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[ML] adds new feature_processors field for data frame analytics #60528

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merged 15 commits into from
Aug 14, 2020
Merged

[ML] adds new feature_processors field for data frame analytics #60528

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497c7a8
[ML] adds new feature_processors field for data frame analytics
benwtrent Jul 27, 2020
517cd85
muting bwc test
benwtrent Aug 4, 2020
1e2ef32
Merge remote-tracking branch 'upstream/master' into feature/ml-dfa-ad…
benwtrent Aug 11, 2020
52405e7
addressing PR comments
benwtrent Aug 11, 2020
4cae33a
fixing test
benwtrent Aug 11, 2020
b8a6df9
adding field processor test
benwtrent Aug 11, 2020
be5c290
fixing test
benwtrent Aug 11, 2020
d419dfc
ensuring output feature uniqueness
benwtrent Aug 11, 2020
21c6999
adding more tests
benwtrent Aug 12, 2020
553d221
Merge branch 'master' into feature/ml-dfa-add-processors
elasticmachine Aug 12, 2020
a8d130f
fixing precommit
benwtrent Aug 12, 2020
eac75c1
Merge branch 'feature/ml-dfa-add-processors' of github.com:benwtrent/…
benwtrent Aug 12, 2020
b923b15
Merge remote-tracking branch 'upstream/master' into feature/ml-dfa-ad…
benwtrent Aug 13, 2020
7ad1520
adjusting output, addressing pr comments
benwtrent Aug 13, 2020
f8951d0
fixing formatting
benwtrent Aug 13, 2020
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44 changes: 40 additions & 4 deletions ...core/src/main/java/org/elasticsearch/xpack/core/ml/dataframe/analyses/Classification.java
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Original file line number Diff line number Diff line change
Expand Up @@ -15,10 +15,14 @@
import org.elasticsearch.common.xcontent.XContentBuilder;
import org.elasticsearch.common.xcontent.XContentParser;
import org.elasticsearch.index.mapper.FieldAliasMapper;
import org.elasticsearch.xpack.core.ml.inference.preprocessing.LenientlyParsedPreProcessor;
import org.elasticsearch.xpack.core.ml.inference.preprocessing.PreProcessor;
import org.elasticsearch.xpack.core.ml.inference.preprocessing.StrictlyParsedPreProcessor;
import org.elasticsearch.xpack.core.ml.inference.trainedmodel.ClassificationConfig;
import org.elasticsearch.xpack.core.ml.inference.trainedmodel.InferenceConfig;
import org.elasticsearch.xpack.core.ml.inference.trainedmodel.PredictionFieldType;
import org.elasticsearch.xpack.core.ml.utils.ExceptionsHelper;
import org.elasticsearch.xpack.core.ml.utils.NamedXContentObjectHelper;

import java.io.IOException;
import java.util.Arrays;
Expand Down Expand Up @@ -46,6 +50,7 @@ public class Classification implements DataFrameAnalysis {
public static final ParseField NUM_TOP_CLASSES = new ParseField("num_top_classes");
public static final ParseField TRAINING_PERCENT = new ParseField("training_percent");
public static final ParseField RANDOMIZE_SEED = new ParseField("randomize_seed");
public static final ParseField FEATURE_PROCESSORS = new ParseField("feature_processors");

private static final String STATE_DOC_ID_SUFFIX = "_classification_state#1";

Expand All @@ -59,6 +64,7 @@ public class Classification implements DataFrameAnalysis {
*/
public static final int MAX_DEPENDENT_VARIABLE_CARDINALITY = 30;

@SuppressWarnings("unchecked")
private static ConstructingObjectParser<Classification, Void> createParser(boolean lenient) {
ConstructingObjectParser<Classification, Void> parser = new ConstructingObjectParser<>(
NAME.getPreferredName(),
Expand All @@ -70,14 +76,21 @@ private static ConstructingObjectParser<Classification, Void> createParser(boole
(ClassAssignmentObjective) a[8],
(Integer) a[9],
(Double) a[10],
(Long) a[11]));
(Long) a[11],
(List<PreProcessor>) a[12]));
parser.declareString(constructorArg(), DEPENDENT_VARIABLE);
BoostedTreeParams.declareFields(parser);
parser.declareString(optionalConstructorArg(), PREDICTION_FIELD_NAME);
parser.declareString(optionalConstructorArg(), ClassAssignmentObjective::fromString, CLASS_ASSIGNMENT_OBJECTIVE);
parser.declareInt(optionalConstructorArg(), NUM_TOP_CLASSES);
parser.declareDouble(optionalConstructorArg(), TRAINING_PERCENT);
parser.declareLong(optionalConstructorArg(), RANDOMIZE_SEED);
parser.declareNamedObjects(optionalConstructorArg(),
(p, c, n) -> lenient ?
p.namedObject(LenientlyParsedPreProcessor.class, n, new PreProcessor.PreProcessorParseContext(true)) :
p.namedObject(StrictlyParsedPreProcessor.class, n, new PreProcessor.PreProcessorParseContext(true)),
(classification) -> {/*TODO should we throw if this is not set?*/},
FEATURE_PROCESSORS);
return parser;
}

Expand Down Expand Up @@ -117,14 +130,16 @@ public static Classification fromXContent(XContentParser parser, boolean ignoreU
private final int numTopClasses;
private final double trainingPercent;
private final long randomizeSeed;
private final List<PreProcessor> featureProcessors;

public Classification(String dependentVariable,
BoostedTreeParams boostedTreeParams,
@Nullable String predictionFieldName,
@Nullable ClassAssignmentObjective classAssignmentObjective,
@Nullable Integer numTopClasses,
@Nullable Double trainingPercent,
@Nullable Long randomizeSeed) {
@Nullable Long randomizeSeed,
@Nullable List<PreProcessor> featureProcessors) {
if (numTopClasses != null && (numTopClasses < 0 || numTopClasses > 1000)) {
throw ExceptionsHelper.badRequestException("[{}] must be an integer in [0, 1000]", NUM_TOP_CLASSES.getPreferredName());
}
Expand All @@ -139,10 +154,11 @@ public Classification(String dependentVariable,
this.numTopClasses = numTopClasses == null ? DEFAULT_NUM_TOP_CLASSES : numTopClasses;
this.trainingPercent = trainingPercent == null ? 100.0 : trainingPercent;
this.randomizeSeed = randomizeSeed == null ? Randomness.get().nextLong() : randomizeSeed;
this.featureProcessors = featureProcessors == null ? Collections.emptyList() : Collections.unmodifiableList(featureProcessors);
}

public Classification(String dependentVariable) {
this(dependentVariable, BoostedTreeParams.builder().build(), null, null, null, null, null);
this(dependentVariable, BoostedTreeParams.builder().build(), null, null, null, null, null, null);
}

public Classification(StreamInput in) throws IOException {
Expand All @@ -161,6 +177,11 @@ public Classification(StreamInput in) throws IOException {
} else {
randomizeSeed = Randomness.get().nextLong();
}
if (in.getVersion().onOrAfter(Version.V_8_0_0)) {
featureProcessors = Collections.unmodifiableList(in.readNamedWriteableList(PreProcessor.class));
} else {
featureProcessors = Collections.emptyList();
}
}

public String getDependentVariable() {
Expand Down Expand Up @@ -191,6 +212,10 @@ public long getRandomizeSeed() {
return randomizeSeed;
}

public List<PreProcessor> getFeatureProcessors() {
return featureProcessors;
}

@Override
public String getWriteableName() {
return NAME.getPreferredName();
Expand All @@ -209,6 +234,9 @@ public void writeTo(StreamOutput out) throws IOException {
if (out.getVersion().onOrAfter(Version.V_7_6_0)) {
out.writeOptionalLong(randomizeSeed);
}
if (out.getVersion().onOrAfter(Version.V_8_0_0)) {
out.writeNamedWriteableList(featureProcessors);
}
}

@Override
Expand All @@ -227,6 +255,9 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
if (version.onOrAfter(Version.V_7_6_0)) {
builder.field(RANDOMIZE_SEED.getPreferredName(), randomizeSeed);
}
if (featureProcessors.isEmpty() == false) {
NamedXContentObjectHelper.writeNamedObjects(builder, params, true, FEATURE_PROCESSORS.getPreferredName(), featureProcessors);
}
builder.endObject();
return builder;
}
Expand All @@ -247,6 +278,10 @@ public Map<String, Object> getParams(FieldInfo fieldInfo) {
}
params.put(NUM_CLASSES, fieldInfo.getCardinality(dependentVariable));
params.put(TRAINING_PERCENT.getPreferredName(), trainingPercent);
if (featureProcessors.isEmpty() == false) {
params.put(FEATURE_PROCESSORS.getPreferredName(),
featureProcessors.stream().map(p -> Collections.singletonMap(p.getName(), p)).collect(Collectors.toList()));
}
return params;
}

Expand Down Expand Up @@ -388,14 +423,15 @@ public boolean equals(Object o) {
&& Objects.equals(predictionFieldName, that.predictionFieldName)
&& Objects.equals(classAssignmentObjective, that.classAssignmentObjective)
&& Objects.equals(numTopClasses, that.numTopClasses)
&& Objects.equals(featureProcessors, that.featureProcessors)
&& trainingPercent == that.trainingPercent
&& randomizeSeed == that.randomizeSeed;
}

@Override
public int hashCode() {
return Objects.hash(dependentVariable, boostedTreeParams, predictionFieldName, classAssignmentObjective,
numTopClasses, trainingPercent, randomizeSeed);
numTopClasses, trainingPercent, randomizeSeed, featureProcessors);
}

public enum ClassAssignmentObjective {
Expand Down
45 changes: 41 additions & 4 deletions ...gin/core/src/main/java/org/elasticsearch/xpack/core/ml/dataframe/analyses/Regression.java
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Original file line number Diff line number Diff line change
Expand Up @@ -15,9 +15,13 @@
import org.elasticsearch.common.xcontent.XContentBuilder;
import org.elasticsearch.common.xcontent.XContentParser;
import org.elasticsearch.index.mapper.NumberFieldMapper;
import org.elasticsearch.xpack.core.ml.inference.preprocessing.LenientlyParsedPreProcessor;
import org.elasticsearch.xpack.core.ml.inference.preprocessing.PreProcessor;
import org.elasticsearch.xpack.core.ml.inference.preprocessing.StrictlyParsedPreProcessor;
import org.elasticsearch.xpack.core.ml.inference.trainedmodel.InferenceConfig;
import org.elasticsearch.xpack.core.ml.inference.trainedmodel.RegressionConfig;
import org.elasticsearch.xpack.core.ml.utils.ExceptionsHelper;
import org.elasticsearch.xpack.core.ml.utils.NamedXContentObjectHelper;

import java.io.IOException;
import java.util.Arrays;
Expand All @@ -28,6 +32,7 @@
import java.util.Map;
import java.util.Objects;
import java.util.Set;
import java.util.stream.Collectors;

import static org.elasticsearch.common.xcontent.ConstructingObjectParser.constructorArg;
import static org.elasticsearch.common.xcontent.ConstructingObjectParser.optionalConstructorArg;
Expand All @@ -42,12 +47,14 @@ public class Regression implements DataFrameAnalysis {
public static final ParseField RANDOMIZE_SEED = new ParseField("randomize_seed");
public static final ParseField LOSS_FUNCTION = new ParseField("loss_function");
public static final ParseField LOSS_FUNCTION_PARAMETER = new ParseField("loss_function_parameter");
public static final ParseField FEATURE_PROCESSORS = new ParseField("feature_processors");

private static final String STATE_DOC_ID_SUFFIX = "_regression_state#1";

private static final ConstructingObjectParser<Regression, Void> LENIENT_PARSER = createParser(true);
private static final ConstructingObjectParser<Regression, Void> STRICT_PARSER = createParser(false);

@SuppressWarnings("unchecked")
private static ConstructingObjectParser<Regression, Void> createParser(boolean lenient) {
ConstructingObjectParser<Regression, Void> parser = new ConstructingObjectParser<>(
NAME.getPreferredName(),
Expand All @@ -59,14 +66,21 @@ private static ConstructingObjectParser<Regression, Void> createParser(boolean l
(Double) a[8],
(Long) a[9],
(LossFunction) a[10],
(Double) a[11]));
(Double) a[11],
(List<PreProcessor>) a[12]));
parser.declareString(constructorArg(), DEPENDENT_VARIABLE);
BoostedTreeParams.declareFields(parser);
parser.declareString(optionalConstructorArg(), PREDICTION_FIELD_NAME);
parser.declareDouble(optionalConstructorArg(), TRAINING_PERCENT);
parser.declareLong(optionalConstructorArg(), RANDOMIZE_SEED);
parser.declareString(optionalConstructorArg(), LossFunction::fromString, LOSS_FUNCTION);
parser.declareDouble(optionalConstructorArg(), LOSS_FUNCTION_PARAMETER);
parser.declareNamedObjects(optionalConstructorArg(),
(p, c, n) -> lenient ?
p.namedObject(LenientlyParsedPreProcessor.class, n, new PreProcessor.PreProcessorParseContext(true)) :
p.namedObject(StrictlyParsedPreProcessor.class, n, new PreProcessor.PreProcessorParseContext(true)),
(regression) -> {/*TODO should we throw if this is not set?*/},
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FEATURE_PROCESSORS);
return parser;
}

Expand All @@ -90,14 +104,16 @@ public static Regression fromXContent(XContentParser parser, boolean ignoreUnkno
private final long randomizeSeed;
private final LossFunction lossFunction;
private final Double lossFunctionParameter;
private final List<PreProcessor> featureProcessors;

public Regression(String dependentVariable,
BoostedTreeParams boostedTreeParams,
@Nullable String predictionFieldName,
@Nullable Double trainingPercent,
@Nullable Long randomizeSeed,
@Nullable LossFunction lossFunction,
@Nullable Double lossFunctionParameter) {
@Nullable Double lossFunctionParameter,
@Nullable List<PreProcessor> featureProcessors) {
if (trainingPercent != null && (trainingPercent < 1.0 || trainingPercent > 100.0)) {
throw ExceptionsHelper.badRequestException("[{}] must be a double in [1, 100]", TRAINING_PERCENT.getPreferredName());
}
Expand All @@ -112,10 +128,11 @@ public Regression(String dependentVariable,
throw ExceptionsHelper.badRequestException("[{}] must be a positive double", LOSS_FUNCTION_PARAMETER.getPreferredName());
}
this.lossFunctionParameter = lossFunctionParameter;
this.featureProcessors = featureProcessors == null ? Collections.emptyList() : Collections.unmodifiableList(featureProcessors);
}

public Regression(String dependentVariable) {
this(dependentVariable, BoostedTreeParams.builder().build(), null, null, null, null, null);
this(dependentVariable, BoostedTreeParams.builder().build(), null, null, null, null, null, null);
}

public Regression(StreamInput in) throws IOException {
Expand All @@ -126,6 +143,11 @@ public Regression(StreamInput in) throws IOException {
randomizeSeed = in.readOptionalLong();
lossFunction = in.readEnum(LossFunction.class);
lossFunctionParameter = in.readOptionalDouble();
if (in.getVersion().onOrAfter(Version.V_8_0_0)) {
featureProcessors = Collections.unmodifiableList(in.readNamedWriteableList(PreProcessor.class));
} else {
featureProcessors = Collections.emptyList();
}
}

public String getDependentVariable() {
Expand Down Expand Up @@ -156,6 +178,10 @@ public Double getLossFunctionParameter() {
return lossFunctionParameter;
}

public List<PreProcessor> getFeatureProcessors() {
return featureProcessors;
}

@Override
public String getWriteableName() {
return NAME.getPreferredName();
Expand All @@ -170,6 +196,9 @@ public void writeTo(StreamOutput out) throws IOException {
out.writeOptionalLong(randomizeSeed);
out.writeEnum(lossFunction);
out.writeOptionalDouble(lossFunctionParameter);
if (out.getVersion().onOrAfter(Version.V_8_0_0)) {
out.writeNamedWriteableList(featureProcessors);
}
}

@Override
Expand All @@ -190,6 +219,9 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws
if (lossFunctionParameter != null) {
builder.field(LOSS_FUNCTION_PARAMETER.getPreferredName(), lossFunctionParameter);
}
if (featureProcessors.isEmpty() == false) {
NamedXContentObjectHelper.writeNamedObjects(builder, params, true, FEATURE_PROCESSORS.getPreferredName(), featureProcessors);
}
builder.endObject();
return builder;
}
Expand All @@ -207,6 +239,10 @@ public Map<String, Object> getParams(FieldInfo fieldInfo) {
if (lossFunctionParameter != null) {
params.put(LOSS_FUNCTION_PARAMETER.getPreferredName(), lossFunctionParameter);
}
if (featureProcessors.isEmpty() == false) {
params.put(FEATURE_PROCESSORS.getPreferredName(),
featureProcessors.stream().map(p -> Collections.singletonMap(p.getName(), p)).collect(Collectors.toList()));
}
return params;
}

Expand Down Expand Up @@ -290,13 +326,14 @@ public boolean equals(Object o) {
&& trainingPercent == that.trainingPercent
&& randomizeSeed == that.randomizeSeed
&& lossFunction == that.lossFunction
&& Objects.equals(featureProcessors, that.featureProcessors)
&& Objects.equals(lossFunctionParameter, that.lossFunctionParameter);
}

@Override
public int hashCode() {
return Objects.hash(dependentVariable, boostedTreeParams, predictionFieldName, trainingPercent, randomizeSeed, lossFunction,
lossFunctionParameter);
lossFunctionParameter, featureProcessors);
}

public enum LossFunction {
Expand Down
16 changes: 8 additions & 8 deletions ...main/java/org/elasticsearch/xpack/core/ml/inference/MlInferenceNamedXContentProvider.java
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Original file line number Diff line number Diff line change
Expand Up @@ -57,23 +57,23 @@ public List<NamedXContentRegistry.Entry> getNamedXContentParsers() {

// PreProcessing Lenient
namedXContent.add(new NamedXContentRegistry.Entry(LenientlyParsedPreProcessor.class, OneHotEncoding.NAME,
OneHotEncoding::fromXContentLenient));
(p, c) -> OneHotEncoding.fromXContentLenient(p, (PreProcessor.PreProcessorParseContext) c)));
namedXContent.add(new NamedXContentRegistry.Entry(LenientlyParsedPreProcessor.class, TargetMeanEncoding.NAME,
TargetMeanEncoding::fromXContentLenient));
(p, c) -> TargetMeanEncoding.fromXContentLenient(p, (PreProcessor.PreProcessorParseContext) c)));
namedXContent.add(new NamedXContentRegistry.Entry(LenientlyParsedPreProcessor.class, FrequencyEncoding.NAME,
FrequencyEncoding::fromXContentLenient));
(p, c) -> FrequencyEncoding.fromXContentLenient(p, (PreProcessor.PreProcessorParseContext) c)));
namedXContent.add(new NamedXContentRegistry.Entry(LenientlyParsedPreProcessor.class, CustomWordEmbedding.NAME,
CustomWordEmbedding::fromXContentLenient));
(p, c) -> CustomWordEmbedding.fromXContentLenient(p)));

// PreProcessing Strict
namedXContent.add(new NamedXContentRegistry.Entry(StrictlyParsedPreProcessor.class, OneHotEncoding.NAME,
OneHotEncoding::fromXContentStrict));
(p, c) -> OneHotEncoding.fromXContentStrict(p, (PreProcessor.PreProcessorParseContext) c)));
namedXContent.add(new NamedXContentRegistry.Entry(StrictlyParsedPreProcessor.class, TargetMeanEncoding.NAME,
TargetMeanEncoding::fromXContentStrict));
(p, c) -> TargetMeanEncoding.fromXContentStrict(p, (PreProcessor.PreProcessorParseContext) c)));
namedXContent.add(new NamedXContentRegistry.Entry(StrictlyParsedPreProcessor.class, FrequencyEncoding.NAME,
FrequencyEncoding::fromXContentStrict));
(p, c) -> FrequencyEncoding.fromXContentStrict(p, (PreProcessor.PreProcessorParseContext) c)));
namedXContent.add(new NamedXContentRegistry.Entry(StrictlyParsedPreProcessor.class, CustomWordEmbedding.NAME,
CustomWordEmbedding::fromXContentStrict));
(p, c) -> CustomWordEmbedding.fromXContentStrict(p)));

// Model Lenient
namedXContent.add(new NamedXContentRegistry.Entry(LenientlyParsedTrainedModel.class, Tree.NAME, Tree::fromXContentLenient));
Expand Down
4 changes: 2 additions & 2 deletions .../core/src/main/java/org/elasticsearch/xpack/core/ml/inference/TrainedModelDefinition.java
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Expand Up @@ -56,8 +56,8 @@ private static ObjectParser<TrainedModelDefinition.Builder, Void> createParser(b
TRAINED_MODEL);
parser.declareNamedObjects(TrainedModelDefinition.Builder::setPreProcessors,
(p, c, n) -> ignoreUnknownFields ?
p.namedObject(LenientlyParsedPreProcessor.class, n, null) :
p.namedObject(StrictlyParsedPreProcessor.class, n, null),
p.namedObject(LenientlyParsedPreProcessor.class, n, PreProcessor.PreProcessorParseContext.DEFAULT) :
p.namedObject(StrictlyParsedPreProcessor.class, n, PreProcessor.PreProcessorParseContext.DEFAULT),
(trainedModelDefBuilder) -> trainedModelDefBuilder.setProcessorsInOrder(true),
PREPROCESSORS);
return parser;
Expand Down
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