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Add an optimizer rule to filter out columns with low variances
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83 changes: 83 additions & 0 deletions
83
spark/spark-2.3/src/main/scala/org/apache/spark/sql/hive/HivemallConf.scala
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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. | ||
| */ | ||
|
|
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| package org.apache.spark.sql.hive | ||
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| import scala.language.implicitConversions | ||
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| import org.apache.spark.internal.config.{ConfigBuilder, ConfigEntry, ConfigReader} | ||
| import org.apache.spark.sql.internal.SQLConf | ||
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| object HivemallConf { | ||
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| /** | ||
| * Implicitly injects the [[HivemallConf]] into [[SQLConf]]. | ||
| */ | ||
| implicit def SQLConfToHivemallConf(conf: SQLConf): HivemallConf = new HivemallConf(conf) | ||
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| private val sqlConfEntries = java.util.Collections.synchronizedMap( | ||
| new java.util.HashMap[String, ConfigEntry[_]]()) | ||
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| private def register(entry: ConfigEntry[_]): Unit = sqlConfEntries.synchronized { | ||
| require(!sqlConfEntries.containsKey(entry.key), | ||
| s"Duplicate SQLConfigEntry. ${entry.key} has been registered") | ||
| sqlConfEntries.put(entry.key, entry) | ||
| } | ||
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| // For testing only | ||
| // TODO: Need to add tests for the configurations | ||
| private[sql] def unregister(entry: ConfigEntry[_]): Unit = sqlConfEntries.synchronized { | ||
| sqlConfEntries.remove(entry.key) | ||
| } | ||
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| def buildConf(key: String): ConfigBuilder = ConfigBuilder(key).onCreate(register) | ||
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| val FEATURE_SELECTION_ENABLED = | ||
| buildConf("spark.sql.optimizer.featureSelection.enabled") | ||
| .doc("Whether feature selections are applied in the optimizer") | ||
| .booleanConf | ||
| .createWithDefault(false) | ||
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| val FEATURE_SELECTION_VARIANCE_THRESHOLD = | ||
| buildConf("spark.sql.optimizer.featureSelection.varianceThreshold") | ||
| .doc("Specifies the threshold of variances to filter out features") | ||
| .doubleConf | ||
| .createWithDefault(0.05) | ||
| } | ||
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| class HivemallConf(conf: SQLConf) { | ||
| import HivemallConf._ | ||
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| private val reader = new ConfigReader(conf.settings) | ||
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| def featureSelectionEnabled: Boolean = getConf(FEATURE_SELECTION_ENABLED) | ||
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| def featureSelectionVarianceThreshold: Double = getConf(FEATURE_SELECTION_VARIANCE_THRESHOLD) | ||
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| /** ********************** SQLConf functionality methods ************ */ | ||
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| /** | ||
| * Return the value of Hivemall configuration property for the given key. If the key is not set | ||
| * yet, return `defaultValue` in [[ConfigEntry]]. | ||
| */ | ||
| private def getConf[T](entry: ConfigEntry[T]): T = { | ||
| require(sqlConfEntries.get(entry.key) == entry, s"$entry is not registered") | ||
| entry.readFrom(reader) | ||
| } | ||
| } |
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71 changes: 71 additions & 0 deletions
71
spark/spark-2.3/src/main/scala/org/apache/spark/sql/optimizer/VarianceThreshold.scala
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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. | ||
| */ | ||
|
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| package org.apache.spark.sql.optimizer | ||
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| import org.apache.spark.sql.catalyst.plans.logical.{Histogram, LogicalPlan, Project, Statistics} | ||
| import org.apache.spark.sql.catalyst.rules.Rule | ||
| import org.apache.spark.sql.internal.SQLConf | ||
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| /** | ||
| * This optimizer rule removes features with low variance; it removes all features whose | ||
| * variance doesn't meet some threshold. You can control this threshold by | ||
| * `spark.sql.optimizer.featureSelection.varianceThreshold` (0.05 by default). | ||
| */ | ||
| class VarianceThreshold(conf: SQLConf) extends Rule[LogicalPlan] { | ||
| import org.apache.spark.sql.hive.HivemallConf._ | ||
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| private def featureSelectionEnabled: Boolean = conf.featureSelectionEnabled | ||
| private def varianceThreshold: Double = conf.featureSelectionVarianceThreshold | ||
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| private def hasColumnHistogram(s: Statistics): Boolean = { | ||
| s.attributeStats.exists { case (_, stat) => | ||
| stat.histogram.isDefined | ||
| } | ||
| } | ||
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| private def satisfyVarianceThreshold(histgramOption: Option[Histogram]): Boolean = { | ||
| // TODO: Since binary types are not supported in histograms but they could frequently appear | ||
| // in user schemas, we would be better to handle the case here. | ||
| histgramOption.forall { hist => | ||
| // TODO: Make the value more precise by using `HistogramBin.ndv` | ||
| val dataSeq = hist.bins.map { bin => (bin.hi + bin.lo) / 2 } | ||
| val avg = dataSeq.sum / dataSeq.length | ||
| val variance = dataSeq.map { d => Math.pow(avg - d, 2.0) }.sum / dataSeq.length | ||
| varianceThreshold < variance | ||
| } | ||
| } | ||
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| override def apply(plan: LogicalPlan): LogicalPlan = plan match { | ||
| case p if featureSelectionEnabled && hasColumnHistogram(p.stats) => | ||
| val attributeStats = p.stats.attributeStats | ||
| val outputAttrs = p.output | ||
| val projectList = outputAttrs.zip(outputAttrs.map { a => attributeStats.get(a)}).flatMap { | ||
| case (_, Some(stat)) if !satisfyVarianceThreshold(stat.histogram) => None | ||
| case (attr, _) => Some(attr) | ||
| } | ||
| if (projectList != outputAttrs) { | ||
| Project(projectList, p) | ||
| } else { | ||
| p | ||
| } | ||
| case p => p | ||
| } | ||
| } |
68 changes: 68 additions & 0 deletions
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spark/spark-2.3/src/test/scala/org/apache/spark/sql/hive/FeatureSelectionRuleSuite.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,68 @@ | ||
| /* | ||
| * 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. | ||
| */ | ||
|
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| package org.apache.spark.sql.hive | ||
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| import org.apache.spark.sql.{QueryTest, Row} | ||
| import org.apache.spark.sql.hive.test.TestHiveSingleton | ||
| import org.apache.spark.sql.internal.SQLConf | ||
| import org.apache.spark.sql.optimizer.VarianceThreshold | ||
| import org.apache.spark.sql.test.SQLTestUtils | ||
| import org.apache.spark.sql.types._ | ||
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| class FeatureSelectionRuleSuite extends SQLTestUtils with TestHiveSingleton { | ||
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| import hiveContext.implicits._ | ||
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| protected override def beforeAll(): Unit = { | ||
| super.beforeAll() | ||
| // Sets user-defined optimization rules for feature selection | ||
| hiveContext.experimental.extraOptimizations = Seq( | ||
| new VarianceThreshold(hiveContext.conf)) | ||
| } | ||
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| test("filter out features with low variances") { | ||
| withSQLConf( | ||
| HivemallConf.FEATURE_SELECTION_ENABLED.key -> "true", | ||
| HivemallConf.FEATURE_SELECTION_VARIANCE_THRESHOLD.key -> "0.1", | ||
| SQLConf.CBO_ENABLED.key -> "true", | ||
| SQLConf.HISTOGRAM_ENABLED.key -> "true") { | ||
| withTable("t") { | ||
| withTempDir { dir => | ||
| Seq((1, "one", 1.0, 1.0), | ||
| (1, "two", 1.1, 2.3), | ||
| (1, "three", 0.9, 3.5), | ||
| (1, "one", 0.9, 10.3)) | ||
| .toDF("c0", "c1", "c2", "c3") | ||
| .write | ||
| .parquet(s"${dir.getAbsolutePath}/t") | ||
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| spark.read.parquet(s"${dir.getAbsolutePath}/t").write.saveAsTable("t") | ||
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| sql("ANALYZE TABLE t COMPUTE STATISTICS FOR COLUMNS c0, c1, c2, c3") | ||
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| // Filters out `c0` and `c2` because of low variances | ||
| val optimizedPlan = sql("SELECT c0, * FROM t").queryExecution.optimizedPlan | ||
| assert(optimizedPlan.output.map(_.name) === Seq("c1", "c3")) | ||
| } | ||
| } | ||
| } | ||
| } | ||
| } |
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