def main():
"""
Just runs some example code.
"""
# load a dataset
anneal_file = helper.get_data_dir() + os.sep + "anneal.arff"
helper.print_info("Loading dataset: " + anneal_file)
loader = Loader("weka.core.converters.ArffLoader")
anneal_data = loader.load_file(anneal_file)
anneal_data.class_is_last()
# perform attribute selection
helper.print_title("Attribute selection")
search = ASSearch(classname="weka.attributeSelection.BestFirst",
options=["-D", "1", "-N", "5"])
evaluation = ASEvaluation(
classname="weka.attributeSelection.CfsSubsetEval",
options=["-P", "1", "-E", "1"])
attsel = AttributeSelection()
attsel.search(search)
attsel.evaluator(evaluation)
attsel.select_attributes(anneal_data)
print("# attributes: " + str(attsel.number_attributes_selected))
print("attributes (as numpy array): " + str(attsel.selected_attributes))
print("attributes (as list): " + str(list(attsel.selected_attributes)))
print("result string:\n" + attsel.results_string)
# perform ranking
helper.print_title("Attribute ranking (2-fold CV)")
search = ASSearch(classname="weka.attributeSelection.Ranker",
options=["-N", "-1"])
evaluation = ASEvaluation("weka.attributeSelection.InfoGainAttributeEval")
attsel = AttributeSelection()
attsel.ranking(True)
attsel.folds(2)
attsel.crossvalidation(True)
attsel.seed(42)
attsel.search(search)
attsel.evaluator(evaluation)
attsel.select_attributes(anneal_data)
print("ranked attributes:\n" + str(attsel.ranked_attributes))
print("result string:\n" + attsel.results_string)
def main():
"""
Just runs some example code.
"""
# load a dataset
anneal_file = helper.get_data_dir() + os.sep + "anneal.arff"
helper.print_info("Loading dataset: " + anneal_file)
loader = Loader("weka.core.converters.ArffLoader")
anneal_data = loader.load_file(anneal_file)
anneal_data.class_is_last()
# perform attribute selection
helper.print_title("Attribute selection")
search = ASSearch(classname="weka.attributeSelection.BestFirst", options=["-D", "1", "-N", "5"])
evaluation = ASEvaluation(classname="weka.attributeSelection.CfsSubsetEval", options=["-P", "1", "-E", "1"])
attsel = AttributeSelection()
attsel.search(search)
attsel.evaluator(evaluation)
attsel.select_attributes(anneal_data)
print("# attributes: " + str(attsel.number_attributes_selected))
print("attributes: " + str(attsel.selected_attributes))
print("result string:\n" + attsel.results_string)
# perform ranking
helper.print_title("Attribute ranking (2-fold CV)")
search = ASSearch(classname="weka.attributeSelection.Ranker", options=["-N", "-1"])
evaluation = ASEvaluation("weka.attributeSelection.InfoGainAttributeEval")
attsel = AttributeSelection()
attsel.ranking(True)
attsel.folds(2)
attsel.crossvalidation(True)
attsel.seed(42)
attsel.search(search)
attsel.evaluator(evaluation)
attsel.select_attributes(anneal_data)
print("ranked attributes:\n" + str(attsel.ranked_attributes))
print("result string:\n" + attsel.results_string)
data_dir = "\\\\egr-1l11qd2\\CLS_lab\\Junya Zhao\\Data driven model _paper [June 25_2018\\FeatureSelection\\EvlSearch\\"
globbed_files = glob.glob(data_dir + "*.csv")
for csv in globbed_files:
data = converters.load_any_file(csv)
data.class_is_last()
search = ASSearch(classname="weka.attributeSelection.EvolutionarySearch",
options=[
"-population-size", "200", "-generations", "500",
"-crossover-probability", "0.6"
])
evaluator = ASEvaluation(classname="weka.attributeSelection.CfsSubsetEval",
options=["-P", "1", "E", "1"])
attsel = AttributeSelection()
attsel.folds(10)
attsel.crossvalidation(True)
attsel.seed(1)
attsel.search(search)
attsel.evaluator(evaluator)
attsel.select_attributes(data)
evl = Evaluation(data)
print("# attributes: " + str(attsel.number_attributes_selected))
print("attributes: " + str(attsel.selected_attributes))
print("result string:\n" + attsel.results_string)
print(evl)
# write the report for each file
with open(f"{csv}._report.csv", "a") as outfile:
outfile.write(attsel.results_string)
#with open(f"{csv}._label.txt","a") as output:
#output.write(str(attsel.selected_attributes))
jvm.stop()
