sklearn聚集聚类链接矩阵

问题描述

我正在尝试绘制完整链接

调整聚类模型

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

调用该函数查找距离，并将其传递到树状图

 距离，权重= get_distances（X，model）
 links_matrix = np.column_stack（[model.children_，距离，权重] ）.astype（float）
 plt.figure（figsize =（20,10））
树状图（linkage_matrix）
 plt.show（）
  

输出树状图：

I'm trying to draw a complete-link scipy.cluster.hierarchy.dendrogram, and I found that scipy.cluster.hierarchy.linkage is slower than sklearn.AgglomerativeClustering.

However, sklearn.AgglomerativeClustering doesn't return the distance between clusters and the number of original observations, which scipy.cluster.hierarchy.dendrogram needs. Is there a way to take them?

解决方案

I made a scipt to do it without modifying sklearn and without recursive functions. Before using note that:

• Merge distance can sometimes decrease with respect to the childrenmerge distance. I added three ways to handle those cases: Take themax, do nothing or increase with the l2 norm. The l2 norm logic has not been verified yet. Please check yourself what suits you best.

Import the packages:

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

Function to compute weights and distances:

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

Make sample data of 2 clusters with 2 subclusters:

# 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)

Sample data:

Fit the clustering model

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

Call the function to find the distances, and pass it to the dendogram

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 dendogram:

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