def handle_predict(argv):
hypothesis = None
model = None
with open(argv[3], "r") as f:
# DONT DO THIS ITS INSECURE. IM INSANE
model = f.readline().strip('\n')
hypothesis = f.readline()
f.close()
hypothesis = literal_eval(hypothesis)
tree = None
tree = DecisionTree()
tree.define_positive_class(lambda x: x.classification == 'en')
tree.define_classes(processing.classes)
tree.define_attributes(processing.attr_definitions)
examples = process_file(argv[4], training=False)
examples = tree.create_examples(examples)
return tree.classify(examples, hypothesis)
def handle_train(argv):
examples = process_file(argv[2], training=True)
tree = None
if argv[4] == "dt":
tree = DecisionTree()
else:
tree = Adaboost()
tree.define_positive_class(lambda x: x.classification == 'en')
tree.define_classes(processing.classes)
tree.define_attributes(processing.attr_definitions)
tree.load_examples(examples)
tree.generate(tree.examples)
with open(argv[3], "w") as f:
f.write(argv[4] + "\n")
f.write(str(tree.tree))
f.close()
tree.print()
def handle_train(argv, size=None, depth=None):
examples = process_file(argv[2], training=True)
tree = None
tree = DecisionTree()
tree.define_positive_class(lambda x: x.classification == 'en')
tree.define_classes(processing.classes)
tree.define_attributes(processing.attr_definitions)
if size:
tree.load_examples(examples[:size])
else:
tree.load_examples(examples)
if depth != None:
tree.generate(tree.examples, depth)
else:
tree.generate(tree.examples)
with open(argv[3], "w") as f:
f.write("dt" + "\n")
f.write(str(tree.tree))
f.close()
tree.print()
def handle_predict(argv):
hypothesis = None
model = None
with open(argv[2], "r") as f:
model = f.readline().strip('\n')
hypothesis = f.readline()
f.close()
hypothesis = literal_eval(hypothesis)
tree = None
if model == "dt":
tree = DecisionTree()
else:
tree = Adaboost()
tree.define_positive_class(lambda x: x.classification == 'en')
tree.define_classes(processing.classes)
tree.define_attributes(processing.attr_definitions)
examples = process_file(argv[3], training=False)
examples = tree.create_examples(examples)
for classification in tree.classify(examples, hypothesis):
print(classification)