author: Diogo Silva

SKL = SciKit-Learn



In [1]:

    
%pylab inline









    



Populating the interactive namespace from numpy and matplotlib



In [2]:

    
%quickref



In [3]:

    
import seaborn as sns



In [4]:

    
cd ~/QCThesis/









    



/home/chiroptera/workspace/QCThesis



In [5]:

    
from sklearn.cluster import KMeans as KMeans_skl
import MyML.cluster.eac as eac
reload(eac)
import MyML.cluster.K_Means3 as K_Means3
reload(K_Means3)
import MyML.metrics.accuracy as determine_ci
reload(determine_ci)









    Out[5]:





<module 'MyML.metrics.accuracy' from 'MyML/metrics/accuracy.pyc'>

Helper functions



In [6]:

    
def stat_my_kmeans(data,nclusters,gtruth,rounds=20):
    nsamples=data.shape[0]
    all_acc = list()
    for r in xrange(rounds):
        iters="converge"
        kmeans_mode="numpy"

        grouper = K_Means3.K_Means(nclusters=nclusters, iters=iters, mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=300)
        grouper._centroid_mode = "index"
        grouper.fit(data)

        ci=determine_ci.ConsistencyIndex(N=nsamples)
        accuracy=ci.score(gtruth,grouper.labels_,format='array')
        all_acc.append(accuracy)
        
        
    return np.mean(all_acc),np.var(all_acc),np.max(all_acc),np.min(all_acc)
    
def stat_skl_kmeans(data,nclusters,gtruth,rounds=20,init='random'):
    nsamples=data.shape[0]
    all_acc = list()
    for r in xrange(rounds):
        iters="converge"
        kmeans_mode="numpy"

        gSKL = KMeans_skl(n_clusters=nclusters,n_init=1,init=init)
        gSKL.fit(data)

        ci=determine_ci.ConsistencyIndex(N=nsamples)
        accuracy=ci.score(gtruth,gSKL.labels_,format='array')
        all_acc.append(accuracy)
        
        
    return np.mean(all_acc),np.var(all_acc),np.max(all_acc),np.min(all_acc)



In [96]:

    
def k_analysis(partition_files,ground_truth,nprots,iters="converge",rounds=20,files=True):
    nsamples=data.shape[0]
    all_acc = list()
    
    for r in xrange(rounds):
        prot_mode="random"

        estimator=eac.EAC(nsamples)
        estimator.fit(partition_files,files=files,assoc_mode='prot', prot_mode=prot_mode, nprot=nprots,build_only=True)

        kmeans_mode = "numpy"
        nclusters = np.unique(ground_truth).shape[0]

        grouper = K_Means3.K_Means(nclusters=nclusters, iters=iters, mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=300)
        grouper._centroid_mode = "index"
        grouper.fit(estimator._coassoc)

        
        sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)
        sklAcc.score(gt,grouper.labels_,format='array')
        
        all_acc.append(sklAcc.accuracy)
    return np.mean(all_acc),np.var(all_acc),np.max(all_acc),np.min(all_acc)

def k_analysis_better(partition_files,ground_truth,nprots,iters="converge",rounds=20,files=True):
    nsamples=data.shape[0]
    all_acc = list()
    
    for r in xrange(rounds):
        prot_mode="random"

        estimator=eac.EAC(nsamples)
        estimator.fit(partition_files,files=files,assoc_mode='prot', prot_mode=prot_mode, nprot=nprots,build_only=True)

        kmeans_mode = "numpy"
        nclusters = np.unique(ground_truth).shape[0]

        grouper = K_Means3.K_Means(nclusters=nclusters, iters=iters, mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=300)
        grouper._centroid_mode = "iter"
        grouper.fit(estimator._coassoc)

        sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)
        sklAcc.score(gt,grouper.labels_,format='array')
        
        all_acc.append(sklAcc.accuracy)
    return np.mean(all_acc),np.var(all_acc),np.max(all_acc),np.min(all_acc)

def k_skl_analysis(partition_files,ground_truth,nprots,rounds=20,files=True):
    nsamples=data.shape[0]
    all_acc = list()
    
    for r in xrange(rounds):
        prot_mode="random"

        estimator=eac.EAC(nsamples)
        estimator.fit(partition_files,files=files,assoc_mode='prot', prot_mode=prot_mode, nprot=nprots,build_only=True)

        kmeans_mode = "numpy"
        nclusters = np.unique(ground_truth).shape[0]


        grouper = KMeans_skl(n_clusters=nclusters,n_init=1,init="random")
        grouper.fit(estimator._coassoc)
        
        sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)
        sklAcc.score(gt,grouper.labels_,format='array')
        
        all_acc.append(sklAcc.accuracy)
    return np.mean(all_acc),np.var(all_acc),np.max(all_acc),np.min(all_acc)

Generate data



In [8]:

    
center1=(0,0)
center2=(10,10)

cov1=1
cov2=1

n1=100
n2=100
nsamples=n1+n2
dim=2

g1 = np.random.normal(loc=center1,scale=cov1,size=(n1,dim))
g2 = np.random.normal(loc=center2,scale=cov2,size=(n2,dim))

data = np.vstack((g1,g2))
gt=np.zeros(data.shape[0],dtype=np.int32)
gt[100:]=1

figData=plt.figure()
plt.plot(g1[:,0],g1[:,1],'.')
plt.plot(g2[:,0],g2[:,1],'.')









    Out[8]:





[<matplotlib.lines.Line2D at 0x7f6d8ca5e050>]

Generate partitions, k=6,10,[4,25]



In [9]:

    
def formatPartition(partition):
    nclusters=np.unique(partition).shape[0]
    finalPartition=[list() for x in xrange(nclusters)]
    for n,c in partition:
        finalPartition[c].append(n)
        
    return finalPartition

def generatePartitions(data,npartitions,nclusters,iters=3):
    
    if type(nclusters) is list:
        clusterRange = True
        min_ncluster=nclusters[0]
        max_ncluster=nclusters[1]
    else:
        clusterRange = False
        k = nclusters
        
    partitions = list()
    
    mode = "numpy"
    for p in xrange(npartitions):
        if clusterRange:
            k = np.random.randint(min_ncluster,max_ncluster)
        
        grouper = K_Means3.K_Means(nclusters=k, iters=iters, mode=mode, cuda_mem='manual',tol=1e-4,max_iters=300)
        grouper._centroid_mode = "index"
        grouper.fit(data)
        partitions.append(grouper.partition)
        
    return partitions
        
def generatePartitionsSKL(data,npartitions,nclusters,iters=3):
    
    if type(nclusters) is list:
        clusterRange = True
        min_ncluster=nclusters[0]
        max_ncluster=nclusters[1]
    else:
        clusterRange = False
        k = nclusters
        
    partitions = list()
    
    mode = "numpy"
    for p in xrange(npartitions):
        if clusterRange:
            k = np.random.randint(min_ncluster,max_ncluster)
        
        gSKL = KMeans_skl(n_clusters=k,n_init=1,init="random",max_iter=iters)
        gSKL.fit(data)
        partitions.append(formatPartition(gSKL.labels_))
        
    return partitions



In [10]:

    
npartitions=30
iters=3

nclusters=10
partitions_my_10 = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)
partitions_skl_10 = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)

if type(nclusters) is not list:
    allGood=True
    for p in xrange(npartitions):
        if len(partitions_my_10[p]) != nclusters:
            print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_my_10[p]))
            allGood=False
        if len(partitions_skl_10[p]) != nclusters:
            print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_skl_10[p]))
            allGood=False
    if allGood:
        print 'All partitions have good number of clusters.'

nclusters=6
partitions_my_6 = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)
partitions_skl_6 = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)

if type(nclusters) is not list:
    allGood=True
    for p in xrange(npartitions):
        if len(partitions_my_6[p]) != nclusters:
            print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_my_6[p]))
            allGood=False
        if len(partitions_skl_6[p]) != nclusters:
            print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_skl_6[p]))
            allGood=False
    if allGood:
        print 'All partitions have good number of clusters.'

nclusters=[4,25]
partitions_my_rand = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)
partitions_skl_rand = generatePartitions(data=data,npartitions=npartitions,nclusters=nclusters,iters=iters)

if type(nclusters) is not list:
    allGood=True
    for p in xrange(npartitions):
        if len(partitions_my_rand[p]) != nclusters:
            print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_my[p]))
            allGood=False
        if len(partitions_skl_rand[p]) != nclusters:
            print 'partition {} of partitions_my has different number of clusters:{}'.format(p,len(partitions_skl[p]))
            allGood=False
    if allGood:
        print 'All partitions have good number of clusters.'









    



All partitions have good number of clusters.
All partitions have good number of clusters.

EAC K-Means

6 clusters per partition



In [11]:

    
# generate coassoc
prot_mode="random"
assoc_mode='full' # prot or full
nprots=nsamples # number of prototypes

partitions_used = partitions_my_10

myEstimator=eac.EAC(nsamples)
myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)

# final clustering with the true number of clusters
true_nclusters = np.unique(gt).shape[0]

# cluster with my K-Means
kmeans_mode = "numpy"

grouper = K_Means3.K_Means(nclusters=true_nclusters, iters="converge", mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=300)
grouper._centroid_mode = "index"
grouper.fit(myEstimator._coassoc)

# cluster with SKL K-Means
gSKL = KMeans_skl(n_clusters=true_nclusters,n_init=1,init="random")
gSKL.fit(myEstimator._coassoc)

# Hungarian accuracy
myAcc = determine_ci.HungarianIndex(nsamples=nsamples)
myAcc.score(gt,grouper.labels_,format='array')

sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)
sklAcc.score(gt,gSKL.labels_,format='array')


print 'My Accuracy:\t',myAcc.accuracy
print 'SKL Accuracy:\t',sklAcc.accuracy

figEAC=plt.figure(figsize=(16,6))
ax1EAC=figEAC.add_subplot(1,2,1)
ax2EAC=figEAC.add_subplot(1,2,2)

for c in np.unique(grouper.labels_):
    clusterData=grouper.labels_==c
    ax1EAC.plot(data[clusterData,0],data[clusterData,1],'.')
ax1EAC.set_title("Final EAC partition with my K-Means")

for c in np.unique(gSKL.labels_):
    clusterData=gSKL.labels_==c
    ax2EAC.plot(data[clusterData,0],data[clusterData,1],'.')
ax2EAC.set_title("Final EAC partition with SKL's K-Means")









    



My Accuracy:	1.0
SKL Accuracy:	1.0






    



MyML/helper/partition.py:56: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  if clusts == None:






    Out[11]:





<matplotlib.text.Text at 0x7f6d8c036190>



In [12]:

    
def accuracy(labels1,labels2):
    sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)
    sklAcc.score(labels1,labels2,format='array')
    return sklAcc.accuracy



In [13]:

    
def seek_bad_coassoc(data,rounds=20):
    for r in xrange(rounds):
        nclusters = 3

        grouper = K_Means3.K_Means(nclusters=nclusters, iters=iters, mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=300)
        grouper._centroid_mode = "index"
        try:
            grouper.fit(data)
        except:
            print "iterations={}".format(grouper.iters_)
            return estimator
        
    return None



In [19]:

    
test_c=myEstimator._coassoc[[1,1]]



In [20]:

    
myEstimator._coassoc









    Out[20]:





array([[ 30.,   1.,   2., ...,   0.,   0.,   0.],
       [  1.,  30.,   1., ...,   0.,   0.,   0.],
       [  2.,   1.,  30., ...,   0.,   0.,   0.],
       ..., 
       [  0.,   0.,   0., ...,  30.,   0.,   4.],
       [  0.,   0.,   0., ...,   0.,  30.,   8.],
       [  0.,   0.,   0., ...,   4.,   8.,  30.]])



In [21]:

    
gSKL = KMeans_skl(n_clusters=2,n_init=1,init="random",max_iter=1)
gSKL.fit(myEstimator._coassoc)
accuracy(gt,gSKL.labels_)









    Out[21]:





1.0



In [23]:

    
reload(K_Means3)









    Out[23]:





<module 'MyML.cluster.K_Means3' from 'MyML/cluster/K_Means3.pyc'>



In [56]:

    
#%%timeit
grouper = K_Means3.K_Means(nclusters=2, iters="converge", mode="numpy", cuda_mem='manual',tol=1e-4,max_iters=300,centroids="random")
grouper.fit(myEstimator._coassoc)
accuracy(gt,grouper.labels_)









    Out[56]:





1.0



In [88]:

    
grouper = K_Means3.K_Means(nclusters=2, iters="converge", mode="numpy", cuda_mem='manual',tol=1e-5,max_iters=300,centroids="random")
grouper._centroid_mode = "iter"
grouper.fit(myEstimator._coassoc)
accuracy(gt,grouper.labels_)









    Out[88]:





1.0



In [97]:

    
partitions_used=partitions_my_rand
print k_skl_analysis(partitions_used,gt,nsamples,rounds=100,files=False)
print k_analysis_better(partitions_used,gt,nsamples,rounds=100,files=False)
print k_analysis(partitions_used,gt,nsamples,rounds=100,files=False)









    



(1.0, 0.0, 1.0, 1.0)
(1.0, 0.0, 1.0, 1.0)
(1.0, 0.0, 1.0, 1.0)



In [356]:

    
def _np_label_fast(data,centroids):

    N,D = data.shape
    C,cD = centroids.shape

    labels = np.zeros(N,dtype=np.int32)

    # first iteration of all datapoints outside loop
    # distance from points to centroid 0
    best_dist = data - centroids[0]
    best_dist = best_dist ** 2
    best_dist = best_dist.sum(axis=1) 


    for c in xrange(1,C):
        # distance from points to centroid c
        dist = data - centroids[c]
        dist = dist ** 2
        dist = dist.sum(axis=1)

        #thisCluster = np.full(N,c,dtype=np.int32)
        #labels = np.where(dist < bestd_ist,thisCluster,labels)
        labels[dist<best_dist] = c
        best_dist = np.minimum(dist,best_dist)

    return labels



In [358]:

    
labels=_np_label_fast(saved_est._coassoc,test_c)



In [369]:

    
labelChangedIndex = np.where(labels[1:] != labels[:-1])[0] + 1



In [371]:

    
labelChangedIndex.size









    Out[371]:





0



In [277]:

    
bad_est=seek_bad_coassoc(saved_est._coassoc,rounds=10000)
print bad_est == None









    



labels:  [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
centroids:  [[  2.   1.   0.   0.   0.   0.   5.   0.   2.   0.   0.   0.   0.   0.
    1.   0.  21.   1.   0.   0.   0.   0.   1.  17.   0.   0.   2.  16.
    0.  30.   0.   2.   0.   1.   0.   7.   0.   0.   0.   1.   0.   1.
    1.   0.   0.  30.   0.   4.   0.   1.   0.   1.   0.  22.   4.   0.
    0.   5.   3.   8.   0.   0.   4.   0.   1.   0.   0.   0.  13.   3.
    1.   0.   0.   1.   0.   0.   0.   0.   0.   3.   2.   0.  30.   1.
    2.  19.  20.   0.   0.   2.   0.   6.   0.   1.   0.   0.   2.   0.
    1.   0.   0.   0.   0.   0.   0.   8.   0.  21.   0.  14.   1.   0.
    0.   0.   1.   0.   0.   0.   0.   3.   0.   0.   6.   1.   1.   3.
    0.   2.   0.   0.   0.   1.   0.   0.   0.   0.   0.   1.   1.   0.
    0.   3.   5.   0.   1.   0.  23.   0.   0.   1.   0.   0.   0.   2.
   19.  11.   0.   6.   2.   0.   0.   0.   0.   0.   0.   0.   0.   0.
    0.   2.   1.  19.   0.  29.   0.   1.   0.   0.   2.   0.   6.   0.
    1.   9.   0.   0.   0.   0.   1.   0.   0.   5.   0.   2.   0.   0.
    1.   0.   3.   0.]
 [  2.   1.   0.   0.   0.   0.   5.   0.   2.   0.   0.   0.   0.   0.
    1.   0.  21.   1.   0.   0.   0.   0.   1.  17.   0.   0.   2.  16.
    0.  30.   0.   2.   0.   1.   0.   7.   0.   0.   0.   1.   0.   1.
    1.   0.   0.  30.   0.   4.   0.   1.   0.   1.   0.  22.   4.   0.
    0.   5.   3.   8.   0.   0.   4.   0.   1.   0.   0.   0.  13.   3.
    1.   0.   0.   1.   0.   0.   0.   0.   0.   3.   2.   0.  30.   1.
    2.  19.  20.   0.   0.   2.   0.   6.   0.   1.   0.   0.   2.   0.
    1.   0.   0.   0.   0.   0.   0.   8.   0.  21.   0.  14.   1.   0.
    0.   0.   1.   0.   0.   0.   0.   3.   0.   0.   6.   1.   1.   3.
    0.   2.   0.   0.   0.   1.   0.   0.   0.   0.   0.   1.   1.   0.
    0.   3.   5.   0.   1.   0.  23.   0.   0.   1.   0.   0.   0.   2.
   19.  11.   0.   6.   2.   0.   0.   0.   0.   0.   0.   0.   0.   0.
    0.   2.   1.  19.   0.  29.   0.   1.   0.   0.   2.   0.   6.   0.
    1.   9.   0.   0.   0.   0.   1.   0.   0.   5.   0.   2.   0.   0.
    1.   0.   3.   0.]
 [  2.   1.   0.   0.   0.   0.   5.   0.   2.   0.   0.   0.   0.   0.
    1.   0.  21.   1.   0.   0.   0.   0.   1.  17.   0.   0.   2.  16.
    0.  30.   0.   2.   0.   1.   0.   7.   0.   0.   0.   1.   0.   1.
    1.   0.   0.  30.   0.   4.   0.   1.   0.   1.   0.  22.   4.   0.
    0.   5.   3.   8.   0.   0.   4.   0.   1.   0.   0.   0.  13.   3.
    1.   0.   0.   1.   0.   0.   0.   0.   0.   3.   2.   0.  30.   1.
    2.  19.  20.   0.   0.   2.   0.   6.   0.   1.   0.   0.   2.   0.
    1.   0.   0.   0.   0.   0.   0.   8.   0.  21.   0.  14.   1.   0.
    0.   0.   1.   0.   0.   0.   0.   3.   0.   0.   6.   1.   1.   3.
    0.   2.   0.   0.   0.   1.   0.   0.   0.   0.   0.   1.   1.   0.
    0.   3.   5.   0.   1.   0.  23.   0.   0.   1.   0.   0.   0.   2.
   19.  11.   0.   6.   2.   0.   0.   0.   0.   0.   0.   0.   0.   0.
    0.   2.   1.  19.   0.  29.   0.   1.   0.   0.   2.   0.   6.   0.
    1.   9.   0.   0.   0.   0.   1.   0.   0.   5.   0.   2.   0.   0.
    1.   0.   3.   0.]]
centroids equal: True
labelChangedIndex:  []
iterations=1
False

Accuracy is usually 100% in both cases (clustering from my K-Means and SciKit-Learn's). This depends on the ensemble. For some ensembles the accuracy on both is always one, for others it sometimes is not in one or both of the K-Means used (mine vs SKL).

The number of prototypes is equal to the number of samples and since there are not repeated prototypes, all the samples are being used. Above are the visualizations of the solutions.

Statistic analysis



In [107]:

    
stat_nprots=nsamples
print "{}\t{}\t{}\t{}\t{}".format("type","mean","var","max","min")
print "skl \t",
for metric in k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100):
    print "{}\t".format(metric),
print "\nmy  \t",
for metric in k_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,iters="converge",rounds=100):
    print "{}\t".format(metric),









    



type	mean	var	max	min
skl 	0.97345	0.0044723475	1.0	0.76	
my  	0.90525	0.0098911875	1.0	0.715



In [108]:

    
print "\nmy  \t",
for metric in k_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,iters="converge",rounds=100):
    print "{}\t".format(metric),









    



my  	0.90835	0.0090310275	1.0	0.725



In [14]:

    
nprots=[5,20,40,60,80,100,120,140,160,180,200]

results_k10=list()
for n in nprots:
    r=k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100)
    results_k10.append(r)
    
mean_k10=[res[0] for res in results_k10]
var_k10=[res[1] for res in results_k10]
best_k10=[res[2] for res in results_k10]
worst_k10=[res[3] for res in results_k10]

plt.plot(mean_k10,label='mean')
plt.plot(best_k10,label='best')
plt.plot(worst_k10,label='worst')
plt.plot([0, 10], [0.5, 0.5], 'k-', lw=1)
plt.title("Analysis of the influence of the number of prototypes")
plt.legend(loc='best')









    Out[14]:





<matplotlib.legend.Legend at 0x7f568a132d10>

10 clusters per partition



In [15]:

    
partitions_used = partitions_my_10

# generate coassoc
prot_mode="random"
assoc_mode='prot' # prot or full
nprots=nsamples # number of prototypes

myEstimator=eac.EAC(nsamples)
myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)

# final clustering with the true number of clusters
true_nclusters = np.unique(gt).shape[0]

# cluster with my K-Means
kmeans_mode = "numpy"

grouper = K_Means3.K_Means(nclusters=true_nclusters, iters="converge", mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=300)
grouper._centroid_mode = "index"
grouper.fit(myEstimator._coassoc)

# cluster with SKL K-Means
gSKL = KMeans_skl(n_clusters=true_nclusters,n_init=1,init="random")
gSKL.fit(myEstimator._coassoc)

# Hungarian accuracy
myAcc = determine_ci.HungarianIndex(nsamples=nsamples)
myAcc.score(gt,grouper.labels_,format='array')

sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)
sklAcc.score(gt,gSKL.labels_,format='array')


print 'My Accuracy:\t',myAcc.accuracy
print 'SKL Accuracy:\t',sklAcc.accuracy

figEAC2=plt.figure(figsize=(16,12))
ax1EAC2=figEAC2.add_subplot(2,2,1)
ax2EAC2=figEAC2.add_subplot(2,2,2)
ax3EAC2=figEAC2.add_subplot(2,2,3)

for c in np.unique(grouper.labels_):
    clusterData=grouper.labels_==c
    ax1EAC2.plot(data[clusterData,0],data[clusterData,1],'.')
ax1EAC2.set_title("Final EAC partition with my K-Means")

for c in np.unique(gSKL.labels_):
    clusterData=gSKL.labels_==c
    ax2EAC2.plot(data[clusterData,0],data[clusterData,1],'.')
ax2EAC2.set_title("Final EAC partition with SKL's K-Means")

nprots=[5,20,40,60,80,100,120,140,160,180,200]
results_k6=list()
for n in nprots:
    r=k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100)
    results_k6.append(r)
    
mean_k6=[res[0] for res in results_k6]
var_k6=[res[1] for res in results_k6]
best_k6=[res[2] for res in results_k6]
worst_k6=[res[3] for res in results_k6]

ax3EAC2.plot(mean_k6)
ax3EAC2.plot(best_k6)
ax3EAC2.plot(worst_k6)
ax3EAC2.plot([0, 10], [0.5, 0.5], 'k-', lw=1)
ax3EAC2.set_title("Analysis of the influence of the number of prototypes (SKL)")

print "\nStatistical analysis"
stat_nprots=nsamples
print "{}\t{}\t{}\t{}\t{}".format("type","mean","var","max","min")
print "skl \t",
for metric in k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100):
    print "{}\t".format(metric),
print "\nmy  \t",
for metric in k_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,iters="converge",rounds=100):
    print "{}\t".format(metric),









    



My Accuracy:	0.7
SKL Accuracy:	1.0

Statistical analysis
type	mean	var	max	min
skl 	0.93705	0.0112285475	1.0	0.685	
my  	0.87935	0.0097233275	1.0	0.675

Random number of clusters per partition



In [16]:

    
partitions_used = partitions_my_rand

# generate coassoc
prot_mode="random"
assoc_mode='prot' # prot or full
nprots=nsamples # number of prototypes

myEstimator=eac.EAC(nsamples)
myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)

# final clustering with the true number of clusters
true_nclusters = np.unique(gt).shape[0]

# cluster with my K-Means
kmeans_mode = "numpy"

grouper = K_Means3.K_Means(nclusters=true_nclusters, iters="converge", mode=kmeans_mode, cuda_mem='manual',tol=1e-4,max_iters=300)
grouper._centroid_mode = "index"
grouper.fit(myEstimator._coassoc)

# cluster with SKL K-Means
gSKL = KMeans_skl(n_clusters=true_nclusters,n_init=1,init="random")
gSKL.fit(myEstimator._coassoc)

# Hungarian accuracy
myAcc = determine_ci.HungarianIndex(nsamples=nsamples)
myAcc.score(gt,grouper.labels_,format='array')

sklAcc = determine_ci.HungarianIndex(nsamples=nsamples)
sklAcc.score(gt,gSKL.labels_,format='array')


print 'My Accuracy:\t',myAcc.accuracy
print 'SKL Accuracy:\t',sklAcc.accuracy

figEAC2=plt.figure(figsize=(16,12))
ax1EAC2=figEAC2.add_subplot(2,2,1)
ax2EAC2=figEAC2.add_subplot(2,2,2)
ax3EAC2=figEAC2.add_subplot(2,2,3)

for c in np.unique(grouper.labels_):
    clusterData=grouper.labels_==c
    ax1EAC2.plot(data[clusterData,0],data[clusterData,1],'.')
ax1EAC2.set_title("Final EAC partition with my K-Means")

for c in np.unique(gSKL.labels_):
    clusterData=gSKL.labels_==c
    ax2EAC2.plot(data[clusterData,0],data[clusterData,1],'.')
ax2EAC2.set_title("Final EAC partition with SKL's K-Means")

nprots=[5,20,40,60,80,100,120,140,160,180,200]
results_k6=list()
for n in nprots:
    r=k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100)
    results_k6.append(r)
    
mean_k6=[res[0] for res in results_k6]
var_k6=[res[1] for res in results_k6]
best_k6=[res[2] for res in results_k6]
worst_k6=[res[3] for res in results_k6]

ax3EAC2.plot(mean_k6)
ax3EAC2.plot(best_k6)
ax3EAC2.plot(worst_k6)
ax3EAC2.plot([0, 10], [0.5, 0.5], 'k-', lw=1)
ax3EAC2.set_title("Analysis of the influence of the number of prototypes (SKL)")

print "\nStatistical analysis"
stat_nprots=nsamples
print "{}\t{}\t{}\t{}\t{}".format("type","mean","var","max","min")
print "skl \t",
for metric in k_skl_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,rounds=100):
    print "{}\t".format(metric),
print "\nmy  \t",
for metric in k_analysis(partitions_used,files=False,ground_truth=gt,nprots=stat_nprots,iters="converge",rounds=100):
    print "{}\t".format(metric),









    



My Accuracy:	0.905
SKL Accuracy:	1.0

Statistical analysis
type	mean	var	max	min
skl 	0.954	0.008803	1.0	0.695	
my  	0.91935	0.0116068275	1.0	0.675



In [159]:

    
plt.pcolor(myEstimator._coassoc)









    Out[159]:





<matplotlib.collections.PolyCollection at 0x7fdb7ff825d0>

K-Means only



In [18]:

    
stat_nprots=nsamples
print "{}\t{}\t{}\t{}\t{}".format("type","mean","var","max","min")
print "my  \t",
for metric in stat_my_kmeans(data,true_nclusters,gt,rounds=100):
    print "{}\t".format(metric),
print "\nskl \t",
for metric in stat_skl_kmeans(data,true_nclusters,gt,rounds=100):
    print "{}\t".format(metric),









    



type	mean	var	max	min
my  	1.0	0.0	1.0	1.0	
skl 	1.0	0.0	1.0	1.0

EAC K-Medoids



In [19]:

    
import MyML.cluster.KMedoids as KMedoids

6 clusters per partition



In [36]:

    
#%%debug
partitions_used = partitions_my_6

# generate coassoc
prot_mode="random"
assoc_mode='full' # prot or full
nprots=50 # number of prototypes

myEstimator=eac.EAC(nsamples)
myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)

# final clustering with the true number of clusters
true_nclusters = np.unique(gt).shape[0]

# compute diassociation from co-assoc
diassoc=myEstimator._coassoc.max()-myEstimator._coassoc

#k-medoids
labels,medoids=KMedoids.cluster(diassoc,k=true_nclusters)

# Hungarian accuracy
acc = determine_ci.HungarianIndex(nsamples=nsamples)
acc.score(gt,labels,format='array')

print 'K-Medoids Accuracy:\t',acc.accuracy









    



K-Medoids Accuracy:	1.0

Statistical analysis



In [22]:

    
class acc_medoids():
    def __init__(self,data,nclusters,gt):
        self.data=data
        self.nsamples=data.shape[0]
        self.nclusters=nclusters
        self.gt=gt
        
    def run(self):
        labels,medoids=KMedoids.cluster(self.data,k=self.nclusters)
        # Hungarian accuracy
        acc = determine_ci.HungarianIndex(nsamples=self.nsamples)
        acc.score(self.gt,labels,format='array')
        return acc.accuracy
    
class acc_skl_kmeans():
    def __init__(self,data,nclusters,gt):
        self.data=data
        self.nclusters=nclusters
        self.nsamples=data.shape[0]
        self.gt=gt
    def run(self):
        # cluster with SKL K-Means
        gSKL = KMeans_skl(n_clusters=self.nclusters,n_init=1,init="random")
        gSKL.fit(self.data)

        # Hungarian accuracy
        sklAcc = determine_ci.HungarianIndex(nsamples=self.nsamples)
        sklAcc.score(self.gt,gSKL.labels_,format='array')
        
        return sklAcc.accuracy

def stat_analysis(method,rounds=20):
    rAll = np.zeros(rounds)
    for r in xrange(rounds):
        rAll[r]=method.run()
    return rAll.mean(),rAll.var(),rAll.max(),rAll.min()



In [23]:

    
rounds=100
diassoc=myEstimator._coassoc.max()-myEstimator._coassoc
x=acc_medoids(diassoc,nclusters=true_nclusters,gt=gt)
print 'diassoc kmedoids\t',stat_analysis(x,rounds=rounds)

x2=acc_skl_kmeans(diassoc,nclusters=true_nclusters,gt=gt)
print 'diassoc kmeans  \t',stat_analysis(x2,rounds=rounds)

x3=acc_medoids(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)
print 'assoc kmedoids  \t',stat_analysis(x3,rounds=rounds)

x4=acc_skl_kmeans(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)
print 'assoc kmeans    \t',stat_analysis(x4,rounds=rounds)









    



diassoc kmedoids	(1.0, 0.0, 1.0, 1.0)
diassoc kmeans  	(1.0, 0.0, 1.0, 1.0)
assoc kmedoids  	(0.87944999999999995, 0.016268947499999999, 0.98999999999999999, 0.65500000000000003)
assoc kmeans    	(1.0, 0.0, 1.0, 1.0)

10 clusters per partition



In [27]:

    
#%%debug
partitions_used = partitions_my_10

# generate coassoc
prot_mode="random"
assoc_mode='full' # prot or full
nprots=50 # number of prototypes

myEstimator=eac.EAC(nsamples)
myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)

# final clustering with the true number of clusters
true_nclusters = np.unique(gt).shape[0]

# compute diassociation from co-assoc
diassoc=myEstimator._coassoc.max()-myEstimator._coassoc

#k-medoids
labels,medoids=KMedoids.cluster(diassoc,k=true_nclusters)

# Hungarian accuracy
acc = determine_ci.HungarianIndex(nsamples=nsamples)
acc.score(gt,labels,format='array')

print 'K-Medoids Accuracy:\t',acc.accuracy









    



K-Medoids Accuracy:	0.79

Statistical analysis



In [28]:

    
rounds=100
diassoc=myEstimator._coassoc.max()-myEstimator._coassoc
x=acc_medoids(diassoc,nclusters=true_nclusters,gt=gt)
print 'diassoc kmedoids\t',stat_analysis(x,rounds=rounds)

x2=acc_skl_kmeans(diassoc,nclusters=true_nclusters,gt=gt)
print 'diassoc kmeans  \t',stat_analysis(x2,rounds=rounds)

x3=acc_medoids(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)
print 'assoc kmedoids  \t',stat_analysis(x3,rounds=rounds)

x4=acc_skl_kmeans(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)
print 'assoc kmeans    \t',stat_analysis(x4,rounds=rounds)









    



diassoc kmedoids	(0.88564999999999983, 0.006913827499999998, 0.98999999999999999, 0.70999999999999996)
diassoc kmeans  	(0.94109999999999983, 0.007578790000000001, 1.0, 0.68500000000000005)
assoc kmedoids  	(0.80559999999999998, 0.0076446399999999994, 0.98999999999999999, 0.57999999999999996)
assoc kmeans    	(0.93274999999999997, 0.0096581875000000036, 1.0, 0.68500000000000005)

Random clusters per partition



In [29]:

    
#%%debug
npartitions=30
nclusters=[4,25]
iters=3
partitions_used = partitions_my_rand

# generate coassoc
prot_mode="random"
assoc_mode='full' # prot or full
nprots=50 # number of prototypes

myEstimator=eac.EAC(nsamples)
myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)

# final clustering with the true number of clusters
true_nclusters = np.unique(gt).shape[0]

# compute diassociation from co-assoc
diassoc=myEstimator._coassoc.max()-myEstimator._coassoc

#k-medoids
labels,medoids=KMedoids.cluster(diassoc,k=true_nclusters)

# Hungarian accuracy
acc = determine_ci.HungarianIndex(nsamples=nsamples)
acc.score(gt,labels,format='array')

print 'K-Medoids Accuracy:\t',acc.accuracy









    



K-Medoids Accuracy:	1.0

Statistical analysis



In [30]:

    
rounds=100
diassoc=myEstimator._coassoc.max()-myEstimator._coassoc
x=acc_medoids(diassoc,nclusters=true_nclusters,gt=gt)
print 'diassoc kmedoids\t',stat_analysis(x,rounds=rounds)

x2=acc_skl_kmeans(diassoc,nclusters=true_nclusters,gt=gt)
print 'diassoc kmeans  \t',stat_analysis(x2,rounds=rounds)

x3=acc_medoids(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)
print 'assoc kmedoids  \t',stat_analysis(x3,rounds=rounds)

x4=acc_skl_kmeans(myEstimator._coassoc,nclusters=true_nclusters,gt=gt)
print 'assoc kmeans    \t',stat_analysis(x4,rounds=rounds)









    



diassoc kmedoids	(1.0, 0.0, 1.0, 1.0)
diassoc kmeans  	(0.94669999999999999, 0.0095141100000000044, 1.0, 0.69499999999999995)
assoc kmedoids  	(0.86844999999999994, 0.019944347500000001, 0.98999999999999999, 0.59999999999999998)
assoc kmeans    	(0.93619999999999992, 0.011716560000000003, 1.0, 0.69499999999999995)



In [31]:

    
from sklearn.metrics.pairwise import pairwise_distances



In [32]:

    
pairwise=pairwise_distances(data)
y=acc_medoids(pairwise,2,gt=gt)
stat_analysis(y,rounds=20)









    Out[32]:





(1.0, 0.0, 1.0, 1.0)

EAC Single link



In [1]:

    
#%%debug
npartitions=30
nclusters=[4,25]
iters=3
partitions_used = partitions_my_rand

# generate coassoc
prot_mode="random"
assoc_mode='full' # prot or full
nprots=50 # number of prototypes

myEstimator=eac.EAC(nsamples)
myEstimator.fit(partitions_used,files=False,assoc_mode=assoc_mode, prot_mode=prot_mode, nprot=nprots,build_only=True)

# final clustering with the true number of clusters
true_nclusters = np.unique(gt).shape[0]

#k-medoids
myEstimator._apply_linkage()
labels = myEstimator._clusterFromLinkage()

# Hungarian accuracy
acc = determine_ci.HungarianIndex(nsamples=nsamples)
acc.score(gt,labels,format='array')

print 'K-Medoids Accuracy:\t',acc.accuracy









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-ec74976ec8a2> in <module>()
      3 nclusters=[4,25]
      4 iters=3
----> 5 partitions_used = partitions_my_rand
      6 
      7 # generate coassoc

NameError: name 'partitions_my_rand' is not defined



In [34]:

    
#generated from: http://tools.medialab.sciences-po.fr/iwanthue/
my_colors=["#D37E30",
"#6F6FD8",
"#3AA579",
"#D5337B",
"#4595B8",
"#3EA729",
"#D150D7",
"#4E6E23",
"#8F4D79",
"#D64430",
"#A1952B",
"#C15257",
"#AA5BB3",
"#6A76B0",
"#8E5723",
"#2A7464",
"#D66C9F",
"#60994E",
"#73A32D",
"#33A74F"]
my_pallete=sns.color_palette(my_colors)
sns.palplot(my_pallete)
sns.set_palette(my_pallete)



In [35]:

    
figX=sns.plt.figure(figsize=(12,90))
for i,p in enumerate(partitions_skl):
    ax=figX.add_subplot(15,2,i+1)
    for c in p:
        ax.plot(data[c,0],data[c,1],'.',markersize=12)
        ax.set_title("partition {}".format(i+1))









    



---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-35-f5557a10f407> in <module>()
      1 figX=sns.plt.figure(figsize=(12,90))
----> 2 for i,p in enumerate(partitions_skl):
      3     ax=figX.add_subplot(15,2,i+1)
      4     for c in p:
      5         ax.plot(data[c,0],data[c,1],'.',markersize=12)

NameError: name 'partitions_skl' is not defined





    





<matplotlib.figure.Figure at 0x7f5686ec5b10>