def decision_tree_all_data():
train, target = load_spambase()
cf = tree.DecisionTree()
cf = cf.fit(train, target, 5)
print_tree(cf.root)
predicts = cf.predict(train)
cm = confusion_matrix(target,predicts)
print "confusion matrix: TN: %s, FP: %s, FN: %s, TP: %s" % (cm[0, 0], cm[0, 1], cm[1, 0], cm[1, 1])
er, acc, fpr, tpr = confusion_matrix_analysis(cm)
print 'Error rate: %f, accuracy: %f, FPR: %f, TPR: %f' % (er, acc, fpr, tpr)
def decision_tree():
train, target = load_spambase()
# 10 fold cross validation
train_size = len(train)
k = 10
test_index_generator = cross_validation.k_fold_cross_validation(train_size, k)
fold = 0
train_accuracy = 0
test_accuracy = 0
train_mse = 0
test_mse = 0
for start, end in test_index_generator:
train_left = train[range(0, start)]
train_right = train[range(end, train_size)]
k_fold_train = np.vstack((train_left, train_right))
test = train[range(start, end)]
target_left = target[range(0, start)]
target_right = target[range(end, train_size)]
train_target = np.append(target_left, target_right)
test_target = target[range(start, end)]
cf = tree.DecisionTree()
cf = cf.fit(k_fold_train, train_target, 5)
print "=========Tree=============="
print_tree(cf.root)
print '=============Train Data Result============'
predict_train = cf.predict(k_fold_train)
cm = confusion_matrix(train_target, predict_train)
print "confusion matrix: TN: %s, FP: %s, FN: %s, TP: %s" % (cm[0, 0], cm[0, 1], cm[1, 0], cm[1, 1])
er, acc, fpr, tpr = confusion_matrix_analysis(cm)
print 'Error rate: %f, accuracy: %f, FPR: %f, TPR: %f' % (er, acc, fpr, tpr)
train_accuracy += acc
print "mse: ", mse(predict_train, train_target), " rmse: ", rmse(predict_train, train_target), " mae: ", mae(predict_train,
train_target)
train_mse += mse(predict_train, train_target)
print '=============Test Data Result============'
predict_test = cf.predict(test)
cm = confusion_matrix(test_target, predict_test)
print "confusion matrix: TN: %s, FP: %s, FN: %s, TP: %s" % (cm[0, 0], cm[0, 1], cm[1, 0], cm[1, 1])
er, acc, fpr, tpr = confusion_matrix_analysis(cm)
print 'Error rate: %f, accuracy: %f, FPR: %f, TPR: %f' % (er, acc, fpr, tpr)
test_accuracy += acc
print "mse: ", mse(predict_test, test_target), " rmse: ", rmse(predict_test, test_target), " mae: ", mae(predict_test,
test_target)
test_mse += mse(predict_test, test_target)
fold += 1
print "Average train acc: %f, average test acc: %f" % (train_accuracy / fold, test_accuracy / fold)
print "Average train mse: %f, average test mse: %f" % (train_mse / fold, test_mse / fold)
def decision_tree_all_data():
train, target = load_spambase()
cf = tree.DecisionTree()
cf = cf.fit(train, target, 5)
print_tree(cf.root)
predicts = cf.predict(train)
cm = confusion_matrix(target, predicts)
print "confusion matrix: TN: %s, FP: %s, FN: %s, TP: %s" % (
cm[0, 0], cm[0, 1], cm[1, 0], cm[1, 1])
er, acc, fpr, tpr = confusion_matrix_analysis(cm)
print 'Error rate: %f, accuracy: %f, FPR: %f, TPR: %f' % (er, acc, fpr,
tpr)
def regression_tree():
print "=========Start Train=============="
train, train_target, test, test_target = load_boston_house()
print len(train), len(test)
classifier = tree.RegressionTree()
classifier = classifier.fit(train, train_target, 2, -1)
print "=========Finish Train=============="
print "=========Tree=============="
print_tree(classifier.root)
print '=============Train Data Result============'
predict = classifier.predict(train)
print "mse: ", mse(predict, train_target), " rmse: ", rmse(predict, train_target), " mae: ", mae(predict,
train_target)
print '=============Test Data Result============'
predict = classifier.predict(test)
print "mse: ", mse(predict, test_target), " rmse: ", rmse(predict, test_target), " mae: ", mae(predict, test_target)
def regression_tree():
print "=========Start Train=============="
train, train_target, test, test_target = load_boston_house()
print len(train), len(test)
classifier = tree.RegressionTree()
classifier = classifier.fit(train, train_target, 2, -1)
print "=========Finish Train=============="
print "=========Tree=============="
print_tree(classifier.root)
print '=============Train Data Result============'
predict = classifier.predict(train)
print "mse: ", mse(predict, train_target), " rmse: ", rmse(
predict, train_target), " mae: ", mae(predict, train_target)
print '=============Test Data Result============'
predict = classifier.predict(test)
print "mse: ", mse(predict, test_target), " rmse: ", rmse(
predict, test_target), " mae: ", mae(predict, test_target)
def decision_tree():
train, target = load_spambase()
# 10 fold cross validation
train_size = len(train)
k = 10
test_index_generator = cross_validation.k_fold_cross_validation(
train_size, k)
fold = 0
train_accuracy = 0
test_accuracy = 0
train_mse = 0
test_mse = 0
for start, end in test_index_generator:
train_left = train[range(0, start)]
train_right = train[range(end, train_size)]
k_fold_train = np.vstack((train_left, train_right))
test = train[range(start, end)]
target_left = target[range(0, start)]
target_right = target[range(end, train_size)]
train_target = np.append(target_left, target_right)
test_target = target[range(start, end)]
cf = tree.DecisionTree()
cf = cf.fit(k_fold_train, train_target, 5)
print "=========Tree=============="
print_tree(cf.root)
print '=============Train Data Result============'
predict_train = cf.predict(k_fold_train)
cm = confusion_matrix(train_target, predict_train)
print "confusion matrix: TN: %s, FP: %s, FN: %s, TP: %s" % (
cm[0, 0], cm[0, 1], cm[1, 0], cm[1, 1])
er, acc, fpr, tpr = confusion_matrix_analysis(cm)
print 'Error rate: %f, accuracy: %f, FPR: %f, TPR: %f' % (er, acc, fpr,
tpr)
train_accuracy += acc
print "mse: ", mse(predict_train, train_target), " rmse: ", rmse(
predict_train,
train_target), " mae: ", mae(predict_train, train_target)
train_mse += mse(predict_train, train_target)
print '=============Test Data Result============'
predict_test = cf.predict(test)
cm = confusion_matrix(test_target, predict_test)
print "confusion matrix: TN: %s, FP: %s, FN: %s, TP: %s" % (
cm[0, 0], cm[0, 1], cm[1, 0], cm[1, 1])
er, acc, fpr, tpr = confusion_matrix_analysis(cm)
print 'Error rate: %f, accuracy: %f, FPR: %f, TPR: %f' % (er, acc, fpr,
tpr)
test_accuracy += acc
print "mse: ", mse(predict_test, test_target), " rmse: ", rmse(
predict_test, test_target), " mae: ", mae(predict_test,
test_target)
test_mse += mse(predict_test, test_target)
fold += 1
print "Average train acc: %f, average test acc: %f" % (
train_accuracy / fold, test_accuracy / fold)
print "Average train mse: %f, average test mse: %f" % (train_mse / fold,
test_mse / fold)
