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ai.h2o.automl.modeling.StackedEnsembleStepsProvider Maven / Gradle / Ivy
package ai.h2o.automl.modeling;
import ai.h2o.automl.*;
import ai.h2o.automl.WorkAllocations.Work;
import ai.h2o.automl.events.EventLogEntry;
import ai.h2o.automl.preprocessing.PreprocessingConfig;
import ai.h2o.automl.preprocessing.TargetEncoding;
import hex.KeyValue;
import hex.Model;
import hex.ensemble.Metalearner;
import hex.ensemble.StackedEnsembleModel;
import hex.ensemble.StackedEnsembleModel.StackedEnsembleParameters;
import hex.glm.GLMModel;
import water.DKV;
import water.Job;
import water.Key;
import water.util.PojoUtils;
import java.util.*;
import java.util.stream.Collectors;
import java.util.stream.IntStream;
import java.util.stream.Stream;
public class StackedEnsembleStepsProvider
implements ModelingStepsProvider
, ModelParametersProvider {
public static class StackedEnsembleSteps extends ModelingSteps {
static final String NAME = Algo.StackedEnsemble.name();
static abstract class StackedEnsembleModelStep extends ModelingStep.ModelStep {
protected final Metalearner.Algorithm _metalearnerAlgo;
StackedEnsembleModelStep(String id, Metalearner.Algorithm algo, int priorityGroup, int weight, AutoML autoML) {
super(NAME, Algo.StackedEnsemble, id, priorityGroup, weight, autoML);
_metalearnerAlgo = algo;
_ignoredConstraints = new AutoML.Constraint[] {AutoML.Constraint.MODEL_COUNT};
}
@Override
protected void setCrossValidationParams(Model.Parameters params) {
//added in the stack: we could probably move this here.
}
@Override
protected void setWeightingParams(Model.Parameters params) {
//Disabled: StackedEnsemble doesn't support weights in score0?
}
@Override
protected void setClassBalancingParams(Model.Parameters params) {
//Disabled
}
@Override
protected PreprocessingConfig getPreprocessingConfig() {
//SE should not have TE applied, the base models already do it.
PreprocessingConfig config = super.getPreprocessingConfig();
config.put(TargetEncoding.CONFIG_ENABLED, false);
return config;
}
@Override
@SuppressWarnings("unchecked")
public boolean canRun() {
Key[] keys = getBaseModels();
Work seWork = getAllocatedWork();
if (!super.canRun()) {
aml().job().update(0, "Skipping this StackedEnsemble");
aml().eventLog().info(EventLogEntry.Stage.ModelTraining, String.format("Skipping StackedEnsemble '%s' due to the exclude_algos option or it is already trained.", _id));
return false;
} else if (keys.length == 0) {
aml().job().update(seWork.consume(), "No base models; skipping this StackedEnsemble");
aml().eventLog().info(EventLogEntry.Stage.ModelTraining, String.format("No base models, due to timeouts or the exclude_algos option. Skipping StackedEnsemble '%s'.", _id));
return false;
} else if (keys.length == 1) {
aml().job().update(seWork.consume(), "Only one base model; skipping this StackedEnsemble");
aml().eventLog().info(EventLogEntry.Stage.ModelTraining, String.format("Skipping StackedEnsemble '%s' since there is only one model to stack", _id));
return false;
} else if (!isCVEnabled() && aml().getBlendingFrame() == null) {
aml().job().update(seWork.consume(), "Cross-validation disabled by the user and no blending frame provided; Skipping this StackedEnsemble");
aml().eventLog().info(EventLogEntry.Stage.ModelTraining, String.format("Cross-validation is disabled by the user and no blending frame was provided; skipping StackedEnsemble '%s'.", _id));
return false;
}
return !hasDoppelganger(keys);
}
@SuppressWarnings("unchecked")
protected boolean hasDoppelganger(Key[] baseModelsKeys) {
Key[] seModels = Arrays
.stream(getTrainedModelsKeys())
.filter(k -> isStackedEnsemble(k))
.toArray(Key[]::new);
Set keySet = new HashSet<>(Arrays.asList(baseModelsKeys));
for (Key seKey: seModels) {
StackedEnsembleModelStep seStep = (StackedEnsembleModelStep)aml().session().getModelingStep(seKey);
if (seStep._metalearnerAlgo != _metalearnerAlgo) continue;
final StackedEnsembleParameters seParams = seKey.get()._parms;
final Key[] seBaseModels = seParams._base_models;
if (seBaseModels.length != baseModelsKeys.length) continue;
if (keySet.equals(new HashSet<>(Arrays.asList(seBaseModels))))
return true; // We already have a SE with the same base models
}
return false;
}
protected abstract Key[] getBaseModels();
protected String getModelType(Key key) {
String keyStr = key.toString();
return keyStr.substring(0, keyStr.indexOf('_'));
}
protected boolean isStackedEnsemble(Key key) {
ModelingStep step = aml().session().getModelingStep(key);
return step != null && step.getAlgo() == Algo.StackedEnsemble;
}
@Override
public StackedEnsembleParameters prepareModelParameters() {
StackedEnsembleParameters params = new StackedEnsembleParameters();
params._valid = (aml().getValidationFrame() == null ? null : aml().getValidationFrame()._key);
params._blending = (aml().getBlendingFrame() == null ? null : aml().getBlendingFrame()._key);
params._keep_levelone_frame = true; //TODO Why is this true? Can be optionally turned off
return params;
}
protected void setMetalearnerParameters(StackedEnsembleParameters params) {
AutoMLBuildSpec buildSpec = aml().getBuildSpec();
params._metalearner_fold_column = buildSpec.input_spec.fold_column;
params._metalearner_nfolds = buildSpec.build_control.nfolds;
params.initMetalearnerParams(_metalearnerAlgo);
params._metalearner_parameters._keep_cross_validation_models = buildSpec.build_control.keep_cross_validation_models;
params._metalearner_parameters._keep_cross_validation_predictions = buildSpec.build_control.keep_cross_validation_predictions;
}
Job stack(String modelName, Key[] baseModels, boolean isLast) {
StackedEnsembleParameters params = prepareModelParameters();
params._base_models = baseModels;
params._keep_base_model_predictions = !isLast; //avoids recomputing some base predictions for each SE
setMetalearnerParameters(params);
if (_metalearnerAlgo == Metalearner.Algorithm.AUTO) setAutoMetalearnerSEParameters(params);
return stack(modelName, params);
}
Job stack(String modelName, StackedEnsembleParameters stackedEnsembleParameters) {
Key modelKey = makeKey(modelName, true);
return trainModel(modelKey, stackedEnsembleParameters);
}
protected void setAutoMetalearnerSEParameters(StackedEnsembleParameters stackedEnsembleParameters) {
// add custom alpha in GLM metalearner
GLMModel.GLMParameters metalearnerParams = (GLMModel.GLMParameters)stackedEnsembleParameters._metalearner_parameters;
metalearnerParams._alpha = new double[]{0.5, 1.0};
if (aml().getResponseColumn().isCategorical()) {
// Add logit transform
stackedEnsembleParameters._metalearner_transform = StackedEnsembleParameters.MetalearnerTransform.Logit;
}
}
}
static class BestOfFamilySEModelStep extends StackedEnsembleModelStep {
public BestOfFamilySEModelStep(String id, int priorityGroup, AutoML autoML) {
this(id, Metalearner.Algorithm.AUTO, priorityGroup, autoML);
}
public BestOfFamilySEModelStep(String id, Metalearner.Algorithm algo, int priorityGroup, AutoML autoML) {
this(id, algo, priorityGroup, DEFAULT_MODEL_TRAINING_WEIGHT, autoML);
}
public BestOfFamilySEModelStep(String id, Metalearner.Algorithm algo, int priorityGroup, int weight, AutoML autoML) {
super((id == null ? "best_of_family_"+algo.name() : id), algo, priorityGroup, weight, autoML);
_description = _description+" (built with "+algo.name()+" metalearner, using top model from each algorithm type)";
}
@Override
@SuppressWarnings("unchecked")
protected Key[] getBaseModels() {
// Set aside List for best models per model type. Meaning best GLM, GBM, DRF, XRT, and DL (5 models).
// This will give another ensemble that is smaller than the original which takes all models into consideration.
List> bestModelsOfEachType = new ArrayList<>();
Set typesOfGatheredModels = new HashSet<>();
for (Key key : getTrainedModelsKeys()) {
// trained models are sorted (taken from leaderboard), so we only need to pick the first of each type (excluding other StackedEnsembles)
String type = getModelType(key);
if (isStackedEnsemble(key) || typesOfGatheredModels.contains(type)) continue;
typesOfGatheredModels.add(type);
bestModelsOfEachType.add(key);
}
return bestModelsOfEachType.toArray(new Key[0]);
}
@Override
protected Job startJob() {
return stack(_provider+"_BestOfFamily", getBaseModels(), false);
}
}
static class BestNModelsSEModelStep extends StackedEnsembleModelStep {
private final int _N;
public BestNModelsSEModelStep(String id, int N, int priorityGroup, AutoML autoML) {
this(id, Metalearner.Algorithm.AUTO, N, priorityGroup, DEFAULT_MODEL_TRAINING_WEIGHT, autoML);
}
public BestNModelsSEModelStep(String id, Metalearner.Algorithm algo, int N, int priorityGroup, int weight, AutoML autoML) {
super((id == null ? "best_"+N+"_"+algo.name() : id), algo, priorityGroup, weight, autoML);
_N = N;
_description = _description+" (built with "+algo.name()+" metalearner, using best "+N+" non-SE models)";
}
@Override
@SuppressWarnings("unchecked")
protected Key[] getBaseModels() {
return Stream.of(getTrainedModelsKeys())
.filter(k -> !isStackedEnsemble(k))
.limit(_N)
.toArray(Key[]::new);
}
@Override
protected Job startJob() {
return stack(_provider+"_Best"+_N, getBaseModels(), false);
}
}
static class AllSEModelStep extends StackedEnsembleModelStep {
public AllSEModelStep(String id, int priorityGroup, AutoML autoML) {
this(id, Metalearner.Algorithm.AUTO, priorityGroup, autoML);
}
public AllSEModelStep(String id, Metalearner.Algorithm algo, int priorityGroup, AutoML autoML) {
this(id, algo, priorityGroup, DEFAULT_MODEL_TRAINING_WEIGHT, autoML);
}
public AllSEModelStep(String id, Metalearner.Algorithm algo, int priorityGroup, int weight, AutoML autoML) {
super((id == null ? "all_"+algo.name() : id), algo, priorityGroup, weight, autoML);
_description = _description+" (built with "+algo.name()+" metalearner, using all AutoML models)";
}
@Override
@SuppressWarnings("unchecked")
protected Key[] getBaseModels() {
return Stream.of(getTrainedModelsKeys())
.filter(k -> !isStackedEnsemble(k))
.toArray(Key[]::new);
}
@Override
protected Job startJob() {
return stack(_provider+"_AllModels", getBaseModels(), false);
}
}
static class MonotonicSEModelStep extends StackedEnsembleModelStep {
public MonotonicSEModelStep(String id, int priorityGroup, AutoML autoML) {
this(id, Metalearner.Algorithm.AUTO, priorityGroup, DEFAULT_MODEL_TRAINING_WEIGHT, autoML);
}
public MonotonicSEModelStep(String id, Metalearner.Algorithm algo, int priorityGroup, int weight, AutoML autoML) {
super((id == null ? "monotonic" : id), algo, priorityGroup, weight, autoML);
_description = _description+" (built with "+algo.name()+" metalearner, using monotonically constrained AutoML models)";
}
boolean hasMonotoneConstrains(Key modelKey) {
Model model = DKV.getGet(modelKey);
try {
KeyValue[] mc = (KeyValue[]) PojoUtils.getFieldValue(
model._parms, "_monotone_constraints",
PojoUtils.FieldNaming.CONSISTENT);
return mc != null && mc.length > 0;
} catch (IllegalArgumentException e) {
return false;
}
}
@Override
public boolean canRun() {
boolean canRun = super.canRun();
if (!canRun) return false;
int monotoneModels=0;
for (Key modelKey: getTrainedModelsKeys()) {
if (hasMonotoneConstrains(modelKey))
monotoneModels++;
if (monotoneModels >= 2)
return true;
}
if (monotoneModels == 1) {
aml().job().update(getAllocatedWork().consume(),
"Only one monotonic base model; skipping this StackedEnsemble");
aml().eventLog().info(EventLogEntry.Stage.ModelTraining,
String.format("Skipping StackedEnsemble '%s' since there is only one monotonic model to stack", _id));
} else {
aml().job().update(getAllocatedWork().consume(),
"No monotonic base model; skipping this StackedEnsemble");
aml().eventLog().info(EventLogEntry.Stage.ModelTraining,
String.format("Skipping StackedEnsemble '%s' since there is no monotonic model to stack", _id));
}
return false;
}
@Override
@SuppressWarnings("unchecked")
protected Key[] getBaseModels() {
return Stream.of(getTrainedModelsKeys())
.filter(k -> !isStackedEnsemble(k) && hasMonotoneConstrains(k))
.toArray(Key[]::new);
}
@Override
protected Job startJob() {
return stack(_provider + "_Monotonic", getBaseModels(), false);
}
}
private final ModelingStep[] defaults;
private final ModelingStep[] optionals;
{
// we're going to cheat a bit: ModelingSteps needs to instantiated by the AutoML instance
// to convert each StepDefinition into one or more ModelingStep(s)
// so at that time, we have access to the entire modeling plan
// and we can dynamically generate the modeling steps that we're going to need.
StepDefinition[] modelingPlan = aml().getBuildSpec().build_models.modeling_plan;
if (Stream.of(modelingPlan).noneMatch(sd -> sd.getName().equals(NAME))) {
defaults = new ModelingStep[0];
optionals = new ModelingStep[0];
} else {
List defaultSeSteps = new ArrayList<>();
// starting to generate the SE for each "base" group
// ie for each group with algo steps.
Set defaultAlgoProviders = Stream.of(Algo.values())
.filter(a -> a != Algo.StackedEnsemble)
.map(Algo::name)
.collect(Collectors.toSet());
int[] baseAlgoGroups = Stream.of(modelingPlan)
.filter(sd -> defaultAlgoProviders.contains(sd.getName()))
.flatMapToInt(sd ->
sd.getAlias() == StepDefinition.Alias.defaults ? IntStream.of(ModelingStep.ModelStep.DEFAULT_MODEL_GROUP)
: sd.getAlias() == StepDefinition.Alias.grids ? IntStream.of(ModelingStep.GridStep.DEFAULT_GRID_GROUP)
: sd.getAlias() == StepDefinition.Alias.all ? IntStream.of(ModelingStep.ModelStep.DEFAULT_MODEL_GROUP, ModelingStep.GridStep.DEFAULT_GRID_GROUP)
: sd.getSteps().stream().flatMapToInt(s -> s.getGroup() == StepDefinition.Step.DEFAULT_GROUP
? IntStream.of(ModelingStep.ModelStep.DEFAULT_MODEL_GROUP, ModelingStep.GridStep.DEFAULT_GRID_GROUP)
: IntStream.of(s.getGroup())))
.distinct().sorted().toArray();
for (int group : baseAlgoGroups) {
defaultSeSteps.add(new BestOfFamilySEModelStep("best_of_family_" + group, group, aml()));
defaultSeSteps.add(new AllSEModelStep("all_" + group, group, aml())); // groups <=0 are ignored.
}
defaults = defaultSeSteps.toArray(new ModelingStep[0]);
// now all the additional SEs are available as optionals (usually requested by id).
int maxBaseGroup = IntStream.of(baseAlgoGroups).max().orElse(0);
List optionalSeSteps = new ArrayList<>();
if (maxBaseGroup > 0) {
int optionalGroup = maxBaseGroup+1;
optionalSeSteps.add(new MonotonicSEModelStep("monotonic", optionalGroup, aml()));
optionalSeSteps.add(new BestOfFamilySEModelStep("best_of_family", optionalGroup, aml()));
optionalSeSteps.add(new AllSEModelStep("all", optionalGroup, aml()));
if (Algo.XGBoost.enabled()) {
optionalSeSteps.add(new BestOfFamilySEModelStep("best_of_family_xgboost", Metalearner.Algorithm.xgboost, optionalGroup, aml()));
optionalSeSteps.add(new AllSEModelStep("all_xgboost", Metalearner.Algorithm.xgboost, optionalGroup, aml()));
}
optionalSeSteps.add(new BestOfFamilySEModelStep("best_of_family_gbm", Metalearner.Algorithm.gbm, optionalGroup, aml()));
optionalSeSteps.add(new AllSEModelStep("all_gbm", Metalearner.Algorithm.gbm, optionalGroup, aml()));
optionalSeSteps.add(new BestOfFamilySEModelStep("best_of_family_xglm", optionalGroup, aml()) {
@Override
protected boolean hasDoppelganger(Key[] baseModelsKeys) {
return false;
}
@Override
protected void setMetalearnerParameters(StackedEnsembleParameters params) {
super.setMetalearnerParameters(params);
GLMModel.GLMParameters metalearnerParams = (GLMModel.GLMParameters) params._metalearner_parameters;
metalearnerParams._lambda_search = true;
}
});
optionalSeSteps.add(new AllSEModelStep("all_xglm", optionalGroup, aml()) {
@Override
protected boolean hasDoppelganger(Key[] baseModelsKeys) {
Set modelTypes = new HashSet<>();
for (Key key : baseModelsKeys) {
String modelType = getModelType(key);
if (modelTypes.contains(modelType)) return false;
modelTypes.add(modelType);
}
return true;
}
@Override
protected void setMetalearnerParameters(StackedEnsembleParameters params) {
super.setMetalearnerParameters(params);
GLMModel.GLMParameters metalearnerParams = (GLMModel.GLMParameters) params._metalearner_parameters;
metalearnerParams._lambda_search = true;
}
});
// optionalSeSteps.add(new BestNModelsSEModelStep("best_20", 20, optionalGroup, aml()));
int card = aml().getResponseColumn().cardinality();
int maxModels = card <= 2 ? 1_000 : Math.max(100, 1_000 / (card - 1));
optionalSeSteps.add(new BestNModelsSEModelStep("best_N", maxModels, optionalGroup, aml()));
}
optionals = optionalSeSteps.toArray(new ModelingStep[0]);
}
}
public StackedEnsembleSteps(AutoML autoML) {
super(autoML);
}
@Override
public String getProvider() {
return NAME;
}
@Override
protected ModelingStep[] getDefaultModels() {
return defaults;
}
@Override
protected ModelingStep[] getOptionals() {
return optionals;
}
}
@Override
public String getName() {
return StackedEnsembleSteps.NAME;
}
@Override
public StackedEnsembleSteps newInstance(AutoML aml) {
return new StackedEnsembleSteps(aml);
}
@Override
public StackedEnsembleParameters newDefaultParameters() {
return new StackedEnsembleParameters();
}
}
