def ID3(data_obj, attributes, parent, tree, limit_depth=False, max_depth=10):
label = data_obj.get_column('label')
if (len(label) <= 0):
# No data
return
elif (len(np.unique(label)) == 1):
# Object of only 1 label in the tree
# Add a node with labels
n = Node(np.unique(label)[0], True)
tree.add_node(n, parent)
elif (len(attributes) <= 0):
# Add majority label to the tree as the node
# first get counts of individual labels
bins, counts = np.unique(label, return_counts=True)
n = Node(bins[np.argmax(counts)], True)
tree.add_node(n, parent)
else:
if (limit_depth):
if (max_depth < 0):
print("Max-depth should be greater than 0. Aborting!!!")
return
# Information gain for each features
info_gain_per_feature = {}
for key in attributes:
info_gain_per_feature[key] = info_gain(data_obj, key)
# print(key + "," + str(info_gain_per_feature[key]))
# Choose the best feature and the possible values
best_feature = max(info_gain_per_feature,
key=info_gain_per_feature.get)
best_feature_values = data_obj.attributes[best_feature].possible_vals
# Add a node
n = Node(best_feature, False)
# Add all possible directions in which node can go
for i in range(len(best_feature_values)):
# partition into subset based on different values
data_subset_obj = data_obj.get_row_subset(best_feature,
best_feature_values[i])
# if non-zero items in the subset data
if (data_subset_obj.raw_data.shape[0] > 0):
n.add_value(best_feature_values[i])
tree.add_node(n, parent)
# Check depth of the tree after adding this node.
if (limit_depth):
depth = tree.get_depth(tree.get_root())
if (depth > max_depth):
# Donot grow the tree instead add label nodes
tree.del_node(n, parent)
# Add majority label to the tree as the node
# first get counts of individual labels
bins, counts = np.unique(label, return_counts=True)
n = Node(bins[np.argmax(counts)], True)
tree.add_node(n, parent)
return
# pop this feature from dictionary
attributes_new = remove_key(attributes, best_feature)
for i in range(len(n.value)):
# partition into subsets based on different values
data_subset_obj = data_obj.get_row_subset(best_feature, n.value[i])
if (parent is None):
new_parent = tree.get_root()
else:
new_parent = parent.child[-1]
ID3(data_subset_obj, attributes_new, new_parent, tree, limit_depth,
max_depth)
from tree import Tree
from tree import Node
myTree = Tree()
#print(myTree.get_root())
n = Node('taste')
n.add_value('o')
p = Node('var')
n.add_value('a')
q = Node('var')
n.add_value('b')
r = Node('var')
r.add_value('c')
s = Node('name')
myTree.add_node(n, myTree.get_root())
print("Traversing the tree after adding 1 node")
myTree.print_tree(myTree.get_root(), 0)
myTree.add_node(p, n)
#myTree.add_node(p,myTree.search_node(myTree.get_root(),n.feature,n.value))
print("Traversing the tree after adding 2 nodes")
myTree.print_tree(myTree.get_root(), 0)
myTree.add_node(q, n)
myTree.add_node(r, n)