GH-15779 add DeepLearning to weak_learners

h2o-algos/src/main/java/hex/adaboost/AdaBoost.java

@@ -3,6 +3,8 @@
 import hex.Model;
 import hex.ModelBuilder;
 import hex.ModelCategory;
+import hex.deeplearning.DeepLearning;
+import hex.deeplearning.DeepLearningModel;
 import hex.glm.GLM;
 import hex.glm.GLMModel;
 import hex.tree.drf.DRF;
@@ -169,6 +171,8 @@ private ModelBuilder chooseWeakLearner(Frame frame) {
                 return getGLMWeakLearner(frame);
             case GBM:
                 return getGBMWeakLearner(frame);
+            case DEEP_LEARNING:
+                return getDeepLearningWeakLearner(frame);
             default:
             case DRF:
                 return getDRFWeakLearner(frame);
@@ -212,6 +216,17 @@ private GBM getGBMWeakLearner(Frame frame) {
         return new GBM(parms);
     }
 
+    private DeepLearning getDeepLearningWeakLearner(Frame frame) {
+        DeepLearningModel.DeepLearningParameters parms = new DeepLearningModel.DeepLearningParameters();
+        parms._train = frame._key;
+        parms._response_column = _parms._response_column;
+        parms._weights_column = _weightsName;
+        parms._seed = _parms._seed;
+        parms._epochs = 10;
+        parms._hidden = new int[]{2};
+        return new DeepLearning(parms);
+    }
+
     public TwoDimTable createModelSummaryTable() {
         List<String> colHeaders = new ArrayList<>();
         List<String> colTypes = new ArrayList<>();

h2o-algos/src/main/java/hex/adaboost/AdaBoostModel.java

@@ -10,7 +10,7 @@
 public class AdaBoostModel extends Model<AdaBoostModel, AdaBoostModel.AdaBoostParameters, AdaBoostModel.AdaBoostOutput> {
     private static final Logger LOG = Logger.getLogger(AdaBoostModel.class);
 
-    public enum Algorithm {DRF, GLM, GBM, AUTO}
+    public enum Algorithm {DRF, GLM, GBM, DEEP_LEARNING,AUTO}
 
     public AdaBoostModel(Key<AdaBoostModel> selfKey, AdaBoostParameters parms,
                          AdaBoostOutput output) {

h2o-algos/src/main/java/hex/deeplearning/DeepLearningModel.java

@@ -2361,6 +2361,13 @@ public boolean isDistributionHuber() {
     }
   }
 
+  @Override
+  public double score(double[] data) {
+    double[] pred = score0(data, new double[_output.nclasses() + 1], 0);
+    score0PostProcessSupervised(pred, data);
+    return pred[0];
+  }
+
 
 }
 

h2o-algos/src/main/java/hex/schemas/AdaBoostV3.java

@@ -29,7 +29,7 @@ public static final class AdaBoostParametersV3 extends ModelParametersSchemaV3<A
         @API(help = "Number of AdaBoost weak learners.", gridable = true)
         public int nlearners;
 
-        @API(help = "Choose a weak learner type. Defaults to AUTO, which means DRF.", gridable = true, values = {"AUTO", "DRF", "GLM", "GBM"})
+        @API(help = "Choose a weak learner type. Defaults to AUTO, which means DRF.", gridable = true, values = {"AUTO", "DRF", "GLM", "GBM", "DEEP_LEARNING"})
         public AdaBoostModel.Algorithm weak_learner;
 
         @API(help="Learning rate (from 0.0 to 1.0)", gridable = true)

h2o-algos/src/test/java/hex/adaboost/AdaBoostTest.java

@@ -102,6 +102,30 @@ public void testBasicTrainGLM() {
         }
     }
 
+    @Test
+    public void testBasicTrainDeepLearning() {
+        try {
+            Scope.enter();
+            Frame train = parseTestFile("smalldata/prostate/prostate.csv");
+            Scope.track(train);
+            String response = "CAPSULE";
+            train.toCategoricalCol(response);
+            AdaBoostModel.AdaBoostParameters p = new AdaBoostModel.AdaBoostParameters();
+            p._nlearners = 20;
+            p._train = train._key;
+            p._seed = 0xDECAF;
+            p._weak_learner = AdaBoostModel.Algorithm.DEEP_LEARNING;
+            p._response_column = response;
+
+            AdaBoost adaBoost = new AdaBoost(p);
+            AdaBoostModel adaBoostModel = adaBoost.trainModel().get();
+            Scope.track_generic(adaBoostModel);
+            assertNotNull(adaBoostModel);
+        } finally {
+            Scope.exit();
+        }
+    }
+
     @Test
     public void testBasicTrainLarge() {
         try {
@@ -488,4 +512,30 @@ public void testBasicTrainAndScoreGBM() {
             Scope.exit();
         }
     }
+
+    @Test
+    public void testBasicTrainAndScoreDeepLearning() {
+        try {
+            Scope.enter();
+            Frame train = Scope.track(parseTestFile("smalldata/prostate/prostate.csv"));
+            String response = "CAPSULE";
+            train.toCategoricalCol(response);
+            AdaBoostModel.AdaBoostParameters p = new AdaBoostModel.AdaBoostParameters();
+            p._train = train._key;
+            p._seed = 0xDECAF;
+            p._nlearners = 50;
+            p._weak_learner = AdaBoostModel.Algorithm.DEEP_LEARNING;
+            p._response_column = response;
+
+            AdaBoost adaBoost = new AdaBoost(p);
+            AdaBoostModel adaBoostModel = adaBoost.trainModel().get();
+            Scope.track_generic(adaBoostModel);
+            assertNotNull(adaBoostModel);
+
+            Frame score = adaBoostModel.score(train);
+            Scope.track(score);
+        } finally {
+            Scope.exit();
+        }
+    }
 }

h2o-py/h2o/estimators/adaboost.py

@@ -29,7 +29,7 @@ def __init__(self,
                  categorical_encoding="auto",  # type: Literal["auto", "enum", "one_hot_internal", "one_hot_explicit", "binary", "eigen", "label_encoder", "sort_by_response", "enum_limited"]
                  weights_column=None,  # type: Optional[str]
                  nlearners=50,  # type: int
-                 weak_learner="auto",  # type: Literal["auto", "drf", "glm", "gbm"]
+                 weak_learner="auto",  # type: Literal["auto", "drf", "glm", "gbm", "deep_learning"]
                  learn_rate=0.5,  # type: float
                  seed=-1,  # type: int
                  ):
@@ -64,7 +64,7 @@ def __init__(self,
         :type nlearners: int
         :param weak_learner: Choose a weak learner type. Defaults to AUTO, which means DRF.
                Defaults to ``"auto"``.
-        :type weak_learner: Literal["auto", "drf", "glm", "gbm"]
+        :type weak_learner: Literal["auto", "drf", "glm", "gbm", "deep_learning"]
         :param learn_rate: Learning rate (from 0.0 to 1.0)
                Defaults to ``0.5``.
         :type learn_rate: float
@@ -180,13 +180,13 @@ def weak_learner(self):
         """
         Choose a weak learner type. Defaults to AUTO, which means DRF.
 
-        Type: ``Literal["auto", "drf", "glm", "gbm"]``, defaults to ``"auto"``.
+        Type: ``Literal["auto", "drf", "glm", "gbm", "deep_learning"]``, defaults to ``"auto"``.
         """
         return self._parms.get("weak_learner")
 
     @weak_learner.setter
     def weak_learner(self, weak_learner):
-        assert_is_type(weak_learner, None, Enum("auto", "drf", "glm", "gbm"))
+        assert_is_type(weak_learner, None, Enum("auto", "drf", "glm", "gbm", "deep_learning"))
         self._parms["weak_learner"] = weak_learner
 
     @property

h2o-r/h2o-package/R/adaboost.R

@@ -25,8 +25,8 @@
 #'        you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get
 #'        an accurate prediction, remove all rows with weight == 0.
 #' @param nlearners Number of AdaBoost weak learners. Defaults to 50.
-#' @param weak_learner Choose a weak learner type. Defaults to AUTO, which means DRF. Must be one of: "AUTO", "DRF", "GLM", "GBM".
-#'        Defaults to AUTO.
+#' @param weak_learner Choose a weak learner type. Defaults to AUTO, which means DRF. Must be one of: "AUTO", "DRF", "GLM", "GBM",
+#'        "DEEP_LEARNING". Defaults to AUTO.
 #' @param learn_rate Learning rate (from 0.0 to 1.0) Defaults to 0.5.
 #' @param seed Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default).
 #'        Defaults to -1 (time-based random number).
@@ -58,7 +58,7 @@ h2o.adaBoost <- function(x,
                          categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
                          weights_column = NULL,
                          nlearners = 50,
-                         weak_learner = c("AUTO", "DRF", "GLM", "GBM"),
+                         weak_learner = c("AUTO", "DRF", "GLM", "GBM", "DEEP_LEARNING"),
                          learn_rate = 0.5,
                          seed = -1)
 {
@@ -110,7 +110,7 @@ h2o.adaBoost <- function(x,
                                          categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
                                          weights_column = NULL,
                                          nlearners = 50,
-                                         weak_learner = c("AUTO", "DRF", "GLM", "GBM"),
+                                         weak_learner = c("AUTO", "DRF", "GLM", "GBM", "DEEP_LEARNING"),
                                          learn_rate = 0.5,
                                          seed = -1,
                                          segment_columns = NULL,