def find_leaf(tree):
leaf = {}
# print(tree)
for key, value in tree.items():
if value[7] == True:
leaf[key] = value
return leaf
def find_leaf(tree):
leaf={}
count=0
for key,value in tree.items():
if value[7]==True:
leaf[key]=value
count+=1
return leaf,count
def predict_example(x, tree):
"""
Predicts the classification label for a single example x using tree by recursively descending the tree until
a label/leaf node is reached.
Returns the predicted label of x according to tree
"""
if tree == 1 or tree == 0: #terminate conditions if leaf node is reached
return tree
else:
for i, j in tree.items():
if ((i[1] == x[i[0]]) == i[2]):
return predict_example(x, tree[i])
def predict_example(x, tree):
"""
Predicts the classification label for a single example x using tree by recursively descending the tree until
a label/leaf node is reached.
Returns the predicted label of x according to tree
"""
# INSERT YOUR CODE HERE. NOTE: THIS IS A RECURSIVE FUNCTION.
for decision_node, child_tree in tree.items():
idx = decision_node[0]
val = decision_node[1]
decision = decision_node[2]
if decision == (x[idx] == val):
if type(child_tree) is not dict:
class_label = child_tree
else:
class_label = predict_example(x, child_tree)
return class_label
def predict(x_test, tree_dict):
result = []
for i in x_test:
try:
(key, value), = tree_dict.items()
except:
result.append(tree_dict)
continue
tree = {}
while type(value).__name__=='dict':
if i[int(key)] <= value[2]:
tree = value[0]
else:
tree = value[1]
if type(tree).__name__=='int32' or type(tree).__name__=='int64':
result.append(tree)
break
(key, value), = tree.items()
return result
def predict_example(x, tree):
"""
Predicts the classification label for a single example x using tree by recursively descending the tree until
a label/leaf node is reached.
Returns the predicted label of x according to tree
"""
if (type(tree) is not dict):
return tree
# key = next(iter(tree))
# if(key[2]==True):
# return predict_example(x,tree.get(key))
# else:
# return predict_example(x,tree.get(next(iter(tree))))
for key, subtree in tree.items():
if (x[key[0]] == key[1]):
if (key[2]):
return predict_example(x, subtree)
else:
if (key[2] == False):
return predict_example(x, subtree)