Fix failed tests

Author: maurever

h2o-algos/src/main/java/hex/knn/KNN.java

@@ -49,7 +49,6 @@ class KNNDriver extends Driver {
         public void computeImpl() {
             KNNModel model = null;
             Frame result = new Frame(Key.make("KNN_distances"));
-            Frame tmpResult = null;
             try {
                 init(true);   // Initialize parameters
                 if (error_count() > 0) {
@@ -72,12 +71,15 @@ public void computeImpl() {
                         query[j] = train.vec(j).chunkForChunkIdx(i).deepCopy();
                     }
                     KNNDistanceTask task = new KNNDistanceTask(_parms._k, query, KNNDistanceFactory.createDistance(_parms._distance), idColumnIndex, idColumn, idType, responseColumnIndex, responseColumn);
-                    tmpResult = task.doAll(train).outputFrame();
+                    Frame tmpResult = task.doAll(train).outputFrame();
+                    Scope.untrack(tmpResult);
+                    
                     // merge result from a chunk
                     result = result.add(tmpResult);
                 }
-                DKV.put(result._key, result);
-                model._output.setDistancesKey(result._key);
+                Key<Frame> key = result._key;
+                DKV.put(key, result);
+                model._output.setDistancesKey(key);
                 Scope.untrack(result);
 
                 model.update(_job);
@@ -90,9 +92,6 @@ public void computeImpl() {
                 if (model != null) {
                     model.unlock(_job);
                 }
-                if (tmpResult != null) {
-                    tmpResult.remove();
-                }
             }
         }
     }

h2o-algos/src/test/java/hex/knn/KNNTest.java

@@ -55,9 +55,7 @@ public void testIris() {
             ModelMetricsMultinomial mm1 = (ModelMetricsMultinomial) knn._output._training_metrics;
             Assert.assertEquals(mm.auc(), mm1.auc(), 0);
 
-            // test after KNN API will be ready
-            //knn.testJavaScoring(fr, preds, 0);
-            
+            knn.testJavaScoring(fr, preds, 0);
         } finally {
             if (knn != null){
                 knn.delete();

h2o-py/tests/testdir_sklearn/pyunit_sklearn_classification_all_estimators.py

@@ -149,7 +149,7 @@ def make_tests(classifier):
 
 
 failing = [
-    'H2OStackedEnsembleClassifier', 'H2OUpliftRandomForestClassifier'  # needs a separate test (requires models as parameters)
+    'H2OStackedEnsembleClassifier', 'H2OUpliftRandomForestClassifier', 'H2OKnnClassifier'  # needs a separate test (requires models as parameters)
 ]
 classifiers = [cls for name, cls in inspect.getmembers(h2o.sklearn, inspect.isclass)
                if name.endswith('Classifier') and name not in ['H2OAutoMLClassifier']+failing]

h2o-py/tests/testdir_sklearn/pyunit_sklearn_generic_all_estimators.py

@@ -200,6 +200,7 @@ def make_tests(classifier):
     'H2OUpliftRandomForestEstimator',  # generic part is not implemented yet
     'H2ODecisionTreeEstimator',  # generic part is not implemented yet
     'H2OAdaBoostEstimator',  # generic part is not implemented yet or test needs to be adjusted just for classification
+    'H2OKnnEstimator'  # generic part is not implemented yet
 ]
 estimators = [cls for name, cls in inspect.getmembers(h2o.sklearn, inspect.isclass)
               if name.endswith('Estimator') and name not in ['H2OAutoMLEstimator'] + failing]

h2o-py/tests/testdir_sklearn/pyunit_sklearn_regression_all_estimators.py

@@ -139,7 +139,8 @@ def make_tests(classifier):
     'H2OStackedEnsembleRegressor',  # needs a separate test (requires models as parameters),
     'H2OUpliftRandomForestRegressor',  # does not support regression yet
     'H2ODecisionTreeRegressor',  # does not support regression yet
-    'H2OAdaBoostRegressor'  # does not support regression yet
+    'H2OAdaBoostRegressor',  # does not support regression yet
+    'H2OKnnRegressor'  # does not support regression
 ]
 regressors = [cls for name, cls in inspect.getmembers(h2o.sklearn, inspect.isclass)
               if name.endswith('Regressor') and name not in ['H2OAutoMLRegressor']+failing]

h2o-py/tests_rest_smoke/testdir_multi_jvm/test_rest_api.py

@@ -23,7 +23,7 @@
 algos = ['coxph', 'kmeans', 'deeplearning', 'drf', 'glm', 'gbm', 'pca', 'naivebayes', 'glrm', 'svd', 'isotonicregression',
          'psvm', 'aggregator', 'word2vec', 'stackedensemble', 'xgboost', 'isolationforest', 'gam',
          'generic', 'targetencoder', 'rulefit', 'extendedisolationforest', 'anovaglm', 'modelselection',
-         'upliftdrf', 'infogram', 'dt', 'adaboost', 'hglm']
+         'upliftdrf', 'infogram', 'dt', 'adaboost', 'hglm', 'knn']
 
 algo_additional_default_params = { 'grep' : { 'regex' : '.*' },
                                    'kmeans' : { 'k' : 2 },

h2o-r/tests/testdir_algos/knn/runit_knn_smoke.R

@@ -5,7 +5,7 @@ source("../../../scripts/h2o-r-test-setup.R")
 
 knn.smoke <- function() {
     iris.hex <- h2o.uploadFile( locate("smalldata/iris/iris.csv"))
-    iris.knn <-  h2o.knn(x=1:4, training_frame=iris.hex, k = 3, distance="euclidean", seed = 1234)
+    iris.knn <-  h2o.knn(x=1:4, y=5, training_frame=iris.hex, k=3 , distance="euclidean", seed=1234)
 
     # Score test data with different default auc_type (previous was "NONE", so no AUC calculation)
     perf <- h2o.performance(iris.knn, test.hex, auc_type="WEIGHTED_OVO")