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Fides 0.7.1 (#44)
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* add hdf5 history writing

* bump version, remove history init

* simplify history

* allow restriction of iterative update schemes

* apply restriction to structured updates

* expand steps

* simplify & fix hdf5 export

* allow disabling curvature condition

* fix flake

* fixes update restriction

* Update test_hessian_approximation.py

* Update requirements.txt

* remove hess approx restriction

* implement hybridfraction method

* fixup

* fix neg eigenvalue handling

* fixup

* remove scaled gradient step, fix handling negative EV

* fixup

* fixup

* fix doc?

* Update hessian_approximation.py

* fix doc?

* fixup

* use eigenvalues from lstsq

* fix: lstsq only returns absolute ev values

* add more errors, less verbose default logging

* fix eigvals

* fix flake

* refactor refine to only be performed in subspace

* fix flake

* fix test

* fix flake

* fix refine step

* reduce logging output

* allow user to set start_id

* fix history tracking

* fixup

* bump version

* fixup

* Update version.py

* fix normalization

* track condition number

* change tracked fval

* only real part of EVs

* fix curvature condition enforcement

* rename posdef_newt

* update fraction min iteration to 25

* track newton steps

* use more copy to avoid shooting ourselves in the foot
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@FFroehlich
FFroehlich authored Nov 16, 2021
1 parent 31aba66 commit db158b4
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Showing 4 changed files with 81 additions and 80 deletions.
28 changes: 12 additions & 16 deletions fides/hessian_approximation.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -56,7 +56,7 @@ def get_mat(self) -> np.ndarray:
:return:
Hessian approximation
"""
return self._hess
return self._hess.copy()

def get_diff(self) -> np.ndarray:
"""
Expand All @@ -65,7 +65,7 @@ def get_diff(self) -> np.ndarray:
:return:
Hessian approximation update
"""
return self._diff
return self._diff.copy()

def set_mat(self, mat: np.ndarray) -> None:
"""
Expand All @@ -78,7 +78,7 @@ def set_mat(self, mat: np.ndarray) -> None:
raise ValueError('Passed matrix had inconsistent '
f'shape, was {mat.shape}, '
f'but should be {self._hess.shape}.')
self._hess = mat
self._hess = mat.copy()

@property
def requires_resfun(self):
Expand All @@ -88,6 +88,10 @@ def requires_resfun(self):
def requires_hess(self):
return False # pragma: no cover

def _update_hess_and_store_diff(self, hess):
self._diff = hess - self._hess
self._hess = hess.copy()


class IterativeHessianApproximation(HessianApproximation):
"""
Expand Down Expand Up @@ -144,11 +148,8 @@ def __init__(self, phi: float, init_with_hess: Optional[bool] = False,
super(Broyden, self).__init__(init_with_hess)

def _compute_update(self, s: np.ndarray, y: np.ndarray):
if y.T.dot(s) <= 0:
self._diff = np.zeros_like(self._hess)
else:
self._diff = broyden_class_update(y, s, self._hess, self.phi,
self.enforce_curv_cond)
self._diff = broyden_class_update(y, s, self._hess, self.phi,
self.enforce_curv_cond)


class BFGS(Broyden):
Expand Down Expand Up @@ -257,8 +258,7 @@ def _switched_update(self, s: np.ndarray, y: np.ndarray,
new_hess = self.hessian_update.get_mat()
else:
new_hess = hess
self._diff = new_hess - self._hess
self._hess = new_hess
self._update_hess_and_store_diff(new_hess)


class HybridFixed(HybridSwitchApproximation):
Expand Down Expand Up @@ -297,7 +297,7 @@ def __init__(self,
Switch from a dynamic approximation to the user provided iterative
scheme as soon as the fraction of iterations where the step is
accepted but the trust region is not update exceeds the user provided
threshold.Threshold check is only performed after 10 iterations.
threshold.Threshold check is only performed after 25 iterations.
The switching is non-reversible. The iterative scheme is
initialized and updated rom the beginning, but only employed after
the switching.
Expand All @@ -316,7 +316,7 @@ def __init__(self,
def update(self, s: np.ndarray, y: np.ndarray, hess: np.ndarray,
tr_nonupdates: int, iterations: int):
self._switched_update(s, y, hess)
if not self._switched and iterations > 10:
if not self._switched and iterations > 25:
self._switched = tr_nonupdates/iterations > self.switch_threshold


Expand Down Expand Up @@ -440,10 +440,6 @@ def requires_hess(self):
def get_structured_diff(self) -> np.ndarray:
return self._structured_diff

def _update_hess_and_store_diff(self, hess):
self._diff = hess - self._hess
self._hess = hess


class SSM(StructuredApproximation):
"""
Expand Down
103 changes: 53 additions & 50 deletions fides/minimize.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -300,7 +300,7 @@ def minimize(self, x0: np.ndarray, start_id: Optional[str] = None):
self.hessian_update.init_mat(len(self.x), funout.hess)
self.hess = self.hessian_update.get_mat()
else:
self.hess = funout.hess
self.hess = funout.hess.copy()

funout.checkdims()

Expand Down Expand Up @@ -353,6 +353,9 @@ def minimize(self, x0: np.ndarray, start_id: Optional[str] = None):
self.update(step, funout_new, funout)
funout = funout_new

if self.get_option(Options.HISTORY_FILE):
self.track_history(accepted, step, funout_new)

if self.get_option(Options.HISTORY_FILE):
with h5py.File(self.get_option(Options.HISTORY_FILE), 'a') as f:
g = f.create_group(self.start_id)
Expand Down Expand Up @@ -436,7 +439,7 @@ def update(self,

self.hess = self.hessian_update.get_mat()
else:
self.hess = funout_new.hess
self.hess = funout_new.hess.copy()
self.check_in_bounds(funout_new.x)
self.fval = funout_new.fval
self.x = funout_new.x
Expand Down Expand Up @@ -659,10 +662,6 @@ def log_step(self, accepted: bool, step: Step, funout: Funout):
iterspaces = max(len(str(self.get_option(Options.MAXITER))), 5) - \
len(str(self.iteration))
steptypespaces = 4 - len(step.type)
reflspaces, trunspaces = [
4 - len(str(count))
for count in [step.reflection_count, step.truncation_count]
]

fval = funout.fval
if not np.isfinite(fval):
Expand All @@ -680,52 +679,56 @@ def log_step(self, accepted: bool, step: Step, funout: Funout):
f' | {int(accepted)}'
)

if self.get_option(Options.HISTORY_FILE):
def track_history(self, accepted: bool, step: Step, funout: Funout):
normdx = norm(step.s + step.s0)
normg = norm(self.grad)

min_ev_hess, max_ev_hess = _min_max_evs(self.hess)
min_ev_hess_update, max_ev_hess_update = np.NaN, np.NaN
min_ev_hess_supdate, max_ev_hess_supdate = np.NaN, np.NaN
if self.hessian_update:
if accepted:
min_ev_hess_update, max_ev_hess_update = \
_min_max_evs(self.hessian_update.get_diff())
else:
min_ev_hess_update, max_ev_hess_update = 0.0, 0.0

min_ev_hess, max_ev_hess = _min_max_evs(self.hess)
min_ev_hess_update, max_ev_hess_update = np.NaN, np.NaN
min_ev_hess_supdate, max_ev_hess_supdate = np.NaN, np.NaN
if self.hessian_update:
if isinstance(self.hessian_update, StructuredApproximation):
if accepted:
min_ev_hess_update, max_ev_hess_update = \
_min_max_evs(self.hessian_update.get_diff())
min_ev_hess_supdate, max_ev_hess_supdate = \
_min_max_evs(
self.hessian_update.get_structured_diff()
)
else:
min_ev_hess_update, max_ev_hess_update = 0.0, 0.0

if isinstance(self.hessian_update, StructuredApproximation):
if accepted:
min_ev_hess_supdate, max_ev_hess_supdate = \
_min_max_evs(
self.hessian_update.get_structured_diff()
)
else:
min_ev_hess_supdate, max_ev_hess_supdate = 0.0, 0.0

update = {
'fval': self.fval,
'tr_ratio': self.tr_ratio,
'tr_radius': self.delta_iter,
'normgrad': normg,
'normstep': normdx,
'theta': step.theta,
'alpha': step.alpha,
'reflections': step.reflection_count,
'truncations': step.truncation_count,
'accept': accepted,
'hess_min_ev': min_ev_hess,
'hess_max_ev': max_ev_hess,
'hess_update_min_ev': min_ev_hess_update,
'hess_update_max_ev': max_ev_hess_update,
'hess_struct_update_min_ev': min_ev_hess_supdate,
'hess_struct_update_max_ev': max_ev_hess_supdate,
'iterations_since_tr_update': self.iterations_since_tr_update,
'step_type': step.type,
'subspace_dim': step.subspace.shape[1],
'posdef_newt': step.posdef_newt
if hasattr(step, 'posdef_newt') else False,
}
for key, val in update.items():
self.history[key].append(val)
min_ev_hess_supdate, max_ev_hess_supdate = 0.0, 0.0

update = {
'fval': funout.fval,
'tr_ratio': self.tr_ratio,
'tr_radius': self.delta_iter,
'normgrad': normg,
'normstep': normdx,
'theta': step.theta,
'alpha': step.alpha,
'reflections': step.reflection_count,
'truncations': step.truncation_count,
'accept': accepted,
'hess_min_ev': min_ev_hess,
'hess_max_ev': max_ev_hess,
'hess_update_min_ev': min_ev_hess_update,
'hess_update_max_ev': max_ev_hess_update,
'hess_struct_update_min_ev': min_ev_hess_supdate,
'hess_struct_update_max_ev': max_ev_hess_supdate,
'iterations_since_tr_update': self.iterations_since_tr_update,
'step_type': step.type,
'subspace_dim': step.subspace.shape[1],
'posdef': step.posdef if hasattr(step, 'posdef') else False,
'newton': step.newton if hasattr(step, 'newton') else False,
'cond_hess': np.linalg.cond(self.hess),
'cond_shess': np.linalg.cond(step.shess),
}
for key, val in update.items():
self.history[key].append(val)

def log_step_initial(self):
"""
Expand Down Expand Up @@ -842,4 +845,4 @@ def get_option(self, option):

def _min_max_evs(mat: np.ndarray):
evs = np.linalg.eigvals(mat)
return np.min(evs), np.max(evs)
return np.real(np.min(evs)), np.real(np.max(evs))
28 changes: 15 additions & 13 deletions fides/steps.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -117,8 +117,8 @@ def __init__(self,
self.og_sc: Union[np.ndarray, None] = None
self.og_ss: Union[np.ndarray, None] = None

self.sg: np.ndarray = sg
self.scaling: csc_matrix = scaling
self.sg: np.ndarray = sg.copy()
self.scaling: csc_matrix = scaling.copy()

self.delta: float = delta
self.theta: float = theta
Expand Down Expand Up @@ -265,23 +265,25 @@ def __init__(self, x, sg, hess, scaling, g_dscaling, delta, theta,
ub, lb, logger):
super().__init__(x, sg, hess, scaling, g_dscaling, delta, theta,
ub, lb, logger)
s_newt, _, _, _ = linalg.lstsq(self.shess, sg)
s_newt *= -1
s_newt = - linalg.lstsq(self.shess, sg)[0]
# lstsq only returns absolute ev values
e, v = np.linalg.eig(self.shess)
self.posdef_newt = np.min(np.real(e)) > \
- np.spacing(1) * np.max(np.abs(e))
self.posdef = np.min(np.real(e)) > - np.spacing(1) * np.max(np.abs(e))

if len(sg) == 1:
s_newt = - sg[0]/self.shess[0]
self.subspace = np.expand_dims(s_newt, 1)
return

if self.posdef_newt:
self.newton = False

if self.posdef:
s_newt = - linalg.lstsq(self.shess, sg)[0]

if norm(s_newt) < delta:
# Case 0 of Fig 12 in [ColemanLi1994]
normalize(s_newt)
self.newton = True
self.subspace = np.expand_dims(s_newt, 1)
return

Expand Down Expand Up @@ -376,8 +378,8 @@ def conj_grad(self, eps):
beta = rp2 / r2

d = -rp + beta * d
z = zp
r = rp
z = zp.copy()
r = rp.copy()


class TRStepReflected(Step):
Expand Down Expand Up @@ -479,9 +481,9 @@ def __init__(self, x, sg, hess, scaling, g_dscaling, delta, theta,
ub, lb, step):
super().__init__(x, sg, hess, scaling, g_dscaling, delta, theta,
ub, lb, step.logger)
self.subspace: np.ndarray = step.subspace
self.chess: np.ndarray = step.chess
self.cg: np.ndarray = step.cg
self.subspace: np.ndarray = step.subspace.copy()
self.chess: np.ndarray = step.chess.copy()
self.cg: np.ndarray = step.cg.copy()
self.constraints = [
NonlinearConstraint(
fun=lambda xc: (norm(self.subspace.dot(xc)) - delta) *
Expand All @@ -498,7 +500,7 @@ def __init__(self, x, sg, hess, scaling, g_dscaling, delta, theta,
ub=(ub - x) / scaling.diagonal()
)
]
self.guess: np.ndarray = step.sc
self.guess: np.ndarray = step.sc.copy()
self.qpval0: float = step.qpval
self.reflection_indices: int = step.reflection_indices
self.truncation_indices: int = step.truncation_indices
Expand Down
2 changes: 1 addition & 1 deletion fides/version.py
View file
Original file line number Diff line number Diff line change
@@ -1 +1 @@
__version__ = "0.7.0"
__version__ = "0.7.1"

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