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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.util.concurrent.atomic.AtomicLong
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
import org.apache.spark.internal.Logging
import org.apache.spark.ml.{Estimator, Model}
import org.apache.spark.ml.param.Param
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.Dataset
/**
* A small wrapper that defines a training session for an estimator, and some methods to log
* useful information during this session.
*
* A new instance is expected to be created within fit().
*
* @param estimator the estimator that is being fit
* @param dataset the training dataset
* @tparam E the type of the estimator
*/
private[ml] class Instrumentation[E <: Estimator[_]] private (
estimator: E, dataset: RDD[_]) extends Logging {
private val id = Instrumentation.counter.incrementAndGet()
private val prefix = {
val className = estimator.getClass.getSimpleName
s"$className-${estimator.uid}-${dataset.hashCode()}-$id: "
}
init()
private def init(): Unit = {
log(s"training: numPartitions=${dataset.partitions.length}" +
s" storageLevel=${dataset.getStorageLevel}")
}
/**
* Logs a message with a prefix that uniquely identifies the training session.
*/
def log(msg: String): Unit = {
logInfo(prefix + msg)
}
/**
* Logs the value of the given parameters for the estimator being used in this session.
*/
def logParams(params: Param[_]*): Unit = {
val pairs: Seq[(String, JValue)] = for {
p <- params
value <- estimator.get(p)
} yield {
val cast = p.asInstanceOf[Param[Any]]
p.name -> parse(cast.jsonEncode(value))
}
log(compact(render(map2jvalue(pairs.toMap))))
}
def logNumFeatures(num: Long): Unit = {
log(compact(render("numFeatures" -> num)))
}
def logNumClasses(num: Long): Unit = {
log(compact(render("numClasses" -> num)))
}
/**
* Logs the successful completion of the training session and the value of the learned model.
*/
def logSuccess(model: Model[_]): Unit = {
log(s"training finished")
}
}
/**
* Some common methods for logging information about a training session.
*/
private[ml] object Instrumentation {
private val counter = new AtomicLong(0)
/**
* Creates an instrumentation object for a training session.
*/
def create[E <: Estimator[_]](
estimator: E, dataset: Dataset[_]): Instrumentation[E] = {
create[E](estimator, dataset.rdd)
}
/**
* Creates an instrumentation object for a training session.
*/
def create[E <: Estimator[_]](
estimator: E, dataset: RDD[_]): Instrumentation[E] = {
new Instrumentation[E](estimator, dataset)
}
}
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