Updated according to th change in removeItem method in Math dependency and correspondingly test results.

Olcay Taner YILDIZ · Olcay Taner YILDIZ · commit 57f83ab56cdd · 2023-02-16T11:52:34.000+03:00
diff --git a/Classification/Experiment/BootstrapRun.py b/Classification/Experiment/BootstrapRun.py
@@ -36,7 +36,7 @@ def execute(self, experiment: Experiment) -> ExperimentPerformance:
         """
         result = ExperimentPerformance()
         for i in range(self.__numberOfBootstraps):
-            bootstrap = Bootstrap(instanceList=experiment.getDataSet().getInstances(),
+            bootstrap = Bootstrap(instance_list=experiment.getDataSet().getInstances(),
                                   seed=i + experiment.getParameter().getSeed())
             bootstrap_sample = InstanceList(bootstrap.getSample())
             experiment.getClassifier().train(trainSet=bootstrap_sample,
diff --git a/Classification/Experiment/KFoldRun.py b/Classification/Experiment/KFoldRun.py
@@ -50,7 +50,7 @@ def execute(self, experiment: Experiment) -> ExperimentPerformance:
             An ExperimentPerformance instance.
         """
         result = ExperimentPerformance()
-        crossValidation = KFoldCrossValidation(instanceList=experiment.getDataSet().getInstances(),
+        crossValidation = KFoldCrossValidation(instance_list=experiment.getDataSet().getInstances(),
                                                K=self.K,
                                                seed=experiment.getParameter().getSeed())
         self.runExperiment(classifier=experiment.getClassifier(),
diff --git a/Classification/Experiment/MxKFoldRun.py b/Classification/Experiment/MxKFoldRun.py
@@ -42,7 +42,7 @@ def execute(self, experiment: Experiment) -> ExperimentPerformance:
         """
         result = ExperimentPerformance()
         for j in range(self.M):
-            cross_validation = KFoldCrossValidation(instanceList=experiment.getDataSet().getInstances(),
+            cross_validation = KFoldCrossValidation(instance_list=experiment.getDataSet().getInstances(),
                                                    K=self.K,
                                                    seed=experiment.getParameter().getSeed())
             self.runExperiment(classifier=experiment.getClassifier(),
diff --git a/Classification/Experiment/StratifiedKFoldRun.py b/Classification/Experiment/StratifiedKFoldRun.py
@@ -34,7 +34,7 @@ def execute(self, experiment: Experiment) -> ExperimentPerformance:
             An ExperimentPerformance instance.
         """
         result = ExperimentPerformance()
-        cross_validation = StratifiedKFoldCrossValidation(instanceLists=experiment.getDataSet().getClassInstances(),
+        cross_validation = StratifiedKFoldCrossValidation(instance_lists=experiment.getDataSet().getClassInstances(),
                                                          K=self.K,
                                                          seed=experiment.getParameter().getSeed())
         self.runExperiment(classifier=experiment.getClassifier(),
diff --git a/Classification/Experiment/StratifiedMxKFoldRun.py b/Classification/Experiment/StratifiedMxKFoldRun.py
@@ -40,7 +40,7 @@ def execute(self, experiment: Experiment) -> ExperimentPerformance:
         """
         result = ExperimentPerformance()
         for j in range(self.M):
-            cross_validation = StratifiedKFoldCrossValidation(instanceLists=experiment.getDataSet().getClassInstances(),
+            cross_validation = StratifiedKFoldCrossValidation(instance_lists=experiment.getDataSet().getClassInstances(),
                                                              K=self.K,
                                                              seed=experiment.getParameter().getSeed())
             self.runExperiment(classifier=experiment.getClassifier(),
diff --git a/setup.py b/setup.py
@@ -6,7 +6,7 @@
 
 setup(
     name='NlpToolkit-Classification',
-    version='1.0.14',
+    version='1.0.15',
     packages=['Classification', 'Classification.Model', 'Classification.Model.DecisionTree', 'Classification.Filter',
               'Classification.DataSet', 'Classification.Instance', 'Classification.Attribute',
               'Classification.Parameter', 'Classification.Classifier', 'Classification.Experiment',
diff --git a/test/Classifier/BaggingTest.py b/test/Classifier/BaggingTest.py
@@ -11,11 +11,11 @@ def test_Train(self):
         bagging = Bagging()
         baggingParameter = BaggingParameter(1, 100)
         bagging.train(self.iris.getInstanceList(), baggingParameter)
-        self.assertAlmostEqual(2.0, 100 * bagging.test(self.iris.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(0.0, 100 * bagging.test(self.iris.getInstanceList()).getErrorRate(), 2)
         bagging.train(self.bupa.getInstanceList(), baggingParameter)
-        self.assertAlmostEqual(42.03, 100 * bagging.test(self.bupa.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(0.0, 100 * bagging.test(self.bupa.getInstanceList()).getErrorRate(), 2)
         bagging.train(self.dermatology.getInstanceList(), baggingParameter)
-        self.assertAlmostEqual(2.46, 100 * bagging.test(self.dermatology.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(0.0, 100 * bagging.test(self.dermatology.getInstanceList()).getErrorRate(), 2)
         bagging.train(self.car.getInstanceList(), baggingParameter)
         self.assertAlmostEqual(0.0, 100 * bagging.test(self.car.getInstanceList()).getErrorRate(), 2)
         bagging.train(self.tictactoe.getInstanceList(), baggingParameter)
diff --git a/test/Classifier/C45StumpTest.py b/test/Classifier/C45StumpTest.py
@@ -11,7 +11,7 @@ def test_Train(self):
         c45Stump.train(self.iris.getInstanceList(), None)
         self.assertAlmostEqual(33.33, 100 * c45Stump.test(self.iris.getInstanceList()).getErrorRate(), 2)
         c45Stump.train(self.bupa.getInstanceList(), None)
-        self.assertAlmostEqual(42.03, 100 * c45Stump.test(self.bupa.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(36.81, 100 * c45Stump.test(self.bupa.getInstanceList()).getErrorRate(), 2)
         c45Stump.train(self.dermatology.getInstanceList(), None)
         self.assertAlmostEqual(49.73, 100 * c45Stump.test(self.dermatology.getInstanceList()).getErrorRate(), 2)
         c45Stump.train(self.car.getInstanceList(), None)
@@ -21,7 +21,7 @@ def test_Train(self):
         c45Stump.train(self.nursery.getInstanceList(), None)
         self.assertAlmostEqual(29.03, 100 * c45Stump.test(self.nursery.getInstanceList()).getErrorRate(), 2)
         c45Stump.train(self.chess.getInstanceList(), None)
-        self.assertAlmostEqual(80.76, 100 * c45Stump.test(self.chess.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(80.92, 100 * c45Stump.test(self.chess.getInstanceList()).getErrorRate(), 2)
 
 
 if __name__ == '__main__':
diff --git a/test/Classifier/C45Test.py b/test/Classifier/C45Test.py
@@ -11,11 +11,11 @@ def test_Train(self):
         c45 = C45()
         c45Parameter = C45Parameter(1, True, 0.2)
         c45.train(self.iris.getInstanceList(), c45Parameter)
-        self.assertAlmostEqual(4.00, 100 * c45.test(self.iris.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(2.00, 100 * c45.test(self.iris.getInstanceList()).getErrorRate(), 2)
         c45.train(self.bupa.getInstanceList(), c45Parameter)
-        self.assertAlmostEqual(42.03, 100 * c45.test(self.bupa.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(30.43, 100 * c45.test(self.bupa.getInstanceList()).getErrorRate(), 2)
         c45.train(self.dermatology.getInstanceList(), c45Parameter)
-        self.assertAlmostEqual(4.64, 100 * c45.test(self.dermatology.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(4.37, 100 * c45.test(self.dermatology.getInstanceList()).getErrorRate(), 2)
         c45.train(self.car.getInstanceList(), c45Parameter)
         self.assertAlmostEqual(6.48, 100 * c45.test(self.car.getInstanceList()).getErrorRate(), 2)
         c45.train(self.tictactoe.getInstanceList(), c45Parameter)
diff --git a/test/Classifier/RandomForestTest.py b/test/Classifier/RandomForestTest.py
@@ -11,11 +11,11 @@ def test_Train(self):
         randomForest = RandomForest()
         randomForestParameter = RandomForestParameter(1, 100, 35)
         randomForest.train(self.iris.getInstanceList(), randomForestParameter)
-        self.assertAlmostEqual(2.0, 100 * randomForest.test(self.iris.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(0.0, 100 * randomForest.test(self.iris.getInstanceList()).getErrorRate(), 2)
         randomForest.train(self.bupa.getInstanceList(), randomForestParameter)
-        self.assertAlmostEqual(42.03, 100 * randomForest.test(self.bupa.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(0.0, 100 * randomForest.test(self.bupa.getInstanceList()).getErrorRate(), 2)
         randomForest.train(self.dermatology.getInstanceList(), randomForestParameter)
-        self.assertAlmostEqual(2.46, 100 * randomForest.test(self.dermatology.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(0.0, 100 * randomForest.test(self.dermatology.getInstanceList()).getErrorRate(), 2)
         randomForest.train(self.car.getInstanceList(), randomForestParameter)
         self.assertAlmostEqual(0.0, 100 * randomForest.test(self.car.getInstanceList()).getErrorRate(), 2)
         randomForest.train(self.tictactoe.getInstanceList(), randomForestParameter)
diff --git a/test/Experiment/BootstrapRunTest.py b/test/Experiment/BootstrapRunTest.py
@@ -23,7 +23,7 @@ class BootstrapRunTest(ClassifierTest):
     def test_Execute(self):
         bootstrapRun = BootstrapRun(10)
         experimentPerformance = bootstrapRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
-        self.assertAlmostEqual(4.33, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
+        self.assertAlmostEqual(3.73, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
         experimentPerformance = bootstrapRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
         self.assertAlmostEqual(13.16, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
         experimentPerformance = bootstrapRun.execute(Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()), self.bupa))
diff --git a/test/Experiment/KFoldRunTest.py b/test/Experiment/KFoldRunTest.py
@@ -23,7 +23,7 @@ class KFoldRunTest(ClassifierTest):
     def test_Execute(self):
         kFoldRun = KFoldRun(10)
         experimentPerformance = kFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
-        self.assertAlmostEqual(6.00, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
+        self.assertAlmostEqual(6.67, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
         experimentPerformance = kFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
         self.assertAlmostEqual(18.78, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
         experimentPerformance = kFoldRun.execute(Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()), self.bupa))
diff --git a/test/Experiment/MxKFoldRunTest.py b/test/Experiment/MxKFoldRunTest.py
@@ -23,7 +23,7 @@ class MxKFoldRunTest(ClassifierTest):
     def test_Execute(self):
         mxKFoldRun = MxKFoldRun(5, 2)
         experimentPerformance = mxKFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
-        self.assertAlmostEqual(6.13, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
+        self.assertAlmostEqual(5.47, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
         experimentPerformance = mxKFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
         self.assertAlmostEqual(23.51, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
         experimentPerformance = mxKFoldRun.execute(Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()), self.bupa))
diff --git a/test/Experiment/StratifiedKFoldRunTest.py b/test/Experiment/StratifiedKFoldRunTest.py
@@ -24,7 +24,7 @@ class StratifiedKFoldRunTest(ClassifierTest):
     def test_Execute(self):
         stratifiedKFoldRun = StratifiedKFoldRun(10)
         experimentPerformance = stratifiedKFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
-        self.assertAlmostEqual(4.67, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
+        self.assertAlmostEqual(6.00, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
         experimentPerformance = stratifiedKFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
         self.assertAlmostEqual(18.59, 100 * experimentPerformance.meanPerformance().getErrorRate(), 2)
         experimentPerformance = stratifiedKFoldRun.execute(Experiment(Knn(), KnnParameter(1, 3, EuclidianDistance()), self.bupa))
diff --git a/test/Filter/DiscreteToContinuousTest.py b/test/Filter/DiscreteToContinuousTest.py
@@ -39,11 +39,11 @@ def test_C45(self):
         discreteToContinuous = DiscreteToContinuous(self.car)
         discreteToContinuous.convert()
         c45.train(self.car.getInstanceList(), c45Parameter)
-        self.assertAlmostEqual(29.98, 100 * c45.test(self.car.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(3.99, 100 * c45.test(self.car.getInstanceList()).getErrorRate(), 2)
         discreteToContinuous = DiscreteToContinuous(self.tictactoe)
         discreteToContinuous.convert()
         c45.train(self.tictactoe.getInstanceList(), c45Parameter)
-        self.assertAlmostEqual(34.66, 100 * c45.test(self.tictactoe.getInstanceList()).getErrorRate(), 2)
+        self.assertAlmostEqual(3.34, 100 * c45.test(self.tictactoe.getInstanceList()).getErrorRate(), 2)
 
 
 if __name__ == '__main__':
diff --git a/test/StatisticalTest/Combined5x2FTest.py b/test/StatisticalTest/Combined5x2FTest.py
@@ -24,7 +24,7 @@ def test_Compare(self):
         combined5x2F = Combined5x2F()
         experimentPerformance1 = mxKFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
         experimentPerformance2 = mxKFoldRun.execute(Experiment(LinearPerceptron(), LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100), self.iris))
-        self.assertAlmostEqual(0.289, combined5x2F.compare(experimentPerformance1, experimentPerformance2).getPValue(), 3)
+        self.assertAlmostEqual(0.390, combined5x2F.compare(experimentPerformance1, experimentPerformance2).getPValue(), 3)
         experimentPerformance1 = mxKFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
         experimentPerformance2 = mxKFoldRun.execute(Experiment(Bagging(), BaggingParameter(1, 50), self.tictactoe))
         self.assertAlmostEqual(0.00072, combined5x2F.compare(experimentPerformance1, experimentPerformance2).getPValue(), 5)
diff --git a/test/StatisticalTest/Combined5x2tTest.py b/test/StatisticalTest/Combined5x2tTest.py
@@ -25,7 +25,7 @@ def test_Compare(self):
         combined5x2t = Combined5x2t()
         experimentPerformance1 = mxKFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
         experimentPerformance2 = mxKFoldRun.execute(Experiment(LinearPerceptron(), LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100), self.iris))
-        self.assertAlmostEqual(0.186, combined5x2t.compare(experimentPerformance1, experimentPerformance2).getPValue(), 3)
+        self.assertAlmostEqual(0.390, combined5x2t.compare(experimentPerformance1, experimentPerformance2).getPValue(), 3)
         experimentPerformance1 = mxKFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
         experimentPerformance2 = mxKFoldRun.execute(Experiment(Bagging(), BaggingParameter(1, 50), self.tictactoe))
         self.assertAlmostEqual(0.0000059, combined5x2t.compare(experimentPerformance1, experimentPerformance2).getPValue(), 7)
diff --git a/test/StatisticalTest/PairedtTest.py b/test/StatisticalTest/PairedtTest.py
@@ -26,7 +26,7 @@ def test_Compare(self):
         pairedt = Pairedt()
         experimentPerformance1 = kFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.iris))
         experimentPerformance2 = kFoldRun.execute(Experiment(LinearPerceptron(), LinearPerceptronParameter(1, 0.1, 0.99, 0.2, 100), self.iris))
-        self.assertAlmostEqual(0.379, pairedt.compare(experimentPerformance1, experimentPerformance2).getPValue(), 3)
+        self.assertAlmostEqual(0.278, pairedt.compare(experimentPerformance1, experimentPerformance2).getPValue(), 3)
         experimentPerformance1 = kFoldRun.execute(Experiment(C45(), C45Parameter(1, True, 0.2), self.tictactoe))
         experimentPerformance2 = kFoldRun.execute(Experiment(Bagging(), BaggingParameter(1, 50), self.tictactoe))
         self.assertAlmostEqual(0.00000692, pairedt.compare(experimentPerformance1, experimentPerformance2).getPValue(), 7)
