def find_threshold(self, data_attr, data_target):
'''
Find threshold of data_attr with respect to data_target
'''
attr_name = data_attr.columns[0]
target_name = data_target.columns[0]
data = pd.concat([data_attr, data_target], axis=1)
data = data.sort_values(attr_name).reset_index(drop=True)
# Retrieve all indexes with different value of target attribute
diff_index = list()
for i in range(len(data) - 1):
if data[target_name].iloc[i] != data[target_name].iloc[i + 1]:
diff_index.append(i)
best_point = 0
best_idx = -1
for i in diff_index:
if (self.gain_ratio):
point = Calculate.gain_ratio(data[attr_name],
data[target_name],
is_continue=True,
split_index=i)
else:
point = Calculate.info_gain(data[attr_name],
data[target_name],
is_continue=True,
split_index=i)
if point > best_point:
best_point = point
best_idx = i
best_splitter = (data[attr_name].iloc[best_idx] +
data[attr_name].iloc[best_idx + 1]) / 2
return [best_splitter, best_point]
def fit(self, data, attributes, target_name):
'''
Built and return decision tree using ID3 algorithm
'''
data_target = data[target_name]
# Data target contains one label
entropy_data_target = Calculate.entropy(data_target)
if entropy_data_target == 0:
value_list = Calculate.get_unique_data(data, target_name)
value_dict = dict()
for key, value in value_list.items():
value_dict[key] = len(value_list[key])
# Set current_node, info_gain, values
tree = Tree(
Node(None,
entropy_data_target,
value_dict,
result=data_target[0],
is_leaf=True))
return tree
# Nothing attribute shall be chosen
if len(attributes) == 0:
# Set current_node, info_gain, values
value_list = Calculate.get_unique_data(data, target_name)
value_dict = dict()
for key, value in value_list.items():
value_dict[key] = len(value_list[key])
tree = Tree(
Node(None,
entropy_data_target,
value_dict,
result=Calculate.most_label(data_target),
is_leaf=True))
return tree
else:
# Find best attribute to be node using either info gain or gain ratio
best_attr = ''
best_point = 0 # Could be Info gain or Gain ratio
for attr in attributes:
if self.gain_ratio:
point = Calculate.gain_ratio(data[attr], data_target)
if point > best_point:
best_point = point
best_attr = attr
else:
point = Calculate.info_gain(data[attr], data_target)
if point > best_point:
best_point = point
best_attr = attr
value_list = Calculate.get_unique_data(data, target_name)
value_dict = dict()
for key, value in value_list.items():
value_dict[key] = len(value_list[key])
# Build decision tree recursively
dtree = Tree(Node(best_attr, best_point, value_dict))
# Delete usage attribute in attributes
attributes.remove(best_attr)
# Scan all posible value to be generated subtree
list_attribute = Calculate.get_unique_data(data, best_attr)
i = 0
for attribute in list_attribute:
data = pd.DataFrame(
data=list_attribute[attribute]).reset_index(drop=True)
data.drop(best_attr, axis=1, inplace=True)
dtree.add_child(self.fit(data, attributes, target_name))
dtree.children[i].value.edge = attribute
i += 1
return dtree
