Aggregations: add cost minimizer to moving_avg aggregation
This commit is contained in:
Zachary Tong
parent
cbcc553912
commit
0f76e656dd
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@ -36,6 +36,7 @@ public class MovAvgBuilder extends PipelineAggregatorBuilder<MovAvgBuilder> {
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private MovAvgModelBuilder modelBuilder;
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private Integer window;
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private Integer predict;
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private Boolean minimize;
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public MovAvgBuilder(String name) {
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super(name, MovAvgPipelineAggregator.TYPE.name());
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@ -94,6 +95,18 @@ public class MovAvgBuilder extends PipelineAggregatorBuilder<MovAvgBuilder> {
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return this;
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}
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/**
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* Determines if the model should be fit to the data using a cost
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* minimizing algorithm.
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*
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* @param minimize If the model should be fit to the underlying data
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* @return Returns the builder to continue chaining
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*/
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public MovAvgBuilder minimize(boolean minimize) {
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this.minimize = minimize;
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return this;
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}
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@Override
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protected XContentBuilder internalXContent(XContentBuilder builder, Params params) throws IOException {
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@ -112,6 +125,9 @@ public class MovAvgBuilder extends PipelineAggregatorBuilder<MovAvgBuilder> {
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if (predict != null) {
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builder.field(MovAvgParser.PREDICT.getPreferredName(), predict);
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}
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if (minimize != null) {
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builder.field(MovAvgParser.MINIMIZE.getPreferredName(), minimize);
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}
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return builder;
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}
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@ -44,6 +44,7 @@ public class MovAvgParser implements PipelineAggregator.Parser {
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public static final ParseField WINDOW = new ParseField("window");
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public static final ParseField SETTINGS = new ParseField("settings");
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public static final ParseField PREDICT = new ParseField("predict");
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public static final ParseField MINIMIZE = new ParseField("minimize");
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private final MovAvgModelParserMapper movAvgModelParserMapper;
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@ -69,6 +70,7 @@ public class MovAvgParser implements PipelineAggregator.Parser {
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Map<String, Object> settings = null;
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String model = "simple";
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int predict = 0;
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Boolean minimize = null;
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while ((token = parser.nextToken()) != XContentParser.Token.END_OBJECT) {
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if (token == XContentParser.Token.FIELD_NAME) {
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@ -124,6 +126,13 @@ public class MovAvgParser implements PipelineAggregator.Parser {
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throw new SearchParseException(context, "Unknown key for a " + token + " in [" + pipelineAggregatorName + "]: ["
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+ currentFieldName + "].", parser.getTokenLocation());
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}
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} else if (token == XContentParser.Token.VALUE_BOOLEAN) {
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if (context.parseFieldMatcher().match(currentFieldName, MINIMIZE)) {
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minimize = parser.booleanValue();
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} else {
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throw new SearchParseException(context, "Unknown key for a " + token + " in [" + pipelineAggregatorName + "]: ["
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+ currentFieldName + "].", parser.getTokenLocation());
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}
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} else {
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throw new SearchParseException(context, "Unexpected token " + token + " in [" + pipelineAggregatorName + "].",
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parser.getTokenLocation());
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@ -155,9 +164,17 @@ public class MovAvgParser implements PipelineAggregator.Parser {
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throw new SearchParseException(context, "Could not parse settings for model [" + model + "].", null, exception);
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}
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// If the user doesn't set a preference for cost minimization, ask what the model prefers
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if (minimize == null) {
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minimize = movAvgModel.minimizeByDefault();
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} else if (minimize && !movAvgModel.canBeMinimized()) {
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// If the user asks to minimize, but this model doesn't support it, throw exception
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throw new SearchParseException(context, "The [" + model + "] model cannot be minimized.", null);
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}
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return new MovAvgPipelineAggregator.Factory(pipelineAggregatorName, bucketsPaths, formatter, gapPolicy, window, predict,
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movAvgModel);
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movAvgModel, minimize);
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}
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@ -48,6 +48,7 @@ import org.joda.time.DateTime;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.List;
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import java.util.ListIterator;
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import java.util.Map;
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import static org.elasticsearch.search.aggregations.pipeline.BucketHelpers.resolveBucketValue;
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@ -81,18 +82,20 @@ public class MovAvgPipelineAggregator extends PipelineAggregator {
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private int window;
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private MovAvgModel model;
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private int predict;
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private boolean minimize;
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public MovAvgPipelineAggregator() {
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}
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public MovAvgPipelineAggregator(String name, String[] bucketsPaths, ValueFormatter formatter, GapPolicy gapPolicy,
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int window, int predict, MovAvgModel model, Map<String, Object> metadata) {
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int window, int predict, MovAvgModel model, boolean minimize, Map<String, Object> metadata) {
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super(name, bucketsPaths, metadata);
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this.formatter = formatter;
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this.gapPolicy = gapPolicy;
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this.window = window;
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this.model = model;
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this.predict = predict;
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this.minimize = minimize;
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}
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@Override
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@ -113,6 +116,12 @@ public class MovAvgPipelineAggregator extends PipelineAggregator {
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int lastValidPosition = 0;
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int counter = 0;
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// Do we need to fit the model parameters to the data?
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if (minimize) {
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assert (model.canBeMinimized());
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model = minimize(buckets, histo, model);
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}
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for (InternalHistogram.Bucket bucket : buckets) {
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Double thisBucketValue = resolveBucketValue(histo, bucket, bucketsPaths()[0], gapPolicy);
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@ -194,6 +203,60 @@ public class MovAvgPipelineAggregator extends PipelineAggregator {
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return factory.create(newBuckets, histo);
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}
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private MovAvgModel minimize(List<? extends InternalHistogram.Bucket> buckets, InternalHistogram histo, MovAvgModel model) {
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int counter = 0;
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EvictingQueue<Double> values = EvictingQueue.create(window);
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double[] test = new double[window];
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ListIterator<? extends InternalHistogram.Bucket> iter = buckets.listIterator(buckets.size());
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// We have to walk the iterator backwards because we don't know if/how many buckets are empty.
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while (iter.hasPrevious() && counter < window) {
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Double thisBucketValue = resolveBucketValue(histo, iter.previous(), bucketsPaths()[0], gapPolicy);
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if (!(thisBucketValue == null || thisBucketValue.equals(Double.NaN))) {
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test[window - counter - 1] = thisBucketValue;
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counter += 1;
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}
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}
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// If we didn't fill the test set, we don't have enough data to minimize.
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// Just return the model with the starting coef
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if (counter < window) {
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return model;
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}
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//And do it again, for the train set. Unfortunately we have to fill an array and then
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//fill an evicting queue backwards :(
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counter = 0;
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double[] train = new double[window];
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while (iter.hasPrevious() && counter < window) {
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Double thisBucketValue = resolveBucketValue(histo, iter.previous(), bucketsPaths()[0], gapPolicy);
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if (!(thisBucketValue == null || thisBucketValue.equals(Double.NaN))) {
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train[window - counter - 1] = thisBucketValue;
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counter += 1;
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}
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}
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// If we didn't fill the train set, we don't have enough data to minimize.
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// Just return the model with the starting coef
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if (counter < window) {
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return model;
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}
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for (double v : train) {
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values.add(v);
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}
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return SimulatedAnealingMinimizer.minimize(model, values, test);
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}
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@Override
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public void doReadFrom(StreamInput in) throws IOException {
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formatter = ValueFormatterStreams.readOptional(in);
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@ -201,6 +264,7 @@ public class MovAvgPipelineAggregator extends PipelineAggregator {
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window = in.readVInt();
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predict = in.readVInt();
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model = MovAvgModelStreams.read(in);
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minimize = in.readBoolean();
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}
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@ -211,6 +275,7 @@ public class MovAvgPipelineAggregator extends PipelineAggregator {
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out.writeVInt(window);
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out.writeVInt(predict);
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model.writeTo(out);
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out.writeBoolean(minimize);
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}
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@ -221,20 +286,22 @@ public class MovAvgPipelineAggregator extends PipelineAggregator {
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private int window;
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private MovAvgModel model;
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private int predict;
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private boolean minimize;
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public Factory(String name, String[] bucketsPaths, ValueFormatter formatter, GapPolicy gapPolicy,
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int window, int predict, MovAvgModel model) {
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int window, int predict, MovAvgModel model, boolean minimize) {
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super(name, TYPE.name(), bucketsPaths);
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this.formatter = formatter;
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this.gapPolicy = gapPolicy;
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this.window = window;
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this.model = model;
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this.predict = predict;
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this.minimize = minimize;
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}
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@Override
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protected PipelineAggregator createInternal(Map<String, Object> metaData) throws IOException {
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return new MovAvgPipelineAggregator(name, bucketsPaths, formatter, gapPolicy, window, predict, model, metaData);
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return new MovAvgPipelineAggregator(name, bucketsPaths, formatter, gapPolicy, window, predict, model, minimize, metaData);
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}
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@Override
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@ -0,0 +1,125 @@
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/*
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* Licensed to Elasticsearch under one or more contributor
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* license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright
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* ownership. Elasticsearch licenses this file to you under
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* the Apache License, Version 2.0 (the "License"); you may
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* not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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package org.elasticsearch.search.aggregations.pipeline.movavg;
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import com.google.common.collect.EvictingQueue;
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import org.elasticsearch.search.aggregations.pipeline.movavg.models.MovAvgModel;
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/**
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* A cost minimizer which will fit a MovAvgModel to the data.
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*
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* This optimizer uses naive simulated annealing. Random solutions in the problem space
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* are generated, compared against the last period of data, and the least absolute deviation
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* is recorded as a cost.
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*
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* If the new cost is better than the old cost, the new coefficients are chosen. If the new
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* solution is worse, there is a temperature-dependent probability it will be randomly selected
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* anyway. This allows the algo to sample the problem space widely. As iterations progress,
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* the temperature decreases and the algorithm rejects poor solutions more regularly,
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* theoretically honing in on a global minimum.
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*/
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public class SimulatedAnealingMinimizer {
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/**
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* Runs the simulated annealing algorithm and produces a model with new coefficients that, theoretically
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* fit the data better and generalizes to future forecasts without overfitting.
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*
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* @param model The MovAvgModel to be optimized for
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* @param train A training set provided to the model, which predictions will be
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* generated from
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* @param test A test set of data to compare the predictions against and derive
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* a cost for the model
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* @return A new, minimized model that (theoretically) better fits the data
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*/
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public static MovAvgModel minimize(MovAvgModel model, EvictingQueue<Double> train, double[] test) {
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double temp = 1;
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double minTemp = 0.0001;
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int iterations = 100;
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double alpha = 0.9;
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MovAvgModel bestModel = model;
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MovAvgModel oldModel = model;
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double oldCost = cost(model, train, test);
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double bestCost = oldCost;
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while (temp > minTemp) {
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for (int i = 0; i < iterations; i++) {
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MovAvgModel newModel = oldModel.neighboringModel();
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double newCost = cost(newModel, train, test);
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double ap = acceptanceProbability(oldCost, newCost, temp);
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if (ap > Math.random()) {
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oldModel = newModel;
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oldCost = newCost;
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if (newCost < bestCost) {
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bestCost = newCost;
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bestModel = newModel;
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}
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}
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}
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temp *= alpha;
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}
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return bestModel;
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}
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/**
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* If the new cost is better than old, return 1.0. Otherwise, return a double that increases
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* as the two costs are closer to each other.
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*
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* @param oldCost Old model cost
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* @param newCost New model cost
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* @param temp Current annealing temperature
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* @return The probability of accepting the new cost over the old
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*/
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private static double acceptanceProbability(double oldCost, double newCost, double temp) {
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return newCost < oldCost ? 1.0 : Math.exp(-(newCost - oldCost) / temp);
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}
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/**
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* Calculates the "cost" of a model. E.g. when run on the training data, how closely do the predictions
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* match the test data
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*
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* Uses Least Absolute Differences to calculate error. Note that this is not scale free, but seems
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* to work fairly well in practice
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*
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* @param model The MovAvgModel we are fitting
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* @param train A training set of data given to the model, which will then generate predictions from
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* @param test A test set of data to compare against the predictions
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* @return A cost, or error, of the model
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*/
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private static double cost(MovAvgModel model, EvictingQueue<Double> train, double[] test) {
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double error = 0;
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double[] predictions = model.predict(train, test.length);
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assert(predictions.length == test.length);
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for (int i = 0; i < predictions.length; i++) {
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error += Math.abs(test[i] - predictions[i]) ;
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}
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return error;
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}
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}
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@ -41,16 +41,33 @@ public class EwmaModel extends MovAvgModel {
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protected static final ParseField NAME_FIELD = new ParseField("ewma");
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/**
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* Controls smoothing of data. Alpha = 1 retains no memory of past values
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* Controls smoothing of data. Also known as "level" value.
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* Alpha = 1 retains no memory of past values
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* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
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* mean of the series). Useful values are somewhere in between
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* mean of the series).
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*/
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private double alpha;
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private final double alpha;
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public EwmaModel(double alpha) {
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this.alpha = alpha;
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}
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@Override
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public boolean canBeMinimized() {
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return true;
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}
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@Override
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public MovAvgModel neighboringModel() {
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double alpha = Math.random();
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return new EwmaModel(alpha);
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}
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@Override
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public MovAvgModel clone() {
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return new EwmaModel(this.alpha);
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}
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@Override
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protected <T extends Number> double[] doPredict(Collection<T> values, int numPredictions) {
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double[] predictions = new double[numPredictions];
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@ -105,8 +122,7 @@ public class EwmaModel extends MovAvgModel {
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@Override
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public MovAvgModel parse(@Nullable Map<String, Object> settings, String pipelineName, int windowSize, ParseFieldMatcher parseFieldMatcher) throws ParseException {
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double alpha = parseDoubleParam(settings, "alpha", 0.5);
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double alpha = parseDoubleParam(settings, "alpha", 0.3);
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return new EwmaModel(alpha);
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}
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@ -114,7 +130,7 @@ public class EwmaModel extends MovAvgModel {
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public static class EWMAModelBuilder implements MovAvgModelBuilder {
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private double alpha = 0.5;
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private Double alpha;
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/**
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* Alpha controls the smoothing of the data. Alpha = 1 retains no memory of past values
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@ -134,7 +150,10 @@ public class EwmaModel extends MovAvgModel {
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public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
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builder.field(MovAvgParser.MODEL.getPreferredName(), NAME_FIELD.getPreferredName());
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builder.startObject(MovAvgParser.SETTINGS.getPreferredName());
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if (alpha != null) {
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builder.field("alpha", alpha);
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}
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builder.endObject();
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return builder;
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}
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@ -39,22 +39,50 @@ public class HoltLinearModel extends MovAvgModel {
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protected static final ParseField NAME_FIELD = new ParseField("holt");
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/**
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* Controls smoothing of data. Alpha = 1 retains no memory of past values
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* Controls smoothing of data. Also known as "level" value.
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* Alpha = 1 retains no memory of past values
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* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
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* mean of the series). Useful values are somewhere in between
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* mean of the series).
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*/
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private double alpha;
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private final double alpha;
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/**
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* Equivalent to <code>alpha</code>, but controls the smoothing of the trend instead of the data
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* Controls smoothing of trend.
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* Beta = 1 retains no memory of past values
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* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
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* mean of the series).
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*/
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private double beta;
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private final double beta;
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public HoltLinearModel(double alpha, double beta) {
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this.alpha = alpha;
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this.beta = beta;
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}
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@Override
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public boolean canBeMinimized() {
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return true;
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}
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@Override
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public MovAvgModel neighboringModel() {
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double newValue = Math.random();
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switch ((int) (Math.random() * 2)) {
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case 0:
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return new HoltLinearModel(newValue, this.beta);
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case 1:
|
||||
return new HoltLinearModel(this.alpha, newValue);
|
||||
default:
|
||||
assert (false): "Random value fell outside of range [0-1]";
|
||||
return new HoltLinearModel(newValue, this.beta); // This should never technically happen...
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public MovAvgModel clone() {
|
||||
return new HoltLinearModel(this.alpha, this.beta);
|
||||
}
|
||||
|
||||
/**
|
||||
* Predicts the next `n` values in the series, using the smoothing model to generate new values.
|
||||
* Unlike the other moving averages, Holt-Linear has forecasting/prediction built into the algorithm.
|
||||
|
|
@ -154,16 +182,17 @@ public class HoltLinearModel extends MovAvgModel {
|
|||
@Override
|
||||
public MovAvgModel parse(@Nullable Map<String, Object> settings, String pipelineName, int windowSize, ParseFieldMatcher parseFieldMatcher) throws ParseException {
|
||||
|
||||
double alpha = parseDoubleParam(settings, "alpha", 0.5);
|
||||
double beta = parseDoubleParam(settings, "beta", 0.5);
|
||||
double alpha = parseDoubleParam(settings, "alpha", 0.3);
|
||||
double beta = parseDoubleParam(settings, "beta", 0.1);
|
||||
return new HoltLinearModel(alpha, beta);
|
||||
}
|
||||
}
|
||||
|
||||
public static class HoltLinearModelBuilder implements MovAvgModelBuilder {
|
||||
|
||||
private double alpha = 0.5;
|
||||
private double beta = 0.5;
|
||||
|
||||
private Double alpha;
|
||||
private Double beta;
|
||||
|
||||
/**
|
||||
* Alpha controls the smoothing of the data. Alpha = 1 retains no memory of past values
|
||||
|
|
@ -195,8 +224,15 @@ public class HoltLinearModel extends MovAvgModel {
|
|||
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
|
||||
builder.field(MovAvgParser.MODEL.getPreferredName(), NAME_FIELD.getPreferredName());
|
||||
builder.startObject(MovAvgParser.SETTINGS.getPreferredName());
|
||||
|
||||
if (alpha != null) {
|
||||
builder.field("alpha", alpha);
|
||||
}
|
||||
|
||||
if (beta != null) {
|
||||
builder.field("beta", beta);
|
||||
}
|
||||
|
||||
builder.endObject();
|
||||
return builder;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -41,6 +41,47 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
|
||||
protected static final ParseField NAME_FIELD = new ParseField("holt_winters");
|
||||
|
||||
/**
|
||||
* Controls smoothing of data. Also known as "level" value.
|
||||
* Alpha = 1 retains no memory of past values
|
||||
* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
|
||||
* mean of the series).
|
||||
*/
|
||||
private final double alpha;
|
||||
|
||||
/**
|
||||
* Controls smoothing of trend.
|
||||
* Beta = 1 retains no memory of past values
|
||||
* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
|
||||
* mean of the series).
|
||||
*/
|
||||
private final double beta;
|
||||
|
||||
/**
|
||||
* Controls smoothing of seasonality.
|
||||
* Gamma = 1 retains no memory of past values
|
||||
* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
|
||||
* mean of the series).
|
||||
*/
|
||||
private final double gamma;
|
||||
|
||||
/**
|
||||
* Periodicity of the data
|
||||
*/
|
||||
private final int period;
|
||||
|
||||
/**
|
||||
* Whether this is a multiplicative or additive HW
|
||||
*/
|
||||
private final SeasonalityType seasonalityType;
|
||||
|
||||
/**
|
||||
* Padding is used to add a very small amount to values, so that zeroes do not interfere
|
||||
* with multiplicative seasonality math (e.g. division by zero)
|
||||
*/
|
||||
private final boolean pad;
|
||||
private final double padding;
|
||||
|
||||
public enum SeasonalityType {
|
||||
ADDITIVE((byte) 0, "add"), MULTIPLICATIVE((byte) 1, "mult");
|
||||
|
||||
|
|
@ -116,27 +157,6 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
}
|
||||
|
||||
|
||||
/**
|
||||
* Controls smoothing of data. Alpha = 1 retains no memory of past values
|
||||
* (e.g. random walk), while alpha = 0 retains infinite memory of past values (e.g.
|
||||
* mean of the series). Useful values are somewhere in between
|
||||
*/
|
||||
private double alpha;
|
||||
|
||||
/**
|
||||
* Equivalent to <code>alpha</code>, but controls the smoothing of the trend instead of the data
|
||||
*/
|
||||
private double beta;
|
||||
|
||||
private double gamma;
|
||||
|
||||
private int period;
|
||||
|
||||
private SeasonalityType seasonalityType;
|
||||
|
||||
private boolean pad;
|
||||
private double padding;
|
||||
|
||||
public HoltWintersModel(double alpha, double beta, double gamma, int period, SeasonalityType seasonalityType, boolean pad) {
|
||||
this.alpha = alpha;
|
||||
this.beta = beta;
|
||||
|
|
@ -150,11 +170,41 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
this.padding = seasonalityType.equals(SeasonalityType.MULTIPLICATIVE) && pad ? 0.0000000001 : 0;
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean minimizeByDefault() {
|
||||
return true;
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean hasValue(int windowLength) {
|
||||
public boolean canBeMinimized() {
|
||||
return true;
|
||||
}
|
||||
|
||||
@Override
|
||||
public MovAvgModel neighboringModel() {
|
||||
double newValue = Math.random();
|
||||
switch ((int) (Math.random() * 3)) {
|
||||
case 0:
|
||||
return new HoltWintersModel(newValue, beta, gamma, period, seasonalityType, pad);
|
||||
case 1:
|
||||
return new HoltWintersModel(alpha, newValue, gamma, period, seasonalityType, pad);
|
||||
case 2:
|
||||
return new HoltWintersModel(alpha, beta, newValue, period, seasonalityType, pad);
|
||||
default:
|
||||
assert (false): "Random value fell outside of range [0-2]";
|
||||
return new HoltWintersModel(newValue, beta, gamma, period, seasonalityType, pad); // This should never technically happen...
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public MovAvgModel clone() {
|
||||
return new HoltWintersModel(alpha, beta, gamma, period, seasonalityType, pad);
|
||||
}
|
||||
|
||||
@Override
|
||||
public boolean hasValue(int valuesAvailable) {
|
||||
// We need at least (period * 2) data-points (e.g. two "seasons")
|
||||
return windowLength >= period * 2;
|
||||
return valuesAvailable >= period * 2;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -198,7 +248,7 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
|
||||
// Smoothed value
|
||||
double s = 0;
|
||||
double last_s = 0;
|
||||
double last_s;
|
||||
|
||||
// Trend value
|
||||
double b = 0;
|
||||
|
|
@ -218,12 +268,11 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
// Calculate the slopes between first and second season for each period
|
||||
for (int i = 0; i < period; i++) {
|
||||
s += vs[i];
|
||||
b += (vs[i] - vs[i + period]) / 2;
|
||||
b += (vs[i + period] - vs[i]) / period;
|
||||
}
|
||||
s /= (double) period;
|
||||
b /= (double) period;
|
||||
last_s = s;
|
||||
last_b = b;
|
||||
|
||||
// Calculate first seasonal
|
||||
if (Double.compare(s, 0.0) == 0 || Double.compare(s, -0.0) == 0) {
|
||||
|
|
@ -247,7 +296,7 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
if (seasonalityType.equals(SeasonalityType.MULTIPLICATIVE)) {
|
||||
seasonal[i] = gamma * (vs[i] / (last_s + last_b )) + (1 - gamma) * seasonal[i - period];
|
||||
} else {
|
||||
seasonal[i] = gamma * (vs[i] - (last_s + last_b )) + (1 - gamma) * seasonal[i - period];
|
||||
seasonal[i] = gamma * (vs[i] - (last_s - last_b )) + (1 - gamma) * seasonal[i - period];
|
||||
}
|
||||
|
||||
last_s = s;
|
||||
|
|
@ -255,18 +304,15 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
}
|
||||
|
||||
double[] forecastValues = new double[numForecasts];
|
||||
int seasonCounter = (values.size() - 1) - period;
|
||||
|
||||
for (int i = 0; i < numForecasts; i++) {
|
||||
for (int i = 1; i <= numForecasts; i++) {
|
||||
int idx = values.size() - period + ((i - 1) % period);
|
||||
|
||||
// TODO perhaps pad out seasonal to a power of 2 and use a mask instead of modulo?
|
||||
if (seasonalityType.equals(SeasonalityType.MULTIPLICATIVE)) {
|
||||
forecastValues[i] = s + (i * b) * seasonal[seasonCounter % values.size()];
|
||||
forecastValues[i-1] = (s + (i * b)) * seasonal[idx];
|
||||
} else {
|
||||
forecastValues[i] = s + (i * b) + seasonal[seasonCounter % values.size()];
|
||||
forecastValues[i-1] = s + (i * b) + seasonal[idx];
|
||||
}
|
||||
|
||||
seasonCounter += 1;
|
||||
}
|
||||
|
||||
return forecastValues;
|
||||
|
|
@ -312,9 +358,9 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
@Override
|
||||
public MovAvgModel parse(@Nullable Map<String, Object> settings, String pipelineName, int windowSize, ParseFieldMatcher parseFieldMatcher) throws ParseException {
|
||||
|
||||
double alpha = parseDoubleParam(settings, "alpha", 0.5);
|
||||
double beta = parseDoubleParam(settings, "beta", 0.5);
|
||||
double gamma = parseDoubleParam(settings, "gamma", 0.5);
|
||||
double alpha = parseDoubleParam(settings, "alpha", 0.3);
|
||||
double beta = parseDoubleParam(settings, "beta", 0.1);
|
||||
double gamma = parseDoubleParam(settings, "gamma", 0.3);
|
||||
int period = parseIntegerParam(settings, "period", 1);
|
||||
|
||||
if (windowSize < 2 * period) {
|
||||
|
|
@ -345,12 +391,12 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
|
||||
public static class HoltWintersModelBuilder implements MovAvgModelBuilder {
|
||||
|
||||
private double alpha = 0.5;
|
||||
private double beta = 0.5;
|
||||
private double gamma = 0.5;
|
||||
private int period = 1;
|
||||
private SeasonalityType seasonalityType = SeasonalityType.ADDITIVE;
|
||||
private boolean pad = true;
|
||||
private Double alpha;
|
||||
private Double beta;
|
||||
private Double gamma;
|
||||
private Integer period;
|
||||
private SeasonalityType seasonalityType;
|
||||
private Boolean pad;
|
||||
|
||||
/**
|
||||
* Alpha controls the smoothing of the data. Alpha = 1 retains no memory of past values
|
||||
|
|
@ -402,12 +448,31 @@ public class HoltWintersModel extends MovAvgModel {
|
|||
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
|
||||
builder.field(MovAvgParser.MODEL.getPreferredName(), NAME_FIELD.getPreferredName());
|
||||
builder.startObject(MovAvgParser.SETTINGS.getPreferredName());
|
||||
|
||||
if (alpha != null) {
|
||||
builder.field("alpha", alpha);
|
||||
}
|
||||
|
||||
if (beta != null) {
|
||||
builder.field("beta", beta);
|
||||
}
|
||||
|
||||
if (gamma != null) {
|
||||
builder.field("gamma", gamma);
|
||||
}
|
||||
|
||||
if (period != null) {
|
||||
builder.field("period", period);
|
||||
builder.field("type", seasonalityType.getName());
|
||||
}
|
||||
|
||||
if (pad != null) {
|
||||
builder.field("pad", pad);
|
||||
}
|
||||
|
||||
if (seasonalityType != null) {
|
||||
builder.field("seasonalityType", seasonalityType);
|
||||
}
|
||||
|
||||
builder.endObject();
|
||||
return builder;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -42,6 +42,22 @@ public class LinearModel extends MovAvgModel {
|
|||
|
||||
protected static final ParseField NAME_FIELD = new ParseField("linear");
|
||||
|
||||
|
||||
@Override
|
||||
public boolean canBeMinimized() {
|
||||
return false;
|
||||
}
|
||||
|
||||
@Override
|
||||
public MovAvgModel neighboringModel() {
|
||||
return new LinearModel();
|
||||
}
|
||||
|
||||
@Override
|
||||
public MovAvgModel clone() {
|
||||
return new LinearModel();
|
||||
}
|
||||
|
||||
@Override
|
||||
protected <T extends Number> double[] doPredict(Collection<T> values, int numPredictions) {
|
||||
double[] predictions = new double[numPredictions];
|
||||
|
|
|
|||
|
|
@ -32,17 +32,42 @@ import java.util.Map;
|
|||
|
||||
public abstract class MovAvgModel {
|
||||
|
||||
/**
|
||||
* Should this model be fit to the data via a cost minimizing algorithm by default?
|
||||
*
|
||||
* @return
|
||||
*/
|
||||
public boolean minimizeByDefault() {
|
||||
return false;
|
||||
}
|
||||
|
||||
/**
|
||||
* Returns if the model can be cost minimized. Not all models have parameters
|
||||
* which can be tuned / optimized.
|
||||
*
|
||||
* @return
|
||||
*/
|
||||
public abstract boolean canBeMinimized();
|
||||
|
||||
/**
|
||||
* Generates a "neighboring" model, where one of the tunable parameters has been
|
||||
* randomly mutated within the allowed range. Used for minimization
|
||||
*
|
||||
* @return
|
||||
*/
|
||||
public abstract MovAvgModel neighboringModel();
|
||||
|
||||
/**
|
||||
* Checks to see this model can produce a new value, without actually running the algo.
|
||||
* This can be used for models that have certain preconditions that need to be met in order
|
||||
* to short-circuit execution
|
||||
*
|
||||
* @param windowLength Length of current window
|
||||
* @param valuesAvailable Number of values in the current window of values
|
||||
* @return Returns `true` if calling next() will produce a value, `false` otherwise
|
||||
*/
|
||||
public boolean hasValue(int windowLength) {
|
||||
public boolean hasValue(int valuesAvailable) {
|
||||
// Default implementation can always provide a next() value
|
||||
return windowLength > 0;
|
||||
return valuesAvailable > 0;
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -85,6 +110,8 @@ public abstract class MovAvgModel {
|
|||
|
||||
/**
|
||||
* Returns an empty set of predictions, filled with NaNs
|
||||
* @param numPredictions Number of empty predictions to generate
|
||||
* @return
|
||||
*/
|
||||
protected double[] emptyPredictions(int numPredictions) {
|
||||
double[] predictions = new double[numPredictions];
|
||||
|
|
@ -100,6 +127,13 @@ public abstract class MovAvgModel {
|
|||
*/
|
||||
public abstract void writeTo(StreamOutput out) throws IOException;
|
||||
|
||||
/**
|
||||
* Clone the model, returning an exact copy
|
||||
*
|
||||
* @return
|
||||
*/
|
||||
public abstract MovAvgModel clone();
|
||||
|
||||
/**
|
||||
* Abstract class which also provides some concrete parsing functionality.
|
||||
*/
|
||||
|
|
|
|||
|
|
@ -40,6 +40,22 @@ public class SimpleModel extends MovAvgModel {
|
|||
|
||||
protected static final ParseField NAME_FIELD = new ParseField("simple");
|
||||
|
||||
|
||||
@Override
|
||||
public boolean canBeMinimized() {
|
||||
return false;
|
||||
}
|
||||
|
||||
@Override
|
||||
public MovAvgModel neighboringModel() {
|
||||
return new SimpleModel();
|
||||
}
|
||||
|
||||
@Override
|
||||
public MovAvgModel clone() {
|
||||
return new SimpleModel();
|
||||
}
|
||||
|
||||
@Override
|
||||
protected <T extends Number> double[] doPredict(Collection<T> values, int numPredictions) {
|
||||
double[] predictions = new double[numPredictions];
|
||||
|
|
|
|||
|
|
@ -25,8 +25,7 @@ import com.google.common.collect.EvictingQueue;
|
|||
import org.elasticsearch.action.index.IndexRequestBuilder;
|
||||
import org.elasticsearch.action.search.SearchPhaseExecutionException;
|
||||
import org.elasticsearch.action.search.SearchResponse;
|
||||
import org.elasticsearch.index.query.RangeQueryBuilder;
|
||||
import org.elasticsearch.search.aggregations.bucket.filter.InternalFilter;
|
||||
import org.elasticsearch.search.SearchParseException;
|
||||
import org.elasticsearch.search.aggregations.bucket.histogram.Histogram;
|
||||
import org.elasticsearch.search.aggregations.bucket.histogram.InternalHistogram;
|
||||
import org.elasticsearch.search.aggregations.bucket.histogram.InternalHistogram.Bucket;
|
||||
|
|
@ -45,7 +44,6 @@ import java.util.*;
|
|||
|
||||
import static org.elasticsearch.common.xcontent.XContentFactory.jsonBuilder;
|
||||
import static org.elasticsearch.search.aggregations.AggregationBuilders.avg;
|
||||
import static org.elasticsearch.search.aggregations.AggregationBuilders.filter;
|
||||
import static org.elasticsearch.search.aggregations.AggregationBuilders.histogram;
|
||||
import static org.elasticsearch.search.aggregations.AggregationBuilders.max;
|
||||
import static org.elasticsearch.search.aggregations.AggregationBuilders.min;
|
||||
|
|
@ -55,8 +53,6 @@ import static org.elasticsearch.search.aggregations.pipeline.PipelineAggregatorB
|
|||
import static org.elasticsearch.test.hamcrest.ElasticsearchAssertions.assertSearchResponse;
|
||||
import static org.hamcrest.Matchers.closeTo;
|
||||
import static org.hamcrest.Matchers.equalTo;
|
||||
import static org.hamcrest.Matchers.greaterThanOrEqualTo;
|
||||
import static org.hamcrest.Matchers.lessThanOrEqualTo;
|
||||
import static org.hamcrest.core.IsNull.notNullValue;
|
||||
import static org.hamcrest.core.IsNull.nullValue;
|
||||
|
||||
|
|
@ -83,7 +79,7 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
|
||||
|
||||
enum MovAvgType {
|
||||
SIMPLE ("simple"), LINEAR("linear"), EWMA("ewma"), HOLT("holt"), HOLT_WINTERS("holt_winters");
|
||||
SIMPLE ("simple"), LINEAR("linear"), EWMA("ewma"), HOLT("holt"), HOLT_WINTERS("holt_winters"), HOLT_BIG_MINIMIZE("holt");
|
||||
|
||||
private final String name;
|
||||
|
||||
|
|
@ -136,7 +132,12 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
|
||||
for (MovAvgType type : MovAvgType.values()) {
|
||||
for (MetricTarget target : MetricTarget.values()) {
|
||||
setupExpected(type, target);
|
||||
if (type.equals(MovAvgType.HOLT_BIG_MINIMIZE)) {
|
||||
setupExpected(type, target, numBuckets);
|
||||
} else {
|
||||
setupExpected(type, target, windowSize);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -169,7 +170,7 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
* @param type The moving average model to use
|
||||
* @param target The document field "target", e.g. _count or a field value
|
||||
*/
|
||||
private void setupExpected(MovAvgType type, MetricTarget target) {
|
||||
private void setupExpected(MovAvgType type, MetricTarget target, int windowSize) {
|
||||
ArrayList<Double> values = new ArrayList<>(numBuckets);
|
||||
EvictingQueue<Double> window = EvictingQueue.create(windowSize);
|
||||
|
||||
|
|
@ -209,6 +210,9 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
case HOLT:
|
||||
values.add(holt(window));
|
||||
break;
|
||||
case HOLT_BIG_MINIMIZE:
|
||||
values.add(holt(window));
|
||||
break;
|
||||
case HOLT_WINTERS:
|
||||
// HW needs at least 2 periods of data to start
|
||||
if (window.size() >= period * 2) {
|
||||
|
|
@ -226,7 +230,7 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
window.offer(metricValue);
|
||||
|
||||
}
|
||||
testValues.put(type.toString() + "_" + target.toString(), values);
|
||||
testValues.put(type.name() + "_" + target.name(), values);
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
@ -349,12 +353,11 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
// Calculate the slopes between first and second season for each period
|
||||
for (int i = 0; i < period; i++) {
|
||||
s += vs[i];
|
||||
b += (vs[i] - vs[i + period]) / 2;
|
||||
b += (vs[i + period] - vs[i]) / period;
|
||||
}
|
||||
s /= (double) period;
|
||||
b /= (double) period;
|
||||
last_s = s;
|
||||
last_b = b;
|
||||
|
||||
// Calculate first seasonal
|
||||
if (Double.compare(s, 0.0) == 0 || Double.compare(s, -0.0) == 0) {
|
||||
|
|
@ -377,18 +380,20 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
if (seasonalityType.equals(HoltWintersModel.SeasonalityType.MULTIPLICATIVE)) {
|
||||
seasonal[i] = gamma * (vs[i] / (last_s + last_b )) + (1 - gamma) * seasonal[i - period];
|
||||
} else {
|
||||
seasonal[i] = gamma * (vs[i] - (last_s + last_b )) + (1 - gamma) * seasonal[i - period];
|
||||
seasonal[i] = gamma * (vs[i] - (last_s - last_b )) + (1 - gamma) * seasonal[i - period];
|
||||
}
|
||||
|
||||
last_s = s;
|
||||
last_b = b;
|
||||
}
|
||||
|
||||
int seasonCounter = (window.size() - 1) - period;
|
||||
int idx = window.size() - period + (0 % period);
|
||||
|
||||
// TODO perhaps pad out seasonal to a power of 2 and use a mask instead of modulo?
|
||||
if (seasonalityType.equals(HoltWintersModel.SeasonalityType.MULTIPLICATIVE)) {
|
||||
return s + (0 * b) * seasonal[seasonCounter % window.size()];
|
||||
return (s + (1 * b)) * seasonal[idx];
|
||||
} else {
|
||||
return s + (0 * b) + seasonal[seasonCounter % window.size()];
|
||||
return s + (1 * b) + seasonal[idx];
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -425,8 +430,8 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
List<? extends Bucket> buckets = histo.getBuckets();
|
||||
assertThat("Size of buckets array is not correct.", buckets.size(), equalTo(mockHisto.size()));
|
||||
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.SIMPLE.toString() + "_" + MetricTarget.COUNT.toString());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.SIMPLE.toString() + "_" + MetricTarget.VALUE.toString());
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.SIMPLE.name() + "_" + MetricTarget.COUNT.name());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.SIMPLE.name() + "_" + MetricTarget.VALUE.name());
|
||||
|
||||
Iterator<? extends Histogram.Bucket> actualIter = buckets.iterator();
|
||||
Iterator<PipelineAggregationHelperTests.MockBucket> expectedBucketIter = mockHisto.iterator();
|
||||
|
|
@ -477,8 +482,8 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
List<? extends Bucket> buckets = histo.getBuckets();
|
||||
assertThat("Size of buckets array is not correct.", buckets.size(), equalTo(mockHisto.size()));
|
||||
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.LINEAR.toString() + "_" + MetricTarget.COUNT.toString());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.LINEAR.toString() + "_" + MetricTarget.VALUE.toString());
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.LINEAR.name() + "_" + MetricTarget.COUNT.name());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.LINEAR.name() + "_" + MetricTarget.VALUE.name());
|
||||
|
||||
Iterator<? extends Histogram.Bucket> actualIter = buckets.iterator();
|
||||
Iterator<PipelineAggregationHelperTests.MockBucket> expectedBucketIter = mockHisto.iterator();
|
||||
|
|
@ -529,8 +534,8 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
List<? extends Bucket> buckets = histo.getBuckets();
|
||||
assertThat("Size of buckets array is not correct.", buckets.size(), equalTo(mockHisto.size()));
|
||||
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.EWMA.toString() + "_" + MetricTarget.COUNT.toString());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.EWMA.toString() + "_" + MetricTarget.VALUE.toString());
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.EWMA.name() + "_" + MetricTarget.COUNT.name());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.EWMA.name() + "_" + MetricTarget.VALUE.name());
|
||||
|
||||
Iterator<? extends Histogram.Bucket> actualIter = buckets.iterator();
|
||||
Iterator<PipelineAggregationHelperTests.MockBucket> expectedBucketIter = mockHisto.iterator();
|
||||
|
|
@ -581,8 +586,8 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
List<? extends Bucket> buckets = histo.getBuckets();
|
||||
assertThat("Size of buckets array is not correct.", buckets.size(), equalTo(mockHisto.size()));
|
||||
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.HOLT.toString() + "_" + MetricTarget.COUNT.toString());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.HOLT.toString() + "_" + MetricTarget.VALUE.toString());
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.HOLT.name() + "_" + MetricTarget.COUNT.name());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.HOLT.name() + "_" + MetricTarget.VALUE.name());
|
||||
|
||||
Iterator<? extends Histogram.Bucket> actualIter = buckets.iterator();
|
||||
Iterator<PipelineAggregationHelperTests.MockBucket> expectedBucketIter = mockHisto.iterator();
|
||||
|
|
@ -618,12 +623,14 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
.modelBuilder(new HoltWintersModel.HoltWintersModelBuilder()
|
||||
.alpha(alpha).beta(beta).gamma(gamma).period(period).seasonalityType(seasonalityType))
|
||||
.gapPolicy(gapPolicy)
|
||||
.minimize(false)
|
||||
.setBucketsPaths("_count"))
|
||||
.subAggregation(movingAvg("movavg_values")
|
||||
.window(windowSize)
|
||||
.modelBuilder(new HoltWintersModel.HoltWintersModelBuilder()
|
||||
.alpha(alpha).beta(beta).gamma(gamma).period(period).seasonalityType(seasonalityType))
|
||||
.gapPolicy(gapPolicy)
|
||||
.minimize(false)
|
||||
.setBucketsPaths("the_metric"))
|
||||
).execute().actionGet();
|
||||
|
||||
|
|
@ -635,8 +642,8 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
List<? extends Bucket> buckets = histo.getBuckets();
|
||||
assertThat("Size of buckets array is not correct.", buckets.size(), equalTo(mockHisto.size()));
|
||||
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.HOLT_WINTERS.toString() + "_" + MetricTarget.COUNT.toString());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.HOLT_WINTERS.toString() + "_" + MetricTarget.VALUE.toString());
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.HOLT_WINTERS.name() + "_" + MetricTarget.COUNT.name());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.HOLT_WINTERS.name() + "_" + MetricTarget.VALUE.name());
|
||||
|
||||
Iterator<? extends Histogram.Bucket> actualIter = buckets.iterator();
|
||||
Iterator<PipelineAggregationHelperTests.MockBucket> expectedBucketIter = mockHisto.iterator();
|
||||
|
|
@ -1038,6 +1045,230 @@ public class MovAvgTests extends ElasticsearchIntegrationTest {
|
|||
|
||||
}
|
||||
|
||||
@Test
|
||||
public void HoltWintersMinimization() {
|
||||
|
||||
SearchResponse response = client()
|
||||
.prepareSearch("idx").setTypes("type")
|
||||
.addAggregation(
|
||||
histogram("histo").field(INTERVAL_FIELD).interval(interval)
|
||||
.extendedBounds(0L, (long) (interval * (numBuckets - 1)))
|
||||
.subAggregation(metric)
|
||||
.subAggregation(movingAvg("movavg_counts")
|
||||
.window(windowSize)
|
||||
.modelBuilder(new HoltWintersModel.HoltWintersModelBuilder()
|
||||
.period(period).seasonalityType(seasonalityType))
|
||||
.gapPolicy(gapPolicy)
|
||||
.minimize(true)
|
||||
.setBucketsPaths("_count"))
|
||||
.subAggregation(movingAvg("movavg_values")
|
||||
.window(windowSize)
|
||||
.modelBuilder(new HoltWintersModel.HoltWintersModelBuilder()
|
||||
.period(period).seasonalityType(seasonalityType))
|
||||
.gapPolicy(gapPolicy)
|
||||
.minimize(true)
|
||||
.setBucketsPaths("the_metric"))
|
||||
).execute().actionGet();
|
||||
|
||||
assertSearchResponse(response);
|
||||
|
||||
InternalHistogram<Bucket> histo = response.getAggregations().get("histo");
|
||||
assertThat(histo, notNullValue());
|
||||
assertThat(histo.getName(), equalTo("histo"));
|
||||
List<? extends Bucket> buckets = histo.getBuckets();
|
||||
assertThat("Size of buckets array is not correct.", buckets.size(), equalTo(mockHisto.size()));
|
||||
|
||||
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.HOLT_WINTERS.name() + "_" + MetricTarget.COUNT.name());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.HOLT_WINTERS.name() + "_" + MetricTarget.VALUE.name());
|
||||
|
||||
Iterator<? extends Histogram.Bucket> actualIter = buckets.iterator();
|
||||
Iterator<PipelineAggregationHelperTests.MockBucket> expectedBucketIter = mockHisto.iterator();
|
||||
Iterator<Double> expectedCountsIter = expectedCounts.iterator();
|
||||
Iterator<Double> expectedValueIter = expectedValues.iterator();
|
||||
|
||||
// The minimizer is stochastic, so just make sure all the values coming back aren't null
|
||||
while (actualIter.hasNext()) {
|
||||
|
||||
Histogram.Bucket actual = actualIter.next();
|
||||
PipelineAggregationHelperTests.MockBucket expected = expectedBucketIter.next();
|
||||
Double expectedCount = expectedCountsIter.next();
|
||||
Double expectedValue = expectedValueIter.next();
|
||||
|
||||
assertThat("keys do not match", ((Number) actual.getKey()).longValue(), equalTo(expected.key));
|
||||
assertThat("doc counts do not match", actual.getDocCount(), equalTo((long)expected.count));
|
||||
|
||||
SimpleValue countMovAvg = actual.getAggregations().get("movavg_counts");
|
||||
SimpleValue valuesMovAvg = actual.getAggregations().get("movavg_values");
|
||||
|
||||
if (expectedCount == null) {
|
||||
//this bucket wasn't supposed to have a value (empty, skipped, etc), so
|
||||
//movavg should be null too
|
||||
assertThat(countMovAvg, nullValue());
|
||||
} else {
|
||||
|
||||
// Note that we don't compare against the mock values, since those are assuming
|
||||
// a non-minimized set of coefficients. Just check for not-nullness
|
||||
assertThat(countMovAvg, notNullValue());
|
||||
}
|
||||
|
||||
if (expectedValue == null) {
|
||||
//this bucket wasn't supposed to have a value (empty, skipped, etc), so
|
||||
//movavg should be null too
|
||||
assertThat(valuesMovAvg, nullValue());
|
||||
} else {
|
||||
|
||||
// Note that we don't compare against the mock values, since those are assuming
|
||||
// a non-minimized set of coefficients. Just check for not-nullness
|
||||
assertThat(valuesMovAvg, notNullValue());
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* If the minimizer is turned on, but there isn't enough data to minimize with, it will simply use
|
||||
* the default settings. Which means our mock histo will match the generated result (which it won't
|
||||
* if the minimizer is actually working, since the coefficients will be different and thus generate different
|
||||
* data)
|
||||
*
|
||||
* We can simulate this by setting the window size == size of histo
|
||||
*/
|
||||
@Test
|
||||
public void minimizeNotEnoughData() {
|
||||
|
||||
SearchResponse response = client()
|
||||
.prepareSearch("idx").setTypes("type")
|
||||
.addAggregation(
|
||||
histogram("histo").field(INTERVAL_FIELD).interval(interval)
|
||||
.extendedBounds(0L, (long) (interval * (numBuckets - 1)))
|
||||
.subAggregation(metric)
|
||||
.subAggregation(movingAvg("movavg_counts")
|
||||
.window(numBuckets)
|
||||
.modelBuilder(new HoltLinearModel.HoltLinearModelBuilder().alpha(alpha).beta(beta))
|
||||
.gapPolicy(gapPolicy)
|
||||
.minimize(true)
|
||||
.setBucketsPaths("_count"))
|
||||
.subAggregation(movingAvg("movavg_values")
|
||||
.window(numBuckets)
|
||||
.modelBuilder(new HoltLinearModel.HoltLinearModelBuilder().alpha(alpha).beta(beta))
|
||||
.gapPolicy(gapPolicy)
|
||||
.minimize(true)
|
||||
.setBucketsPaths("the_metric"))
|
||||
).execute().actionGet();
|
||||
|
||||
assertSearchResponse(response);
|
||||
|
||||
InternalHistogram<Bucket> histo = response.getAggregations().get("histo");
|
||||
assertThat(histo, notNullValue());
|
||||
assertThat(histo.getName(), equalTo("histo"));
|
||||
List<? extends Bucket> buckets = histo.getBuckets();
|
||||
assertThat("Size of buckets array is not correct.", buckets.size(), equalTo(mockHisto.size()));
|
||||
|
||||
List<Double> expectedCounts = testValues.get(MovAvgType.HOLT_BIG_MINIMIZE.name() + "_" + MetricTarget.COUNT.name());
|
||||
List<Double> expectedValues = testValues.get(MovAvgType.HOLT_BIG_MINIMIZE.name() + "_" + MetricTarget.VALUE.name());
|
||||
|
||||
Iterator<? extends Histogram.Bucket> actualIter = buckets.iterator();
|
||||
Iterator<PipelineAggregationHelperTests.MockBucket> expectedBucketIter = mockHisto.iterator();
|
||||
Iterator<Double> expectedCountsIter = expectedCounts.iterator();
|
||||
Iterator<Double> expectedValuesIter = expectedValues.iterator();
|
||||
|
||||
while (actualIter.hasNext()) {
|
||||
assertValidIterators(expectedBucketIter, expectedCountsIter, expectedValuesIter);
|
||||
|
||||
Histogram.Bucket actual = actualIter.next();
|
||||
PipelineAggregationHelperTests.MockBucket expected = expectedBucketIter.next();
|
||||
Double expectedCount = expectedCountsIter.next();
|
||||
Double expectedValue = expectedValuesIter.next();
|
||||
|
||||
assertThat("keys do not match", ((Number) actual.getKey()).longValue(), equalTo(expected.key));
|
||||
assertThat("doc counts do not match", actual.getDocCount(), equalTo((long)expected.count));
|
||||
|
||||
assertBucketContents(actual, expectedCount, expectedValue);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Only some models can be minimized, should throw exception for: simple, linear
|
||||
*/
|
||||
@Test
|
||||
public void checkIfNonTunableCanBeMinimized() {
|
||||
|
||||
try {
|
||||
client()
|
||||
.prepareSearch("idx").setTypes("type")
|
||||
.addAggregation(
|
||||
histogram("histo").field(INTERVAL_FIELD).interval(interval)
|
||||
.extendedBounds(0L, (long) (interval * (numBuckets - 1)))
|
||||
.subAggregation(metric)
|
||||
.subAggregation(movingAvg("movavg_counts")
|
||||
.window(numBuckets)
|
||||
.modelBuilder(new SimpleModel.SimpleModelBuilder())
|
||||
.gapPolicy(gapPolicy)
|
||||
.minimize(true)
|
||||
.setBucketsPaths("_count"))
|
||||
).execute().actionGet();
|
||||
fail("Simple Model cannot be minimized, but an exception was not thrown");
|
||||
} catch (SearchPhaseExecutionException e) {
|
||||
// All good
|
||||
}
|
||||
|
||||
try {
|
||||
client()
|
||||
.prepareSearch("idx").setTypes("type")
|
||||
.addAggregation(
|
||||
histogram("histo").field(INTERVAL_FIELD).interval(interval)
|
||||
.extendedBounds(0L, (long) (interval * (numBuckets - 1)))
|
||||
.subAggregation(metric)
|
||||
.subAggregation(movingAvg("movavg_counts")
|
||||
.window(numBuckets)
|
||||
.modelBuilder(new LinearModel.LinearModelBuilder())
|
||||
.gapPolicy(gapPolicy)
|
||||
.minimize(true)
|
||||
.setBucketsPaths("_count"))
|
||||
).execute().actionGet();
|
||||
fail("Linear Model cannot be minimized, but an exception was not thrown");
|
||||
} catch (SearchPhaseExecutionException e) {
|
||||
// all good
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* These models are all minimizable, so they should not throw exceptions
|
||||
*/
|
||||
@Test
|
||||
public void checkIfTunableCanBeMinimized() {
|
||||
|
||||
MovAvgModelBuilder[] builders = new MovAvgModelBuilder[]{
|
||||
new EwmaModel.EWMAModelBuilder(),
|
||||
new HoltLinearModel.HoltLinearModelBuilder(),
|
||||
new HoltWintersModel.HoltWintersModelBuilder()
|
||||
};
|
||||
|
||||
for (MovAvgModelBuilder builder : builders) {
|
||||
try {
|
||||
client()
|
||||
.prepareSearch("idx").setTypes("type")
|
||||
.addAggregation(
|
||||
histogram("histo").field(INTERVAL_FIELD).interval(interval)
|
||||
.extendedBounds(0L, (long) (interval * (numBuckets - 1)))
|
||||
.subAggregation(metric)
|
||||
.subAggregation(movingAvg("movavg_counts")
|
||||
.window(numBuckets)
|
||||
.modelBuilder(builder)
|
||||
.gapPolicy(gapPolicy)
|
||||
.minimize(true)
|
||||
.setBucketsPaths("_count"))
|
||||
).execute().actionGet();
|
||||
} catch (SearchPhaseExecutionException e) {
|
||||
fail("Model [" + builder.toString() + "] can be minimized, but an exception was thrown");
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
||||
|
||||
private void assertValidIterators(Iterator expectedBucketIter, Iterator expectedCountsIter, Iterator expectedValuesIter) {
|
||||
if (!expectedBucketIter.hasNext()) {
|
||||
|
|
|
|||
|
|
@ -44,6 +44,8 @@ A `moving_avg` aggregation looks like this in isolation:
|
|||
|`model` |The moving average weighting model that we wish to use |Optional |`simple`
|
||||
|`gap_policy` |Determines what should happen when a gap in the data is encountered. |Optional |`insert_zero`
|
||||
|`window` |The size of window to "slide" across the histogram. |Optional |`5`
|
||||
|`minimize` |If the model should be algorithmically minimized. See <<movavg-minimizer, Minimization>> for more
|
||||
details |Optional |`false` for most models
|
||||
|`settings` |Model-specific settings, contents which differ depending on the model specified. |Optional |
|
||||
|===
|
||||
|
||||
|
|
@ -100,6 +102,7 @@ the values from a `simple` moving average tend to "lag" behind the real data.
|
|||
"the_movavg":{
|
||||
"moving_avg":{
|
||||
"buckets_path": "the_sum",
|
||||
"window" : 30,
|
||||
"model" : "simple"
|
||||
}
|
||||
}
|
||||
|
|
@ -135,6 +138,7 @@ the "lag" behind the data's mean, since older points have less influence.
|
|||
"the_movavg":{
|
||||
"moving_avg":{
|
||||
"buckets_path": "the_sum",
|
||||
"window" : 30,
|
||||
"model" : "linear"
|
||||
}
|
||||
}
|
||||
|
|
@ -165,7 +169,9 @@ setting. Small values make the weight decay slowly, which provides greater smoo
|
|||
portion of the window. Larger valuers make the weight decay quickly, which reduces the impact of older values on the
|
||||
moving average. This tends to make the moving average track the data more closely but with less smoothing.
|
||||
|
||||
The default value of `alpha` is `0.5`, and the setting accepts any float from 0-1 inclusive.
|
||||
The default value of `alpha` is `0.3`, and the setting accepts any float from 0-1 inclusive.
|
||||
|
||||
The EWMA model can be <<movavg-minimizer, Minimized>>
|
||||
|
||||
[source,js]
|
||||
--------------------------------------------------
|
||||
|
|
@ -173,6 +179,7 @@ The default value of `alpha` is `0.5`, and the setting accepts any float from 0-
|
|||
"the_movavg":{
|
||||
"moving_avg":{
|
||||
"buckets_path": "the_sum",
|
||||
"window" : 30,
|
||||
"model" : "ewma",
|
||||
"settings" : {
|
||||
"alpha" : 0.5
|
||||
|
|
@ -204,7 +211,9 @@ smoothed data). The trend value is also exponentially weighted.
|
|||
|
||||
Values are produced by multiplying the level and trend components.
|
||||
|
||||
The default value of `alpha` and `beta` is `0.5`, and the settings accept any float from 0-1 inclusive.
|
||||
The default value of `alpha` is `0.3` and `beta` is `0.1`. The settings accept any float from 0-1 inclusive.
|
||||
|
||||
The Holt-Linear model can be <<movavg-minimizer, Minimized>>
|
||||
|
||||
[source,js]
|
||||
--------------------------------------------------
|
||||
|
|
@ -212,6 +221,7 @@ The default value of `alpha` and `beta` is `0.5`, and the settings accept any fl
|
|||
"the_movavg":{
|
||||
"moving_avg":{
|
||||
"buckets_path": "the_sum",
|
||||
"window" : 30,
|
||||
"model" : "holt",
|
||||
"settings" : {
|
||||
"alpha" : 0.5,
|
||||
|
|
@ -270,15 +280,18 @@ Additive seasonality is the default; it can also be specified by setting `"type"
|
|||
when the seasonal affect is additive to your data. E.g. you could simply subtract the seasonal effect to "de-seasonalize"
|
||||
your data into a flat trend.
|
||||
|
||||
The default value of `alpha`, `beta` and `gamma` is `0.5`, and the settings accept any float from 0-1 inclusive.
|
||||
The default values of `alpha` and `gamma` are `0.3` while `beta` is `0.1`. The settings accept any float from 0-1 inclusive.
|
||||
The default value of `period` is `1`.
|
||||
|
||||
The additive Holt-Winters model can be <<movavg-minimizer, Minimized>>
|
||||
|
||||
[source,js]
|
||||
--------------------------------------------------
|
||||
{
|
||||
"the_movavg":{
|
||||
"moving_avg":{
|
||||
"buckets_path": "the_sum",
|
||||
"window" : 30,
|
||||
"model" : "holt_winters",
|
||||
"settings" : {
|
||||
"type" : "add",
|
||||
|
|
@ -301,9 +314,11 @@ image::images/pipeline_movavg/triple.png[]
|
|||
Multiplicative is specified by setting `"type": "mult"`. This variety is preferred when the seasonal affect is
|
||||
multiplied against your data. E.g. if the seasonal affect is x5 the data, rather than simply adding to it.
|
||||
|
||||
The default value of `alpha`, `beta` and `gamma` is `0.5`, and the settings accept any float from 0-1 inclusive.
|
||||
The default values of `alpha` and `gamma` are `0.3` while `beta` is `0.1`. The settings accept any float from 0-1 inclusive.
|
||||
The default value of `period` is `1`.
|
||||
|
||||
The multiplicative Holt-Winters model can be <<movavg-minimizer, Minimized>>
|
||||
|
||||
[WARNING]
|
||||
======
|
||||
Multiplicative Holt-Winters works by dividing each data point by the seasonal value. This is problematic if any of
|
||||
|
|
@ -319,6 +334,7 @@ you can disable this behavior with `pad: false`
|
|||
"the_movavg":{
|
||||
"moving_avg":{
|
||||
"buckets_path": "the_sum",
|
||||
"window" : 30,
|
||||
"model" : "holt_winters",
|
||||
"settings" : {
|
||||
"type" : "mult",
|
||||
|
|
@ -347,6 +363,7 @@ as your buckets:
|
|||
"the_movavg":{
|
||||
"moving_avg":{
|
||||
"buckets_path": "the_sum",
|
||||
"window" : 30,
|
||||
"model" : "simple",
|
||||
"predict" 10
|
||||
}
|
||||
|
|
@ -381,3 +398,53 @@ fluctuations into the model:
|
|||
[[holt_winters_prediction_global]]
|
||||
.Holt-Winters moving average with window of size 120, predict = 25, alpha = 0.8, beta = 0.2, gamma = 0.7, period = 30
|
||||
image::images/pipeline_movavg/triple_prediction.png[]
|
||||
|
||||
[[movavg-minimizer]]
|
||||
==== Minimization
|
||||
|
||||
Some of the models (EWMA, Holt-Linear, Holt-Winters) require one or more parameters to be configured. Parameter choice
|
||||
can be tricky and sometimes non-intuitive. Furthermore, small deviations in these parameters can sometimes have a drastic
|
||||
effect on the output moving average.
|
||||
|
||||
For that reason, the three "tunable" models can be algorithmically *minimized*. Minimization is a process where parameters
|
||||
are tweaked until the predictions generated by the model closely match the output data. Minimization is not fullproof
|
||||
and can be susceptible to overfitting, but it often gives better results than hand-tuning.
|
||||
|
||||
Minimization is disabled by default for `ewma` and `holt_linear`, while it is enabled by default for `holt_winters`.
|
||||
Minimization is most useful with Holt-Winters, since it helps improve the accuracy of the predictions. EWMA and
|
||||
Holt-Linear are not great predictors, and mostly used for smoothing data, so minimization is less useful on those
|
||||
models.
|
||||
|
||||
Minimization is enabled/disabled via the `minimize` parameter:
|
||||
|
||||
[source,js]
|
||||
--------------------------------------------------
|
||||
{
|
||||
"the_movavg":{
|
||||
"moving_avg":{
|
||||
"buckets_path": "the_sum",
|
||||
"model" : "holt_winters",
|
||||
"window" : 30,
|
||||
"minimize" : true, <1>
|
||||
"settings" : {
|
||||
"period" : 7
|
||||
}
|
||||
}
|
||||
}
|
||||
--------------------------------------------------
|
||||
<1> Minimization is enabled with the `minimize` parameter
|
||||
|
||||
When enabled, minimization will find the optimal values for `alpha`, `beta` and `gamma`. The user should still provide
|
||||
appropriate values for `window`, `period` and `type`.
|
||||
|
||||
[WARNING]
|
||||
======
|
||||
Minimization works by running a stochastic process called *simulated annealing*. This process will usually generate
|
||||
a good solution, but is not guaranteed to find the global optimum. It also requires some amount of additional
|
||||
computational power, since the model needs to be re-run multiple times as the values are tweaked. The run-time of
|
||||
minimization is linear to the size of the window being processed: excessively large windows may cause latency.
|
||||
|
||||
Finally, minimization fits the model to the last `n` values, where `n = window`. This generally produces
|
||||
better forecasts into the future, since the parameters are tuned around the end of the series. It can, however, generate
|
||||
poorer fitting moving averages at the beginning of the series.
|
||||
======
|
||||
Loading…
Reference in New Issue