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[ML] Introduce randomize_seed setting for regression and classification #49990

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Dec 10, 2019
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[ML] Introduce randomize_seed setting for regression and classification #49990

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cb520d9
[ML] Introduce randomize_seed setting for regression and classification
dimitris-athanasiou Dec 2, 2019
a349e73
Fix tests
dimitris-athanasiou Dec 9, 2019
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27 changes: 23 additions & 4 deletions ...t/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/Classification.java
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Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,7 @@ public static Builder builder(String dependentVariable) {
static final ParseField PREDICTION_FIELD_NAME = new ParseField("prediction_field_name");
static final ParseField TRAINING_PERCENT = new ParseField("training_percent");
static final ParseField NUM_TOP_CLASSES = new ParseField("num_top_classes");
static final ParseField RANDOMIZE_SEED = new ParseField("randomize_seed");

private static final ConstructingObjectParser<Classification, Void> PARSER =
new ConstructingObjectParser<>(
Expand All @@ -63,7 +64,8 @@ public static Builder builder(String dependentVariable) {
(Double) a[5],
(String) a[6],
(Double) a[7],
(Integer) a[8]));
(Integer) a[8],
(Long) a[9]));

static {
PARSER.declareString(ConstructingObjectParser.constructorArg(), DEPENDENT_VARIABLE);
Expand All @@ -75,6 +77,7 @@ public static Builder builder(String dependentVariable) {
PARSER.declareString(ConstructingObjectParser.optionalConstructorArg(), PREDICTION_FIELD_NAME);
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), TRAINING_PERCENT);
PARSER.declareInt(ConstructingObjectParser.optionalConstructorArg(), NUM_TOP_CLASSES);
PARSER.declareLong(ConstructingObjectParser.optionalConstructorArg(), RANDOMIZE_SEED);
}

private final String dependentVariable;
Expand All @@ -86,10 +89,11 @@ public static Builder builder(String dependentVariable) {
private final String predictionFieldName;
private final Double trainingPercent;
private final Integer numTopClasses;
private final Long randomizeSeed;

private Classification(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
@Nullable Integer maximumNumberTrees, @Nullable Double featureBagFraction, @Nullable String predictionFieldName,
@Nullable Double trainingPercent, @Nullable Integer numTopClasses) {
@Nullable Double trainingPercent, @Nullable Integer numTopClasses, @Nullable Long randomizeSeed) {
this.dependentVariable = Objects.requireNonNull(dependentVariable);
this.lambda = lambda;
this.gamma = gamma;
Expand All @@ -99,6 +103,7 @@ private Classification(String dependentVariable, @Nullable Double lambda, @Nulla
this.predictionFieldName = predictionFieldName;
this.trainingPercent = trainingPercent;
this.numTopClasses = numTopClasses;
this.randomizeSeed = randomizeSeed;
}

@Override
Expand Down Expand Up @@ -138,6 +143,10 @@ public Double getTrainingPercent() {
return trainingPercent;
}

public Long getRandomizeSeed() {
return randomizeSeed;
}

public Integer getNumTopClasses() {
return numTopClasses;
}
Expand Down Expand Up @@ -167,6 +176,9 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
if (trainingPercent != null) {
builder.field(TRAINING_PERCENT.getPreferredName(), trainingPercent);
}
if (randomizeSeed != null) {
builder.field(RANDOMIZE_SEED.getPreferredName(), randomizeSeed);
}
if (numTopClasses != null) {
builder.field(NUM_TOP_CLASSES.getPreferredName(), numTopClasses);
}
Expand All @@ -177,7 +189,7 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
@Override
public int hashCode() {
return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName,
trainingPercent, numTopClasses);
trainingPercent, randomizeSeed, numTopClasses);
}

@Override
Expand All @@ -193,6 +205,7 @@ public boolean equals(Object o) {
&& Objects.equals(featureBagFraction, that.featureBagFraction)
&& Objects.equals(predictionFieldName, that.predictionFieldName)
&& Objects.equals(trainingPercent, that.trainingPercent)
&& Objects.equals(randomizeSeed, that.randomizeSeed)
&& Objects.equals(numTopClasses, that.numTopClasses);
}

Expand All @@ -211,6 +224,7 @@ public static class Builder {
private String predictionFieldName;
private Double trainingPercent;
private Integer numTopClasses;
private Long randomizeSeed;

private Builder(String dependentVariable) {
this.dependentVariable = Objects.requireNonNull(dependentVariable);
Expand Down Expand Up @@ -251,14 +265,19 @@ public Builder setTrainingPercent(Double trainingPercent) {
return this;
}

public Builder setRandomizeSeed(Long randomizeSeed) {
this.randomizeSeed = randomizeSeed;
return this;
}

public Builder setNumTopClasses(Integer numTopClasses) {
this.numTopClasses = numTopClasses;
return this;
}

public Classification build() {
return new Classification(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName,
trainingPercent, numTopClasses);
trainingPercent, numTopClasses, randomizeSeed);
}
}
}
29 changes: 24 additions & 5 deletions client/rest-high-level/src/main/java/org/elasticsearch/client/ml/dataframe/Regression.java
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Expand Up @@ -48,6 +48,7 @@ public static Builder builder(String dependentVariable) {
static final ParseField FEATURE_BAG_FRACTION = new ParseField("feature_bag_fraction");
static final ParseField PREDICTION_FIELD_NAME = new ParseField("prediction_field_name");
static final ParseField TRAINING_PERCENT = new ParseField("training_percent");
static final ParseField RANDOMIZE_SEED = new ParseField("randomize_seed");

private static final ConstructingObjectParser<Regression, Void> PARSER =
new ConstructingObjectParser<>(
Expand All @@ -61,7 +62,8 @@ public static Builder builder(String dependentVariable) {
(Integer) a[4],
(Double) a[5],
(String) a[6],
(Double) a[7]));
(Double) a[7],
(Long) a[8]));

static {
PARSER.declareString(ConstructingObjectParser.constructorArg(), DEPENDENT_VARIABLE);
Expand All @@ -72,6 +74,7 @@ public static Builder builder(String dependentVariable) {
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), FEATURE_BAG_FRACTION);
PARSER.declareString(ConstructingObjectParser.optionalConstructorArg(), PREDICTION_FIELD_NAME);
PARSER.declareDouble(ConstructingObjectParser.optionalConstructorArg(), TRAINING_PERCENT);
PARSER.declareLong(ConstructingObjectParser.optionalConstructorArg(), RANDOMIZE_SEED);
}

private final String dependentVariable;
Expand All @@ -82,10 +85,11 @@ public static Builder builder(String dependentVariable) {
private final Double featureBagFraction;
private final String predictionFieldName;
private final Double trainingPercent;
private final Long randomizeSeed;

private Regression(String dependentVariable, @Nullable Double lambda, @Nullable Double gamma, @Nullable Double eta,
@Nullable Integer maximumNumberTrees, @Nullable Double featureBagFraction, @Nullable String predictionFieldName,
@Nullable Double trainingPercent) {
@Nullable Double trainingPercent, @Nullable Long randomizeSeed) {
this.dependentVariable = Objects.requireNonNull(dependentVariable);
this.lambda = lambda;
this.gamma = gamma;
Expand All @@ -94,6 +98,7 @@ private Regression(String dependentVariable, @Nullable Double lambda, @Nullable
this.featureBagFraction = featureBagFraction;
this.predictionFieldName = predictionFieldName;
this.trainingPercent = trainingPercent;
this.randomizeSeed = randomizeSeed;
}

@Override
Expand Down Expand Up @@ -133,6 +138,10 @@ public Double getTrainingPercent() {
return trainingPercent;
}

public Long getRandomizeSeed() {
return randomizeSeed;
}

@Override
public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException {
builder.startObject();
Expand All @@ -158,14 +167,17 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
if (trainingPercent != null) {
builder.field(TRAINING_PERCENT.getPreferredName(), trainingPercent);
}
if (randomizeSeed != null) {
builder.field(RANDOMIZE_SEED.getPreferredName(), randomizeSeed);
}
builder.endObject();
return builder;
}

@Override
public int hashCode() {
return Objects.hash(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName,
trainingPercent);
trainingPercent, randomizeSeed);
}

@Override
Expand All @@ -180,7 +192,8 @@ public boolean equals(Object o) {
&& Objects.equals(maximumNumberTrees, that.maximumNumberTrees)
&& Objects.equals(featureBagFraction, that.featureBagFraction)
&& Objects.equals(predictionFieldName, that.predictionFieldName)
&& Objects.equals(trainingPercent, that.trainingPercent);
&& Objects.equals(trainingPercent, that.trainingPercent)
&& Objects.equals(randomizeSeed, that.randomizeSeed);
}

@Override
Expand All @@ -197,6 +210,7 @@ public static class Builder {
private Double featureBagFraction;
private String predictionFieldName;
private Double trainingPercent;
private Long randomizeSeed;

private Builder(String dependentVariable) {
this.dependentVariable = Objects.requireNonNull(dependentVariable);
Expand Down Expand Up @@ -237,9 +251,14 @@ public Builder setTrainingPercent(Double trainingPercent) {
return this;
}

public Builder setRandomizeSeed(Long randomizeSeed) {
this.randomizeSeed = randomizeSeed;
return this;
}

public Regression build() {
return new Regression(dependentVariable, lambda, gamma, eta, maximumNumberTrees, featureBagFraction, predictionFieldName,
trainingPercent);
trainingPercent, randomizeSeed);
}
}
}
2 changes: 2 additions & 0 deletions client/rest-high-level/src/test/java/org/elasticsearch/client/MachineLearningIT.java
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Expand Up @@ -1291,6 +1291,7 @@ public void testPutDataFrameAnalyticsConfig_GivenRegression() throws Exception {
.setAnalysis(org.elasticsearch.client.ml.dataframe.Regression.builder("my_dependent_variable")
.setPredictionFieldName("my_dependent_variable_prediction")
.setTrainingPercent(80.0)
.setRandomizeSeed(42L)
.build())
.setDescription("this is a regression")
.build();
Expand Down Expand Up @@ -1326,6 +1327,7 @@ public void testPutDataFrameAnalyticsConfig_GivenClassification() throws Excepti
.setAnalysis(org.elasticsearch.client.ml.dataframe.Classification.builder("my_dependent_variable")
.setPredictionFieldName("my_dependent_variable_prediction")
.setTrainingPercent(80.0)
.setRandomizeSeed(42L)
.setNumTopClasses(1)
.build())
.setDescription("this is a classification")
Expand Down
4 changes: 3 additions & 1 deletion ...h-level/src/test/java/org/elasticsearch/client/documentation/MlClientDocumentationIT.java
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Expand Up @@ -2975,7 +2975,8 @@ public void testPutDataFrameAnalytics() throws Exception {
.setFeatureBagFraction(0.4) // <6>
.setPredictionFieldName("my_prediction_field_name") // <7>
.setTrainingPercent(50.0) // <8>
.setNumTopClasses(1) // <9>
.setRandomizeSeed(1234L) // <9>
.setNumTopClasses(1) // <10>
.build();
// end::put-data-frame-analytics-classification

Expand All @@ -2988,6 +2989,7 @@ public void testPutDataFrameAnalytics() throws Exception {
.setFeatureBagFraction(0.4) // <6>
.setPredictionFieldName("my_prediction_field_name") // <7>
.setTrainingPercent(50.0) // <8>
.setRandomizeSeed(1234L) // <9>
.build();
// end::put-data-frame-analytics-regression

Expand Down
1 change: 1 addition & 0 deletions ...t-high-level/src/test/java/org/elasticsearch/client/ml/dataframe/ClassificationTests.java
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Expand Up @@ -34,6 +34,7 @@ public static Classification randomClassification() {
.setFeatureBagFraction(randomBoolean() ? null : randomDoubleBetween(0.0, 1.0, false))
.setPredictionFieldName(randomBoolean() ? null : randomAlphaOfLength(10))
.setTrainingPercent(randomBoolean() ? null : randomDoubleBetween(1.0, 100.0, true))
.setRandomizeSeed(randomBoolean() ? null : randomLong())
.setNumTopClasses(randomBoolean() ? null : randomIntBetween(0, 10))
.build();
}
Expand Down
4 changes: 3 additions & 1 deletion docs/java-rest/high-level/ml/put-data-frame-analytics.asciidoc
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Expand Up @@ -119,7 +119,8 @@ include-tagged::{doc-tests-file}[{api}-classification]
<6> The fraction of features which will be used when selecting a random bag for each candidate split. A double in (0, 1].
<7> The name of the prediction field in the results object.
<8> The percentage of training-eligible rows to be used in training. Defaults to 100%.
<9> The number of top classes to be reported in the results. Defaults to 2.
<9> The seed to be used by the random generator that picks which rows are used in training.
<10> The number of top classes to be reported in the results. Defaults to 2.

===== Regression

Expand All @@ -138,6 +139,7 @@ include-tagged::{doc-tests-file}[{api}-regression]
<6> The fraction of features which will be used when selecting a random bag for each candidate split. A double in (0, 1].
<7> The name of the prediction field in the results object.
<8> The percentage of training-eligible rows to be used in training. Defaults to 100%.
<9> The seed to be used by the random generator that picks which rows are used in training.

==== Analyzed fields

Expand Down
4 changes: 4 additions & 0 deletions docs/reference/ml/df-analytics/apis/dfanalyticsresources.asciidoc
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Expand Up @@ -204,6 +204,8 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=prediction_field_name]

include::{docdir}/ml/ml-shared.asciidoc[tag=training_percent]

include::{docdir}/ml/ml-shared.asciidoc[tag=randomize_seed]


[float]
[[regression-resources-advanced]]
Expand Down Expand Up @@ -252,6 +254,8 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=prediction_field_name]

include::{docdir}/ml/ml-shared.asciidoc[tag=training_percent]

include::{docdir}/ml/ml-shared.asciidoc[tag=randomize_seed]


[float]
[[classification-resources-advanced]]
Expand Down
4 changes: 3 additions & 1 deletion docs/reference/ml/df-analytics/apis/put-dfanalytics.asciidoc
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Expand Up @@ -397,7 +397,8 @@ PUT _ml/data_frame/analytics/student_performance_mathematics_0.3
{
"regression": {
"dependent_variable": "G3",
"training_percent": 70 <1>
"training_percent": 70, <1>
"randomize_seed": 19673948271 <2>
}
}
}
Expand All @@ -406,6 +407,7 @@ PUT _ml/data_frame/analytics/student_performance_mathematics_0.3

<1> The `training_percent` defines the percentage of the data set that will be used
for training the model.
<2> The `randomize_seed` is the seed used to randomly pick which data is used for training.


[[ml-put-dfanalytics-example-c]]
Expand Down
9 changes: 9 additions & 0 deletions docs/reference/ml/ml-shared.asciidoc
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Expand Up @@ -681,6 +681,15 @@ those that contain arrays) won’t be included in the calculation for used
percentage. Defaults to `100`.
end::training_percent[]

tag::randomize_seed[]
`randomize_seed`::
(Optional, long) Defines the seed to the random generator that is used to pick
which documents will be used for training. By default it is randomly generated.
Set it to a specific value to ensure the same documents are used for training
assuming other related parameters (e.g. `source`, `analyzed_fields`, etc.) are the same.
end::randomize_seed[]


tag::use-null[]
Defines whether a new series is used as the null series when there is no value
for the by or partition fields. The default value is `false`.
Expand Down
3 changes: 2 additions & 1 deletion ...ore/src/main/java/org/elasticsearch/xpack/core/ml/dataframe/DataFrameAnalyticsConfig.java
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Expand Up @@ -225,7 +225,8 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
builder.field(DEST.getPreferredName(), dest);

builder.startObject(ANALYSIS.getPreferredName());
builder.field(analysis.getWriteableName(), analysis);
builder.field(analysis.getWriteableName(), analysis,
new MapParams(Collections.singletonMap(VERSION.getPreferredName(), version == null ? null : version.toString())));
builder.endObject();

if (params.paramAsBoolean(ToXContentParams.FOR_INTERNAL_STORAGE, false)) {
Expand Down
4 changes: 2 additions & 2 deletions ...e/src/main/java/org/elasticsearch/xpack/core/ml/dataframe/analyses/BoostedTreeParams.java
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Expand Up @@ -49,7 +49,7 @@ static void declareFields(AbstractObjectParser<?, Void> parser) {
private final Integer maximumNumberTrees;
private final Double featureBagFraction;

BoostedTreeParams(@Nullable Double lambda,
public BoostedTreeParams(@Nullable Double lambda,
@Nullable Double gamma,
@Nullable Double eta,
@Nullable Integer maximumNumberTrees,
Expand All @@ -76,7 +76,7 @@ static void declareFields(AbstractObjectParser<?, Void> parser) {
this.featureBagFraction = featureBagFraction;
}

BoostedTreeParams() {
public BoostedTreeParams() {
this(null, null, null, null, null);
}

Expand Down
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