def branch_node(node, df, threshold, Y, regression=False):
"""
:param node: Node object defined in Stats
:param df: The dataframe being used by the tree
:param threshold: max branching depth
:param Y: Feature to predict
:return: void
"""
print 'Branching Level : ' + str(node.level)
data = node.get_node_data(df)
print 'Length of data ' + str(len(data)) + ' len df: ' + str(len(df))
feature, label = mytree.find_best_feature_and_label_for_split(data, Y, regression)
print 'feature: {} label: {}'.format(feature, label)
if feature is not None and node.level < threshold:
A_array, B_array = node.split(feature, df[feature], label)
print ' A : {} B: {}'.format(sum(A_array), sum(B_array))
node.add_left(A_array)
node.add_right(B_array)
branch_node(node.left, df, threshold, Y, regression)
branch_node(node.right, df, threshold, Y, regression)
else:
if not regression:
predict = 0
prob = mystats.binary_probability(data, Y)
print 'PROBABILITY ' + str(prob)
if prob >= .5:
predict = 1
error = mystats.binary_error(data, Y, predict)
else:
print str(feature) +'is fueaturea ' + str(label) + str(node.presence)
predict = float(sum(data[Y]))/len(data[Y])
error = mystats.compute_MSE(predict, list(data[Y]))
node.leaf(predict, error)
def test_node(node, df, Y, regression=False):
"""
:param node: Node object defined in Stats
:param df: The dataframe being used by the tree
:param Y: Feature to predict
:return: void
"""
print 'Testing Branching Level : ' + str(node.level)
data = node.get_node_data(df)
print 'Length of TEST data ' + str(len(data)) + ' len df: ' + str(len(df))
feature = node.label['feature']
label = node.label['criteria']
if feature is not '':
print 'feature ' + feature
#print df[feature]
A_array, B_array = node.split(feature, df[feature], label)
print 'Test A : {} B: {}'.format(sum(A_array), sum(B_array))
node.left.set_presence(A_array)
node.right.set_presence(B_array)
if node.left is not None:
test_node(node.left, df, Y, regression)
if node.right is not None:
test_node(node.right, df, Y, regression)
else:
predict = node.predict
if not regression:
error = mystats.binary_error(data, Y, predict)
else:
error = mystats.compute_MSE(predict, list(data[Y]))
node.test_leaf(error)
def regression_line_housing_no_libs():
"""
Solution for HW1 prob 2
"""
print('Homework 1 problem 2 - No Libraries - Regression Line')
print('Housing Dataset')
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], train['MEDV'])
print 'Y_fit'
print Y_fit
#for i in range(0, len(Y_fit)):
# print str(Y_fit[i]) + ' -- ' + str(train['MEDV'][i])
row_sums = np.zeros(len(Y_fit[0]))
for col in Y_fit:
for i in range(0, len(col)):
row_sums[i] += col[i]
print row_sums
col_MSE = {}
for i, col in enumerate(columns):
col_fit = row_sums[i] # Y_fit[i] + Y_fit[-1]
col_MSE[col] = mystats.compute_MSE(col_fit, train['MEDV'])
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)
