## 0 Ideas for the competition
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https://blog.csdn.net/dc_sinor?type=blog
1 What is Random Forest?
Random forest belongs to the Bagging (abbreviation of Bootstrap AGgregation) method in integrated learning. If a graph is used to represent the relationship between them as follows:
Decision Tree – Decision Tree
Before explaining random forests, we need to mention decision trees. Decision tree is a very simple algorithm, which is highly explanatory and conforms to human intuitive thinking. This is a supervised learning algorithm based on if-then-else rules. The above picture can intuitively express the logic of the decision tree.
Random Forest – Random Forest | RF
A random forest is composed of many decision trees, and there is no correlation between different decision trees.
When we perform a classification task, when a new input sample enters, each decision tree in the forest is judged and classified separately. Each decision tree will get its own classification result, which classification in the classification result of the decision tree At most, then Random Forest will take this result as the final result.
2 Construction process of random deep forest
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- A sample with a sample size of N is drawn N times with replacement, one sample is drawn each time, and finally N samples are formed. The selected N samples are used to train a decision tree as samples at the root node of the decision tree.
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- When each sample has M attributes, when each node of the decision tree needs to be split, m attributes are randomly selected from the M attributes, satisfying the condition m << M. Then use a certain strategy (for example, information gain) from these m attributes to select 1 attribute as the splitting attribute of the node.
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- In the process of forming the decision tree, each node must be split according to step 2 (it is easy to understand, if the attribute selected by the node next time is the attribute used when its parent node was just split, the node has reached the leaf node, there is no need to continue splitting). Until it can no longer be divided. Note that no pruning is performed throughout the formation of the decision tree.
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- Follow steps 1-3 to build a large number of decision trees, thus forming a random forest.
3 Advantages and disadvantages of random forest
3.1 Advantages
- It can produce very high-dimensional (many features) data without dimensionality reduction or feature selection
- It can judge the importance of features
- Interactions between different features can be judged
- Not easy to overfit
- The training speed is relatively fast, and it is easy to make a parallel method
- It is relatively simple to implement
- For unbalanced datasets, it balances the errors.
- Accuracy can still be maintained if a large fraction of features are missing.
3.2 Disadvantages
- Random forests have been shown to overfit on some noisy classification or regression problems.
- For data with attributes with different values, attributes with more value divisions will have a greater impact on random forests, so the attribute weights produced by random forests on such data are not credible
4 Implementation of Random Deep Forest Algorithm
Dataset: https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/
import csv
from random import seed
from random import randrange
from math import sqrt
def loadCSV(filename):#加载数据，一行行的存入列表
dataSet = []
with open(filename, 'r') as file:
csvReader = csv.reader(file)
for line in csvReader:
dataSet.append(line)
return dataSet
# 除了标签列，其他列都转换为float类型
def column_to_float(dataSet):
featLen = len(dataSet[0]) - 1
for data in dataSet:
for column in range(featLen):
data[column] = float(data[column].strip())
# 将数据集随机分成N块，方便交叉验证，其中一块是测试集，其他四块是训练集
def spiltDataSet(dataSet, n_folds):
fold_size = int(len(dataSet) / n_folds)
dataSet_copy = list(dataSet)
dataSet_spilt = []
for i in range(n_folds):
fold = []
while len(fold) < fold_size: # 这里不能用if，if只是在第一次判断时起作用，while执行循环，直到条件不成立
index = randrange(len(dataSet_copy))
fold.append(dataSet_copy.pop(index)) # pop() 函数用于移除列表中的一个元素（默认最后一个元素），并且返回该元素的值。
dataSet_spilt.append(fold)
return dataSet_spilt
# 构造数据子集
def get_subsample(dataSet, ratio):
subdataSet = []
lenSubdata = round(len(dataSet) * ratio)#返回浮点数
while len(subdataSet) < lenSubdata:
index = randrange(len(dataSet) - 1)
subdataSet.append(dataSet[index])
# print len(subdataSet)
return subdataSet
# 分割数据集
def data_spilt(dataSet, index, value):
left = []
right = []
for row in dataSet:
if row[index] < value:
left.append(row)
else:
right.append(row)
return left, right
# 计算分割代价
def spilt_loss(left, right, class_values):
loss = 0.0
for class_value in class_values:
left_size = len(left)
if left_size != 0: # 防止除数为零
prop = [row[-1] for row in left].count(class_value) / float(left_size)
loss += (prop * (1.0 - prop))
right_size = len(right)
if right_size != 0:
prop = [row[-1] for row in right].count(class_value) / float(right_size)
loss += (prop * (1.0 - prop))
return loss
# 选取任意的n个特征，在这n个特征中，选取分割时的最优特征
def get_best_spilt(dataSet, n_features):
features = []
class_values = list(set(row[-1] for row in dataSet))
b_index, b_value, b_loss, b_left, b_right = 999, 999, 999, None, None
while len(features) < n_features:
index = randrange(len(dataSet[0]) - 1)
if index not in features:
features.append(index)
# print 'features:',features
for index in features:#找到列的最适合做节点的索引，（损失最小）
for row in dataSet:
left, right = data_spilt(dataSet, index, row[index])#以它为节点的，左右分支
loss = spilt_loss(left, right, class_values)
if loss < b_loss:#寻找最小分割代价
b_index, b_value, b_loss, b_left, b_right = index, row[index], loss, left, right
# print b_loss
# print type(b_index)
return {
'index': b_index, 'value': b_value, 'left': b_left, 'right': b_right}
# 决定输出标签
def decide_label(data):
output = [row[-1] for row in data]
return max(set(output), key=output.count)
# 子分割，不断地构建叶节点的过程对对对
def sub_spilt(root, n_features, max_depth, min_size, depth):
left = root['left']
# print left
right = root['right']
del (root['left'])
del (root['right'])
# print depth
if not left or not right:
root['left'] = root['right'] = decide_label(left + right)
# print 'testing'
return
if depth > max_depth:
root['left'] = decide_label(left)
root['right'] = decide_label(right)
return
if len(left) < min_size:
root['left'] = decide_label(left)
else:
root['left'] = get_best_spilt(left, n_features)
# print 'testing_left'
sub_spilt(root['left'], n_features, max_depth, min_size, depth + 1)
if len(right) < min_size:
root['right'] = decide_label(right)
else:
root['right'] = get_best_spilt(right, n_features)
# print 'testing_right'
sub_spilt(root['right'], n_features, max_depth, min_size, depth + 1)
# 构造决策树
def build_tree(dataSet, n_features, max_depth, min_size):
root = get_best_spilt(dataSet, n_features)
sub_spilt(root, n_features, max_depth, min_size, 1)
return root
# 预测测试集结果
def predict(tree, row):
predictions = []
if row[tree['index']] < tree['value']:
if isinstance(tree['left'], dict):
return predict(tree['left'], row)
else:
return tree['left']
else:
if isinstance(tree['right'], dict):
return predict(tree['right'], row)
else:
return tree['right']
# predictions=set(predictions)
def bagging_predict(trees, row):
predictions = [predict(tree, row) for tree in trees]
return max(set(predictions), key=predictions.count)
# 创建随机森林
def random_forest(train, test, ratio, n_feature, max_depth, min_size, n_trees):
trees = []
for i in range(n_trees):
train = get_subsample(train, ratio)#从切割的数据集中选取子集
tree = build_tree(train, n_features, max_depth, min_size)
# print 'tree %d: '%i,tree
trees.append(tree)
# predict_values = [predict(trees,row) for row in test]
predict_values = [bagging_predict(trees, row) for row in test]
return predict_values
# 计算准确率
def accuracy(predict_values, actual):
correct = 0
for i in range(len(actual)):
if actual[i] == predict_values[i]:
correct += 1
return correct / float(len(actual))
if __name__ == '__main__':
seed(1)
dataSet = loadCSV('sonar-all-data.csv')
column_to_float(dataSet)#dataSet
n_folds = 5
max_depth = 15
min_size = 1
ratio = 1.0
# n_features=sqrt(len(dataSet)-1)
n_features = 15
n_trees = 10
folds = spiltDataSet(dataSet, n_folds)#先是切割数据集
scores = []
for fold in folds:
train_set = folds[
:] # 此处不能简单地用train_set=folds，这样用属于引用,那么当train_set的值改变的时候，folds的值也会改变，所以要用复制的形式。（L[:]）能够复制序列，D.copy() 能够复制字典，list能够生成拷贝 list(L)
train_set.remove(fold)#选好训练集
# print len(folds)
train_set = sum(train_set, []) # 将多个fold列表组合成一个train_set列表
# print len(train_set)
test_set = []
for row in fold:
row_copy = list(row)
row_copy[-1] = None
test_set.append(row_copy)
# for row in test_set:
# print row[-1]
actual = [row[-1] for row in fold]
predict_values = random_forest(train_set, test_set, ratio, n_features, max_depth, min_size, n_trees)
accur = accuracy(predict_values, actual)
scores.append(accur)
print ('Trees is %d' % n_trees)
print ('scores:%s' % scores)
print ('mean score:%s' % (sum(scores) / float(len(scores))))