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GH-16046 UpliftDRF Implement Cross Validation #16048

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GH-16046 UpliftDRF Implement Cross Validation #16048

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47225b8
Implement Uplift cross validation
maurever Jan 12, 2024
cf87bb2
Fix auuc2 MR bug
maurever Mar 27, 2024
c83d0e3
Implement AUUC reduce method correctly
maurever May 5, 2024
5c6869b
Implement AUUC reduce correctly
maurever May 8, 2024
af461bc
Change default auuc_nbins
maurever May 10, 2024
8745111
Build project changes
maurever May 15, 2024
d4c70fa
Fix randomForest tests to pass on master
maurever May 15, 2024
1dfae13
Debug tests failed on jenkins
maurever May 22, 2024
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2 changes: 1 addition & 1 deletion h2o-algos/src/main/java/hex/generic/GenericModel.java
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Original file line number Diff line number Diff line change
Expand Up @@ -137,7 +137,7 @@ public ModelMetrics.MetricBuilder makeMetricBuilder(String[] domain) {
case AnomalyDetection:
return new ModelMetricsAnomaly.MetricBuilderAnomaly();
case BinomialUplift:
return new ModelMetricsBinomialUplift.MetricBuilderBinomialUplift(domain, null);
return new ModelMetricsBinomialUplift.MetricBuilderBinomialUplift(domain, null, _parms._auuc_nbins,AUUC.calculateProbs(_parms._auuc_nbins));
default:
throw H2O.unimpl();
}
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2 changes: 1 addition & 1 deletion h2o-algos/src/main/java/hex/tree/SharedTreeModel.java
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Expand Up @@ -166,7 +166,7 @@ public boolean forceStrictlyReproducibleHistograms() {
case Binomial: return new ModelMetricsBinomial.MetricBuilderBinomial(domain);
case Multinomial: return new ModelMetricsMultinomial.MetricBuilderMultinomial(_output.nclasses(),domain, _parms._auc_type);
case Regression: return new ModelMetricsRegression.MetricBuilderRegression();
case BinomialUplift: return new ModelMetricsBinomialUplift.MetricBuilderBinomialUplift(domain, ((UpliftDRFModel.UpliftDRFOutput)_output)._defaultAuucThresholds);
case BinomialUplift: return new ModelMetricsBinomialUplift.MetricBuilderBinomialUplift(domain, ((UpliftDRFModel.UpliftDRFOutput)_output)._defaultAuucThresholds, _parms._auuc_nbins, AUUC.calculateProbs( _parms._auuc_nbins));
default: throw H2O.unimpl();
}
}
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8 changes: 3 additions & 5 deletions h2o-algos/src/main/java/hex/tree/uplift/UpliftDRF.java
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Expand Up @@ -97,11 +97,9 @@ public ModelCategory[] can_build() {
if (hasOffsetCol())
error("_offset_column", "Offsets are not yet supported for Uplift DRF.");
if (hasWeightCol())
error("_weight_column", "Weights are not yet supported for Uplift DRF.");
if (hasFoldCol())
error("_fold_column", "Cross-validation is not yet supported for Uplift DRF.");
if (_parms._nfolds > 0)
error("_nfolds", "Cross-validation is not yet supported for Uplift DRF.");
if(!_parms._weights_column.equals("__internal_cv_weights__")) {
error("_weight_column", "Weights are not yet supported for Uplift DRF.");
}
if (_nclass == 1)
error("_distribution", "UpliftDRF currently support binomial classification problems only.");
if (_nclass > 2 || _parms._distribution.equals(DistributionFamily.multinomial))
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2 changes: 1 addition & 1 deletion h2o-algos/src/main/java/hex/tree/uplift/UpliftDRFModel.java
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Expand Up @@ -84,7 +84,7 @@ public void initActualParamValues() {
}

@Override public ModelMetrics.MetricBuilder makeMetricBuilder(String[] domain) {
return new ModelMetricsBinomialUplift.MetricBuilderBinomialUplift(domain, _output._defaultAuucThresholds);
return new ModelMetricsBinomialUplift.MetricBuilderBinomialUplift(domain, _output._defaultAuucThresholds, _parms._auuc_nbins, AUUC.calculateProbs(_parms._auuc_nbins));
}

@Override
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82 changes: 49 additions & 33 deletions h2o-algos/src/test/java/hex/tree/uplift/UpliftDRFTest.java
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Expand Up @@ -41,6 +41,8 @@ public void testBasicTrain() {
p._response_column = "conversion";
p._seed = 0xDECAF;
p._ntrees = 3;
p._score_each_iteration = true;
p._auuc_nbins = 450;

UpliftDRF udrf = new UpliftDRF(p);
UpliftDRFModel model = udrf.trainModel().get();
Expand Down Expand Up @@ -153,17 +155,16 @@ public void testBasicTrainErrorDoNotSupportMultinomialResponseColumn() {
}
}


@Test(expected = H2OModelBuilderIllegalArgumentException.class)
public void testBasicTrainErrorDoNotSupportNfolds() {
public void testBasicTrainErrorDoNotSupportOffset() {
try {
Scope.enter();
Frame train = generateFrame();
UpliftDRFModel.UpliftDRFParameters p = new UpliftDRFModel.UpliftDRFParameters();
p._train = train._key;
p._treatment_column = "treatment";
p._response_column = "conversion";
p._nfolds = 10;
p._offset_column = "C1";

UpliftDRF udrf = new UpliftDRF(p);
udrf.trainModel().get();
Expand All @@ -173,61 +174,75 @@ public void testBasicTrainErrorDoNotSupportNfolds() {
}

@Test(expected = H2OModelBuilderIllegalArgumentException.class)
public void testBasicTrainErrorDoNotSupportFoldColumn() {
public void testBasicTrainErrorDoNotSupportDistribution() {
try {
Scope.enter();
Frame train = generateFrame();
UpliftDRFModel.UpliftDRFParameters p = new UpliftDRFModel.UpliftDRFParameters();
p._train = train._key;
p._treatment_column = "treatment";
p._response_column = "conversion";
p._fold_column = "C0";
p._distribution = DistributionFamily.multinomial;

UpliftDRF udrf = new UpliftDRF(p);
udrf.trainModel().get();
} finally {
Scope.exit();
}
}

@Test(expected = H2OModelBuilderIllegalArgumentException.class)
public void testBasicTrainErrorDoNotSupportOffset() {
@Test
public void testBasicTrainSupportEarlyStoppingAUUC() {
try {
Scope.enter();
Frame train = generateFrame();
int ntrees = 42;
UpliftDRFModel.UpliftDRFParameters p = new UpliftDRFModel.UpliftDRFParameters();
p._train = train._key;
p._treatment_column = "treatment";
p._response_column = "conversion";
p._offset_column = "C1";
p._stopping_metric = ScoreKeeper.StoppingMetric.AUUC;
p._stopping_rounds = 2;
p._ntrees = ntrees;
p._score_each_iteration = true;

UpliftDRF udrf = new UpliftDRF(p);
udrf.trainModel().get();
UpliftDRFModel model = udrf.trainModel().get();
Scope.track_generic(model);
assertNotNull(model);
assertTrue(model._output._treeStats._num_trees < ntrees);
} finally {
Scope.exit();
}
}

@Test(expected = H2OModelBuilderIllegalArgumentException.class)
public void testBasicTrainErrorDoNotSupportDistribution() {
@Test
public void testBasicTrainSupportEarlyStoppingATE() {
try {
Scope.enter();
Frame train = generateFrame();
int ntrees = 42;
UpliftDRFModel.UpliftDRFParameters p = new UpliftDRFModel.UpliftDRFParameters();
p._train = train._key;
p._treatment_column = "treatment";
p._response_column = "conversion";
p._distribution = DistributionFamily.multinomial;
p._stopping_metric = ScoreKeeper.StoppingMetric.ATE;
p._stopping_rounds = 2;
p._ntrees = ntrees;
p._score_each_iteration = true;

UpliftDRF udrf = new UpliftDRF(p);
udrf.trainModel().get();
UpliftDRFModel model = udrf.trainModel().get();
Scope.track_generic(model);
assertNotNull(model);
assertTrue(model._output._treeStats._num_trees < ntrees);
} finally {
Scope.exit();
}
}

@Test
public void testBasicTrainSupportEarlyStoppingAUUC() {
public void testBasicTrainSupportEarlyStoppingATT() {
try {
Scope.enter();
Frame train = generateFrame();
Expand All @@ -236,7 +251,7 @@ public void testBasicTrainSupportEarlyStoppingAUUC() {
p._train = train._key;
p._treatment_column = "treatment";
p._response_column = "conversion";
p._stopping_metric = ScoreKeeper.StoppingMetric.AUUC;
p._stopping_metric = ScoreKeeper.StoppingMetric.ATT;
p._stopping_rounds = 2;
p._ntrees = ntrees;
p._score_each_iteration = true;
Expand All @@ -252,7 +267,7 @@ public void testBasicTrainSupportEarlyStoppingAUUC() {
}

@Test
public void testBasicTrainSupportEarlyStoppingATE() {
public void testBasicTrainSupportEarlyStoppingATC() {
try {
Scope.enter();
Frame train = generateFrame();
Expand All @@ -261,7 +276,7 @@ public void testBasicTrainSupportEarlyStoppingATE() {
p._train = train._key;
p._treatment_column = "treatment";
p._response_column = "conversion";
p._stopping_metric = ScoreKeeper.StoppingMetric.ATE;
p._stopping_metric = ScoreKeeper.StoppingMetric.ATC;
p._stopping_rounds = 2;
p._ntrees = ntrees;
p._score_each_iteration = true;
Expand All @@ -277,7 +292,7 @@ public void testBasicTrainSupportEarlyStoppingATE() {
}

@Test
public void testBasicTrainSupportEarlyStoppingATT() {
public void testBasicTrainSupportEarlyStoppingQini() {
try {
Scope.enter();
Frame train = generateFrame();
Expand All @@ -286,7 +301,7 @@ public void testBasicTrainSupportEarlyStoppingATT() {
p._train = train._key;
p._treatment_column = "treatment";
p._response_column = "conversion";
p._stopping_metric = ScoreKeeper.StoppingMetric.ATT;
p._stopping_metric = ScoreKeeper.StoppingMetric.qini;
p._stopping_rounds = 2;
p._ntrees = ntrees;
p._score_each_iteration = true;
Expand All @@ -302,50 +317,51 @@ public void testBasicTrainSupportEarlyStoppingATT() {
}

@Test
public void testBasicTrainSupportEarlyStoppingATC() {
public void testBasicTrainSupportCV() {
try {
Scope.enter();
Frame train = generateFrame();
int ntrees = 42;
int ntrees = 10;
UpliftDRFModel.UpliftDRFParameters p = new UpliftDRFModel.UpliftDRFParameters();
p._train = train._key;
p._treatment_column = "treatment";
p._response_column = "conversion";
p._stopping_metric = ScoreKeeper.StoppingMetric.ATC;
p._stopping_rounds = 2;
p._ntrees = ntrees;
p._score_each_iteration = true;
p._nfolds = 3;
p._auuc_nbins = 400;

UpliftDRF udrf = new UpliftDRF(p);
UpliftDRFModel model = udrf.trainModel().get();
Scope.track_generic(model);
assertNotNull(model);
assertTrue(model._output._treeStats._num_trees < ntrees);
} finally {
Scope.exit();
}
}

@Test
public void testBasicTrainSupportEarlyStoppingQini() {
public void testSupportCVCriteo() {
try {
Scope.enter();
Frame train = generateFrame();
int ntrees = 42;
Frame train = Scope.track(parseTestFile("smalldata/uplift/criteo_uplift_13k.csv"));
train.toCategoricalCol("treatment");
train.toCategoricalCol("conversion");
UpliftDRFModel.UpliftDRFParameters p = new UpliftDRFModel.UpliftDRFParameters();
p._train = train._key;
p._ignored_columns = new String[]{"visit", "exposure"};
p._treatment_column = "treatment";
p._response_column = "conversion";
p._stopping_metric = ScoreKeeper.StoppingMetric.qini;
p._stopping_rounds = 2;
p._ntrees = ntrees;
p._seed = 0xDECAF;
p._ntrees = 11;
p._score_each_iteration = true;
p._nfolds = 3;
p._auuc_nbins = 50;

UpliftDRF udrf = new UpliftDRF(p);
UpliftDRFModel model = udrf.trainModel().get();
Scope.track_generic(model);
assertNotNull(model);
assertTrue(model._output._treeStats._num_trees < ntrees);
} finally {
Scope.exit();
}
Expand Down
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