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package ai.h2o.automl;
import ai.h2o.automl.AutoMLBuildSpec.AutoMLCustomParameters;
import ai.h2o.automl.ModelSelectionStrategies.LeaderboardHolder;
import ai.h2o.automl.ModelSelectionStrategy.Selection;
import ai.h2o.automl.events.EventLog;
import ai.h2o.automl.events.EventLogEntry;
import ai.h2o.automl.events.EventLogEntry.Stage;
import ai.h2o.automl.WorkAllocations.JobType;
import ai.h2o.automl.WorkAllocations.Work;
import ai.h2o.automl.leaderboard.Leaderboard;
import ai.h2o.automl.preprocessing.PreprocessingConfig;
import ai.h2o.automl.preprocessing.PreprocessingStep;
import hex.Model;
import hex.Model.Parameters.FoldAssignmentScheme;
import hex.ModelBuilder;
import hex.ModelContainer;
import hex.ScoreKeeper.StoppingMetric;
import hex.grid.Grid;
import hex.grid.GridSearch;
import hex.grid.HyperSpaceSearchCriteria;
import hex.grid.HyperSpaceSearchCriteria.RandomDiscreteValueSearchCriteria;
import jsr166y.CountedCompleter;
import org.apache.commons.lang.builder.ToStringBuilder;
import water.*;
import water.exceptions.H2OIllegalArgumentException;
import water.util.ArrayUtils;
import water.util.Countdown;
import water.util.EnumUtils;
import water.util.Log;
import java.util.*;
import java.util.function.Consumer;
import java.util.function.Predicate;
/**
* Parent class defining common properties and common logic for actual {@link AutoML} training steps.
*/
public abstract class ModelingStep extends Iced {
protected enum SeedPolicy {
/** No seed will be used (= random). */
None,
/** The global AutoML seed will be used. */
Global,
/** The seed is incremented for each model, starting from the global seed if there is one. */
Incremental
}
static Predicate isDefaultModel = w -> w._type == JobType.ModelBuild;
static Predicate isExplorationWork = w -> w._type == JobType.ModelBuild || w._type == JobType.HyperparamSearch;
static Predicate isExploitationWork = w -> w._type == JobType.Selection;
protected Job startSearch(
final Key resultKey,
final MP baseParams,
final Map hyperParams,
final HyperSpaceSearchCriteria searchCriteria)
{
applyPreprocessing(baseParams);
aml().eventLog().info(Stage.ModelTraining, "AutoML: starting "+resultKey+" hyperparameter search")
.setNamedValue("start_"+_provider+"_"+_id, new Date(), EventLogEntry.epochFormat.get());
return GridSearch.startGridSearch(
resultKey,
baseParams,
hyperParams,
new GridSearch.SimpleParametersBuilderFactory<>(),
searchCriteria,
GridSearch.SEQUENTIAL_MODEL_BUILDING
);
}
@SuppressWarnings("unchecked")
protected Job startModel(
final Key resultKey,
final MP params
) {
Job job = new Job<>(resultKey, ModelBuilder.javaName(_algo.urlName()), _description);
applyPreprocessing(params);
ModelBuilder builder = ModelBuilder.make(_algo.urlName(), job, (Key) resultKey);
builder._parms = params;
aml().eventLog().info(Stage.ModelTraining, "AutoML: starting "+resultKey+" model training")
.setNamedValue("start_"+_provider+"_"+_id, new Date(), EventLogEntry.epochFormat.get());
try {
builder.init(false); // validate parameters
if (builder._messages.length > 0) {
for (ModelBuilder.ValidationMessage vm : builder._messages) {
if (vm.log_level() == Log.WARN) {
aml().eventLog().warn(Stage.ModelTraining, vm.field()+" param, "+vm.message());
} else if (vm.log_level() == Log.ERRR) {
aml().eventLog().error(Stage.ModelTraining, vm.field()+" param, "+vm.message());
}
}
}
return builder.trainModelOnH2ONode();
} catch (H2OIllegalArgumentException exception) {
aml().eventLog().error(Stage.ModelTraining, "Skipping training of model "+resultKey+" due to exception: "+exception);
onDone(null);
return null;
}
}
private transient AutoML _aml;
protected final IAlgo _algo;
protected final String _provider;
protected final String _id;
protected int _weight;
protected int _priorityGroup;
protected AutoML.Constraint[] _ignoredConstraints = new AutoML.Constraint[0]; // whether or not to ignore the max_models/max_runtime constraints
protected String _description;
protected Work _work;
private final transient List> _onDone = new ArrayList<>();
StepDefinition _fromDef;
transient final Predicate _isSamePriorityGroup = w -> w._priorityGroup == _priorityGroup;
protected ModelingStep(String provider, IAlgo algo, String id, int priorityGroup, int weight, AutoML autoML) {
assert priorityGroup >= 0;
_provider = provider;
_algo = algo;
_id = id;
_priorityGroup = priorityGroup;
_weight = weight;
_aml = autoML;
_description = provider+" "+id;
}
public String getProvider() {
return _provider;
}
public String getId() {
return _id;
}
public IAlgo getAlgo() {
return _algo;
}
public int getWeight() {
return _weight;
}
public int getPriorityGroup() {
return _priorityGroup;
}
public boolean isResumable() {
return false;
}
/**
* @return true iff we can call {@link #run()} on this modeling step to start a new job.
*/
public boolean canRun() {
Work work = getAllocatedWork();
return work != null && work._weight > 0;
}
/**
* Execute this modeling step, returning the job associated to it if any.
* @return
*/
public Job run() {
Job job = startJob();
if (job != null && job._result != null) {
register(job._result);
if (isResumable()) aml().session().addResumableKey(job._result);
}
return job;
}
/**
* @return an {@link Iterator} for the potential sub-steps provided by this modeling step.
*/
public Iterator iterateSubSteps() {
return Collections.emptyIterator();
}
/**
* @param id
* @return the sub-step (if any) with the given identifier, or null if there's no sub-step
*/
protected Optional getSubStep(String id) {
return Optional.empty();
}
protected abstract JobType getJobType();
/**
* Starts a new {@link Job} as part of this step.
* @return the newly started job.
*/
protected abstract Job startJob();
protected void onDone(Job job) {
for (Consumer exec : _onDone) {
exec.accept(job);
}
_onDone.clear();
};
protected void register(Key key) {
aml().session().registerKeySource(key, this);
}
protected AutoML aml() {
return _aml;
}
/**
* @return the total work allocated for this step.
*/
protected Work getAllocatedWork() {
if (_work == null) {
_work = getWorkAllocations().getAllocation(_id, _algo);
}
return _work;
}
/**
* Creates the {@link Work} instance representing the total work handled by this step.
* @return
*/
protected Work makeWork() {
return new Work(getId(), getAlgo(), getJobType(), getPriorityGroup(), getWeight());
}
protected Key makeKey(String name, boolean withCounter) {
return aml().makeKey(name, null, withCounter);
}
protected WorkAllocations getWorkAllocations() {
return aml()._workAllocations;
}
/**
* @return the models trained until now, sorted by the default leaderboard metric.
*/
protected Model[] getTrainedModels() {
return aml().leaderboard().getModels();
}
protected Key[] getTrainedModelsKeys() {
return aml().leaderboard().getModelKeys();
}
protected boolean isCVEnabled() {
return aml().isCVEnabled();
}
@Override
public boolean equals(Object o) {
if (this == o) return true;
if (o == null || getClass() != o.getClass()) return false;
ModelingStep that = (ModelingStep) o;
return _provider.equals(that._provider) && _id.equals(that._id);
}
@Override
public int hashCode() {
return Objects.hash(_provider, _id);
}
/**
* Assign common parameters to the model params before building the model or set of models.
* This includes:
*
*
data-related parameters: frame/columns parameters, class distribution.
*
cross-validation parameters/
*
memory-optimization: if certain objects build during training should be kept after training or not/
*
model management: checkpoints.
*
* @param params the model parameters to which the common parameters will be added.
*/
protected void setCommonModelBuilderParams(Model.Parameters params) {
params._train = aml()._trainingFrame._key;
if (null != aml()._validationFrame)
params._valid = aml()._validationFrame._key;
AutoMLBuildSpec buildSpec = aml().getBuildSpec();
params._response_column = buildSpec.input_spec.response_column;
params._ignored_columns = buildSpec.input_spec.ignored_columns;
setCrossValidationParams(params);
setWeightingParams(params);
setClassBalancingParams(params);
params._keep_cross_validation_models = buildSpec.build_control.keep_cross_validation_models;
params._keep_cross_validation_fold_assignment = buildSpec.build_control.nfolds != 0 && buildSpec.build_control.keep_cross_validation_fold_assignment;
params._export_checkpoints_dir = buildSpec.build_control.export_checkpoints_dir;
}
protected void setCrossValidationParams(Model.Parameters params) {
AutoMLBuildSpec buildSpec = aml().getBuildSpec();
params._keep_cross_validation_predictions = aml().getBlendingFrame() == null ? true : buildSpec.build_control.keep_cross_validation_predictions;
params._fold_column = buildSpec.input_spec.fold_column;
if (buildSpec.input_spec.fold_column == null) {
params._nfolds = buildSpec.build_control.nfolds;
if (buildSpec.build_control.nfolds > 1) {
// TODO: below allow the user to specify this (vs Modulo)
params._fold_assignment = FoldAssignmentScheme.Modulo;
}
}
}
protected void setWeightingParams(Model.Parameters params) {
AutoMLBuildSpec buildSpec = aml().getBuildSpec();
params._weights_column = buildSpec.input_spec.weights_column;
}
protected void setClassBalancingParams(Model.Parameters params) {
AutoMLBuildSpec buildSpec = aml().getBuildSpec();
if (buildSpec.build_control.balance_classes) {
params._balance_classes = buildSpec.build_control.balance_classes;
params._class_sampling_factors = buildSpec.build_control.class_sampling_factors;
params._max_after_balance_size = buildSpec.build_control.max_after_balance_size;
}
}
protected void setCustomParams(Model.Parameters params) {
AutoMLCustomParameters customParams = aml().getBuildSpec().build_models.algo_parameters;
if (customParams == null) return;
customParams.applyCustomParameters(_algo, params);
}
protected void applyPreprocessing(Model.Parameters params) {
if (aml().getPreprocessing() == null) return;
for (PreprocessingStep preprocessingStep : aml().getPreprocessing()) {
PreprocessingStep.Completer complete = preprocessingStep.apply(params, getPreprocessingConfig());
_onDone.add(j -> complete.run());
}
}
protected PreprocessingConfig getPreprocessingConfig() {
return new PreprocessingConfig();
}
/**
* Configures early-stopping for the model or set of models to be built.
*
* @param parms the model parameters to which the stopping criteria will be added.
* @param defaults the default parameters for the corresponding {@link ModelBuilder}.
*/
protected void setStoppingCriteria(Model.Parameters parms, Model.Parameters defaults) {
// If the caller hasn't set ModelBuilder stopping criteria, set it from our global criteria.
AutoMLBuildSpec buildSpec = aml().getBuildSpec();
//FIXME: Do we really need to compare with defaults before setting the buildSpec value instead?
// This can create subtle bugs: e.g. if dev wanted to enforce a stopping criteria for a specific algo/model,
// he wouldn't be able to enforce the default value, that would always be overridden by buildSpec.
// We should instead provide hooks and ensure that properties are always set in the following order:
// 1. defaults, 2. user defined, 3. internal logic/algo specific based on the previous state (esp. handling of AUTO properties).
if (parms._stopping_metric == defaults._stopping_metric)
parms._stopping_metric = buildSpec.build_control.stopping_criteria.stopping_metric();
if (parms._stopping_metric == StoppingMetric.AUTO) {
String sort_metric = getSortMetric();
parms._stopping_metric = sort_metric == null ? StoppingMetric.AUTO
: sort_metric.equals("auc") ? StoppingMetric.logloss
: metricValueOf(sort_metric);
}
if (parms._stopping_rounds == defaults._stopping_rounds)
parms._stopping_rounds = buildSpec.build_control.stopping_criteria.stopping_rounds();
if (parms._stopping_tolerance == defaults._stopping_tolerance)
parms._stopping_tolerance = buildSpec.build_control.stopping_criteria.stopping_tolerance();
}
/**
* @param parms the model parameters to which the stopping criteria will be added.
* @param defaults the default parameters for the corresponding {@link ModelBuilder}.
* @param seedPolicy the policy defining how the seed will be assigned to the model parameters.
*/
protected void setSeed(Model.Parameters parms, Model.Parameters defaults, SeedPolicy seedPolicy) {
AutoMLBuildSpec buildSpec = aml().getBuildSpec();
// Don't use the same exact seed so that, e.g., if we build two GBMs they don't do the same row and column sampling.
if (parms._seed == defaults._seed) {
switch (seedPolicy) {
case Global:
parms._seed = buildSpec.build_control.stopping_criteria.seed();
break;
case Incremental:
parms._seed = _aml._incrementalSeed.get() == defaults._seed ? defaults._seed : _aml._incrementalSeed.getAndIncrement();
break;
default:
break;
}
}
}
protected void initTimeConstraints(Model.Parameters parms, double upperLimit) {
AutoMLBuildSpec buildSpec = aml().getBuildSpec();
if (parms._max_runtime_secs == 0) {
double maxPerModel = buildSpec.build_control.stopping_criteria.max_runtime_secs_per_model();
parms._max_runtime_secs = upperLimit <= 0 ? maxPerModel : Math.min(maxPerModel, upperLimit);
}
}
private String getSortMetric() {
//ensures that the sort metric is always updated according to the defaults set by leaderboard
Leaderboard leaderboard = aml().leaderboard();
return leaderboard == null ? null : leaderboard.getSortMetric();
}
private static StoppingMetric metricValueOf(String name) {
if (name == null) return StoppingMetric.AUTO;
switch (name) {
case "mean_residual_deviance": return StoppingMetric.deviance;
default:
try {
return EnumUtils.valueOf(StoppingMetric.class, name);
} catch (IllegalArgumentException ignored) { }
return StoppingMetric.AUTO;
}
}
/**
* Step designed to build a single/default model.
*/
public static abstract class ModelStep extends ModelingStep {
public static final int DEFAULT_MODEL_TRAINING_WEIGHT = 10;
public static final int DEFAULT_MODEL_GROUP = 1;
public ModelStep(String provider, IAlgo algo, String id, AutoML autoML) {
this(provider, algo, id, DEFAULT_MODEL_GROUP, DEFAULT_MODEL_TRAINING_WEIGHT, autoML);
}
public ModelStep(String provider, IAlgo algo, String id, int priorityGroup, int weight, AutoML autoML) {
super(provider, algo, id, priorityGroup, weight, autoML);
}
@Override
protected JobType getJobType() {
return JobType.ModelBuild;
}
public abstract Model.Parameters prepareModelParameters();
@Override
protected Job startJob() {
return trainModel(prepareModelParameters());
}
protected Job trainModel(Model.Parameters parms) {
return trainModel(null, parms);
}
/**
* @param key (optional) model key.
* @param parms the model builder params.
* @return a started training model.
*/
protected Job trainModel(Key key, Model.Parameters parms) {
String algoName = ModelBuilder.algoName(_algo.urlName());
if (null == key) key = makeKey(algoName, true);
Model.Parameters defaults = ModelBuilder.make(_algo.urlName(), null, null)._parms;
initTimeConstraints(parms, 0);
setCommonModelBuilderParams(parms);
setSeed(parms, defaults, SeedPolicy.Incremental);
setStoppingCriteria(parms, defaults);
setCustomParams(parms);
// override model's max_runtime_secs to ensure that the total max_runtime doesn't exceed expectations
if (ArrayUtils.contains(_ignoredConstraints, AutoML.Constraint.TIMEOUT)) {
parms._max_runtime_secs = 0;
} else {
Work work = getAllocatedWork();
// double maxAssignedTimeSecs = aml().timeRemainingMs() / 1e3; // legacy
// double maxAssignedTimeSecs = aml().timeRemainingMs() * getWorkAllocations().remainingWorkRatio(work) / 1e3; //including default models in the distribution of the time budget.
// double maxAssignedTimeSecs = aml().timeRemainingMs() * getWorkAllocations().remainingWorkRatio(work, isDefaultModel) / 1e3; //PUBDEV-7595
double maxAssignedTimeSecs = aml().timeRemainingMs() * getWorkAllocations().remainingWorkRatio(work, _isSamePriorityGroup) / 1e3; // Models from a priority group + SEs
parms._max_runtime_secs = parms._max_runtime_secs == 0
? maxAssignedTimeSecs
: Math.min(parms._max_runtime_secs, maxAssignedTimeSecs);
}
Log.debug("Training model: " + algoName + ", time remaining (ms): " + aml().timeRemainingMs());
aml().eventLog().debug(Stage.ModelTraining, parms._max_runtime_secs == 0
? "No time limitation for "+key
: "Time assigned for "+key+": "+parms._max_runtime_secs+"s");
return startModel(key, parms);
}
}
/**
* Step designed to build multiple models using a (random) grid search.
*/
public static abstract class GridStep extends ModelingStep {
public static final int DEFAULT_GRID_TRAINING_WEIGHT = 30;
public static final int DEFAULT_GRID_GROUP = 2;
protected static final int GRID_STOPPING_ROUND_FACTOR = 2;
public GridStep(String provider, IAlgo algo, String id, AutoML autoML) {
this(provider, algo, id, DEFAULT_GRID_GROUP, DEFAULT_GRID_TRAINING_WEIGHT, autoML);
}
public GridStep(String provider, IAlgo algo, String id, int priorityGroup, int weight, AutoML autoML) {
super(provider, algo, id, priorityGroup, weight, autoML);
}
@Override
protected JobType getJobType() {
return JobType.HyperparamSearch;
}
@Override
public boolean isResumable() {
return true;
}
public abstract Model.Parameters prepareModelParameters();
public abstract Map prepareSearchParameters();
@Override
protected Job startJob() {
return hyperparameterSearch(prepareModelParameters(), prepareSearchParameters());
}
@Override
@SuppressWarnings("unchecked")
protected Key makeKey(String name, boolean withCounter) {
return aml().makeKey(name, "grid", withCounter);
}
protected Job hyperparameterSearch(Model.Parameters baseParms, Map searchParms) {
return hyperparameterSearch(null, baseParms, searchParms);
}
/**
* @param key optional grid key
* @param baseParms ModelBuilder parameter values that are common across all models in the search.
* @param searchParms hyperparameter search space,
* @return the started hyperparameter search job.
*/
protected Job hyperparameterSearch(Key key, Model.Parameters baseParms, Map searchParms) {
Model.Parameters defaults;
try {
defaults = baseParms.getClass().newInstance();
} catch (Exception e) {
aml().eventLog().warn(Stage.ModelTraining, "Internal error doing hyperparameter search");
throw new H2OIllegalArgumentException("Hyperparameter search can't create a new instance of Model.Parameters subclass: " + baseParms.getClass());
}
initTimeConstraints(baseParms, 0);
setCommonModelBuilderParams(baseParms);
// grid seed is provided later through the searchCriteria
setStoppingCriteria(baseParms, defaults);
setCustomParams(baseParms);
AutoMLBuildSpec buildSpec = aml().getBuildSpec();
RandomDiscreteValueSearchCriteria searchCriteria = (RandomDiscreteValueSearchCriteria)buildSpec.build_control.stopping_criteria.getSearchCriteria().clone();
setSearchCriteria(searchCriteria, baseParms);
if (null == key) key = makeKey(_provider, true);
aml().trackKeys(key);
Log.debug("Hyperparameter search: "+_provider+", time remaining (ms): "+aml().timeRemainingMs());
aml().eventLog().debug(Stage.ModelTraining, searchCriteria.max_runtime_secs() == 0
? "No time limitation for "+key
: "Time assigned for "+key+": "+searchCriteria.max_runtime_secs()+"s");
return startSearch(
key,
baseParms,
searchParms,
searchCriteria
);
}
protected void setSearchCriteria(RandomDiscreteValueSearchCriteria searchCriteria, Model.Parameters baseParms) {
Work work = getAllocatedWork();
// for time limit, this is allocated in proportion of the entire work budget.
double maxAssignedTimeSecs = ArrayUtils.contains(_ignoredConstraints, AutoML.Constraint.TIMEOUT)
? 0
: aml().timeRemainingMs() * getWorkAllocations().remainingWorkRatio(work, _isSamePriorityGroup) / 1e3;
// SE predicate can be removed if/when we decide to include SEs in the max_models limit
// for models limit, this is not assigned in the same proportion as for time,
// as the exploitation phase is not supposed to "add" models but just to replace some by better ones,
// instead, allocation is done in proportion of the entire exploration budget.
int maxAssignedModels = (int) Math.ceil(aml().remainingModels() * getWorkAllocations().remainingWorkRatio(work, isExplorationWork.and(w -> w._algo != Algo.StackedEnsemble)));
searchCriteria.set_max_runtime_secs(searchCriteria.max_runtime_secs() == 0
? maxAssignedTimeSecs
: Math.min(searchCriteria.max_runtime_secs(), maxAssignedTimeSecs));
searchCriteria.set_max_models(searchCriteria.max_models() == 0
? maxAssignedModels
: Math.min(searchCriteria.max_models(), maxAssignedModels));
searchCriteria.set_stopping_rounds(baseParms._stopping_rounds * GRID_STOPPING_ROUND_FACTOR);
}
}
/**
* Step designed to train some models (or not) and then deciding to make a selection
* and add and/or remove models to/from the current leaderboard.
*/
public static abstract class SelectionStep extends ModelingStep {
public static final int DEFAULT_SELECTION_TRAINING_WEIGHT = 20;
public static final int DEFAULT_SELECTION_GROUP = 3;
public SelectionStep(String provider, IAlgo algo, String id, AutoML autoML) {
this(provider, algo, id, DEFAULT_SELECTION_GROUP, DEFAULT_SELECTION_TRAINING_WEIGHT, autoML);
}
public SelectionStep(String provider, IAlgo algo, String id, int priorityGroup, int weight, AutoML autoML) {
super(provider, algo, id, priorityGroup, weight, autoML);
}
@Override
protected JobType getJobType() {
return JobType.Selection;
}
@Override
@SuppressWarnings("unchecked")
protected Key makeKey(String name, boolean withCounter) {
return aml().makeKey(name, "selection", withCounter);
}
private LeaderboardHolder makeLeaderboard(String name, EventLog eventLog) {
Leaderboard amlLeaderboard = aml().leaderboard();
EventLog tmpEventLog = eventLog == null ? EventLog.getOrMake(Key.make(name)) : eventLog;
Leaderboard tmpLeaderboard = Leaderboard.getOrMake(
name,
tmpEventLog,
amlLeaderboard.leaderboardFrame(),
amlLeaderboard.getSortMetric()
);
return new LeaderboardHolder() {
@Override
public Leaderboard get() {
return tmpLeaderboard;
}
@Override
public void cleanup() {
//by default, just empty the leaderboard and remove the container without touching anything model-related.
tmpLeaderboard.removeModels(tmpLeaderboard.getModelKeys(), false);
tmpLeaderboard.remove(false);
if (eventLog == null) {
tmpEventLog.remove();
}
}
};
}
protected LeaderboardHolder makeTmpLeaderboard(String name) {
return makeLeaderboard("tmp_"+name, null);
}
@Override
protected Job startJob() {
Key[] trainedModelKeys = getTrainedModelsKeys();
Key key = makeKey(_provider+"_"+_id, false);
aml().trackKeys(key);
Job job = new Job<>(key, Models.class.getName(), _description);
Work work = getAllocatedWork();
double maxAssignedTimeSecs = ArrayUtils.contains(_ignoredConstraints, AutoML.Constraint.TIMEOUT)
? 0
: aml().timeRemainingMs() * getWorkAllocations().remainingWorkRatio(work) / 1e3;
aml().eventLog().debug(Stage.ModelTraining, maxAssignedTimeSecs == 0
? "No time limitation for "+key
: "Time assigned for "+key+": "+maxAssignedTimeSecs+"s");
return job.start(new H2O.H2OCountedCompleter() {
Models result = new Models(key, Model.class, job);
Key selectionKey = Key.make(key+"_select");
EventLog selectionEventLog = EventLog.getOrMake(selectionKey);
// EventLog selectionEventLog = aml().eventLog();
LeaderboardHolder selectionLeaderboard = makeLeaderboard(selectionKey.toString(), selectionEventLog);
{
result.delete_and_lock(job);
}
@Override
public void compute2() {
Countdown countdown = Countdown.fromSeconds(maxAssignedTimeSecs);
ModelingStepsExecutor localExecutor = new ModelingStepsExecutor(selectionLeaderboard.get(), selectionEventLog, countdown);
localExecutor.start();
Job innerTraining = startTraining(selectionKey, maxAssignedTimeSecs);
localExecutor.monitor(innerTraining, SelectionStep.this, job);
Log.debug("Selection leaderboard " + selectionLeaderboard.get()._key, selectionLeaderboard.get().toLogString());
Selection selection = getSelectionStrategy().select(trainedModelKeys, selectionLeaderboard.get().getModelKeys());
Leaderboard lb = aml().leaderboard();
Log.debug("Selection result for job " + key, ToStringBuilder.reflectionToString(selection));
lb.removeModels(selection._remove, false); // do remove the model immediately from DKV: if it were part of a grid, it prevents the grid from being resumed.
aml().trackKeys(selection._remove);
lb.addModels(selection._add);
result.unlock(job);
result.addModels(selection._add);
tryComplete();
}
@Override
public void onCompletion(CountedCompleter caller) {
Keyed.remove(selectionKey, new Futures(), false); // don't cascade: tmp models removal is is done using the logic below.
selectionLeaderboard.get().removeModels(trainedModelKeys, false); // if original models were added to selection leaderboard, just remove them.
selectionLeaderboard.get().removeModels( // for newly trained models, fully remove those that don't appear in the result container.
Arrays.stream(selectionLeaderboard.get().getModelKeys()).filter(k -> !ArrayUtils.contains(result.getModelKeys(), k)).toArray(Key[]::new),
true
);
selectionLeaderboard.cleanup();
if (!aml().eventLog()._key.equals(selectionEventLog._key)) selectionEventLog.remove();
super.onCompletion(caller);
}
@Override
public boolean onExceptionalCompletion(Throwable ex, CountedCompleter caller) {
result.unlock(job._key, false);
Keyed.remove(selectionKey);
selectionLeaderboard.get().remove();
if (!aml().eventLog()._key.equals(selectionEventLog._key)) selectionEventLog.remove();
return super.onExceptionalCompletion(ex, caller);
}
}, work._weight, maxAssignedTimeSecs);
}
protected abstract Job startTraining(Key result, double maxRuntimeSecs);
protected abstract ModelSelectionStrategy getSelectionStrategy();
protected Job asModelsJob(Job job, Key result){
Job jModels = new Job<>(result, Models.class.getName(), job._description); // can use the same result key as original job, as it is dropped once its result is read
return jModels.start(new H2O.H2OCountedCompleter() {
Models models = new Models(result, Model.class, jModels);
{
models.delete_and_lock(jModels);
}
@Override
public void compute2() {
ModelingStepsExecutor.ensureStopRequestPropagated(job, jModels, ModelingStepsExecutor.DEFAULT_POLLING_INTERVAL_IN_MILLIS);
Keyed res = job.get();
models.unlock(jModels);
if (res instanceof Model) {
models.addModel(res.getKey());
} else if (res instanceof ModelContainer) {
models.addModels(((ModelContainer)res).getModelKeys());
res.remove(false);
} else {
throw new H2OIllegalArgumentException("Can only convert jobs producing a single Model or ModelContainer.");
}
tryComplete();
}
}, job._work, job._max_runtime_msecs);
}
}
/**
* Step designed to dynamically choose to train a model or another, a grid or anything else,
* based on the current automl workflow history.
*/
public static abstract class DynamicStep extends ModelingStep {
public static final int DEFAULT_DYNAMIC_TRAINING_WEIGHT = 20;
public static final int DEFAULT_DYNAMIC_GROUP = 100;
public static class VirtualAlgo implements IAlgo {
public VirtualAlgo() {}
@Override
public String name() {
return "virtual";
}
}
private transient Collection _subSteps;
public DynamicStep(String provider, String id, AutoML autoML) {
this(provider, id, DEFAULT_DYNAMIC_GROUP, DEFAULT_DYNAMIC_TRAINING_WEIGHT, autoML);
}
public DynamicStep(String provider, String id, int priorityGroup, int weight, AutoML autoML) {
super(provider, new VirtualAlgo(), id, priorityGroup, weight, autoML);
}
@Override
public boolean canRun() {
// this step is designed to delegate its work to sub-steps by default,
// so the parent step itself has nothing to run.
return false;
}
@Override
protected Job startJob() {
// see comment in canRun().
return null;
}
@Override
protected JobType getJobType() {
return JobType.Dynamic;
}
@Override
@SuppressWarnings("unchecked")
protected Key makeKey(String name, boolean withCounter) {
return aml().makeKey(name, "decision", withCounter);
}
private void initSubSteps() {
if (_subSteps == null) {
_subSteps = prepareModelingSteps();
}
}
@Override
public Iterator iterateSubSteps() {
initSubSteps();
return _subSteps.iterator();
}
@Override
protected Optional getSubStep(String id) {
initSubSteps();
return _subSteps.stream()
.filter(step -> step._id.equals(id))
.findFirst();
}
protected abstract Collection prepareModelingSteps();
}
}
