def test_branch_likelihood_hky(self):
"""Test likelihood function"""
# params
bgfreq = [0.2, 0.3, 0.3, 0.2]
kappa = 1.59
seqlen = 100
div = 0.1
t0 = 0
t1 = 0.5
step = 0.001
# prep probabilities
probs1 = []
probs2 = []
for i in xrange(seqlen):
if random.random() < div:
for j in xrange(4):
probs1.append(0.01)
probs2.append(0.01)
k = random.randint(1, 4)
probs1[-k] = 1.0
k = (k % 4) + 1
probs2[-k] = 1.0
else:
for j in xrange(4):
probs1.append(0.2)
probs2.append(0.2)
k = random.randint(1, 4)
probs1[-k] = 1.0
probs2[-k] = 1.0
# estimate MLE
mle = spidir.mle_distance_hky(probs1, probs2, seqlen, bgfreq, kappa, t0, t1)
x = list(frange(0, 0.5, 0.01))
y = [spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
top = x[argmax(y)]
print div, top, mle
prep_dir("test/output/branch_likelihood/")
rplot_start("test/output/branch_likelihood/branch_function.pdf")
rplot("plot", x, y, t="l", xlab="distance", ylab="likelihood")
# rp.lines([div, div], [-1e300, 0], col="green")
rp.lines([top, top], [-1e300, 0], col="blue")
rp.lines([mle, mle], [-1e300, 0], col="red")
rplot_end(True)
# ================================
# 1st derivative
x = list(frange(0, 0.5, 0.01))
dy = [spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
dy2 = [
(
spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa, t + 0.01)
- spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa, t)
)
/ 0.01
for t in x
]
rplot_start("test/output/branch_likelihood/deriv_branch_function.pdf")
rplot("plot", x, dy2, t="l", xlab="distance", ylab="d/dt likelihood")
rp.lines([min(x), max(x)], [0, 0], col="black")
rp.lines(x, dy, col="grey")
# rp.lines([div, div], [-1e300, 0], col="green")
rp.lines([top, top], [-1e300, 0], col="blue")
rp.lines([mle, mle], [-1e300, 0], col="red")
rplot_end(True)
# =============================
# 2nd derivative
x = list(frange(0, 0.5, 0.01))
d2y = [spidir.branch_likelihood_hky_deriv2(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
d2y2 = [
(
spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq, kappa, t + 0.01)
- spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq, kappa, t)
)
/ 0.01
for t in x
]
rplot_start("test/output/branch_likelihood/deriv2_branch_function.pdf")
rplot("plot", x, d2y2, t="l", xlab="distance", ylab="d^2/dt^2 likelihood")
rp.lines([min(x), max(x)], [0, 0], col="black")
rp.lines(x, d2y, col="grey")
# rp.lines([div, div], [-1e300, 0], col="green")
# rp.lines([top, top], [-1e300, 0], col="blue")
# rp.lines([mle, mle], [-1e300, 0], col="red")
rplot_end(True)
def test_branch_likelihood_hky(self):
"""Test likelihood function"""
# params
bgfreq = [.2, .3, .3, .2]
kappa = 1.59
seqlen = 100
div = .1
t0 = 0
t1 = .5
step = .001
# prep probabilities
probs1 = []
probs2 = []
for i in xrange(seqlen):
if random.random() < div:
for j in xrange(4):
probs1.append(.01)
probs2.append(.01)
k = random.randint(1, 4)
probs1[-k] = 1.0
k = (k % 4) + 1
probs2[-k] = 1.0
else:
for j in xrange(4):
probs1.append(.2)
probs2.append(.2)
k = random.randint(1, 4)
probs1[-k] = 1.0
probs2[-k] = 1.0
# estimate MLE
mle = spidir.mle_distance_hky(probs1, probs2, seqlen, bgfreq, kappa,
t0, t1)
x = list(frange(0, .5, .01))
y = [
spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa,
t) for t in x
]
top = x[argmax(y)]
print div, top, mle
prep_dir("test/output/branch_likelihood/")
rplot_start("test/output/branch_likelihood/branch_function.pdf")
rplot("plot", x, y, t="l", xlab="distance", ylab="likelihood")
#rp.lines([div, div], [-1e300, 0], col="green")
rp.lines([top, top], [-1e300, 0], col="blue")
rp.lines([mle, mle], [-1e300, 0], col="red")
rplot_end(True)
#================================
# 1st derivative
x = list(frange(0, .5, .01))
dy = [
spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq,
kappa, t) for t in x
]
dy2 = [(spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq,
kappa, t + .01) -
spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq,
kappa, t)) / .01 for t in x]
rplot_start("test/output/branch_likelihood/deriv_branch_function.pdf")
rplot("plot", x, dy2, t="l", xlab="distance", ylab="d/dt likelihood")
rp.lines([min(x), max(x)], [0, 0], col="black")
rp.lines(x, dy, col="grey")
#rp.lines([div, div], [-1e300, 0], col="green")
rp.lines([top, top], [-1e300, 0], col="blue")
rp.lines([mle, mle], [-1e300, 0], col="red")
rplot_end(True)
#=============================
# 2nd derivative
x = list(frange(0, .5, .01))
d2y = [
spidir.branch_likelihood_hky_deriv2(probs1, probs2, seqlen, bgfreq,
kappa, t) for t in x
]
d2y2 = [(spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
bgfreq, kappa, t + .01) -
spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
bgfreq, kappa, t)) / .01
for t in x]
rplot_start("test/output/branch_likelihood/deriv2_branch_function.pdf")
rplot("plot",
x,
d2y2,
t="l",
xlab="distance",
ylab="d^2/dt^2 likelihood")
rp.lines([min(x), max(x)], [0, 0], col="black")
rp.lines(x, d2y, col="grey")
#rp.lines([div, div], [-1e300, 0], col="green")
#rp.lines([top, top], [-1e300, 0], col="blue")
#rp.lines([mle, mle], [-1e300, 0], col="red")
rplot_end(True)
def _test_calc_lktable_row(self):
"""test the function CalcLktbaleRow"""
def branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):
model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
model2 = spidir.make_hky_matrix(bgfreq, kappa, 0)
logl = 0.0
for j in xrange(seqlen):
s = sum(
bgfreq[k]
* sum(model1[k][x] * probs1[4 * j + x] for x in xrange(4))
* sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
for k in xrange(4)
)
logl += safelog(s, e)
return logl
def dbranchlk(probs1, probs2, seqlen, bgfreq, kappa, t):
model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)
dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)
logl = 0.0
for j in xrange(seqlen):
ds = sum(
bgfreq[k]
* sum(dmodel1[k][x] * probs1[4 * j + x] for x in xrange(4))
* sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
for k in xrange(4)
)
s = sum(
bgfreq[k]
* sum(model1[k][x] * probs1[4 * j + x] for x in xrange(4))
* sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
for k in xrange(4)
)
logl += safediv(ds, s, INF)
return logl
def d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):
model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)
dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)
d2model1 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, t)
d2model2 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, 0.0)
logl = 0.0
for j in xrange(seqlen):
g = sum(
bgfreq[k]
* sum(model1[k][x] * probs1[4 * j + x] for x in xrange(4))
* sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
for k in xrange(4)
)
dg = sum(
bgfreq[k]
* sum(dmodel1[k][x] * probs1[4 * j + x] for x in xrange(4))
* sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
for k in xrange(4)
)
d2g = sum(
bgfreq[k]
* sum(d2model1[k][x] * probs1[4 * j + x] for x in xrange(4))
* sum(model2[k][y] * probs2[4 * j + y] for y in xrange(4))
for k in xrange(4)
)
logl += -safediv(dg * dg, g * g, INF) + safediv(d2g, g, INF)
return logl
bgfreq = [0.25, 0.25, 0.25, 0.25]
kappa = 1.59
seqlen = 100
# prep probabilities
div = 0.1
probs1 = []
probs2 = []
for i in xrange(seqlen):
if random.random() < div:
for j in xrange(4):
probs1.append(0.01)
probs2.append(0.01)
k = random.randint(1, 4)
probs1[-k] = 1.0
k = (k % 4) + 1
probs2[-k] = 1.0
else:
for j in xrange(4):
probs1.append(0.2)
probs2.append(0.2)
k = random.randint(1, 4)
probs1[-k] = 1.0
probs2[-k] = 1.0
# probs1 = [0.0, 0.0, 1.0, 0.0] + \
# [1.0, 0.0, 0.0, 0.0] * 5
# probs2 = [0.0, 0.0, 0.0, 1.0] + \
# [1.0, 0.0, 0.0, 0.0] * 5
# seqlen = len(probs1) / 4
x = list(frange(0, 1.0, 0.01))
y = [spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
y2 = [branchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
prep_dir("test/output/branch_likelihood_simple/")
rplot_start("test/output/branch_likelihood_simple/cmp_c_py.pdf")
rplot("plot", x, y, t="l")
rp.lines(x, y2, t="l", col="red")
rplot_end(True)
x = list(frange(0, 1.0, 0.01))
y = [spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
y2 = [dbranchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
rplot_start("test/output/branch_likelihood_simple/cmp_c_py_deriv.pdf")
rplot("plot", x, y, t="l")
rp.lines(x, y2, t="l", col="red")
rplot_end(True)
x = list(frange(0, 1.0, 0.01))
y = [spidir.branch_likelihood_hky_deriv2(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
# y = [(spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
# bgfreq, kappa, t+.01) -
# spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
# bgfreq, kappa, t)) / .01
# for t in x]
y2 = [d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
rplot_start("test/output/branch_likelihood_simple/cmp_c_py_deriv2.pdf")
rplot("plot", x, y, t="l")
rp.lines(x, y2, t="l", col="red")
rplot_end(True)
def _test_calc_lktable_row(self):
"""test the function CalcLktbaleRow"""
def branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):
model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
model2 = spidir.make_hky_matrix(bgfreq, kappa, 0)
logl = 0.0
for j in xrange(seqlen):
s = sum(bgfreq[k] * sum(model1[k][x] * probs1[4 * j + x]
for x in xrange(4)) *
sum(model2[k][y] * probs2[4 * j + y]
for y in xrange(4)) for k in xrange(4))
logl += safelog(s, e)
return logl
def dbranchlk(probs1, probs2, seqlen, bgfreq, kappa, t):
model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)
dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)
logl = 0.0
for j in xrange(seqlen):
ds = sum(bgfreq[k] * sum(dmodel1[k][x] * probs1[4 * j + x]
for x in xrange(4)) *
sum(model2[k][y] * probs2[4 * j + y]
for y in xrange(4)) for k in xrange(4))
s = sum(bgfreq[k] * sum(model1[k][x] * probs1[4 * j + x]
for x in xrange(4)) *
sum(model2[k][y] * probs2[4 * j + y]
for y in xrange(4)) for k in xrange(4))
logl += safediv(ds, s, INF)
return logl
def d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t):
model1 = spidir.make_hky_matrix(bgfreq, kappa, t)
model2 = spidir.make_hky_matrix(bgfreq, kappa, 0.0)
dmodel1 = spidir.make_hky_deriv_matrix(bgfreq, kappa, t)
dmodel2 = spidir.make_hky_deriv_matrix(bgfreq, kappa, 0.0)
d2model1 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, t)
d2model2 = spidir.make_hky_deriv2_matrix(bgfreq, kappa, 0.0)
logl = 0.0
for j in xrange(seqlen):
g = sum(bgfreq[k] * sum(model1[k][x] * probs1[4 * j + x]
for x in xrange(4)) *
sum(model2[k][y] * probs2[4 * j + y]
for y in xrange(4)) for k in xrange(4))
dg = sum(bgfreq[k] * sum(dmodel1[k][x] * probs1[4 * j + x]
for x in xrange(4)) *
sum(model2[k][y] * probs2[4 * j + y]
for y in xrange(4)) for k in xrange(4))
d2g = sum(bgfreq[k] * sum(d2model1[k][x] * probs1[4 * j + x]
for x in xrange(4)) *
sum(model2[k][y] * probs2[4 * j + y]
for y in xrange(4)) for k in xrange(4))
logl += - safediv(dg*dg, g*g, INF) + \
safediv(d2g, g, INF)
return logl
bgfreq = [.25, .25, .25, .25]
kappa = 1.59
seqlen = 100
# prep probabilities
div = .1
probs1 = []
probs2 = []
for i in xrange(seqlen):
if random.random() < div:
for j in xrange(4):
probs1.append(.01)
probs2.append(.01)
k = random.randint(1, 4)
probs1[-k] = 1.0
k = (k % 4) + 1
probs2[-k] = 1.0
else:
for j in xrange(4):
probs1.append(.2)
probs2.append(.2)
k = random.randint(1, 4)
probs1[-k] = 1.0
probs2[-k] = 1.0
#probs1 = [0.0, 0.0, 1.0, 0.0] + \
# [1.0, 0.0, 0.0, 0.0] * 5
#probs2 = [0.0, 0.0, 0.0, 1.0] + \
# [1.0, 0.0, 0.0, 0.0] * 5
#seqlen = len(probs1) / 4
x = list(frange(0, 1.0, .01))
y = [
spidir.branch_likelihood_hky(probs1, probs2, seqlen, bgfreq, kappa,
t) for t in x
]
y2 = [branchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
prep_dir("test/output/branch_likelihood_simple/")
rplot_start("test/output/branch_likelihood_simple/cmp_c_py.pdf")
rplot("plot", x, y, t="l")
rp.lines(x, y2, t="l", col="red")
rplot_end(True)
x = list(frange(0, 1.0, .01))
y = [
spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen, bgfreq,
kappa, t) for t in x
]
y2 = [dbranchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
rplot_start("test/output/branch_likelihood_simple/cmp_c_py_deriv.pdf")
rplot("plot", x, y, t="l")
rp.lines(x, y2, t="l", col="red")
rplot_end(True)
x = list(frange(0, 1.0, .01))
y = [
spidir.branch_likelihood_hky_deriv2(probs1, probs2, seqlen, bgfreq,
kappa, t) for t in x
]
#y = [(spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
# bgfreq, kappa, t+.01) -
# spidir.branch_likelihood_hky_deriv(probs1, probs2, seqlen,
# bgfreq, kappa, t)) / .01
# for t in x]
y2 = [d2branchlk(probs1, probs2, seqlen, bgfreq, kappa, t) for t in x]
rplot_start("test/output/branch_likelihood_simple/cmp_c_py_deriv2.pdf")
rplot("plot", x, y, t="l")
rp.lines(x, y2, t="l", col="red")
rplot_end(True)
