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[7.x][ML] Rename outlier score setting to feature_influence_threshold (#43705) (#43734) 

Renames outlier score setting `minimum_score_to_write_feature_influence`
to `feature_influence_threshold`.
This commit is contained in:
Dimitris Athanasiou 2019-06-28 13:28:25 +03:00 committed by GitHub
parent cab879118d
commit 86c853a7c2
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 41 additions and 44 deletions
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33
client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/OutlierDetection.java
View File

@ -47,8 +47,7 @@ public class OutlierDetection implements DataFrameAnalysis {
public static final ParseField NAME = new ParseField("outlier_detection");
static final ParseField N_NEIGHBORS = new ParseField("n_neighbors");
static final ParseField METHOD = new ParseField("method");
public static final ParseField MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE =
new ParseField("minimum_score_to_write_feature_influence");
public static final ParseField FEATURE_INFLUENCE_THRESHOLD = new ParseField("feature_influence_threshold");
private static ObjectParser<Builder, Void> PARSER = new ObjectParser<>(NAME.getPreferredName(), true, Builder::new);
@ -60,23 +59,23 @@ public class OutlierDetection implements DataFrameAnalysis {
}
throw new IllegalArgumentException("Unsupported token [" + p.currentToken() + "]");
}, METHOD, ObjectParser.ValueType.STRING);
PARSER.declareDouble(Builder::setMinScoreToWriteFeatureInfluence, MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE);
PARSER.declareDouble(Builder::setFeatureInfluenceThreshold, FEATURE_INFLUENCE_THRESHOLD);
}
private final Integer nNeighbors;
private final Method method;
private final Double minScoreToWriteFeatureInfluence;
private final Double featureInfluenceThreshold;
/**
* Constructs the outlier detection configuration
* @param nNeighbors The number of neighbors. Leave unspecified for dynamic detection.
* @param method The method. Leave unspecified for a dynamic mixture of methods.
* @param minScoreToWriteFeatureInfluence The min outlier score required to calculate feature influence. Defaults to 0.1.
* @param featureInfluenceThreshold The min outlier score required to calculate feature influence. Defaults to 0.1.
*/
private OutlierDetection(@Nullable Integer nNeighbors, @Nullable Method method, @Nullable Double minScoreToWriteFeatureInfluence) {
private OutlierDetection(@Nullable Integer nNeighbors, @Nullable Method method, @Nullable Double featureInfluenceThreshold) {
this.nNeighbors = nNeighbors;
this.method = method;
this.minScoreToWriteFeatureInfluence = minScoreToWriteFeatureInfluence;
this.featureInfluenceThreshold = featureInfluenceThreshold;
}
@Override
@ -92,8 +91,8 @@ public class OutlierDetection implements DataFrameAnalysis {
return method;
}
public Double getMinScoreToWriteFeatureInfluence() {
return minScoreToWriteFeatureInfluence;
public Double getFeatureInfluenceThreshold() {
return featureInfluenceThreshold;
}
@Override
@ -105,8 +104,8 @@ public class OutlierDetection implements DataFrameAnalysis {
if (method != null) {
builder.field(METHOD.getPreferredName(), method);
}
if (minScoreToWriteFeatureInfluence != null) {
builder.field(MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE.getPreferredName(), minScoreToWriteFeatureInfluence);
if (featureInfluenceThreshold != null) {
builder.field(FEATURE_INFLUENCE_THRESHOLD.getPreferredName(), featureInfluenceThreshold);
}
builder.endObject();
return builder;
@ -120,12 +119,12 @@ public class OutlierDetection implements DataFrameAnalysis {
OutlierDetection other = (OutlierDetection) o;
return Objects.equals(nNeighbors, other.nNeighbors)
&& Objects.equals(method, other.method)
&& Objects.equals(minScoreToWriteFeatureInfluence, other.minScoreToWriteFeatureInfluence);
&& Objects.equals(featureInfluenceThreshold, other.featureInfluenceThreshold);
}
@Override
public int hashCode() {
return Objects.hash(nNeighbors, method, minScoreToWriteFeatureInfluence);
return Objects.hash(nNeighbors, method, featureInfluenceThreshold);
}
@Override
@ -150,7 +149,7 @@ public class OutlierDetection implements DataFrameAnalysis {
private Integer nNeighbors;
private Method method;
private Double minScoreToWriteFeatureInfluence;
private Double featureInfluenceThreshold;
private Builder() {}
@ -164,13 +163,13 @@ public class OutlierDetection implements DataFrameAnalysis {
return this;
}
public Builder setMinScoreToWriteFeatureInfluence(Double minScoreToWriteFeatureInfluence) {
this.minScoreToWriteFeatureInfluence = minScoreToWriteFeatureInfluence;
public Builder setFeatureInfluenceThreshold(Double featureInfluenceThreshold) {
this.featureInfluenceThreshold = featureInfluenceThreshold;
return this;
}
public OutlierDetection build() {
return new OutlierDetection(nNeighbors, method, minScoreToWriteFeatureInfluence);
return new OutlierDetection(nNeighbors, method, featureInfluenceThreshold);
}
}
}

8
client/rest-high-level/src/test/java/org/elasticsearch/client/ml/dataframe/OutlierDetectionTests.java
View File

@ -33,7 +33,7 @@ public class OutlierDetectionTests extends AbstractXContentTestCase<OutlierDetec
return OutlierDetection.builder()
.setNNeighbors(randomBoolean() ? null : randomIntBetween(1, 20))
.setMethod(randomBoolean() ? null : randomFrom(OutlierDetection.Method.values()))
.setMinScoreToWriteFeatureInfluence(randomBoolean() ? null : randomDoubleBetween(0.0, 1.0, true))
.setFeatureInfluenceThreshold(randomBoolean() ? null : randomDoubleBetween(0.0, 1.0, true))
.build();
}
@ -56,7 +56,7 @@ public class OutlierDetectionTests extends AbstractXContentTestCase<OutlierDetec
OutlierDetection outlierDetection = OutlierDetection.createDefault();
assertNull(outlierDetection.getNNeighbors());
assertNull(outlierDetection.getMethod());
assertNull(outlierDetection.getMinScoreToWriteFeatureInfluence());
assertNull(outlierDetection.getFeatureInfluenceThreshold());
}
public void testGetParams_GivenExplicitValues() {
@ -64,10 +64,10 @@ public class OutlierDetectionTests extends AbstractXContentTestCase<OutlierDetec
OutlierDetection.builder()
.setNNeighbors(42)
.setMethod(OutlierDetection.Method.LDOF)
.setMinScoreToWriteFeatureInfluence(0.5)
.setFeatureInfluenceThreshold(0.5)
.build();
assertThat(outlierDetection.getNNeighbors(), equalTo(42));
assertThat(outlierDetection.getMethod(), equalTo(OutlierDetection.Method.LDOF));
assertThat(outlierDetection.getMinScoreToWriteFeatureInfluence(), closeTo(0.5, 1E-9));
assertThat(outlierDetection.getFeatureInfluenceThreshold(), closeTo(0.5, 1E-9));
}
}

34
x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/dataframe/analyses/OutlierDetection.java
View File

@ -27,8 +27,7 @@ public class OutlierDetection implements DataFrameAnalysis {
public static final ParseField N_NEIGHBORS = new ParseField("n_neighbors");
public static final ParseField METHOD = new ParseField("method");
public static final ParseField MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE =
new ParseField("minimum_score_to_write_feature_influence");
public static final ParseField FEATURE_INFLUENCE_THRESHOLD = new ParseField("feature_influence_threshold");
private static final ConstructingObjectParser<OutlierDetection, Void> LENIENT_PARSER = createParser(true);
private static final ConstructingObjectParser<OutlierDetection, Void> STRICT_PARSER = createParser(false);
@ -43,7 +42,7 @@ public class OutlierDetection implements DataFrameAnalysis {
}
throw new IllegalArgumentException("Unsupported token [" + p.currentToken() + "]");
}, METHOD, ObjectParser.ValueType.STRING);
parser.declareDouble(ConstructingObjectParser.optionalConstructorArg(), MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE);
parser.declareDouble(ConstructingObjectParser.optionalConstructorArg(), FEATURE_INFLUENCE_THRESHOLD);
return parser;
}
@ -53,27 +52,26 @@ public class OutlierDetection implements DataFrameAnalysis {
private final Integer nNeighbors;
private final Method method;
private final Double minScoreToWriteFeatureInfluence;
private final Double featureInfluenceThreshold;
/**
* Constructs the outlier detection configuration
* @param nNeighbors The number of neighbors. Leave unspecified for dynamic detection.
* @param method The method. Leave unspecified for a dynamic mixture of methods.
* @param minScoreToWriteFeatureInfluence The min outlier score required to calculate feature influence. Defaults to 0.1.
* @param featureInfluenceThreshold The min outlier score required to calculate feature influence. Defaults to 0.1.
*/
public OutlierDetection(@Nullable Integer nNeighbors, @Nullable Method method, @Nullable Double minScoreToWriteFeatureInfluence) {
public OutlierDetection(@Nullable Integer nNeighbors, @Nullable Method method, @Nullable Double featureInfluenceThreshold) {
if (nNeighbors != null && nNeighbors <= 0) {
throw ExceptionsHelper.badRequestException("[{}] must be a positive integer", N_NEIGHBORS.getPreferredName());
}
if (minScoreToWriteFeatureInfluence != null && (minScoreToWriteFeatureInfluence < 0.0 || minScoreToWriteFeatureInfluence > 1.0)) {
throw ExceptionsHelper.badRequestException("[{}] must be in [0, 1]",
MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE.getPreferredName());
if (featureInfluenceThreshold != null && (featureInfluenceThreshold < 0.0 || featureInfluenceThreshold > 1.0)) {
throw ExceptionsHelper.badRequestException("[{}] must be in [0, 1]", FEATURE_INFLUENCE_THRESHOLD.getPreferredName());
}
this.nNeighbors = nNeighbors;
this.method = method;
this.minScoreToWriteFeatureInfluence = minScoreToWriteFeatureInfluence;
this.featureInfluenceThreshold = featureInfluenceThreshold;
}
/**
@ -86,7 +84,7 @@ public class OutlierDetection implements DataFrameAnalysis {
public OutlierDetection(StreamInput in) throws IOException {
nNeighbors = in.readOptionalVInt();
method = in.readBoolean() ? in.readEnum(Method.class) : null;
minScoreToWriteFeatureInfluence = in.readOptionalDouble();
featureInfluenceThreshold = in.readOptionalDouble();
}
@Override
@ -105,7 +103,7 @@ public class OutlierDetection implements DataFrameAnalysis {
out.writeBoolean(false);
}
out.writeOptionalDouble(minScoreToWriteFeatureInfluence);
out.writeOptionalDouble(featureInfluenceThreshold);
}
@Override
@ -117,8 +115,8 @@ public class OutlierDetection implements DataFrameAnalysis {
if (method != null) {
builder.field(METHOD.getPreferredName(), method);
}
if (minScoreToWriteFeatureInfluence != null) {
builder.field(MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE.getPreferredName(), minScoreToWriteFeatureInfluence);
if (featureInfluenceThreshold != null) {
builder.field(FEATURE_INFLUENCE_THRESHOLD.getPreferredName(), featureInfluenceThreshold);
}
builder.endObject();
return builder;
@ -131,12 +129,12 @@ public class OutlierDetection implements DataFrameAnalysis {
OutlierDetection that = (OutlierDetection) o;
return Objects.equals(nNeighbors, that.nNeighbors)
&& Objects.equals(method, that.method)
&& Objects.equals(minScoreToWriteFeatureInfluence, that.minScoreToWriteFeatureInfluence);
&& Objects.equals(featureInfluenceThreshold, that.featureInfluenceThreshold);
}
@Override
public int hashCode() {
return Objects.hash(nNeighbors, method, minScoreToWriteFeatureInfluence);
return Objects.hash(nNeighbors, method, featureInfluenceThreshold);
}
@Override
@ -148,8 +146,8 @@ public class OutlierDetection implements DataFrameAnalysis {
if (method != null) {
params.put(METHOD.getPreferredName(), method);
}
if (minScoreToWriteFeatureInfluence != null) {
params.put(MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE.getPreferredName(), minScoreToWriteFeatureInfluence);
if (featureInfluenceThreshold != null) {
params.put(FEATURE_INFLUENCE_THRESHOLD.getPreferredName(), featureInfluenceThreshold);
}
return params;
}

2
x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/job/persistence/ElasticsearchMappings.java
View File

@ -432,7 +432,7 @@ public class ElasticsearchMappings {
.startObject(OutlierDetection.METHOD.getPreferredName())
.field(TYPE, KEYWORD)
.endObject()
.startObject(OutlierDetection.MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE.getPreferredName())
.startObject(OutlierDetection.FEATURE_INFLUENCE_THRESHOLD.getPreferredName())
.field(TYPE, DOUBLE)
.endObject()
.endObject()

2
x-pack/plugin/core/src/main/java/org/elasticsearch/xpack/core/ml/job/results/ReservedFieldNames.java
View File

@ -286,7 +286,7 @@ public final class ReservedFieldNames {
OutlierDetection.NAME.getPreferredName(),
OutlierDetection.N_NEIGHBORS.getPreferredName(),
OutlierDetection.METHOD.getPreferredName(),
OutlierDetection.MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE.getPreferredName(),
OutlierDetection.FEATURE_INFLUENCE_THRESHOLD.getPreferredName(),
ElasticsearchMappings.CONFIG_TYPE,

2
x-pack/plugin/core/src/test/java/org/elasticsearch/xpack/core/ml/dataframe/analyses/OutlierDetectionTests.java
View File

@ -53,7 +53,7 @@ public class OutlierDetectionTests extends AbstractSerializingTestCase<OutlierDe
assertThat(params.size(), equalTo(3));
assertThat(params.get(OutlierDetection.N_NEIGHBORS.getPreferredName()), equalTo(42));
assertThat(params.get(OutlierDetection.METHOD.getPreferredName()), equalTo(OutlierDetection.Method.LDOF));
assertThat((Double) params.get(OutlierDetection.MINIMUM_SCORE_TO_WRITE_FEATURE_INFLUENCE.getPreferredName()),
assertThat((Double) params.get(OutlierDetection.FEATURE_INFLUENCE_THRESHOLD.getPreferredName()),
is(closeTo(0.42, 1E-9)));
}
}

4
x-pack/plugin/src/test/resources/rest-api-spec/test/ml/data_frame_analytics_crud.yml
View File

@ -170,7 +170,7 @@ setup:
"outlier_detection":{
"n_neighbors": 5,
"method": "lof",
"minimum_score_to_write_feature_influence": 0.0
"feature_influence_threshold": 0.0
}
}
}
@ -180,7 +180,7 @@ setup:
- match: { dest.index: "index-dest" }
- match: { analysis.outlier_detection.n_neighbors: 5 }
- match: { analysis.outlier_detection.method: "lof" }
- match: { analysis.outlier_detection.minimum_score_to_write_feature_influence: 0.0 }
- match: { analysis.outlier_detection.feature_influence_threshold: 0.0 }
- is_true: create_time
- is_true: version