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ai.h2o.automl.modeling.XGBoostSteps Maven / Gradle / Ivy
package ai.h2o.automl.modeling;
import ai.h2o.automl.*;
import ai.h2o.automl.ModelSelectionStrategies.KeepBestN;
import ai.h2o.automl.events.EventLogEntry;
import hex.Model;
import hex.genmodel.utils.DistributionFamily;
import hex.grid.GridSearch;
import hex.grid.HyperSpaceSearchCriteria.SequentialSearchCriteria;
import hex.grid.HyperSpaceSearchCriteria.StoppingCriteria;
import hex.grid.SequentialWalker;
import hex.tree.xgboost.XGBoostModel;
import hex.tree.xgboost.XGBoostModel.XGBoostParameters;
import water.Job;
import water.Key;
import java.util.*;
public class XGBoostSteps extends ModelingSteps {
static final String NAME = Algo.XGBoost.name();
static XGBoostParameters prepareModelParameters(AutoML aml, boolean emulateLightGBM) {
XGBoostParameters params = new XGBoostParameters();
if (emulateLightGBM) {
params._tree_method = XGBoostParameters.TreeMethod.hist;
params._grow_policy = XGBoostParameters.GrowPolicy.lossguide;
}
// setDistribution: no way to identify gaussian, poisson, laplace? using descriptive statistics?
params._distribution = aml.getResponseColumn().isBinary() && !(aml.getResponseColumn().isNumeric()) ? DistributionFamily.bernoulli
: aml.getResponseColumn().isCategorical() ? DistributionFamily.multinomial
: DistributionFamily.AUTO;
params._score_tree_interval = 5;
params._ntrees = 10000;
// params._min_split_improvement = 0.01f;
return params;
}
static abstract class XGBoostModelStep extends ModelingStep.ModelStep {
boolean _emulateLightGBM;
XGBoostModelStep(String id, AutoML autoML, boolean emulateLightGBM) {
super(NAME, Algo.XGBoost, id, autoML);
_emulateLightGBM = emulateLightGBM;
}
public XGBoostParameters prepareModelParameters() {
XGBoostParameters params = XGBoostSteps.prepareModelParameters(aml(), _emulateLightGBM);
if (aml().getBuildSpec().build_control.balance_classes && aml().getDistributionFamily().equals(DistributionFamily.bernoulli)) {
double[] dist = aml().getClassDistribution();
params._scale_pos_weight = (float) (dist[0] / dist[1]);
}
return params;
}
}
static abstract class XGBoostGridStep extends ModelingStep.GridStep {
boolean _emulateLightGBM;
public XGBoostGridStep(String id, AutoML autoML, boolean emulateLightGBM) {
super(NAME, Algo.XGBoost, id, autoML);
_emulateLightGBM = emulateLightGBM;
}
public XGBoostParameters prepareModelParameters() {
return XGBoostSteps.prepareModelParameters(aml(), _emulateLightGBM);
}
}
static abstract class XGBoostExploitationStep extends ModelingStep.SelectionStep {
boolean _emulateLightGBM;
protected XGBoostModel getBestXGB() {
return getBestXGBs(1).get(0);
}
protected List getBestXGBs(int topN) {
List xgbs = new ArrayList<>();
for (Model model : getTrainedModels()) {
if (model instanceof XGBoostModel) {
xgbs.add((XGBoostModel) model);
}
if (xgbs.size() == topN) break;
}
return xgbs;
}
@Override
public boolean canRun() {
return super.canRun() && getBestXGBs(1).size() > 0;
}
public XGBoostExploitationStep(String id, AutoML autoML, boolean emulateLightGBM) {
super(NAME, Algo.XGBoost, id, autoML);
_emulateLightGBM = emulateLightGBM;
if (autoML.getBuildSpec().build_models.exploitation_ratio > 0)
_ignoredConstraints = new AutoML.Constraint[] { AutoML.Constraint.MODEL_COUNT };
}
}
private final ModelingStep[] defaults = new XGBoostModelStep[] {
new XGBoostModelStep("def_1", aml(), false) {
@Override
public XGBoostParameters prepareModelParameters() {
//XGB 1 (medium depth)
XGBoostParameters params = super.prepareModelParameters();
params._max_depth = 10;
params._min_rows = 5;
params._sample_rate = 0.6;
params._col_sample_rate = 0.8;
params._col_sample_rate_per_tree = 0.8;
if (_emulateLightGBM) {
params._max_leaves = 1 << params._max_depth;
params._max_depth = params._max_depth * 2;
}
return params;
}
},
new XGBoostModelStep("def_2", aml(), false) {
@Override
public XGBoostParameters prepareModelParameters() {
//XGB 2 (deep)
XGBoostParameters params = super.prepareModelParameters();
params._max_depth = 15;
params._min_rows = 10;
params._sample_rate = 0.6;
params._col_sample_rate = 0.8;
params._col_sample_rate_per_tree = 0.8;
if (_emulateLightGBM) {
params._max_leaves = 1 << params._max_depth;
params._max_depth = params._max_depth * 2;
}
return params;
}
},
new XGBoostModelStep("def_3", aml(), false) {
@Override
public XGBoostParameters prepareModelParameters() {
//XGB 3 (shallow)
XGBoostParameters params = super.prepareModelParameters();
params._max_depth = 5;
params._min_rows = 3;
params._sample_rate = 0.8;
params._col_sample_rate = 0.8;
params._col_sample_rate_per_tree = 0.8;
if (_emulateLightGBM) {
params._max_leaves = 1 << params._max_depth;
params._max_depth = params._max_depth * 2;
}
return params;
}
},
};
static class DefaultXGBoostGridStep extends XGBoostGridStep {
public DefaultXGBoostGridStep(String id, AutoML autoML) {
super(id, autoML, false);
}
@Override
public XGBoostParameters prepareModelParameters() {
XGBoostParameters params = super.prepareModelParameters();
// Reset scale pos weight so we can grid search the parameter
params._scale_pos_weight = (new XGBoostParameters())._scale_pos_weight;
return params;
}
@Override
public Map prepareSearchParameters() {
Map searchParams = new HashMap<>();
// searchParams.put("_ntrees", new Integer[]{100, 1000, 10000}); // = _n_estimators
if (_emulateLightGBM) {
searchParams.put("_max_leaves", new Integer[]{1 << 5, 1 << 10, 1 << 15, 1 << 20});
searchParams.put("_max_depth", new Integer[]{10, 20, 50});
} else {
searchParams.put("_max_depth", new Integer[]{3, 6, 9, 12, 15});
if (aml().getWeightsColumn() == null || aml().getWeightsColumn().isInt()) {
searchParams.put("_min_rows", new Double[]{1.0, 3.0, 5.0, 10.0, 15.0, 20.0}); // = _min_child_weight
} else {
searchParams.put("_min_rows", new Double[]{0.01, 0.1, 1.0, 3.0, 5.0, 10.0, 15.0, 20.0}); // = _min_child_weight
}
}
searchParams.put("_sample_rate", new Double[]{0.6, 0.8, 1.0}); // = _subsample
searchParams.put("_col_sample_rate", new Double[]{0.6, 0.8, 1.0}); // = _colsample_bylevel"
searchParams.put("_col_sample_rate_per_tree", new Double[]{0.7, 0.8, 0.9, 1.0}); // = _colsample_bytree: start higher to always use at least about 40% of columns
// searchParams.put("_min_split_improvement", new Float[]{0.01f, 0.05f, 0.1f, 0.5f, 1f, 5f, 10f, 50f}); // = _gamma
// searchParams.put("_tree_method", new XGBoostParameters.TreeMethod[]{XGBoostParameters.TreeMethod.auto});
searchParams.put("_booster", new XGBoostParameters.Booster[]{ // include gblinear? cf. https://0xdata.atlassian.net/browse/PUBDEV-7254
XGBoostParameters.Booster.gbtree, //default, let's use it more often: note that some combinations may be trained multiple time by the RGS then.
XGBoostParameters.Booster.gbtree,
XGBoostParameters.Booster.dart
});
searchParams.put("_reg_lambda", new Float[]{0.001f, 0.01f, 0.1f, 1f, 10f, 100f});
searchParams.put("_reg_alpha", new Float[]{0.001f, 0.01f, 0.1f, 0.5f, 1f});
if (aml().getBuildSpec().build_control.balance_classes && aml().getDistributionFamily().equals(DistributionFamily.bernoulli)) {
double[] dist = aml().getClassDistribution();
final float negPosRatio = (float)(dist[0] / dist[1]);
final float imbalanceRatio = negPosRatio < 1 ? 1 / negPosRatio : negPosRatio;
searchParams.put("_scale_pos_weight", new Float[]{1.f, negPosRatio});
searchParams.put("_max_delta_step", new Float[]{0f, Math.min(5f, imbalanceRatio / 2), Math.min(10f, imbalanceRatio)});
}
return searchParams;
}
}
private final ModelingStep[] grids = new XGBoostGridStep[] {
new DefaultXGBoostGridStep("grid_1", aml()),
/*
new DefaultXGBoostGridStep("grid_1_resume", aml()) {
@Override
protected void setSearchCriteria(RandomDiscreteValueSearchCriteria searchCriteria, Model.Parameters baseParms) {
super.setSearchCriteria(searchCriteria, baseParms);
searchCriteria.set_stopping_rounds(0);
}
@Override
@SuppressWarnings("unchecked")
protected Job startJob() {
Key[] resumedGrid = aml().getResumableKeys(_provider, "grid_1");
if (resumedGrid.length == 0) return null;
return hyperparameterSearch(resumedGrid[0], prepareModelParameters(), prepareSearchParameters());
}
}
*/
};
private final ModelingStep[] exploitation = new ModelingStep[] {
new XGBoostExploitationStep("lr_annealing", aml(), false) {
Key resultKey = null;
@Override
protected Job startTraining(Key result, double maxRuntimeSecs) {
resultKey = result;
XGBoostModel bestXGB = getBestXGB();
aml().eventLog().info(EventLogEntry.Stage.ModelSelection, "Retraining best XGBoost with learning rate annealing: "+bestXGB._key);
XGBoostParameters params = (XGBoostParameters) bestXGB._input_parms.clone();
params._max_runtime_secs = 0; // reset max runtime
params._learn_rate_annealing = 0.99;
initTimeConstraints(params, maxRuntimeSecs);
setStoppingCriteria(params, new XGBoostParameters());
return asModelsJob(startModel(Key.make(result+"_model"), params), result);
}
@Override
protected ModelSelectionStrategy getSelectionStrategy() {
return (originalModels, newModels) ->
new KeepBestN<>(1, () -> makeTmpLeaderboard(Objects.toString(resultKey, _provider+"_"+_id)))
.select(new Key[] { getBestXGB()._key }, newModels);
}
},
new XGBoostExploitationStep("lr_search", aml(), false) {
Key resultKey = null;
@Override
protected ModelSelectionStrategy getSelectionStrategy() {
return (originalModels, newModels) ->
new KeepBestN<>(1, () -> makeTmpLeaderboard(Objects.toString(resultKey, _provider+"_"+_id)))
.select(new Key[] { getBestXGB()._key }, newModels);
}
@Override
protected Job startTraining(Key result, double maxRuntimeSecs) {
resultKey = result;
XGBoostModel bestXGB = getBestXGBs(1).get(0);
aml().eventLog().info(EventLogEntry.Stage.ModelSelection, "Applying learning rate search on best XGBoost: "+bestXGB._key);
XGBoostParameters params = (XGBoostParameters) bestXGB._input_parms.clone();
XGBoostParameters defaults = new XGBoostParameters();
params._max_runtime_secs = 0; // reset max runtime
initTimeConstraints(params, 0); // ensure we have a max runtime per model in the grid
setStoppingCriteria(params, defaults); // keep the same seed as the bestXGB
// keep stopping_rounds fixed, but increases score_tree_interval when lowering learn rate
int sti = params._score_tree_interval;
Object[][] hyperParams = new Object[][] {
new Object[] {"_learn_rate", "_score_tree_interval"},
new Object[] { 0.5 , sti },
new Object[] { 0.2 , 2*sti },
new Object[] { 0.1 , 3*sti },
new Object[] { 0.05 , 4*sti },
new Object[] { 0.02 , 5*sti },
new Object[] { 0.01 , 6*sti },
new Object[] { 0.005 , 7*sti },
new Object[] { 0.002 , 8*sti },
new Object[] { 0.001 , 9*sti },
new Object[] { 0.0005, 10*sti },
};
/*
Object[][] hyperParams = new Object[][] {
new Object[] {"_learn_rate", "_score_tree_interval"},
new Object[] { 0.5 , sti },
new Object[] { 0.2 , (1<<1)*sti },
new Object[] { 0.1 , (1<<2)*sti },
new Object[] { 0.05 , (1<<3)*sti },
new Object[] { 0.02 , (1<<4)*sti },
new Object[] { 0.01 , (1<<5)*sti },
new Object[] { 0.005 , (1<<6)*sti },
new Object[] { 0.002 , (1<<7)*sti },
new Object[] { 0.001 , (1<<8)*sti },
new Object[] { 0.0005, (1<<9)*sti },
};
*/
aml().eventLog().info(EventLogEntry.Stage.ModelTraining, "AutoML: starting "+resultKey+" model training")
.setNamedValue("start_"+_provider+"_"+_id, new Date(), EventLogEntry.epochFormat.get());
return asModelsJob(GridSearch.startGridSearch(
Key.make(result+"_grid"),
new SequentialWalker<>(
params,
hyperParams,
new GridSearch.SimpleParametersBuilderFactory<>(),
new SequentialSearchCriteria(StoppingCriteria.create()
.maxRuntimeSecs((int)maxRuntimeSecs)
.stoppingMetric(params._stopping_metric)
.stoppingRounds(3) // enforcing this as we define the sequence and it is quite small.
.stoppingTolerance(params._stopping_tolerance)
.build())
),
GridSearch.SEQUENTIAL_MODEL_BUILDING
), result);
}
}
};
public XGBoostSteps(AutoML autoML) {
super(autoML);
}
@Override
public String getProvider() {
return NAME;
}
@Override
protected ModelingStep[] getDefaultModels() {
return defaults;
}
@Override
protected ModelingStep[] getGrids() {
return grids;
}
@Override
protected ModelingStep[] getOptionals() {
return exploitation;
}
}
