def decision_spambase_set_no_libs():
"""
Solution for HW1 prob 1
"""
print('Homework 1 problem 1 - No Libraries - Regression Decision tree')
print('Spambase Dataset')
spam_data = utils.load_and_normalize_spam_data()
test, train = utils.split_test_and_train(spam_data)
print str(len(train)) + " # in training set <--> # in test " + str(len(test))
node = mytree.Node(np.ones(len(train)))
branch_node(node, train, 5, 'is_spam')
#node.show_children_tree()
node.show_children_tree(follow=False)
model = mytree.Tree(node)
model.print_leaves()
print 'Trained model error is : ' + str(model.error())
node.presence = np.ones(len(test))
test_node(node, test, 'is_spam')
test_tree = mytree.Tree(node)
prediction = test_tree.predict_obj()
test_tree.print_leaves_test()
print 'predict sum: ' + str(sum(prediction))
print 'MSE:' + str(test_tree.error_test())
[tp, tn, fp, fn] = mystats.get_performance_stats(test['is_spam'].as_matrix(), prediction)
print 'TP: {}\tFP: {}\nTN: {}\tFN: {}'.format(tp, fp, tn, fn)
print 'Accuracy: ' + str(mystats.compute_accuracy(tp,tn, fp,fn))
print 'MSE: ' + str(mystats.compute_MSE_arrays(prediction, test['is_spam']))
