LinearRegressionStreamProcessor.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.MultipleLinearRegressionCalculator;
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 methods supported by this function are
* timeseries:regress(int/long/float/double y, int/long/float/double x1, int/long/float/double x2 ...)
* and
* timeseries:regress(int calcInterval, int batchSize, double confidenceInterval, int/long/float/double y,
* int/long/float/double x1, int/long/float/double x2 ...).
*/
@Extension(
name = "regress",
namespace = "timeseries",
description = "This allows user to specify the batch size (optional) that defines the number of events" +
" to be considered for the calculation of regression.",
parameters = {
@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 = "batch.size",
description = "The maximum number of events that shoukd be used for a regression calculation.",
type = {DataType.INT},
optional = true,
defaultValue = "1000000000"),
@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(s) of the independent variable.",
type = {DataType.DOUBLE})
},
examples = {
@Example(
syntax = "from StockExchangeStream#timeseries:regress(10, 100000, 0.95, Y, X1, X2, X3)\n" +
"select *\n" +
"insert into StockForecaster",
description = "This query submits a calculation interval (every 10 events)," +
" a batch size (100,000 events), a confidence interval (0.95)," +
" a dependent input stream (Y) and" +
" 3 independent input streams (X1, X2, X3) that are used to perform linear regression" +
" between Y and all the X streams."
)
}
)
public class LinearRegressionStreamProcessor 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 = 0;
@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 regression
paramCount = attributeExpressionLength;
// Capture constant inputs
if (attributeExpressionExecutors[0] instanceof ConstantExpressionExecutor) {
paramCount = paramCount - 3;
paramPosition = 3;
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) {
regressionCalculator = new MultipleLinearRegressionCalculator(paramCount, calcInterval, batchSize, ci);
} else {
regressionCalculator = new SimpleLinearRegressionCalculator(paramCount, calcInterval, batchSize, ci);
}
// Add attributes for standard error and all beta values
String betaVal;
ArrayList<Attribute> attributes = new ArrayList<Attribute>(paramCount);
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));
}
return attributes;
}
@Override
protected void process(ComplexEventChunk<StreamEvent> streamEventChunk, Processor nextProcessor,
StreamEventCloner streamEventCloner, ComplexEventPopulater complexEventPopulater) {
synchronized (this) {
while (streamEventChunk.hasNext()) {
ComplexEvent complexEvent = streamEventChunk.next();
Object[] inputData = new Object[attributeExpressionLength - paramPosition];
for (int i = paramPosition; i < attributeExpressionLength; i++) {
inputData[i - paramPosition] = attributeExpressionExecutors[i].execute(complexEvent);
}
Object[] outputData = regressionCalculator.calculateLinearRegression(inputData);
// Skip processing if user has specified calculation interval
if (outputData == null) {
streamEventChunk.remove();
} else {
complexEventPopulater.populateComplexEvent(complexEvent, outputData);
}
}
}
nextProcessor.process(streamEventChunk);
}
@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) {
}
}