def main():
for cell in cells:
for stim in stims:
CLS_D = mio.load(os.path.join(
path, "%s.%s.%s.combined.csv" % (cell, stim, "cls")),
delimit_c=",")
DCOR_D = mio.load(os.path.join(
path, "%s.%s.%s.combined.csv" % (cell, stim, "dcor")),
delimit_c=",")
stem = "%s-%s-%s-Network" % (team, cell, stim)
save_files(CLS_D, DCOR_D, 0.5, os.path.join(out_path_exp, stem))
fp = open(os.path.join(out_path_exp, "%s-Network-Writeup.txt" % team), "w")
fp.write(" ")
fp.close()
CLS_D = mio.load(os.path.join(path, "insilico.all.cls.combined.csv"))
DCOR_D = mio.load(os.path.join(path, "insilico.all.dcor.combined.csv"))
#TeamName-Network-Insilico.sif
#TeamName-Network-Insilico.eda
stem = "%s-Network-Insilico" % (team)
save_files(CLS_D, DCOR_D, 0.5, os.path.join(out_path_insil, stem))
fp = open(
os.path.join(out_path_insil, "%s-Network-Insilico-Writeup.txt" % team),
"w")
fp.write(" ")
fp.close()
def main():
make_dir(out_path_exp)
for cell in CELLS:
for stim in STIMS:
CLS_D = mio.load(os.path.join(path,"%s.%s.cls.tab" % (cell, stim)), delimit_c="\t")
DCOR_D = mio.load(os.path.join(path,"%s.%s.dcor.tab" % (cell, stim)), delimit_c="\t")
DB_D = mio.load(os.path.join(path,"%s.%s.db.tab" % (cell, stim)), delimit_c="\t")
ADJ_D = mio.load(os.path.join(path,"%s.%s.adj.tab" % (cell, stim)), delimit_c="\t")
assert CLS_D['M'].shape==DCOR_D['M'].shape
assert CLS_D['M'].shape[0]==CLS_D['M'].shape[1]
assert CLS_D['row_ids']==DCOR_D['row_ids']
assert CLS_D['row_ids']==CLS_D['col_ids']
assert DCOR_D['row_ids']==DCOR_D['col_ids']
assert ADJ_D['row_ids']==DCOR_D['col_ids']
assert DB_D['row_ids']==DCOR_D['col_ids']
stem = "%s-%s-%s-Network" % (team,cell,stim)
adj_to_contest(D=ADJ_D, path_stem=os.path.join(out_path_exp,stem))
dot_file = "pretty_sep16_%s_%s.dot"%(cell,stim)
pretty_dot(ADJ_D, DB_D, DCOR_D, CLS_D, os.path.join(path, dot_file))
# required as part of submission
fp = open(os.path.join(out_path_exp,"%s-Network-Writeup.txt"%team),"w")
fp.write(" ")
fp.close()
def main():
# Experimental Data
# ----------------------------------------
make_dir(out_path_exp)
for cell in cells:
for stim in stims:
CLS_D = mio.load(os.path.join(path,"%s.%s.cls.tab" % (cell, stim)), delimit_c="\t")
DCOR_D = mio.load(os.path.join(path,"%s.%s.dcor.tab" % (cell, stim)), delimit_c="\t")
#BT20.EGF.xnet.adj.tab
XNET_D = mio.load(os.path.join(path,"%s.%s.xnet.adj.tab" % (cell, stim)), delimit_c="\t")
ADJ_D = mio.load(os.path.join(path,"%s.%s.inh-combined.adj.tab" % (cell, stim)), delimit_c="\t")
stem = "%s-%s-%s-Network" % (team,cell,stim)
adj_to_contest(D=ADJ_D, path_stem=os.path.join(out_path_exp,stem))
dot_file = "pretty_sep9_%s_%s.dot"%(cell,stim)
pretty_dot(ADJ_D, XNET_D, DCOR_D, CLS_D, os.path.join(path, dot_file))
# required as part of submission
fp = open(os.path.join(out_path_exp,"%s-Network-Writeup.txt"%team),"w")
fp.write(" ")
fp.close()
# In Silico Data
# ----------------------------------------
make_dir(out_path_insil)
ADJ_D = mio.load(os.path.join(path,"insilico.all.sep9.adj.tab"), delimit_c="\t")
#TeamName-Network-Insilico.sif
#TeamName-Network-Insilico.eda
stem = "%s-Network-Insilico"%team
adj_to_contest(ADJ_D, os.path.join(out_path_insil,stem))
fp = open(os.path.join(out_path_insil,"%s-Network-Insilico-Writeup.txt"%team),"w")
fp.write(" ")
fp.close()
def main():
script.main(fname="D.expr.gold.CLS.apr.19.tab")
D = mio.load("D.expr.gold.CLS.apr.19.tab")
CLS = all_pairs_bool_dist(D['M'])
CHECK = mio.load("gold.R.dists.tab")['M']
print CLS
print CHECK
assert np.all(CLS == CHECK)
def load_extend_training_datas():
extend_features_dict = matrix_io.load('extend_train_features.pkl', float)
extend_labels_dict = matrix_io.load('extend_train_labels.pkl', np.int32)
extend_features = extend_features_dict['M']
extend_labels = extend_labels_dict['M']
features_mat = np.mat(extend_features) / 255.0
labels_mat = np.zeros([len(extend_labels), 10])
for i in xrange(len(extend_labels)):
labels_mat[i, extend_labels[i]] = 1
return features_mat, labels_mat
def main():
GOLD_D = mio.load("data/gold_standard_network.csv", dtype=str)
MIM_D = mio.load("data/mim_msa_cov.csv", dtype=str)
# HACK for biovis: load no weaks
YATES_D = mio.load("data/biovis_gold/gold_0.32_dot_noweak_noclust.adj.csv", dtype=str)
Y2_D = mio.load("data/biovis_gold/gold_0.32_dot_noweak_noclust.adj.csv", dtype=str)
#YATES_D = mio.load("data/gold_0.32_dot_nw.adj.csv", dtype=str)
#Y2_D = mio.load("data/gold.paths.dcor0.32.k2.tab", dtype=str)
assert GOLD_D["row_ids"] == GOLD_D["col_ids"]
assert MIM_D["row_ids"] == MIM_D["col_ids"]
assert YATES_D["row_ids"] == YATES_D["col_ids"]
assert GOLD_D["row_ids"] == MIM_D["row_ids"]
assert Y2_D["row_ids"] == Y2_D["col_ids"]
assert Y2_D["row_ids"] == YATES_D["row_ids"]
# align YATES and GOLD
GOLDi = [ YATES_D['row_ids'].index(x) if x in YATES_D['row_ids'] else None for x in GOLD_D['row_ids'] ]
GOLDiy = filter(lambda s: s is not None, GOLDi)
GOLDin = [i for i,s in enumerate(GOLDi) if s is not None]
#print np.array(YATES_D['row_ids'])[GOLDiy]
#print np.array(GOLD_D['row_ids'])[GOLDin]
#print MIM_D['M']
#print GOLD_D['M']
#r = test(GOLD_D['M'], MIM_D['M'], GOLD_D['row_ids'])
#print r
# fp = open("/Users/z/Desktop/mim-cov.csv", "w")
# fp.write(",".join([""]+GOLD_D['col_ids'])+"\n")
# for i,row in enumerate(r['R']):
# fp.write(GOLD_D['row_ids'][i]+",")
# fp.write(",".join(row)+"\n")
# fp.close()
# COMPARE GOLD WITH MIM
print "GOLD VS MIM"
R_G_MIM = test_mods(GOLD_D['M'], MIM_D['M'], GOLD_D['row_ids'])
print
# COMPARE GOLD WITH YATES
assert list(np.array(YATES_D['row_ids'])[GOLDiy]) == list(np.array(GOLD_D['row_ids'])[GOLDin])
row_ids=list(np.array(GOLD_D['row_ids'])[GOLDin])
print
G = GOLD_D['M'][GOLDin,:][:,GOLDin]
Y = YATES_D['M'][GOLDiy,:][:,GOLDiy]
Y2 = Y2_D['M'][GOLDiy,:][:,GOLDiy]
print "GOLD VS YATES EDGES"
R_G_Y = test_mods(G,Y, row_ids)
print
print "GOLD VS YATES WITH PATHS"
R_G_Y2 = test_mods(G,Y2,row_ids)
print
def main(fname=None, pkl=True, **kwds):
assert fname
if isinstance(pkl, basestring) and pkl.lower() in ('f','false','none'): pkl = False
if 'b' in kwds: kwds['b'] = float(kwds['b'])
if 'z_th' in kwds: kwds['z_th'] = float(kwds['z_th'])
if 'err_th' in kwds: kwds['err_th'] = float(kwds['err_th'])
if 'd_th' in kwds: kwds['d_th'] = float(kwds['d_th'])
if 'r_th' in kwds: kwds['r_th'] = float(kwds['r_th'])
print "Loading data..."
D = mio.load(fname)
print "Computing all pairs boolean class for a (%d x %d) data matrix (%d x %d result matrix)..." % \
(D['M'].shape[0], D['M'].shape[1], D['M'].shape[0], D['M'].shape[0])
CLS, steps, b = compute_all_bool(D['M'], **kwds)
z = kwds.get('z_th', 3.0)
r = kwds.get('r_th', 2/3)
if r < 0.5:
print "WARNING: r<0.5, (r=%f)... are you sure?" % r
err = kwds.get('err_th', 0.1)
fname_out = '%s.b%.4f.z%.2f.r%.2f.err%.2f.bool.tab' % (fname, b, z, r, err)
print "Saving %s..." % (fname_out)
mio.save(CLS, fname_out, fmt="%d", row_ids=D['row_ids'], col_ids=D['row_ids'])
steps_fname = fname+".steps.txt"
print "Saving high/low thresholds to %s in original row order..." % steps_fname
open(steps_fname,"w").write("\n".join(("%f"%x for x in steps)))
if pkl:
fname_pkl_out = fname_out.rpartition('.')[0]+'.pkl'
print "Saving %s..." % (fname_pkl_out)
pickle.dump(CLS, open(fname_pkl_out,"w"), protocol=-1)
def main(fname=None, pkl=True, **kwds):
assert fname
if isinstance(pkl, basestring) and pkl.lower() in ('f', 'false', 'none'):
pkl = False
if 'err' in kwds: kwds['err'] = int(kwds['err'])
if 'th' in kwds: kwds['th'] = float(kwds['th'])
D = mio.load(fname)
print "Computing all pairs weak boolean class..."
print "Computing all pairs weak class for a (%d x %d) data matrix (%d x %d result matrix)..." % \
(D['M'].shape[0], D['M'].shape[1], D['M'].shape[0], D['M'].shape[0])
WEAK, err, th = compute_all_weak(D['M'], **kwds)
print "Used parameters err=%d, cutoff th=%f" % (err, th)
fname_out = "%s.err%d.th%.4f.weak.tab" % (fname, err, th)
print "Saving %s..." % (fname_out)
mio.save(WEAK,
fname_out,
fmt="%d",
row_ids=D['row_ids'],
col_ids=D['row_ids'])
if pkl:
fname_pkl_out = fname_out.rpartition('.')[0] + '.pkl'
print "Saving %s..." % (fname_pkl_out)
pickle.dump(WEAK, open(fname_pkl_out, "w"), protocol=-1)
return WEAK
def main(fname=None, pkl=True, algorithm="3", outtag="", **kwds):
assert fname
if isinstance(pkl, basestring) and pkl.lower() in ('f','false','none'): pkl = False
print "Loading data from %s..." % fname
D = mio.load(fname)
print "Computing all pairs (euclidean) distance correlation from a (%d x %d) data matrix to a (%d x %d) result matrix..." % (D['M'].shape[0], D['M'].shape[1], D['M'].shape[0], D['M'].shape[0])
print "Computing all pairs (euclidean) distance correlation..."
if algorithm == "1":
print "Using Algorithm 1: single dot product, n^2*m memory"
DCOR = compute_all_dcor(D['M'], **kwds)
elif algorithm == "2":
print "Using Algorithm 2: multiple dot products, n*m memory"
DCOR = compute_all_dcor_2(D['M'], **kwds)
elif algorithm == "3":
print "Using Algorithm 3: multiple dot products, n*m memory, n choose 2 savings"
DCOR = compute_all_dcor_3(D['M'], **kwds)
else:
raise Exception, "Unknown algorithm %s" % algorithm
if outtag and outtag[-1] != ".":
outtag += "."
fname_out = '%s.%sdcor.tab' % (fname, outtag)
print "Saving %s..." % (fname_out)
mio.save(DCOR, fname_out, fmt="%.4f", row_ids=D['row_ids'], col_ids=D['row_ids'])
if pkl:
fname_pkl_out = fname_out.rpartition('.')[0]+'.pkl'
print "Saving %s..." % (fname_pkl_out)
pickle.dump(DCOR, open(fname_pkl_out,"w"), protocol=-1)
return DCOR
def main(fname=None, pkl=True, **kwds):
assert fname
if isinstance(pkl, basestring) and pkl.lower() in ('f', 'false', 'none'):
pkl = False
if 'b' in kwds: kwds['b'] = float(kwds['b'])
if 'z_th' in kwds: kwds['z_th'] = float(kwds['z_th'])
if 'err_th' in kwds: kwds['err_th'] = float(kwds['err_th'])
if 'd_th' in kwds: kwds['d_th'] = float(kwds['d_th'])
if 'r_th' in kwds: kwds['r_th'] = float(kwds['r_th'])
print "Loading data..."
D = mio.load(fname)
print "Computing all pairs boolean class for a (%d x %d) data matrix (%d x %d result matrix)..." % \
(D['M'].shape[0], D['M'].shape[1], D['M'].shape[0], D['M'].shape[0])
CLS, steps, b = compute_all_bool(D['M'], **kwds)
z = kwds.get('z_th', 3.0)
r = kwds.get('r_th', 2 / 3)
if r < 0.5:
print "WARNING: r<0.5, (r=%f)... are you sure?" % r
err = kwds.get('err_th', 0.1)
fname_out = '%s.b%.4f.z%.2f.r%.2f.err%.2f.bool.tab' % (fname, b, z, r, err)
print "Saving %s..." % (fname_out)
mio.save(CLS,
fname_out,
fmt="%d",
row_ids=D['row_ids'],
col_ids=D['row_ids'])
steps_fname = fname + ".steps.txt"
print "Saving high/low thresholds to %s in original row order..." % steps_fname
open(steps_fname, "w").write("\n".join(("%f" % x for x in steps)))
if pkl:
fname_pkl_out = fname_out.rpartition('.')[0] + '.pkl'
print "Saving %s..." % (fname_pkl_out)
pickle.dump(CLS, open(fname_pkl_out, "w"), protocol=-1)
def main():
# Experimental Data
# ----------------------------------------
make_dir(out_path_exp)
for cell in cells:
for stim in stims:
CLS_D = mio.load(os.path.join(path,
"%s.%s.cls.tab" % (cell, stim)),
delimit_c="\t")
DCOR_D = mio.load(os.path.join(path,
"%s.%s.dcor.tab" % (cell, stim)),
delimit_c="\t")
#BT20.EGF.xnet.adj.tab
XNET_D = mio.load(os.path.join(path, "%s.%s.xnet.adj.tab" %
(cell, stim)),
delimit_c="\t")
ADJ_D = mio.load(os.path.join(
path, "%s.%s.inh-combined.adj.tab" % (cell, stim)),
delimit_c="\t")
stem = "%s-%s-%s-Network" % (team, cell, stim)
adj_to_contest(D=ADJ_D, path_stem=os.path.join(out_path_exp, stem))
dot_file = "pretty_sep9_%s_%s.dot" % (cell, stim)
pretty_dot(ADJ_D, XNET_D, DCOR_D, CLS_D,
os.path.join(path, dot_file))
# required as part of submission
fp = open(os.path.join(out_path_exp, "%s-Network-Writeup.txt" % team), "w")
fp.write(" ")
fp.close()
# In Silico Data
# ----------------------------------------
make_dir(out_path_insil)
ADJ_D = mio.load(os.path.join(path, "insilico.all.sep9.adj.tab"),
delimit_c="\t")
#TeamName-Network-Insilico.sif
#TeamName-Network-Insilico.eda
stem = "%s-Network-Insilico" % team
adj_to_contest(ADJ_D, os.path.join(out_path_insil, stem))
fp = open(
os.path.join(out_path_insil, "%s-Network-Insilico-Writeup.txt" % team),
"w")
fp.write(" ")
fp.close()
def main(weighted=True, min_d=0.4, colored=False):
if isinstance(weighted, basestring) and weighted.lower() in ("f", "false", "0", "na","null"):
weighted = False
if isinstance(colored, basestring) and colored.lower() in ("f", "false", "0", "na","null"):
colored = False
min_d = float(min_d)
CLS = mio.load("data/gold.celegans.gse2180.cls.csv")
DCOR = mio.load("data/gold.celegans.gse2180.dcor.csv")
if colored:
colors = load_colors("data/gold.celegans.gse2180.phase.colors.txt")
else:
colors = None
print "digraph {"
print FONT_STRING
if colors:
for k,v in colors.items():
print '"%s"[color="%s",style=filled,fontcolor=white]' % (k, v)
for edge in csv_to_graphvis(CLS, DCOR, min_d, weighted=weighted):
print edge
print "}"
def main():
for cell in cells:
for stim in stims:
CLS_D = mio.load(os.path.join(path,"%s.%s.%s.combined.csv" % (cell, stim, "cls")), delimit_c=",")
DCOR_D = mio.load(os.path.join(path,"%s.%s.%s.combined.csv" % (cell, stim, "dcor")), delimit_c=",")
stem = "%s-%s-%s-Network" % (team,cell,stim)
save_files(CLS_D, DCOR_D, 0.5, os.path.join(out_path_exp,stem))
fp = open(os.path.join(out_path_exp,"%s-Network-Writeup.txt"%team),"w")
fp.write(" ")
fp.close()
CLS_D = mio.load(os.path.join(path,"insilico.all.cls.combined.csv"))
DCOR_D = mio.load(os.path.join(path,"insilico.all.dcor.combined.csv"))
#TeamName-Network-Insilico.sif
#TeamName-Network-Insilico.eda
stem = "%s-Network-Insilico" % (team)
save_files(CLS_D, DCOR_D, 0.5, os.path.join(out_path_insil,stem))
fp = open(os.path.join(out_path_insil,"%s-Network-Insilico-Writeup.txt"%team),"w")
fp.write(" ")
fp.close()
def main():
ADJ_D = mio.load("data/gold_0.32_dot_nw.adj.csv")
M = np.array(ADJ_D['M'],dtype=int)
print M
P = np.zeros(M.shape, dtype=np.int)
for i in xrange(M.shape[1]):
js = list(get_connected(M, i, k=2))
P[js,i] = 1
print P
print M==P
assert ADJ_D['row_ids'] == ADJ_D['col_ids']
mio.save(P, open("data/gold.paths.dcor0.32.k2.tab","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d")
def main():
make_dir(out_path_exp)
for cell in CELLS:
for stim in STIMS:
CLS_D = mio.load(os.path.join(path,
"%s.%s.cls.tab" % (cell, stim)),
delimit_c="\t")
DCOR_D = mio.load(os.path.join(path,
"%s.%s.dcor.tab" % (cell, stim)),
delimit_c="\t")
DB_D = mio.load(os.path.join(path, "%s.%s.db.tab" % (cell, stim)),
delimit_c="\t")
ADJ_D = mio.load(os.path.join(path,
"%s.%s.adj.tab" % (cell, stim)),
delimit_c="\t")
assert CLS_D['M'].shape == DCOR_D['M'].shape
assert CLS_D['M'].shape[0] == CLS_D['M'].shape[1]
assert CLS_D['row_ids'] == DCOR_D['row_ids']
assert CLS_D['row_ids'] == CLS_D['col_ids']
assert DCOR_D['row_ids'] == DCOR_D['col_ids']
assert ADJ_D['row_ids'] == DCOR_D['col_ids']
assert DB_D['row_ids'] == DCOR_D['col_ids']
stem = "%s-%s-%s-Network" % (team, cell, stim)
adj_to_contest(D=ADJ_D, path_stem=os.path.join(out_path_exp, stem))
dot_file = "pretty_sep16_%s_%s.dot" % (cell, stim)
pretty_dot(ADJ_D, DB_D, DCOR_D, CLS_D,
os.path.join(path, dot_file))
# required as part of submission
fp = open(os.path.join(out_path_exp, "%s-Network-Writeup.txt" % team), "w")
fp.write(" ")
fp.close()
def main(fname=None, as_rows=True, use_weak=False):
assert fname
if isinstance(as_rows,
basestring) and as_rows.lower() in ('f', 'false', 'none'):
as_rows = False
if not use_weak:
print "Loading boolean class enumeration matrix", fname
else:
print "Loading weak class enumeration matrix", fname
D = mio.load(fname, dtype=np.int)
M = D['M']
# verify that enumeration matrices look credible
if not use_weak:
Z = np.in1d(M, np.array([0, 1, 2, 3, 4, 5, 6, 7]))
if not np.all(Z):
print "%d invalid values in M." % (np.sum(~Z))
print "up to 20 unrecognized values include..."
zz = M[Z]
print np.unique(zz)[1:np.min(20, len(zz))]
else:
assert np.all(np.in1d(M, np.array([0, 1, 2, 3, 4, 5])))
if not as_rows:
print "Computing distance between all pairs of columns..."
M = np.transpose(M)
else:
print "Computing distance between all pairs of rows..."
if not use_weak:
print "Computing Boolean Class distance"
DIST = all_pairs_bool_dist(M)
fname_out = fname + '.booldist.tab'
else:
print "Computing Weak Class distance"
DIST = all_pairs_weak_dist(M)
fname_out = fname + '.weakdist.tab'
print "Saving boolean class distance matrix as", fname_out
if as_rows:
ids = D.get('row_ids', None)
mio.save(DIST, fp=fname_out, row_ids=ids, col_ids=ids, fmt="%d")
else:
ids = D.get('col_ids', None)
mio.save(DIST, fp=fname_out, row_ids=ids, col_ids=ids, fmt="%d")
return fname_out
def main():
# 1: load adj matrix
ADJ_D = mio.load(ADJ_FNAME, dtype=np.int, force_row_ids=True, force_col_ids=True)
assert len(ADJ_D['row_ids']) == len(ADJ_D['col_ids'])
assert len(ADJ_D['row_ids']) == ADJ_D['M'].shape[0]
assert ADJ_D['M'].shape[0] == ADJ_D['M'].shape[1]
# 2.1: find paths k=3
P3 = paths.fill_paths(ADJ_D["M"], k=3)
mio.save(Pinf, open("data/all_k61_0.5_dot_nw.adj.paths.k3.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
# 2.2: find paths k=2
P2 = paths.fill_paths(ADJ_D["M"], k=2)
mio.save(Pinf, open("data/all_k61_0.5_dot_nw.adj.paths.k2.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
# 3: load ranks
ranks = load_ranks(open(RANKS_FNAME))
print ranks
def main(fname=None, as_rows=True, use_weak=False):
assert fname
if isinstance(as_rows,basestring) and as_rows.lower() in ('f','false','none'): as_rows = False
if not use_weak:
print "Loading boolean class enumeration matrix", fname
else:
print "Loading weak class enumeration matrix", fname
D = mio.load(fname, dtype=np.int)
M = D['M']
# verify that enumeration matrices look credible
if not use_weak:
Z = np.in1d(M,np.array([0,1,2,3,4,5,6,7]))
if not np.all(Z):
print "%d invalid values in M." % (np.sum(~Z))
print "up to 20 unrecognized values include..."
zz = M[Z]
print np.unique(zz)[1:np.min(20, len(zz))]
else:
assert np.all(np.in1d(M,np.array([0,1,2,3,4,5])))
if not as_rows:
print "Computing distance between all pairs of columns..."
M = np.transpose(M)
else:
print "Computing distance between all pairs of rows..."
if not use_weak:
print "Computing Boolean Class distance"
DIST = all_pairs_bool_dist(M)
fname_out = fname+'.booldist.tab'
else:
print "Computing Weak Class distance"
DIST = all_pairs_weak_dist(M)
fname_out = fname+'.weakdist.tab'
print "Saving boolean class distance matrix as", fname_out
if as_rows:
ids = D.get('row_ids',None)
mio.save(DIST, fp=fname_out, row_ids=ids, col_ids=ids, fmt="%d")
else:
ids = D.get('col_ids',None)
mio.save(DIST, fp=fname_out, row_ids=ids, col_ids=ids, fmt="%d")
return fname_out
def main(fname=None, pkl=True, **kwds):
assert fname
if isinstance(pkl, basestring) and pkl.lower() in ('f','false','none'): pkl = False
if 'err' in kwds: kwds['err'] = int(kwds['err'])
if 'th' in kwds: kwds['th'] = float(kwds['th'])
D = mio.load(fname)
print "Computing all pairs weak boolean class..."
print "Computing all pairs weak class for a (%d x %d) data matrix (%d x %d result matrix)..." % \
(D['M'].shape[0], D['M'].shape[1], D['M'].shape[0], D['M'].shape[0])
WEAK, err, th = compute_all_weak(D['M'], **kwds)
print "Used parameters err=%d, cutoff th=%f" % (err, th)
fname_out = "%s.err%d.th%.4f.weak.tab" % (fname, err, th)
print "Saving %s..." % (fname_out)
mio.save(WEAK, fname_out, fmt="%d", row_ids=D['row_ids'], col_ids=D['row_ids'])
if pkl:
fname_pkl_out = fname_out.rpartition('.')[0]+'.pkl'
print "Saving %s..." % (fname_pkl_out)
pickle.dump(WEAK, open(fname_pkl_out,"w"), protocol=-1)
return WEAK
def main():
DCOR = script.main(fname=FNAME)
COR = compute_all_ppc_numpy(M)
SCI = compute_all_pcc_scipy(M)
print np.all(np.abs(COR - SCI) < 0.0000000000001)
DCOR = compute_all_dcor(M)
DCORL = loop_dcor(M)
print np.all(np.abs(DCOR - DCORL) < 0.0000000000001)
DCOR2 = compute_all_dcor_2(M)
print np.all(np.abs(DCOR - DCOR2) < 0.0000000000001)
DCOR3 = compute_all_dcor_3(M)
print np.all(np.abs(DCOR - DCOR3) < 0.0000000000001)
print np.all(np.abs(DCOR - DCOR3) < 0.1)
KUN = mio.load("kungold.tab")['M']
print KUN
print "kun", np.all(np.abs(DCOR - KUN) < 0.001)
print "kun", np.all(np.abs(DCORL - KUN) < 0.001)
print DCOR[5, 3], DCOR[3, 5]
print DCOR3[5, 3], DCOR3[3, 5]
def main():
v = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
H = np.matrix([[0, 0, 0, 1], [1, 1, 1, 0], [0, 0, 0, 1]])
L = np.matrix([[1, 1, 0, 0], [0, 0, 0, 1], [1, 1, 1, 0]])
print stepfit(v)
D = mio.load("nice.may3.Eg.expr.gold.celegans.csv")
M = D['M']
Steps = []
for row in M:
Steps.append(stepfit(row)[0])
b = 0.3
#CLS = all_pairs_bool(M, Steps, b, z_th=2.7)
CLS = all_pairs_bool(M, Steps, b)
print CLS
print D.keys()
mio.save(CLS,
"nice.test.tab",
fmt="%d",
row_ids=D['row_ids'],
col_ids=D['row_ids'])
print "OK: saved result to nice.test.tab"
def main():
DCOR = script.main(fname=FNAME)
COR = compute_all_ppc_numpy(M)
SCI = compute_all_pcc_scipy(M)
print np.all(np.abs(COR-SCI) < 0.0000000000001)
DCOR = compute_all_dcor(M)
DCORL = loop_dcor(M)
print np.all(np.abs(DCOR-DCORL) < 0.0000000000001)
DCOR2 = compute_all_dcor_2(M)
print np.all(np.abs(DCOR-DCOR2) < 0.0000000000001)
DCOR3 = compute_all_dcor_3(M)
print np.all(np.abs(DCOR-DCOR3) < 0.0000000000001)
print np.all(np.abs(DCOR-DCOR3) < 0.1)
KUN = mio.load("kungold.tab")['M']
print KUN
print "kun", np.all(np.abs(DCOR-KUN) < 0.001)
print "kun", np.all(np.abs(DCORL-KUN) < 0.001)
print DCOR[5,3], DCOR[3,5]
print DCOR3[5,3], DCOR3[3,5]
def main(fname=None, n=100000, dep="dcor", do_abs=False):
assert fname
n = int(n)
if isinstance(do_abs, basestring):
do_abs = not do_abs.lower() in ('f','false','none')
D = mio.load(fname)
M = D['M']
#POOL = multiprocessing.Pool()
if dep=="dcor":
import dcor
d = dcor.dcor
f = lambda x: f_dep(M,d)
hist_bins = 20
hist_range = (0,1)
elif dep=="pcc":
import scipy.stats
d = lambda a,b: scipy.stats.pearsonr(a,b)[0]
f = lambda x: f_dep(M,d)
if do_abs:
hist_bins = 20
hist_range = (0,1)
else:
hist_bins = 40
hist_range = (-1,1)
else:
raise Exception, "Unrecognized dependency measure '%s'" % dep
#Z = np.array(POOL.map(f, xrange(n)))
Z = np.array(map(f, xrange(n)))
if do_abs:
Z = np.abs(Z)
Z.sort()
i = 1
print "n=%d" % n
print
while i<=n:
print i, i/n, Z[-i]
i *= 10
print
print np.histogram(Z, range=hist_range, bins=hist_bins)
def main():
# In Silico Data Only
# ----------------------------------------
INFILE = os.path.join(path,"insilico.manual.sep9.adj.tab")
make_dir(out_path_insil)
ADJ_D = mio.load(INFILE, delimit_c="\t")
stem = "%s-Network-Insilico"%team
#submission files into submission directory
#RM = directed_unweighted_trifilt(ADJ_D)
#print np.sum(RM)
#ADJ_D['M'][RM==1] = 0
adj_to_contest(ADJ_D, os.path.join(out_path_insil,stem))
# nice signed directed network visualization
directed_dot(ADJ_D, outfile=INFILE+".dot")
print "Wrote .dot visualization to %s" % INFILE+".dot"
# token writeup into submission directory
fp = open(os.path.join(out_path_insil,"%s-Network-Insilico-Writeup.txt"%team),"w")
fp.write(" ")
fp.close()
print "Wrote submission files to %s" % out_path_insil
def main():
# In Silico Data Only
# ----------------------------------------
INFILE = os.path.join(path, "insilico.manual.sep9.adj.tab")
make_dir(out_path_insil)
ADJ_D = mio.load(INFILE, delimit_c="\t")
stem = "%s-Network-Insilico" % team
#submission files into submission directory
#RM = directed_unweighted_trifilt(ADJ_D)
#print np.sum(RM)
#ADJ_D['M'][RM==1] = 0
adj_to_contest(ADJ_D, os.path.join(out_path_insil, stem))
# nice signed directed network visualization
directed_dot(ADJ_D, outfile=INFILE + ".dot")
print "Wrote .dot visualization to %s" % INFILE + ".dot"
# token writeup into submission directory
fp = open(
os.path.join(out_path_insil, "%s-Network-Insilico-Writeup.txt" % team),
"w")
fp.write(" ")
fp.close()
print "Wrote submission files to %s" % out_path_insil
def main(fname=None, pkl=True, algorithm="3", outtag="", **kwds):
assert fname
if isinstance(pkl, basestring) and pkl.lower() in ('f', 'false', 'none'):
pkl = False
print "Loading data from %s..." % fname
D = mio.load(fname)
print "Computing all pairs (euclidean) distance correlation from a (%d x %d) data matrix to a (%d x %d) result matrix..." % (
D['M'].shape[0], D['M'].shape[1], D['M'].shape[0], D['M'].shape[0])
print "Computing all pairs (euclidean) distance correlation..."
if algorithm == "1":
print "Using Algorithm 1: single dot product, n^2*m memory"
DCOR = compute_all_dcor(D['M'], **kwds)
elif algorithm == "2":
print "Using Algorithm 2: multiple dot products, n*m memory"
DCOR = compute_all_dcor_2(D['M'], **kwds)
elif algorithm == "3":
print "Using Algorithm 3: multiple dot products, n*m memory, n choose 2 savings"
DCOR = compute_all_dcor_3(D['M'], **kwds)
else:
raise Exception, "Unknown algorithm %s" % algorithm
if outtag and outtag[-1] != ".":
outtag += "."
fname_out = '%s.%sdcor.tab' % (fname, outtag)
print "Saving %s..." % (fname_out)
mio.save(DCOR,
fname_out,
fmt="%.4f",
row_ids=D['row_ids'],
col_ids=D['row_ids'])
if pkl:
fname_pkl_out = fname_out.rpartition('.')[0] + '.pkl'
print "Saving %s..." % (fname_pkl_out)
pickle.dump(DCOR, open(fname_pkl_out, "w"), protocol=-1)
return DCOR
def load_extend_test_datas():
extend_features_dict = matrix_io.load('extend_test_features.pkl', float)
extend_features = extend_features_dict['M']
features_mat = np.mat(extend_features) / 255.0
return features_mat
def matrix_files_to_flat_graphviz_file(cls_fname=None, dcor_fname=None, out_fname=None, ignore_fname=None, color_fname=None, weak_fname=None, min_d=0, weighted=True, plot_na=False, rank_cluster_fname=None, ignore_nodes=None, do_rank_clust=True, rank_clust_names=False, **kwds):
"""From matrix file names and parameters, write resulting graphviz output to file."""
assert cls_fname and dcor_fname and out_fname
weighted = str_true_false(weighted)
plot_na = str_true_false(plot_na)
min_d = float(min_d)
assert min_d >= 0
if ignore_nodes is not None:
ignore_nodes = ignore_nodes.split(',')
if do_rank_clust in ("F",'f','false','FALSE','None', "False", False, None):
do_rank_clust = False
else:
do_rank_clust = True
CLS_D = mio.load(cls_fname)
DCOR_D = mio.load(dcor_fname)
if color_fname:
node_styles = load_colors_as_node_style_dict(open(color_fname))
else:
node_styles = None
if rank_clust_names in ("F",'f','false','FALSE','None', False, None):
rank_clust_names = None
else:
rank_clust_names = CLS_D['row_ids']
if rank_cluster_fname is not None:
rank_clusters = load_rank_clusters(open(rank_cluster_fname), rank_clust_names)
else:
rank_clusters = None
print rank_clusters
if not do_rank_clust:
print "kill ranks..."
rank_clusters = None
assert CLS_D['row_ids'] == CLS_D['col_ids']
assert DCOR_D['row_ids'] == DCOR_D['col_ids']
assert CLS_D['row_ids'] == DCOR_D['row_ids']
names = CLS_D['row_ids']
CLS, DCOR = CLS_D['M'], DCOR_D['M']
assert np.size(CLS,0) == np.size(CLS,1)
assert np.shape(CLS) == np.shape(DCOR)
if weak_fname:
WEAK_D = mio.load(weak_fname)
assert WEAK_D['row_ids'] == WEAK_D['col_ids']
assert WEAK_D['row_ids'] == names
WEAK = WEAK_D["M"]
assert np.shape(WEAK) == np.shape(CLS)
else:
WEAK = None
if ignore_fname:
IGNORE_D = mio.load(ignore_fname, force_row_ids=True, force_col_ids=True)
assert IGNORE_D['row_ids'] == IGNORE_D['col_ids']
try:
assert IGNORE_D['row_ids'] == names
except AssertionError:
print IGNORE_D['row_ids']
print names
raise
IGNORE = IGNORE_D["M"]
assert np.shape(IGNORE) == np.shape(CLS)
else:
IGNORE = None
out = open(out_fname, "w")
G = print_graphviz(names=names, out=out, CLS=CLS, DCOR=DCOR, WEAK=WEAK, IGNORE=IGNORE, node_styles=node_styles, min_d=min_d, weighted=weighted, plot_na=plot_na, rank_clusters=rank_clusters, ignore_nodes=ignore_nodes, **kwds)
out.close()
return G
from __init__ import * import matrix_io as mio from clusters_prototype_lib import * import subprocess min_d=0.13 TAB_PREFIX="/Users/z/Dropbox/biostat/brca/GSE7307.e2fnets.jun19/tab/" cls_fname=TAB_PREFIX+"jun20.R.GSE7307.TF.BOOL.syms.tab" dcor_fname=TAB_PREFIX+"jun20.R.GSE7307.TF.DCOR.syms.tab" weak_fname=TAB_PREFIX+"jun20.R.GSE7307.TF.WEAK.syms.tab" graphviz_cmd="dot" outpath_prefix="/Users/z/Desktop/brca_e2f_custom" clusts_fname="/Users/z/Dropbox/biostat/brca/GSE7307.e2fnets.jun19/tab/jun19.GSE7307.k299.gsplom.clust.names.e2fcustom.txt" out_path="/Users/z/Desktop/" CLS_D = mio.load(cls_fname) DCOR_D = mio.load(dcor_fname) WEAK_D = mio.load(weak_fname) CLS = CLS_D['M'] DCOR = DCOR_D['M'] WEAK = WEAK_D['M'] node_names = DCOR_D['row_ids'] CLUSTS = load_clusters(open(clusts_fname)) print CLS.shape, DCOR.shape, WEAK.shape # Convert cluster names into row ID indices (indexed from zero) # NOTE: C is a list, not a dict. It is ordered in same order as clust_names C, clust_names = clust_names_to_row_num_list(CLUSTS, node_names) print C print clust_names
def main(pkl_fname=None, row_fname=None, col_fname=None, outdir=None, sig=None, doabs=False, diag=1):
"""
pkl_fname: path to pickled numpy dependency matrix
row_fname: path to labeled text matrix with row ids, maybe col ids
col_fname: optional path to labeled text matrix with col ids
sig: float of minimum significance
doabs: flag of whether to use absolute value for significance testing
diag: if matrix is symmetric, the value of the diagonal
"""
assert pkl_fname and row_fname and outdir
make_dir(outdir)
if doabs:
abstxt = "T"
else:
abstxt = "F"
out_fname = os.path.join(outdir, os.path.basename(pkl_fname.rpartition('.')[0]))
if sig:
out_fname += ".sig%f" % sig
if doabs:
out_fname += ".absT"
out_fname += ".tab"
print "Text matrix will be saved to: %s" % out_fname
M = pickle.load(open(pkl_fname))
# Get row and column labels.
try:
D_row = mio.load(row_fname)
row_names = np.array(D_row['row_ids'])
except AssertionError:
row_names = np.array([s.strip('\n\r') for s in open(row_fname)])
if col_fname is None:
col_names = np.array(D_row['col_ids'])
else:
if row_fname == col_fname:
col_names = row_names
else:
try:
D_col = mio.load(col_fname)
col_names = np.array(D_col['row_ids']) # Use row IDs as column IDs in Dependency Matrix
except AssertionError:
col_names = np.array([s.strip('\n\r') for s in open(col_fname)])
if len(row_names) == np.size(M,0) and len(col_names) == np.size(M,1):
print "Number of rows(%d) and column(%d) names fit matrix size (%d,%d)." % \
(len(row_names), len(col_names), np.size(M,0), np.size(M,1))
else:
n = len(row_names)
if np.size(M,0) == n*(n-1)//2:
print "Matrix seems to be n choose 2 upper triangle matrix. Converting to full matrix..."
M = distance.squareform(M)
if diag is not None:
print "Forcing diagonal to be:", diag
for i in xrange(n):
M[i,i] = diag
else:
raise Exception, "Unknown matrix size %s given #row_ids(%d), #col_ids(%d)" % \
(np.shape(M), len(row_names), len(col_names))
# Remove insignificant rows and columns; align row/col names
original_dim = M.shape
if sig is not None:
sig = float(sig)
if not doabs:
col_max = np.amax(M,0)
row_max = np.amax(M,1)
else:
col_max = np.amax(np.abs(M),0)
row_max = np.amax(np.abs(M),1)
M = M[row_max>=sig,:][:,col_max>=sig]
row_names = row_names[row_max>=sig]
col_names = col_names[col_max>=sig]
new_dim = M.shape
# Dump to text
now_timestamp = datetime.datetime.now().isoformat('_')
header = ["Generated on %s from pickled matrix file %s" % (now_timestamp, pkl_fname),
"Original dimensions: %s, New dimensions: %s" % (original_dim, new_dim),
"sig: %s, abs: %s" % (str(sig), str(abstxt))]
print "\n".join(header)
fp = open(out_fname, "w")
mio.save(M, fp, ftype="txt", delimit_c="\t", row_ids=list(row_names), col_ids=list(col_names), headers=header)
fp.close()
print "Tab matrix saved to %s." % out_fname
return out_fname
#!/usr/bin/python """Hack script and notes to generate Jun19 BRCA network-of-networks. python script.py min_d=0.30 cls_fname=data/D.expr.gold.CLS.apr.19.tab dcor_fname=data/D.expr.gold.DCOR.apr.19.tab color_fname=data/gold.celegans.phase.colors.genes.txt graphviz_cmd=dot weak_fname=data/gold.weak.tab weighted=False outpath_prefix=~/Desktop/gold_0.30_dot_nw """ from clusters_prototype_lib import * import matrix_io as mio import subprocess DIR = "/Users/z/Dropbox/biostat/brca/GSE7307.e2fnets/" PDF_PTN = DIR + "jun19_e2f_clust_pdfs/c%s.fdp.dot.pdf" SUFFIXES = ['117', '150', '83', '82', '80', '51'] INTER_DCOR_D = mio.load(DIR+"GSE7307.TF.R.299.interest.inter.DCOR.jun19.tab") INTER_BOOL_D = mio.load(DIR+"GSE7307.TF.R.299.interest.inter.BOOL.jun19.tab") INTER_WEAK_D = mio.load(DIR+"GSE7307.TF.R.299.interest.inter.WEAK.jun19.tab") print INTER_DCOR_D['row_ids'] print SUFFIXES assert INTER_DCOR_D['row_ids'] == SUFFIXES assert INTER_DCOR_D['row_ids'] == INTER_BOOL_D['row_ids'] assert INTER_DCOR_D['row_ids'] == INTER_WEAK_D['row_ids'] assert INTER_DCOR_D['row_ids'] == INTER_DCOR_D['col_ids'] C_plots = [] for c in SUFFIXES: print PDF_PTN%c w,h = get_pdf_file_size(PDF_PTN%c) C_plots.append((w,h)) clust_out = DIR+"e2f.jun20.clusts.dot" fp = open(clust_out,"w")
from script_weak import *
import matrix_io as mio
D = mio.load("nice.may3.Eg.expr.gold.celegans.csv")
WEAK = main("nice.may3.Eg.expr.gold.celegans.csv")
print WEAK
print WEAK[3,6], WEAK[6,3]
r3 = np.array(D['M']>0.2,dtype=np.int)[3,]
r6 = np.array(D['M']>0.2,dtype=np.int)[6,]
print D['row_ids'][3], r3
print D['row_ids'][6], r6
print r3-r6
In [12]: %timeit DCOR = compute_all_dcor(M)
100 loops, best of 3: 5.01 ms per loop
In [19]: %timeit DCORL = test.loop_dcor(M)
10 loops, best of 3: 56.4 ms per loop
"""
from __init__ import *
import matrix_io as mio
import script
import numpy as np
from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform
import dcor
FNAME = "nice.may3.Eg.expr.gold.celegans.csv"
M = mio.load(FNAME)['M']
def loop_dcor(M):
m = M.shape[0]
D = np.zeros((m, m))
for i, rowi in enumerate(M):
for j, rowj in enumerate(M):
D[i, j] = dcor.dcor(rowi, rowj)
return D
def main():
DCOR = script.main(fname=FNAME)
COR = compute_all_ppc_numpy(M)
SCI = compute_all_pcc_scipy(M)
In [12]: %timeit DCOR = compute_all_dcor(M)
100 loops, best of 3: 5.01 ms per loop
In [19]: %timeit DCORL = test.loop_dcor(M)
10 loops, best of 3: 56.4 ms per loop
"""
from __init__ import *
import matrix_io as mio
import script
import numpy as np
from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform
import dcor
FNAME="nice.may3.Eg.expr.gold.celegans.csv"
M = mio.load(FNAME)['M']
def loop_dcor(M):
m = M.shape[0]
D = np.zeros((m,m))
for i,rowi in enumerate(M):
for j,rowj in enumerate(M):
D[i,j] = dcor.dcor(rowi,rowj)
return D
def main():
DCOR = script.main(fname=FNAME)
COR = compute_all_ppc_numpy(M)
SCI = compute_all_pcc_scipy(M)
print np.all(np.abs(COR-SCI) < 0.0000000000001)
DCOR = compute_all_dcor(M)
def main():
# 1: load adj matrix
ADJ_D = mio.load(ADJ_FNAME, dtype=np.int, force_row_ids=True, force_col_ids=True)
assert len(ADJ_D['row_ids']) == len(ADJ_D['col_ids'])
assert len(ADJ_D['row_ids']) == ADJ_D['M'].shape[0]
assert ADJ_D['M'].shape[0] == ADJ_D['M'].shape[1]
# 2: find all paths
Pinf = paths.fill_paths(ADJ_D["M"], k=None)
mio.save(Pinf, open("data/all_k61_0.5_dot_nw.adj.paths.kinf.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(ADJ_D["M"]!=Pinf)
P2 = paths.fill_paths(ADJ_D["M"], k=2)
mio.save(P2, open("data/all_k61_0.5_dot_nw.adj.paths.k2.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P2!=Pinf)
P3 = paths.fill_paths(ADJ_D["M"], k=3)
mio.save(P3, open("data/all_k61_0.5_dot_nw.adj.paths.k3.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P3!=Pinf)
P4 = paths.fill_paths(ADJ_D["M"], k=4)
mio.save(P4, open("data/all_k61_0.5_dot_nw.adj.paths.k4.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P4!=Pinf)
P5 = paths.fill_paths(ADJ_D["M"], k=5)
mio.save(P5, open("data/all_k61_0.5_dot_nw.adj.paths.k5.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P5!=Pinf)
P6 = paths.fill_paths(ADJ_D["M"], k=6)
mio.save(P6, open("data/all_k61_0.5_dot_nw.adj.paths.k6.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P6!=Pinf)
P7 = paths.fill_paths(ADJ_D["M"], k=7)
mio.save(P7, open("data/all_k61_0.5_dot_nw.adj.paths.k7.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P7!=Pinf)
P8 = paths.fill_paths(ADJ_D["M"], k=8)
mio.save(P8, open("data/all_k61_0.5_dot_nw.adj.paths.k8.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P8!=Pinf)
P9 = paths.fill_paths(ADJ_D["M"], k=9)
mio.save(P9, open("data/all_k61_0.5_dot_nw.adj.paths.k9.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P9!=Pinf)
P10 = paths.fill_paths(ADJ_D["M"], k=10)
mio.save(P10, open("data/all_k61_0.5_dot_nw.adj.paths.k10.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P10!=Pinf)
P11 = paths.fill_paths(ADJ_D["M"], k=11)
mio.save(P11, open("data/all_k61_0.5_dot_nw.adj.paths.k11.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P11!=Pinf)
P12 = paths.fill_paths(ADJ_D["M"], k=12)
mio.save(P12, open("data/all_k61_0.5_dot_nw.adj.paths.k12.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P12!=Pinf)
P13 = paths.fill_paths(ADJ_D["M"], k=13)
mio.save(P13, open("data/all_k61_0.5_dot_nw.adj.paths.k13.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P13!=Pinf)
P14 = paths.fill_paths(ADJ_D["M"], k=14)
mio.save(P14, open("data/all_k61_0.5_dot_nw.adj.paths.k14.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P14!=Pinf)
P15 = paths.fill_paths(ADJ_D["M"], k=15)
mio.save(P15, open("data/all_k61_0.5_dot_nw.adj.paths.k15.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P15!=Pinf)
P16 = paths.fill_paths(ADJ_D["M"], k=16)
mio.save(P15, open("data/all_k61_0.5_dot_nw.adj.paths.k16.csv","w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
print np.sum(P16!=Pinf)
from script_weak import *
import matrix_io as mio
D = mio.load("nice.may3.Eg.expr.gold.celegans.csv")
WEAK = main("nice.may3.Eg.expr.gold.celegans.csv")
print WEAK
print WEAK[3, 6], WEAK[6, 3]
r3 = np.array(D['M'] > 0.2, dtype=np.int)[3, ]
r6 = np.array(D['M'] > 0.2, dtype=np.int)[6, ]
print D['row_ids'][3], r3
print D['row_ids'][6], r6
print r3 - r6
"""
digraph {
graph [fontname = "helvetica", nodesep=0.300000, splines=ortho, ranksep=0.400000, rank=same];
node [fontname = "helvetica", color="#000000", style=filled, fillcolor="#ffffff"];
edge [fontname = "helvetica", penwidth=1];
"154" -> "107"[color="#222222", penwidth=4.512063];
"107" -> "343"[color="#4197c7", constraint=false, dir=none, penwidth=6.054689,style=dashed];
}
"""
import matrix_io as mio
import numpy as np
D = mio.load("KO.adj.matrix.tab", dtype=np.int)
M = D['M']
assert M.shape[0]==M.shape[1], M.shape
n = M.shape[0]
for i in xrange(n):
for j in xrange(n):
options = {}
if M[i,j] == 1: # weak activator
options.update({'color':'#339900'})
elif M[i,j] == 2: # strong activator
options.update({'color':'#00ff00'})
elif M[i,j] == -1: # weak repressor
options.update({'color':'orange', 'arrowhead':'tee'})
elif M[i,j] == -2: # strong repressor
options.update({'color':'red', 'arrowhead':'tee'})
else:
continue
opts = ", ".join(['%s="%s"'%(k,v) for k,v in options.items()])
print '"%s" -> "%s"[%s]' % (D['col_ids'][j], D['row_ids'][i], opts)
def main():
# 1: load adj matrix
ADJ_D = mio.load(ADJ_FNAME, dtype=np.int, force_row_ids=True, force_col_ids=True)
DCOR_D = mio.load(DCOR_FNAME, force_row_ids=True, force_col_ids=True)
assert len(ADJ_D['row_ids']) == len(ADJ_D['col_ids'])
assert len(ADJ_D['row_ids']) == ADJ_D['M'].shape[0]
assert DCOR_D["row_ids"] == ADJ_D['row_ids']
assert DCOR_D["row_ids"] == DCOR_D["col_ids"]
assert ADJ_D['M'].shape[0] == ADJ_D['M'].shape[1]
n = ADJ_D['M'].shape[0]
# 2.1: find paths k=2
P2 = paths.fill_paths(ADJ_D["M"], k=2)
mio.save(P2, open(P2_FNAME,"w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
# 2.2: find paths k=3
P3 = paths.fill_paths(ADJ_D["M"], k=3)
mio.save(P3, open(P3_FNAME,"w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
name_order = ADJ_D['row_ids']
# 3.1: load ranks
# (I compiled this list manually) Note that rank elements are 0 indexed in ADJ matrix
ranks = load_ranks_named(open(RANKS_FNAME), name_order)
#print ranks
#print name_order
#sys.exit(1)
node_clusters = []
# 4: find clusters in same rank at k=2
Ignore_Clust = np.zeros(ADJ_D['M'].shape, dtype=np.bool)
for lvl, r in enumerate(ranks):
CC = group_in_same_rank(r, P2)
rnp = np.array(r)
c = [map(str,rnp[cc]+1) for cc in CC]
node_clusters.append(c)
# Ignore edges in equal rank clusters
for node in r:
adj = set(np.nonzero(ADJ_D['M'][:,node])[0])
for e in (adj & set(r)):
Ignore_Clust[node,e] = True
# Also remove corresponding opposite direction
if ADJ_D['M'][node,e] == ADJ_D['M'][e,node] == 1:
Ignore_Clust[e,node] = True
print "Clusters"
for c in node_clusters:
print c
print
# 4.5 attempt to hide lower strength edges without disconnecting nodes
DCOR = DCOR_D['M']
AD = ADJ_D['M'].copy()
Ignore_Low = np.zeros(ADJ_D['M'].shape, dtype=np.bool)
for i in range(n):
for j in range(n):
if i == j: continue
if DCOR[i,j] < DCOR_TH and AD[j,i]:
# edge exists from i to j and it is under dCor thresh. can we remove it?
AD[j,i] = 0
# undirected edge
if AD[i,j]:
AD[i,j] = 0
if np.sum(AD[:,i]) == 0 or np.sum(AD[j:,]) == 0 or \
np.sum(AD[:,j]) == 0 or np.sum(AD[i:,]) == 0:
# no, it disconnects something
AD[i,j] = 1
AD[j,i] = 1
else:
Ignore_Low[i,j] = True
Ignore_Low[j,i] = True
# directed edge
else:
if np.sum(AD[:,i]) == 0 or np.sum(AD[j:,]) == 0:
# no, it disconnects something
AD[j,i] = 1
else:
Ignore_Low[j,i] = True
NL = count_edges(Ignore_Low)
assert np.sum(Ignore_Low & ADJ_D['M']) == np.sum(Ignore_Low)
print "Too low:", NL
# 5: look for redudant directed edges between levels at least 2 levels apart
# remove edge if path of equal length already exists
Ignore_Far = np.zeros(ADJ_D['M'].shape, dtype=np.bool)
A = ADJ_D['M'].copy()
A = A & (~Ignore_Low)
n_far_edges = 0
for lvl in xrange(len(ranks)-2):
this_rank = ranks[lvl]
for dlvl in xrange(lvl+2,len(ranks)):
delta = dlvl-lvl
that_rank = ranks[dlvl]
for top in this_rank:
for low in that_rank:
if A[low,top]: # adj is col->row
n_far_edges += 1
A[low,top] = 0 # try removing this link
# is there an alternate path of equal length to this node?
conn = paths.is_path(A,top,delta+1)
if not low in conn:
A[low,top] = 1 # I guess we need this edge...
else:
Ignore_Far[low,top] = True
# also remove an associated undirected edge
if A[top,low]:
Ignore_Far[top,low] = True
A[top,low] = 0
print "# Far edges", n_far_edges
# 6: Print Stats
assert np.sum(Ignore_Clust & Ignore_Far)==0
NT = count_edges(ADJ_D['M'])
NS = count_edges(Ignore_Clust)
NF = count_edges(Ignore_Far)
print "Total:", NT
print "Same Level:", NS
print "Redundant Far:", NF
n_rm = NS['total']+NF['total']+NL['total'] # this is wrong
#$print "removed:", n_rm
#print "reduction:", n_rm/NT['total']
# 7: Save Edge Ignore Matrix
Ignore = Ignore_Clust | Ignore_Far | Ignore_Low
NI = count_edges(Ignore)
print "Ignored", NI
print np.sum(Ignore)
print np.sum(Ignore_Clust | Ignore_Far)
print "Save Ignore Matrix at:", IGNORE_FNAME
mio.save(Ignore, open(IGNORE_FNAME, "w"), ftype="txt", row_ids=ADJ_D['row_ids'], col_ids=ADJ_D['col_ids'], fmt="%d", delimit_c=",")
