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Merge pull request #776 from zwt233/Auto-ML
Add Early Stopping Function and a new acquisition function(GP-UCB)
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60
...on-angel/automl/src/main/scala/com/tencent/angel/spark/automl/tuner/acquisition/UCB.scala
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| /* | ||
| * Tencent is pleased to support the open source community by making Angel available. | ||
| * | ||
| * Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved. | ||
| * | ||
| * Licensed 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 | ||
| * | ||
| * https://opensource.org/licenses/Apache-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. | ||
| * | ||
| */ | ||
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| package com.tencent.angel.spark.automl.tuner.acquisition | ||
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| import com.tencent.angel.spark.automl.tuner.surrogate.Surrogate | ||
| import org.apache.commons.logging.{Log, LogFactory} | ||
| import org.apache.spark.ml.linalg.{Vector, Vectors} | ||
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| /** | ||
| * Expected improvement. | ||
| * | ||
| * @param surrogate | ||
| * @param beta : Controls the upper confidence bound | ||
| * Assume : | ||
| * - t: number of iteration | ||
| * - d: dimension of optimization space | ||
| * - v: hyperparameter v = 1 | ||
| * - delta: small constant 0.1 (prob of regret) | ||
| * Suggest value：beta = sqrt( v* (2* log( (t**(d/2. + 2))*(pi**2)/(3. * delta) ))) | ||
| */ | ||
| class UCB( | ||
| override val surrogate: Surrogate, | ||
| val beta: Double = 100) | ||
| extends Acquisition(surrogate) { | ||
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| val LOG: Log = LogFactory.getLog(classOf[Surrogate]) | ||
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| override def compute(X: Vector, derivative: Boolean = false): (Double, Vector) = { | ||
| val pred = surrogate.predict(X) // (mean, variance) | ||
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| val m: Double = pred._1 | ||
| val s: Double = Math.sqrt(pred._2) | ||
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| if (s == 0) { | ||
| // if std is zero, we have observed x on all instances | ||
| // using a RF, std should be never exactly 0.0 | ||
| (0.0, Vectors.dense(new Array[Double](X.size))) | ||
| } else { | ||
| val ucb = m + beta*s | ||
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| (ucb, Vectors.dense(new Array[Double](X.size))) | ||
| } | ||
| } | ||
| } |
79 changes: 79 additions & 0 deletions
79
...gel/automl/src/main/scala/com/tencent/angel/spark/automl/tuner/config/EarlyStopping.scala
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,79 @@ | ||
| /* | ||
| * Tencent is pleased to support the open source community by making Angel available. | ||
| * | ||
| * Copyright (C) 2017-2018 THL A29 Limited, a Tencent company. All rights reserved. | ||
| * | ||
| * Licensed 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 | ||
| * | ||
| * https://opensource.org/licenses/Apache-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. | ||
| * | ||
| */ | ||
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| package com.tencent.angel.spark.automl.tuner.config | ||
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| /** | ||
| * A single configuration | ||
| * | ||
| * @param patience : How long to wait after last time validation loss improved. | ||
| * Default: 5 | ||
| * @param minimize : Whether to minimize or maximize the val_score | ||
| * Default: false | ||
| */ | ||
| class EarlyStopping(patience:Int=5, | ||
| var minDelta:Double = 0.0, | ||
| minimize:Boolean=false) { | ||
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| var counter: Int = 0 | ||
| var bestScore: Double = Double.NegativeInfinity | ||
| var earlyStop: Boolean = false | ||
| val pat = patience | ||
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| def greater(a: Double, b: Double): Boolean = { | ||
| if (a > b) { | ||
| return true | ||
| } | ||
| else { | ||
| return false | ||
| } | ||
| } | ||
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| var monitorOp = greater _ | ||
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| def less(a: Double, b: Double): Boolean = { | ||
| if (a > b) { | ||
| return false | ||
| } | ||
| else { | ||
| return true | ||
| } | ||
| } | ||
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| if (minimize) { | ||
| monitorOp = less _ | ||
| minDelta = -minDelta | ||
| bestScore = Double.PositiveInfinity | ||
| } | ||
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| def update(val_score: Double): Unit = { | ||
| val score = val_score | ||
| if (monitorOp(score - minDelta, bestScore)) { | ||
| bestScore = score | ||
| counter = 0 | ||
| } | ||
| else { | ||
| counter += 1 | ||
| println(s"EarlyStopping counter: ${counter} out of ${patience}") | ||
| if (counter >= patience) { | ||
| earlyStop = true | ||
| } | ||
| } | ||
| } | ||
| } |
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