def dist_tree(training_distances, training_labels, outpath = "/tmp/training_distances_avg_tree"):
import jrs_labels_tree
print "","building label vs label distance matrix using averaging..."
#dmatrix = jrs_io.load_data(open("/tmp/dist_tree_dmatrix.txt"), cast_method=float)
#print "","loaded matrix =",len(dmatrix),"x",len(dmatrix[0]),"..."
#import numpy
#print numpy.array(dmatrix)
print "","training on matrix =",len(training_distances),"x",len(training_distances[0]),"/",len(training_labels)," labels' sets..."
dmatrix = jrs_labels_tree.build_sim_matrix_labels(training_distances, training_labels)
print "","clearing diagonal..."
for i in xrange(len(dmatrix)):
dmatrix[i][i] = 0.0
minval = min_matrix(dmatrix)
maxval = max_matrix(dmatrix)
print numpy.array(dmatrix)
print "","minval:",minval
print "","maxval:",maxval
jrs_io.store_data(open("/tmp/dist_tree_dmatrix.txt","w"), dmatrix)
print "","transforming dmatrix by substracting minval"
dmatrix_t = [[(e-minval) for e in row ] for row in dmatrix ]
dmatrix = dmatrix_t
minval = min_matrix(dmatrix)
maxval = max_matrix(dmatrix)
print numpy.array(dmatrix)
print "","minval:",minval
print "","maxval:",maxval
jrs_io.store_data(open("/tmp/dist_tree_dmatrix_t.txt","w"), dmatrix)
#dmatrix = jrs_io.load_data(open("/tmp/dist_tree_dmatrix_t.txt"), cast_method=float)
#print "","loaded matrix =",len(dmatrix),"x",len(dmatrix[0]),"..."
#import numpy
#print numpy.array(dmatrix)
treelabels = [str(i+1) for i in range(len(single_labels))]
print "","treelabels:", treelabels
def gen_tree(outph, hd):
phylo_tree = jrs_labels_tree.upgma(treelabels, dmatrix, agreggation_method = 'a', anonclades = hd)
print "","writing tree to:",outph
jrs_labels_tree.write_tree(phylo_tree, outph)
print "","dict_tree=",jrs_labels_tree.phylotree2dicttree(phylo_tree)
gen_tree(outpath, False)
gen_tree(outpath+"_hd", True)
print "Shuffling labels..."
labels_shuffled = [labels[ix] for ix in order]
jrs_io.store_labels(open(labels_path+"_shuffled","w"), labels_shuffled)
print "Loading distances' file:", distance_matrix_path
distances = jrs_io.load_data(open(distance_matrix_path), lambda x: x)
try: print "",len(distances), "x",len(distances[0])
except: pass
print "Extending order..."
order = order + range(n, len(distances))
print "Extended order:", order
print "Shuffling columns"
distances_tmp = []
for row in distances:
new_row = [ row[ix] for ix in order ]
distances_tmp.append(new_row)
print "Shuffling rows"
distances_shuffled = []
for ix in order:
distances_shuffled.append(distances_tmp[ix])
fout = open(distance_matrix_path+"_shuffled","w")
print "Storing to ",fout
jrs_io.store_data(fout, distances_shuffled)
print "Shuffling labels..."
labels_shuffled = [labels[ix] for ix in order]
jrs_io.store_labels(open(labels_path + "_shuffled", "w"), labels_shuffled)
print "Loading distances' file:", distance_matrix_path
distances = jrs_io.load_data(open(distance_matrix_path), lambda x: x)
try:
print "", len(distances), "x", len(distances[0])
except:
pass
print "Extending order..."
order = order + range(n, len(distances))
print "Extended order:", order
print "Shuffling columns"
distances_tmp = []
for row in distances:
new_row = [row[ix] for ix in order]
distances_tmp.append(new_row)
print "Shuffling rows"
distances_shuffled = []
for ix in order:
distances_shuffled.append(distances_tmp[ix])
fout = open(distance_matrix_path + "_shuffled", "w")
print "Storing to ", fout
jrs_io.store_data(fout, distances_shuffled)
