srowen commented on a change in pull request #31535: URL: https://github.com/apache/spark/pull/31535#discussion_r575287498
########## File path: mllib/src/main/scala/org/apache/spark/ml/tuning/ParamRandomBuilder.scala ########## @@ -0,0 +1,210 @@ +/* + * 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.tuning + +import org.apache.spark.annotation.Since +import org.apache.spark.ml.param._ +import org.apache.spark.ml.tuning.RandomRanges._ + +case class Limits[T: Numeric](x: T, y: T) + +private[ml] abstract class RandomT[T: Numeric] { + def randomT(): T + def randomTLog(n: Int): T +} + +abstract class Generator[T: Numeric] { + def apply(lim: Limits[T]): RandomT[T] +} + +object RandomRanges { + + private val rnd = new scala.util.Random + + private[tuning] def randomBigInt0To(x: BigInt): BigInt = { + var randVal = BigInt(x.bitLength, rnd) + while (randVal > x) { + randVal = BigInt(x.bitLength, rnd) + } + randVal + } + + private[ml] def bigIntBetween(lower: BigInt, upper: BigInt): BigInt = { + val diff: BigInt = upper - lower + randomBigInt0To(diff) + lower + } + + private def randomBigDecimalBetween(lower: BigDecimal, upper: BigDecimal): BigDecimal = { + val zeroCenteredRnd: BigDecimal = BigDecimal(rnd.nextDouble() - 0.5) + val range: BigDecimal = upper - lower + val halfWay: BigDecimal = lower + range / 2 + (zeroCenteredRnd * range) + halfWay + } + + implicit object DoubleGenerator extends Generator[Double] { + def apply(limits: Limits[Double]): RandomT[Double] = new RandomT[Double] { + import limits._ + val lower: Double = math.min(x, y) + val upper: Double = math.max(x, y) + + override def randomTLog(n: Int): Double = + RandomRanges.randomLog(lower, upper, n) + + override def randomT(): Double = + randomBigDecimalBetween(BigDecimal(lower), BigDecimal(upper)).doubleValue + } + } + + implicit object FloatGenerator extends Generator[Float] { + def apply(limits: Limits[Float]): RandomT[Float] = new RandomT[Float] { + import limits._ + val lower: Float = math.min(x, y) + val upper: Float = math.max(x, y) + + override def randomTLog(n: Int): Float = + RandomRanges.randomLog(lower, upper, n).toFloat + + override def randomT(): Float = + randomBigDecimalBetween(BigDecimal(lower), BigDecimal(upper)).floatValue + } + } + + implicit object IntGenerator extends Generator[Int] { + def apply(limits: Limits[Int]): RandomT[Int] = new RandomT[Int] { + import limits._ + val lower: Int = math.min(x, y) + val upper: Int = math.max(x, y) + + override def randomTLog(n: Int): Int = + RandomRanges.randomLog(lower, upper, n).toInt + + override def randomT(): Int = + bigIntBetween(BigInt(lower), BigInt(upper)).intValue + } + } + + implicit object LongGenerator extends Generator[Long] { + def apply(limits: Limits[Long]): RandomT[Long] = new RandomT[Long] { + import limits._ + val lower: Long = math.min(x, y) + val upper: Long = math.max(x, y) + + override def randomTLog(n: Int): Long = + RandomRanges.randomLog(lower, upper, n).toLong + + override def randomT(): Long = + bigIntBetween(BigInt(lower), BigInt(upper)).longValue + } + } + + private[ml] def logN(x: Double, base: Int): Double = math.log(x) / math.log(base) + + private[ml] def randomLog(lower: Double, upper: Double, n: Int): Double = { + val logLower: Double = logN(lower, n) + val logUpper: Double = logN(upper, n) + val logLimits: Limits[Double] = Limits(logLower, logUpper) + val rndLogged: RandomT[Double] = RandomRanges(logLimits) + math.pow(n, rndLogged.randomT()) + } + + private[ml] def apply[T: Generator](lim: Limits[T])(implicit t: Generator[T]): RandomT[T] = t(lim) + +} + +/** + * "For any distribution over a sample space with a finite maximum, the maximum of 60 random + * observations lies within the top 5% of the true maximum, with 95% probability" + * - Evaluating Machine Learning Models by Alice Zheng + * https://www.oreilly.com/library/view/evaluating-machine-learning/9781492048756/ch04.html + * + * Note: if you want more sophisticated hyperparameter tuning, consider Python libraries + * such as Hyperopt. + */ +@Since("3.2.0") +class ParamRandomBuilder extends ParamGridBuilder { + @Since("3.2.0") Review comment: You don't have to annotate all the methods - the class annotation implies it's 'since' 3.2.0 already. OK to remove to keep it simpler. ########## File path: mllib/src/main/scala/org/apache/spark/ml/tuning/ParamRandomBuilder.scala ########## @@ -0,0 +1,210 @@ +/* + * 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.tuning + +import org.apache.spark.annotation.Since +import org.apache.spark.ml.param._ +import org.apache.spark.ml.tuning.RandomRanges._ + +case class Limits[T: Numeric](x: T, y: T) + +private[ml] abstract class RandomT[T: Numeric] { + def randomT(): T + def randomTLog(n: Int): T +} + +abstract class Generator[T: Numeric] { + def apply(lim: Limits[T]): RandomT[T] +} + +object RandomRanges { + + private val rnd = new scala.util.Random + + private[tuning] def randomBigInt0To(x: BigInt): BigInt = { + var randVal = BigInt(x.bitLength, rnd) + while (randVal > x) { + randVal = BigInt(x.bitLength, rnd) + } + randVal + } + + private[ml] def bigIntBetween(lower: BigInt, upper: BigInt): BigInt = { + val diff: BigInt = upper - lower + randomBigInt0To(diff) + lower + } + + private def randomBigDecimalBetween(lower: BigDecimal, upper: BigDecimal): BigDecimal = { + val zeroCenteredRnd: BigDecimal = BigDecimal(rnd.nextDouble() - 0.5) + val range: BigDecimal = upper - lower + val halfWay: BigDecimal = lower + range / 2 + (zeroCenteredRnd * range) + halfWay + } + + implicit object DoubleGenerator extends Generator[Double] { + def apply(limits: Limits[Double]): RandomT[Double] = new RandomT[Double] { + import limits._ + val lower: Double = math.min(x, y) + val upper: Double = math.max(x, y) + + override def randomTLog(n: Int): Double = + RandomRanges.randomLog(lower, upper, n) + + override def randomT(): Double = + randomBigDecimalBetween(BigDecimal(lower), BigDecimal(upper)).doubleValue + } + } + + implicit object FloatGenerator extends Generator[Float] { + def apply(limits: Limits[Float]): RandomT[Float] = new RandomT[Float] { + import limits._ + val lower: Float = math.min(x, y) + val upper: Float = math.max(x, y) + + override def randomTLog(n: Int): Float = + RandomRanges.randomLog(lower, upper, n).toFloat + + override def randomT(): Float = + randomBigDecimalBetween(BigDecimal(lower), BigDecimal(upper)).floatValue + } + } + + implicit object IntGenerator extends Generator[Int] { + def apply(limits: Limits[Int]): RandomT[Int] = new RandomT[Int] { + import limits._ + val lower: Int = math.min(x, y) + val upper: Int = math.max(x, y) + + override def randomTLog(n: Int): Int = + RandomRanges.randomLog(lower, upper, n).toInt + + override def randomT(): Int = + bigIntBetween(BigInt(lower), BigInt(upper)).intValue + } + } + + implicit object LongGenerator extends Generator[Long] { + def apply(limits: Limits[Long]): RandomT[Long] = new RandomT[Long] { + import limits._ + val lower: Long = math.min(x, y) + val upper: Long = math.max(x, y) + + override def randomTLog(n: Int): Long = + RandomRanges.randomLog(lower, upper, n).toLong + + override def randomT(): Long = + bigIntBetween(BigInt(lower), BigInt(upper)).longValue + } + } + + private[ml] def logN(x: Double, base: Int): Double = math.log(x) / math.log(base) + + private[ml] def randomLog(lower: Double, upper: Double, n: Int): Double = { + val logLower: Double = logN(lower, n) + val logUpper: Double = logN(upper, n) + val logLimits: Limits[Double] = Limits(logLower, logUpper) + val rndLogged: RandomT[Double] = RandomRanges(logLimits) + math.pow(n, rndLogged.randomT()) + } + + private[ml] def apply[T: Generator](lim: Limits[T])(implicit t: Generator[T]): RandomT[T] = t(lim) + +} + +/** + * "For any distribution over a sample space with a finite maximum, the maximum of 60 random + * observations lies within the top 5% of the true maximum, with 95% probability" + * - Evaluating Machine Learning Models by Alice Zheng + * https://www.oreilly.com/library/view/evaluating-machine-learning/9781492048756/ch04.html + * + * Note: if you want more sophisticated hyperparameter tuning, consider Python libraries + * such as Hyperopt. + */ +@Since("3.2.0") +class ParamRandomBuilder extends ParamGridBuilder { + @Since("3.2.0") + def addRandom[T: Generator](param: Param[T], lim: Limits[T], n: Int): this.type = { + val gen: RandomT[T] = RandomRanges(lim) + addGrid(param, (1 to n).map { _: Int => gen.randomT() }) + } + + @Since("3.2.0") + def addLog10Random[T: Generator](param: Param[T], lim: Limits[T], n: Int): this.type = + addLogRandom(param, lim, n, 10) + + @Since("3.2.0") + def addLog2Random[T: Generator](param: Param[T], lim: Limits[T], n: Int): this.type = Review comment: I was going to say just go with one, natural log, but, I kind of like base-10 as more useful. Base 2 I'm not as sure about but I get it - batch size 16, 32, 64 etc ########## File path: mllib/src/main/scala/org/apache/spark/ml/tuning/ParamRandomBuilder.scala ########## @@ -0,0 +1,145 @@ +/* + * 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.tuning + +import org.apache.spark.annotation.Since +import org.apache.spark.ml.param._ + +case class Limits[T: Numeric](x: T, y: T) + +abstract class RandomT[T: Numeric] { + def randomT(): T + def randomTLog(n: Int): T +} + +abstract class Generator[T: Numeric] { + def apply(lim: Limits[T]): RandomT[T] +} + +object RandomRanges { + + val rnd = new scala.util.Random + + private[tuning] def randomBigInt0To(x: BigInt): BigInt = { + var randVal = BigInt(x.bitLength, rnd) + while (randVal > x) { + randVal = BigInt(x.bitLength, rnd) + } + randVal + } + + def bigIntBetween(lower: BigInt, upper: BigInt): BigInt = { + val diff: BigInt = upper - lower + randomBigInt0To(diff) + lower + } + + private def randomBigDecimalBetween(lower: BigDecimal, upper: BigDecimal): BigDecimal = { + val zeroCenteredRnd: BigDecimal = BigDecimal(rnd.nextDouble() - 0.5) + val range: BigDecimal = upper - lower + val halfWay: BigDecimal = lower + range / 2 + (zeroCenteredRnd * range) + halfWay + } + + implicit object DoubleGenerator extends Generator[Double] { + def apply(limits: Limits[Double]): RandomT[Double] = new RandomT[Double] { + import limits._ + val lower: Double = math.min(x, y) + val upper: Double = math.max(x, y) + + override def randomTLog(n: Int): Double = + RandomRanges.randomLog(lower, upper, n) + + override def randomT(): Double = + randomBigDecimalBetween(BigDecimal(lower), BigDecimal(upper)).doubleValue + } + } + + implicit object FloatGenerator extends Generator[Float] { + def apply(limits: Limits[Float]): RandomT[Float] = new RandomT[Float] { + import limits._ + val lower: Float = math.min(x, y) + val upper: Float = math.max(x, y) + + override def randomTLog(n: Int): Float = + RandomRanges.randomLog(lower, upper, n).toFloat + + override def randomT(): Float = + randomBigDecimalBetween(BigDecimal(lower), BigDecimal(upper)).floatValue + } + } + + implicit object IntGenerator extends Generator[Int] { + def apply(limits: Limits[Int]): RandomT[Int] = new RandomT[Int] { + import limits._ + val lower: Int = math.min(x, y) + val upper: Int = math.max(x, y) + + override def randomTLog(n: Int): Int = + RandomRanges.randomLog(lower, upper, n).toInt + + override def randomT(): Int = + bigIntBetween(BigInt(lower), BigInt(upper)).intValue + } + } + + implicit object LongGenerator extends Generator[Long] { Review comment: It's no big deal either way, but yeah I'd remove it to keep this simple. Do the implicits need to be public? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
