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Add Support for Covariance Function (#9236)
* finished simple cov, before testing, before group by * added inner segment tests, before fixing precision issues with floats * declared precision var * aligned tests type with aggfunc to cast to double before summing * added inter seg tests * finished aggregation group by, added tests for aggregation group by, before testing invalid inputs * added tear down * style * stylish * added distinct inter seg tests * style fix * added comments for test clarity * fixed typo * added test with filter * filter * added covar_samp * before fixing basequerytests * cleaned distinct instance tests * add bessel corrector * i love math * added javadoc on formula, addressed comments * got rid of duplicate comments * updated javadoc for best test guidance * reduced division, handled 0 case * trigger test * trigger test
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.../java/org/apache/pinot/core/query/aggregation/function/CovarianceAggregationFunction.java
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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.pinot.core.query.aggregation.function; | ||
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| import com.google.common.base.Preconditions; | ||
| import java.util.ArrayList; | ||
| import java.util.List; | ||
| import java.util.Map; | ||
| import org.apache.pinot.common.request.context.ExpressionContext; | ||
| import org.apache.pinot.common.utils.DataSchema; | ||
| import org.apache.pinot.core.common.BlockValSet; | ||
| import org.apache.pinot.core.query.aggregation.AggregationResultHolder; | ||
| import org.apache.pinot.core.query.aggregation.ObjectAggregationResultHolder; | ||
| import org.apache.pinot.core.query.aggregation.groupby.GroupByResultHolder; | ||
| import org.apache.pinot.core.query.aggregation.groupby.ObjectGroupByResultHolder; | ||
| import org.apache.pinot.segment.local.customobject.CovarianceTuple; | ||
| import org.apache.pinot.segment.spi.AggregationFunctionType; | ||
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| /** | ||
| * Aggregation function which returns the population covariance of 2 expressions. | ||
| * COVAR_POP(exp1, exp2) = mean(exp1 * exp2) - mean(exp1) * mean(exp2) | ||
| * COVAR_SAMP(exp1, exp2) = (sum(exp1 * exp2) - sum(exp1) * sum(exp2)) / (count - 1) | ||
| * | ||
| * Population covariance between two random variables X and Y is defined as either | ||
| * covarPop(X,Y) = E[(X - E[X]) * (Y - E[Y])] or | ||
| * covarPop(X,Y) = E[X*Y] - E[X] * E[Y], | ||
| * here E[X] represents mean of X | ||
| * @see <a href="https://en.wikipedia.org/wiki/Covariance">Covariance</a> | ||
| * The calculations here are based on the second definition shown above. | ||
| * Sample covariance = covarPop(X, Y) * besselCorrection | ||
| * @see <a href="https://en.wikipedia.org/wiki/Bessel%27s_correction">Bessel's correction</a> | ||
| */ | ||
| public class CovarianceAggregationFunction implements AggregationFunction<CovarianceTuple, Double> { | ||
| private static final double DEFAULT_FINAL_RESULT = Double.NEGATIVE_INFINITY; | ||
| protected final ExpressionContext _expression1; | ||
| protected final ExpressionContext _expression2; | ||
| protected final boolean _isSample; | ||
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| public CovarianceAggregationFunction(List<ExpressionContext> arguments, boolean isSample) { | ||
| _expression1 = arguments.get(0); | ||
| _expression2 = arguments.get(1); | ||
| _isSample = isSample; | ||
| } | ||
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| @Override | ||
| public AggregationFunctionType getType() { | ||
| if (_isSample) { | ||
| return AggregationFunctionType.COVARSAMP; | ||
| } | ||
| return AggregationFunctionType.COVARPOP; | ||
| } | ||
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| @Override | ||
| public String getColumnName() { | ||
| return getType().getName() + "_" + _expression1 + "_" + _expression2; | ||
| } | ||
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| @Override | ||
| public String getResultColumnName() { | ||
| return getType().getName().toLowerCase() + "(" + _expression1 + "," + _expression2 + ")"; | ||
| } | ||
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| @Override | ||
| public List<ExpressionContext> getInputExpressions() { | ||
| ArrayList<ExpressionContext> inputExpressions = new ArrayList<>(); | ||
| inputExpressions.add(_expression1); | ||
| inputExpressions.add(_expression2); | ||
| return inputExpressions; | ||
| } | ||
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| @Override | ||
| public AggregationResultHolder createAggregationResultHolder() { | ||
| return new ObjectAggregationResultHolder(); | ||
| } | ||
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| @Override | ||
| public GroupByResultHolder createGroupByResultHolder(int initialCapacity, int maxCapacity) { | ||
| return new ObjectGroupByResultHolder(initialCapacity, maxCapacity); | ||
| } | ||
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| @Override | ||
| public void aggregate(int length, AggregationResultHolder aggregationResultHolder, | ||
| Map<ExpressionContext, BlockValSet> blockValSetMap) { | ||
| double[] values1 = getValSet(blockValSetMap, _expression1); | ||
| double[] values2 = getValSet(blockValSetMap, _expression2); | ||
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| double sumX = 0.0; | ||
| double sumY = 0.0; | ||
| double sumXY = 0.0; | ||
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| for (int i = 0; i < length; i++) { | ||
| sumX += values1[i]; | ||
| sumY += values2[i]; | ||
| sumXY += values1[i] * values2[i]; | ||
| } | ||
| setAggregationResult(aggregationResultHolder, sumX, sumY, sumXY, length); | ||
| } | ||
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| protected void setAggregationResult(AggregationResultHolder aggregationResultHolder, double sumX, double sumY, | ||
| double sumXY, long count) { | ||
| CovarianceTuple covarianceTuple = aggregationResultHolder.getResult(); | ||
| if (covarianceTuple == null) { | ||
| aggregationResultHolder.setValue(new CovarianceTuple(sumX, sumY, sumXY, count)); | ||
| } else { | ||
| covarianceTuple.apply(sumX, sumY, sumXY, count); | ||
| } | ||
| } | ||
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| protected void setGroupByResult(int groupKey, GroupByResultHolder groupByResultHolder, double sumX, double sumY, | ||
| double sumXY, long count) { | ||
| CovarianceTuple covarianceTuple = groupByResultHolder.getResult(groupKey); | ||
| if (covarianceTuple == null) { | ||
| groupByResultHolder.setValueForKey(groupKey, new CovarianceTuple(sumX, sumY, sumXY, count)); | ||
| } else { | ||
| covarianceTuple.apply(sumX, sumY, sumXY, count); | ||
| } | ||
| } | ||
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| private double[] getValSet(Map<ExpressionContext, BlockValSet> blockValSetMap, ExpressionContext expression) { | ||
| BlockValSet blockValSet = blockValSetMap.get(expression); | ||
| //TODO: Add MV support for covariance | ||
| Preconditions.checkState(blockValSet.isSingleValue(), | ||
| "Covariance function currently only supports single-valued column"); | ||
| switch (blockValSet.getValueType().getStoredType()) { | ||
| case INT: | ||
| case LONG: | ||
| case FLOAT: | ||
| case DOUBLE: | ||
| return blockValSet.getDoubleValuesSV(); | ||
| default: | ||
| throw new IllegalStateException( | ||
| "Cannot compute covariance for non-numeric type: " + blockValSet.getValueType()); | ||
| } | ||
| } | ||
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| @Override | ||
| public void aggregateGroupBySV(int length, int[] groupKeyArray, GroupByResultHolder groupByResultHolder, | ||
| Map<ExpressionContext, BlockValSet> blockValSetMap) { | ||
| double[] values1 = getValSet(blockValSetMap, _expression1); | ||
| double[] values2 = getValSet(blockValSetMap, _expression2); | ||
| for (int i = 0; i < length; i++) { | ||
| setGroupByResult(groupKeyArray[i], groupByResultHolder, values1[i], values2[i], values1[i] * values2[i], 1L); | ||
| } | ||
| } | ||
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| @Override | ||
| public void aggregateGroupByMV(int length, int[][] groupKeysArray, GroupByResultHolder groupByResultHolder, | ||
| Map<ExpressionContext, BlockValSet> blockValSetMap) { | ||
| double[] values1 = getValSet(blockValSetMap, _expression1); | ||
| double[] values2 = getValSet(blockValSetMap, _expression2); | ||
| for (int i = 0; i < length; i++) { | ||
| for (int groupKey : groupKeysArray[i]) { | ||
| setGroupByResult(groupKey, groupByResultHolder, values1[i], values2[i], values1[i] * values2[i], 1L); | ||
| } | ||
| } | ||
| } | ||
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| @Override | ||
| public CovarianceTuple extractAggregationResult(AggregationResultHolder aggregationResultHolder) { | ||
| CovarianceTuple covarianceTuple = aggregationResultHolder.getResult(); | ||
| if (covarianceTuple == null) { | ||
| return new CovarianceTuple(0.0, 0.0, 0.0, 0L); | ||
| } else { | ||
| return covarianceTuple; | ||
| } | ||
| } | ||
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| @Override | ||
| public CovarianceTuple extractGroupByResult(GroupByResultHolder groupByResultHolder, int groupKey) { | ||
| return groupByResultHolder.getResult(groupKey); | ||
| } | ||
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| @Override | ||
| public CovarianceTuple merge(CovarianceTuple intermediateResult1, CovarianceTuple intermediateResult2) { | ||
| intermediateResult1.apply(intermediateResult2); | ||
| return intermediateResult1; | ||
| } | ||
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| @Override | ||
| public DataSchema.ColumnDataType getIntermediateResultColumnType() { | ||
| return DataSchema.ColumnDataType.OBJECT; | ||
| } | ||
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| @Override | ||
| public DataSchema.ColumnDataType getFinalResultColumnType() { | ||
| return DataSchema.ColumnDataType.DOUBLE; | ||
| } | ||
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| @Override | ||
| public Double extractFinalResult(CovarianceTuple covarianceTuple) { | ||
| long count = covarianceTuple.getCount(); | ||
| if (count == 0L) { | ||
| return DEFAULT_FINAL_RESULT; | ||
| } else { | ||
| double sumX = covarianceTuple.getSumX(); | ||
| double sumY = covarianceTuple.getSumY(); | ||
| double sumXY = covarianceTuple.getSumXY(); | ||
| if (_isSample) { | ||
| if (count - 1 == 0L) { | ||
| return DEFAULT_FINAL_RESULT; | ||
| } | ||
| // sample cov = population cov * (count / (count - 1)) | ||
| return (sumXY / (count - 1)) - (sumX * sumY) / (count * (count - 1)); | ||
| } | ||
| return (sumXY / count) - (sumX * sumY) / (count * count); | ||
| } | ||
| } | ||
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| @Override | ||
| public String toExplainString() { | ||
| StringBuilder stringBuilder = new StringBuilder(getType().getName()).append('('); | ||
| int numArguments = getInputExpressions().size(); | ||
| if (numArguments > 0) { | ||
| stringBuilder.append(getInputExpressions().get(0).toString()); | ||
| for (int i = 1; i < numArguments; i++) { | ||
| stringBuilder.append(", ").append(getInputExpressions().get(i).toString()); | ||
| } | ||
| } | ||
| return stringBuilder.append(')').toString(); | ||
| } | ||
| } |
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