[ML] Rename score fields (elastic/x-pack-elasticsearch#769)

* Rename normalized_probability to record_score
* Rename anomaly_score on influencers to influencer_score
* Remove record level anomaly_score
* Remove bucket level max_normalized_probability

Relates elastic/x-pack-elasticsearch#678

Original commit: elastic/x-pack-elasticsearch@c2c05e73d8

plugin/src/main/java/org/elasticsearch/xpack/ml/action/GetBucketsAction.java

@@ -69,7 +69,6 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
         public static final ParseField START = new ParseField("start");
         public static final ParseField END = new ParseField("end");
         public static final ParseField ANOMALY_SCORE = new ParseField("anomaly_score");
-        public static final ParseField MAX_NORMALIZED_PROBABILITY = new ParseField("max_normalized_probability");
         public static final ParseField TIMESTAMP = new ParseField("timestamp");
 
         private static final ObjectParser<Request, Void> PARSER = new ObjectParser<>(NAME, Request::new);
@@ -86,7 +85,6 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
             PARSER.declareBoolean(Request::setIncludeInterim, INCLUDE_INTERIM);
             PARSER.declareObject(Request::setPageParams, PageParams.PARSER, PageParams.PAGE);
             PARSER.declareDouble(Request::setAnomalyScore, ANOMALY_SCORE);
-            PARSER.declareDouble(Request::setMaxNormalizedProbability, MAX_NORMALIZED_PROBABILITY);
             PARSER.declareString(Request::setPartitionValue, PARTITION_VALUE);
         }
 
@@ -107,7 +105,6 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
         private String end;
         private PageParams pageParams;
         private Double anomalyScore;
-        private Double maxNormalizedProbability;
 
         Request() {
         }
@@ -121,14 +118,13 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
         }
 
         public void setTimestamp(String timestamp) {
-            if (pageParams != null || start != null || end != null || anomalyScore != null || maxNormalizedProbability != null) {
+            if (pageParams != null || start != null || end != null || anomalyScore != null) {
                 throw new IllegalArgumentException("Param [" + TIMESTAMP.getPreferredName() + "] is incompatible with ["
                                 + PageParams.FROM.getPreferredName() + ","
                                 + PageParams.SIZE.getPreferredName() + ","
                                 + START.getPreferredName() + ","
                                 + END.getPreferredName() + ","
-                                + ANOMALY_SCORE.getPreferredName() + ","
+                                + ANOMALY_SCORE.getPreferredName() + "]");
-                                + MAX_NORMALIZED_PROBABILITY.getPreferredName() + "]");
             }
             this.timestamp = ExceptionsHelper.requireNonNull(timestamp, Result.TIMESTAMP.getPreferredName());
         }
@@ -213,18 +209,6 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
             this.anomalyScore = anomalyScore;
         }
 
-        public double getMaxNormalizedProbability() {
-            return maxNormalizedProbability;
-        }
-
-        public void setMaxNormalizedProbability(double maxNormalizedProbability) {
-            if (timestamp != null) {
-                throw new IllegalArgumentException("Param [" + MAX_NORMALIZED_PROBABILITY.getPreferredName() + "] is incompatible with ["
-                        + TIMESTAMP.getPreferredName() + "].");
-            }
-            this.maxNormalizedProbability = maxNormalizedProbability;
-        }
-
         @Override
         public ActionRequestValidationException validate() {
             return null;
@@ -241,7 +225,6 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
             start = in.readOptionalString();
             end = in.readOptionalString();
             anomalyScore = in.readOptionalDouble();
-            maxNormalizedProbability = in.readOptionalDouble();
             pageParams = in.readOptionalWriteable(PageParams::new);
         }
 
@@ -256,7 +239,6 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
             out.writeOptionalString(start);
             out.writeOptionalString(end);
             out.writeOptionalDouble(anomalyScore);
-            out.writeOptionalDouble(maxNormalizedProbability);
             out.writeOptionalWriteable(pageParams);
         }
 
@@ -284,9 +266,6 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
             if (anomalyScore != null) {
                 builder.field(ANOMALY_SCORE.getPreferredName(), anomalyScore);
             }
-            if (maxNormalizedProbability != null) {
-                builder.field(MAX_NORMALIZED_PROBABILITY.getPreferredName(), maxNormalizedProbability);
-            }
             builder.endObject();
             return builder;
         }
@@ -294,7 +273,7 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
         @Override
         public int hashCode() {
             return Objects.hash(jobId, timestamp, partitionValue, expand, includeInterim,
-                    anomalyScore, maxNormalizedProbability, pageParams, start, end);
+                    anomalyScore, pageParams, start, end);
         }
 
         @Override
@@ -312,7 +291,6 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
                     Objects.equals(expand, other.expand) &&
                     Objects.equals(includeInterim, other.includeInterim) &&
                     Objects.equals(anomalyScore, other.anomalyScore) &&
-                    Objects.equals(maxNormalizedProbability, other.maxNormalizedProbability) &&
                     Objects.equals(pageParams, other.pageParams) &&
                     Objects.equals(start, other.start) &&
                     Objects.equals(end, other.end);
@@ -410,7 +388,6 @@ public class GetBucketsAction extends Action<GetBucketsAction.Request, GetBucket
                             .start(request.start)
                             .end(request.end)
                             .anomalyScoreThreshold(request.anomalyScore)
-                            .normalizedProbabilityThreshold(request.maxNormalizedProbability)
                             .partitionValue(request.partitionValue);
 
             if (request.pageParams != null) {

plugin/src/main/java/org/elasticsearch/xpack/ml/action/GetInfluencersAction.java

@@ -96,7 +96,7 @@ extends Action<GetInfluencersAction.Request, GetInfluencersAction.Response, GetI
         private boolean includeInterim = false;
         private PageParams pageParams = new PageParams();
         private double anomalyScoreFilter = 0.0;
-        private String sort = Influencer.ANOMALY_SCORE.getPreferredName();
+        private String sort = Influencer.INFLUENCER_SCORE.getPreferredName();
         private boolean decending = false;
 
         Request() {

plugin/src/main/java/org/elasticsearch/xpack/ml/action/GetRecordsAction.java

@@ -66,10 +66,9 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
         public static final ParseField START = new ParseField("start");
         public static final ParseField END = new ParseField("end");
         public static final ParseField INCLUDE_INTERIM = new ParseField("include_interim");
-        public static final ParseField ANOMALY_SCORE_FILTER = new ParseField("anomaly_score");
+        public static final ParseField RECORD_SCORE_FILTER = new ParseField("record_score");
         public static final ParseField SORT = new ParseField("sort");
         public static final ParseField DESCENDING = new ParseField("desc");
-        public static final ParseField MAX_NORMALIZED_PROBABILITY = new ParseField("normalized_probability");
         public static final ParseField PARTITION_VALUE = new ParseField("partition_value");
 
         private static final ObjectParser<Request, Void> PARSER = new ObjectParser<>(NAME, Request::new);
@@ -83,8 +82,7 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
             PARSER.declareBoolean(Request::setDecending, DESCENDING);
             PARSER.declareBoolean(Request::setIncludeInterim, INCLUDE_INTERIM);
             PARSER.declareObject(Request::setPageParams, PageParams.PARSER, PageParams.PAGE);
-            PARSER.declareDouble(Request::setAnomalyScore, ANOMALY_SCORE_FILTER);
+            PARSER.declareDouble(Request::setRecordScore, RECORD_SCORE_FILTER);
-            PARSER.declareDouble(Request::setMaxNormalizedProbability, MAX_NORMALIZED_PROBABILITY);
         }
 
         public static Request parseRequest(String jobId, XContentParser parser) {
@@ -100,10 +98,9 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
         private String end;
         private boolean includeInterim = false;
         private PageParams pageParams = new PageParams();
-        private double anomalyScoreFilter = 0.0;
+        private double recordScoreFilter = 0.0;
-        private String sort = Influencer.ANOMALY_SCORE.getPreferredName();
+        private String sort = Influencer.INFLUENCER_SCORE.getPreferredName();
         private boolean decending = false;
-        private double maxNormalizedProbability = 0.0;
         private String partitionValue;
 
         Request() {
@@ -156,12 +153,12 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
             return pageParams;
         }
 
-        public double getAnomalyScoreFilter() {
+        public double getRecordScoreFilter() {
-            return anomalyScoreFilter;
+            return recordScoreFilter;
         }
 
-        public void setAnomalyScore(double anomalyScoreFilter) {
+        public void setRecordScore(double recordScoreFilter) {
-            this.anomalyScoreFilter = anomalyScoreFilter;
+            this.recordScoreFilter = recordScoreFilter;
         }
 
         public String getSort() {
@@ -172,14 +169,6 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
             this.sort = ExceptionsHelper.requireNonNull(sort, SORT.getPreferredName());
         }
 
-        public double getMaxNormalizedProbability() {
-            return maxNormalizedProbability;
-        }
-
-        public void setMaxNormalizedProbability(double maxNormalizedProbability) {
-            this.maxNormalizedProbability = maxNormalizedProbability;
-        }
-
         public String getPartitionValue() {
             return partitionValue;
         }
@@ -203,8 +192,7 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
             end = in.readOptionalString();
             sort = in.readOptionalString();
             decending = in.readBoolean();
-            anomalyScoreFilter = in.readDouble();
+            recordScoreFilter = in.readDouble();
-            maxNormalizedProbability = in.readDouble();
             partitionValue = in.readOptionalString();
         }
 
@@ -218,8 +206,7 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
             out.writeOptionalString(end);
             out.writeOptionalString(sort);
             out.writeBoolean(decending);
-            out.writeDouble(anomalyScoreFilter);
+            out.writeDouble(recordScoreFilter);
-            out.writeDouble(maxNormalizedProbability);
             out.writeOptionalString(partitionValue);
         }
 
@@ -231,9 +218,8 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
             builder.field(END.getPreferredName(), end);
             builder.field(SORT.getPreferredName(), sort);
             builder.field(DESCENDING.getPreferredName(), decending);
-            builder.field(ANOMALY_SCORE_FILTER.getPreferredName(), anomalyScoreFilter);
+            builder.field(RECORD_SCORE_FILTER.getPreferredName(), recordScoreFilter);
             builder.field(INCLUDE_INTERIM.getPreferredName(), includeInterim);
-            builder.field(MAX_NORMALIZED_PROBABILITY.getPreferredName(), maxNormalizedProbability);
             builder.field(PageParams.PAGE.getPreferredName(), pageParams);
             if (partitionValue != null) {
                 builder.field(PARTITION_VALUE.getPreferredName(), partitionValue);
@@ -244,7 +230,7 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
 
         @Override
         public int hashCode() {
-            return Objects.hash(jobId, start, end, sort, decending, anomalyScoreFilter, includeInterim, maxNormalizedProbability,
+            return Objects.hash(jobId, start, end, sort, decending, recordScoreFilter, includeInterim,
                     pageParams, partitionValue);
         }
 
@@ -262,9 +248,8 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
                     Objects.equals(end, other.end) &&
                     Objects.equals(sort, other.sort) &&
                     Objects.equals(decending, other.decending) &&
-                    Objects.equals(anomalyScoreFilter, other.anomalyScoreFilter) &&
+                    Objects.equals(recordScoreFilter, other.recordScoreFilter) &&
                     Objects.equals(includeInterim, other.includeInterim) &&
-                    Objects.equals(maxNormalizedProbability, other.maxNormalizedProbability) &&
                     Objects.equals(pageParams, other.pageParams) &&
                     Objects.equals(partitionValue, other.partitionValue);
         }
@@ -362,7 +347,7 @@ public class GetRecordsAction extends Action<GetRecordsAction.Request, GetRecord
                     .epochEnd(request.end)
                     .from(request.pageParams.getFrom())
                     .size(request.pageParams.getSize())
-                    .anomalyScoreThreshold(request.anomalyScoreFilter)
+                    .recordScore(request.recordScoreFilter)
                     .sortField(request.sort)
                     .sortDescending(request.decending)
                     .build();

plugin/src/main/java/org/elasticsearch/xpack/ml/job/persistence/BucketsQueryBuilder.java

@@ -22,8 +22,6 @@ import java.util.Objects;
  * <li>IncludeInterim- Include interim results. Default = false</li>
  * <li>anomalyScoreThreshold- Return only buckets with an anomalyScore &gt;=
  * this value. Default = 0.0</li>
- * <li>normalizedProbabilityThreshold- Return only buckets with a
- * maxNormalizedProbability &gt;= this value. Default = 0.0</li>
  * <li>start- The start bucket time. A bucket with this timestamp will be
  * included in the results. If 0 all buckets up to <code>endEpochMs</code> are
  * returned. Default = -1</li>
@@ -66,13 +64,6 @@ public final class BucketsQueryBuilder {
         return this;
     }
 
-    public BucketsQueryBuilder normalizedProbabilityThreshold(Double normalizedProbability) {
-        if (normalizedProbability != null) {
-            bucketsQuery.normalizedProbability = normalizedProbability;
-        }
-        return this;
-    }
-
     /**
      * @param partitionValue Not set if null or empty
      */
@@ -133,8 +124,7 @@ public final class BucketsQueryBuilder {
         private int size = DEFAULT_SIZE;
         private boolean expand = false;
         private boolean includeInterim = false;
-        private double anomalyScoreFilter = 0.0d;
+        private double anomalyScoreFilter = 0.0;
-        private double normalizedProbability = 0.0d;
         private String start;
         private String end;
         private String timestamp;
@@ -162,10 +152,6 @@ public final class BucketsQueryBuilder {
             return anomalyScoreFilter;
         }
 
-        public double getNormalizedProbability() {
-            return normalizedProbability;
-        }
-
         public String getStart() {
             return start;
         }
@@ -195,7 +181,7 @@ public final class BucketsQueryBuilder {
 
         @Override
         public int hashCode() {
-            return Objects.hash(from, size, expand, includeInterim, anomalyScoreFilter, normalizedProbability, start, end,
+            return Objects.hash(from, size, expand, includeInterim, anomalyScoreFilter, start, end,
                     timestamp, partitionValue, sortField, sortDescending);
         }
 
@@ -221,7 +207,6 @@ public final class BucketsQueryBuilder {
                     Objects.equals(end, other.end) &&
                     Objects.equals(timestamp, other.timestamp) &&
                     Objects.equals(anomalyScoreFilter, other.anomalyScoreFilter) &&
-                    Objects.equals(normalizedProbability, other.normalizedProbability) &&
                     Objects.equals(partitionValue, other.partitionValue) &&
                     Objects.equals(sortField, other.sortField) &&
                     this.sortDescending == other.sortDescending;

plugin/src/main/java/org/elasticsearch/xpack/ml/job/persistence/ElasticsearchMappings.java

@@ -145,9 +145,6 @@ public class ElasticsearchMappings {
                             .startObject(Bucket.INITIAL_ANOMALY_SCORE.getPreferredName())
                                 .field(TYPE, DOUBLE)
                             .endObject()
-                            .startObject(Bucket.MAX_NORMALIZED_PROBABILITY.getPreferredName())
-                                .field(TYPE, DOUBLE)
-                            .endObject()
                             .startObject(Bucket.IS_INTERIM.getPreferredName())
                                 .field(TYPE, BOOLEAN)
                             .endObject()
@@ -175,9 +172,6 @@ public class ElasticsearchMappings {
                                     .startObject(Bucket.INITIAL_ANOMALY_SCORE.getPreferredName())
                                         .field(TYPE, DOUBLE)
                                     .endObject()
-                                    .startObject(AnomalyRecord.ANOMALY_SCORE.getPreferredName())
-                                        .field(TYPE, DOUBLE)
-                                    .endObject()
                                     .startObject(AnomalyRecord.PROBABILITY.getPreferredName())
                                         .field(TYPE, DOUBLE)
                                     .endObject()
@@ -230,7 +224,7 @@ public class ElasticsearchMappings {
                                     .startObject(AnomalyRecord.PARTITION_FIELD_VALUE.getPreferredName())
                                         .field(TYPE, KEYWORD)
                                     .endObject()
-                                    .startObject(Bucket.MAX_NORMALIZED_PROBABILITY.getPreferredName())
+                                    .startObject(PerPartitionMaxProbabilities.MAX_RECORD_SCORE.getPreferredName())
                                         .field(TYPE, DOUBLE)
                                     .endObject()
                                 .endObject()
@@ -343,10 +337,10 @@ public class ElasticsearchMappings {
             .field(TYPE, KEYWORD)
             .field(COPY_TO, ALL_FIELD_VALUES)
         .endObject()
-        .startObject(AnomalyRecord.NORMALIZED_PROBABILITY.getPreferredName())
+        .startObject(AnomalyRecord.RECORD_SCORE.getPreferredName())
             .field(TYPE, DOUBLE)
         .endObject()
-        .startObject(AnomalyRecord.INITIAL_NORMALIZED_PROBABILITY.getPreferredName())
+        .startObject(AnomalyRecord.INITIAL_RECORD_SCORE.getPreferredName())
             .field(TYPE, DOUBLE)
         .endObject()
         .startObject(AnomalyRecord.CAUSES.getPreferredName())
@@ -415,7 +409,13 @@ public class ElasticsearchMappings {
     }
 
     private static XContentBuilder addInfluencerFieldsToMapping(XContentBuilder builder) throws IOException {
-        builder.startObject(Influencer.INFLUENCER_FIELD_NAME.getPreferredName())
+        builder.startObject(Influencer.INFLUENCER_SCORE.getPreferredName())
+            .field(TYPE, DOUBLE)
+        .endObject()
+        .startObject(Influencer.INITIAL_INFLUENCER_SCORE.getPreferredName())
+            .field(TYPE, DOUBLE)
+        .endObject()
+        .startObject(Influencer.INFLUENCER_FIELD_NAME.getPreferredName())
             .field(TYPE, KEYWORD)
         .endObject()
         .startObject(Influencer.INFLUENCER_FIELD_VALUE.getPreferredName())

plugin/src/main/java/org/elasticsearch/xpack/ml/job/persistence/InfluencersQueryBuilder.java

@@ -25,8 +25,6 @@ import java.util.Objects;
  * <li>end- The end influencer timestamp. Influencers up to but NOT including this
  * timestamp are returned. If 0 all influencers from <code>start</code> are
  * returned. Default = -1</li>
- * <li>partitionValue Set the bucket's max normalized probability to this
- * partition field value's max normalized probability. Default = null</li>
  * </ul>
  */
 public final class InfluencersQueryBuilder {
@@ -96,7 +94,7 @@ public final class InfluencersQueryBuilder {
         private double anomalyScoreFilter = 0.0d;
         private String start;
         private String end;
-        private String sortField = Influencer.ANOMALY_SCORE.getPreferredName();
+        private String sortField = Influencer.INFLUENCER_SCORE.getPreferredName();
         private boolean sortDescending = false;
 
         public int getFrom() {

plugin/src/main/java/org/elasticsearch/xpack/ml/job/persistence/JobProvider.java

@@ -99,7 +99,7 @@ public class JobProvider {
     private static final Logger LOGGER = Loggers.getLogger(JobProvider.class);
 
     private static final List<String> SECONDARY_SORT = Arrays.asList(
-            AnomalyRecord.ANOMALY_SCORE.getPreferredName(),
+            AnomalyRecord.RECORD_SCORE.getPreferredName(),
             AnomalyRecord.OVER_FIELD_VALUE.getPreferredName(),
             AnomalyRecord.PARTITION_FIELD_VALUE.getPreferredName(),
             AnomalyRecord.BY_FIELD_VALUE.getPreferredName(),
@@ -330,7 +330,6 @@ public class JobProvider {
         } else {
             rfb.timeRange(Result.TIMESTAMP.getPreferredName(), query.getStart(), query.getEnd())
                     .score(Bucket.ANOMALY_SCORE.getPreferredName(), query.getAnomalyScoreFilter())
-                    .score(Bucket.MAX_NORMALIZED_PROBABILITY.getPreferredName(), query.getNormalizedProbability())
                     .interim(Bucket.IS_INTERIM.getPreferredName(), query.isIncludeInterim());
         }
 
@@ -399,9 +398,6 @@ public class JobProvider {
                     errorHandler.accept(item2.getFailure());
                     return;
                 }
-                List<PerPartitionMaxProbabilities> partitionProbs =
-                        handlePartitionMaxNormailizedProbabilitiesResponse(item2.getResponse());
-                mergePartitionScoresIntoBucket(partitionProbs, buckets.results(), query.getPartitionValue());
 
                 if (query.isExpand()) {
                     Iterator<Bucket> bucketsToExpand = buckets.results().stream()
@@ -433,23 +429,6 @@ public class JobProvider {
         }
     }
 
-    void mergePartitionScoresIntoBucket(List<PerPartitionMaxProbabilities> partitionProbs, List<Bucket> buckets, String partitionValue) {
-        Iterator<PerPartitionMaxProbabilities> itr = partitionProbs.iterator();
-        PerPartitionMaxProbabilities partitionProb = itr.hasNext() ? itr.next() : null;
-        for (Bucket b : buckets) {
-            if (partitionProb == null) {
-                b.setMaxNormalizedProbability(0.0);
-            } else {
-                if (partitionProb.getTimestamp().equals(b.getTimestamp())) {
-                    b.setMaxNormalizedProbability(partitionProb.getMaxProbabilityForPartition(partitionValue));
-                    partitionProb = itr.hasNext() ? itr.next() : null;
-                } else {
-                    b.setMaxNormalizedProbability(0.0);
-                }
-            }
-        }
-    }
-
     private SearchRequest createPartitionMaxNormailizedProbabilitiesRequest(String jobId, Object epochStart, Object epochEnd,
                                                                             String partitionFieldValue) {
         QueryBuilder timeRangeQuery = new ResultsFilterBuilder()
@@ -643,8 +622,7 @@ public class JobProvider {
                         Consumer<Exception> errorHandler, Client client) {
         QueryBuilder fb = new ResultsFilterBuilder()
                 .timeRange(Result.TIMESTAMP.getPreferredName(), query.getStart(), query.getEnd())
-                .score(AnomalyRecord.ANOMALY_SCORE.getPreferredName(), query.getAnomalyScoreThreshold())
+                .score(AnomalyRecord.RECORD_SCORE.getPreferredName(), query.getRecordScoreThreshold())
-                .score(AnomalyRecord.NORMALIZED_PROBABILITY.getPreferredName(), query.getNormalizedProbabilityThreshold())
                 .interim(AnomalyRecord.IS_INTERIM.getPreferredName(), query.isIncludeInterim())
                 .term(AnomalyRecord.PARTITION_FIELD_VALUE.getPreferredName(), query.getPartitionFieldValue()).build();
         FieldSortBuilder sb = null;

plugin/src/main/java/org/elasticsearch/xpack/ml/job/persistence/RecordsQueryBuilder.java

@@ -17,10 +17,8 @@ package org.elasticsearch.xpack.ml.job.persistence;
  * <li>SortField- The field to sort results by if <code>null</code> no sort is
  * applied. Default = null</li>
  * <li>SortDescending- Sort in descending order. Default = true</li>
- * <li>anomalyScoreThreshold- Return only buckets with an anomalyScore &gt;=
+ * <li>recordScoreThreshold- Return only records with a record_score &gt;=
  * this value. Default = 0.0</li>
- * <li>normalizedProbabilityThreshold. Return only buckets with a
- * maxNormalizedProbability &gt;= this value. Default = 0.0</li>
  * <li>start- The start bucket time. A bucket with this timestamp will be
  * included in the results. If 0 all buckets up to <code>endEpochMs</code> are
  * returned. Default = -1</li>
@@ -70,13 +68,8 @@ public final class RecordsQueryBuilder {
         return this;
     }
 
-    public RecordsQueryBuilder anomalyScoreThreshold(double anomalyScoreFilter) {
+    public RecordsQueryBuilder recordScore(double recordScore) {
-        recordsQuery.anomalyScoreFilter = anomalyScoreFilter;
+        recordsQuery.recordScore = recordScore;
-        return this;
-    }
-
-    public RecordsQueryBuilder normalizedProbability(double normalizedProbability) {
-        recordsQuery.normalizedProbability = normalizedProbability;
         return this;
     }
 
@@ -100,8 +93,7 @@ public final class RecordsQueryBuilder {
         private boolean includeInterim = false;
         private String sortField;
         private boolean sortDescending = true;
-        private double anomalyScoreFilter = 0.0d;
+        private double recordScore = 0.0;
-        private double normalizedProbability = 0.0d;
         private String partitionFieldValue;
         private String start;
         private String end;
@@ -123,12 +115,8 @@ public final class RecordsQueryBuilder {
             return sortDescending;
         }
 
-        public double getAnomalyScoreThreshold() {
+        public double getRecordScoreThreshold() {
-            return anomalyScoreFilter;
+            return recordScore;
-        }
-
-        public double getNormalizedProbabilityThreshold() {
-            return normalizedProbability;
         }
 
         public String getPartitionFieldValue() {

plugin/src/main/java/org/elasticsearch/xpack/ml/job/process/normalizer/BucketNormalizable.java

@@ -140,22 +140,18 @@ public class BucketNormalizable extends Normalizable {
 
     @Override
     public boolean setMaxChildrenScore(ChildType childrenType, double maxScore) {
-        double oldScore = 0.0;
         switch (childrenType) {
             case BUCKET_INFLUENCER:
-                oldScore = bucket.getAnomalyScore();
+                double oldScore = bucket.getAnomalyScore();
                 bucket.setAnomalyScore(maxScore);
-                break;
+                return maxScore != oldScore;
             case RECORD:
-                oldScore = bucket.getMaxNormalizedProbability();
-                bucket.setMaxNormalizedProbability(maxScore);
-                break;
             case PARTITION_SCORE:
-                break;
+                return false;
             default:
                 throw new IllegalArgumentException("Invalid type: " + childrenType);
         }
-        return maxScore != oldScore;
+
     }
 
     @Override

plugin/src/main/java/org/elasticsearch/xpack/ml/job/process/normalizer/InfluencerNormalizable.java

@@ -61,12 +61,12 @@ class InfluencerNormalizable extends AbstractLeafNormalizable {
 
     @Override
     public double getNormalizedScore() {
-        return influencer.getAnomalyScore();
+        return influencer.getInfluencerScore();
     }
 
     @Override
     public void setNormalizedScore(double normalizedScore) {
-        influencer.setAnomalyScore(normalizedScore);
+        influencer.setInfluencerScore(normalizedScore);
     }
 
     @Override

plugin/src/main/java/org/elasticsearch/xpack/ml/job/process/normalizer/PartitionScoreNormalizable.java

@@ -62,12 +62,12 @@ public class PartitionScoreNormalizable extends AbstractLeafNormalizable {
 
     @Override
     public double getNormalizedScore() {
-        return score.getAnomalyScore();
+        return score.getRecordScore();
     }
 
     @Override
     public void setNormalizedScore(double normalizedScore) {
-        score.setAnomalyScore(normalizedScore);
+        score.setRecordScore(normalizedScore);
     }
 
     @Override

plugin/src/main/java/org/elasticsearch/xpack/ml/job/process/normalizer/RecordNormalizable.java

@@ -63,17 +63,17 @@ class RecordNormalizable extends AbstractLeafNormalizable {
 
     @Override
     public double getNormalizedScore() {
-        return record.getNormalizedProbability();
+        return record.getRecordScore();
     }
 
     @Override
     public void setNormalizedScore(double normalizedScore) {
-        record.setNormalizedProbability(normalizedScore);
+        record.setRecordScore(normalizedScore);
     }
 
     @Override
     public void setParentScore(double parentScore) {
-        record.setAnomalyScore(parentScore);
+        // nothing to do
     }
 
     @Override

plugin/src/main/java/org/elasticsearch/xpack/ml/job/results/AnomalyRecord.java

@@ -76,9 +76,8 @@ public class AnomalyRecord extends ToXContentToBytes implements Writeable {
     /**
      * Normalization
      */
-    public static final ParseField ANOMALY_SCORE = new ParseField("anomaly_score");
+    public static final ParseField RECORD_SCORE = new ParseField("record_score");
-    public static final ParseField NORMALIZED_PROBABILITY = new ParseField("normalized_probability");
+    public static final ParseField INITIAL_RECORD_SCORE = new ParseField("initial_record_score");
-    public static final ParseField INITIAL_NORMALIZED_PROBABILITY = new ParseField("initial_normalized_probability");
 
     public static final ConstructingObjectParser<AnomalyRecord, Void> PARSER =
             new ConstructingObjectParser<>(RESULT_TYPE_VALUE, true,
@@ -99,9 +98,8 @@ public class AnomalyRecord extends ToXContentToBytes implements Writeable {
         PARSER.declareInt(ConstructingObjectParser.constructorArg(), SEQUENCE_NUM);
         PARSER.declareString((anomalyRecord, s) -> {}, Result.RESULT_TYPE);
         PARSER.declareDouble(AnomalyRecord::setProbability, PROBABILITY);
-        PARSER.declareDouble(AnomalyRecord::setAnomalyScore, ANOMALY_SCORE);
+        PARSER.declareDouble(AnomalyRecord::setRecordScore, RECORD_SCORE);
-        PARSER.declareDouble(AnomalyRecord::setNormalizedProbability, NORMALIZED_PROBABILITY);
+        PARSER.declareDouble(AnomalyRecord::setInitialRecordScore, INITIAL_RECORD_SCORE);
-        PARSER.declareDouble(AnomalyRecord::setInitialNormalizedProbability, INITIAL_NORMALIZED_PROBABILITY);
         PARSER.declareInt(AnomalyRecord::setDetectorIndex, DETECTOR_INDEX);
         PARSER.declareBoolean(AnomalyRecord::setInterim, IS_INTERIM);
         PARSER.declareString(AnomalyRecord::setByFieldName, BY_FIELD_NAME);
@@ -141,10 +139,9 @@ public class AnomalyRecord extends ToXContentToBytes implements Writeable {
     private String overFieldValue;
     private List<AnomalyCause> causes;
 
-    private double anomalyScore;
+    private double recordScore;
-    private double normalizedProbability;
 
-    private double initialNormalizedProbability;
+    private double initialRecordScore;
 
     private final Date timestamp;
     private final long bucketSpan;
@@ -184,9 +181,8 @@ public class AnomalyRecord extends ToXContentToBytes implements Writeable {
         if (in.readBoolean()) {
             causes = in.readList(AnomalyCause::new);
         }
-        anomalyScore = in.readDouble();
+        recordScore = in.readDouble();
-        normalizedProbability = in.readDouble();
+        initialRecordScore = in.readDouble();
-        initialNormalizedProbability = in.readDouble();
         timestamp = new Date(in.readLong());
         bucketSpan = in.readLong();
         if (in.readBoolean()) {
@@ -226,9 +222,8 @@ public class AnomalyRecord extends ToXContentToBytes implements Writeable {
         if (hasCauses) {
             out.writeList(causes);
         }
-        out.writeDouble(anomalyScore);
+        out.writeDouble(recordScore);
-        out.writeDouble(normalizedProbability);
+        out.writeDouble(initialRecordScore);
-        out.writeDouble(initialNormalizedProbability);
         out.writeLong(timestamp.getTime());
         out.writeLong(bucketSpan);
         boolean hasInfluencers = influences != null;
@@ -244,9 +239,8 @@ public class AnomalyRecord extends ToXContentToBytes implements Writeable {
         builder.field(Job.ID.getPreferredName(), jobId);
         builder.field(Result.RESULT_TYPE.getPreferredName(), RESULT_TYPE_VALUE);
         builder.field(PROBABILITY.getPreferredName(), probability);
-        builder.field(ANOMALY_SCORE.getPreferredName(), anomalyScore);
+        builder.field(RECORD_SCORE.getPreferredName(), recordScore);
-        builder.field(NORMALIZED_PROBABILITY.getPreferredName(), normalizedProbability);
+        builder.field(INITIAL_RECORD_SCORE.getPreferredName(), initialRecordScore);
-        builder.field(INITIAL_NORMALIZED_PROBABILITY.getPreferredName(), initialNormalizedProbability);
         builder.field(BUCKET_SPAN.getPreferredName(), bucketSpan);
         builder.field(DETECTOR_INDEX.getPreferredName(), detectorIndex);
         builder.field(SEQUENCE_NUM.getPreferredName(), sequenceNum);
@@ -350,28 +344,20 @@ public class AnomalyRecord extends ToXContentToBytes implements Writeable {
         this.detectorIndex = detectorIndex;
     }
 
-    public double getAnomalyScore() {
+    public double getRecordScore() {
-        return anomalyScore;
+        return recordScore;
     }
 
-    public void setAnomalyScore(double anomalyScore) {
+    public void setRecordScore(double recordScore) {
-        this.anomalyScore = anomalyScore;
+        this.recordScore = recordScore;
     }
 
-    public double getNormalizedProbability() {
+    public double getInitialRecordScore() {
-        return normalizedProbability;
+        return initialRecordScore;
     }
 
-    public void setNormalizedProbability(double normalizedProbability) {
+    public void setInitialRecordScore(double initialRecordScore) {
-        this.normalizedProbability = normalizedProbability;
+        this.initialRecordScore = initialRecordScore;
-    }
-
-    public double getInitialNormalizedProbability() {
-        return initialNormalizedProbability;
-    }
-
-    public void setInitialNormalizedProbability(double initialNormalizedProbability) {
-        this.initialNormalizedProbability = initialNormalizedProbability;
     }
 
     public Date getTimestamp() {
@@ -523,8 +509,8 @@ public class AnomalyRecord extends ToXContentToBytes implements Writeable {
 
     @Override
     public int hashCode() {
-        return Objects.hash(jobId, detectorIndex, sequenceNum, bucketSpan, probability, anomalyScore,
+        return Objects.hash(jobId, detectorIndex, sequenceNum, bucketSpan, probability,
-                normalizedProbability, initialNormalizedProbability, typical, actual,
+                recordScore, initialRecordScore, typical, actual,
                 function, functionDescription, fieldName, byFieldName, byFieldValue, correlatedByFieldValue,
                 partitionFieldName, partitionFieldValue, overFieldName, overFieldValue,
                 timestamp, isInterim, causes, influences, jobId);
@@ -548,9 +534,8 @@ public class AnomalyRecord extends ToXContentToBytes implements Writeable {
                 && this.sequenceNum == that.sequenceNum
                 && this.bucketSpan == that.bucketSpan
                 && this.probability == that.probability
-                && this.anomalyScore == that.anomalyScore
+                && this.recordScore == that.recordScore
-                && this.normalizedProbability == that.normalizedProbability
+                && this.initialRecordScore == that.initialRecordScore
-                && this.initialNormalizedProbability == that.initialNormalizedProbability
                 && Objects.deepEquals(this.typical, that.typical)
                 && Objects.deepEquals(this.actual, that.actual)
                 && Objects.equals(this.function, that.function)

plugin/src/main/java/org/elasticsearch/xpack/ml/job/results/Bucket.java

@@ -38,7 +38,6 @@ public class Bucket extends ToXContentToBytes implements Writeable {
 
     public static final ParseField ANOMALY_SCORE = new ParseField("anomaly_score");
     public static final ParseField INITIAL_ANOMALY_SCORE = new ParseField("initial_anomaly_score");
-    public static final ParseField MAX_NORMALIZED_PROBABILITY = new ParseField("max_normalized_probability");
     public static final ParseField IS_INTERIM = new ParseField("is_interim");
     public static final ParseField RECORD_COUNT = new ParseField("record_count");
     public static final ParseField EVENT_COUNT = new ParseField("event_count");
@@ -74,7 +73,6 @@ public class Bucket extends ToXContentToBytes implements Writeable {
         PARSER.declareLong(ConstructingObjectParser.constructorArg(), BUCKET_SPAN);
         PARSER.declareDouble(Bucket::setAnomalyScore, ANOMALY_SCORE);
         PARSER.declareDouble(Bucket::setInitialAnomalyScore, INITIAL_ANOMALY_SCORE);
-        PARSER.declareDouble(Bucket::setMaxNormalizedProbability, MAX_NORMALIZED_PROBABILITY);
         PARSER.declareBoolean(Bucket::setInterim, IS_INTERIM);
         PARSER.declareInt(Bucket::setRecordCount, RECORD_COUNT);
         PARSER.declareLong(Bucket::setEventCount, EVENT_COUNT);
@@ -90,7 +88,6 @@ public class Bucket extends ToXContentToBytes implements Writeable {
     private final long bucketSpan;
     private double anomalyScore;
     private double initialAnomalyScore;
-    private double maxNormalizedProbability;
     private int recordCount;
     private List<AnomalyRecord> records = new ArrayList<>();
     private long eventCount;
@@ -112,7 +109,6 @@ public class Bucket extends ToXContentToBytes implements Writeable {
         this.bucketSpan = other.bucketSpan;
         this.anomalyScore = other.anomalyScore;
         this.initialAnomalyScore = other.initialAnomalyScore;
-        this.maxNormalizedProbability = other.maxNormalizedProbability;
         this.recordCount = other.recordCount;
         this.records = new ArrayList<>(other.records);
         this.eventCount = other.eventCount;
@@ -130,7 +126,6 @@ public class Bucket extends ToXContentToBytes implements Writeable {
         anomalyScore = in.readDouble();
         bucketSpan = in.readLong();
         initialAnomalyScore = in.readDouble();
-        maxNormalizedProbability = in.readDouble();
         recordCount = in.readInt();
         records = in.readList(AnomalyRecord::new);
         eventCount = in.readLong();
@@ -148,7 +143,6 @@ public class Bucket extends ToXContentToBytes implements Writeable {
         out.writeDouble(anomalyScore);
         out.writeLong(bucketSpan);
         out.writeDouble(initialAnomalyScore);
-        out.writeDouble(maxNormalizedProbability);
         out.writeInt(recordCount);
         out.writeList(records);
         out.writeLong(eventCount);
@@ -167,7 +161,6 @@ public class Bucket extends ToXContentToBytes implements Writeable {
         builder.field(ANOMALY_SCORE.getPreferredName(), anomalyScore);
         builder.field(BUCKET_SPAN.getPreferredName(), bucketSpan);
         builder.field(INITIAL_ANOMALY_SCORE.getPreferredName(), initialAnomalyScore);
-        builder.field(MAX_NORMALIZED_PROBABILITY.getPreferredName(), maxNormalizedProbability);
         builder.field(RECORD_COUNT.getPreferredName(), recordCount);
         if (!records.isEmpty()) {
             builder.field(RECORDS.getPreferredName(), records);
@@ -225,14 +218,6 @@ public class Bucket extends ToXContentToBytes implements Writeable {
         this.initialAnomalyScore = initialAnomalyScore;
     }
 
-    public double getMaxNormalizedProbability() {
-        return maxNormalizedProbability;
-    }
-
-    public void setMaxNormalizedProbability(double maxNormalizedProbability) {
-        this.maxNormalizedProbability = maxNormalizedProbability;
-    }
-
     public int getRecordCount() {
         return recordCount;
     }
@@ -316,19 +301,19 @@ public class Bucket extends ToXContentToBytes implements Writeable {
         Optional<PartitionScore> first = partitionScores.stream().filter(s -> partitionValue.equals(s.getPartitionFieldValue()))
                 .findFirst();
 
-        return first.isPresent() ? first.get().getInitialAnomalyScore() : 0.0;
+        return first.isPresent() ? first.get().getInitialRecordScore() : 0.0;
     }
 
     public double partitionAnomalyScore(String partitionValue) {
         Optional<PartitionScore> first = partitionScores.stream().filter(s -> partitionValue.equals(s.getPartitionFieldValue()))
                 .findFirst();
 
-        return first.isPresent() ? first.get().getAnomalyScore() : 0.0;
+        return first.isPresent() ? first.get().getRecordScore() : 0.0;
     }
 
     @Override
     public int hashCode() {
-        return Objects.hash(jobId, timestamp, eventCount, initialAnomalyScore, anomalyScore, maxNormalizedProbability, recordCount, records,
+        return Objects.hash(jobId, timestamp, eventCount, initialAnomalyScore, anomalyScore, recordCount, records,
                 isInterim, bucketSpan, bucketInfluencers);
     }
 
@@ -349,8 +334,8 @@ public class Bucket extends ToXContentToBytes implements Writeable {
 
         return Objects.equals(this.jobId, that.jobId) && Objects.equals(this.timestamp, that.timestamp)
                 && (this.eventCount == that.eventCount) && (this.bucketSpan == that.bucketSpan)
+                && (this.recordCount == that.recordCount)
                 && (this.anomalyScore == that.anomalyScore) && (this.initialAnomalyScore == that.initialAnomalyScore)
-                && (this.maxNormalizedProbability == that.maxNormalizedProbability) && (this.recordCount == that.recordCount)
                 && Objects.equals(this.records, that.records) && Objects.equals(this.isInterim, that.isInterim)
                 && Objects.equals(this.bucketInfluencers, that.bucketInfluencers);
     }

plugin/src/main/java/org/elasticsearch/xpack/ml/job/results/Influencer.java

@@ -37,8 +37,8 @@ public class Influencer extends ToXContentToBytes implements Writeable {
     public static final ParseField BUCKET_SPAN = new ParseField("bucket_span");
     public static final ParseField INFLUENCER_FIELD_NAME = new ParseField("influencer_field_name");
     public static final ParseField INFLUENCER_FIELD_VALUE = new ParseField("influencer_field_value");
-    public static final ParseField INITIAL_ANOMALY_SCORE = new ParseField("initial_anomaly_score");
+    public static final ParseField INITIAL_INFLUENCER_SCORE = new ParseField("initial_influencer_score");
-    public static final ParseField ANOMALY_SCORE = new ParseField("anomaly_score");
+    public static final ParseField INFLUENCER_SCORE = new ParseField("influencer_score");
 
     // Used for QueryPage
     public static final ParseField RESULTS_FIELD = new ParseField("influencers");
@@ -64,8 +64,8 @@ public class Influencer extends ToXContentToBytes implements Writeable {
         PARSER.declareInt(ConstructingObjectParser.constructorArg(), SEQUENCE_NUM);
         PARSER.declareString((influencer, s) -> {}, Result.RESULT_TYPE);
         PARSER.declareDouble(Influencer::setProbability, PROBABILITY);
-        PARSER.declareDouble(Influencer::setAnomalyScore, ANOMALY_SCORE);
+        PARSER.declareDouble(Influencer::setInfluencerScore, INFLUENCER_SCORE);
-        PARSER.declareDouble(Influencer::setInitialAnomalyScore, INITIAL_ANOMALY_SCORE);
+        PARSER.declareDouble(Influencer::setInitialInfluencerScore, INITIAL_INFLUENCER_SCORE);
         PARSER.declareBoolean(Influencer::setInterim, Bucket.IS_INTERIM);
     }
 
@@ -76,8 +76,8 @@ public class Influencer extends ToXContentToBytes implements Writeable {
     private String influenceField;
     private String influenceValue;
     private double probability;
-    private double initialAnomalyScore;
+    private double initialInfluencerScore;
-    private double anomalyScore;
+    private double influencerScore;
     private boolean isInterim;
 
     public Influencer(String jobId, String fieldName, String fieldValue, Date timestamp, long bucketSpan, int sequenceNum) {
@@ -95,8 +95,8 @@ public class Influencer extends ToXContentToBytes implements Writeable {
         influenceField = in.readString();
         influenceValue = in.readString();
         probability = in.readDouble();
-        initialAnomalyScore = in.readDouble();
+        initialInfluencerScore = in.readDouble();
-        anomalyScore = in.readDouble();
+        influencerScore = in.readDouble();
         isInterim = in.readBoolean();
         bucketSpan = in.readLong();
         sequenceNum = in.readInt();
@@ -109,8 +109,8 @@ public class Influencer extends ToXContentToBytes implements Writeable {
         out.writeString(influenceField);
         out.writeString(influenceValue);
         out.writeDouble(probability);
-        out.writeDouble(initialAnomalyScore);
+        out.writeDouble(initialInfluencerScore);
-        out.writeDouble(anomalyScore);
+        out.writeDouble(influencerScore);
         out.writeBoolean(isInterim);
         out.writeLong(bucketSpan);
         out.writeInt(sequenceNum);
@@ -126,8 +126,8 @@ public class Influencer extends ToXContentToBytes implements Writeable {
         if (ReservedFieldNames.isValidFieldName(influenceField)) {
             builder.field(influenceField, influenceValue);
         }
-        builder.field(ANOMALY_SCORE.getPreferredName(), anomalyScore);
+        builder.field(INFLUENCER_SCORE.getPreferredName(), influencerScore);
-        builder.field(INITIAL_ANOMALY_SCORE.getPreferredName(), initialAnomalyScore);
+        builder.field(INITIAL_INFLUENCER_SCORE.getPreferredName(), initialInfluencerScore);
         builder.field(PROBABILITY.getPreferredName(), probability);
         builder.field(SEQUENCE_NUM.getPreferredName(), sequenceNum);
         builder.field(BUCKET_SPAN.getPreferredName(), bucketSpan);
@@ -165,20 +165,20 @@ public class Influencer extends ToXContentToBytes implements Writeable {
         return influenceValue;
     }
 
-    public double getInitialAnomalyScore() {
+    public double getInitialInfluencerScore() {
-        return initialAnomalyScore;
+        return initialInfluencerScore;
     }
 
-    public void setInitialAnomalyScore(double influenceScore) {
+    public void setInitialInfluencerScore(double score) {
-        initialAnomalyScore = influenceScore;
+        initialInfluencerScore = score;
     }
 
-    public double getAnomalyScore() {
+    public double getInfluencerScore() {
-        return anomalyScore;
+        return influencerScore;
     }
 
-    public void setAnomalyScore(double score) {
+    public void setInfluencerScore(double score) {
-        anomalyScore = score;
+        influencerScore = score;
     }
 
     public boolean isInterim() {
@@ -191,8 +191,8 @@ public class Influencer extends ToXContentToBytes implements Writeable {
 
     @Override
     public int hashCode() {
-        return Objects.hash(jobId, timestamp, influenceField, influenceValue, initialAnomalyScore, anomalyScore, probability, isInterim,
+        return Objects.hash(jobId, timestamp, influenceField, influenceValue, initialInfluencerScore,
-                bucketSpan, sequenceNum);
+                influencerScore, probability, isInterim, bucketSpan, sequenceNum);
     }
 
     @Override
@@ -213,8 +213,8 @@ public class Influencer extends ToXContentToBytes implements Writeable {
         return Objects.equals(jobId, other.jobId) && Objects.equals(timestamp, other.timestamp)
                 && Objects.equals(influenceField, other.influenceField)
                 && Objects.equals(influenceValue, other.influenceValue)
-                && Double.compare(initialAnomalyScore, other.initialAnomalyScore) == 0
+                && Double.compare(initialInfluencerScore, other.initialInfluencerScore) == 0
-                && Double.compare(anomalyScore, other.anomalyScore) == 0 && Double.compare(probability, other.probability) == 0
+                && Double.compare(influencerScore, other.influencerScore) == 0 && Double.compare(probability, other.probability) == 0
                 && (isInterim == other.isInterim) && (bucketSpan == other.bucketSpan) && (sequenceNum == other.sequenceNum);
     }
 }

plugin/src/main/java/org/elasticsearch/xpack/ml/job/results/PartitionScore.java

@@ -21,8 +21,8 @@ public class PartitionScore extends ToXContentToBytes implements Writeable {
 
     private final String partitionFieldValue;
     private final String partitionFieldName;
-    private final double initialAnomalyScore;
+    private final double initialRecordScore;
-    private double anomalyScore;
+    private double recordScore;
     private double probability;
 
     public static final ConstructingObjectParser<PartitionScore, Void> PARSER = new ConstructingObjectParser<>(
@@ -32,24 +32,24 @@ public class PartitionScore extends ToXContentToBytes implements Writeable {
     static {
         PARSER.declareString(ConstructingObjectParser.constructorArg(), AnomalyRecord.PARTITION_FIELD_NAME);
         PARSER.declareString(ConstructingObjectParser.constructorArg(), AnomalyRecord.PARTITION_FIELD_VALUE);
-        PARSER.declareDouble(ConstructingObjectParser.constructorArg(), Bucket.INITIAL_ANOMALY_SCORE);
+        PARSER.declareDouble(ConstructingObjectParser.constructorArg(), AnomalyRecord.INITIAL_RECORD_SCORE);
-        PARSER.declareDouble(ConstructingObjectParser.constructorArg(), AnomalyRecord.ANOMALY_SCORE);
+        PARSER.declareDouble(ConstructingObjectParser.constructorArg(), AnomalyRecord.RECORD_SCORE);
         PARSER.declareDouble(ConstructingObjectParser.constructorArg(), AnomalyRecord.PROBABILITY);
     }
 
-    public PartitionScore(String fieldName, String fieldValue, double initialAnomalyScore, double anomalyScore, double probability) {
+    public PartitionScore(String fieldName, String fieldValue, double initialRecordScore, double recordScore, double probability) {
         partitionFieldName = fieldName;
         partitionFieldValue = fieldValue;
-        this.initialAnomalyScore = initialAnomalyScore;
+        this.initialRecordScore = initialRecordScore;
-        this.anomalyScore = anomalyScore;
+        this.recordScore = recordScore;
         this.probability = probability;
     }
 
     public PartitionScore(StreamInput in) throws IOException {
         partitionFieldName = in.readString();
         partitionFieldValue = in.readString();
-        initialAnomalyScore = in.readDouble();
+        initialRecordScore = in.readDouble();
-        anomalyScore = in.readDouble();
+        recordScore = in.readDouble();
         probability = in.readDouble();
     }
 
@@ -57,8 +57,8 @@ public class PartitionScore extends ToXContentToBytes implements Writeable {
     public void writeTo(StreamOutput out) throws IOException {
         out.writeString(partitionFieldName);
         out.writeString(partitionFieldValue);
-        out.writeDouble(initialAnomalyScore);
+        out.writeDouble(initialRecordScore);
-        out.writeDouble(anomalyScore);
+        out.writeDouble(recordScore);
         out.writeDouble(probability);
     }
 
@@ -67,23 +67,23 @@ public class PartitionScore extends ToXContentToBytes implements Writeable {
         builder.startObject();
         builder.field(AnomalyRecord.PARTITION_FIELD_NAME.getPreferredName(), partitionFieldName);
         builder.field(AnomalyRecord.PARTITION_FIELD_VALUE.getPreferredName(), partitionFieldValue);
-        builder.field(Bucket.INITIAL_ANOMALY_SCORE.getPreferredName(), initialAnomalyScore);
+        builder.field(AnomalyRecord.INITIAL_RECORD_SCORE.getPreferredName(), initialRecordScore);
-        builder.field(AnomalyRecord.ANOMALY_SCORE.getPreferredName(), anomalyScore);
+        builder.field(AnomalyRecord.RECORD_SCORE.getPreferredName(), recordScore);
         builder.field(AnomalyRecord.PROBABILITY.getPreferredName(), probability);
         builder.endObject();
         return builder;
     }
 
-    public double getInitialAnomalyScore() {
+    public double getInitialRecordScore() {
-        return initialAnomalyScore;
+        return initialRecordScore;
     }
 
-    public double getAnomalyScore() {
+    public double getRecordScore() {
-        return anomalyScore;
+        return recordScore;
     }
 
-    public void setAnomalyScore(double anomalyScore) {
+    public void setRecordScore(double recordScore) {
-        this.anomalyScore = anomalyScore;
+        this.recordScore = recordScore;
     }
 
     public String getPartitionFieldName() {
@@ -104,7 +104,7 @@ public class PartitionScore extends ToXContentToBytes implements Writeable {
 
     @Override
     public int hashCode() {
-        return Objects.hash(partitionFieldName, partitionFieldValue, probability, initialAnomalyScore, anomalyScore);
+        return Objects.hash(partitionFieldName, partitionFieldValue, probability, initialRecordScore, recordScore);
     }
 
     @Override
@@ -122,6 +122,6 @@ public class PartitionScore extends ToXContentToBytes implements Writeable {
         // id is excluded from the test as it is generated by the datastore
         return Objects.equals(this.partitionFieldValue, that.partitionFieldValue)
                 && Objects.equals(this.partitionFieldName, that.partitionFieldName) && (this.probability == that.probability)
-                && (this.initialAnomalyScore == that.initialAnomalyScore) && (this.anomalyScore == that.anomalyScore);
+                && (this.initialRecordScore == that.initialRecordScore) && (this.recordScore == that.recordScore);
     }
 }

plugin/src/main/java/org/elasticsearch/xpack/ml/job/results/PerPartitionMaxProbabilities.java

@@ -43,6 +43,7 @@ public class PerPartitionMaxProbabilities extends ToXContentToBytes implements W
      * Field Names
      */
     public static final ParseField PER_PARTITION_MAX_PROBABILITIES = new ParseField("per_partition_max_probabilities");
+    public static final ParseField MAX_RECORD_SCORE = new ParseField("max_record_score");
 
     @SuppressWarnings("unchecked")
     public static final ConstructingObjectParser<PerPartitionMaxProbabilities, Void> PARSER =
@@ -85,7 +86,7 @@ public class PerPartitionMaxProbabilities extends ToXContentToBytes implements W
         this.jobId = records.get(0).getJobId();
         this.timestamp = records.get(0).getTimestamp();
         this.bucketSpan = records.get(0).getBucketSpan();
-        this.perPartitionMaxProbabilities = calcMaxNormalizedProbabilityPerPartition(records);
+        this.perPartitionMaxProbabilities = calcMaxRecordScorePerPartition(records);
     }
 
     public PerPartitionMaxProbabilities(StreamInput in) throws IOException {
@@ -123,7 +124,7 @@ public class PerPartitionMaxProbabilities extends ToXContentToBytes implements W
         Optional<PartitionProbability> first =
                 perPartitionMaxProbabilities.stream().filter(pp -> partitionValue.equals(pp.getPartitionValue())).findFirst();
 
-        return first.isPresent() ? first.get().getMaxNormalizedProbability() : 0.0;
+        return first.isPresent() ? first.get().getMaxRecordScore() : 0.0;
     }
 
     /**
@@ -150,10 +151,10 @@ public class PerPartitionMaxProbabilities extends ToXContentToBytes implements W
         }
     }
 
-    private List<PartitionProbability> calcMaxNormalizedProbabilityPerPartition(List<AnomalyRecord> anomalyRecords) {
+    private List<PartitionProbability> calcMaxRecordScorePerPartition(List<AnomalyRecord> anomalyRecords) {
         Map<String, Double> maxValueByPartition = anomalyRecords.stream().collect(
                 Collectors.groupingBy(AnomalyRecord::getPartitionFieldValue,
-                Collector.of(DoubleMaxBox::new, (m, ar) -> m.accept(ar.getNormalizedProbability()),
+                Collector.of(DoubleMaxBox::new, (m, ar) -> m.accept(ar.getRecordScore()),
                         DoubleMaxBox::combine, DoubleMaxBox::value)));
 
         List<PartitionProbability> pProbs = new ArrayList<>();
@@ -200,7 +201,7 @@ public class PerPartitionMaxProbabilities extends ToXContentToBytes implements W
     }
 
     /**
-     * Class for partitionValue, maxNormalizedProb pairs
+     * Class for partitionValue, maxRecordScore pairs
      */
     public static class PartitionProbability extends ToXContentToBytes implements Writeable  {
 
@@ -210,48 +211,48 @@ public class PerPartitionMaxProbabilities extends ToXContentToBytes implements W
 
         static {
             PARSER.declareString(ConstructingObjectParser.constructorArg(), AnomalyRecord.PARTITION_FIELD_VALUE);
-            PARSER.declareDouble(ConstructingObjectParser.constructorArg(), Bucket.MAX_NORMALIZED_PROBABILITY);
+            PARSER.declareDouble(ConstructingObjectParser.constructorArg(), MAX_RECORD_SCORE);
         }
 
         private final String partitionValue;
-        private final double maxNormalizedProbability;
+        private final double maxRecordScore;
 
-        PartitionProbability(String partitionName, double maxNormalizedProbability) {
+        PartitionProbability(String partitionName, double maxRecordScore) {
             this.partitionValue = partitionName;
-            this.maxNormalizedProbability = maxNormalizedProbability;
+            this.maxRecordScore = maxRecordScore;
         }
 
         public PartitionProbability(StreamInput in) throws IOException {
             partitionValue = in.readString();
-            maxNormalizedProbability = in.readDouble();
+            maxRecordScore = in.readDouble();
         }
 
         public String getPartitionValue() {
             return partitionValue;
         }
 
-        public double getMaxNormalizedProbability() {
+        public double getMaxRecordScore() {
-            return maxNormalizedProbability;
+            return maxRecordScore;
         }
 
         @Override
         public void writeTo(StreamOutput out) throws IOException {
             out.writeString(partitionValue);
-            out.writeDouble(maxNormalizedProbability);
+            out.writeDouble(maxRecordScore);
         }
 
         @Override
         public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
             builder.startObject()
                     .field(AnomalyRecord.PARTITION_FIELD_VALUE.getPreferredName(), partitionValue)
-                    .field(Bucket.MAX_NORMALIZED_PROBABILITY.getPreferredName(), maxNormalizedProbability)
+                    .field(MAX_RECORD_SCORE.getPreferredName(), maxRecordScore)
                     .endObject();
             return builder;
         }
 
         @Override
         public int hashCode() {
-            return Objects.hash(partitionValue, maxNormalizedProbability);
+            return Objects.hash(partitionValue, maxRecordScore);
         }
 
         @Override
@@ -267,7 +268,7 @@ public class PerPartitionMaxProbabilities extends ToXContentToBytes implements W
             PartitionProbability that = (PartitionProbability) other;
 
             return Objects.equals(this.partitionValue, that.partitionValue)
-                    && this.maxNormalizedProbability == that.maxNormalizedProbability;
+                    && this.maxRecordScore == that.maxRecordScore;
         }
     }
 }

plugin/src/main/java/org/elasticsearch/xpack/ml/job/results/ReservedFieldNames.java

@@ -10,7 +10,6 @@ import org.elasticsearch.xpack.ml.job.config.Job;
 import org.elasticsearch.xpack.ml.job.process.autodetect.state.ModelSizeStats;
 import org.elasticsearch.xpack.ml.job.process.autodetect.state.ModelSnapshot;
 import org.elasticsearch.xpack.ml.job.persistence.ElasticsearchMappings;
-import org.elasticsearch.xpack.ml.job.process.autodetect.state.Quantiles;
 
 import java.util.Arrays;
 import java.util.HashSet;
@@ -72,16 +71,14 @@ public final class ReservedFieldNames {
             AnomalyRecord.OVER_FIELD_NAME.getPreferredName(),
             AnomalyRecord.OVER_FIELD_VALUE.getPreferredName(),
             AnomalyRecord.CAUSES.getPreferredName(),
-            AnomalyRecord.ANOMALY_SCORE.getPreferredName(),
+            AnomalyRecord.RECORD_SCORE.getPreferredName(),
-            AnomalyRecord.NORMALIZED_PROBABILITY.getPreferredName(),
+            AnomalyRecord.INITIAL_RECORD_SCORE.getPreferredName(),
-            AnomalyRecord.INITIAL_NORMALIZED_PROBABILITY.getPreferredName(),
             AnomalyRecord.BUCKET_SPAN.getPreferredName(),
             AnomalyRecord.SEQUENCE_NUM.getPreferredName(),
 
             Bucket.ANOMALY_SCORE.getPreferredName(),
             Bucket.BUCKET_INFLUENCERS.getPreferredName(),
             Bucket.BUCKET_SPAN.getPreferredName(),
-            Bucket.MAX_NORMALIZED_PROBABILITY.getPreferredName(),
             Bucket.IS_INTERIM.getPreferredName(),
             Bucket.RECORD_COUNT.getPreferredName(),
             Bucket.EVENT_COUNT.getPreferredName(),
@@ -121,8 +118,8 @@ public final class ReservedFieldNames {
             Influencer.PROBABILITY.getPreferredName(),
             Influencer.INFLUENCER_FIELD_NAME.getPreferredName(),
             Influencer.INFLUENCER_FIELD_VALUE.getPreferredName(),
-            Influencer.INITIAL_ANOMALY_SCORE.getPreferredName(),
+            Influencer.INITIAL_INFLUENCER_SCORE.getPreferredName(),
-            Influencer.ANOMALY_SCORE.getPreferredName(),
+            Influencer.INFLUENCER_SCORE.getPreferredName(),
             Influencer.BUCKET_SPAN.getPreferredName(),
             Influencer.SEQUENCE_NUM.getPreferredName(),
 
@@ -148,6 +145,7 @@ public final class ReservedFieldNames {
             ModelSnapshot.LATEST_RESULT_TIME.getPreferredName(),
 
             PerPartitionMaxProbabilities.PER_PARTITION_MAX_PROBABILITIES.getPreferredName(),
+            PerPartitionMaxProbabilities.MAX_RECORD_SCORE.getPreferredName(),
 
             Result.RESULT_TYPE.getPreferredName(),
             Result.TIMESTAMP.getPreferredName()

plugin/src/main/java/org/elasticsearch/xpack/ml/rest/results/RestGetBucketsAction.java

@@ -71,11 +71,6 @@ public class RestGetBucketsAction extends BaseRestHandler {
                 request.setAnomalyScore(
                         Double.parseDouble(restRequest.param(GetBucketsAction.Request.ANOMALY_SCORE.getPreferredName(), "0.0")));
             }
-            if (restRequest.hasParam(GetBucketsAction.Request.MAX_NORMALIZED_PROBABILITY.getPreferredName())) {
-                request.setMaxNormalizedProbability(
-                        Double.parseDouble(restRequest.param(
-                                GetBucketsAction.Request.MAX_NORMALIZED_PROBABILITY.getPreferredName(), "0.0")));
-            }
             if (restRequest.hasParam(GetBucketsAction.Request.PARTITION_VALUE.getPreferredName())) {
                 request.setPartitionValue(restRequest.param(GetBucketsAction.Request.PARTITION_VALUE.getPreferredName()));
             }

plugin/src/main/java/org/elasticsearch/xpack/ml/rest/results/RestGetInfluencersAction.java

@@ -50,7 +50,7 @@ public class RestGetInfluencersAction extends BaseRestHandler {
             request.setAnomalyScore(
                     Double.parseDouble(restRequest.param(GetInfluencersAction.Request.ANOMALY_SCORE.getPreferredName(), "0.0")));
             request.setSort(restRequest.param(GetInfluencersAction.Request.SORT_FIELD.getPreferredName(),
-                    Influencer.ANOMALY_SCORE.getPreferredName()));
+                    Influencer.INFLUENCER_SCORE.getPreferredName()));
             request.setDecending(restRequest.paramAsBoolean(GetInfluencersAction.Request.DESCENDING_SORT.getPreferredName(), true));
         }
 

plugin/src/main/java/org/elasticsearch/xpack/ml/rest/results/RestGetRecordsAction.java

@@ -45,13 +45,11 @@ public class RestGetRecordsAction extends BaseRestHandler {
             request.setIncludeInterim(restRequest.paramAsBoolean(GetRecordsAction.Request.INCLUDE_INTERIM.getPreferredName(), false));
             request.setPageParams(new PageParams(restRequest.paramAsInt(PageParams.FROM.getPreferredName(), PageParams.DEFAULT_FROM),
                     restRequest.paramAsInt(PageParams.SIZE.getPreferredName(), PageParams.DEFAULT_SIZE)));
-            request.setAnomalyScore(
+            request.setRecordScore(
-                    Double.parseDouble(restRequest.param(GetRecordsAction.Request.ANOMALY_SCORE_FILTER.getPreferredName(), "0.0")));
+                    Double.parseDouble(restRequest.param(GetRecordsAction.Request.RECORD_SCORE_FILTER.getPreferredName(), "0.0")));
             request.setSort(restRequest.param(GetRecordsAction.Request.SORT.getPreferredName(),
-                    AnomalyRecord.NORMALIZED_PROBABILITY.getPreferredName()));
+                    AnomalyRecord.RECORD_SCORE.getPreferredName()));
             request.setDecending(restRequest.paramAsBoolean(GetRecordsAction.Request.DESCENDING.getPreferredName(), true));
-            request.setMaxNormalizedProbability(
-                    Double.parseDouble(restRequest.param(GetRecordsAction.Request.MAX_NORMALIZED_PROBABILITY.getPreferredName(), "0.0")));
             String partitionValue = restRequest.param(GetRecordsAction.Request.PARTITION_VALUE.getPreferredName());
             if (partitionValue != null) {
                 request.setPartitionValue(partitionValue);

plugin/src/test/java/org/elasticsearch/xpack/ml/action/GetBucketActionRequestTests.java

@@ -19,9 +19,6 @@ public class GetBucketActionRequestTests extends AbstractStreamableXContentTestC
         if (randomBoolean()) {
             request.setTimestamp(String.valueOf(randomLong()));
         } else {
-            if (randomBoolean()) {
-                request.setMaxNormalizedProbability(randomDouble());
-            }
             if (randomBoolean()) {
                 request.setPartitionValue(randomAsciiOfLengthBetween(1, 20));
             }

plugin/src/test/java/org/elasticsearch/xpack/ml/action/GetBucketActionResponseTests.java

@@ -58,9 +58,6 @@ public class GetBucketActionResponseTests extends AbstractStreamableTestCase<Get
             if (randomBoolean()) {
                 bucket.setInterim(randomBoolean());
             }
-            if (randomBoolean()) {
-                bucket.setMaxNormalizedProbability(randomDouble());
-            }
             if (randomBoolean()) {
                 int size = randomInt(10);
                 List<PartitionScore> partitionScores = new ArrayList<>(size);
@@ -89,7 +86,6 @@ public class GetBucketActionResponseTests extends AbstractStreamableTestCase<Get
                 List<AnomalyRecord> records = new ArrayList<>(size);
                 for (int i = 0; i < size; i++) {
                     AnomalyRecord anomalyRecord = new AnomalyRecord(jobId, new Date(randomLong()), randomNonNegativeLong(), sequenceNum++);
-                    anomalyRecord.setAnomalyScore(randomDouble());
                     anomalyRecord.setActual(Collections.singletonList(randomDouble()));
                     anomalyRecord.setTypical(Collections.singletonList(randomDouble()));
                     anomalyRecord.setProbability(randomDouble());

plugin/src/test/java/org/elasticsearch/xpack/ml/action/GetInfluencersActionResponseTests.java

@@ -23,8 +23,8 @@ public class GetInfluencersActionResponseTests extends AbstractStreamableTestCas
         for (int j = 0; j < listSize; j++) {
             Influencer influencer = new Influencer(randomAsciiOfLengthBetween(1, 20), randomAsciiOfLengthBetween(1, 20),
                     randomAsciiOfLengthBetween(1, 20), new Date(randomNonNegativeLong()), randomNonNegativeLong(), j + 1);
-            influencer.setAnomalyScore(randomDouble());
+            influencer.setInfluencerScore(randomDouble());
-            influencer.setInitialAnomalyScore(randomDouble());
+            influencer.setInitialInfluencerScore(randomDouble());
             influencer.setProbability(randomDouble());
             influencer.setInterim(randomBoolean());
             hits.add(influencer);

plugin/src/test/java/org/elasticsearch/xpack/ml/action/GetRecordsActionRequestTests.java

@@ -38,14 +38,11 @@ public class GetRecordsActionRequestTests extends AbstractStreamableXContentTest
             request.setDecending(randomBoolean());
         }
         if (randomBoolean()) {
-            request.setAnomalyScore(randomDouble());
+            request.setRecordScore(randomDouble());
         }
         if (randomBoolean()) {
             request.setIncludeInterim(randomBoolean());
         }
-        if (randomBoolean()) {
-            request.setMaxNormalizedProbability(randomDouble());
-        }
         if (randomBoolean()) {
             int from = randomInt(PageParams.MAX_FROM_SIZE_SUM);
             int maxSize = PageParams.MAX_FROM_SIZE_SUM - from;

plugin/src/test/java/org/elasticsearch/xpack/ml/integration/MlJobIT.java

@@ -51,7 +51,7 @@ public class MlJobIT extends ESRestTestCase {
             "\"result_type\": { \"type\" : \"keyword\" }," +
             "\"timestamp\": { \"type\" : \"date\" }, " +
             "\"anomaly_score\": { \"type\" : \"double\" }, " +
-            "\"normalized_probability\": { \"type\" : \"double\" }, " +
+            "\"record_score\": { \"type\" : \"double\" }, " +
             "\"over_field_value\": { \"type\" : \"keyword\" }, " +
             "\"partition_field_value\": { \"type\" : \"keyword\" }, " +
             "\"by_field_value\": { \"type\" : \"keyword\" }, " +

plugin/src/test/java/org/elasticsearch/xpack/ml/job/persistence/BucketsQueryBuilderTests.java

@@ -17,7 +17,6 @@ public class BucketsQueryBuilderTests extends ESTestCase {
         assertEquals(false, query.isIncludeInterim());
         assertEquals(false, query.isExpand());
         assertEquals(0.0, query.getAnomalyScoreFilter(), 0.0001);
-        assertEquals(0.0, query.getNormalizedProbability(), 0.0001);
         assertNull(query.getStart());
         assertNull(query.getEnd());
         assertEquals("timestamp", query.getSortField());
@@ -31,7 +30,6 @@ public class BucketsQueryBuilderTests extends ESTestCase {
                 .includeInterim(true)
                 .expand(true)
                 .anomalyScoreThreshold(50.0d)
-                .normalizedProbabilityThreshold(70.0d)
                 .start("1000")
                 .end("2000")
                 .partitionValue("foo")
@@ -44,7 +42,6 @@ public class BucketsQueryBuilderTests extends ESTestCase {
         assertEquals(true, query.isIncludeInterim());
         assertEquals(true, query.isExpand());
         assertEquals(50.0d, query.getAnomalyScoreFilter(), 0.00001);
-        assertEquals(70.0d, query.getNormalizedProbability(), 0.00001);
         assertEquals("1000", query.getStart());
         assertEquals("2000", query.getEnd());
         assertEquals("foo", query.getPartitionValue());
@@ -59,7 +56,6 @@ public class BucketsQueryBuilderTests extends ESTestCase {
                 .includeInterim(true)
                 .expand(true)
                 .anomalyScoreThreshold(50.0d)
-                .normalizedProbabilityThreshold(70.0d)
                 .start("1000")
                 .end("2000")
                 .partitionValue("foo");
@@ -70,7 +66,6 @@ public class BucketsQueryBuilderTests extends ESTestCase {
                 .includeInterim(true)
                 .expand(true)
                 .anomalyScoreThreshold(50.0d)
-                .normalizedProbabilityThreshold(70.0d)
                 .start("1000")
                 .end("2000")
                 .partitionValue("foo");
@@ -85,7 +80,6 @@ public class BucketsQueryBuilderTests extends ESTestCase {
         .includeInterim(true)
         .expand(true)
         .anomalyScoreThreshold(50.0d)
-        .normalizedProbabilityThreshold(70.0d)
         .start("1000")
         .end("2000")
         .partitionValue("foo");
@@ -97,10 +91,9 @@ public class BucketsQueryBuilderTests extends ESTestCase {
         .includeInterim(true)
         .expand(true)
         .anomalyScoreThreshold(50.1d)
-        .normalizedProbabilityThreshold(70.0d)
         .start("1000")
         .end("2000")
         .partitionValue("foo");
         assertFalse(query.build().equals(query2.build()));
     }
-}
+}

plugin/src/test/java/org/elasticsearch/xpack/ml/job/persistence/InfluencersQueryBuilderTests.java

@@ -19,7 +19,7 @@ public class InfluencersQueryBuilderTests extends ESTestCase {
         assertEquals(0.0, query.getAnomalyScoreFilter(), 0.0001);
         assertNull(query.getStart());
         assertNull(query.getEnd());
-        assertEquals(Influencer.ANOMALY_SCORE.getPreferredName(), query.getSortField());
+        assertEquals(Influencer.INFLUENCER_SCORE.getPreferredName(), query.getSortField());
         assertFalse(query.isSortDescending());
     }
 
@@ -84,4 +84,4 @@ public class InfluencersQueryBuilderTests extends ESTestCase {
         .end("2000");
         assertFalse(query.build().equals(query2.build()));
     }
-}
+}

plugin/src/test/java/org/elasticsearch/xpack/ml/job/persistence/JobProviderTests.java

@@ -297,8 +297,7 @@ public class JobProviderTests extends ESTestCase {
         Client client = getMockedClient(queryBuilder -> {queryBuilderHolder[0] = queryBuilder;}, response);
         JobProvider provider = createProvider(client);
 
-        BucketsQueryBuilder bq = new BucketsQueryBuilder().from(from).size(size).anomalyScoreThreshold(0.0)
+        BucketsQueryBuilder bq = new BucketsQueryBuilder().from(from).size(size).anomalyScoreThreshold(1.0);
-                .normalizedProbabilityThreshold(1.0);
 
         @SuppressWarnings({"unchecked", "rawtypes"})
         QueryPage<Bucket>[] holder = new QueryPage[1];
@@ -308,7 +307,7 @@ public class JobProviderTests extends ESTestCase {
         QueryBuilder query = queryBuilderHolder[0];
         String queryString = query.toString();
         assertTrue(
-                queryString.matches("(?s).*max_normalized_probability[^}]*from. : 1\\.0.*must_not[^}]*term[^}]*is_interim.*value. : .true" +
+                queryString.matches("(?s).*anomaly_score[^}]*from. : 1\\.0.*must_not[^}]*term[^}]*is_interim.*value. : .true" +
                         ".*"));
     }
 
@@ -333,7 +332,7 @@ public class JobProviderTests extends ESTestCase {
         JobProvider provider = createProvider(client);
 
         BucketsQueryBuilder bq = new BucketsQueryBuilder().from(from).size(size).anomalyScoreThreshold(5.1)
-                .normalizedProbabilityThreshold(10.9).includeInterim(true);
+                .includeInterim(true);
 
         @SuppressWarnings({"unchecked", "rawtypes"})
         QueryPage<Bucket>[] holder = new QueryPage[1];
@@ -342,7 +341,6 @@ public class JobProviderTests extends ESTestCase {
         assertEquals(1L, buckets.count());
         QueryBuilder query = queryBuilderHolder[0];
         String queryString = query.toString();
-        assertTrue(queryString.matches("(?s).*max_normalized_probability[^}]*from. : 10\\.9.*"));
         assertTrue(queryString.matches("(?s).*anomaly_score[^}]*from. : 5\\.1.*"));
         assertFalse(queryString.matches("(?s).*is_interim.*"));
     }
@@ -371,7 +369,6 @@ public class JobProviderTests extends ESTestCase {
         bq.from(from);
         bq.size(size);
         bq.anomalyScoreThreshold(5.1);
-        bq.normalizedProbabilityThreshold(10.9);
         bq.includeInterim(true);
 
         @SuppressWarnings({"unchecked", "rawtypes"})
@@ -381,7 +378,6 @@ public class JobProviderTests extends ESTestCase {
         assertEquals(1L, buckets.count());
         QueryBuilder query = queryBuilderHolder[0];
         String queryString = query.toString();
-        assertTrue(queryString.matches("(?s).*max_normalized_probability[^}]*from. : 10\\.9.*"));
         assertTrue(queryString.matches("(?s).*anomaly_score[^}]*from. : 5\\.1.*"));
         assertFalse(queryString.matches("(?s).*is_interim.*"));
     }
@@ -488,8 +484,8 @@ public class JobProviderTests extends ESTestCase {
         JobProvider provider = createProvider(client);
 
         RecordsQueryBuilder rqb = new RecordsQueryBuilder().from(from).size(size).epochStart(String.valueOf(now.getTime()))
-                .epochEnd(String.valueOf(now.getTime())).includeInterim(true).sortField(sortfield).anomalyScoreThreshold(11.1)
+                .epochEnd(String.valueOf(now.getTime())).includeInterim(true).sortField(sortfield)
-                .normalizedProbability(2.2);
+                .recordScore(2.2);
 
         @SuppressWarnings({"unchecked", "rawtypes"})
         QueryPage<AnomalyRecord>[] holder = new QueryPage[1];
@@ -545,8 +541,7 @@ public class JobProviderTests extends ESTestCase {
         rqb.epochEnd(String.valueOf(now.getTime()));
         rqb.includeInterim(true);
         rqb.sortField(sortfield);
-        rqb.anomalyScoreThreshold(11.1);
+        rqb.recordScore(2.2);
-        rqb.normalizedProbability(2.2);
 
         @SuppressWarnings({"unchecked", "rawtypes"})
         QueryPage<AnomalyRecord>[] holder = new QueryPage[1];
@@ -729,27 +724,27 @@ public class JobProviderTests extends ESTestCase {
         Date now = new Date();
         List<Map<String, Object>> source = new ArrayList<>();
 
-        Map<String, Object> recordMap1 = new HashMap<>();
+        Map<String, Object> influencerMap1 = new HashMap<>();
-        recordMap1.put("job_id", "foo");
+        influencerMap1.put("job_id", "foo");
-        recordMap1.put("probability", 0.555);
+        influencerMap1.put("probability", 0.555);
-        recordMap1.put("influencer_field_name", "Builder");
+        influencerMap1.put("influencer_field_name", "Builder");
-        recordMap1.put("timestamp", now.getTime());
+        influencerMap1.put("timestamp", now.getTime());
-        recordMap1.put("influencer_field_value", "Bob");
+        influencerMap1.put("influencer_field_value", "Bob");
-        recordMap1.put("initial_anomaly_score", 22.2);
+        influencerMap1.put("initial_influencer_score", 22.2);
-        recordMap1.put("anomaly_score", 22.6);
+        influencerMap1.put("influencer_score", 22.6);
-        recordMap1.put("bucket_span", 123);
+        influencerMap1.put("bucket_span", 123);
-        recordMap1.put("sequence_num", 1);
+        influencerMap1.put("sequence_num", 1);
         Map<String, Object> recordMap2 = new HashMap<>();
         recordMap2.put("job_id", "foo");
         recordMap2.put("probability", 0.99);
         recordMap2.put("influencer_field_name", "Builder");
         recordMap2.put("timestamp", now.getTime());
         recordMap2.put("influencer_field_value", "James");
-        recordMap2.put("initial_anomaly_score", 5.0);
+        recordMap2.put("initial_influencer_score", 5.0);
-        recordMap2.put("anomaly_score", 5.0);
+        recordMap2.put("influencer_score", 5.0);
         recordMap2.put("bucket_span", 123);
         recordMap2.put("sequence_num", 2);
-        source.add(recordMap1);
+        source.add(influencerMap1);
         source.add(recordMap2);
 
         int from = 4;
@@ -775,15 +770,15 @@ public class JobProviderTests extends ESTestCase {
         assertEquals("Builder", records.get(0).getInfluencerFieldName());
         assertEquals(now, records.get(0).getTimestamp());
         assertEquals(0.555, records.get(0).getProbability(), 0.00001);
-        assertEquals(22.6, records.get(0).getAnomalyScore(), 0.00001);
+        assertEquals(22.6, records.get(0).getInfluencerScore(), 0.00001);
-        assertEquals(22.2, records.get(0).getInitialAnomalyScore(), 0.00001);
+        assertEquals(22.2, records.get(0).getInitialInfluencerScore(), 0.00001);
 
         assertEquals("James", records.get(1).getInfluencerFieldValue());
         assertEquals("Builder", records.get(1).getInfluencerFieldName());
         assertEquals(now, records.get(1).getTimestamp());
         assertEquals(0.99, records.get(1).getProbability(), 0.00001);
-        assertEquals(5.0, records.get(1).getAnomalyScore(), 0.00001);
+        assertEquals(5.0, records.get(1).getInfluencerScore(), 0.00001);
-        assertEquals(5.0, records.get(1).getInitialAnomalyScore(), 0.00001);
+        assertEquals(5.0, records.get(1).getInitialInfluencerScore(), 0.00001);
     }
 
     public void testInfluencers_WithInterim() throws InterruptedException, ExecutionException, IOException {
@@ -791,28 +786,28 @@ public class JobProviderTests extends ESTestCase {
         Date now = new Date();
         List<Map<String, Object>> source = new ArrayList<>();
 
-        Map<String, Object> recordMap1 = new HashMap<>();
+        Map<String, Object> influencerMap1 = new HashMap<>();
-        recordMap1.put("job_id", "foo");
+        influencerMap1.put("job_id", "foo");
-        recordMap1.put("probability", 0.555);
+        influencerMap1.put("probability", 0.555);
-        recordMap1.put("influencer_field_name", "Builder");
+        influencerMap1.put("influencer_field_name", "Builder");
-        recordMap1.put("timestamp", now.getTime());
+        influencerMap1.put("timestamp", now.getTime());
-        recordMap1.put("influencer_field_value", "Bob");
+        influencerMap1.put("influencer_field_value", "Bob");
-        recordMap1.put("initial_anomaly_score", 22.2);
+        influencerMap1.put("initial_influencer_score", 22.2);
-        recordMap1.put("anomaly_score", 22.6);
+        influencerMap1.put("influencer_score", 22.6);
-        recordMap1.put("bucket_span", 123);
+        influencerMap1.put("bucket_span", 123);
-        recordMap1.put("sequence_num", 1);
+        influencerMap1.put("sequence_num", 1);
-        Map<String, Object> recordMap2 = new HashMap<>();
+        Map<String, Object> influencerMap2 = new HashMap<>();
-        recordMap2.put("job_id", "foo");
+        influencerMap2.put("job_id", "foo");
-        recordMap2.put("probability", 0.99);
+        influencerMap2.put("probability", 0.99);
-        recordMap2.put("influencer_field_name", "Builder");
+        influencerMap2.put("influencer_field_name", "Builder");
-        recordMap2.put("timestamp", now.getTime());
+        influencerMap2.put("timestamp", now.getTime());
-        recordMap2.put("influencer_field_value", "James");
+        influencerMap2.put("influencer_field_value", "James");
-        recordMap2.put("initial_anomaly_score", 5.0);
+        influencerMap2.put("initial_influencer_score", 5.0);
-        recordMap2.put("anomaly_score", 5.0);
+        influencerMap2.put("influencer_score", 5.0);
-        recordMap2.put("bucket_span", 123);
+        influencerMap2.put("bucket_span", 123);
-        recordMap2.put("sequence_num", 2);
+        influencerMap2.put("sequence_num", 2);
-        source.add(recordMap1);
+        source.add(influencerMap1);
-        source.add(recordMap2);
+        source.add(influencerMap2);
 
         int from = 4;
         int size = 3;
@@ -837,15 +832,15 @@ public class JobProviderTests extends ESTestCase {
         assertEquals("Builder", records.get(0).getInfluencerFieldName());
         assertEquals(now, records.get(0).getTimestamp());
         assertEquals(0.555, records.get(0).getProbability(), 0.00001);
-        assertEquals(22.6, records.get(0).getAnomalyScore(), 0.00001);
+        assertEquals(22.6, records.get(0).getInfluencerScore(), 0.00001);
-        assertEquals(22.2, records.get(0).getInitialAnomalyScore(), 0.00001);
+        assertEquals(22.2, records.get(0).getInitialInfluencerScore(), 0.00001);
 
         assertEquals("James", records.get(1).getInfluencerFieldValue());
         assertEquals("Builder", records.get(1).getInfluencerFieldName());
         assertEquals(now, records.get(1).getTimestamp());
         assertEquals(0.99, records.get(1).getProbability(), 0.00001);
-        assertEquals(5.0, records.get(1).getAnomalyScore(), 0.00001);
+        assertEquals(5.0, records.get(1).getInfluencerScore(), 0.00001);
-        assertEquals(5.0, records.get(1).getInitialAnomalyScore(), 0.00001);
+        assertEquals(5.0, records.get(1).getInitialInfluencerScore(), 0.00001);
     }
 
     public void testModelSnapshots() throws InterruptedException, ExecutionException, IOException {
@@ -951,80 +946,13 @@ public class JobProviderTests extends ESTestCase {
         assertTrue(queryString.matches("(?s).*snapshot_id.*value. : .snappyId.*description.*value. : .description1.*"));
     }
 
-    public void testMergePartitionScoresIntoBucket() throws InterruptedException, ExecutionException {
+    private AnomalyRecord createAnomalyRecord(String partitionFieldValue, Date timestamp, double recordScore) {
-        MockClientBuilder clientBuilder = new MockClientBuilder(CLUSTER_NAME);
-
-        JobProvider provider = createProvider(clientBuilder.build());
-
-        List<PerPartitionMaxProbabilities> partitionMaxProbs = new ArrayList<>();
-
-        List<AnomalyRecord> records = new ArrayList<>();
-        records.add(createAnomalyRecord("partitionValue1", new Date(2), 1.0));
-        records.add(createAnomalyRecord("partitionValue2", new Date(2), 4.0));
-        partitionMaxProbs.add(new PerPartitionMaxProbabilities(records));
-
-        records.clear();
-        records.add(createAnomalyRecord("partitionValue1", new Date(3), 2.0));
-        records.add(createAnomalyRecord("partitionValue2", new Date(3), 1.0));
-        partitionMaxProbs.add(new PerPartitionMaxProbabilities(records));
-
-        records.clear();
-        records.add(createAnomalyRecord("partitionValue1", new Date(5), 3.0));
-        records.add(createAnomalyRecord("partitionValue2", new Date(5), 2.0));
-        partitionMaxProbs.add(new PerPartitionMaxProbabilities(records));
-
-        List<Bucket> buckets = new ArrayList<>();
-        buckets.add(createBucketAtEpochTime(1));
-        buckets.add(createBucketAtEpochTime(2));
-        buckets.add(createBucketAtEpochTime(3));
-        buckets.add(createBucketAtEpochTime(4));
-        buckets.add(createBucketAtEpochTime(5));
-        buckets.add(createBucketAtEpochTime(6));
-
-        provider.mergePartitionScoresIntoBucket(partitionMaxProbs, buckets, "partitionValue1");
-        assertEquals(0.0, buckets.get(0).getMaxNormalizedProbability(), 0.001);
-        assertEquals(1.0, buckets.get(1).getMaxNormalizedProbability(), 0.001);
-        assertEquals(2.0, buckets.get(2).getMaxNormalizedProbability(), 0.001);
-        assertEquals(0.0, buckets.get(3).getMaxNormalizedProbability(), 0.001);
-        assertEquals(3.0, buckets.get(4).getMaxNormalizedProbability(), 0.001);
-        assertEquals(0.0, buckets.get(5).getMaxNormalizedProbability(), 0.001);
-
-        provider.mergePartitionScoresIntoBucket(partitionMaxProbs, buckets, "partitionValue2");
-        assertEquals(0.0, buckets.get(0).getMaxNormalizedProbability(), 0.001);
-        assertEquals(4.0, buckets.get(1).getMaxNormalizedProbability(), 0.001);
-        assertEquals(1.0, buckets.get(2).getMaxNormalizedProbability(), 0.001);
-        assertEquals(0.0, buckets.get(3).getMaxNormalizedProbability(), 0.001);
-        assertEquals(2.0, buckets.get(4).getMaxNormalizedProbability(), 0.001);
-        assertEquals(0.0, buckets.get(5).getMaxNormalizedProbability(), 0.001);
-    }
-
-    private AnomalyRecord createAnomalyRecord(String partitionFieldValue, Date timestamp, double normalizedProbability) {
         AnomalyRecord record = new AnomalyRecord("foo", timestamp, 600, 42);
         record.setPartitionFieldValue(partitionFieldValue);
-        record.setNormalizedProbability(normalizedProbability);
+        record.setRecordScore(recordScore);
         return record;
     }
 
-    public void testMergePartitionScoresIntoBucket_WithEmptyScoresList() throws InterruptedException, ExecutionException {
-        MockClientBuilder clientBuilder = new MockClientBuilder(CLUSTER_NAME);
-
-        JobProvider provider = createProvider(clientBuilder.build());
-
-        List<PerPartitionMaxProbabilities> scores = new ArrayList<>();
-
-        List<Bucket> buckets = new ArrayList<>();
-        buckets.add(createBucketAtEpochTime(1));
-        buckets.add(createBucketAtEpochTime(2));
-        buckets.add(createBucketAtEpochTime(3));
-        buckets.add(createBucketAtEpochTime(4));
-
-        provider.mergePartitionScoresIntoBucket(scores, buckets, "partitionValue");
-        assertEquals(0.0, buckets.get(0).getMaxNormalizedProbability(), 0.001);
-        assertEquals(0.0, buckets.get(1).getMaxNormalizedProbability(), 0.001);
-        assertEquals(0.0, buckets.get(2).getMaxNormalizedProbability(), 0.001);
-        assertEquals(0.0, buckets.get(3).getMaxNormalizedProbability(), 0.001);
-    }
-
     public void testRestoreStateToStream() throws Exception {
         Map<String, Object> categorizerState = new HashMap<>();
         categorizerState.put("catName", "catVal");
@@ -1120,7 +1048,6 @@ public class JobProviderTests extends ESTestCase {
 
     private Bucket createBucketAtEpochTime(long epoch) {
         Bucket b = new Bucket("foo", new Date(epoch), 123);
-        b.setMaxNormalizedProbability(10.0);
         return b;
     }
 

plugin/src/test/java/org/elasticsearch/xpack/ml/job/persistence/JobResultsPersisterTests.java

@@ -40,7 +40,6 @@ public class JobResultsPersisterTests extends ESTestCase {
         bucket.setAnomalyScore(99.9);
         bucket.setEventCount(57);
         bucket.setInitialAnomalyScore(88.8);
-        bucket.setMaxNormalizedProbability(42.0);
         bucket.setProcessingTimeMs(8888);
         bucket.setRecordCount(1);
 
@@ -54,7 +53,6 @@ public class JobResultsPersisterTests extends ESTestCase {
 
         // We are adding a record but it shouldn't be persisted as part of the bucket
         AnomalyRecord record = new AnomalyRecord(JOB_ID, new Date(), 600, 2);
-        record.setAnomalyScore(99.8);
         bucket.setRecords(Arrays.asList(record));
 
         JobResultsPersister persister = new JobResultsPersister(Settings.EMPTY, client);
@@ -65,7 +63,6 @@ public class JobResultsPersisterTests extends ESTestCase {
         String s = ((IndexRequest)bulkRequest.requests().get(0)).source().utf8ToString();
         assertTrue(s.matches(".*anomaly_score.:99\\.9.*"));
         assertTrue(s.matches(".*initial_anomaly_score.:88\\.8.*"));
-        assertTrue(s.matches(".*max_normalized_probability.:42\\.0.*"));
         assertTrue(s.matches(".*record_count.:1.*"));
         assertTrue(s.matches(".*event_count.:57.*"));
         assertTrue(s.matches(".*bucket_span.:123456.*"));
@@ -92,7 +89,6 @@ public class JobResultsPersisterTests extends ESTestCase {
         actuals.add(5.0);
         actuals.add(5.1);
         r1.setActual(actuals);
-        r1.setAnomalyScore(99.8);
         r1.setByFieldName("byName");
         r1.setByFieldValue("byValue");
         r1.setCorrelatedByFieldValue("testCorrelations");
@@ -100,8 +96,8 @@ public class JobResultsPersisterTests extends ESTestCase {
         r1.setFieldName("testFieldName");
         r1.setFunction("testFunction");
         r1.setFunctionDescription("testDescription");
-        r1.setInitialNormalizedProbability(23.4);
+        r1.setInitialRecordScore(23.4);
-        r1.setNormalizedProbability(0.005);
+        r1.setRecordScore(0.005);
         r1.setOverFieldName("overName");
         r1.setOverFieldValue("overValue");
         r1.setPartitionFieldName("partName");
@@ -120,9 +116,8 @@ public class JobResultsPersisterTests extends ESTestCase {
         String s = ((IndexRequest) bulkRequest.requests().get(0)).source().utf8ToString();
         assertTrue(s.matches(".*detector_index.:3.*"));
         assertTrue(s.matches(".*\"probability\":0\\.1.*"));
-        assertTrue(s.matches(".*\"anomaly_score\":99\\.8.*"));
+        assertTrue(s.matches(".*\"record_score\":0\\.005.*"));
-        assertTrue(s.matches(".*\"normalized_probability\":0\\.005.*"));
+        assertTrue(s.matches(".*initial_record_score.:23.4.*"));
-        assertTrue(s.matches(".*initial_normalized_probability.:23.4.*"));
         assertTrue(s.matches(".*bucket_span.:42.*"));
         assertTrue(s.matches(".*by_field_name.:.byName.*"));
         assertTrue(s.matches(".*by_field_value.:.byValue.*"));
@@ -144,8 +139,8 @@ public class JobResultsPersisterTests extends ESTestCase {
 
         List<Influencer> influencers = new ArrayList<>();
         Influencer inf = new Influencer(JOB_ID, "infName1", "infValue1", new Date(), 600, 1);
-        inf.setAnomalyScore(16);
+        inf.setInfluencerScore(16);
-        inf.setInitialAnomalyScore(55.5);
+        inf.setInitialInfluencerScore(55.5);
         inf.setProbability(0.4);
         influencers.add(inf);
 
@@ -158,8 +153,8 @@ public class JobResultsPersisterTests extends ESTestCase {
         assertTrue(s.matches(".*probability.:0\\.4.*"));
         assertTrue(s.matches(".*influencer_field_name.:.infName1.*"));
         assertTrue(s.matches(".*influencer_field_value.:.infValue1.*"));
-        assertTrue(s.matches(".*initial_anomaly_score.:55\\.5.*"));
+        assertTrue(s.matches(".*initial_influencer_score.:55\\.5.*"));
-        assertTrue(s.matches(".*anomaly_score.:16\\.0.*"));
+        assertTrue(s.matches(".*influencer_score.:16\\.0.*"));
     }
 
     @SuppressWarnings({"unchecked", "rawtypes"})

plugin/src/test/java/org/elasticsearch/xpack/ml/job/process/autodetect/output/AutodetectResultsParserTests.java

@@ -30,7 +30,7 @@ public class AutodetectResultsParserTests extends ESTestCase {
 
     public static final String METRIC_OUTPUT_SAMPLE = "[{\"bucket\": {\"job_id\":\"foo\",\"timestamp\":1359450000000,"
             + "\"bucket_span\":22, \"records\":[],"
-            + "\"max_normalized_probability\":0, \"anomaly_score\":0,\"record_count\":0,\"event_count\":806,\"bucket_influencers\":["
+            + "\"anomaly_score\":0,\"record_count\":0,\"event_count\":806,\"bucket_influencers\":["
             + "{\"sequence_num\":1,\"timestamp\":1359450000000,\"bucket_span\":22,\"job_id\":\"foo\",\"anomaly_score\":0,"
             + "\"probability\":0.0, \"influencer_field_name\":\"bucket_time\","
             + "\"initial_anomaly_score\":0.0}]}},{\"quantiles\": {\"job_id\":\"foo\", \"quantile_state\":\"[normalizer 1.1, normalizer 2" +
@@ -50,7 +50,7 @@ public class AutodetectResultsParserTests extends ESTestCase {
             + "\"probability\":0.0473552,\"by_field_name\":\"airline\",\"by_field_value\":\"SWA\", \"typical\":[152.148],"
             + "\"actual\":[96.6425],\"field_name\":\"responsetime\",\"function\":\"min\",\"partition_field_name\":\"\","
             + "\"partition_field_value\":\"\"}],"
-            + "\"initial_anomaly_score\":0.0140005, \"anomaly_score\":20.22688, \"max_normalized_probability\":10.5688, \"record_count\":4,"
+            + "\"initial_anomaly_score\":0.0140005, \"anomaly_score\":20.22688, \"record_count\":4,"
             + "\"event_count\":820,\"bucket_influencers\":[{\"timestamp\":1359453600000,\"bucket_span\":22,\"sequence_num\":5,"
             + "\"job_id\":\"foo\", \"raw_anomaly_score\":0.0140005, \"probability\":0.01,\"influencer_field_name\":\"bucket_time\","
             + "\"initial_anomaly_score\":20.22688,\"anomaly_score\":20.22688} ,{\"timestamp\":1359453600000,\"bucket_span\":22,"
@@ -65,168 +65,168 @@ public class AutodetectResultsParserTests extends ESTestCase {
             + "\"sequence_num\":1,\"function\":\"max\","
             + "\"causes\":[{\"probability\":1.38951e-08,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"mail.google.com\",\"function\":\"max\",\"typical\":[101534],\"actual\":[9.19027e+07]}],"
-            + "\"normalized_probability\":100,\"anomaly_score\":44.7324},{\"probability\":3.86587e-07,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":100,\"anomaly_score\":44.7324},{\"probability\":3.86587e-07,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"armmf.adobe.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":3.86587e-07,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"armmf.adobe.com\",\"function\":\"max\",\"typical\":[101534],\"actual\":[3.20093e+07]}],"
-            + "\"normalized_probability\":89.5834,\"anomaly_score\":44.7324},{\"probability\":0.00500083,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":89.5834,\"anomaly_score\":44.7324},{\"probability\":0.00500083,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"0.docs.google.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.00500083,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"0.docs.google.com\",\"function\":\"max\",\"typical\":[101534],\"actual\":[6.61812e+06]}],"
-            + "\"normalized_probability\":1.19856,\"anomaly_score\":44.7324},{\"probability\":0.0152333,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":1.19856,\"anomaly_score\":44.7324},{\"probability\":0.0152333,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"emea.salesforce.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.0152333,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"emea.salesforce.com\",\"function\":\"max\",\"typical\":[101534],\"actual\":[5.36373e+06]}],"
-            + "\"normalized_probability\":0.303996,\"anomaly_score\":44.7324}],\"raw_anomaly_score\":1.30397,\"anomaly_score\":44.7324,"
+            + "\"record_score\":0.303996,\"anomaly_score\":44.7324}],\"raw_anomaly_score\":1.30397,\"anomaly_score\":44.7324,"
-            + "\"max_normalized_probability\":100,\"record_count\":4,\"event_count\":1235}" + ",{\"flush\":\"testing2\"}"
+            + "\"record_count\":4,\"event_count\":1235}" + ",{\"flush\":\"testing2\"}"
             + ",{\"timestamp\":1379590800,\"records\":[{\"probability\":1.9008e-08,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"mail.google.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":1.9008e-08,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"mail.google.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[1.1498e+08]}],"
-            + "\"normalized_probability\":93.6213,\"anomaly_score\":1.19192},{\"probability\":1.01013e-06,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":93.6213,\"anomaly_score\":1.19192},{\"probability\":1.01013e-06,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"armmf.adobe.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":1.01013e-06,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"armmf.adobe.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[3.25808e+07]}],"
-            + "\"normalized_probability\":86.5825,\"anomaly_score\":1.19192},{\"probability\":0.000386185,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":86.5825,\"anomaly_score\":1.19192},{\"probability\":0.000386185,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"0.docs.google.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.000386185,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"0.docs.google.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[3.22855e+06]}],"
-            + "\"normalized_probability\":17.1179,\"anomaly_score\":1.19192},{\"probability\":0.00208033,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":17.1179,\"anomaly_score\":1.19192},{\"probability\":0.00208033,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"docs.google.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.00208033,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"docs.google.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[1.43328e+06]}],"
-            + "\"normalized_probability\":3.0692,\"anomaly_score\":1.19192},{\"probability\":0.00312988,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":3.0692,\"anomaly_score\":1.19192},{\"probability\":0.00312988,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"booking2.airasia.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.00312988,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"booking2.airasia.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[1.15764e+06]}],"
-            + "\"normalized_probability\":1.99532,\"anomaly_score\":1.19192},{\"probability\":0.00379229,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":1.99532,\"anomaly_score\":1.19192},{\"probability\":0.00379229,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.facebook.com\",\"function\":\"max\",\"causes\":["
             + "{\"probability\":0.00379229,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.facebook.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[1.0443e+06]}],"
-            + "\"normalized_probability\":1.62352,\"anomaly_score\":1.19192},{\"probability\":0.00623576,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":1.62352,\"anomaly_score\":1.19192},{\"probability\":0.00623576,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.airasia.com\",\"function\":\"max\",\"causes\":["
             + "{\"probability\":0.00623576,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.airasia.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[792699]}],"
-            + "\"normalized_probability\":0.935134,\"anomaly_score\":1.19192},{\"probability\":0.00665308,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.935134,\"anomaly_score\":1.19192},{\"probability\":0.00665308,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.google.com\",\"function\":\"max\",\"causes\":["
             + "{\"probability\":0.00665308,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.google.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[763985]}],"
-            + "\"normalized_probability\":0.868119,\"anomaly_score\":1.19192},{\"probability\":0.00709315,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.868119,\"anomaly_score\":1.19192},{\"probability\":0.00709315,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"0.drive.google.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.00709315,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"0.drive.google.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[736442]}],"
-            + "\"normalized_probability\":0.805994,\"anomaly_score\":1.19192},{\"probability\":0.00755789,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.805994,\"anomaly_score\":1.19192},{\"probability\":0.00755789,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"resources2.news.com.au\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.00755789,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"resources2.news.com.au\",\"function\":\"max\",\"typical\":[31356],\"actual\":[709962]}],"
-            + "\"normalized_probability\":0.748239,\"anomaly_score\":1.19192},{\"probability\":0.00834974,\"field_name\":"
+            + "\"record_score\":0.748239,\"anomaly_score\":1.19192},{\"probability\":0.00834974,\"field_name\":"
             + "\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.calypso.net.au\",\"function\":\"max\","
             + "\"causes\":[{\"probability\":0.00834974,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.calypso.net.au\",\"function\":\"max\",\"typical\":[31356],\"actual\":[669968]}],"
-            + "\"normalized_probability\":0.664644,\"anomaly_score\":1.19192},{\"probability\":0.0107711,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.664644,\"anomaly_score\":1.19192},{\"probability\":0.0107711,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"ad.yieldmanager.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.0107711,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"ad.yieldmanager.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[576067]}],"
-            + "\"normalized_probability\":0.485277,\"anomaly_score\":1.19192},{\"probability\":0.0123367,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.485277,\"anomaly_score\":1.19192},{\"probability\":0.0123367,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.google-analytics.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.0123367,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.google-analytics.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[530594]}],"
-            + "\"normalized_probability\":0.406783,\"anomaly_score\":1.19192},{\"probability\":0.0125647,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.406783,\"anomaly_score\":1.19192},{\"probability\":0.0125647,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"bs.serving-sys.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.0125647,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"bs.serving-sys.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[524690]}],"
-            + "\"normalized_probability\":0.396986,\"anomaly_score\":1.19192},{\"probability\":0.0141652,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.396986,\"anomaly_score\":1.19192},{\"probability\":0.0141652,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.google.com.au\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.0141652,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.google.com.au\",\"function\":\"max\",\"typical\":[31356],\"actual\":[487328]}],"
-            + "\"normalized_probability\":0.337075,\"anomaly_score\":1.19192},{\"probability\":0.0141742,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.337075,\"anomaly_score\":1.19192},{\"probability\":0.0141742,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"resources1.news.com.au\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.0141742,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"resources1.news.com.au\",\"function\":\"max\",\"typical\":[31356],\"actual\":[487136]}],"
-            + "\"normalized_probability\":0.336776,\"anomaly_score\":1.19192},{\"probability\":0.0145263,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.336776,\"anomaly_score\":1.19192},{\"probability\":0.0145263,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"b.mail.google.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.0145263,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"b.mail.google.com\",\"function\":\"max\",\"typical\":[31356],\"actual\":[479766]}],"
-            + "\"normalized_probability\":0.325385,\"anomaly_score\":1.19192},{\"probability\":0.0151447,\"field_name\":\"sum_cs_bytes_\","
+            + "\"record_score\":0.325385,\"anomaly_score\":1.19192},{\"probability\":0.0151447,\"field_name\":\"sum_cs_bytes_\","
             + "\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.rei.com\",\"function\":\"max\",\"causes\":[{"
             + "\"probability\":0.0151447,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.rei" +
             ".com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[467450]}],\"normalized_probability\":0.306657,\"anomaly_score\":1" +
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[467450]}],\"record_score\":0.306657,\"anomaly_score\":1" +
             ".19192},"
             + "{\"probability\":0.0164073,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"s3.amazonaws.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0164073,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"s3.amazonaws.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[444511]}],\"normalized_probability\":0.272805,\"anomaly_score\":1" +
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[444511]}],\"record_score\":0.272805,\"anomaly_score\":1" +
             ".19192},"
             + "{\"probability\":0.0201927,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"0-p-06-ash2.channel.facebook.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0201927,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"0-p-06-ash2.channel.facebook.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[389243]}],\"normalized_probability\":0.196685,\"anomaly_score\":1" +
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[389243]}],\"record_score\":0.196685,\"anomaly_score\":1" +
             ".19192},"
             + "{\"probability\":0.0218721,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"booking.airasia.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0218721,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"booking.airasia.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[369509]}],\"normalized_probability\":0.171353,"
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[369509]}],\"record_score\":0.171353,"
             + "\"anomaly_score\":1.19192},{\"probability\":0.0242411,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.yammer.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0242411,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.yammer.com\"," +
             "\"function\":\"max\","
-            + "\"typical\":[31356],\"actual\":[345295]}],\"normalized_probability\":0.141585,\"anomaly_score\":1.19192},"
+            + "\"typical\":[31356],\"actual\":[345295]}],\"record_score\":0.141585,\"anomaly_score\":1.19192},"
             + "{\"probability\":0.0258232,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"safebrowsing-cache.google.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0258232,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"safebrowsing-cache.google.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[331051]}],\"normalized_probability\":0.124748,\"anomaly_score\":1" +
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[331051]}],\"record_score\":0.124748,\"anomaly_score\":1" +
             ".19192},"
             + "{\"probability\":0.0259695,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"fbcdn-profile-a.akamaihd.net\",\"function\":\"max\",\"causes\":[{\"probability\":0.0259695,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"fbcdn-profile-a.akamaihd.net\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[329801]}],\"normalized_probability\":0.123294,\"anomaly_score\":1" +
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[329801]}],\"record_score\":0.123294,\"anomaly_score\":1" +
             ".19192},"
             + "{\"probability\":0.0268874,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.oag.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0268874,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.oag.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[322200]}],\"normalized_probability\":0.114537,"
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[322200]}],\"record_score\":0.114537,"
             + "\"anomaly_score\":1.19192},{\"probability\":0.0279146,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"booking.qatarairways.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0279146,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"booking.qatarairways.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[314153]}],\"normalized_probability\":0.105419,\"anomaly_score\":1" +
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[314153]}],\"record_score\":0.105419,\"anomaly_score\":1" +
             ".19192},"
             + "{\"probability\":0.0309351,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"resources3.news.com.au\",\"function\":\"max\",\"causes\":[{\"probability\":0.0309351,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"resources3.news.com.au\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[292918]}],\"normalized_probability\":0.0821156,\"anomaly_score\":1" +
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[292918]}],\"record_score\":0.0821156,\"anomaly_score\":1" +
             ".19192}"
             + ",{\"probability\":0.0335204,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"resources0.news.com.au\",\"function\":\"max\",\"causes\":[{\"probability\":0.0335204,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"resources0.news.com.au\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[277136]}],\"normalized_probability\":0.0655063,\"anomaly_score\":1" +
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[277136]}],\"record_score\":0.0655063,\"anomaly_score\":1" +
             ".19192}"
             + ",{\"probability\":0.0354927,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.southwest.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0354927,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.southwest.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[266310]}],\"normalized_probability\":0.0544615,"
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[266310]}],\"record_score\":0.0544615,"
             + "\"anomaly_score\":1.19192},{\"probability\":0.0392043,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"syndication.twimg.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0392043,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"syndication.twimg.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[248276]}],\"normalized_probability\":0.0366913,\"anomaly_score\":1" +
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[248276]}],\"record_score\":0.0366913,\"anomaly_score\":1" +
             ".19192}"
             + ",{\"probability\":0.0400853,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\""
             + ",\"over_field_value\":\"mts0.google.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0400853,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"mts0.google.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[244381]}],\"normalized_probability\":0.0329562,"
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[244381]}],\"record_score\":0.0329562,"
             + "\"anomaly_score\":1.19192},{\"probability\":0.0407335,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"www.onthegotours.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0407335,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"www.onthegotours.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[241600]}],\"normalized_probability\":0.0303116,"
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[241600]}],\"record_score\":0.0303116,"
             + "\"anomaly_score\":1.19192},{\"probability\":0.0470889,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"chatenabled.mail.google.com\",\"function\":\"max\",\"causes\":[{\"probability\":0.0470889,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"chatenabled.mail.google.com\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[217573]}],\"normalized_probability\":0.00823738,"
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[217573]}],\"record_score\":0.00823738,"
             + "\"anomaly_score\":1.19192},{\"probability\":0.0491243,\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\","
             + "\"over_field_value\":\"googleads.g.doubleclick.net\",\"function\":\"max\",\"causes\":[{\"probability\":0.0491243,"
             + "\"field_name\":\"sum_cs_bytes_\",\"over_field_name\":\"cs_host\",\"over_field_value\":\"googleads.g.doubleclick.net\","
-            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[210926]}],\"normalized_probability\":0.00237509,"
+            + "\"function\":\"max\",\"typical\":[31356],\"actual\":[210926]}],\"record_score\":0.00237509,"
-            + "\"anomaly_score\":1.19192}],\"raw_anomaly_score\":1.26918,\"anomaly_score\":1.19192,\"max_normalized_probability\":93.6213,"
+            + "\"anomaly_score\":1.19192}],\"raw_anomaly_score\":1.26918,\"anomaly_score\":1.19192,"
             + "\"record_count\":34,\"event_count\":1159}" + "]";
 
     public void testParser() throws IOException {

plugin/src/test/java/org/elasticsearch/xpack/ml/job/process/normalizer/BucketNormalizableTests.java

@@ -41,12 +41,11 @@ public class BucketNormalizableTests extends ESTestCase {
         bucket.setBucketInfluencers(Arrays.asList(bucketInfluencer1, bucketInfluencer2));
 
         bucket.setAnomalyScore(88.0);
-        bucket.setMaxNormalizedProbability(2.0);
 
         AnomalyRecord record1 = new AnomalyRecord("foo", bucket.getTimestamp(), 600, 3);
-        record1.setNormalizedProbability(1.0);
+        record1.setRecordScore(1.0);
         AnomalyRecord record2 = new AnomalyRecord("foo", bucket.getTimestamp(), 600, 4);
-        record2.setNormalizedProbability(2.0);
+        record2.setRecordScore(2.0);
         bucket.setRecords(Arrays.asList(record1, record2));
 
         List<PartitionScore> partitionScores = new ArrayList<>();
@@ -149,10 +148,9 @@ public class BucketNormalizableTests extends ESTestCase {
         BucketNormalizable bucketNormalizable = new BucketNormalizable(bucket, INDEX_NAME);
 
         assertTrue(bucketNormalizable.setMaxChildrenScore(Normalizable.ChildType.BUCKET_INFLUENCER, 95.0));
-        assertTrue(bucketNormalizable.setMaxChildrenScore(Normalizable.ChildType.RECORD, 42.0));
+        assertFalse(bucketNormalizable.setMaxChildrenScore(Normalizable.ChildType.RECORD, 42.0));
 
         assertEquals(95.0, bucket.getAnomalyScore(), EPSILON);
-        assertEquals(42.0, bucket.getMaxNormalizedProbability(), EPSILON);
     }
 
     public void testSetMaxChildrenScore_GivenSameScores() {
@@ -162,7 +160,6 @@ public class BucketNormalizableTests extends ESTestCase {
         assertFalse(bucketNormalizable.setMaxChildrenScore(Normalizable.ChildType.RECORD, 2.0));
 
         assertEquals(88.0, bucket.getAnomalyScore(), EPSILON);
-        assertEquals(2.0, bucket.getMaxNormalizedProbability(), EPSILON);
     }
 
     public void testSetParentScore() {

plugin/src/test/java/org/elasticsearch/xpack/ml/job/process/normalizer/InfluencerNormalizableTests.java

@@ -19,8 +19,8 @@ public class InfluencerNormalizableTests extends ESTestCase {
     @Before
     public void setUpInfluencer() {
         influencer = new Influencer("foo", "airline", "AAL", new Date(), 600, 1);
-        influencer.setAnomalyScore(1.0);
+        influencer.setInfluencerScore(1.0);
-        influencer.setInitialAnomalyScore(2.0);
+        influencer.setInitialInfluencerScore(2.0);
         influencer.setProbability(0.05);
     }
 
@@ -66,7 +66,7 @@ public class InfluencerNormalizableTests extends ESTestCase {
         normalizable.setNormalizedScore(99.0);
 
         assertEquals(99.0, normalizable.getNormalizedScore(), EPSILON);
-        assertEquals(99.0, influencer.getAnomalyScore(), EPSILON);
+        assertEquals(99.0, influencer.getInfluencerScore(), EPSILON);
     }
 
     public void testGetChildrenTypes() {

plugin/src/test/java/org/elasticsearch/xpack/ml/job/process/normalizer/ScoresUpdaterTests.java

@@ -124,7 +124,6 @@ public class ScoresUpdaterTests extends ESTestCase {
         Bucket bucket = generateBucket(new Date(0));
         bucket.setAnomalyScore(42.0);
         bucket.addBucketInfluencer(createTimeBucketInfluencer(bucket.getTimestamp(), 0.04, 42.0));
-        bucket.setMaxNormalizedProbability(50.0);
 
         Deque<Bucket> buckets = new ArrayDeque<>();
         buckets.add(bucket);
@@ -166,14 +165,12 @@ public class ScoresUpdaterTests extends ESTestCase {
             Bucket bucket = generateBucket(new Date(i * 1000));
             bucket.setAnomalyScore(42.0);
             bucket.addBucketInfluencer(createTimeBucketInfluencer(bucket.getTimestamp(), 0.04, 42.0));
-            bucket.setMaxNormalizedProbability(50.0);
             batch1.add(bucket);
         }
 
         Bucket secondBatchBucket = generateBucket(new Date(10000 * 1000));
         secondBatchBucket.addBucketInfluencer(createTimeBucketInfluencer(secondBatchBucket.getTimestamp(), 0.04, 42.0));
         secondBatchBucket.setAnomalyScore(42.0);
-        secondBatchBucket.setMaxNormalizedProbability(50.0);
         Deque<Bucket> batch2 = new ArrayDeque<>();
         batch2.add(secondBatchBucket);
 
@@ -193,7 +190,6 @@ public class ScoresUpdaterTests extends ESTestCase {
         Bucket bucket1 = generateBucket(new Date(0));
         bucket1.setAnomalyScore(42.0);
         bucket1.addBucketInfluencer(createTimeBucketInfluencer(bucket1.getTimestamp(), 0.04, 42.0));
-        bucket1.setMaxNormalizedProbability(50.0);
         List<BatchedResultsIterator.ResultWithIndex<AnomalyRecord>> records = new ArrayList<>();
         Date date = new Date();
         for (int i=0; i<100000; i++) {
@@ -203,7 +199,6 @@ public class ScoresUpdaterTests extends ESTestCase {
         Bucket bucket2 = generateBucket(new Date(10000 * 1000));
         bucket2.addBucketInfluencer(createTimeBucketInfluencer(bucket2.getTimestamp(), 0.04, 42.0));
         bucket2.setAnomalyScore(42.0);
-        bucket2.setMaxNormalizedProbability(50.0);
 
         Deque<Bucket> batch = new ArrayDeque<>();
         batch.add(bucket1);
@@ -240,7 +235,6 @@ public class ScoresUpdaterTests extends ESTestCase {
         Bucket bucket = generateBucket(new Date(2509200000L));
         bucket.setAnomalyScore(42.0);
         bucket.addBucketInfluencer(createTimeBucketInfluencer(bucket.getTimestamp(), 0.04, 42.0));
-        bucket.setMaxNormalizedProbability(50.0);
 
         Deque<Bucket> buckets = new ArrayDeque<>();
         buckets.add(bucket);
@@ -258,7 +252,6 @@ public class ScoresUpdaterTests extends ESTestCase {
         Bucket bucket = generateBucket(new Date(3600000));
         bucket.setAnomalyScore(42.0);
         bucket.addBucketInfluencer(createTimeBucketInfluencer(bucket.getTimestamp(), 0.04, 42.0));
-        bucket.setMaxNormalizedProbability(50.0);
 
         Deque<Bucket> buckets = new ArrayDeque<>();
         buckets.add(bucket);
@@ -277,7 +270,6 @@ public class ScoresUpdaterTests extends ESTestCase {
         Bucket bucket = generateBucket(new Date(2700000));
         bucket.setAnomalyScore(42.0);
         bucket.addBucketInfluencer(createTimeBucketInfluencer(bucket.getTimestamp(), 0.04, 42.0));
-        bucket.setMaxNormalizedProbability(50.0);
 
         Deque<Bucket> buckets = new ArrayDeque<>();
         buckets.add(bucket);

plugin/src/test/java/org/elasticsearch/xpack/ml/job/results/AnomalyRecordTests.java

@@ -30,10 +30,9 @@ public class AnomalyRecordTests extends AbstractSerializingTestCase<AnomalyRecor
         AnomalyRecord anomalyRecord = new AnomalyRecord(jobId, new Date(randomNonNegativeLong()), randomNonNegativeLong(), sequenceNum);
         anomalyRecord.setActual(Collections.singletonList(randomDouble()));
         anomalyRecord.setTypical(Collections.singletonList(randomDouble()));
-        anomalyRecord.setAnomalyScore(randomDouble());
         anomalyRecord.setProbability(randomDouble());
-        anomalyRecord.setNormalizedProbability(randomDouble());
+        anomalyRecord.setRecordScore(randomDouble());
-        anomalyRecord.setInitialNormalizedProbability(randomDouble());
+        anomalyRecord.setInitialRecordScore(randomDouble());
         anomalyRecord.setInterim(randomBoolean());
         if (randomBoolean()) {
             anomalyRecord.setFieldName(randomAsciiOfLength(12));

plugin/src/test/java/org/elasticsearch/xpack/ml/job/results/BucketTests.java

@@ -52,9 +52,6 @@ public class BucketTests extends AbstractSerializingTestCase<Bucket> {
         if (randomBoolean()) {
             bucket.setInterim(randomBoolean());
         }
-        if (randomBoolean()) {
-            bucket.setMaxNormalizedProbability(randomDouble());
-        }
         if (randomBoolean()) {
             int size = randomInt(10);
             List<PartitionScore> partitionScores = new ArrayList<>(size);
@@ -130,16 +127,6 @@ public class BucketTests extends AbstractSerializingTestCase<Bucket> {
         assertTrue(b1.equals(b2));
     }
 
-    public void testEquals_GivenDifferentMaxNormalizedProbability() {
-        Bucket bucket1 = new Bucket("foo", new Date(123), 123);
-        bucket1.setMaxNormalizedProbability(55.0);
-        Bucket bucket2 = new Bucket("foo", new Date(123), 123);
-        bucket2.setMaxNormalizedProbability(55.1);
-
-        assertFalse(bucket1.equals(bucket2));
-        assertFalse(bucket2.equals(bucket1));
-    }
-
     public void testEquals_GivenDifferentEventCount() {
         Bucket bucket1 = new Bucket("foo", new Date(123), 123);
         bucket1.setEventCount(3);
@@ -183,9 +170,9 @@ public class BucketTests extends AbstractSerializingTestCase<Bucket> {
 
     public void testEquals_GivenSameNumberOfRecordsButDifferent() {
         AnomalyRecord anomalyRecord1 = new AnomalyRecord("foo", new Date(123), 123, 1);
-        anomalyRecord1.setAnomalyScore(1.0);
+        anomalyRecord1.setRecordScore(1.0);
         AnomalyRecord anomalyRecord2 = new AnomalyRecord("foo", new Date(123), 123, 2);
-        anomalyRecord1.setAnomalyScore(2.0);
+        anomalyRecord1.setRecordScore(2.0);
 
         Bucket bucket1 = new Bucket("foo", new Date(123), 123);
         bucket1.setRecords(Arrays.asList(anomalyRecord1));
@@ -231,7 +218,6 @@ public class BucketTests extends AbstractSerializingTestCase<Bucket> {
         bucket1.setInitialAnomalyScore(92.0);
         bucket1.setEventCount(134);
         bucket1.setInterim(true);
-        bucket1.setMaxNormalizedProbability(33.3);
         bucket1.setRecordCount(4);
         bucket1.setRecords(Arrays.asList(record));
         bucket1.addBucketInfluencer(bucketInfluencer);
@@ -241,7 +227,6 @@ public class BucketTests extends AbstractSerializingTestCase<Bucket> {
         bucket2.setInitialAnomalyScore(92.0);
         bucket2.setEventCount(134);
         bucket2.setInterim(true);
-        bucket2.setMaxNormalizedProbability(33.3);
         bucket2.setRecordCount(4);
         bucket2.setRecords(Arrays.asList(record));
         bucket2.addBucketInfluencer(bucketInfluencer);

plugin/src/test/java/org/elasticsearch/xpack/ml/job/results/InfluencerTests.java

@@ -16,12 +16,12 @@ import java.util.Date;
 
 public class InfluencerTests extends AbstractSerializingTestCase<Influencer> {
 
-    public  Influencer createTestInstance(String jobId) {
+    public Influencer createTestInstance(String jobId) {
         Influencer influencer = new Influencer(jobId, randomAsciiOfLengthBetween(1, 20), randomAsciiOfLengthBetween(1, 20),
                 new Date(randomNonNegativeLong()), randomNonNegativeLong(), randomIntBetween(1, 1000));
         influencer.setInterim(randomBoolean());
-        influencer.setAnomalyScore(randomDouble());
+        influencer.setInfluencerScore(randomDouble());
-        influencer.setInitialAnomalyScore(randomDouble());
+        influencer.setInitialInfluencerScore(randomDouble());
         influencer.setProbability(randomDouble());
         return influencer;
     }
@@ -50,12 +50,12 @@ public class InfluencerTests extends AbstractSerializingTestCase<Influencer> {
     }
 
     public void testToXContentDoesNotIncludeNameValueFieldWhenReservedWord() throws IOException {
-        Influencer influencer = new Influencer("foo", AnomalyRecord.ANOMALY_SCORE.getPreferredName(), "bar", new Date(), 300L, 0);
+        Influencer influencer = new Influencer("foo", Influencer.INFLUENCER_SCORE.getPreferredName(), "bar", new Date(), 300L, 0);
         XContentBuilder builder = toXContent(influencer, XContentType.JSON);
         XContentParser parser = createParser(builder);
-        Object serialisedFieldValue = parser.map().get(AnomalyRecord.ANOMALY_SCORE.getPreferredName());
+        Object serialisedFieldValue = parser.map().get(Influencer.INFLUENCER_SCORE.getPreferredName());
+        assertNotNull(serialisedFieldValue);
         assertNotEquals("bar", serialisedFieldValue);
-        assertEquals(0.0, (Double)serialisedFieldValue, 0.0001);
     }
 
 }

plugin/src/test/java/org/elasticsearch/xpack/ml/job/results/PerPartitionMaxProbabilitiesTests.java

@@ -52,9 +52,9 @@ public class PerPartitionMaxProbabilitiesTests extends AbstractSerializingTestCa
         assertEquals(2, pProbs.size());
         for (PerPartitionMaxProbabilities.PartitionProbability pProb : pProbs) {
             if (pProb.getPartitionValue().equals("A")) {
-                assertEquals(40.0, pProb.getMaxNormalizedProbability(), 0.0001);
+                assertEquals(40.0, pProb.getMaxRecordScore(), 0.0001);
             } else {
-                assertEquals(90.0, pProb.getMaxNormalizedProbability(), 0.0001);
+                assertEquals(90.0, pProb.getMaxRecordScore(), 0.0001);
             }
         }
     }
@@ -73,10 +73,10 @@ public class PerPartitionMaxProbabilitiesTests extends AbstractSerializingTestCa
         assertEquals(90.0, ppMax.getMaxProbabilityForPartition("B"), 0.0001);
     }
 
-    private AnomalyRecord createAnomalyRecord(String partitionFieldValue, double normalizedProbability) {
+    private AnomalyRecord createAnomalyRecord(String partitionFieldValue, double recordScore) {
         AnomalyRecord record = new AnomalyRecord("foo", new Date(), 600, 1);
         record.setPartitionFieldValue(partitionFieldValue);
-        record.setNormalizedProbability(normalizedProbability);
+        record.setRecordScore(recordScore);
         return record;
     }
 }

plugin/src/test/resources/rest-api-spec/api/xpack.ml.get_buckets.json

@@ -50,10 +50,6 @@
         "anomaly_score": {
           "type": "double",
           "description": "Filter for the most anomalous buckets"
-        },
-        "max_normalized_probability": {
-          "type": "double",
-          "description": "Filter for buckets containing the most anomalous records"
         }
       }
     },

plugin/src/test/resources/rest-api-spec/api/xpack.ml.get_influencers.json

@@ -31,9 +31,9 @@
           "type": "string",
           "description": "end timestamp for the requested influencers"
         },
-        "anomaly_score": {
+        "influencer_score": {
           "type": "double",
-          "description": "anomaly score threshold for the requested influencers"
+          "description": "influencer score threshold for the requested influencers"
         },
         "sort": {
           "type": "string",

plugin/src/test/resources/rest-api-spec/api/xpack.ml.get_records.json

@@ -36,10 +36,7 @@
           "type": "string",
           "description": "End time filter for records"
         },
-        "anomaly_score": {
+        "record_score": {
-          "type": "double"
-        },
-        "max_normalized_probability": {
           "type": "double"
         },
         "partition_value": {

plugin/src/test/resources/rest-api-spec/test/ml/jobs_get_result_influencers.yaml

@@ -10,8 +10,8 @@ setup:
                   type: keyword
                 "timestamp":
                   type: date
-                "anomaly_score":
+                "influencer_score":
-                  type: float
+                  type: double
                 "result_type":
                   type: keyword
   - do:
@@ -25,7 +25,7 @@ setup:
             "timestamp": "2016-06-01T00:00:00Z",
             "influencer_field_name": "foo",
             "influencer_field_value": "bar",
-            "anomaly_score": 80.0,
+            "influencer_score": 80.0,
             "result_type" : "influencer",
             "bucket_span" : 1,
             "sequence_num" : 1
@@ -42,7 +42,7 @@ setup:
             "timestamp": "2016-06-02T00:00:00Z",
             "influencer_field_name": "foo",
             "influencer_field_value": "zoo",
-            "anomaly_score": 50.0,
+            "influencer_score": 50.0,
             "result_type" : "influencer",
             "bucket_span" : 1,
             "sequence_num" : 2

plugin/src/test/resources/rest-api-spec/test/ml/jobs_get_result_records.yaml

@@ -23,7 +23,7 @@ setup:
             "job_id": "farequote",
             "result_type": "record",
             "timestamp": "2016-06-01T00:00:00Z",
-            "anomaly_score": 60.0,
+            "record_score": 60.0,
             "bucket_span": 1,
             "sequence_num": 1
           }
@@ -38,7 +38,7 @@ setup:
               "job_id": "farequote",
               "result_type": "record",
               "timestamp": "2016-06-02T00:00:00Z",
-              "anomaly_score": 80.0,
+              "record_score": 80.0,
               "bucket_span": 1,
               "sequence_num": 2
             }

plugin/src/test/resources/rest-api-spec/test/ml/revert_model_snapshot.yaml

@@ -101,7 +101,7 @@ setup:
                   "timestamp": "2016-06-02T00:00:00Z",
                   "influencer_field_name": "foo",
                   "influencer_field_value": "zoo",
-                  "anomaly_score": 50.0,
+                  "influencer_score": 50.0,
                   "bucket_span": 1,
                   "sequence_num": 3
                 }
@@ -118,7 +118,7 @@ setup:
               "timestamp": "2016-05-01T00:00:00Z",
               "influencer_field_name": "foo",
               "influencer_field_value": "zoo",
-              "anomaly_score": 50.0,
+              "influencer_score": 50.0,
               "bucket_span": 1,
               "sequence_num": 4
             }