def part2():
"""randomly choose 5%, 10%, 20%, 50%, 100% samples to train, and choose 10 sets each time"""
plt.figure()
for trainFileName, testFileName, key in [
('../diabetes_train.arff', '../diabetes_test.arff', 'diabetes'),
('../heart_train.arff', '../heart_test.arff', 'heart')
]:
attribute, trainset = data_provider(trainFileName)
testAttribute, testset = data_provider(testFileName)
m = 4
avgPoints = []
maxPoints = []
minPoints = []
for rate in (0.05, 0.1, 0.2, 0.5, 1):
accuracys = []
for newTrainset in selectSample(trainset, rate):
root = TreeNode(newTrainset, attribute)
curTree = DecisionTree(root)
curTree.createTree(root, m)
trueSamples = 0
falseSamples = 0
for instance in testset:
if curTree.predict(root, instance) == instance[-1]:
trueSamples += 1
else:
falseSamples += 1
accuracys.append(
float(trueSamples) / (trueSamples + falseSamples))
accuracy = float(sum(accuracys)) / len(accuracys)
avgPoints.append([int(rate * 100), accuracy])
maxPoints.append([int(rate * 100), max(accuracys)])
minPoints.append([int(rate * 100), min(accuracys)])
mapping = {'diabetes': 1, 'heart': 2}
ax = plt.subplot(1, 2, mapping[key])
ax.set_xlim(0, 105)
ax.set_ylim(0.45, 0.9)
ax.set_ylabel('accuracy')
ax.set_title(key)
ax.plot([x[0] for x in avgPoints], [x[1] for x in avgPoints],
label='average')
ax.plot([x[0] for x in maxPoints], [x[1] for x in maxPoints],
label='maximum')
ax.plot([x[0] for x in minPoints], [x[1] for x in minPoints],
label='minimum')
ax.legend()
plt.xlabel('dataset sample percentage')
plt.savefig('../part2.pdf')
def part3():
points = {}
plt.figure()
for trainFileName, testFileName, key in [
('../diabetes_train.arff', '../diabetes_test.arff', 'diabetes'),
('../heart_train.arff', '../heart_test.arff', 'heart')
]:
attribute, trainset = data_provider(trainFileName)
testAttribute, testset = data_provider(testFileName)
root = TreeNode(trainset, attribute)
curTree = DecisionTree(root)
points = []
for m in (2, 5, 10, 20):
curTree.createTree(root, m)
trueSamples = 0
falseSamples = 0
for instance in testset:
if curTree.predict(root, instance) == instance[-1]:
trueSamples += 1
else:
falseSamples += 1
points.append(
[m, float(trueSamples) / (trueSamples + falseSamples)])
mapping = {'diabetes': 1, 'heart': 2}
for x, y in points:
ax = plt.subplot(2, 1, mapping[key])
ax.set_xlim(0, 22)
ax.set_ylim(0.6, 0.8)
ax.set_ylabel('accuracy')
ax.set_title(key)
plt.annotate('%.3f' % y, xy=(x - 0.02, y + 0.02))
plt.annotate('m=%d' % x, xy=(x - 0.02, y - 0.07))
ax.plot(x, y, 'o-')
plt.xlabel('tree number m')
plt.savefig('../part3.pdf')
sys.exit()
trainFileName = sys.argv[1]
testFileName = sys.argv[2]
try:
m = int(sys.argv[3])
except:
print >> sys.stderr, "[ERROR] [m] should be in integer!"
sys.exit()
attribute, trainset = data_provider(trainFileName)
testAttribute, testset = data_provider(testFileName)
try:
assert (testAttribute == attribute)
except AssertionError:
print >> sys.stderr, "[ERROR] pls check the attributes of test data."
sys.exit()
# train
root = TreeNode(trainset, attribute)
curTree = DecisionTree(root)
curTree.createTree(root, m)
curTree.printTree(root, 0)
# test
print '<Predictions for the Test Set Instances>'
index = 1
for instance in testset:
print '{}: Actual: {} Predicted: {}'.format(
index, instance[-1], curTree.predict(root, instance))
index += 1
