# load a dataset
iris_file = "HairEyeColor.csv"
print("Loading dataset: " + iris_file)
loader = Loader(classname="weka.core.converters.CSVLoader")
iris_data = loader.load_file(iris_file)
print (iris_data.num_attributes)
iris_data.set_class_index(iris_data.num_attributes() - 1)
# build a classifier and output model
print ("Training J48 classifier on iris")
classifier = Classifier(classname="weka.test.Regression")
#classifier = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.5"])
# Instead of using 'options=["-C", "0.3"]' in the constructor, we can also set the "confidenceFactor"
# property of the J48 classifier itself. However, being of type float rather than double, we need
# to convert it to the correct type first using the double_to_float function:
#classifier.set_property("confidenceFactor", types.double_to_float(0.3))
classifier.build_classifier(iris_data)
print(classifier)
print(classifier.graph())
#plot_graph.plot_dot_graph(classifier.graph())
evaluation = Evaluation(iris_data) # initialize with priors
evaluation.crossvalidate_model(classifier, iris_data, 10, Random(42)) # 10-fold CV
print(evaluation.to_summary())
print("pctCorrect: " + str(evaluation.percent_correct()))
print("incorrect: " + str(evaluation.incorrect()))
jvm.stop()
