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k means， kernel k means and hierarchical clustering

admin 11月 13, 2020 0

In this post, K means algorithm, Kernel K means and Hierarchical Clustering were implemented by Python. 

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import numpy as np
from matplotlib import cm
import matplotlib.pyplot as plt
%matplotlib inline

Data Generator

Gaussian Data Generator

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def ():
    nPerClass = 200
    mu0 = np.array([-1, -2])
    mu1 = np.array([1, 1])
    Sigma0 = np.array([[1, 0.8],[0.8, 1]])*0.5
    Sigma1 = np.array([[1, 0.8], [0.8, 1]])*0.6
    X1 = np.random.multivariate_normal(mu0, Sigma0, nPerClass)
    X2 = np.random.multivariate_normal(mu1, Sigma1, nPerClass)
    X_out = np.concatenate((X1, X2))
    return X_out

Ring Data Generator

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def generateRingData():
    nPerClass = 50;
    rMax = 10;
    rMin = 2;
    X = np.random.rand(nPerClass*20, 2)*rMax*2 - rMax
    keepers = np.logical_and(np.linalg.norm(X, axis=1)>2, np.linalg.norm(X, axis=1)<4)
    X1 = X[keepers, :]
    keepers = np.logical_and(np.linalg.norm(X, axis=1)>8, np.linalg.norm(X, axis=1)<10)
    X2 = X[keepers, :]
    X_out = np.concatenate((X1, X2))
    return X_out

Spiral Data Generator

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def wrapTopi(x):
    xwrap=np.remainder(x, 2*np.pi)
    mask = np.abs(xwrap)>np.pi
    xwrap[mask] -= 2*np.pi * np.sign(xwrap[mask])
    return xwrap

def generateSpiralData():
    nPerClass = 100;
    rMax = 10;
    rMin = 2;
    X = np.random.rand(nPerClass*20, 2)*rMax*2 - rMax
    r = np.sqrt(np.sum(X**2, axis=1))
    theta = np.arctan2(X[:,1], X[:,0])
    a = 1;
    b = 1;
    thetaHat = (r-a)/b;
    keepers = np.logical_and(np.abs(theta - wrapTopi(thetaHat))<0.7, r < rMax, r > rMin)
    X1 = X[keepers,:]
    a = 4;
    b = 1;
    thetaHat = (r-a)/b;
    keepers = np.logical_and(np.abs(theta - wrapTopi(thetaHat))<0.7, r < rMax, r > rMin)
    X2 = X[keepers,:]
    X_out = np.concatenate((X1, X2))
    point_filter = np.linalg.norm(X_out, axis=1)>rMin
    X_out = X_out[point_filter]
    return X_out
 
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def Standardized(data):
    feature_mean = np.mean(data, axis=0)
    feature_std = np.std(data, axis=0)
    data = (data - feature_mean)/feature_std
    return data
 
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data1 = generateGaussianData()
data2 = generateRingData()
data3 = generateSpiralData()
 
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data1 = Standardized(data1)
data2 = Standardized(data2)
data3 = Standardized(data3)
 
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f, (ax1, ax2, ax3) = plt.subplots(1, 3,figsize=(13,4))
ax1.grid(True)
x = data1[:,0].tolist()
y = data1[:,1].tolist()
ax1.scatter(x, y, marker='o', linewidths=0.001)

ax2.grid(True)
x = data2[:,0].tolist()
y = data2[:,1].tolist()
ax2.scatter(x, y, marker='o', linewidths=0.001)

ax3.grid(True)
x = data3[:,0].tolist()
y = data3[:,1].tolist()
ax3.scatter(x, y, marker='o', linewidths=0.001)

plt.show()

png

K means

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def EuclideanDistance(p1, p2):
    return np.linalg.norm(p1-p2)
def Kmeans(data, k, epoch):
    n, p = data.shape
    c_idx = np.random.choice(n, k, replace=False)
    centers = data[c_idx]
    clusters = {}
    epoch_count = epoch
    diff_sum = np.linalg.norm(centers)   
    while diff_sum > 0.0000001 and epoch_count > 0:
        
        epoch_count -= 1
        for i in range(k):
            clusters['cluster_'+str(i)] = []
        for i in range(n):
            distances = np.zeros(k)
            for j in range(k):
                distances[j] = EuclideanDistance(data[i], centers[j])
            min_idx = np.argmin(distances)
            clusters['cluster_'+str(min_idx)].append(i)
        diff_sum = 0
        for j in range(k):
            new_cent = np.mean(data[clusters['cluster_'+str(j)]], axis=0)
            diff_sum += np.linalg.norm(centers[j] - new_cent) 
            centers[j] = new_cent ## update center     
    return centers, clusters
 
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centers1, clusters1 = Kmeans(data1, 2, 10000)
centers2, clusters2 = Kmeans(data2, 2, 10000)
centers3, clusters3 = Kmeans(data3, 2, 10000)
 
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plt.figure(figsize=(14,4))
plt.subplot(131)
plt.grid(True)   
colors = cm.rainbow(np.linspace(0, 1, 2))
## Gaunssian
for j in range(2):
    cent = centers1[j]
    plt.scatter(cent[0], cent[1], marker='x', c=colors[j], s=100)
    points = data1[clusters1['cluster_'+str(j)]]
    plt.scatter(points[:, 0].tolist(), points[:,1].tolist(), c=colors[j], label='cls '+str(j))
plt.legend(loc='lower right')
plt.title('Gaussian Data:K means')
## Ring    
plt.subplot(132)
plt.grid(True)   
for j in range(2):
    cent = centers2[j]
    plt.scatter(cent[0], cent[1], marker='x', c=colors[j], s=100)
    points = data2[clusters2['cluster_'+str(j)]]
    plt.scatter(points[:, 0].tolist(), points[:,1].tolist(), c=colors[j], label='cls '+str(j))
plt.legend(loc='lower right')
plt.title('Ring data:K means')

## Spiral
plt.subplot(133)
plt.grid(True)   
for j in range(2):
    cent = centers3[j]
    plt.scatter(cent[0], cent[1], marker='x', c=colors[j], s=100)
    points = data3[clusters3['cluster_'+str(j)]]
    plt.scatter(points[:, 0].tolist(), points[:,1].tolist(), c=colors[j], label='cls '+str(j))
plt.legend(loc='lower right')
plt.title('Spiral Data: K means')

plt.show()

png

Hierarchical Clustering

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import heapq
class PriorityQueue:
    def  __init__(self):
        self.heap = []
        self.count = 0
    def push(self, item, priority):
        entry = (priority, item)
        heapq.heappush(self.heap, entry)
        self.count += 1
    def pop(self):
        (_, item) = heapq.heappop(self.heap)
        self.count -= 1
        return item
    def isEmpty(self):
        return len(self.heap) == 0
    
class Pair:
    def __init__(self, cluster1, cluster2, dist):
        self.cluster1 = cluster1 # a list
        self.cluster2 = cluster2 # a list
        self.pair_dist = dist
    def __str__(self):
        return "cluster1: " + str(self.cluster1) + "n" + "cluster2: " + str(self.cluster2) + "n" + "dist: " + str(self.pair_dist)
    
def computeMinDist(cluster1, cluster2, distmap):
    sub_distmap = distmap[cluster1,:][:, cluster2]
    return np.min(sub_distmap)
 
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def HierarchicalCluster(data, k):
    n, p = data.shape
    clustersSet = []
    ## input all individual points as clusters 
    for i in range(n):
        clustersSet.append([i])
    ## initial a heap to sort distance between pair of clusters 
    queue = PriorityQueue()
    ## compute a distance map
    distmap = np.zeros((n, n))
    for i in range(n-1):
        for j in range(i+1, n):
            dist = np.linalg.norm(data[i]- data[j])
            distmap[i][j] = dist
            distmap[j][i] = dist
    ## push all pair in the lowest level into a heap
    for i in range(n-1):
        for j in range(i+1, n):
            dist = distmap[i][j]
            pair = Pair([i], [j], dist)
            queue.push(pair, dist)
    ##
    deletedCluster = []
    while(len(clustersSet) > k):
        pair = queue.pop()
        if pair.cluster1 in deletedCluster or pair.cluster2 in deletedCluster:
            continue
        new_cluster = pair.cluster1 + pair.cluster2 ## merge two cluster
        clustersSet.remove(pair.cluster1)
        clustersSet.remove(pair.cluster2)
        deletedCluster.append(pair.cluster1)
        deletedCluster.append(pair.cluster2)
        for cluster_t in clustersSet:
            dist_t = computeMinDist(new_cluster, cluster_t, distmap)
            pair_t = Pair(new_cluster, cluster_t, dist_t)
            queue.push(pair_t, dist_t) 
        clustersSet.append(new_cluster)
    return clustersSet
 
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clusters1 = HierarchicalCluster(data1, 2)
clusters2 = HierarchicalCluster(data2, 2)
clusters3 = HierarchicalCluster(data3, 2)
 
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plt.figure(figsize=(14,4))
plt.subplot(131)
plt.grid(True)   
colors = cm.rainbow(np.linspace(0, 1, 2))
## Gaunssian
for j in range(2):
    points = data1[clusters1[j]]
    plt.scatter(points[:, 0].tolist(), points[:,1].tolist(), c=colors[j], label='cls '+str(j))
plt.legend(loc='lower right')
plt.title('Gaussian Data:Hierarchical Clustering')
## Ring    
plt.subplot(132)
plt.grid(True)   
for j in range(2):
    points = data2[clusters2[j]]
    plt.scatter(points[:, 0].tolist(), points[:,1].tolist(), c=colors[j], label='cls '+str(j))
plt.legend(loc='lower right')
plt.title('Ring data:Hierarchical Clustering')

## Spiral
plt.subplot(133)
plt.grid(True)   
for j in range(2):
    points = data3[clusters3[j]]
    plt.scatter(points[:, 0].tolist(), points[:,1].tolist(), c=colors[j], label='cls '+str(j))
plt.legend(loc='lower right')
plt.title('Spiral Data: Hierarchical Clustering')

plt.show()

png

Kernel K means

The implementation of K means algorithms with Kernel is shown as the code below. For a valid Kernel, it is an inner product of the data in some Reproducing Kernel Hilbert Space.

The distance of $phi(x_1)$ and $phi(x_2)$ can be defined as $||phi(x_1) - phi(x_2)||^2_2$ using the square of L2 distance.

The center of a cluster $pi_k$is as below, $|pi_k|$ is the number of points in this cluster, $c_k$ is the center of this cluster.

For a point $x_j$, its distance to a cluster center is

From the equations above, we know that we can use K means in a RKHS space without knowing how the data is mapped to that space. Only know the inner product $K(x_i, x_j)$ is enough to implement the Kernel K means algorithms. 

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def KernelKmeans(data, k, epoch, kernel):
    n, p = data.shape
    c_idx = np.random.choice(n, k, replace=False)
    clusters = {}
    epoch_count = 10000
    GramMatrix = np.zeros((n, n))
    ## initialization
    for i in range(n):
        for j in range(i, n):
            GramMatrix[i][j] = kernel(data[i], data[j])
            GramMatrix[j][i] = GramMatrix[i][j]
    for i in range(k):
        clusters['cluster_'+str(i)] = []
        clusters['cluster_'+str(i)].append(c_idx[i])
        
    diff_sum = 1
    while diff_sum > 0.000:
        epoch_count -= 1
        ## use all points in a cluster to update
        new_cluster = [[] for i in range(k)]
        for i in range(n):
            distances = np.zeros(k)
            for j in range(k):
                ## we do not know the center in that high feature space
                term1 = GramMatrix[i][i]
                points = clusters['cluster_'+str(j)]
                size = len(points)
                term2 = 2* np.sum(GramMatrix[i][points]) / size
                term3 = np.sum(GramMatrix[points, :][:, points])/(size**2)
                distances[j] = term1 - term2 + term3
            min_idx = np.argmin(distances)
            new_cluster[min_idx].append(i)
        diff_sum = 0
        for j in range(k):
            old_cent = np.mean(data[clusters['cluster_'+str(j)]], axis=0)
            new_cent = np.mean(data[new_cluster[j]], axis=0)
            diff_sum += np.linalg.norm(old_cent - new_cent)
            clusters['cluster_'+str(j)] = new_cluster[j]
        del new_cluster
    return clusters
 
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def RBFKernel(point1, point2, sigma=0.4):
    return np.exp(-np.linalg.norm(point1-point2)**2 / (2 * (sigma** 2)))
def trivialKernel(point1, point2):
    return 2*np.linalg.norm(point1)*np.linalg.norm(point2)
def LinearKernel(point1, point2):
    return np.dot(point1, point2)
def polynomialKernel(point1, point2):
    return (np.dot(point1, point2))**3
def CustomKernel(point1, point2, sigma=0.4):
    return np.exp(-np.linalg.norm(point1**3-point2**3)**2/(2*(sigma**2)))

Ring Data Using Kernel K means

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clusters1 = KernelKmeans(data2, 2, 1000, trivialKernel)
clusters2 = KernelKmeans(data2, 2, 1000, CustomKernel)
 
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f, (ax1, ax2) = plt.subplots(1, 2, figsize=(9,4))
plt.tight_layout()

colors = cm.rainbow(np.linspace(0, 1, 2))
ax1.grid(True)
## Trivial Kernel
for j in range(2):
    points = data2[clusters2['cluster_'+str(j)]]
    ax1.scatter(points[:, 0].tolist(), points[:,1].tolist(), c=colors[j], label='cls '+str(j))
ax1.legend(loc='lower right')
ax1.set_title('Ring Data: K means with low dimensional Kernel')
## Complicated Kernel
ax2.grid(True)   
for j in range(2):
    points = data2[clusters2['cluster_'+str(j)]]
    ax2.scatter(points[:, 0].tolist(), points[:,1].tolist(), c=colors[j], label='cls '+str(j))
ax2.legend(loc='lower right')
ax2.set_title('Ring data:H K means with a Custom RBF Kernel')
plt.show()

png

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