def main(args):
"""
Loads a dataset, shuffles it, splits it into train/test set. Trains J48 with training set and
evaluates the built model on the test set.
The predictions get recorded in two different ways:
1. in-memory via the test_model method
2. directly to file (more memory efficient), but a separate run of making predictions
:param args: the commandline arguments (optional, can be dataset filename)
:type args: list
"""
# load a dataset
if len(args) <= 1:
data_file = helper.get_data_dir() + os.sep + "vote.arff"
else:
data_file = args[1]
helper.print_info("Loading dataset: " + data_file)
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(data_file)
data.class_is_last()
# generate train/test split of randomized data
train, test = data.train_test_split(66.0, Random(1))
# build classifier
cls = Classifier(classname="weka.classifiers.trees.J48")
cls.build_classifier(train)
print(cls)
# evaluate and record predictions in memory
helper.print_title("recording predictions in-memory")
output = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.CSV",
options=["-distribution"])
evl = Evaluation(train)
evl.test_model(cls, test, output=output)
print(evl.summary())
helper.print_info("Predictions:")
print(output.buffer_content())
# record/output predictions separately
helper.print_title("recording/outputting predictions separately")
outputfile = helper.get_tmp_dir() + "/j48_vote.csv"
output = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.CSV",
options=["-distribution", "-suppress", "-file", outputfile])
output.header = test
output.print_all(cls, test)
helper.print_info("Predictions stored in:" + outputfile)
# by using "-suppress" we don't store the output in memory, the following statement won't output anything
print(output.buffer_content())
classifier = Classifier(classname="weka.classifiers.trees.M5P",
options=["-U", "-M", "500.0"])
print("\n--> building:")
print(classifier.to_commandline())
classifier.build_classifier(dataA)
print("\n--> classifier:\n")
print(classifier)
print("\n--> graph:\n")
print(classifier.graph)
outputfile = helper.get_tmp_dir() + "/result.csv"
output = PredictionOutput(
classname='weka.classifiers.evaluation.output.prediction.CSV',
options=["-distribution", "-suppress", "-file", outputfile])
print("\n--> Output:\n")
output.header = dataA
output.print_all(classifier, dataA)
helper.print_info("Predictions stored in:" + outputfile)
print(output.buffer_content())
Eval = Evaluation(dataA)
Eval.test_model(classifier, dataA, output=output)
print(Eval.summary())
ListEval = []
Corr = []
Corrf = []
ListEval = Eval.summary().split('Mean absolute error')
print("ListEval :")
print(ListEval)
Corr = ListEval[0].split('\n')
Corrf = Corr[1].split('Correlation coefficient ')
print("Corrf :")
