def run_test(fileName, max_k):
cache_dir = './cache'
D = 2.
T = 3.
L = 1.
print >> sys.stderr, "FILE: ", fileName
print fileName
host, paras, phi = newickFormatReader.getInput(fileName)
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
f = open('%s/README' % cache_dir, 'w')
f.write('This directory holds a cache of reconciliation graph for the TreeLife data set')
f.close()
cache_location = '%s/%s.graph' % (cache_dir, os.path.split(fileName)[1])
recon_count_location = '%s/%s.count' % (cache_dir, os.path.split(fileName)[1])
if not(os.path.isfile(cache_location)) or not(os.path.isfile(recon_count_location)):
print >> sys.stderr, 'A reconciliation graph has not been built yet for this newick file'
print >> sys.stderr, 'Doing so now and caching it in {%s}...' % cache_location
DictGraph, numRecon = DP.DP(host, paras, phi, D, T, L)
f = open(cache_location, 'w+')
g = open(recon_count_location, 'w+')
f.write(repr(DictGraph))
g.write(str(numRecon))
f.close()
g.close()
print >> sys.stderr, 'Loading reonciliation graph from cache'
f = open(cache_location)
g = open(recon_count_location)
DictGraph = eval(f.read())
numRecon = float(g.read())
f.close()
g.close()
## Only consider running algorithm for reconciliations with more than
# threshold MPRs
if (numRecon < recon_threshold):
print >> sys.stderr, 'Too few reconciliations: ', numRecon
return
else:
print >> sys.stderr, 'Reconciliation Count: ', numRecon
scoresList, dictReps = Greedy.Greedy(DictGraph, paras)
print >> sys.stderr, 'Found cluster representatives using point-collecting'
graph = ReconGraph.ReconGraph(DictGraph)
setReps = [ReconGraph.dictRecToSetRec(graph, dictRep) for dictRep in dictReps]
random.seed(0)
extra_reps = [KMeans.get_template(graph) for i in xrange(max_k)]
representatives = setReps + extra_reps
print >> sys.stderr, 'Starting K Means algorithm ... '
print >> sys.stderr, 'Printing Average and Maximum cluster radius at each step'
for i in xrange(1, max_k + 1):
# print 'k = %d' % i
KMeans.k_means(graph, 10, i, 0, representatives[:i])
# KMeans.k_means(graph, 10, i, seed, None)
dist_sum = 0
n = 10
for _ in xrange(n):
reps = [KMeans.get_weighted_template(graph) for _ in xrange(i)]
average, maximum = KMeans.cluster_quality(graph, reps)
dist_sum += average
gc.collect()
print float(dist_sum) / n
def run_test(fileName, max_k):
cache_dir = './cache'
D = 2.
T = 3.
L = 1.
print >> sys.stderr, "FILE: ", fileName
print fileName
host, paras, phi = newickFormatReader.getInput(fileName)
if not os.path.exists(cache_dir):
os.makedirs(cache_dir)
f = open('%s/README' % cache_dir, 'w')
f.write('This directory holds a cache of reconciliation graph for the TreeLife data set')
f.close()
cache_location = '%s/%s.graph' % (cache_dir, os.path.split(fileName)[1])
recon_count_location = '%s/%s.count' % (cache_dir, os.path.split(fileName)[1])
if not(os.path.isfile(cache_location)) or not(os.path.isfile(recon_count_location)):
print >> sys.stderr, 'A reconciliation graph has not been built yet for this newick file'
print >> sys.stderr, 'Doing so now and caching it in {%s}...' % cache_location
DictGraph, numRecon = DP.DP(host, paras, phi, D, T, L)
f = open(cache_location, 'w+')
g = open(recon_count_location, 'w+')
f.write(repr(DictGraph))
g.write(str(numRecon))
f.close()
g.close()
print >> sys.stderr, 'Loading reonciliation graph from cache'
f = open(cache_location)
g = open(recon_count_location)
DictGraph = eval(f.read())
numRecon = float(g.read())
f.close()
g.close()
## Only consider running algorithm for reconciliations with more than
# threshold MPRs
if (numRecon < recon_threshold):
print >> sys.stderr, 'Too few reconciliations: ', numRecon
return
else:
print >> sys.stderr, 'Reconciliation Count: ', numRecon
scoresList, dictReps = Greedy.Greedy(DictGraph, paras)
graph = ReconGraph.ReconGraph(DictGraph)
representatives = [ReconGraph.dictRecToSetRec(graph, dictReps[0])]
## Debug info
## Modifies the graph
## Checking for the case when there is an error in likelihood
print >> sys.stderr, "== Checking for likelihoods over 1 =="
found = False
for key in DictGraph.keys():
children = DictGraph[key]
for child in children[:-1]:
if child[-1] > 1:
# Attempt to round to fix large float math errors
roundedValue = round(child[-1])
if roundedValue != 1.0:
print >> sys.stderr, "ERR FOUND: ", key, child
found = True
if not(found):
print >> sys.stderr, "NO ERR(s)"
print >> sys.stderr, "== End of over 1 checks. =="
print >> sys.stderr, 'Starting K-centers algorithm ... '
for i in xrange(2, max_k + 2):
d, newrep = maximize(graph,representatives)
if not all(d_i > 0 for d_i in d):
print >> sys.stderr, "Distance vector contains 0", d
break
print i-1, min(d),
representatives.append(newrep)
dist_sum = 0
n = 10
for _ in xrange(n):
reps = [KMeans.get_weighted_template(graph) for _ in xrange(i-1)]
dist_sum += min_d(maximize(graph,reps))
print float(dist_sum) / n
print >> sys.stderr, "Finished k centers algorithm ..."
