Merge pull request #405 from kaituo/missing

Fix confidence adjustment when all input values are missing

Java/core/src/main/java/com/amazon/randomcutforest/preprocessor/ImputePreprocessor.java

@@ -88,7 +88,31 @@ public float[] getScaledShingledInput(double[] inputPoint, long timestamp, int[]
      */
     @Override
     protected void updateTimestamps(long timestamp) {
-        if (previousTimeStamps[0] == previousTimeStamps[1]) {
+        /*
+         * For imputations done on timestamps other than the current one (specified by
+         * the timestamp parameter), the timestamp of the imputed tuple matches that of
+         * the input tuple, and we increment numberOfImputed. For imputations done at
+         * the current timestamp (if all input values are missing), the timestamp of the
+         * imputed tuple is the current timestamp, and we increment numberOfImputed.
+         *
+         * To check if imputed values are still present in the shingle, we use the first
+         * condition (previousTimeStamps[0] == previousTimeStamps[1]). This works
+         * because previousTimeStamps has a size equal to the shingle size and is filled
+         * with the current timestamp. However, there are scenarios where we might miss
+         * decrementing numberOfImputed:
+         *
+         * 1. Not all values in the shingle are imputed. 2. We accumulated
+         * numberOfImputed when the current timestamp had missing values.
+         *
+         * As a result, this could cause the data quality measure to decrease
+         * continuously since we are always counting missing values that should
+         * eventually be reset to zero. The second condition <pre> timestamp >
+         * previousTimeStamps[previousTimeStamps.length-1] && numberOfImputed > 0 </pre>
+         * will decrement numberOfImputed when we move to a new timestamp, provided
+         * numberOfImputed is greater than zero.
+         */
+        if (previousTimeStamps[0] == previousTimeStamps[1]
+                || (timestamp > previousTimeStamps[previousTimeStamps.length - 1] && numberOfImputed > 0)) {
             numberOfImputed = numberOfImputed - 1;
         }
         super.updateTimestamps(timestamp);
@@ -333,7 +357,10 @@ protected float[] generateShingle(double[] inputTuple, long timestamp, int[] mis
             }
         }
 
-        updateForest(changeForest, input, timestamp, forest, false);
+        // last parameter isFullyImputed = if we miss everything in inputTuple?
+        // This would ensure dataQuality is decreasing if we impute whenever
+        updateForest(changeForest, input, timestamp, forest,
+                missingValues != null ? missingValues.length == inputTuple.length : false);
         if (changeForest) {
             updateTimeStampDeviations(timestamp, lastInputTimeStamp);
             transformer.updateDeviation(input, savedInput, missingValues);

Java/core/src/main/java/com/amazon/randomcutforest/state/preprocessor/PreprocessorMapper.java

@@ -62,6 +62,7 @@ public Preprocessor toModel(PreprocessorState state, long seed) {
         preprocessor.setPreviousTimeStamps(state.getPreviousTimeStamps());
         preprocessor.setNormalizeTime(state.isNormalizeTime());
         preprocessor.setFastForward(state.isFastForward());
+        preprocessor.setNumberOfImputed(state.getNumberOfImputed());
         return preprocessor;
     }
 
@@ -94,6 +95,7 @@ public PreprocessorState toState(Preprocessor model) {
         state.setTimeStampDeviationStates(getStates(model.getTimeStampDeviations(), deviationMapper));
         state.setDataQualityStates(getStates(model.getDataQuality(), deviationMapper));
         state.setFastForward(model.isFastForward());
+        state.setNumberOfImputed(model.getNumberOfImputed());
         return state;
     }
 

Java/core/src/main/java/com/amazon/randomcutforest/state/preprocessor/PreprocessorState.java

@@ -59,4 +59,5 @@ public class PreprocessorState implements Serializable {
     private DeviationState[] dataQualityStates;
     private DeviationState[] timeStampDeviationStates;
     private boolean fastForward;
+    private int numberOfImputed;
 }

Java/parkservices/src/test/java/com/amazon/randomcutforest/parkservices/MissingValueTest.java

@@ -0,0 +1,116 @@
+/*
+ * Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License").
+ * You may not use this file except in compliance with the License.
+ * A copy of the License is located at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * or in the "license" file accompanying this file. This file 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 com.amazon.randomcutforest.parkservices;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.util.ArrayList;
+import java.util.List;
+import java.util.Random;
+
+import org.junit.jupiter.params.ParameterizedTest;
+import org.junit.jupiter.params.provider.EnumSource;
+
+import com.amazon.randomcutforest.config.ForestMode;
+import com.amazon.randomcutforest.config.ImputationMethod;
+import com.amazon.randomcutforest.config.Precision;
+import com.amazon.randomcutforest.config.TransformMethod;
+
+public class MissingValueTest {
+    @ParameterizedTest
+    @EnumSource(ImputationMethod.class)
+    public void testConfidence(ImputationMethod method) {
+        // Create and populate a random cut forest
+
+        int shingleSize = 4;
+        int numberOfTrees = 50;
+        int sampleSize = 256;
+        Precision precision = Precision.FLOAT_32;
+        int baseDimensions = 1;
+
+        long count = 0;
+
+        int dimensions = baseDimensions * shingleSize;
+        ThresholdedRandomCutForest forest = new ThresholdedRandomCutForest.Builder<>().compact(true)
+                .dimensions(dimensions).randomSeed(0).numberOfTrees(numberOfTrees).shingleSize(shingleSize)
+                .sampleSize(sampleSize).precision(precision).anomalyRate(0.01).imputationMethod(method)
+                .fillValues(new double[] { 3 }).forestMode(ForestMode.STREAMING_IMPUTE)
+                .transformMethod(TransformMethod.NORMALIZE).autoAdjust(true).build();
+
+        // Define the size and range
+        int size = 400;
+        double min = 200.0;
+        double max = 240.0;
+
+        // Generate the list of doubles
+        List<Double> randomDoubles = generateUniformRandomDoubles(size, min, max);
+
+        double lastConfidence = 0;
+        for (double val : randomDoubles) {
+            double[] point = new double[] { val };
+            long newStamp = 100 * count;
+            if (count >= 300 && count < 325) {
+                // drop observations
+                AnomalyDescriptor result = forest.process(new double[] { Double.NaN }, newStamp,
+                        generateIntArray(point.length));
+                if (count > 300) {
+                    // confidence start decreasing after 1 missing point
+                    assertTrue(result.getDataConfidence() < lastConfidence, "count " + count);
+                }
+                lastConfidence = result.getDataConfidence();
+                float[] rcfPoint = result.getRCFPoint();
+                double scale = result.getScale()[0];
+                double shift = result.getShift()[0];
+                double[] actual = new double[] { (rcfPoint[3] * scale) + shift };
+                if (method == ImputationMethod.ZERO) {
+                    assertEquals(0, actual[0], 0.001d);
+                } else if (method == ImputationMethod.FIXED_VALUES) {
+                    assertEquals(3.0d, actual[0], 0.001d);
+                }
+            } else {
+                AnomalyDescriptor result = forest.process(point, newStamp);
+                if ((count > 100 && count < 300) || count >= 326) {
+                    // The first 65+ observations gives 0 confidence.
+                    // Confidence start increasing after 1 observed point
+                    assertTrue(result.getDataConfidence() > lastConfidence);
+                }
+                lastConfidence = result.getDataConfidence();
+            }
+            ++count;
+        }
+    }
+
+    public static int[] generateIntArray(int size) {
+        int[] intArray = new int[size];
+        for (int i = 0; i < size; i++) {
+            intArray[i] = i;
+        }
+        return intArray;
+    }
+
+    public static List<Double> generateUniformRandomDoubles(int size, double min, double max) {
+        List<Double> randomDoubles = new ArrayList<>(size);
+        Random random = new Random(0);
+
+        for (int i = 0; i < size; i++) {
+            double randomValue = min + (max - min) * random.nextDouble();
+            randomDoubles.add(randomValue);
+        }
+
+        return randomDoubles;
+    }
+}