深度森林(gcforest)原理讲解以及代码实现

GcForest原理

gcforest采用Cascade结构，也就是多层级结构，每层由四个随机森林组成，两个随机森林和两个极端森林，每个极端森林包含1000(超参数)个完全随机树，每个森林都会对的数据进行训练，每个森林都输出结果，我们把这个结果叫做森林生成的类向量，为了避免过拟合，我们喂给每个森林训练的数据都是通过k折交叉验证的，每一层最后生成四个类向量，下一层以上一层的四个类向量以及原有的数据为新的train data进行训练，如此叠加，最后一层将类向量进行平均，作为预测结果

个人认为这种结构非常类似于神经网络，神经网络的每个单位是神经元，而深度森林的单位元却是随机森林，单个随机森林在性能上强于单个神经元的，这就是使得深度森林很多时候尽管层级和基础森林树不多，也能取得好的结果的主要原因

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GcForest代码实现原理图

我们需要做出一个layer的结构，每个layer由二种四个森林组成

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每个layer都输出两个结果：每个森林的预测结果、四个森林的预测的平均结果

为了防止过拟合我们的layer都由k折交叉验证产生

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同时为了保留数据全部的特征我们将得到的小layer叠在一起定义一个Biger Layer

之后我们就可以构建深度森林了

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GcForest代码

layer.py

extraTree(极端树)使用的所有的样本，只是特征是随机选取的，因为分裂是随机的，所以在某种程度上比随机森林得到的结果更加好

from sklearn.ensemble import ExtraTreesRegressor#引入极端森林回归
from sklearn.ensemble import RandomForestRegressor#引入随机森林回归
import numpy as npclass Layer:#定义层类def __init__(self, n_estimators, num_forests, max_depth=30, min_samples_leaf=1):self.num_forests = num_forests  # 定义森林数self.n_estimators = n_estimators  # 每个森林的树个数self.max_depth = max_depth#每一颗树的最大深度self.min_samples_leaf = min_samples_leaf#树会生长到所有叶子都分到一个类，或者某节点所代表的样本数已小于min_samples_leafself.model = []#最后产生的类向量def train(self, train_data, train_label, weight, val_data):#训练函数val_prob = np.zeros([self.num_forests, val_data.shape[0]])#定义出该层的类向量，有self.num_forersts行，val_data.shape[0]列，这里我们认为val_data应该就是我们的weightfor forest_index in range(self.num_forests):#对具体的layer内的森林进行构建if forest_index % 2 == 0:#如果是第偶数个设为随机森林clf = RandomForestRegressor(n_estimators=self.n_estimators#子树的个数,n_jobs=-1, #cpu并行树，-1表示和cpu的核数相同max_depth=self.max_depth,#最大深度min_samples_leaf=self.min_samples_leaf)clf.fit(train_data, train_label, weight)#weight是取样比重Sample weightsval_prob[forest_index, :] = clf.predict(val_data)#记录类向量else:#如果是第奇数个就设为极端森林clf = ExtraTreesRegressor(n_estimators=self.n_estimators,#森林所含树的个数n_jobs=-1, #并行数max_depth=self.max_depth,#最大深度min_samples_leaf=self.min_samples_leaf)#最小叶子限制clf.fit(train_data, train_label, weight)val_prob[forest_index, :] = clf.predict(val_data)#记录类向量self.model.append(clf)#组建layer层val_avg = np.sum(val_prob, axis=0)#按列进行求和val_avg /= self.num_forests#求平均val_concatenate = val_prob.transpose((1, 0))#对记录的类向量矩阵进行转置return [val_avg, val_concatenate]#返回平均结果和转置后的类向量矩阵def predict(self, test_data):#定义预测函数，也是最后一层的功能predict_prob = np.zeros([self.num_forests, test_data.shape[0]])for forest_index, clf in enumerate(self.model):predict_prob[forest_index, :] = clf.predict(test_data)predict_avg = np.sum(predict_prob, axis=0)predict_avg /= self.num_forestspredict_concatenate = predict_prob.transpose((1, 0))return [predict_avg, predict_concatenate]class KfoldWarpper:#定义每个树进行训练的所用的数据def __init__(self, num_forests, n_estimators, n_fold, kf, layer_index, max_depth=31, min_samples_leaf=1):#包括森林树，森林使用树的个数，k折的个数，k-折交叉验证，第几层，最大深度，最小叶子节点限制self.num_forests = num_forestsself.n_estimators = n_estimatorsself.n_fold = n_foldself.kf = kfself.layer_index = layer_indexself.max_depth = max_depthself.min_samples_leaf = min_samples_leafself.model = []def train(self, train_data, train_label, weight):num_samples, num_features = train_data.shapeval_prob = np.empty([num_samples])val_prob_concatenate = np.empty([num_samples, self.num_forests])#创建新的空矩阵，num_samples行，num_forest列，用于放置预测结果for train_index, test_index in self.kf:#进行k折交叉验证，在train_data里创建交叉验证的补充X_train = train_data[train_index, :]#选出训练集X_val = train_data[test_index, :]#验证集y_train = train_label[train_index]#训练标签weight_train = weight[train_index]#训练集对应的权重layer = Layer(self.n_estimators, self.num_forests, self.max_depth, self.min_samples_leaf)#加入层val_prob[test_index], val_prob_concatenate[test_index, :] = \layer.train(X_train, y_train, weight_train, X_val)#记录输出的结果self.model.append(layer)#在模型中填充层级，这也是导致程序吃资源的部分，每次进行return [val_prob, val_prob_concatenate]def predict(self, test_data):#定义预测函数，用做下一层的训练数据test_prob = np.zeros([test_data.shape[0]])test_prob_concatenate = np.zeros([test_data.shape[0], self.num_forests])for layer in self.model:temp_prob, temp_prob_concatenate = \layer.predict(test_data)test_prob += temp_probtest_prob_concatenate += temp_prob_concatenatetest_prob /= self.n_foldtest_prob_concatenate /= self.n_foldreturn [test_prob, test_prob_concatenate]

gcforest.py

from sklearn.model_selection import KFold
from layer import *
import numpy as npdef compute_loss(target, predict):#对数误差函数temp = np.log(abs(target + 1)) - np.log(abs(predict + 1))res = np.dot(temp, temp) / len(temp)#向量点成后平均return resclass gcForest:#定义gcforest模型def __init__(self, num_estimator, num_forests, max_layer=2, max_depth=31, n_fold=5):self.num_estimator = num_estimatorself.num_forests = num_forestsself.n_fold = n_foldself.max_depth = max_depthself.max_layer = max_layerself.model = []def train(self,train_data, train_label, weight):num_samples, num_features = train_data.shape# basis processtrain_data_new = train_data.copy()# return valueval_p = []best_train_loss = 0.0layer_index = 0best_layer_index = 0bad = 0kf = KFold(2,True,self.n_fold).split(train_data_new.shape[0])
#这里加入k折交叉验证while layer_index < self.max_layer:print("layer " + str(layer_index))layer = KfoldWarpper(self.num_forests, self.num_estimator, self.n_fold, kf, layer_index, self.max_depth, 1)#其实这一个layer是个夹心layer，是2层layer的平均结果val_prob, val_stack= layer.train(train_data_new, train_label, weight)#使用该层进行训练	train_data_new = np.concatenate([train_data, val_stack], axis=1)#将该层的训练结果也加入到train_data中temp_val_loss = compute_loss(train_label, val_prob)print("val   loss:" + str(temp_val_loss))if best_train_loss < temp_val_loss:#用于控制加入的层数，如果加入的层数较多，且误差没有下降也停止运行bad += 1else:bad = 0best_train_loss = temp_val_lossbest_layer_index = layer_indexif bad >= 3:breaklayer_index = layer_index + 1self.model.append(layer)for index in range(len(self.model), best_layer_index + 1, -1):#删除多余的layerself.model.pop()def predict(self, test_data):test_data_new = test_data.copy()test_prob = []for layer in self.model:predict, test_stack = layer.predict(test_data_new)test_data_new = np.concatenate([test_data, test_stack], axis=1)return predict

test.py

import numpy as np
from gcForest import *
from time import timedef load_data():train_data = np.load()  train_label = np.load()train_weight = np.load()test_data = np.load()test_label = np.load()test_file = np.load() return [train_data, train_label, train_weight, test_data, test_label, test_file]if __name__ == '__main__':train_data, train_label, train_weight, test_data, test_label, test_file = load_data()clf = gcForest(num_estimator = 100, num_forests = 4, max_layer=2, max_depth=100, n_fold=5)start = time()clf.train(train_data, train_label, train_weight)end = time()print("fitting time: " + str(end - start) + " sec")start = time()prediction = clf.predict(test_data)end = time()print("prediction time: " + str(end - start) + " sec")result = {}for index, item in enumerate(test_file):if item not in result:result[item] = prediction[index]else:result[item] = (result[item] + prediction[index]) / 2print(result)