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 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)
