sim, m_clusters, similarity_aggregation_method_m)
new_leaf2clusters, new_tree = tree_clustering.generate_3level_tree(
sim_matrix_l, clustering_l, similarity_aggregator_m,
clustering_m)
elif clustering_method == "3lkmedoids":
clustering_l = lambda sim: kmedoids.kmedoids_clustering(
sim, l_clusters, 10000)
clustering_m = lambda sim: kmedoids.kmedoids_clustering(
sim, m_clusters, 10000)
new_leaf2clusters, new_tree = tree_clustering.generate_3level_tree(
sim_matrix_l, clustering_l, similarity_aggregator_m,
clustering_m)
elif clustering_method == "upgma":
new_tree = tree_clustering.generate_upgma_tree(
sim_matrix_l, similarity_aggregation_method_m)
new_leaf2clusters = trees.bottomup2topdown_tree_converter(
new_tree)
else:
print "[build_msc_tree] [Error] Unknown method of building tree!"
sys.exit(-4)
print "[build_msc_tree] new tree=", str(
trees.map_tree_leaves(new_tree, ix2msc))
print "[build_msc_tree] --------------------------------------------------------"
print "[build_msc_tree] Calculating similarity indexes..."
comparision_result = tree_distance.get_selected_indexes(msc_leaf2clusters, new_leaf2clusters, \
similarity_indexes,\
bonding_calc, membership_calc, membership_bonding)
print "[build_msc_tree] [end] iteration=", iterno, " l_clusters=", l_clusters, " m_clusters=", m_clusters, " comparision_result=", comparision_result
iteration_results.append(comparision_result)
lm2avgixs[(l_clusters,
m_clusters)] = stats.avg_lstdict(iteration_results)
import sys,os
sys.path.append(r'../')
sys.path.append(r'../../')
sys.path.append(r'../../../')
sys.path.append(r'../../../../')
import tree
from tree import trees
t = [[[u'01A60']], [[u'05C05']], [[u'11H55']], [[u'35Q60']], [[u'57M25']], [[u'65D17']], [[u'68Q25']], [[u'68Q42']], [[u'68R10']], [[u'90B50']], [[u'91B14']], [[u'91B28']], [[u'60K25', u'90B22']], [[u'68Q05'], [u'68Q10'], [u'68Q15'], [u'68Q80']], [[u'35R35'], [u'45K05'], [u'65M06'], [u'65M12'], [u'65R20'], [u'65Z05'], [u'76M12'], [u'76P05'], [u'76S05'], [u'82C40'], [u'65M15', u'65M60', u'65N15', u'65N25', u'65N30', u'65N55', u'74S05', u'76M10']], [[u'11B37'], [u'11D41'], [u'11J70'], [u'11J72'], [u'11J81'], [u'11J86'], [u'11K06'], [u'11K38'], [u'11K65'], [u'11M06'], [u'11M41'], [u'11R04'], [u'11Y40'], [u'11G05', u'11G40'], [u'11G09', u'11R58'], [u'11R18', u'11R23', u'11R27', u'11R29', u'11R32', u'11R33', u'11R37', u'11R42', u'11R70'], [u'11N25', u'11N37']], [[u'58J65'], [u'60B05'], [u'60B10'], [u'60B15'], [u'60E15'], [u'60F05'], [u'60F10'], [u'60F15'], [u'60F17'], [u'60G07'], [u'60G15'], [u'60G40'], [u'60G42'], [u'60G44'], [u'60G48'], [u'60G51'], [u'60G57'], [u'60G60'], [u'60H05'], [u'60H07'], [u'60H15'], [u'60H99'], [u'60J10'], [u'60J25'], [u'60J35'], [u'60J45'], [u'60J60'], [u'60J80'], [u'60K35'], [u'60K40'], [u'62E20'], [u'60G17', u'60J55', u'60J65'], [u'60H10', u'60H20'], [u'60G50', u'60K37']], [[u'35A20'], [u'35B05'], [u'35B25'], [u'35B40'], [u'35B45'], [u'35B65'], [u'35D05'], [u'35D10'], [u'35G10'], [u'35J15'], [u'35J20'], [u'35J25'], [u'35J70'], [u'35K20'], [u'35K55'], [u'35K57'], [u'35K60'], [u'35K65'], [u'35L60'], [u'35L75'], [u'35Q53'], [u'35Q55'], [u'35Q99'], [u'35J60', u'35J65'], [u'35L45', u'35L65'], [u'35L15', u'35L70']], [[u'34L99'], [u'35J10'], [u'35P05'], [u'35P15'], [u'35P20'], [u'35Q40'], [u'35R30'], [u'47F05'], [u'47N50'], [u'35P25', u'47A40']], [[u'20M35'], [u'68Q45'], [u'68Q70']], [[u'17B37'], [u'22E40'], [u'32M05'], [u'32M15'], [u'37D40'], [u'43A80'], [u'43A85'], [u'53C15'], [u'53C20'], [u'53C21'], [u'53C22'], [u'53C23'], [u'53C25'], [u'53C30'], [u'53C35'], [u'53C40'], [u'53C42'], [u'53C50'], [u'53C55'], [u'53D50'], [u'58E20'], [u'58J20'], [u'58J35'], [u'58J50'], [u'58J60'], [u'49Q05', u'53A10'], [u'17B10', u'17B20', u'17B35', u'22E30', u'22E45', u'22E46', u'22E47']], [[u'34A60'], [u'49J10'], [u'49J20'], [u'49J40'], [u'49J45'], [u'49K15'], [u'49K20'], [u'49L25'], [u'49N60'], [u'49Q10'], [u'49Q15'], [u'49Q20'], [u'65K10'], [u'74B20']], [[u'12H05'], [u'12H25'], [u'14F10'], [u'32C38'], [u'34M99'], [u'35A27'], [u'35S05'], [u'39A10'], [u'58J15'], [u'58J40'], [u'58J47'], [u'65H10']], [[u'34G20'], [u'42B20'], [u'42B25'], [u'46E35'], [u'46G20'], [u'46S10'], [u'47B38'], [u'47D03']], [[u'35Q75'], [u'46N50'], [u'46N99'], [u'53B50'], [u'53Z05'], [u'58Z05']], [[u'11G18'], [u'11G35'], [u'11S40'], [u'14C05'], [u'14C25'], [u'14C30'], [u'14E05'], [u'14E15'], [u'14F30'], [u'14F40'], [u'14G05'], [u'14G10'], [u'14G20'], [u'14G35'], [u'14G40'], [u'14H20'], [u'14H30'], [u'14H45'], [u'14H50'], [u'14J17'], [u'14J30'], [u'14L05'], [u'14L30'], [u'14M17'], [u'14N10'], [u'14P10'], [u'14P25'], [u'20G05'], [u'14C20', u'14D20', u'14F05', u'14H10', u'14H60', u'14J10'], [u'11F33', u'11F67', u'11F80'], [u'14H40', u'14K10', u'14K15'], [u'11F70', u'22E50']], [[u'90B35'], [u'90B80'], [u'90C08'], [u'90C27'], [u'90C29'], [u'90C30'], [u'90C35'], [u'90C05', u'90C10']], [[u'32B05'], [u'32B20'], [u'32C30'], [u'32J15'], [u'32L10'], [u'32S05'], [u'32S25'], [u'32S40'], [u'32U05'], [u'37C85'], [u'53C12'], [u'57M50'], [u'57N10'], [u'57R30'], [u'58C25'], [u'32D15', u'32V40'], [u'32A25', u'32W05'], [u'32S65', u'37F75']], [[u'28D05'], [u'30D05'], [u'37A99'], [u'37D99'], [u'37F10']], [[u'35B27'], [u'74K20']], [[u'37C75'], [u'37G99'], [u'37J35'], [u'37J99'], [u'58E05'], [u'34C25', u'37J45']], [[u'90B25'], [u'90B30']], [[u'03B40'], [u'68Q55'], [u'68Q60'], [u'68Q65']], [[u'62H25'], [u'62H30']], [[u'11B85'], [u'37B10'], [u'68R15']], [[u'35L05'], [u'93B05'], [u'93C20'], [u'93D15']], [[u'35Q35'], [u'76B47'], [u'76M25'], [u'35Q30', u'76D05', u'76N10']], [[u'18B25'], [u'18B30'], [u'18C10'], [u'18D05'], [u'18D10'], [u'18F15'], [u'18G30'], [u'18G55'], [u'55P62']], [[u'62G05'], [u'62G07'], [u'62G10'], [u'62M05']], [[u'20F36'], [u'20D10', u'20D30', u'20E15', u'20F05', u'20K20']]]
trees.bottomup2topdown_tree_converter(t)
wrongprefix2count = {}
for h in t:
for m in h:
for l1 in m:
for l2 in m:
if l1[:2] != l2[:2]:
print l1,l2
wrongprefix2count[(l1[:2],l2[:2])] = wrongprefix2count.get( (l1[:2],l2[:2]), 0) + 1
wrongprefix2count[(l2[:2],l1[:2])] = wrongprefix2count.get( (l2[:2],l1[:2]), 0) + 1
print wrongprefix2count
for h in t:
for m in h:
if len(m)!=2: continue
for l1 in m:
for l2 in m:
if l1[:2] != l2[:2]:
print l1,l2
print "[build_msc_tree] [start] iteration=",iterno," l_clusters=",l_clusters," m_clusters=",m_clusters
if clustering_method == "msc":
new_leaf2clusters, new_tree = msc_leaf2clusters, msc_tree
elif clustering_method == "rand":
new_leaf2clusters, new_tree = random_tree.get_random_tree_leaf2clusters(msc2ix.values())
elif clustering_method == "3lupgma":
clustering_l = lambda sim: upgma.upgma_clustering(sim, l_clusters, similarity_aggregation_method_m)
clustering_m = lambda sim: upgma.upgma_clustering(sim, m_clusters, similarity_aggregation_method_m)
new_leaf2clusters, new_tree = tree_clustering.generate_3level_tree(sim_matrix_l, clustering_l, similarity_aggregator_m, clustering_m)
elif clustering_method == "3lkmedoids":
clustering_l = lambda sim: kmedoids.kmedoids_clustering(sim, l_clusters, 10000)
clustering_m = lambda sim: kmedoids.kmedoids_clustering(sim, m_clusters, 10000)
new_leaf2clusters, new_tree = tree_clustering.generate_3level_tree(sim_matrix_l, clustering_l, similarity_aggregator_m, clustering_m)
elif clustering_method == "upgma":
new_tree = tree_clustering.generate_upgma_tree(sim_matrix_l, similarity_aggregation_method_m)
new_leaf2clusters = trees.bottomup2topdown_tree_converter(new_tree)
else:
print "[build_msc_tree] [Error] Unknown method of building tree!"
sys.exit(-4)
print "[build_msc_tree] new tree=",str(trees.map_tree_leaves(new_tree, ix2msc))
print "[build_msc_tree] --------------------------------------------------------"
print "[build_msc_tree] Calculating similarity indexes..."
comparision_result = tree_distance.get_selected_indexes(msc_leaf2clusters, new_leaf2clusters, \
similarity_indexes,\
bonding_calc, membership_calc, membership_bonding)
print "[build_msc_tree] [end] iteration=",iterno," l_clusters=",l_clusters," m_clusters=",m_clusters," comparision_result=",comparision_result
iteration_results.append(comparision_result)
lm2avgixs[(l_clusters, m_clusters)] = stats.avg_lstdict(iteration_results)
lm2stdixs[(l_clusters, m_clusters)] = stats.std_lstdict(iteration_results)
[u'57N10'], [u'57R30'], [u'58C25'], [u'32D15', u'32V40'],
[u'32A25', u'32W05'], [u'32S65', u'37F75']],
[[u'28D05'], [u'30D05'], [u'37A99'], [u'37D99'], [u'37F10']],
[[u'35B27'], [u'74K20']],
[[u'37C75'], [u'37G99'], [u'37J35'], [u'37J99'], [u'58E05'],
[u'34C25', u'37J45']], [[u'90B25'], [u'90B30']],
[[u'03B40'], [u'68Q55'], [u'68Q60'], [u'68Q65']], [[u'62H25'],
[u'62H30']],
[[u'11B85'], [u'37B10'], [u'68R15']],
[[u'35L05'], [u'93B05'], [u'93C20'], [u'93D15']],
[[u'35Q35'], [u'76B47'], [u'76M25'], [u'35Q30', u'76D05', u'76N10']],
[[u'18B25'], [u'18B30'], [u'18C10'], [u'18D05'], [u'18D10'], [u'18F15'],
[u'18G30'], [u'18G55'], [u'55P62']],
[[u'62G05'], [u'62G07'], [u'62G10'], [u'62M05']],
[[u'20F36'], [u'20D10', u'20D30', u'20E15', u'20F05', u'20K20']]]
trees.bottomup2topdown_tree_converter(t)
wrongprefix2count = {}
for h in t:
for m in h:
for l1 in m:
for l2 in m:
if l1[:2] != l2[:2]:
print l1, l2
wrongprefix2count[(l1[:2],
l2[:2])] = wrongprefix2count.get(
(l1[:2], l2[:2]), 0) + 1
wrongprefix2count[(l2[:2],
l1[:2])] = wrongprefix2count.get(
(l2[:2], l1[:2]), 0) + 1
print wrongprefix2count