def __init__(self):
self.instances1 = Instance.read('./data/test1.dat')
dt = DecisionTree(self.instances1, Id3())
dpath = dt.train()
dpath.dump('./data/test1.dat.path')
self.path1 = DecisionTreeResult.load('./data/test1.dat.path')
self.instances2 = Instance.read('./data/test2.dat')
dt = DecisionTree(self.instances2, Id3())
dpath = dt.train()
dpath.dump('./data/test2.dat.path')
self.path2 = DecisionTreeResult.load('./data/test2.dat.path')
self.dtr = DecisionTreeRefiner()
def training(train_filepath):
train_data = Instance.read(train_filepath)
algo = Id3()
dt = DecisionTree(train_data, algo)
path = dt.train()
return path
def validation(valid_filepath, path):
validation_data = Instance.read(valid_filepath)
r = DecisionTreeRefiner()
result = r.refine(path, validation_data)
return result
def __init__(self):
self.instances1 = Instance.read('./data/test1.dat')
self.instances2 = Instance.read('./data/test2.dat')
self.gt1 = DecisionTreeResult([
Path([('a', True), ('b', True), ('c', True)], 0),
Path([('a', True), ('b', True), ('c', False)], 1),
Path([('a', True), ('b', False)], 0),
Path([('a', False), ('c', True)], 1),
Path([('a', False), ('c', False), ('q', True)], 1),
Path([('a', False), ('c', False), ('q', False), ('e', True)], 1),
Path([('a', False), ('c', False), ('q', False), ('e', False)], 0)
])
self.gt2 = DecisionTreeResult([
Path([('a', True), ('b', True), ('c', True)], 0),
Path([('a', True), ('b', True), ('c', False)], 1),
Path([('a', True), ('b', False)], 0),
Path([('a', False), ('c', True)], 1),
Path([('a', False), ('c', False), ('e', True)], 1),
Path([('a', False), ('c', False), ('e', False)], 0)
])
from data import Instance
from algo import Id3, Gini
from fmeasure import Fmeasure
import time
import optparse
import os
parser = optparse.OptionParser(usage="usage: %prog [options] filepath")
parser.add_option("-a", type="choice", choices=['id3', 'gini', 'f'],
dest="algo", help="algorithm for decision tree", default="id3")
(options, args) = parser.parse_args()
if len(args) == 0:
parser.error("needs filepath")
start_time = time.clock()
filepath = args[0]
instances = Instance.read(filepath)
print '%s used (#pos: %s, #neg: %s)' % (filepath,
len([d for d in instances if d.label == 1]),
len([d for d in instances if d.label != 1]))
if options.algo == 'id3':
algo = Id3()
elif options.algo == 'gini':
algo = Gini()
elif options.algo == 'f':
relnum = len([d for d in instances if d.label == 1])
algo = Fmeasure(relnum)
dt = DecisionTree(instances, algo)
dpath = dt.train()
print "Paths generated:"