def distance_pruner(zlst):
#Prunes cluster tree i.e. zlst with distance between their centroids
if args['v'] > -1:
sys.stderr.write("\n#Pruning data based on eucledian distance between clusters.\n")
print zlst," #Old zlst before distprune"
import dist
z0 = zlst[0]
pairs = []
for _i,i in enumerate(data[z0]):
for _j,j in enumerate(data[z0]):
if i != j:
if dist.dist(i,j,z0,indep,nump) < 0.3:
if [_i,_j] not in pairs and [_j,_i] not in pairs:
pairs.append(['__'+str(_i+1),'__'+str(_j+1)])
def repaired(pairs):
for i in pairs:
for j in pairs:
if i != j :
if i[0] in j or i[1] in j:
pairs = [list(set(i+j))]+\
[k for k in pairs if k not in [i,j]]
return repaired(pairs)
return pairs
repairs = repaired(pairs)
ps = []
for i in repairs:
ps+=i
for i in zlst[1:]:
if i not in ps:
repairs.append([i])
temp_row = {}
for ind,p in enumerate(repairs):
temp_row[ind] = []
for i in p:
temp_row[ind] += data[i]
col = colname[z0]
for Z in zlst:
reader.removeTable(Z)
zlst = [None]
for ind,value in enumerate(temp_row.values()):
Z = '__'+str(ind+1)
reader.makeTable(col,Z)
for r in value:
reader.addRow(r[:len(r)-1]+[ind],Z)
zlst.append(Z)
xy_lib.buildzero(zlst,'',args['e'])
if args['v'] > -1:
print zlst," #New zlst after distprune"
return zlst
def distance_pruner(zlst):
#Prunes cluster tree i.e. zlst with distance between their centroids
if args['v'] > -1:
sys.stderr.write(
"\n#Pruning data based on eucledian distance between clusters.\n")
print zlst, " #Old zlst before distprune"
import dist
z0 = zlst[0]
pairs = []
for _i, i in enumerate(data[z0]):
for _j, j in enumerate(data[z0]):
if i != j:
if dist.dist(i, j, z0, indep, nump) < 0.3:
if [_i, _j] not in pairs and [_j, _i] not in pairs:
pairs.append(['__' + str(_i + 1), '__' + str(_j + 1)])
def repaired(pairs):
for i in pairs:
for j in pairs:
if i != j:
if i[0] in j or i[1] in j:
pairs = [list(set(i+j))]+\
[k for k in pairs if k not in [i,j]]
return repaired(pairs)
return pairs
repairs = repaired(pairs)
ps = []
for i in repairs:
ps += i
for i in zlst[1:]:
if i not in ps:
repairs.append([i])
temp_row = {}
for ind, p in enumerate(repairs):
temp_row[ind] = []
for i in p:
temp_row[ind] += data[i]
col = colname[z0]
for Z in zlst:
reader.removeTable(Z)
zlst = [None]
for ind, value in enumerate(temp_row.values()):
Z = '__' + str(ind + 1)
reader.makeTable(col, Z)
for r in value:
reader.addRow(r[:len(r) - 1] + [ind], Z)
zlst.append(Z)
xy_lib.buildzero(zlst, '', args['e'])
if args['v'] > -1:
print zlst, " #New zlst after distprune"
return zlst
def tshortener(z,zlst,colname,data,dep,indep,patt=1.0,discretize=True):
#The infogain techniques of pruning columns and discretization
class Bucket:
#class for each column with splitted pairs of data
def __init__(self,name):
self.pairs = [] #unsorted row pairs
self.name = name
self.wsum = 0
self.dinds = {} #sorted split indexs
self.lo = {}
self.hi = {}
def addpairs(self,pairs):
self.pairs.append(pairs)
def addwsum(self,wsum):
self.wsum = wsum
def __repr__(self):
s = 'n: '+str(self.name)+":"
s += ' l: '+str(len(self.pairs))
s += ' e: '+str(self.wsum)+'\n'
return s
from globfile import buckets
outcols = []
for key,value in buckets.items():
buckets[key] = None
for Z in zlst[1:]:
for c in indep[Z]:
if c == 'C_id': continue
if c not in buckets.keys():
buckets[c] = Bucket(c)
elif buckets[c] is None:
buckets[c] = Bucket(c)
ind = colname[Z].index(c)
cind = colname[Z].index('C_id')
for r in data[Z]:
buckets[c].addpairs((r[ind],str(r[cind])))
reader.removeTable(Z)
buckets = weighted_entropies(buckets)
vals = buckets.values()[:]
vals.sort(key=lambda x: x.wsum,reverse=False)
for i in range(0,int(len(vals)*patt)):
outcols.append(vals[i].name)
zshort = 'shortenedz'
outcols = [i for i in colname[z] if i in outcols]
print outcols,"#infogained"
#Convert outcols to discrete attributes
if discretize:
outcols = [c[1:] for c in outcols]
print outcols,"#discretized"
reader.makeTable(outcols+dep[z],zshort)
for r in data[z]:
temp = []
for i,c in enumerate(colname[z]):
if discretize:
if c[1:] in outcols or c in dep[z]:
temp.append(r[i])
else:
if c in outcols+dep[z]:
temp.append(r[i])
reader.addRow(temp,zshort)
if discretize: discretizer(zshort,buckets)
for Z in zlst:
reader.removeTable(Z)
#discretizer(zshort,buckets)
return zshort
def tshortener(z, zlst, colname, data, dep, indep, patt=1.0, discretize=True):
#The infogain techniques of pruning columns and discretization
class Bucket:
#class for each column with splitted pairs of data
def __init__(self, name):
self.pairs = [] #unsorted row pairs
self.name = name
self.wsum = 0
self.dinds = {} #sorted split indexs
self.lo = {}
self.hi = {}
def addpairs(self, pairs):
self.pairs.append(pairs)
def addwsum(self, wsum):
self.wsum = wsum
def __repr__(self):
s = 'n: ' + str(self.name) + ":"
s += ' l: ' + str(len(self.pairs))
s += ' e: ' + str(self.wsum) + '\n'
return s
from globfile import buckets
outcols = []
for key, value in buckets.items():
buckets[key] = None
for Z in zlst[1:]:
for c in indep[Z]:
if c == 'C_id': continue
if c not in buckets.keys():
buckets[c] = Bucket(c)
elif buckets[c] is None:
buckets[c] = Bucket(c)
ind = colname[Z].index(c)
cind = colname[Z].index('C_id')
for r in data[Z]:
buckets[c].addpairs((r[ind], str(r[cind])))
reader.removeTable(Z)
buckets = weighted_entropies(buckets)
vals = buckets.values()[:]
vals.sort(key=lambda x: x.wsum, reverse=False)
for i in range(0, int(len(vals) * patt)):
outcols.append(vals[i].name)
zshort = 'shortenedz'
outcols = [i for i in colname[z] if i in outcols]
print outcols, "#infogained"
#Convert outcols to discrete attributes
if discretize:
outcols = [c[1:] for c in outcols]
print outcols, "#discretized"
reader.makeTable(outcols + dep[z], zshort)
for r in data[z]:
temp = []
for i, c in enumerate(colname[z]):
if discretize:
if c[1:] in outcols or c in dep[z]:
temp.append(r[i])
else:
if c in outcols + dep[z]:
temp.append(r[i])
reader.addRow(temp, zshort)
if discretize: discretizer(zshort, buckets)
for Z in zlst:
reader.removeTable(Z)
#discretizer(zshort,buckets)
return zshort
