LinearRegressionOutlierStreamProcessor.java
/*
* Copyright (c) 2016, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
* WSO2 Inc. 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.wso2.extension.siddhi.execution.timeseries;
import org.wso2.extension.siddhi.execution.timeseries.linreg.RegressionCalculator;
import org.wso2.extension.siddhi.execution.timeseries.linreg.SimpleLinearRegressionCalculator;
import org.wso2.siddhi.annotation.Example;
import org.wso2.siddhi.annotation.Extension;
import org.wso2.siddhi.annotation.Parameter;
import org.wso2.siddhi.annotation.util.DataType;
import org.wso2.siddhi.core.config.SiddhiAppContext;
import org.wso2.siddhi.core.event.ComplexEvent;
import org.wso2.siddhi.core.event.ComplexEventChunk;
import org.wso2.siddhi.core.event.stream.StreamEvent;
import org.wso2.siddhi.core.event.stream.StreamEventCloner;
import org.wso2.siddhi.core.event.stream.populater.ComplexEventPopulater;
import org.wso2.siddhi.core.exception.SiddhiAppCreationException;
import org.wso2.siddhi.core.executor.ConstantExpressionExecutor;
import org.wso2.siddhi.core.executor.ExpressionExecutor;
import org.wso2.siddhi.core.query.processor.Processor;
import org.wso2.siddhi.core.query.processor.stream.StreamProcessor;
import org.wso2.siddhi.core.util.config.ConfigReader;
import org.wso2.siddhi.query.api.definition.AbstractDefinition;
import org.wso2.siddhi.query.api.definition.Attribute;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
/**
* The outlier function takes in a dependent event stream (Y), an independent event stream (X) and
* a user specified range for outliers, and returns whether the current event is an outlier,
* based on the regression equation that fits historical data.
*/
@Extension(
name = "outlier",
namespace = "timeseries",
description = "This allows user to specify a batch size (optional) that defines the number of events " +
"to be considered for the calculation of regression when finding outliers.",
parameters = {
@Parameter(name = "batch.size",
description = "The maximum number of events that shoukd be used for a regression calculation.",
type = {DataType.INT}),
@Parameter(name = "range",
description = "The number of standard deviations from the regression calculation.",
type = {DataType.INT, DataType.LONG}),
@Parameter(name = "calculation.interval",
description = "The frequency with which the regression calculation should be carried out.",
type = {DataType.INT},
optional = true,
defaultValue = "1"),
@Parameter(name = "confidence.interval",
description = "The confidence interval to be used for a regression calculation.",
optional = true,
defaultValue = "0.95",
type = {DataType.DOUBLE}),
@Parameter(name = "y.stream",
description = "The data stream of the dependent variable.",
type = {DataType.DOUBLE}),
@Parameter(name = "x.stream",
description = "The data stream of the independent variable.",
type = {DataType.DOUBLE})
},
examples = {
@Example(
syntax = "from StockExchangeStream#timeseries:outlier(2, Y, X)\n" +
"select *\n" +
"insert into StockForecaster;",
description = "This query submits the number of standard deviations to be used as" +
" a range (2), a dependent input stream (Y) and" +
" an independent input stream (X) that are used to" +
" perform linear regression between Y and X." +
" It returns an output that indicates whether the current event is an outlier or not."
)
}
)
public class LinearRegressionOutlierStreamProcessor extends StreamProcessor {
private int paramCount = 0; // Number of x variables +1
private int calcInterval = 1; // The frequency of regression calculation
private int batchSize = 1000000000; // Maximum # of events, used for regression calculation
private double ci = 0.95; // Confidence Interval
private RegressionCalculator regressionCalculator = null;
private int paramPosition = 1;
private Object[] coefficients;
@Override
protected void process(ComplexEventChunk<StreamEvent> streamEventChunk, Processor nextProcessor,
StreamEventCloner streamEventCloner, ComplexEventPopulater complexEventPopulater) {
synchronized (this) {
while (streamEventChunk.hasNext()) {
ComplexEvent complexEvent = streamEventChunk.next();
Boolean result = false; // Becomes true if its an outlier
Object[] inputData = new Object[attributeExpressionLength - paramPosition];
double range = ((Number) attributeExpressionExecutors[paramPosition - 1]
.execute(complexEvent)).doubleValue();
for (int i = paramPosition; i < attributeExpressionLength; i++) {
inputData[i - paramPosition] = attributeExpressionExecutors[i].execute(complexEvent);
}
if (coefficients != null) {
// Get the current Y value and X value
double nextY = ((Number) inputData[0]).doubleValue();
double nextX = ((Number) inputData[1]).doubleValue();
// Get the last computed regression coefficients
double stdError = ((Number) coefficients[0]).doubleValue();
double beta0 = ((Number) coefficients[1]).doubleValue();
double beta1 = ((Number) coefficients[2]).doubleValue();
// Forecast Y based on current coefficients and next X value
double forecastY = beta0 + beta1 * nextX;
// Create the normal range based on user provided range parameter and current std error
double upLimit = forecastY + range * stdError;
double downLimit = forecastY - range * stdError;
// Check whether next Y value is an outlier based on the next X value
// and the current regression equation
if (nextY < downLimit || nextY > upLimit) {
result = true;
}
}
// Perform regression including X and Y of current event
coefficients = regressionCalculator.calculateLinearRegression(inputData);
if (coefficients == null) {
streamEventChunk.remove();
} else {
Object[] outputData = new Object[coefficients.length + 1];
System.arraycopy(coefficients, 0, outputData, 0, coefficients.length);
outputData[coefficients.length] = result;
complexEventPopulater.populateComplexEvent(complexEvent, outputData);
}
}
}
nextProcessor.process(streamEventChunk);
}
@Override
protected List<Attribute> init(AbstractDefinition abstractDefinition, ExpressionExecutor[] expressionExecutors,
ConfigReader configReader, SiddhiAppContext siddhiAppContext) {
final int simpleLinregInputParamCount = 2; // Number of Input parameters in a simple linear forecast
paramCount = attributeExpressionLength - 1;
if (attributeExpressionExecutors[1] instanceof ConstantExpressionExecutor) {
paramCount = paramCount - 3;
paramPosition = 4;
try {
calcInterval = ((Integer) attributeExpressionExecutors[0].execute(null));
batchSize = ((Integer) attributeExpressionExecutors[1].execute(null));
} catch (ClassCastException c) {
throw new SiddhiAppCreationException("Calculation interval," +
" batch size and range should be of type int");
}
try {
ci = ((Double) attributeExpressionExecutors[2].execute(null));
} catch (ClassCastException c) {
throw new SiddhiAppCreationException("Confidence interval should be of type double");
}
if (!(0 <= ci && ci <= 1)) {
throw new SiddhiAppCreationException("Confidence interval should be a value between 0 and 1");
}
}
// Pick the appropriate regression calculator
if (paramCount > simpleLinregInputParamCount) {
throw new SiddhiAppCreationException("Outlier Function is available only for simple linear regression");
} else {
regressionCalculator = new SimpleLinearRegressionCalculator(paramCount, calcInterval, batchSize, ci);
}
// Create attributes for standard error and all beta values and the outlier result
String betaVal;
ArrayList<Attribute> attributes = new ArrayList<Attribute>(paramCount + 1);
attributes.add(new Attribute("stderr", Attribute.Type.DOUBLE));
for (int itr = 0; itr < paramCount; itr++) {
betaVal = "beta" + itr;
attributes.add(new Attribute(betaVal, Attribute.Type.DOUBLE));
}
attributes.add(new Attribute("outlier", Attribute.Type.BOOL));
return attributes;
}
@Override
public void start() {
}
@Override
public void stop() {
}
@Override
public synchronized Map<String, Object> currentState() {
Map<String, Object> state = new HashMap<String, Object>();
return state;
}
@Override
public synchronized void restoreState(Map<String, Object> state) {
}
}