/
benchmarks.py
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/
benchmarks.py
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import bacteria as ba
import numpy as np
import random
import fused_L2 as fl
import scipy.optimize
def single_species_benchmarks():
repeats = 2
lamPs = np.array([0])#np.logspace(0, 3, 10)
lamSs = np.array([0])
lamRs = np.logspace(0, 3, 5)
iron_conds = range(ba.iron_conds)
timeseries_conds = range(ba.timeseries_conds)
subtilis_conds = range(ba.subtilis_conds)
outf = file('benchmark_results/single_species_bench','w')
for lamP in lamPs:
for lamS in lamSs:
for lamR in lamRs:
acc_s = 0
acc_a = 0
for rep in range(repeats):
random.shuffle(iron_conds)
random.shuffle(timeseries_conds)
random.shuffle(subtilis_conds)
sub_s = subtilis_conds[0:int(len(subtilis_conds)/2)]
sub_i = iron_conds[0:int(len(iron_conds)/2)]
sub_t = timeseries_conds[0:int(len(timeseries_conds)/2)]
ba.run_both(lamP=lamP, lamR=lamR, lamS=lamS, outf='tmp', sub_s = sub_s, sub_i = sub_i, sub_t = sub_t)
sub_st = subtilis_conds[int(len(subtilis_conds)/2):len(subtilis_conds)]
sub_it = iron_conds[int(len(iron_conds)/2):len(iron_conds)]
sub_tt = timeseries_conds[int(len(timeseries_conds)/2):len(timeseries_conds)]
(bs_e, bs_t, bs_genes, bs_tfs) = ba.load_B_subtilis(sub_st)
(ba_e, ba_t, ba_genes, ba_tfs) = ba.load_B_anthracis(sub_it, sub_tt)
eval_s = ba.eval_prediction('tmp_subtilis', bs_e, bs_t, bs_genes, bs_tfs,'R2')
eval_a = ba.eval_prediction('tmp_anthracis', ba_e, ba_t, ba_genes, ba_tfs,'R2')
acc_s += eval_s
acc_a += eval_a
writestr = 'lamP=%f\tlamS=%f\tlamR=%f\tsubtilis=%f\tanthracis=%f\n' % (lamP, lamS, lamR, acc_s/repeats, acc_a/repeats)
outf.write(writestr)
print writestr
outf.close()
def multi_species_benchmarks():
repeats = 2
lamPs = np.array([1])#np.logspace(0, 3, 10)
lamSs = np.logspace(0, 2, 5)
lamRs = np.logspace(0, 2, 5)
iron_conds = range(ba.iron_conds)
timeseries_conds = range(ba.timeseries_conds)
subtilis_conds = range(ba.subtilis_conds)
outf = file('benchmark_results/multi_species_bench','w')
for lamP in lamPs:
for lamS in lamSs:
for lamR in lamRs:
acc_s = 0
acc_a = 0
for rep in range(repeats):
random.shuffle(iron_conds)
random.shuffle(timeseries_conds)
random.shuffle(subtilis_conds)
sub_s = subtilis_conds[0:int(len(subtilis_conds)/2)]
sub_i = iron_conds[0:int(len(iron_conds)/2)]
sub_t = timeseries_conds[0:int(len(timeseries_conds)/2)]
ba.run_both(lamP=lamP, lamR=lamR, lamS=lamS, outf='tmp', sub_s = sub_s, sub_i = sub_i, sub_t = sub_t)
sub_st = subtilis_conds[int(len(subtilis_conds)/2):len(subtilis_conds)]
sub_it = iron_conds[int(len(iron_conds)/2):len(iron_conds)]
sub_tt = timeseries_conds[int(len(timeseries_conds)/2):len(timeseries_conds)]
(bs_e, bs_t, bs_genes, bs_tfs) = ba.load_B_subtilis(sub_st)
(ba_e, ba_t, ba_genes, ba_tfs) = ba.load_B_anthracis(sub_it, sub_tt)
eval_s = ba.eval_prediction('tmp_subtilis', bs_e, bs_t, bs_genes, bs_tfs,'R2')
eval_a = ba.eval_prediction('tmp_anthracis', ba_e, ba_t, ba_genes, ba_tfs,'R2')
acc_s += eval_s
acc_a += eval_a
writestr = 'lamP=%f\tlamS=%f\tlamR=%f\tsubtilis=%f\tanthracis=%f\n' % (lamP, lamS, lamR, acc_s/repeats, acc_a/repeats)
outf.write(writestr)
print writestr
outf.close()
def multi_species_benchmarks2(solver):
repeats = 50
lamPs = np.array([1])#np.logspace(0, 3, 10)
lamSs = [0]#+list(np.logspace(-1.5, 1.5, 3))
lamRs = [3]#np.logspace(0, 2, 5)
iron_conds = range(ba.iron_conds)
timeseries_conds = range(ba.timeseries_conds)
subtilis_conds = range(ba.subtilis_conds)
outf = file('benchmark_results/multi_species_bench4','w')
(BS_priors, sign) = ba.load_priors('gsSDnamesWithActivitySign082213','B_subtilis')
orth = ba.load_orth('bs_ba_ortho_804',['B_anthracis','B_subtilis']) #TODO make the order impossible to get
for lamP in lamPs:
for lamS in lamSs:
for lamR in lamRs:
acc_s = 0
acc_a = 0
aupr = 0
corr = 0
for rep in range(repeats):
random.shuffle(iron_conds)
random.shuffle(timeseries_conds)
random.shuffle(subtilis_conds)
sub_s_i = int(len(subtilis_conds)/5)
sub_i_i = int(len(iron_conds)*0.75)
sub_t_i = int(len(timeseries_conds)*0.75)
sub_s = subtilis_conds[0:sub_s_i]
sub_i = iron_conds[0:sub_i_i]
sub_t = timeseries_conds[0:sub_t_i]
if solver=='run_both':
ba.run_both(lamP=lamP, lamR=lamR, lamS=lamS, outf='tmp2', sub_s = sub_s, sub_i = sub_i, sub_t = sub_t)
if solver=='run_both_adjust':
ba.run_both_adjust(lamP=lamP, lamR=lamR, lamS=lamS, outf='tmp2', sub_s=sub_s, sub_i=sub_i, sub_t=sub_t)
if solver=='run_both_refit':
ba.run_both_refit(lamP=lamP, lamR=lamR, lamS=lamS, outf='tmp2', sub_s = sub_s, sub_i = sub_i, sub_t = sub_t, it=10,k=20)
if solver=='scad':
ba.run_both(lamP=lamP, lamR=lamR, lamS=lamS, outf='tmp2', sub_s=sub_s, sub_i=sub_i, sub_t=sub_t, it=10, k=20, it_s=10)
sub_st = subtilis_conds[sub_s_i:]
sub_it = iron_conds[sub_i_i:]
sub_tt = timeseries_conds[sub_t_i:]
(bs_e, bs_t, bs_genes, bs_tfs) = ba.load_B_subtilis(sub_st)
(ba_e, ba_t, ba_genes, ba_tfs) = ba.load_B_anthracis(sub_it, sub_tt)
corr += ba.betas_fused_corr('tmp2_subtilis', 'tmp2_anthracis', orth)
print corr/rep
eval_s = ba.eval_prediction('tmp2_subtilis', bs_e, bs_t, bs_genes, bs_tfs,'R2')
eval_a = ba.eval_prediction('tmp2_anthracis', ba_e, ba_t, ba_genes, ba_tfs,'R2')
(net, bs_genes, bs_tfs) = ba.load_network('tmp2_subtilis')
aupr += ba.eval_network_pr(net, bs_genes, bs_tfs, BS_priors)
print aupr/rep
acc_s += eval_s
acc_a += eval_a
writestr = 'lamP=%f\tlamS=%f\tlamR=%f\tsubtilis=%f\tanthracis=%f\taupr=%f\tcorr_bs=%f\n' % (lamP, lamS, lamR, acc_s/repeats, acc_a/repeats, aupr/repeats, corr/repeats)
outf.write(writestr)
print writestr
outf.close()
def multi_species_benchmarks_opt1(lamSR):
repeats = 50
lamP = 1
#lamSs = [0.3,0.5,0.7,1]#+list(np.logspace(-1.5, 1.5, 3))
#lamRs = [3,5,7]#np.logspace(0, 2, 5)
iron_conds = range(ba.iron_conds)
timeseries_conds = range(ba.timeseries_conds)
subtilis_conds = range(ba.subtilis_conds)
(BS_priors, sign) = ba.load_priors('gsSDnamesWithActivitySign082213','B_subtilis')
orth = ba.load_orth('bs_ba_ortho_804',['B_anthracis','B_subtilis']) #TODO make the order impossible to get
lamR = lamSR[1]
lamS = lamSR[0]
acc_s = 0
acc_a = 0
aupr = 0
corr = 0
for rep in range(repeats):
random.shuffle(iron_conds)
random.shuffle(timeseries_conds)
random.shuffle(subtilis_conds)
sub_s_i = int(len(subtilis_conds)/8)
sub_i_i = int(len(iron_conds)*0.75)
sub_t_i = int(len(timeseries_conds)*0.75)
sub_s = subtilis_conds[0:sub_s_i]
sub_i = iron_conds[0:sub_i_i]
sub_t = timeseries_conds[0:sub_t_i]
ba.run_both_adjust(lamP=lamP, lamR=lamR, lamS=lamS, outf='tmp2', sub_s=sub_s, sub_i=sub_i, sub_t=sub_t)
sub_st = subtilis_conds[sub_s_i:]
sub_it = iron_conds[sub_i_i:]
sub_tt = timeseries_conds[sub_t_i:]
(bs_e, bs_t, bs_genes, bs_tfs) = ba.load_B_subtilis(sub_st)
(ba_e, ba_t, ba_genes, ba_tfs) = ba.load_B_anthracis(sub_it, sub_tt)
corr += ba.betas_fused_corr('tmp2_subtilis', 'tmp2_anthracis', orth)
print corr/(rep+1)
eval_s = ba.eval_prediction('tmp2_subtilis', bs_e, bs_t, bs_genes, bs_tfs,'R2')
eval_a = ba.eval_prediction('tmp2_anthracis', ba_e, ba_t, ba_genes, ba_tfs,'R2')
(net, bs_genes, bs_tfs) = ba.load_network('tmp2_subtilis')
aupr += ba.eval_network_pr(net, bs_genes, bs_tfs, BS_priors)
print aupr/(rep+1)
acc_s += eval_s
acc_a += eval_a
return aupr/repeats
def multi_species_benchmarks_opt2(lamR):
repeats = 50
lamP = 1
#lamSs = [0.3,0.5,0.7,1]#+list(np.logspace(-1.5, 1.5, 3))
#lamRs = [3,5,7]#np.logspace(0, 2, 5)
iron_conds = range(ba.iron_conds)
timeseries_conds = range(ba.timeseries_conds)
subtilis_conds = range(ba.subtilis_conds)
(BS_priors, sign) = ba.load_priors('gsSDnamesWithActivitySign082213','B_subtilis')
orth = ba.load_orth('bs_ba_ortho_804',['B_anthracis','B_subtilis']) #TODO make the order impossible to get
lamR = lamR[0]
lamS = 0
acc_s = 0
acc_a = 0
aupr = 0
corr = 0
for rep in range(repeats):
random.shuffle(iron_conds)
random.shuffle(timeseries_conds)
random.shuffle(subtilis_conds)
sub_s_i = int(len(subtilis_conds)/8)
sub_i_i = int(len(iron_conds)*0.75)
sub_t_i = int(len(timeseries_conds)*0.75)
sub_s = subtilis_conds[0:sub_s_i]
sub_i = iron_conds[0:sub_i_i]
sub_t = timeseries_conds[0:sub_t_i]
ba.run_both_adjust(lamP=lamP, lamR=lamR, lamS=lamS, outf='tmp2', sub_s=sub_s, sub_i=sub_i, sub_t=sub_t)
sub_st = subtilis_conds[sub_s_i:]
sub_it = iron_conds[sub_i_i:]
sub_tt = timeseries_conds[sub_t_i:]
(bs_e, bs_t, bs_genes, bs_tfs) = ba.load_B_subtilis(sub_st)
(ba_e, ba_t, ba_genes, ba_tfs) = ba.load_B_anthracis(sub_it, sub_tt)
corr += ba.betas_fused_corr('tmp2_subtilis', 'tmp2_anthracis', orth)
print corr/(rep+1)
eval_s = ba.eval_prediction('tmp2_subtilis', bs_e, bs_t, bs_genes, bs_tfs,'R2')
eval_a = ba.eval_prediction('tmp2_anthracis', ba_e, ba_t, ba_genes, ba_tfs,'R2')
(net, bs_genes, bs_tfs) = ba.load_network('tmp2_subtilis')
aupr += ba.eval_network_pr(net, bs_genes, bs_tfs, BS_priors)
print aupr/(rep+1)
acc_s += eval_s
acc_a += eval_a
return aupr/repeats
def optimize():
res1 = scipy.optimize.minimize(multi_species_benchmarks_opt1, [0.5, 0.5], method='Nelder-Mead', options={'maxiter':40})
res2 = scipy.optimize.minimize(multi_species_benchmarks_opt2, [0.5], method='Nelder-Mead', options={'maxiter':40})
return (res1, res2)
def test_scad():
subtilis_conds = range(ba.subtilis_conds)
random.shuffle(subtilis_conds)
sub_s = subtilis_conds#[0:int(len(subtilis_conds)*0.05)]
ba.run_both_scad(lamP=1, lamR=2, lamS=0.1, outf='sub_scad_5.0', sub_s = sub_s, sub_i = [], sub_t = [], it=1, k=50, it_s=3)
def bench_lam(s, lamS):
subtilis_conds = range(ba.subtilis_conds)
random.shuffle(subtilis_conds)
sub_s = subtilis_conds[0:int(len(subtilis_conds)*0.025)]
ba.run_both(lamP=1, lamR=2, lamS=0, outf=s+'ft_0', sub_s = sub_s, sub_i = [], sub_t = [])
ba.run_both(lamP=1, lamR=2, lamS=lamS, outf=s+'ft_s', sub_s = sub_s, sub_i = [], sub_t = [])
def rescale_points_cov(c, points):
(s, v, d) = np.linalg.svd(c)
print (s, v, d)
points = np.dot(s, np.dot(np.diag(v), points))
return points
def make_cov_ellipses(r1, r2, s):
points = np.zeros((2, 100))
thetas = np.linspace(0, 2*np.pi, 100)
for theta_i in range(100):
theta = thetas[theta_i]
points[0, theta_i] = np.cos(theta)
points[1, theta_i] = np.sin(theta)
c0 = make_cov(r1, r2, 0, True)
c1 = make_cov(r1, r2, s, True)
c2 = make_cov(r1, r2, s, False)
points0 = rescale_points_cov(c0, points)
points1 = rescale_points_cov(c1, points)
points2 = rescale_points_cov(c2, points)
from matplotlib import pyplot as plt
plt.plot(points0[0,:], points0[1,:], 'r')
plt.hold(True)
plt.plot(points2[0,:], points2[1,:], 'g')
plt.plot(points1[0,:], points1[1,:], 'b')
plt.legend(['no fusion', 'unadjusted ridge', 'adjusted ridge'])
plt.xlim(-2,2)
plt.ylim(-2,2)
plt.xlabel('coefficient 1')
plt.ylabel('coefficient 2')
plt.show()
def make_invcov(R1, R2, S, adj):
if adj:
(R1, R2) = fl.adjust(R1, R2, S)
einv1 = np.zeros((2,2))
einv2 = np.zeros((2,2))
einv1[0,0] = S + R1
einv1[0,1] = -S
einv1[1,0] = -S
einv1[1,1] = S+R2
return einv1
def make_cov(r1, r2, s, adj):
e1 = make_invcov(r1, r2, s, adj)
return np.linalg.inv(e1)
#make a pretty picture
def draw_scad(lamS, a):
from matplotlib import pyplot as plt
b1vals = np.linspace(-10,10,100)
fuse_constraint = fl.constraint(fl.coefficient(0, 0, 0), fl.coefficient(1, 0, 0), 1)
fuse_constraints = [fuse_constraint]
b2 = np.zeros((1,1))
b1 = np.zeros((1,1))
penalties = []
for b1val in b1vals:
b1[0,0] = b1val
fc = fl.scad([b1, b2], fuse_constraints, lamS, a)
penalties.append(fc[0].lam)
pv = np.cumsum(penalties*b1vals)
plt.plot(b1vals, pv - np.min(pv))
plt.hold(True)
plt.plot([a, a], [0, np.max(pv-np.min(pv))], '--k')
plt.plot([-a, -a], [0, np.max(pv-np.min(pv))], '--k')
plt.plot([lamS, lamS], [0, np.max(pv-np.min(pv))], 'k')
plt.plot([-lamS, -lamS], [0, np.max(pv-np.min(pv))], 'k')
#plt.ylim(0,12)
plt.xlabel('|B0 - B1|')
plt.ylabel('saturating penalty')
#plt.figure()
#plt.plot(b1vals, penalties)
#plt.ylim(-0.1,1.1)
#plt.xlabel('|B0 - B1|')
#plt.ylabel('L2 fusion penalty weight')
#plt.show()
#multi_species_benchmarks2()