def label_uniqueness(data):
'''Counting the number of defferent labels in the dataset input:dataset output:Number of labels '''
label_uniq = {}
for x in data:
label = x[len(x) - 1]
if label not in label_uniq:
label_uniq[label] = 0
label_uniq[label] += 1
return label_uniq
pass
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這個就是tool文件裏面的。 建立節點node：
class node:
'''Tree node '''
def __init__(self , fea = -1, value = None, results = None, right = None, left = None):
''' initialization function :param fea:column index value :param value:split value :param results:The class belongs to :param right:right side :param left:left side '''
self.fea = fea
self.value = value
self.results = results
self.right = right
self.left = left
pass
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class decision_tree(object):
def build_tree(self,data):
'''Create decision tree input:data output:root '''if len(data) == 0:
return node()
currentGini = tool.cal_gini(data)
bestGain = 0.0
bestCriterria = None # store the optimal cutting point
bestSets = None # store two datasets which have been splited
feature_num = len(data[0]) - 1 # Number of featuresfor fea in range(0 , feature_num):
feature_values = {}
for sample in data:
feature_values[sample[fea]] = 1 # store the value in the demension fea possiblyfor value in feature_values.keys():
(set_first, set_second) = self.split_tree(data, fea, value)
nowGini = float(len(set_first) * tool.cal_gini(set_first) + len(set_second) * tool.cal_gini(set_second)) / len(data)
gain = currentGini - nowGini
if gain > bestGain and len(set_first) > 0 and len(set_second) > 0:
bestGain = gain
bestCriterria = (fea , value)
bestSets = (set_first , set_second)
pass
if bestGain > 0:
right = self.build_tree(bestSets[0])
left = self.build_tree(bestSets[1])
return node(fea = bestCriterria[0], value = bestCriterria[1], right = right, left = left)
else:
return node(results=tool.label_uniqueness(data))
def split_tree(self , data , fea , value):
'''split the dataset according demension and value input:data output:two data '''
set_first = []
set_second = []
for x in data:
if x[fea] >= value:
set_first.append(x)
else:
set_second.append(x)
return (set_first, set_second)
pass
def predict(self, sample, tree):
'''prediction input:sample, the tree which we have been built output:label '''if tree.results != None:
return tree.results
else:
val_sample = sample[tree.fea]
branch = None
if val_sample >= tree.value:
branch = tree.right
else:
branch = tree.left
return self.predict(sample, branch)
def predcit_samples(self, samples, tree):
predictions = []
for sample in samples:
predictions.append(self.predict(sample, tree))
return predictions
pass
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