Why i'm getting this error? python-weka-wrapper 3-CodePudding

Some days ago i executed this code and goes correctly but right now it started to show me that error:

----------------------------------------------------------------------------
TEST MODEL
----------------------------------------------------------------------------

Encountered exception while evaluating classifier, skipping!
- Classifier: weka.classifiers.meta.AttributeSelectedClassifier -E "weka.attributeSelection.InfoGainAttributeEval " -S "weka.attributeSelection.Ranker -T -1.7976931348623157E308 -N 5" -W weka.classifiers.meta.Bagging -- -P 100 -S 1 -num-slots 1 -I 100 -W weka.classifiers.meta.FilteredClassifier -- -F "weka.filters.supervised.instance.SMOTE -C 0 -K 3 -P 250.0 -S 1" -S 1 -W weka.classifiers.meta.CostSensitiveClassifier -- -cost-matrix "[0.0 2.0; 1.0 0.0]" -S 1 -W weka.classifiers.trees.ADTree -- -B 10 -E -3 -S 1
java.lang.Exception: Cannot use 0 neighbors!
    weka.filters.supervised.instance.SMOTE.doSMOTE(SMOTE.java:539)
    weka.filters.supervised.instance.SMOTE.batchFinished(SMOTE.java:489)
    weka.filters.Filter.useFilter(Filter.java:708)
    weka.classifiers.meta.FilteredClassifier.setUp(FilteredClassifier.java:719)
    weka.classifiers.meta.FilteredClassifier.buildClassifier(FilteredClassifier.java:794)
    weka.classifiers.ParallelIteratedSingleClassifierEnhancer.buildClassifiers(ParallelIteratedSingleClassifierEnhancer.java:229)
    weka.classifiers.meta.Bagging.buildClassifier(Bagging.java:709)
    weka.classifiers.meta.AttributeSelectedClassifier.buildClassifier(AttributeSelectedClassifier.java:513)
    weka.classifiers.evaluation.Evaluation.crossValidateModel(Evaluation.java:843)
    weka.classifiers.Evaluation.crossValidateModel(Evaluation.java:392)
    weka.classifiers.meta.multisearch.DefaultEvaluationTask.doRun(DefaultEvaluationTask.java:98)
    weka.classifiers.meta.multisearch.AbstractEvaluationTask.call(AbstractEvaluationTask.java:113)
    weka.classifiers.meta.multisearch.AbstractEvaluationTask.call(AbstractEvaluationTask.java:34)
    java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
    java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
    java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
    java.base/java.lang.Thread.run(Thread.java:829)

    at weka.filters.supervised.instance.SMOTE.doSMOTE(SMOTE.java:539)
    at weka.filters.supervised.instance.SMOTE.batchFinished(SMOTE.java:489)
    at weka.filters.Filter.useFilter(Filter.java:708)
    at weka.classifiers.meta.FilteredClassifier.setUp(FilteredClassifier.java:719)
    at weka.classifiers.meta.FilteredClassifier.buildClassifier(FilteredClassifier.java:794)
    at weka.classifiers.ParallelIteratedSingleClassifierEnhancer.buildClassifiers(ParallelIteratedSingleClassifierEnhancer.java:229)
    at weka.classifiers.meta.Bagging.buildClassifier(Bagging.java:709)
    at weka.classifiers.meta.AttributeSelectedClassifier.buildClassifier(AttributeSelectedClassifier.java:513)
    at weka.classifiers.evaluation.Evaluation.crossValidateModel(Evaluation.java:843)
    at weka.classifiers.Evaluation.crossValidateModel(Evaluation.java:392)
    at weka.classifiers.meta.multisearch.DefaultEvaluationTask.doRun(DefaultEvaluationTask.java:98)
    at weka.classifiers.meta.multisearch.AbstractEvaluationTask.call(AbstractEvaluationTask.java:113)
    at weka.classifiers.meta.multisearch.AbstractEvaluationTask.call(AbstractEvaluationTask.java:34)
    at java.base/java.util.concurrent.FutureTask.run(FutureTask.java:264)
    at java.base/java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1128)
    at java.base/java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:628)
    at java.base/java.lang.Thread.run(Thread.java:829)

And this is my code:

base_model_3 = Classifier(classname="weka.classifiers.trees.ADTree", 
                  options=["-B", "10", "-E", "-3", "-S", "1"])


CostS_cls_model_3 = SingleClassifierEnhancer(classname="weka.classifiers.meta.CostSensitiveClassifier", 
                                options =["-cost-matrix", "[0.0 2.0; 1.0 0.0]", "-S", "1"])
CostS_cls_model_3.classifier = base_model_3


smote_model_3 = Filter(classname="weka.filters.supervised.instance.SMOTE", 
               options=["-C", "0", "-K", "3", "-P", "250.0", "-S", "1"])
fc_model_3 = FilteredClassifier(options=["-S","1"])
fc_model_3.filter = smote_model_3
fc_model_3.classifier = CostS_cls_model_3


bagging_cls_model_3 = SingleClassifierEnhancer(classname="weka.classifiers.meta.Bagging",
                         options=["-P", "100", "-S", "1", "-num-slots", "1", "-I", "100"])
bagging_cls_model_3.classifier = fc_model_3


AttS_cls_model_3 = AttributeSelectedClassifier()
AttS_cls_model_3.search = from_commandline('weka.attributeSelection.Ranker -T -1.7976931348623157E308 -N 61', classname=get_classname(ASSearch))
AttS_cls_model_3.evaluator = from_commandline('weka.attributeSelection.InfoGainAttributeEval', classname=get_classname(ASEvaluation))
AttS_cls_model_3.classifier = bagging_cls_model_3


multisearch_cls_model_3 = MultiSearch(options = ["-S", "1","-class-label","1"])
multisearch_cls_model_3.evaluation = "FM"
multisearch_cls_model_3.search = ["-sample-size", "100", "-initial-folds", "2", "-subsequent-folds", "10",
                          "-initial-test-set", ".", "-subsequent-test-set", ".", "-num-slots", "1"]                        
mparam_model_3 = MathParameter()
mparam_model_3.prop = "search.numToSelect"
mparam_model_3.minimum = 5.0
mparam_model_3.maximum = 134.0
mparam_model_3.step = 1.0
mparam_model_3.base = 10.0
mparam_model_3.expression = "I"
multisearch_cls_model_3.parameters = [mparam_model_3]
multisearch_cls_model_3.classifier = AttS_cls_model_3


MissingValues = Filter(classname="weka.filters.unsupervised.attribute.ReplaceMissingValues")
fc_model_3_MV = FilteredClassifier(options=["-S","1"])
fc_model_3_MV.filter = MissingValues
fc_model_3_MV.classifier = multisearch_cls_model_3


print("----------------------------------------------------------------------------")
print("CROSS VALIDATION")
print("----------------------------------------------------------------------------")
evl_model_3 = Evaluation(data_modelo_3_encoded)
evl_model_3.crossvalidate_model(fc_model_3_MV, data_modelo_3_encoded, 10, Random(1))
print(evl_model_3.summary())

conf_matrix = evl_model_3.confusion_matrix
plt.figure(figsize=(8,8))
sns.heatmap(conf_matrix, xticklabels = Labels, yticklabels = Labels, annot = True, fmt = "f", linewidth = 2)
plt.title("Confusion Matrix")
plt.ylabel("True Class")
plt.xlabel("Predicted Class")
plt.show()

print("----------------------------------------------------------------------------")
print("TEST MODEL")
print("----------------------------------------------------------------------------")
train_model_3, test_model_3 = data_modelo_3_encoded.train_test_split(70.0, Random(1))

fc_model_3_MV.build_classifier(train_model_3)
evl_model_3 = Evaluation(test_model_3)
evl_model_3.test_model(fc_model_3_MV, test_model_3)
print("")
print("=== Setup ===")
print("Classifier: ")
print(fc_model_3_MV.to_commandline())
print("----------------------------------------------------------------------------")
print("Dataset: ")
print(test_model_3.relationname)
print("----------------------------------------------------------------------------")
print("")
print(evl_model_3.summary("=== "   str(10)   " -fold Cross-Validation ==="))
print("----------------------------------------------------------------------------")
print(evl_model_3.class_details())
print("----------------------------------------------------------------------------")
plcls.plot_roc(evl_model_3, class_index=[0, 1], wait=True)
print("----------------------------------------------------------------------------")

conf_matrix = evl_model_3.confusion_matrix
plt.figure(figsize=(8,8))
sns.heatmap(conf_matrix, xticklabels = Labels, yticklabels = Labels, annot = True, fmt = "f", linewidth = 2)
plt.title("Confusion Matrix")
plt.ylabel("True Class")
plt.xlabel("Predicted Class")
plt.show()

It works correctly until the "Test Model" zone but i did not change anything, just add a confusion matrix for show the results but don't understand why appears that error.

The data base for this model is unbalanced but this error did not appear before.

CodePudding user response：

It is possible that SMOTE is very sensitive to there being enough instances per class (I don't know anything about the filter's inner workings). I was able to recreate this problem when using the UCI dataset labor. However, when I changed the train/test split to 90/10 (as is used by your cross-validation), it worked.