sklearn聚集聚类链接矩阵

Presian Abarov:

我试图绘制一个完整链接scipy.cluster.hierarchy.dendrogram,但发现它scipy.cluster.hierarchy.linkage比慢sklearn.AgglomerativeClustering

但是,sklearn.AgglomerativeClustering不会返回聚类之间的距离和所需的原始观测值的数量scipy.cluster.hierarchy.dendrogram有办法带走他们吗?

Arjan Groen:

我做了一个步骤,无需修改sklearn和递归函数。使用前请注意:

  • 合并距离有时会相对于子级合并距离减小。我添加了三种处理这些情况的方法:采取最大,不采取任何措施或以l2范数增加。l2规范逻辑尚未验证。请检查自己最适合您的。

导入软件包:

from sklearn.cluster import AgglomerativeClustering
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.hierarchy import dendrogram

计算权重和距离的功能:

def get_distances(X,model,mode='l2'):
    distances = []
    weights = []
    children=model.children_
    dims = (X.shape[1],1)
    distCache = {}
    weightCache = {}
    for childs in children:
        c1 = X[childs[0]].reshape(dims)
        c2 = X[childs[1]].reshape(dims)
        c1Dist = 0
        c1W = 1
        c2Dist = 0
        c2W = 1
        if childs[0] in distCache.keys():
            c1Dist = distCache[childs[0]]
            c1W = weightCache[childs[0]]
        if childs[1] in distCache.keys():
            c2Dist = distCache[childs[1]]
            c2W = weightCache[childs[1]]
        d = np.linalg.norm(c1-c2)
        cc = ((c1W*c1)+(c2W*c2))/(c1W+c2W)

        X = np.vstack((X,cc.T))

        newChild_id = X.shape[0]-1

        # How to deal with a higher level cluster merge with lower distance:
        if mode=='l2':  # Increase the higher level cluster size suing an l2 norm
            added_dist = (c1Dist**2+c2Dist**2)**0.5 
            dNew = (d**2 + added_dist**2)**0.5
        elif mode == 'max':  # If the previrous clusters had higher distance, use that one
            dNew = max(d,c1Dist,c2Dist)
        elif mode == 'actual':  # Plot the actual distance.
            dNew = d


        wNew = (c1W + c2W)
        distCache[newChild_id] = dNew
        weightCache[newChild_id] = wNew

        distances.append(dNew)
        weights.append( wNew)
    return distances, weights

使用2个子群集制作2个群集的样本数据:

# Make 4 distributions, two of which form a bigger cluster
X1_1 = np.random.randn(25,2)+[8,1.5]
X1_2 = np.random.randn(25,2)+[8,-1.5]
X2_1 = np.random.randn(25,2)-[8,3]
X2_2 = np.random.randn(25,2)-[8,-3]

# Merge the four distributions
X = np.vstack([X1_1,X1_2,X2_1,X2_2])

# Plot the clusters
colors = ['r']*25 + ['b']*25 + ['g']*25 + ['y']*25
plt.scatter(X[:,0],X[:,1],c=colors)

样本数据:

聚类样本数据

拟合聚类模型

model = AgglomerativeClustering(n_clusters=2,linkage="ward")
model.fit(X)

调用该函数以查找距离,并将其传递给树状图

distance, weight = get_distances(X,model)
linkage_matrix = np.column_stack([model.children_, distance, weight]).astype(float)
plt.figure(figsize=(20,10))
dendrogram(linkage_matrix)
plt.show()

Ouput树状图: 在此处输入图片说明

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