集成学习lgb库调参的粒子群方法

时间 2021-04-24

标签算法 app dom ide 函数学习测试 lua code orm 栏目 HTML 繁體版

原文原文链接

粒子群算法是模拟鸟群蜂群的觅食行为的一种算法。基本思想是经过群体中个体之间的协做和信息共享来寻找最优解。试着想一下一群鸟在寻找食物，在这个区域中只有一只虫子，全部的鸟都不知道食物在哪。可是它们知道本身的当前位置距离食物有多远，同时它们知道离食物最近的鸟的位置。想一下这时候会发生什么？算法

鸟A：哈哈哈原来虫子离我最近！
鸟B,C,D：我得赶忙往 A 那里过去看看！
同时各只鸟在位置不停变化时候离食物的距离也不断变化，因此必定有过离食物最近的位置，这也是它们的一个参考。鸟某某：我刚刚的位置好像靠近了食物，我得往那里靠近！app

公式请自行百度知乎dom

具体代码流程以下：
ide

本文主要描述如何用粒子群方法搜索到一个适合lgb的参数
调整参数通常的步骤以下：函数

*设定基础参数{parm0}，基础评判指标{metrics0}；学习

在训练集上作cross-validation，作训练集/交叉验证集上误差/方差与树棵树的关系图；测试

判断模型是过拟合 or 欠拟合，更新相应参数{parm1}；lua

重复二、3步，肯定树的棵树nestimators；code

采用参数{parm1}、nestimators，训练模型，并应用到测试集；
最好损失函数的评估部分要随机对原数据取样用一半数据去训练而后预测另一半数据使参数向方差变小的方向移动*orm

先要定一个损失函数：

def gini_coef(wealths):
    cum_wealths = np.cumsum(sorted(np.append(wealths, 0)))
    sum_wealths = cum_wealths[-1]
    xarray = np.array(range(0, len(cum_wealths))) / np.float(len(cum_wealths)-1)
    yarray = cum_wealths / sum_wealths
    B = np.trapz(yarray, x=xarray)
    A = 0.5 - B
    return A / (A+B)

固然也能够传入训练数据的标签值和预测值作协方差这里采用基尼系数做为损失函数

定义一个评估函数：用于评估该参数版本的效果如何：

def evaluate(train1 , feature_use,parent):
    np.set_printoptions(suppress=True)
    print("*************************************")
    print(parent)
    model_lgb = lgb.LGBMRegressor(objective='regression',
                                  min_sum_hessian_in_leaf=parent[0],
                                  learning_rate=parent[1],
                                  bagging_fraction=parent[2],
                                  feature_fraction=parent[3],
                                  num_leaves=int(parent[4]),
                                  n_estimators=int(parent[5]),
                                  max_bin=int(parent[6]),
                                  bagging_freq=int(parent[7]),
                                  feature_fraction_seed=int(parent[8]),
                                  min_data_in_leaf=int(parent[9]),
                                  is_unbalance = True
                                  )
    targetme = train1['target']

    X_train, X_test, y_train, y_test = train_test_split(train1[feature_use] , targetme, test_size=0.5)

    model_lgb.fit(X_train.fillna(-1), y_train)
    y_pred = model_lgb.predict(X_test.fillna(-1))
    return gini_coef(y_pred)

参数初始化代码：

## 参数初始化
# 粒子群算法中的两个参数
c1 = 1.49445
c2 = 1.49445
maxgen= 50  # 进化次数
sizepop= 100  # 种群规模

Vmax1=0.1
Vmin1=-0.1

## 产生初始粒子和速度
pop=[]
V = []
fitness =[]
for i in range(sizepop):
    # 随机产生一个种群
    temp_pop =[]
    temp_v = []

    min_sum_hessian_in_leaf = random.random()
    temp_pop.append(min_sum_hessian_in_leaf)
    temp_v.append(random.random())

    learning_rate = random.uniform(0.001,0.2)
    temp_pop.append(learning_rate)
    temp_v.append(random.random())

    bagging_fraction = random.uniform(0.5,1)
    temp_pop.append(bagging_fraction)
    temp_v.append(random.random())

    feature_fraction = random.uniform(0.3,1)
    temp_pop.append(feature_fraction)
    temp_v.append(random.random())

    num_leaves = random.randint(3,100)
    temp_pop.append(num_leaves)
    temp_v.append(random.randint(-3,3))

    n_estimators = random.randint(800,1200)
    temp_pop.append(n_estimators)
    temp_v.append(random.randint(-3,3))

    max_bin = random.randint(100,500)
    temp_pop.append(max_bin)
    temp_v.append(random.randint(-3,3))

    bagging_freq = random.randint(1,10)
    temp_pop.append(bagging_freq)
    temp_v.append(random.randint(-3,3))

    feature_fraction_seed = random.randint(1,10)
    temp_pop.append(feature_fraction_seed)
    temp_v.append(random.randint(-3,3))

    min_data_in_leaf = random.randint(1,20)
    temp_pop.append(min_data_in_leaf)
    temp_v.append(random.randint(-3,3))

    pop.append(temp_pop) # 初始种群
    V.append(temp_v) # 初始化速度
    # 计算适应度
    fitness.append(evaluate(train1,feature_use ,temp_pop))  # 染色体的适应度  end

pop = np.array(pop)
V = np.array(V)

# 个体极值和群体极值
bestfitness =min(fitness)
bestIndex = fitness.index(bestfitness)
zbest=pop[bestIndex,:]   #全局最佳
gbest=pop               #个体最佳
fitnessgbest=fitness   #个体最佳适应度值
fitnesszbest=bestfitness   #全局最佳适应度值

开始迭代寻优：

count = 0
## 迭代寻优
for i in range(maxgen):
    for j in range(sizepop):
        count = count + 1
        print(count)
        # 速度更新
        V[j,:] = V[j,:] + c1 * random.random() * (gbest[j,:] - pop[j,:]) + c2 * random.random() * (zbest - pop[j,:])
        if(V[j,0]<-0.1):
            V[j,0]=-0.1
        if(V[j,0]>0.1):
            V[j,0]=0.1
        if(V[j,1]<-0.02):
            V[j,1]=-0.02
        if(V[j,1]>0.02):
            V[j,1]=0.02
        if(V[j,2]<-0.1):
            V[j,2]=-0.1
        if(V[j,2]>0.1):
            V[j,2]=0.1
        if(V[j,3]<-0.1):
            V[j,3]=-0.1
        if(V[j,3]>0.1):
            V[j,3]=0.1
        if(V[j,4]<-2):
            V[j,4]=-2
        if(V[j,4]>2):
            V[j,4]=2
        if(V[j,5]<-10):
            V[j,5]=-10
        if(V[j,5]>10):
            V[j,5]=10
        if(V[j,6]<-5):
            V[j,6]=-5
        if(V[j,6]>5):
            V[j,6]=5
        if(V[j,7]<-1):
            V[j,7]=-1
        if(V[j,7]>1):
            V[j,7]=1
        if(V[j,8]<-1):
            V[j,8]=-1
        if(V[j,8]>1):
            V[j,8]=1
        if(V[j,9]<-1):
            V[j,9]=-1
        if(V[j,9]>1):
            V[j,9]=1

        pop[j,:]=pop[j,:]+0.5*V[j,:]

        if(pop[j,0]<0):
            pop[j,0]=0.001
        if (pop[j, 0] > 1):
            pop[j, 0] = 0.9

        if (pop[j, 1] < 0):
            pop[j, 1] = 0.001
        if (pop[j, 1] > 0.2):
            pop[j, 1] = 0.2

        if (pop[j, 2] < 0.5):
            pop[j, 2] = 0.5
        if (pop[j, 2] > 1):
            pop[j, 2] = 1

        if (pop[j, 3] < 0.3):
            pop[j, 3] = 0.3
        if (pop[j, 3] > 1):
            pop[j, 3] = 1

        if (pop[j, 4] < 3):
            pop[j, 4] =3
        if (pop[j, 4] > 100):
            pop[j, 4] = 100

        if (pop[j, 5] < 800):
            pop[j, 5] = 800
        if (pop[j, 5] > 1200):
            pop[j, 5] = 1200

        if (pop[j, 6] < 100):
            pop[j, 6] = 100
        if (pop[j, 6] > 500):
            pop[j, 6] = 500

        if (pop[j, 7] < 1):
            pop[j, 7] = 1
        if (pop[j, 7] > 10):
            pop[j, 7] = 10

        if (pop[j, 8] < 1):
            pop[j, 8] = 1
        if (pop[j, 8] > 10):
            pop[j, 8] = 10

        if (pop[j, 9] < 1):
            pop[j, 9] = 1
        if (pop[j, 9] > 20):
            pop[j, 9] = 20
        fitness[j] = evaluate(train1,feature_use,pop[j,:])

    for k in range(1,sizepop):
        if(fitness[k] > fitnessgbest[k]):
            gbest[k,:] = pop[k,:]
            fitnessgbest[k] = fitness[k]

        #群体最优更新
        if fitness[k] > fitnesszbest:
            zbest = pop[k,:]
            fitnesszbest = fitness[k]

载入参数进行预测：

# 采用lgb回归预测模型，具体参数设置以下
model_lgb = lgb.LGBMRegressor(objective='regression',
                              min_sum_hessian_in_leaf=zbest[0],
                              learning_rate=zbest[1],
                              bagging_fraction=zbest[2],
                              feature_fraction=zbest[3],
                              num_leaves=int(zbest[4]),
                              n_estimators=int(zbest[5]),
                              max_bin=int(zbest[6]),
                              bagging_freq=int(zbest[7]),
                              feature_fraction_seed=int(zbest[8]),
                              min_data_in_leaf=int(zbest[9]),
                              is_unbalance=True)
targetme=train1['target']

model_lgb.fit(train1[feature_use].fillna(-1), train1['target'])
y_pred = model_lgb.predict(test1[feature_use].fillna(-1))
print("lgb success")

上文中的 train是 pandas中的dataframe类型，下图为这个代码运行起来的状况

有技术交流的能够扫描如下