def evaluate(self):
# split contains list of lists i.e, list of [train, test]
split = self.split_data(self.points)
open("output/accuracy_%s_%s.csv" % (self.name, self.method), 'w').close()
for train, test in split:
dtree = decision_tree(self.all_data, self.features, self.categories, self.method, len(train))
Decision_tree = dtree.build_decision_tree(train)
self.get_accuracy(Decision_tree, test)
def evaluate(self):
# split contains list of lists i.e, list of [train, test]
split = self.split_data(self.points)
open("output/accuracy_%s_%s.csv" % (self.name, self.method),
'w').close()
for train, test in split:
dtree = decision_tree(self.all_data, self.features,
self.categories, self.method, len(train))
Decision_tree = dtree.build_decision_tree(train)
self.get_accuracy(Decision_tree, test)
def evaluate(path, name, method, k):
Data, points_index, points, features, categories = get_data(
path, name, method)
split = split_data(points, k)
open("output/accuracy_%s_%s.csv" % (name, method), 'w').close()
# split contains list of lists i.e, list of [train, test]
accuracy = []
for train, test in split:
dtree = decision_tree(Data, features, categories, method, len(train))
Decision_tree = dtree.build_decision_tree(train)
get_accuracy(Decision_tree, Data, test, features, name, method)
def evaluate(self):
# split contains list of lists i.e, list of [train, test]
split = self.split_data(self.points)
open(
"output/accuracy_%s_%s_%s.csv" %
(self.name, self.method, self.beam_size), 'w').close()
for train, test in split:
# Generate initial set of trees
dtree = decision_tree(self.all_data,
self.features, self.categories, self.method,
len(train), self.beam_size)
trees, keep_going = dtree.initialize_trees(train)
trees, prev_acc = self.keep_best_m(trees, train)
acc_sum = [sum(prev_acc)]
# Iterate till we can not improve further
while keep_going:
tmp_trees = []
keep_going = False
for tree in trees:
new_trees, going = dtree.extend(tree)
tmp_trees += new_trees
keep_going = keep_going or going
trees, curr_acc = self.keep_best_m(tmp_trees, train)
acc_sum.append(sum(curr_acc))
if len(acc_sum) > 2:
if acc_sum[-1] - acc_sum[-3] <= 0:
keep_going = False
i = 0
while i < len(curr_acc) and curr_acc[i] == 1:
i += 1
if i == len(curr_acc):
keep_going = False
# trees has forest of trees
# As we have final set of trees, checking accuracy and generating results.
self.get_accuracy(trees, test)