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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.param.shared
import java.io.PrintWriter
import scala.reflect.ClassTag
/**
* Code generator for shared params (sharedParams.scala). Run under the Spark folder with
* {{{
* build/sbt "mllib/runMain org.apache.spark.ml.param.shared.SharedParamsCodeGen"
* }}}
*/
private[shared] object SharedParamsCodeGen {
def main(args: Array[String]): Unit = {
val params = Seq(
ParamDesc[Double]("regParam", "regularization parameter (>= 0)",
isValid = "ParamValidators.gtEq(0)"),
ParamDesc[Int]("maxIter", "maximum number of iterations (>= 0)",
isValid = "ParamValidators.gtEq(0)"),
ParamDesc[String]("featuresCol", "features column name", Some("\"features\"")),
ParamDesc[String]("labelCol", "label column name", Some("\"label\"")),
ParamDesc[String]("predictionCol", "prediction column name", Some("\"prediction\"")),
ParamDesc[String]("rawPredictionCol", "raw prediction (a.k.a. confidence) column name",
Some("\"rawPrediction\"")),
ParamDesc[String]("probabilityCol", "Column name for predicted class conditional" +
" probabilities. Note: Not all models output well-calibrated probability estimates!" +
" These probabilities should be treated as confidences, not precise probabilities",
Some("\"probability\"")),
ParamDesc[String]("varianceCol", "Column name for the biased sample variance of prediction"),
ParamDesc[Double]("threshold",
"threshold in binary classification prediction, in range [0, 1]", Some("0.5"),
isValid = "ParamValidators.inRange(0, 1)", finalMethods = false),
ParamDesc[Array[Double]]("thresholds", "Thresholds in multi-class classification" +
" to adjust the probability of predicting each class." +
" Array must have length equal to the number of classes, with values >= 0." +
" The class with largest value p/t is predicted, where p is the original probability" +
" of that class and t is the class' threshold",
isValid = "(t: Array[Double]) => t.forall(_ >= 0)", finalMethods = false),
ParamDesc[String]("inputCol", "input column name"),
ParamDesc[Array[String]]("inputCols", "input column names"),
ParamDesc[String]("outputCol", "output column name", Some("uid + \"__output\"")),
ParamDesc[Int]("checkpointInterval", "set checkpoint interval (>= 1) or " +
"disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed " +
"every 10 iterations", isValid = "(interval: Int) => interval == -1 || interval >= 1"),
ParamDesc[Boolean]("fitIntercept", "whether to fit an intercept term", Some("true")),
ParamDesc[String]("handleInvalid", "how to handle invalid entries. Options are skip (which " +
"will filter out rows with bad values), or error (which will throw an error). More " +
"options may be added later",
isValid = "ParamValidators.inArray(Array(\"skip\", \"error\"))"),
ParamDesc[Boolean]("standardization", "whether to standardize the training features" +
" before fitting the model", Some("true")),
ParamDesc[Long]("seed", "random seed", Some("this.getClass.getName.hashCode.toLong")),
ParamDesc[Double]("elasticNetParam", "the ElasticNet mixing parameter, in range [0, 1]." +
" For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty",
isValid = "ParamValidators.inRange(0, 1)"),
ParamDesc[Double]("tol", "the convergence tolerance for iterative algorithms (>= 0)",
isValid = "ParamValidators.gtEq(0)"),
ParamDesc[Double]("stepSize", "Step size to be used for each iteration of optimization (>" +
" 0)", isValid = "ParamValidators.gt(0)"),
ParamDesc[String]("weightCol", "weight column name. If this is not set or empty, we treat " +
"all instance weights as 1.0"),
ParamDesc[String]("solver", "the solver algorithm for optimization. If this is not set or " +
"empty, default value is 'auto'", Some("\"auto\"")))
val code = genSharedParams(params)
val file = "src/main/scala/org/apache/spark/ml/param/shared/sharedParams.scala"
val writer = new PrintWriter(file)
writer.write(code)
writer.close()
}
/** Description of a param. */
private case class ParamDesc[T: ClassTag](
name: String,
doc: String,
defaultValueStr: Option[String] = None,
isValid: String = "",
finalMethods: Boolean = true) {
require(name.matches("[a-z][a-zA-Z0-9]*"), s"Param name $name is invalid.")
require(doc.nonEmpty) // TODO: more rigorous on doc
def paramTypeName: String = {
val c = implicitly[ClassTag[T]].runtimeClass
c match {
case _ if c == classOf[Int] => "IntParam"
case _ if c == classOf[Long] => "LongParam"
case _ if c == classOf[Float] => "FloatParam"
case _ if c == classOf[Double] => "DoubleParam"
case _ if c == classOf[Boolean] => "BooleanParam"
case _ if c.isArray && c.getComponentType == classOf[String] => s"StringArrayParam"
case _ if c.isArray && c.getComponentType == classOf[Double] => s"DoubleArrayParam"
case _ => s"Param[${getTypeString(c)}]"
}
}
def valueTypeName: String = {
val c = implicitly[ClassTag[T]].runtimeClass
getTypeString(c)
}
private def getTypeString(c: Class[_]): String = {
c match {
case _ if c == classOf[Int] => "Int"
case _ if c == classOf[Long] => "Long"
case _ if c == classOf[Float] => "Float"
case _ if c == classOf[Double] => "Double"
case _ if c == classOf[Boolean] => "Boolean"
case _ if c == classOf[String] => "String"
case _ if c.isArray => s"Array[${getTypeString(c.getComponentType)}]"
}
}
}
/** Generates the HasParam trait code for the input param. */
private def genHasParamTrait(param: ParamDesc[_]): String = {
val name = param.name
val Name = name(0).toUpper +: name.substring(1)
val Param = param.paramTypeName
val T = param.valueTypeName
val doc = param.doc
val defaultValue = param.defaultValueStr
val defaultValueDoc = defaultValue.map { v =>
s" (default: $v)"
}.getOrElse("")
val setDefault = defaultValue.map { v =>
s"""
| setDefault($name, $v)
|""".stripMargin
}.getOrElse("")
val isValid = if (param.isValid != "") {
", " + param.isValid
} else {
""
}
val methodStr = if (param.finalMethods) {
"final def"
} else {
"def"
}
s"""
|/**
| * Trait for shared param $name$defaultValueDoc.
| */
|private[ml] trait Has$Name extends Params {
|
| /**
| * Param for $doc.
| * @group param
| */
| final val $name: $Param = new $Param(this, "$name", "$doc"$isValid)
|$setDefault
| /** @group getParam */
| $methodStr get$Name: $T = $$($name)
|}
|""".stripMargin
}
/** Generates Scala source code for the input params with header. */
private def genSharedParams(params: Seq[ParamDesc[_]]): String = {
val header =
"""/*
| * 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.param.shared
|
|import org.apache.spark.ml.param._
|
|// DO NOT MODIFY THIS FILE! It was generated by SharedParamsCodeGen.
|
|// scalastyle:off
|""".stripMargin
val footer = "// scalastyle:on\n"
val traits = params.map(genHasParamTrait).mkString
header + traits + footer
}
}
|
