I am getting an attribute error but I couldn't figure out the reason. I am attaching my code please have a look and let me know how to fix.
The error I am getting is:
AttributeError Traceback (most recent call last)
<ipython-input-1-f3085e096816> in <module>
164 my_tree = build_tree(training_data)
165
--> 166 print_tree(my_tree)
167
168 testing_data = [
<ipython-input-1-f3085e096816> in print_tree(node, spacing)
134 return
135
--> 136 print(spacing + str(node.question))
137
138 print(spacing + "--> True")
AttributeError: 'NoneType' object has no attribute 'question'
# Generating data
training_data = [
["Green", 3, "Mango"],
["Yellow", 3, "Mango"],
["Red", 1, "Grape"],
["Red", 1, "Grape"],
["Yellow", 3, "Lemon"],
]
# Column Labels
header = ["color", "diameter", "label"]
def unique_vals(rows, col):
"""Find unique values for a column in dataset."""
return set([row[col] for row in rows])
def class_counts(rows):
"""Counts # of each typre of eg. in datset."""
counts = {}
for row in rows:
label = row[-1]
if label not in counts:
counts[label] = 0
counts[label] += 1
return counts
def is_numeric(value):
"""Check if a value is numeric"""
return isinstance(value, int) or isinstance(value, float)
# Spliting dataset based on questions
class Question:
def __init__(self, column, value):
self.column = column
self.value = value
def match(self, example):
# compare feature values
val = example[self.column]
if is_numeric(val):
return val >= self.value
else:
return val == self.value
def __repr__(self):
condition = "=="
if is_numeric(self.value):
condition = ">="
return "Is %s %s %s?" % (
header[self.column], condition, str(self.value))
def partition(rows, question):
true_rows, false_rows = [], []
for row in rows:
if question.match(row):
true_rows.append(row)
else:
false_rows.append(row)
return true_rows, false_rows
# Entropy
def gini(rows):
counts = class_counts(rows)
impurity = 1
for lbl in counts:
prob_if_lbl = counts[lbl]/float(len(rows))
impurity -= prob_if_lbl**2
return impurity
def info_gain(left, right, current_uncertainity):
p = float(len(left))/len(left) + len(right)
return current_uncertainity - p * gini(left) - (1-p) * gini(right)
def find_best_split(rows):
best_gain = 0
best_question = None
current_uncertainity = gini(rows)
n_features = len(rows[0]) - 1
for col in range(n_features):
values = set([row[col] for row in rows])
for val in values:
question = Question(col, val)
true_rows, false_rows = partition(rows, question)
if len(true_rows) == 0 or len(false_rows) == 0:
continue
gain = info_gain(true_rows, false_rows, current_uncertainity)
if gain >= best_gain:
best_gain, best_question = gain, question
return best_gain, best_question
# Defining tree
class leaf:
def __init__(self, rows):
self.predicitons = class_counts(rows)
class Decision_Node:
def __init__(self,
question,
true_branch,
false_branch):
self.question = question
self.true_branch = true_branch
self.false_branch = false_branch
def build_tree(rows):
gain, question = find_best_split(rows)
if gain == 0:
return leaf(rows)
true_rows, false_rows = partition(rows, question)
true_branch = build_tree(true_rows)
false_branch = build_tree(false_rows)
def print_tree(node, spacing=""):
if isinstance(node, leaf):
print (spacing + "Predict", node.predicitons)
return
print(spacing + str(node.question))
print(spacing + "--> True")
print_tree(node.true_branch, spacing + " ")
print(spacing + "--> False")
print_tree(node.false_branch, spacing + " ")
def classify(row,node):
if isinstance(node,leaf):
return node.predicitons
if node.question.match(row):
return classify(row, node.true_branch)
else:
return classify(row, node.false_branch)
def print_leaf(counts):
total = sum(counts.values()) * 1.0
probs = {}
for lbl in counts.keys():
probs[lbl] = str(int(counts[lbl] / total * 100)) + "%"
return probs
if __name__ == "__main__":
my_tree = build_tree(training_data)
print_tree(my_tree)
testing_data = [
["Green", 3, "Mango"],
["Yellow", 4, "Mango"],
["Red", 2, "Grape"],
["Red", 1, "Grape"],
["Yellow", 3, "Lemon"],
]
for row in testing_data:
print("Actual: %s. Predicted: %s" %
(row[-1], print_leaf(classify(row, my_tree))))
