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Rename to num_top_feature_important_values
1 parent 5283cd9 commit 1f255b1

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14 files changed

+73
-73
lines changed

14 files changed

+73
-73
lines changed

‎client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/Classification.java

+15-15
Original file line numberDiff line numberDiff line change
@@ -46,7 +46,7 @@ public static Builder builder(String dependentVariable) {
4646
static final ParseField ETA = new ParseField("eta");
4747
static final ParseField MAXIMUM_NUMBER_TREES = new ParseField("maximum_number_trees");
4848
static final ParseField FEATURE_BAG_FRACTION = new ParseField("feature_bag_fraction");
49-
static final ParseField TOP_FEATURE_IMPORTANCE_VALUES = new ParseField("top_feature_importance_values");
49+
static final ParseField NUM_TOP_FEATURE_IMPORTANCE_VALUES = new ParseField("num_top_feature_importance_values");
5050
static final ParseField PREDICTION_FIELD_NAME = new ParseField("prediction_field_name");
5151
static final ParseField TRAINING_PERCENT = new ParseField("training_percent");
5252
static final ParseField NUM_TOP_CLASSES = new ParseField("num_top_classes");
@@ -76,7 +76,7 @@ public static Builder builder(String dependentVariable) {
7676
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), ETA);
7777
PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), MAXIMUM_NUMBER_TREES);
7878
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), FEATURE_BAG_FRACTION);
79-
PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), TOP_FEATURE_IMPORTANCE_VALUES);
79+
PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), NUM_TOP_FEATURE_IMPORTANCE_VALUES);
8080
PARSER.declareString(ConstructingObjectParser.optionalConstructorArg(), PREDICTION_FIELD_NAME);
8181
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), TRAINING_PERCENT);
8282
PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), NUM_TOP_CLASSES);
@@ -89,23 +89,23 @@ public static Builder builder(String dependentVariable) {
8989
private final Double eta;
9090
private final Integer maximumNumberTrees;
9191
private final Double featureBagFraction;
92-
private final Integer topFeatureImportanceValues;
92+
private final Integer numTopFeatureImportanceValues;
9393
private final String predictionFieldName;
9494
private final Double trainingPercent;
9595
private final Integer numTopClasses;
9696
private final Long randomizeSeed;
9797

9898
private Classification(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
9999
@Nullable Integer maximumNumberTrees, @Nullable Double featureBagFraction,
100-
@Nullable Integer topFeatureImportanceValues, @Nullable String predictionFieldName,
100+
@Nullable Integer numTopFeatureImportanceValues, @Nullable String predictionFieldName,
101101
@Nullable Double trainingPercent, @Nullable Integer numTopClasses, @Nullable Long randomizeSeed) {
102102
this.dependentVariable = Objects.requireNonNull(dependentVariable);
103103
this.lambda = lambda;
104104
this.gamma = gamma;
105105
this.eta = eta;
106106
this.maximumNumberTrees = maximumNumberTrees;
107107
this.featureBagFraction = featureBagFraction;
108-
this.topFeatureImportanceValues = topFeatureImportanceValues;
108+
this.numTopFeatureImportanceValues = numTopFeatureImportanceValues;
109109
this.predictionFieldName = predictionFieldName;
110110
this.trainingPercent = trainingPercent;
111111
this.numTopClasses = numTopClasses;
@@ -141,8 +141,8 @@ public Double getFeatureBagFraction() {
141141
return featureBagFraction;
142142
}
143143

144-
public Integer getTopFeatureImportanceValues() {
145-
return topFeatureImportanceValues;
144+
public Integer getNumTopFeatureImportanceValues() {
145+
return numTopFeatureImportanceValues;
146146
}
147147

148148
public String getPredictionFieldName() {
@@ -180,8 +180,8 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
180180
if (featureBagFraction != null) {
181181
builder.field(FEATURE_BAG_FRACTION.getPreferredName(), featureBagFraction);
182182
}
183-
if (topFeatureImportanceValues != null) {
184-
builder.field(TOP_FEATURE_IMPORTANCE_VALUES.getPreferredName(), topFeatureImportanceValues);
183+
if (numTopFeatureImportanceValues != null) {
184+
builder.field(NUM_TOP_FEATURE_IMPORTANCE_VALUES.getPreferredName(), numTopFeatureImportanceValues);
185185
}
186186
if (predictionFieldName != null) {
187187
builder.field(PREDICTION_FIELD_NAME.getPreferredName(), predictionFieldName);
@@ -201,7 +201,7 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
201201

202202
@Override
203203
public int hashCode() {
204-
return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, topFeatureImportanceValues,
204+
return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, numTopFeatureImportanceValues,
205205
predictionFieldName, trainingPercent, randomizeSeed, numTopClasses);
206206
}
207207

@@ -216,7 +216,7 @@ public boolean equals(Object o) {
216216
&& Objects.equals(eta, that.eta)
217217
&& Objects.equals(maximumNumberTrees, that.maximumNumberTrees)
218218
&& Objects.equals(featureBagFraction, that.featureBagFraction)
219-
&& Objects.equals(topFeatureImportanceValues, that.topFeatureImportanceValues)
219+
&& Objects.equals(numTopFeatureImportanceValues, that.numTopFeatureImportanceValues)
220220
&& Objects.equals(predictionFieldName, that.predictionFieldName)
221221
&& Objects.equals(trainingPercent, that.trainingPercent)
222222
&& Objects.equals(randomizeSeed, that.randomizeSeed)
@@ -235,7 +235,7 @@ public static class Builder {
235235
private Double eta;
236236
private Integer maximumNumberTrees;
237237
private Double featureBagFraction;
238-
private Integer topFeatureImportanceValues;
238+
private Integer numTopFeatureImportanceValues;
239239
private String predictionFieldName;
240240
private Double trainingPercent;
241241
private Integer numTopClasses;
@@ -270,8 +270,8 @@ public Builder setFeatureBagFraction(Double featureBagFraction) {
270270
return this;
271271
}
272272

273-
public Builder setTopFeatureImportanceValues(Integer topFeatureImportanceValues) {
274-
this.topFeatureImportanceValues = topFeatureImportanceValues;
273+
public Builder setNumTopFeatureImportanceValues(Integer numTopFeatureImportanceValues) {
274+
this.numTopFeatureImportanceValues = numTopFeatureImportanceValues;
275275
return this;
276276
}
277277

@@ -297,7 +297,7 @@ public Builder setNumTopClasses(Integer numTopClasses) {
297297

298298
public Classification build() {
299299
return new Classification(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction,
300-
topFeatureImportanceValues, predictionFieldName, trainingPercent, numTopClasses, randomizeSeed);
300+
numTopFeatureImportanceValues, predictionFieldName, trainingPercent, numTopClasses, randomizeSeed);
301301
}
302302
}
303303
}

‎client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/Regression.java

+17-17
Original file line numberDiff line numberDiff line change
@@ -46,7 +46,7 @@ public static Builder builder(String dependentVariable) {
4646
static final ParseField ETA = new ParseField("eta");
4747
static final ParseField MAXIMUM_NUMBER_TREES = new ParseField("maximum_number_trees");
4848
static final ParseField FEATURE_BAG_FRACTION = new ParseField("feature_bag_fraction");
49-
static final ParseField TOP_FEATURE_IMPORTANCE_VALUES = new ParseField("top_feature_importance_values");
49+
static final ParseField NUM_TOP_FEATURE_IMPORTANCE_VALUES = new ParseField("num_top_feature_importance_values");
5050
static final ParseField PREDICTION_FIELD_NAME = new ParseField("prediction_field_name");
5151
static final ParseField TRAINING_PERCENT = new ParseField("training_percent");
5252
static final ParseField RANDOMIZE_SEED = new ParseField("randomize_seed");
@@ -74,7 +74,7 @@ public static Builder builder(String dependentVariable) {
7474
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), ETA);
7575
PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), MAXIMUM_NUMBER_TREES);
7676
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), FEATURE_BAG_FRACTION);
77-
PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), TOP_FEATURE_IMPORTANCE_VALUES);
77+
PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), NUM_TOP_FEATURE_IMPORTANCE_VALUES);
7878
PARSER.declareString(ConstructingObjectParser.optionalConstructorArg(), PREDICTION_FIELD_NAME);
7979
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), TRAINING_PERCENT);
8080
PARSER.declareLong(ConstructingObjectParser.optionalConstructorArg(), RANDOMIZE_SEED);
@@ -86,22 +86,22 @@ public static Builder builder(String dependentVariable) {
8686
private final Double eta;
8787
private final Integer maximumNumberTrees;
8888
private final Double featureBagFraction;
89-
private final Integer topFeatureImportanceValues;
89+
private final Integer numTopFeatureImportanceValues;
9090
private final String predictionFieldName;
9191
private final Double trainingPercent;
9292
private final Long randomizeSeed;
9393

94-
private Regression(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
94+
private Regression(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
9595
@Nullable Integer maximumNumberTrees, @Nullable Double featureBagFraction,
96-
@Nullable Integer topFeatureImportanceValues, @Nullable String predictionFieldName,
96+
@Nullable Integer numTopFeatureImportanceValues, @Nullable String predictionFieldName,
9797
@Nullable Double trainingPercent, @Nullable Long randomizeSeed) {
9898
this.dependentVariable = Objects.requireNonNull(dependentVariable);
9999
this.lambda = lambda;
100100
this.gamma = gamma;
101101
this.eta = eta;
102102
this.maximumNumberTrees = maximumNumberTrees;
103103
this.featureBagFraction = featureBagFraction;
104-
this.topFeatureImportanceValues = topFeatureImportanceValues;
104+
this.numTopFeatureImportanceValues = numTopFeatureImportanceValues;
105105
this.predictionFieldName = predictionFieldName;
106106
this.trainingPercent = trainingPercent;
107107
this.randomizeSeed = randomizeSeed;
@@ -136,8 +136,8 @@ public Double getFeatureBagFraction() {
136136
return featureBagFraction;
137137
}
138138

139-
public Integer getTopFeatureImportanceValues() {
140-
return topFeatureImportanceValues;
139+
public Integer getNumTopFeatureImportanceValues() {
140+
return numTopFeatureImportanceValues;
141141
}
142142

143143
public String getPredictionFieldName() {
@@ -171,8 +171,8 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
171171
if (featureBagFraction != null) {
172172
builder.field(FEATURE_BAG_FRACTION.getPreferredName(), featureBagFraction);
173173
}
174-
if (topFeatureImportanceValues != null) {
175-
builder.field(TOP_FEATURE_IMPORTANCE_VALUES.getPreferredName(), topFeatureImportanceValues);
174+
if (numTopFeatureImportanceValues != null) {
175+
builder.field(NUM_TOP_FEATURE_IMPORTANCE_VALUES.getPreferredName(), numTopFeatureImportanceValues);
176176
}
177177
if (predictionFieldName != null) {
178178
builder.field(PREDICTION_FIELD_NAME.getPreferredName(), predictionFieldName);
@@ -189,7 +189,7 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
189189

190190
@Override
191191
public int hashCode() {
192-
return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, topFeatureImportanceValues,
192+
return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, numTopFeatureImportanceValues,
193193
predictionFieldName, trainingPercent, randomizeSeed);
194194
}
195195

@@ -204,7 +204,7 @@ public boolean equals(Object o) {
204204
&& Objects.equals(eta, that.eta)
205205
&& Objects.equals(maximumNumberTrees, that.maximumNumberTrees)
206206
&& Objects.equals(featureBagFraction, that.featureBagFraction)
207-
&& Objects.equals(topFeatureImportanceValues, that.topFeatureImportanceValues)
207+
&& Objects.equals(numTopFeatureImportanceValues, that.numTopFeatureImportanceValues)
208208
&& Objects.equals(predictionFieldName, that.predictionFieldName)
209209
&& Objects.equals(trainingPercent, that.trainingPercent)
210210
&& Objects.equals(randomizeSeed, that.randomizeSeed);
@@ -222,7 +222,7 @@ public static class Builder {
222222
private Double eta;
223223
private Integer maximumNumberTrees;
224224
private Double featureBagFraction;
225-
private Integer topFeatureImportanceValues;
225+
private Integer numTopFeatureImportanceValues;
226226
private String predictionFieldName;
227227
private Double trainingPercent;
228228
private Long randomizeSeed;
@@ -256,8 +256,8 @@ public Builder setFeatureBagFraction(Double featureBagFraction) {
256256
return this;
257257
}
258258

259-
public Builder setTopFeatureImportanceValues(Integer topFeatureImportanceValues) {
260-
this.topFeatureImportanceValues = topFeatureImportanceValues;
259+
public Builder setNumTopFeatureImportanceValues(Integer numTopFeatureImportanceValues) {
260+
this.numTopFeatureImportanceValues = numTopFeatureImportanceValues;
261261
return this;
262262
}
263263

@@ -277,8 +277,8 @@ public Builder setRandomizeSeed(Long randomizeSeed) {
277277
}
278278

279279
public Regression build() {
280-
return new Regression(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, topFeatureImportanceValues,
281-
predictionFieldName, trainingPercent, randomizeSeed);
280+
return new Regression(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction,
281+
numTopFeatureImportanceValues, predictionFieldName, trainingPercent, randomizeSeed);
282282
}
283283
}
284284
}

‎client/rest-high-level/src/test/java/org/elasticsearch/client/MachineLearningIT.java

+2-2
Original file line numberDiff line numberDiff line change
@@ -1299,7 +1299,7 @@ public void testPutDataFrameAnalyticsConfig_GivenRegression() throws Exception {
12991299
.setEta(1.0)
13001300
.setMaximumNumberTrees(10)
13011301
.setFeatureBagFraction(0.5)
1302-
.setTopFeatureImportanceValues(3)
1302+
.setNumTopFeatureImportanceValues(3)
13031303
.build())
13041304
.setDescription("this is a regression")
13051305
.build();
@@ -1342,7 +1342,7 @@ public void testPutDataFrameAnalyticsConfig_GivenClassification() throws Excepti
13421342
.setEta(1.0)
13431343
.setMaximumNumberTrees(10)
13441344
.setFeatureBagFraction(0.5)
1345-
.setTopFeatureImportanceValues(3)
1345+
.setNumTopFeatureImportanceValues(3)
13461346
.build())
13471347
.setDescription("this is a classification")
13481348
.build();

‎client/rest-high-level/src/test/java/org/elasticsearch/client/documentation/MlClientDocumentationIT.java

+3-3
Original file line numberDiff line numberDiff line change
@@ -2975,7 +2975,7 @@ public void testPutDataFrameAnalytics() throws Exception {
29752975
.setEta(5.5) // <4>
29762976
.setMaximumNumberTrees(50) // <5>
29772977
.setFeatureBagFraction(0.4) // <6>
2978-
.setTopFeatureImportanceValues(3) // <7>
2978+
.setNumTopFeatureImportanceValues(3) // <7>
29792979
.setPredictionFieldName("my_prediction_field_name") // <8>
29802980
.setTrainingPercent(50.0) // <9>
29812981
.setRandomizeSeed(1234L) // <10>
@@ -2990,7 +2990,7 @@ public void testPutDataFrameAnalytics() throws Exception {
29902990
.setEta(5.5) // <4>
29912991
.setMaximumNumberTrees(50) // <5>
29922992
.setFeatureBagFraction(0.4) // <6>
2993-
.setTopFeatureImportanceValues(3) // <7>
2993+
.setNumTopFeatureImportanceValues(3) // <7>
29942994
.setPredictionFieldName("my_prediction_field_name") // <8>
29952995
.setTrainingPercent(50.0) // <9>
29962996
.setRandomizeSeed(1234L) // <10>
@@ -3672,7 +3672,7 @@ public void testPutTrainedModel() throws Exception {
36723672
}
36733673
{
36743674
PutTrainedModelRequest request = new PutTrainedModelRequest(trainedModelConfig);
3675-
3675+
36763676
// tag::put-trained-model-execute-listener
36773677
ActionListener<PutTrainedModelResponse> listener = new ActionListener<>() {
36783678
@Override

‎client/rest-high-level/src/test/java/org/elasticsearch/client/ml/dataframe/ClassificationTests.java

+1-1
Original file line numberDiff line numberDiff line change
@@ -32,7 +32,7 @@ public static Classification randomClassification() {
3232
.setEta(randomBoolean() ? null : randomDoubleBetween(0.001, 1.0, true))
3333
.setMaximumNumberTrees(randomBoolean() ? null : randomIntBetween(1, 2000))
3434
.setFeatureBagFraction(randomBoolean() ? null : randomDoubleBetween(0.0, 1.0, false))
35-
.setTopFeatureImportanceValues(randomBoolean() ? null : randomIntBetween(0, Integer.MAX_VALUE))
35+
.setNumTopFeatureImportanceValues(randomBoolean() ? null : randomIntBetween(0, Integer.MAX_VALUE))
3636
.setPredictionFieldName(randomBoolean() ? null : randomAlphaOfLength(10))
3737
.setTrainingPercent(randomBoolean() ? null : randomDoubleBetween(1.0, 100.0, true))
3838
.setRandomizeSeed(randomBoolean() ? null : randomLong())

‎client/rest-high-level/src/test/java/org/elasticsearch/client/ml/dataframe/RegressionTests.java

+1-1
Original file line numberDiff line numberDiff line change
@@ -32,7 +32,7 @@ public static Regression randomRegression() {
3232
.setEta(randomBoolean() ? null : randomDoubleBetween(0.001, 1.0, true))
3333
.setMaximumNumberTrees(randomBoolean() ? null : randomIntBetween(1, 2000))
3434
.setFeatureBagFraction(randomBoolean() ? null : randomDoubleBetween(0.0, 1.0, false))
35-
.setTopFeatureImportanceValues(randomBoolean() ? null : randomIntBetween(0, Integer.MAX_VALUE))
35+
.setNumTopFeatureImportanceValues(randomBoolean() ? null : randomIntBetween(0, Integer.MAX_VALUE))
3636
.setPredictionFieldName(randomBoolean() ? null : randomAlphaOfLength(10))
3737
.setTrainingPercent(randomBoolean() ? null : randomDoubleBetween(1.0, 100.0, true))
3838
.build();

‎docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc

+4-4
Original file line numberDiff line numberDiff line change
@@ -148,9 +148,9 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
148148
(Optional, long)
149149
include::{docdir}/ml/ml-shared.asciidoc[tag=randomize-seed]
150150

151-
`analysis`.`classification`.`top_feature_importance_values`::::
151+
`analysis`.`classification`.`num_top_feature_importance_values`::::
152152
(Optional, integer)
153-
include::{docdir}/ml/ml-shared.asciidoc[tag=top-feature-importance-values]
153+
include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-feature-importance-values]
154154

155155
`analysis`.`classification`.`training_percent`::::
156156
(Optional, integer)
@@ -231,9 +231,9 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=lambda]
231231
(Optional, string)
232232
include::{docdir}/ml/ml-shared.asciidoc[tag=prediction-field-name]
233233

234-
`analysis`.`regression`.`top_feature_importance_values`::::
234+
`analysis`.`regression`.`num_top_feature_importance_values`::::
235235
(Optional, integer)
236-
include::{docdir}/ml/ml-shared.asciidoc[tag=top-feature-importance-values]
236+
include::{docdir}/ml/ml-shared.asciidoc[tag=num-top-feature-importance-values]
237237

238238
`analysis`.`regression`.`training_percent`::::
239239
(Optional, integer)

‎docs/reference/ml/ml-shared.asciidoc

+2-2
Original file line numberDiff line numberDiff line change
@@ -640,12 +640,12 @@ tag::indices[]
640640
An array of index names. Wildcards are supported. For example:
641641
`["it_ops_metrics", "server*"]`.
642642

643-
tag::top-feature-importance-values[]
643+
tag::num-top-feature-importance-values[]
644644
Advanced configuration option. If set, feature importance for the top
645645
most important features will be computed. Importance is calculated
646646
using the SHAP (SHapley Additive exPlanations) method as described in
647647
https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf[Lundberg, S. M., & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. In NeurIPS 2017.].
648-
end::top-feature-importance-values[]
648+
end::num-top-feature-importance-values[]
649649

650650
+
651651
--

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