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GH-16056 xgboost support gblinear parameters (#16166)
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* GH-16056 - add gblinear parameters to xgboost

* test that top_k works -> feature selector must me changed

* Add updater parameter to by able to set shotgun
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@valenad1
valenad1 committed May 4, 2024
1 parent e33ffb7 commit fe4cf5e
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Showing 2 changed files with 94 additions and 6 deletions.
30 changes: 25 additions & 5 deletions h2o-extensions/xgboost/src/main/java/hex/tree/xgboost/XGBoostModel.java
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,12 @@ public enum DMatrixType {
public enum Backend {
auto, gpu, cpu
}
public enum FeatureSelector {
cyclic, shuffle, random, greedy, thrifty
}
public enum Updater {
gpu_hist, shotgun, coord_descent, gpu_coord_descent,
}

// H2O GBM options
public boolean _quiet_mode = true;
Expand Down Expand Up @@ -141,6 +147,12 @@ public enum Backend {
public int[] _gpu_id; // which GPU to use
public Backend _backend = Backend.auto;

// GBLiner specific (booster == gblinear)
// lambda, alpha support also for gbtree
public FeatureSelector _feature_selector = FeatureSelector.cyclic;
public int _top_k;
public Updater _updater;

public String _eval_metric;
public boolean _score_eval_metric_only;

Expand Down Expand Up @@ -378,6 +390,10 @@ public static Map<String, Object> createParamsMap(XGBoostParameters p, int nClas
params.put("one_drop", p._one_drop ? "1" : "0");
params.put("skip_drop", p._skip_drop);
}
if (p._booster == XGBoostParameters.Booster.gblinear) {
params.put("feature_selector", p._feature_selector.toString());
params.put("top_k", p._top_k);
}
XGBoostParameters.Backend actualBackend = getActualBackend(p, true);
XGBoostParameters.TreeMethod actualTreeMethod = getActualTreeMethod(p);
if (actualBackend == XGBoostParameters.Backend.gpu) {
Expand All @@ -387,17 +403,17 @@ public static Map<String, Object> createParamsMap(XGBoostParameters p, int nClas
params.put("gpu_id", 0);
}
// we are setting updater rather than tree_method here to keep CPU predictor, which is faster
if (p._booster == XGBoostParameters.Booster.gblinear) {
if (p._booster == XGBoostParameters.Booster.gblinear && p._updater == null) {
LOG.info("Using gpu_coord_descent updater.");
params.put("updater", "gpu_coord_descent");
params.put("updater", XGBoostParameters.Updater.gpu_coord_descent.toString());
} else {
LOG.info("Using gpu_hist tree method.");
params.put("max_bin", p._max_bins);
params.put("tree_method", "gpu_hist");
params.put("tree_method", XGBoostParameters.Updater.gpu_hist.toString());
}
} else if (p._booster == XGBoostParameters.Booster.gblinear) {
} else if (p._booster == XGBoostParameters.Booster.gblinear && p._updater == null) {
LOG.info("Using coord_descent updater.");
params.put("updater", "coord_descent");
params.put("updater", XGBoostParameters.Updater.coord_descent.toString());
} else if (H2O.CLOUD.size() > 1 && p._tree_method == XGBoostParameters.TreeMethod.auto &&
p._monotone_constraints != null) {
LOG.info("Using hist tree method for distributed computation with monotone_constraints.");
Expand All @@ -410,6 +426,10 @@ public static Map<String, Object> createParamsMap(XGBoostParameters p, int nClas
params.put("max_bin", p._max_bins);
}
}
if (p._updater != null) {
LOG.info("Using user-provided updater.");
params.put("updater", p._updater.toString());
}
if (p._min_child_weight != 1) {
LOG.info("Using user-provided parameter min_child_weight instead of min_rows.");
params.put("min_child_weight", p._min_child_weight);
Expand Down
70 changes: 69 additions & 1 deletion h2o-extensions/xgboost/src/test/java/hex/tree/xgboost/XGBoostTest.java
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Original file line number Diff line number Diff line change
Expand Up @@ -3279,5 +3279,73 @@ public void testWarnEvalMetricOnlyWithouEvalMetric() {
Scope.exit();
}
}


@Test
public void testGBLinearTopKAndFeatureSelector() {
Scope.enter();
try {
String response = "CAPSULE";
Frame train = parseAndTrackTestFile("./smalldata/logreg/prostate_train.csv");
train.toCategoricalCol(response);

XGBoostModel.XGBoostParameters parms = new XGBoostModel.XGBoostParameters();
parms._ntrees = 1;
parms._train = train._key;
parms._response_column = response;
parms._booster = XGBoostModel.XGBoostParameters.Booster.gblinear;
parms._top_k = 2;
parms._feature_selector = XGBoostModel.XGBoostParameters.FeatureSelector.greedy;

ModelBuilder job = new hex.tree.xgboost.XGBoost(parms);

XGBoostModel xgboost = (XGBoostModel) job.trainModel().get();
Scope.track_generic(xgboost);
assertNotNull(xgboost);

Frame score = xgboost.score(train);
Scope.track(score);

parms._top_k = 100;
ModelBuilder jobTopKChanged = new hex.tree.xgboost.XGBoost(parms);

XGBoostModel xgboostTopKChanged = (XGBoostModel) jobTopKChanged.trainModel().get();
Scope.track_generic(xgboostTopKChanged);
assertNotNull(xgboostTopKChanged);

Frame scoreTopKChanged = xgboostTopKChanged.score(train);
Scope.track(scoreTopKChanged);
assertNotEquals("top_k should affect the predictions", score.toTwoDimTable().get(0,1), scoreTopKChanged.toTwoDimTable().get(0,1));
}
finally {
Scope.exit();
}
}


@Test
public void testGBLinearShotgun() {
Scope.enter();
try {
String response = "CAPSULE";
Frame train = parseAndTrackTestFile("./smalldata/logreg/prostate_train.csv");
train.toCategoricalCol(response);

XGBoostModel.XGBoostParameters parms = new XGBoostModel.XGBoostParameters();
parms._ntrees = 1;
parms._train = train._key;
parms._response_column = response;
parms._booster = XGBoostModel.XGBoostParameters.Booster.gblinear;
parms._updater = XGBoostModel.XGBoostParameters.Updater.shotgun;
parms._feature_selector = XGBoostModel.XGBoostParameters.FeatureSelector.shuffle;

ModelBuilder job = new hex.tree.xgboost.XGBoost(parms);
XGBoostModel xgboost = (XGBoostModel) job.trainModel().get();
assertNotNull(xgboost);
Scope.track_generic(xgboost);
assertEquals("updater should be changed", xgboost._output._native_parameters.get(1,1), XGBoostModel.XGBoostParameters.Updater.shotgun.toString());
}
finally {
Scope.exit();
}
}
}

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