diff --git a/pom.xml b/pom.xml
index 029599c2..a14482ec 100644
--- a/pom.xml
+++ b/pom.xml
@@ -32,7 +32,7 @@
         be accompanied by a bump in version number, regardless of how minor the change.
     -->
 
-    <version>0.7.1</version>
+    <version>0.7.2</version>
 
 
     <repositories>
diff --git a/src/main/java/quickml/supervised/Utils.java b/src/main/java/quickml/supervised/Utils.java
index f0a6eb57..400bebdc 100644
--- a/src/main/java/quickml/supervised/Utils.java
+++ b/src/main/java/quickml/supervised/Utils.java
@@ -63,7 +63,7 @@ public static PredictionMapResults calcResultpredictionsWithoutAttrs(Classifier
         return new PredictionMapResults(results);
     }
 
-    public static void sortTrainingInstancesByTime(List<ClassifierInstance> trainingData, final DateTimeExtractor<ClassifierInstance> dateTimeExtractor) {
+    public static void sortTrainingInstancesByTime(List<? extends ClassifierInstance> trainingData, final DateTimeExtractor<ClassifierInstance> dateTimeExtractor) {
         Collections.sort(trainingData, new Comparator<ClassifierInstance>() {
             @Override
             public int compare(ClassifierInstance o1, ClassifierInstance o2) {
diff --git a/src/main/java/quickml/supervised/classifier/StaticBuilders.java b/src/main/java/quickml/supervised/classifier/StaticBuilders.java
index 20406097..51789c96 100644
--- a/src/main/java/quickml/supervised/classifier/StaticBuilders.java
+++ b/src/main/java/quickml/supervised/classifier/StaticBuilders.java
@@ -20,6 +20,7 @@
 import quickml.supervised.classifier.randomForest.RandomForestBuilder;
 import quickml.supervised.crossValidation.ClassifierLossChecker;
 import quickml.supervised.crossValidation.data.OutOfTimeData;
+import quickml.supervised.crossValidation.data.TrainingDataCycler;
 import quickml.supervised.crossValidation.lossfunctions.ClassifierLogCVLossFunction;
 import quickml.supervised.crossValidation.lossfunctions.ClassifierLossFunction;
 import quickml.supervised.crossValidation.lossfunctions.LossFunction;
@@ -47,7 +48,7 @@
 public class StaticBuilders {
     private static final Logger logger = LoggerFactory.getLogger(StaticBuilders.class);
 
-    public static Pair<Map<String, Object>, DownsamplingClassifier> getOptimizedDownsampledRandomForest(List<ClassifierInstance> trainingData, int rebuildsPerValidation, double fractionOfDataForValidation, ClassifierLossFunction lossFunction, DateTimeExtractor dateTimeExtractor,  Map<String, FieldValueRecommender> config) {
+    public static <T extends ClassifierInstance> Pair<Map<String, Object>, DownsamplingClassifier>  getOptimizedDownsampledRandomForest(List<T> trainingData, int rebuildsPerValidation, double fractionOfDataForValidation, ClassifierLossFunction lossFunction, DateTimeExtractor dateTimeExtractor,  Map<String, FieldValueRecommender> config) {
         /**
          * @param rebuildsPerValidation is the number of times the model will be rebuilt with a new training set while estimating the loss of a model
          *                              with a prarticular set of hyperparameters
@@ -57,27 +58,29 @@ public static Pair<Map<String, Object>, DownsamplingClassifier> getOptimizedDown
 
         int timeSliceHours = getTimeSliceHours(trainingData, rebuildsPerValidation, dateTimeExtractor);
         double crossValidationFraction = 0.2;
-        PredictiveModelOptimizer optimizer=  new PredictiveModelOptimizerBuilder<Classifier, ClassifierInstance>()
+        TrainingDataCycler<T> outOfTimeData = new OutOfTimeData<T>(trainingData, crossValidationFraction, timeSliceHours, dateTimeExtractor);
+        ClassifierLossChecker<T> classifierInstanceClassifierLossChecker = new ClassifierLossChecker<>(lossFunction);
+        PredictiveModelOptimizer optimizer=  new PredictiveModelOptimizerBuilder<Classifier, T>()
                         .modelBuilder(new RandomForestBuilder<>())
-                        .dataCycler(new OutOfTimeData<>(trainingData, crossValidationFraction, timeSliceHours,dateTimeExtractor))
-                        .lossChecker(new ClassifierLossChecker<>(lossFunction))
+                        .dataCycler(outOfTimeData)
+                        .lossChecker(classifierInstanceClassifierLossChecker)
                         .valuesToTest(config)
                         .iterations(3).build();
         Map<String, Object> bestParams =  optimizer.determineOptimalConfig();
 
-        RandomForestBuilder<ClassifierInstance> randomForestBuilder = new RandomForestBuilder<>(new TreeBuilder<>().attributeIgnoringStrategy(new IgnoreAttributesWithConstantProbability(0.7))).numTrees(24);
-        DownsamplingClassifierBuilder<ClassifierInstance> downsamplingClassifierBuilder = new DownsamplingClassifierBuilder<>(randomForestBuilder,0.1);
+        RandomForestBuilder<T> randomForestBuilder = new RandomForestBuilder<T>(new TreeBuilder<T>().attributeIgnoringStrategy(new IgnoreAttributesWithConstantProbability(0.7))).numTrees(24);
+        DownsamplingClassifierBuilder<T> downsamplingClassifierBuilder = new DownsamplingClassifierBuilder<>(randomForestBuilder,0.1);
         downsamplingClassifierBuilder.updateBuilderConfig(bestParams);
 
         DownsamplingClassifier downsamplingClassifier = downsamplingClassifierBuilder.buildPredictiveModel(trainingData);
         return new Pair<Map<String, Object>, DownsamplingClassifier>(bestParams, downsamplingClassifier);
     }
-    public static Pair<Map<String, Object>, DownsamplingClassifier> getOptimizedDownsampledRandomForest(List<ClassifierInstance> trainingData,  int rebuildsPerValidation, double fractionOfDataForValidation, ClassifierLossFunction lossFunction, DateTimeExtractor dateTimeExtractor) {
+    public static Pair<Map<String, Object>, DownsamplingClassifier> getOptimizedDownsampledRandomForest(List<? extends ClassifierInstance> trainingData,  int rebuildsPerValidation, double fractionOfDataForValidation, ClassifierLossFunction lossFunction, DateTimeExtractor dateTimeExtractor) {
         Map<String, FieldValueRecommender> config = createConfig();
         return getOptimizedDownsampledRandomForest(trainingData,  rebuildsPerValidation, fractionOfDataForValidation, lossFunction, dateTimeExtractor, config);
     }
 
-    private static int getTimeSliceHours(List<ClassifierInstance> trainingData, int rebuildsPerValidation, DateTimeExtractor<ClassifierInstance> dateTimeExtractor) {
+    private static int getTimeSliceHours(List<? extends ClassifierInstance> trainingData, int rebuildsPerValidation, DateTimeExtractor<ClassifierInstance> dateTimeExtractor) {
 
         Utils.sortTrainingInstancesByTime(trainingData, dateTimeExtractor);
         DateTime latestDateTime = dateTimeExtractor.extractDateTime(trainingData.get(trainingData.size()-1));
diff --git a/src/test/java/quickml/supervised/classifier/StaticBuildersTest.java b/src/test/java/quickml/supervised/classifier/StaticBuildersTest.java
index 52ef2c7d..4457f2ea 100644
--- a/src/test/java/quickml/supervised/classifier/StaticBuildersTest.java
+++ b/src/test/java/quickml/supervised/classifier/StaticBuildersTest.java
@@ -19,14 +19,14 @@
 public class StaticBuildersTest {
     private static final Logger logger = LoggerFactory.getLogger(StaticBuildersTest.class);
 
-
+@Test
     public void getOptimizedDownsampledRandomForestIntegrationTest() throws Exception {
         double fractionOfDataForValidation = .2;
         int rebuildsPerValidation = 1;
         List<ClassifierInstance> trainingData = getAdvertisingInstances().subList(0, 3000);
         OnespotDateTimeExtractor dateTimeExtractor = new OnespotDateTimeExtractor();
         Pair<Map<String, Object>, DownsamplingClassifier> downsamplingClassifierPair =
-                StaticBuilders.getOptimizedDownsampledRandomForest(trainingData, rebuildsPerValidation, fractionOfDataForValidation, new WeightedAUCCrossValLossFunction(1.0), dateTimeExtractor);
+                StaticBuilders.<ClassifierInstance>getOptimizedDownsampledRandomForest(trainingData, rebuildsPerValidation, fractionOfDataForValidation, new WeightedAUCCrossValLossFunction(1.0), dateTimeExtractor);
         logger.info("logged weighted auc loss should be between 0.25 and 0.28");
     }
 }
\ No newline at end of file