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Posted to reviews@spark.apache.org by GitBox <gi...@apache.org> on 2021/02/11 15:00:48 UTC
[GitHub] [spark] srowen commented on a change in pull request #31535: [SPARK-34415][ML] Randomization in hyperparameter optimization
srowen commented on a change in pull request #31535:
URL: https://github.com/apache/spark/pull/31535#discussion_r574568879
##########
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)
Review comment:
I think these can be private[ml] or at least private[spark]? best to not expose whatever we dont' have to
##########
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 = {
Review comment:
I doubt BigDecimal or BigInt is worth supporting? I have not seen that used as a hyperparam
##########
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:
Likewise not sure Long is even needed; is there a use case?
##########
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] {
+ 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
+ }
+ }
+
+ def logN(x: Double, base: Int): Double = math.log(x) / math.log(base)
+
+ 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())
+ }
+
+ 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("3.1.0")
Review comment:
3.2.0
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