def g(params):
"""differentiable piece of objective in η problem."""
y = params[1:]
if log_transform:
M = utils.M(self.n, t, np.exp(y))
else:
M = utils.M(self.n, t, y)
L = self.C @ M
ξ = self.mu0 * L.sum(1)
if folded:
ξ = utils.fold(ξ)
else:
r = expit(params[0])
ξ = (1 - r) * ξ + r * self.AM_freq @ ξ
loss_term = loss(np.squeeze(ξ), x)
y_delta = y - y_ref
ridge_term = (ridge_penalty / 2) * (y_delta.T @ Γ @ y_delta)
return loss_term + ridge_term
def set_eta(self, eta: hst.eta):
r"""Set pre-specified demographic history :math:`\eta(t)`
Args:
eta: demographic history object
"""
self.η = eta
t = self.η.arrays()[0]
self.M = utils.M(self.n, t, self.η.y)
self.L = self.C @ self.M
self.r = 0
def g(logy):
"""differentiable piece of objective in η problem"""
L = self.C @ utils.M(self.n, t, np.exp(logy))
if mask is not None:
loss_term = loss(z, x[mask, :], L[mask, :])
else:
loss_term = loss(z, x, L)
spline_term = (α_spline / 2) * ((D1 @ logy)**2).sum()
# generalized Tikhonov
logy_delta = logy - logy_ref
ridge_term = (α_ridge / 2) * (logy_delta.T @ Γ @ logy_delta)
return loss_term + spline_term + ridge_term
def test_constant_history(self):
u"""test expected SFS under constant demography and mutation rate
against the analytic formula from Fu (1995)
"""
n = 198
η0 = 3e4
μ0 = 40
change_points = np.array([])
η = histories.eta(change_points, np.array([η0]))
t, y = η.arrays()
μ = histories.mu(change_points, np.array([[μ0]]))
ξ_mushi = np.squeeze(utils.C(n) @ utils.M(n, t, y) @ μ.Z)
ξ_Fu = 2 * η0 * μ0 / np.arange(1, n)
self.assertTrue(np.isclose(ξ_mushi, ξ_Fu).all(),
msg=f'\nξ_mushi:\n{ξ_mushi}\nξ_Fu:\n{ξ_Fu}')
def simulate(self,
eta: hst.eta,
mu: Union[hst.mu, np.float64],
seed: int = None) -> None:
r"""Simulate a SFS under the Poisson random field model (no linkage)
assigns simulated SFS to ``X`` attribute
Args:
eta: demographic history
mu: mutation spectrum history (or constant rate)
seed: random seed
"""
onp.random.seed(seed)
t, y = eta.arrays()
M = utils.M(self.n, t, y)
L = self.C @ M
if type(mu) == hst.mu:
if not eta.check_grid(mu):
raise ValueError('η(t) and μ(t) must use the same time grid')
else:
mu = hst.mu(eta.change_points, mu * np.ones_like(y))
self.X = poisson.rvs(L @ mu.Z)
self.mutation_types = mu.mutation_types
def infer_history(
self, # noqa: C901
change_points: np.array,
mu0: np.float64,
eta: hst.eta = None,
eta_ref: hst.eta = None,
mu_ref: hst.mu = None,
infer_eta: bool = True,
infer_mu: bool = True,
alpha_tv: np.float64 = 0,
alpha_spline: np.float64 = 0,
alpha_ridge: np.float64 = 0,
beta_tv: np.float64 = 0,
beta_spline: np.float64 = 0,
beta_rank: np.float64 = 0,
hard: bool = False,
beta_ridge: np.float64 = 0,
max_iter: int = 1000,
s0: int = 1,
max_line_iter=100,
gamma: np.float64 = 0.8,
tol: np.float64 = 0,
loss: str = 'prf',
mask: np.array = None,
verbose: bool = False) -> None:
r"""Perform sequential inference to fit :math:`\eta(t)` and
:math:`\mu(t)`
Args:
change_points: epoch change points (ordered times > 0)
mu0: total mutation rate (per genome per generation)
eta: initial demographic history. By default, a
constant MLE is computed
eta_ref: reference demographic history for ridge penalty. If
``None``, the constant MLE is used
mu_ref: reference MuSH for ridge penalty. If None, the constant
MLE is used
infer_eta: perform :math:`\eta` inference if ``True``
infer_mu: perform :math:`\mu` inference if ``True``
loss: loss function, 'prf' for Poisson random field, 'kl' for
Kullback-Leibler divergence, 'lsq' for least-squares
mask: array of bools, with False indicating exclusion of that
frequency
alpha_tv: total variation penalty on :math:`\eta(t)`
alpha_spline: L2 on first differences penalty on :math:`\eta(t)`
alpha_ridge: L2 for strong convexity penalty on :math:`\eta(t)`
hard: hard rank penalty on :math:`\mu(t)` (non-convex)
beta_tv: total variation penalty on :math:`\mu(t)`
beta_spline: penalty on :math:`\mu(t)`
beta_rank: rank penalty on :math:`\mu(t)`
beta_ridge: L2 penalty on :math:`\mu(t)`
max_iter: maximum number of proximal gradient steps
tol: relative tolerance in objective function (if ``0``, not used)
s0: max step size
max_line_iter: maximum number of line search steps
gamma: step size shrinkage rate for line search
verbose: print verbose messages if ``True``
"""
# pithify reg paramter names
α_tv = alpha_tv
α_spline = alpha_spline
α_ridge = alpha_ridge
β_tv = beta_tv
β_spline = beta_spline
β_rank = beta_rank
β_ridge = beta_ridge
assert self.X is not None, 'use simulate() to generate data first'
if self.X is None:
raise ValueError('use simulate() to generate data first')
if mask is not None:
assert len(mask) == self.X.shape[0], 'mask must have n-1 elements'
# ininitialize with MLE constant η and μ
x = self.X.sum(1, keepdims=True)
μ_total = hst.mu(change_points,
mu0 * np.ones((len(change_points) + 1, 1)))
t, z = μ_total.arrays()
# number of segregating variants in each mutation type
S = self.X.sum(0, keepdims=True)
if eta is not None:
self.η = eta
elif self.η is None:
# Harmonic number
H = (1 / np.arange(1, self.n - 1)).sum()
# constant MLE
y = (S.sum() / 2 / H / mu0) * np.ones(len(z))
self.η = hst.eta(change_points, y)
μ_const = hst.mu(self.η.change_points,
mu0 * (S / S.sum()) * np.ones(
(self.η.m, self.X.shape[1])),
mutation_types=self.mutation_types.values)
if self.μ is None:
self.μ = μ_const
self.M = utils.M(self.n, t, self.η.y)
self.L = self.C @ self.M
# badness of fit
if loss == 'prf':
def loss(*args, **kwargs):
return -utils.prf(*args, **kwargs)
elif loss == 'kl':
loss = utils.d_kl
elif loss == 'lsq':
loss = utils.lsq
else:
raise ValueError(f'unrecognized loss argument {loss}')
# some matrices we'll need for the first difference penalties
D = (np.eye(self.η.m, k=0) - np.eye(self.η.m, k=-1))
# W matrix deals with boundary condition
W = np.eye(self.η.m)
W = index_update(W, index[0, 0], 0)
D1 = W @ D # 1st difference matrix
# D2 = D1.T @ D1 # 2nd difference matrix
if infer_eta:
if verbose:
print('inferring η(t)', flush=True)
# Accelerated proximal gradient method: our objective function
# decomposes as f = g + h, where g is differentiable and h is not.
# https://people.eecs.berkeley.edu/~elghaoui/Teaching/EE227A/lecture18.pdf
# Tikhonov matrix
if eta_ref is None:
eta_ref = self.η
Γ = np.diag(np.ones_like(eta_ref.y))
else:
# - log(1 - CDF)
Γ = np.diag(-np.log(utils.tmrca_sf(t, eta_ref.y, self.n))[:-1])
logy_ref = np.log(eta_ref.y)
@jit
def g(logy):
"""differentiable piece of objective in η problem"""
L = self.C @ utils.M(self.n, t, np.exp(logy))
if mask is not None:
loss_term = loss(z, x[mask, :], L[mask, :])
else:
loss_term = loss(z, x, L)
spline_term = (α_spline / 2) * ((D1 @ logy)**2).sum()
# generalized Tikhonov
logy_delta = logy - logy_ref
ridge_term = (α_ridge / 2) * (logy_delta.T @ Γ @ logy_delta)
return loss_term + spline_term + ridge_term
if α_tv > 0:
@jit
def h(logy):
"""nondifferentiable piece of objective in η problem"""
return α_tv * np.abs(D1 @ logy).sum()
def prox(logy, s):
"""total variation prox operator"""
return ptv.tv1_1d(logy, s * α_tv)
else:
@jit
def h(logy):
return 0
def prox(logy, s):
return logy
# initial iterate
logy = np.log(self.η.y)
logy = opt.acc_prox_grad_method(logy,
g,
jit(grad(g)),
h,
prox,
tol=tol,
max_iter=max_iter,
s0=s0,
max_line_iter=max_line_iter,
gamma=gamma,
verbose=verbose)
y = np.exp(logy)
self.η = hst.eta(self.η.change_points, y)
self.M = utils.M(self.n, t, y)
self.L = self.C @ self.M
if infer_mu and len(self.mutation_types) > 1:
if verbose:
print('inferring μ(t) conditioned on η(t)', flush=True)
if mu_ref is None:
mu_ref = μ_const
# Tikhonov matrix
Γ = np.diag(np.ones_like(self.η.y))
else:
# - log(1 - CDF)
Γ = np.diag(-np.log(utils.tmrca_sf(t, self.η.y, self.n))[:-1])
# orthonormal basis for Aitchison simplex
# NOTE: instead of Gram-Schmidt could try SVD of clr transformed X
# https://en.wikipedia.org/wiki/Compositional_data#Isometric_logratio_transform
basis = cmp._gram_schmidt_basis(self.μ.Z.shape[1])
# initial iterate in inverse log-ratio transform
Z = cmp.ilr(self.μ.Z, basis)
Z_const = cmp.ilr(μ_const.Z, basis)
Z_ref = cmp.ilr(mu_ref.Z, basis)
@jit
def g(Z):
"""differentiable piece of objective in μ problem"""
if mask is not None:
loss_term = loss(mu0 * cmp.ilr_inv(Z, basis),
self.X[mask, :], self.L[mask, :])
else:
loss_term = loss(mu0 * cmp.ilr_inv(Z, basis), self.X,
self.L)
spline_term = (β_spline / 2) * ((D1 @ Z)**2).sum()
# generalized Tikhonov
Z_delta = Z - Z_ref
ridge_term = (β_ridge / 2) * np.trace(Z_delta.T @ Γ @ Z_delta)
return loss_term + spline_term + ridge_term
if β_tv and β_rank:
@jit
def h1(Z):
"""1st nondifferentiable piece of objective in μ problem"""
return β_tv * np.abs(D1 @ Z).sum()
shape = Z.T.shape
w = β_tv * onp.ones(shape)
w[:, -1] = 0
w = w.flatten()[:-1]
def prox1(Z, s):
"""total variation prox operator on row dimension
"""
return ptv.tv1w_1d(Z.T, s * w).reshape(shape).T
@jit
def h2(Z):
"""2nd nondifferentiable piece of objective in μ problem"""
σ = np.linalg.svd(Z - Z_const, compute_uv=False)
return β_rank * np.linalg.norm(σ, 0 if hard else 1)
def prox2(Z, s):
"""singular value thresholding"""
U, σ, Vt = np.linalg.svd(Z - Z_const, full_matrices=False)
if hard:
σ = index_update(σ, index[σ <= s * β_rank], 0)
else:
σ = np.maximum(0, σ - s * β_rank)
Σ = np.diag(σ)
return Z_const + U @ Σ @ Vt
Z = opt.three_op_prox_grad_method(Z,
g,
jit(grad(g)),
h1,
prox1,
h2,
prox2,
tol=tol,
max_iter=max_iter,
s0=s0,
max_line_iter=max_line_iter,
gamma=gamma,
ls_tol=0,
verbose=verbose)
else:
if β_tv:
@jit
def h(Z):
"""nondifferentiable piece of objective in μ problem"""
return β_tv * np.abs(D1 @ Z).sum()
shape = Z.T.shape
w = β_tv * onp.ones(shape)
w[:, -1] = 0
w = w.flatten()[:-1]
def prox(Z, s):
"""total variation prox operator on row dimension
"""
return ptv.tv1w_1d(Z.T, s * w).reshape(shape).T
elif β_rank:
@jit
def h(Z):
"""nondifferentiable piece of objective in μ problem"""
σ = np.linalg.svd(Z - Z_const, compute_uv=False)
return β_rank * np.linalg.norm(σ, 0 if hard else 1)
def prox(Z, s):
"""singular value thresholding"""
U, σ, Vt = np.linalg.svd(Z - Z_const,
full_matrices=False)
if hard:
σ = index_update(σ, index[σ <= s * β_rank], 0)
else:
σ = np.maximum(0, σ - s * β_rank)
Σ = np.diag(σ)
return Z_const + U @ Σ @ Vt
else:
@jit
def h(Z):
return 0
@jit
def prox(Z, s):
return Z
Z = opt.acc_prox_grad_method(Z,
g,
jit(grad(g)),
h,
prox,
tol=tol,
max_iter=max_iter,
s0=s0,
max_line_iter=max_line_iter,
gamma=gamma,
verbose=verbose)
self.μ = hst.mu(self.η.change_points,
mu0 * cmp.ilr_inv(Z, basis),
mutation_types=self.mutation_types.values)
def infer_mush(
self,
*trend_penalty: Tuple[int, np.float64],
ridge_penalty: np.float64 = 0,
rank_penalty: np.float64 = 0,
hard: bool = False,
mu_ref: hst.mu = None,
misid_penalty: np.float64 = 1e-4,
loss: str = "prf",
max_iter: int = 100,
tol: np.float64 = 0,
line_search_kwargs: Dict = {},
trend_kwargs: Dict = {},
verbose: bool = False,
) -> None:
r"""Infer mutation spectrum history :math:`\mu(t)`
Args:
trend_penalty: tuple ``(k, λ)`` for :math:`k`-th order trend
penalty (can pass multiple for mixed trends)
ridge_penalty: ridge penalty
rank_penalty: rank penalty
hard: hard rank penalty (non-convex)
mu_ref: reference MuSH for ridge penalty. If None, the constant
MLE is used
misid_penalty: ridge parameter to shrink misid rates to aggregate
rate
loss: loss function from :mod:`~mushi.loss_functions` module
max_iter: maximum number of optimization steps
tol: relative tolerance in objective function (if ``0``, not used)
line_search_kwargs: line search keyword arguments,
see :py:class:`mushi.optimization.LineSearcher`
trend_kwargs: keyword arguments for trend filtering,
see :py:meth:`mushi.optimization.TrendFilter.run`
verbose: print verbose messages if ``True``
Examples:
Suppose ``ksfs`` is a ``kSFS`` object, and the demography has
already been fit with ``infer_eta``. The following fits a
mutation spectrum history with 0-th order (piecewise constant) trend
penalization of strength 100.
>>> ksfs.infer_mush((0, 1e2))
Alternatively, a mixed trend solution, with constant and cubic
pieces, and with rank penalization 100, is fit with
>>> ksfs.infer_mush((0, 1e2), (3, 1e1), rank_penalty=1e2)
The attribute ``ksfs.mu`` is now set and accessable for plotting
(see :class:`~mushi.mu`).
"""
if self.X is None:
raise TypeError("use simulate() to generate data first")
self._check_eta()
if self.r is None or self.r == 0:
raise ValueError("ancestral misidentification rate has not been "
"inferred, possibly due to folded SFS inference")
if self.mutation_types is None:
raise ValueError("k-SFS must contain multiple mutation types")
# number of segregating variants in each mutation type
S = self.X.sum(0, keepdims=True)
# ininitialize with MLE constant μ
μ_const = hst.mu(
self.η.change_points,
self.mu0 * (S / S.sum()) * np.ones((self.η.m, self.X.shape[1])),
mutation_types=self.mutation_types.values,
)
if self.μ is None:
self.μ = μ_const
t = μ_const.arrays()[0]
self.M = utils.M(self.n, t, self.η.y)
self.L = self.C @ self.M
# badness of fit
loss = getattr(loss_functions, loss)
# rescale trend penalties to be comparable between orders and time grids
# filter zeros from trend penalties
trend_penalty = tuple((k, (self.μ.m**k / onp.math.factorial(k)) * λ)
for k, λ in trend_penalty if λ > 0)
if mu_ref is None:
mu_ref = μ_const
# Tikhonov matrix
Γ = np.diag(np.ones_like(self.η.y))
else:
self.μ.check_grid(mu_ref)
# - log(1 - CDF)
Γ = np.diag(-np.log(utils.tmrca_sf(t, self.η.y, self.n))[:-1])
# orthonormal basis for Aitchison simplex
# NOTE: instead of Gram-Schmidt could try SVD of clr transformed X
# https://en.wikipedia.org/wiki/Compositional_data#Isometric_logratio_transform
basis = cmp._gram_schmidt_basis(self.μ.Z.shape[1])
check_orth = True if self.μ.Z.shape[1] > 2 else False
# constand MLE and reference mush
Z_const = cmp.ilr(μ_const.Z, basis, check_orth)
Z_ref = cmp.ilr(mu_ref.Z, basis, check_orth)
# weights for relating misid rates to aggregate misid rate from eta step
misid_weights = self.X.sum(0) / self.X.sum()
# reference composition for weighted misid (if all rates are equal)
misid_ref = cmp.ilr(misid_weights, basis, check_orth)
# In the following, params will hold the weighted misid composition in
# the first row and the mush composition at each time in the remaining rows
@jit
def g(params):
"""differentiable piece of objective in μ problem."""
r = self.r * cmp.ilr_inv(params[0, :], basis) / misid_weights
Z = params[1:, :]
Ξ = self.L @ (self.mu0 * cmp.ilr_inv(Z, basis))
Ξ = Ξ * (1 - r) + self.AM_freq @ Ξ @ (self.AM_mut *
r[:, np.newaxis])
loss_term = loss(Ξ, self.X)
Z_delta = Z - Z_ref
ridge_term = (ridge_penalty / 2) * np.sum(Z_delta * (Γ @ Z_delta))
misid_delta = params[0, :] - misid_ref
misid_ridge_term = misid_penalty * np.sum(misid_delta**2)
return loss_term + ridge_term + misid_ridge_term
if trend_penalty:
@jit
def h_trend(params):
"""trend filtering penalty."""
return sum(
λ *
np.linalg.norm(np.diff(params[1:, :], k + 1, axis=0), 1)
for k, λ in trend_penalty)
def prox_trend(params, s):
"""trend filtering prox operator (no jit due to ptv module)"""
k, sλ = zip(*((k, s * λ) for k, λ in trend_penalty))
trend_filterer = opt.TrendFilter(k, sλ)
return params.at[1:, :].set(
trend_filterer.run(params[1:, :], **trend_kwargs))
if rank_penalty:
if self.mutation_types.size < 3:
raise ValueError(
"kSFS must have more than 2 mutation types for"
" rank penalization")
@jit
def h_rank(params):
"""2nd nondifferentiable piece of objective in μ problem."""
if hard:
return rank_penalty * np.linalg.matrix_rank(params[1:, :] -
Z_const)
# else:
return rank_penalty * np.linalg.norm(params[1:, :] - Z_const,
"nuc")
def prox_rank(params, s):
"""singular value thresholding."""
U, σ, Vt = np.linalg.svd(params[1:, :] - Z_const,
full_matrices=False)
if hard:
σ = σ.at[σ <= s * rank_penalty].set(0)
else:
σ = np.maximum(0, σ - s * rank_penalty)
Σ = np.diag(σ)
return params.at[1:, :].set(Z_const + U @ Σ @ Vt)
if not hard:
prox_rank = jit(prox_rank)
# optimizer
if trend_penalty and rank_penalty:
optimizer = opt.ThreeOpProxGrad(
g,
jit(grad(g)),
h_trend,
prox_trend,
h_rank,
prox_rank,
verbose=verbose,
**line_search_kwargs,
)
else:
if trend_penalty:
h = h_trend
prox = prox_trend
elif rank_penalty:
h = h_rank
prox = prox_rank
else:
@jit
def h(params):
return 0
@jit
def prox(params, s):
return params
optimizer = opt.AccProxGrad(g,
jit(grad(g)),
h,
prox,
verbose=verbose,
**line_search_kwargs)
# initial point (note initial row is for misid rates)
# ---------------------------------------------------
params = np.zeros((self.μ.m + 1, self.mutation_types.size - 1))
# misid rate for each mutation type
if self.r_vector is not None:
r = self.r_vector
else:
r = self.r * np.ones(self.mutation_types.size)
params = params.at[0, :].set(
cmp.ilr(misid_weights * r, basis, check_orth))
# ilr transformed mush
ilr_mush = cmp.ilr(self.μ.Z, basis, check_orth)
# make sure it's a column vector if only 2 mutation types
if ilr_mush.ndim == 1:
ilr_mush = ilr_mush[:, np.newaxis]
params = params.at[1:, :].set(ilr_mush)
# ---------------------------------------------------
# run optimizer
params = optimizer.run(params, tol=tol, max_iter=max_iter)
# update attributes
self.r_vector = self.r * cmp.ilr_inv(params[0, :],
basis) / misid_weights
self.μ = hst.mu(
self.η.change_points,
self.mu0 * cmp.ilr_inv(params[1:, :], basis),
mutation_types=self.mutation_types.values,
)
def infer_eta(
self,
mu0: np.float64,
*trend_penalty: Tuple[int, np.float64],
ridge_penalty: np.float64 = 0,
folded: bool = False,
pts: np.float64 = 100,
ta: np.float64 = None,
log_transform: bool = True,
eta: hst.eta = None,
eta_ref: hst.eta = None,
loss: str = "prf",
max_iter: int = 100,
tol: np.float64 = 0,
line_search_kwargs: Dict = {},
trend_kwargs: Dict = {},
verbose: bool = False,
) -> None:
r"""Infer demographic history :math:`\eta(t)`
Args:
mu0: total mutation rate (per genome per generation)
trend_penalty: tuple ``(k, λ)`` for :math:`k`-th order trend
penalty (can pass multiple for mixed trends)
ridge_penalty: ridge penalty
folded: if ``False``, infer :math:`\eta(t)` using unfolded SFS. If
``True``, can only be used with ``infer_mu=False``, and
infer :math:`\eta(t)` using folded SFS.
pts: number of points for time discretization
ta: time (in WF generations ago) of oldest change point in time
discretization. If ``None``, set automatically based on
10 * E[TMRCA] under MLE constant demography
log_transform: fit :math:`\log\eta(t)`
eta: initial demographic history. By default, a constant MLE is
computed
eta_ref: reference demographic history for ridge penalty. If
``None``, the constant MLE is used
loss: loss function name from :mod:`~mushi.loss_functions` module
max_iter: maximum number of optimization steps
tol: relative tolerance in objective function (if ``0``, not used)
line_search_kwargs: line search keyword arguments,
see :py:meth:`mushi.optimization.LineSearcher`
trend_kwargs: keyword arguments for trend filtering,
see :py:meth:`mushi.optimization.TrendFilter.run`
verbose: print verbose messages if ``True``
Examples:
Suppose ``ksfs`` is a ``kSFS`` object. Then the following fits a
demographic history with 0-th order (piecewise constant) trend
penalization of strength 100, assuming a mutation rate of 10
mutations per genome per generation.
>>> mu0 = 1
>>> ksfs.infer_eta(mu0, (0, 1e2))
Alternatively, a mixed trend solution, with constant and cubic
pieces, is fit with
>>> ksfs.infer_eta(mu0, (0, 1e2), (3, 1e1))
The attribute ``ksfs.eta`` is now set and accessable for plotting
(see :class:`~mushi.eta`).
"""
if self.X is None:
raise TypeError("use simulate() to generate data first")
# total SFS
if self.X.ndim == 1:
x = self.X
else:
x = self.X.sum(1)
# fold the spectrum if inference is on folded SFS
if folded:
x = utils.fold(x)
# constant MLE
# Harmonic number
H = (1 / np.arange(1, self.n - 1)).sum()
N_const = self.X.sum() / 2 / H / mu0
if ta is None:
tmrca_exp = 4 * N_const * (1 - 1 / self.n)
ta = 10 * tmrca_exp
change_points = np.logspace(0, np.log10(ta), pts)
# ininitialize with MLE constant η
if eta is not None:
self.set_eta(eta)
elif self.η is None:
y = N_const * np.ones(change_points.size + 1)
self.η = hst.eta(change_points, y)
t = self.η.arrays()[0]
self.mu0 = mu0
# badness of fit
loss = getattr(loss_functions, loss)
# Accelerated proximal gradient method: our objective function
# decomposes as f = g + h, where g is differentiable and h is not.
# https://people.eecs.berkeley.edu/~elghaoui/Teaching/EE227A/lecture18.pdf
# rescale trend penalties to be comparable between orders and time grids
# filter zeros from trend penalties
trend_penalty = tuple((k, (self.η.m**k / onp.math.factorial(k)) * λ)
for k, λ in trend_penalty if λ > 0)
# Tikhonov matrix
if eta_ref is None:
eta_ref = self.η
Γ = np.diag(np.ones_like(eta_ref.y))
else:
self.η.check_grid(eta_ref)
# - log(1 - CDF)
Γ = np.diag(-np.log(utils.tmrca_sf(t, eta_ref.y, self.n))[:-1])
y_ref = np.log(eta_ref.y) if log_transform else eta_ref.y
# In the following, the parameter vector params will contain the
# misid rate in params[0], and y in params[1:]
@jit
def g(params):
"""differentiable piece of objective in η problem."""
y = params[1:]
if log_transform:
M = utils.M(self.n, t, np.exp(y))
else:
M = utils.M(self.n, t, y)
L = self.C @ M
ξ = self.mu0 * L.sum(1)
if folded:
ξ = utils.fold(ξ)
else:
r = expit(params[0])
ξ = (1 - r) * ξ + r * self.AM_freq @ ξ
loss_term = loss(np.squeeze(ξ), x)
y_delta = y - y_ref
ridge_term = (ridge_penalty / 2) * (y_delta.T @ Γ @ y_delta)
return loss_term + ridge_term
@jit
def h(params):
"""nondifferentiable piece of objective in η problem."""
return sum(λ * np.linalg.norm(np.diff(params[1:], k + 1), 1)
for k, λ in trend_penalty)
def prox(params, s):
"""trend filtering prox operator (no jit due to ptv module)"""
if trend_penalty:
k, sλ = zip(*((k, s * λ) for k, λ in trend_penalty))
trend_filterer = opt.TrendFilter(k, sλ)
params = params.at[1:].set(
trend_filterer.run(params[1:], **trend_kwargs))
if log_transform:
return params
# else:
# clip to minimum population size of 1
return np.clip(params, 1)
# optimizer
optimizer = opt.AccProxGrad(g,
jit(grad(g)),
h,
prox,
verbose=verbose,
**line_search_kwargs)
# initial point
params = np.concatenate(
(np.array([logit(1e-2)]),
np.log(self.η.y) if log_transform else self.η.y))
# run optimization
params = optimizer.run(params, tol=tol, max_iter=max_iter)
if not folded:
self.r = expit(params[0])
y = np.exp(params[1:]) if log_transform else params[1:]
self.η = hst.eta(self.η.change_points, y)
self.M = utils.M(self.n, t, y)
self.L = self.C @ self.M
def simulate(
self,
eta: hst.eta,
mu: Union[hst.mu, np.float64],
r: np.float64 = 0,
seed: int = None,
) -> None:
r"""Simulate a :math:`k`-SFS under the Poisson random field model
(no linkage), assign to ``X`` attribute
Args:
eta: demographic history
mu: mutation spectrum history (or constant mutation rate)
r: ancestral state misidentification rate (default 0)
seed: random seed
Examples:
Define sample size:
>>> ksfs = mushi.kSFS(n=10)
Define demographic history and MuSH:
>>> eta = mushi.eta(np.array([1, 100, 10000]), np.array([1e4, 1e4, 1e2, 1e4]))
>>> mush = mushi.mu(eta.change_points, np.ones((4, 4)),
... ['AAA>ACA', 'ACA>AAA', 'TCC>TTC', 'GAA>GGA'])
Define ancestral misidentification rate:
>>> r = 0.03
Set random seed:
>>> seed = 0
Run simulation and print simulated :math:`k`-SFS
>>> ksfs.simulate(eta, mush, r, seed)
>>> ksfs.as_df() # doctest: +NORMALIZE_WHITESPACE
mutation type AAA>ACA ACA>AAA TCC>TTC GAA>GGA
sample frequency
1 1118 1123 1106 1108
2 147 128 120 98
3 65 55 66 60
4 49 52 64 46
5 44 43 34 36
6 35 28 36 33
7 23 32 24 35
8 34 32 24 24
9 52 41 57 56
"""
onp.random.seed(seed)
t, y = eta.arrays()
M = utils.M(self.n, t, y)
L = self.C @ M
if type(mu) == hst.mu:
eta.check_grid(mu)
Ξ = L @ mu.Z
self.mutation_types = mu.mutation_types
self.AM_mut = utils.mutype_misid(self.mutation_types)
self.X = np.array(
poisson.rvs((1 - r) * Ξ + r * self.AM_freq @ Ξ @ self.AM_mut))
else:
ξ = mu * L.sum(1)
self.X = np.array(poisson.rvs((1 - r) * ξ + r * self.AM_freq @ ξ))
