def subCluster(n,clustr,distMat):
'''
Takes a clustering csv (clustr) and distance matrix (distMat) as inputs
For largest two clusters (by probability mass), builds reduced/sliced distMat
Breaks the largest two clusters into n subclusters via k-medoids
'''
import operator
import sklearn
from sklearn import metrics
#get the unigram probs
allChords = csv.reader(open('50ms 3 SDSets.csv','r',newline='\n'))
uniProbs = {}
for row in allChords:
uniProbs[row[0]] = int(row[1])
uniProbs = getProbsFromFreqs(uniProbs)
#get the distance matrix
diMat = []
dists = csv.reader(open(distMat, 'r',newline='\n'))
for row in dists:
diMat.append(row)
disArr = np.array(diMat)#pairwise dist mat (gen Manh?) as strings
diArr = disArr.astype(float)#now as floats
#print(diArr)
#get the kludgy lookup list that relates chord labels to distMat rows
lkps = {}
lkp = csv.reader(open('ndistMat_lookups_rev.csv','r',newline='\n'))
for row in lkp:
lkps[row[1]] = row[0]
#get the medoids and membership from prev clustering
meds = {}#dict of medoids: each medoid keys a list of [chord, chord,...]
medP = {}#dict of total unigram tallies keyed by medoid
clsts = csv.reader(open(clustr, 'r',newline='\n'))
i=0
for row in clsts:
i+=1
if i < 3: continue
if row[2] not in meds:
meds[row[2]] = []
medP[row[2]] = 0
meds[row[2]].append(row[0])
medP[row[2]] += int(row[1])
#list of medoids sorted by descending unigram probability captured
sorted_medP = sorted(medP.items(), key=operator.itemgetter(1), reverse=True)
#take the two biggest clusters and generate a new intra-clus disMat
ri = str(random.randint(0,5000))#silly rn for csv bookkeeping
sils = []#list silhouettes of the two biggest clusters
newclus = []#format: [origin chord, unigram prob, cluster assign, medoid, distance to medoid]
for j in range(2):
subcl_id = []#this will be a list of row indices for new_distMat
subcl = meds[sorted_medP[j][0]]#all the chord names in med
for chd in subcl:
subcl_id.append(lkps[chd])#the numerical maps for those chords
rows = np.array(subcl_id, dtype=np.intp)
new_distMat = diArr[np.ix_(rows, rows)]#distance matrix for subcluster
#kmedoids on the subcluster
clus_and_med = cluster(new_distMat,n)
new_meds = [subcl[m] for m in clus_and_med[1]]
msil = sklearn.metrics.silhouette_score(new_distMat,clus_and_med[0],metric='precomputed')
print(len(clus_and_med[0]),new_distMat.shape,msil)
sils.append([ri+'_'+str(j),msil])
#print(new_meds)
for l,oc in enumerate(subcl):
newclus.append([oc,uniProbs[oc],clus_and_med[0][l],subcl[clus_and_med[0][l]],new_distMat[l,clus_and_med[0][l]]])
#now dump the new clusterings into a csv
csvName = 'subClus/subcluster test'+ri+'.csv'
file = open(csvName, 'w',newline='\n')
lw = csv.writer(file)
lw.writerow(['origin chord','uprob','cluster','medoid','distance'])
for row in newclus:
lw.writerow(row)
file2 = open('subClus/subClus_silh.csv','a',newline='\n')
lw2 = csv.writer(file2)
for row in sils:
lw2.writerow(row)
def metaCluster(fld,k):
'''
For a collection of (locally-convergent) clusterings in fld
Take each origin chord and track its cluster IDs across clusterings
Compare the resulting membership vectors and cluster by THEIR similarity
Hamming distance (how many entries are not the same) seems most appropriate
'''
listing = os.listdir(fld)
clusDict = {}#dict of cluster assignments across runs: clusDict[origin chord]=[assign 1, assign 2, ...]
ocs = []#will be list of keys from clusDict
clusMat = []#will be list of cluster assignments in order of ocs, also from clusDict
#Pull chord names and probabilities for topN chords
allChords = csv.reader(open('50ms 3 SDSets.csv','r',newline='\n'))
uniProbs = {}
for row in allChords:
#Make a list of the topN most unigram-probable sds
uniProbs[row[0]] = int(row[1])
uniProbs = getProbsFromFreqs(uniProbs)
for f in listing:
#Get clustering data
address = fld + f
allOCs = csv.reader(open(address,'r',newline='\n'))
lstOfOCs = []
for row in allOCs:
lstOfOCs.append(row)#should be topN of these
#for each origin chord, append its assignment to the relevant clusDict entry list
for j,oc in enumerate(lstOfOCs):
if j < 2: continue#cut the two header rows
if not oc[0] in clusDict: clusDict[oc[0]] = []#make one if there isn't one
clusDict[oc[0]].append(oc[2])#stick the (int) cluster assignment in dict
#build (stable, ordered) ocs and clusMat lists
for key in clusDict.keys():
ocs.append(key)#this tells us what the rows of clusMat refer to
clusMat.append(clusDict[key])#this is what we'll cluster
print('clusDict',clusDict)
print('first row of oc list and first row of clusMat')
print(ocs[0],clusMat[0])
#now, calculate hamming distances between rows/ocs
distMat = pdist(clusMat,metric='hamming')
distMat_sq = squareform(distMat)#redundant, square
print(distMat_sq)
#kmedoids
clus_and_med = cluster(distMat_sq,k)
meds = [ocs[med] for med in clus_and_med[1]]
clus = []#format: [origin chord, unigram prob, cluster assignment, medoid, distance from medoid]
for l,oc in enumerate(ocs):
clus.append([oc,uniProbs[oc],clus_and_med[0][l],ocs[clus_and_med[0][l]],distMat_sq[l,clus_and_med[0][l]]])
#send out the csv
csvName = 'metacluster test.csv'
file = open(csvName, 'w',newline='\n')
lw = csv.writer(file)
lw.writerow(meds)
lw.writerow(['origin chord','uprob','cluster','medoid','distance'])
for row in clus:
lw.writerow(row)