
with DefaultReadWriteTest {
test("default read/write") {
val myParams = new MyParams("my_params")
testDefaultReadWrite(myParams)
}
}
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