粒子群算法是模拟鸟群蜂群的觅食行为的一种算法。基本思想是通过群体中个体之间的协作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,所有的鸟都不知道食物在哪。但是它们知道自己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?

鸟A:哈哈哈原来虫子离我最近!
鸟B,C,D:我得赶紧往 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,所以一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!

公式请自行百度 知乎

具体代码流程如下:
集成学习lgb库调参的粒子群方法

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
调整参数一般的步骤如下:

*设定基础参数{parm0},基础评判指标{metrics0};

在训练集上做cross-validation,做训练集/交叉验证集上偏差/方差与树棵树的关系图;

判断模型是过拟合 or 欠拟合,更新相应参数{parm1};

重复2、3步,确定树的棵树nestimators;

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 然后预测另外一半数据 使参数向方差变小的方向移动*

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)

当然也可以传入训练数据的标签值 和预测值做协方差 这里采用基尼系数作为损失函数

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,具体参数设置如下
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情况
集成学习lgb库调参的粒子群方法

有技术交流的可以扫描以下
集成学习lgb库调参的粒子群方法