def run_scr(lamP, lamR, lamS, outf, sub):
#(ba,tf, genes, tfs) = load_bacteria('B_subtilis.csv','tfNames.txt',range(250))
(ba,tf, genes, tfs) = load_B_subtilis(sub)
(BC_priors, sign) = load_priors('gsSDnamesWithActivitySign082213','B_subtilis')
orth = []#load_orth('',['B_subtilis'])
print ba.shape
organisms = ['B_subtilis']
gene_ls = [genes]
tf_ls = [tfs]
Xs = [tf]
Ys = [ba]
priors = BC_priors
Bs = fl.solve_ortho_direct_refit(organisms, gene_ls, tf_ls, Xs, Ys, orth, priors, lamP, lamR, lamS, 0, 100)
#Bs = fl.solve_ortho_direct(organisms, gene_ls, tf_ls, Xs, Ys, orth, priors, lamP, lamR, lamS)
Bs_str_l = []
Bs_str_l.append('\t'.join(tfs))
for gi in range(len(genes)):
gene = genes[gi]
regulators = Bs[0][:, gi]
Bs_str_l.append(gene +'\t'+ '\t'.join(map(str, regulators)))
f = file(outf, 'w')
f.write('\n'.join(Bs_str_l))
f.close()
def test_linearBs_refit(b1, b2, fusiongroups, xsamples1, xsamples2, noise_std1, noise_std2, p_falsep, p_falseneg, lamP, lamR, lamS, it, k, organisms):
# TFs = [map(lambda x: str(x)+'tfa', range(b1.shape[0])), map(lambda x: str(x)+'tfb', range(b2.shape[0]))]
# Gs = [map(lambda x: str(x)+'ga', range(b1.shape[1])), map(lambda x: str(x)+'gb', range(b2.shape[1]))]
TFs = [map(lambda x: x, range(b1.shape[0])), map(lambda x: x, range(b2.shape[0]))]
Gs = [map(lambda x: x, range(b1.shape[1])), map(lambda x: x, range(b2.shape[1]))]
xdims1 = (xsamples1, b1.shape[0])
xdims2 = (xsamples2, b2.shape[0])
(x1, y1) = generate_from_linear(xdims1, b1, noise_std1)
(x2, y2) = generate_from_linear(xdims2, b2, noise_std2)
(x1test, y1test) = generate_from_linear(xdims1, b1, noise_std1)
(x2test, y2test) = generate_from_linear(xdims2, b2, noise_std2)
p1 = messwpriors(b1, p_falsep, p_falseneg)
p2 = messwpriors(b2, p_falsep, p_falseneg)
priorset = []
for i in range(len(p1)):
tf = fr.one_gene(TFs[0][p1[i][0]], 0)
gene = fr.one_gene(Gs[0][p1[i][1]], 0)
priorset.append((tf,gene))
for i in range(len(p2)):
tf = fr.one_gene(TFs[1][p2[i][0]], 1)
gene = fr.one_gene(Gs[1][p2[i][1]], 1)
priorset.append((tf,gene))
#organisms = ['a','b']
#constraints=[]
#for i in range(len(fusiongroups)):
# fg = fusiongroups[i]
# for j in range(len(fg[0])):
# for m in range(len(fg[1])):
# coeff1 = fr.coefficient(0,fg[0][j][0], fg[0][j][1])
# coeff2 = fr.coefficient(1,fg[1][m][0], fg[1][m][1])
# constr = fr.constraint(coeff1,coeff2, lamS)
# constraints.append(constr)
#bs_solve = fr.solve_ortho_direct_refit_bench(organisms, Gs, TFs, [x1, x2], [y1, y2], constraints, priorset, lamP, lamR, lamS, it, k)
bs_solve = fr.solve_ortho_direct_refit(organisms, Gs, TFs, [x1, x2], [y1, y2], fusiongroups, priorset, lamP, lamR, lamS, it, k)
err1 = B_err(bs_solve[0], b1)
err2 = B_err(bs_solve[1], b2)
support_score1 = check_support(bs_solve[0], b1)
support_score2 = check_support(bs_solve[1], b2)
err1p = pred_err_grps(bs_solve[0], x1test, y1test)
err2p = pred_err_grps(bs_solve[1], x2test, y2test)
return (bs_solve, err1, err2, err1p, err2p, support_score1, support_score2)
def run_both_refit(lamP, lamR, lamS, outf, sub_s, sub_i, sub_t, it, k):
(bs_e, bs_t , bs_genes, bs_tfs) = load_B_subtilis(sub_s)
(BS_priors, sign) = load_priors('gsSDnamesWithActivitySign082213','B_subtilis')
(ba_e, ba_t, ba_genes, ba_tfs) = load_B_anthracis(sub_i, sub_t)
(BA_priors, sign) = ([], [])
Xs = [bs_t, ba_t]
Ys = [bs_e, ba_e]
priors = BS_priors + BA_priors
orth = load_orth('bs_ba_ortho_804',['B_anthracis','B_subtilis'])
#orth = load_orth('',['B_subtilis'])
organisms = ['B_subtilis','B_anthracis']
#ortht = random_orth(bs_tfs, ba_tfs, organisms, 250)
#orthg = random_orth(bs_genes, ba_genes, organisms, 2500)
#orth = ortht+orthg
#print orth
#return
gene_ls = [bs_genes, ba_genes]
tf_ls = [bs_tfs, ba_tfs]
Bs = fl.solve_ortho_direct_refit(organisms, gene_ls, tf_ls, Xs, Ys, orth, priors, lamP, lamR, lamS, it, k)
write_bs(bs_genes, bs_tfs, Bs[0], outf+'_subtilis')
write_bs(ba_genes, ba_tfs, Bs[1], outf+'_anthracis')