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']))
def regression_line_spam_no_libs():
"""
Solution for HW1 prob 2
"""
print('Homework 1 problem 2 - No Libraries - Regression Line')
print('Spam Dataset')
spam_data = utils.load_and_normalize_spam_data()
test, train = utils.split_test_and_train(spam_data)
columns = train.columns[:-1]
Y_fit = mystats.linear_regression_points(train[columns], train['is_spam'])
#print 'Y_fit'
#print Y_fit
#for i in range(0, len(Y_fit)):
# print str(Y_fit[i]) + ' -- ' + str(train['is_spam'][i])
col_MSE = {}
for i, col in enumerate(columns):
col_fit = Y_fit[i] + Y_fit[-1]
col_MSE[col] = mystats.compute_MSE_arrays(col_fit, train['is_spam'])
print col_MSE
RMSE = np.sqrt(col_MSE.values())
average_MSE = utils.average(col_MSE.values())
average_RMSE = utils.average(RMSE)
print 'Average MSE: ' + str(average_MSE)
print 'Average RMSE: ' + str(average_RMSE)
def test_regression_line_housing_no_libs():
"""
Testing 2 variable solution for HW1 prob 2
"""
print('Testing linear regression with 2 columns')
test, train = utils.load_and_normalize_housing_set()
print str(len(train)) + " # in training set <--> # in test " + str(len(test))
columns = train.columns[:-1]
Y_fit = mystats.linear_regression_points(train[columns[0]], train['MEDV'])
#for i, col in enumerate(columns):
print 'Y_fit'
print Y_fit
for i in range(0, len(Y_fit)):
print str(Y_fit[i]) + ' -- ' + str(train['MEDV'][i])
print train[columns[0]]
#myplot.points([train[columns[0]], train['MEDV']])
#myplot.points([train[columns[0]], list(Y_fit[0])])
myplot.fit_v_point([train[columns[0]], train['MEDV'], list(Y_fit[0] + Y_fit[-1])])
col_MSE = {}
print columns[0]
i = 0
col = 'CRIM'
col_fit = Y_fit[i] + Y_fit[-1]
col_MSE[col] = mystats.compute_MSE_arrays(col_fit, train['MEDV'])
print col_MSE
def decision_housing_set_no_libs():
"""
Solution for HW1 prob 1
"""
print('Homework 1 problem 1 - No Libraries - Regression Decision tree')
print('Housing Dataset')
test, train = utils.load_and_normalize_housing_set()
# The following 2 lines are for debugging
#train = utils.train_subset(train, ['ZN','CRIM', 'TAX', 'DIS', 'MEDV'], n=50)
#test = utils.train_subset(test, ['ZN', 'CRIM', 'TAX', 'DIS', 'MEDV'], n=3)
print str(len(train)) + " # in training set <--> # in test " + str(len(test))
node = mytree.Node(np.ones(len(train)))
branch_node(node, train, 2, 'MEDV', regression=True)
#node.show_children_tree()
node.show_children_tree(follow=False)
model = mytree.Tree(node)
model.print_leaves()
model.print_tree(train)
print 'Trained model error is : ' + str(model.error())
train_prediction = model.predict_obj()
print 'Training MSE is: ' + str(mystats.compute_MSE_arrays(train_prediction, train['MEDV']))
sys.exit()
node.presence = np.ones(len(test))
test_node(node, test, 'MEDV', regression=True)
test_tree = mytree.Tree(node)
prediction = test_tree.predict_obj()
#raw_input()
print 'predict sum: ' + str(sum(prediction))
test_tree.print_leaves_test()
print 'ERROR: ' + str(test_tree.error_test())
print prediction
print 'train'
print train['MEDV']
print 'test'
print test['MEDV']
MSE = mystats.compute_MSE_arrays(prediction, test['MEDV'])
print 'MSE: ' + str(MSE)
print 'RMSE: ' + str(np.sqrt(MSE))
test_tree.print_tree(test, long=False)
