1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
|
from typing import IO, Optional, Union
import numpy as np
import scipy.optimize as opt
from ase import Atoms
from ase.optimize.optimize import Optimizer
class Converged(Exception):
pass
class OptimizerConvergenceError(Exception):
pass
class SciPyOptimizer(Optimizer):
"""General interface for SciPy optimizers
Only the call to the optimizer is still needed
"""
def __init__(
self,
atoms: Atoms,
logfile: Union[IO, str] = '-',
trajectory: Optional[str] = None,
callback_always: bool = False,
alpha: float = 70.0,
**kwargs,
):
"""Initialize object
Parameters
----------
atoms: :class:`~ase.Atoms`
The Atoms object to relax.
trajectory: str
Trajectory file used to store optimisation path.
logfile: file object or str
If *logfile* is a string, a file with that name will be opened.
Use '-' for stdout.
callback_always: bool
Should the callback be run after each force call (also in the
linesearch)
alpha: float
Initial guess for the Hessian (curvature of energy surface). A
conservative value of 70.0 is the default, but number of needed
steps to converge might be less if a lower value is used. However,
a lower value also means risk of instability.
kwargs : dict, optional
Extra arguments passed to
:class:`~ase.optimize.optimize.Optimizer`.
"""
restart = None
Optimizer.__init__(self, atoms, restart, logfile, trajectory, **kwargs)
self.force_calls = 0
self.callback_always = callback_always
self.H0 = alpha
self.max_steps = 0
def x0(self):
"""Return x0 in a way SciPy can use
This class is mostly usable for subclasses wanting to redefine the
parameters (and the objective function)"""
return self.optimizable.get_positions().reshape(-1)
def f(self, x):
"""Objective function for use of the optimizers"""
self.optimizable.set_positions(x.reshape(-1, 3))
# Scale the problem as SciPy uses I as initial Hessian.
return self.optimizable.get_potential_energy() / self.H0
def fprime(self, x):
"""Gradient of the objective function for use of the optimizers"""
self.optimizable.set_positions(x.reshape(-1, 3))
self.force_calls += 1
if self.callback_always:
self.callback(x)
# Remember that forces are minus the gradient!
# Scale the problem as SciPy uses I as initial Hessian.
return - self.optimizable.get_forces().reshape(-1) / self.H0
def callback(self, x):
"""Callback function to be run after each iteration by SciPy
This should also be called once before optimization starts, as SciPy
optimizers only calls it after each iteration, while ase optimizers
call something similar before as well.
:meth:`callback`() can raise a :exc:`Converged` exception to signal the
optimisation is complete. This will be silently ignored by
:meth:`run`().
"""
if self.nsteps < self.max_steps:
self.nsteps += 1
f = self.optimizable.get_forces()
self.log(f)
self.call_observers()
if self.converged(f):
raise Converged
def run(self, fmax=0.05, steps=100000000):
self.fmax = fmax
try:
# As SciPy does not log the zeroth iteration, we do that manually
if self.nsteps == 0:
self.log()
self.call_observers()
self.max_steps = steps + self.nsteps
# Scale the problem as SciPy uses I as initial Hessian.
self.call_fmin(fmax / self.H0, steps)
except Converged:
pass
return self.converged()
def dump(self, data):
pass
def load(self):
pass
def call_fmin(self, fmax, steps):
raise NotImplementedError
class SciPyFminCG(SciPyOptimizer):
"""Non-linear (Polak-Ribiere) conjugate gradient algorithm"""
def call_fmin(self, fmax, steps):
output = opt.fmin_cg(self.f,
self.x0(),
fprime=self.fprime,
# args=(),
gtol=fmax * 0.1, # Should never be reached
norm=np.inf,
# epsilon=
maxiter=steps,
full_output=1,
disp=0,
# retall=0,
callback=self.callback)
warnflag = output[-1]
if warnflag == 2:
raise OptimizerConvergenceError(
'Warning: Desired error not necessarily achieved '
'due to precision loss')
class SciPyFminBFGS(SciPyOptimizer):
"""Quasi-Newton method (Broydon-Fletcher-Goldfarb-Shanno)"""
def call_fmin(self, fmax, steps):
output = opt.fmin_bfgs(self.f,
self.x0(),
fprime=self.fprime,
# args=(),
gtol=fmax * 0.1, # Should never be reached
norm=np.inf,
# epsilon=1.4901161193847656e-08,
maxiter=steps,
full_output=1,
disp=0,
# retall=0,
callback=self.callback)
warnflag = output[-1]
if warnflag == 2:
raise OptimizerConvergenceError(
'Warning: Desired error not necessarily achieved '
'due to precision loss')
class SciPyGradientlessOptimizer(Optimizer):
"""General interface for gradient less SciPy optimizers
Only the call to the optimizer is still needed
Note: If you redefine x0() and f(), you don't even need an atoms object.
Redefining these also allows you to specify an arbitrary objective
function.
XXX: This is still a work in progress
"""
def __init__(
self,
atoms: Atoms,
logfile: Union[IO, str] = '-',
trajectory: Optional[str] = None,
callback_always: bool = False,
**kwargs,
):
"""Initialize object
Parameters
----------
atoms: :class:`~ase.Atoms`
The Atoms object to relax.
trajectory: str
Trajectory file used to store optimisation path.
logfile: file object or str
If *logfile* is a string, a file with that name will be opened.
Use '-' for stdout.
callback_always: bool
Should the callback be run after each force call (also in the
linesearch)
alpha: float
Initial guess for the Hessian (curvature of energy surface). A
conservative value of 70.0 is the default, but number of needed
steps to converge might be less if a lower value is used. However,
a lower value also means risk of instability.
kwargs : dict, optional
Extra arguments passed to
:class:`~ase.optimize.optimize.Optimizer`.
"""
restart = None
Optimizer.__init__(self, atoms, restart, logfile, trajectory, **kwargs)
self.function_calls = 0
self.callback_always = callback_always
def x0(self):
"""Return x0 in a way SciPy can use
This class is mostly usable for subclasses wanting to redefine the
parameters (and the objective function)"""
return self.optimizable.get_positions().reshape(-1)
def f(self, x):
"""Objective function for use of the optimizers"""
self.optimizable.set_positions(x.reshape(-1, 3))
self.function_calls += 1
# Scale the problem as SciPy uses I as initial Hessian.
return self.optimizable.get_potential_energy()
def callback(self, x):
"""Callback function to be run after each iteration by SciPy
This should also be called once before optimization starts, as SciPy
optimizers only calls it after each iteration, while ase optimizers
call something similar before as well.
"""
# We can't assume that forces are available!
# f = self.optimizable.get_forces()
# self.log(f)
self.call_observers()
# if self.converged(f):
# raise Converged
self.nsteps += 1
def run(self, ftol=0.01, xtol=0.01, steps=100000000):
self.xtol = xtol
self.ftol = ftol
# As SciPy does not log the zeroth iteration, we do that manually
self.callback(None)
try:
# Scale the problem as SciPy uses I as initial Hessian.
self.call_fmin(xtol, ftol, steps)
except Converged:
pass
return self.converged()
def dump(self, data):
pass
def load(self):
pass
def call_fmin(self, xtol, ftol, steps):
raise NotImplementedError
class SciPyFmin(SciPyGradientlessOptimizer):
"""Nelder-Mead Simplex algorithm
Uses only function calls.
XXX: This is still a work in progress
"""
def call_fmin(self, xtol, ftol, steps):
opt.fmin(self.f,
self.x0(),
# args=(),
xtol=xtol,
ftol=ftol,
maxiter=steps,
# maxfun=None,
# full_output=1,
disp=0,
# retall=0,
callback=self.callback)
class SciPyFminPowell(SciPyGradientlessOptimizer):
"""Powell's (modified) level set method
Uses only function calls.
XXX: This is still a work in progress
"""
def __init__(self, *args, **kwargs):
"""Parameters:
direc: float
How much to change x to initially. Defaults to 0.04.
"""
direc = kwargs.pop('direc', None)
SciPyGradientlessOptimizer.__init__(self, *args, **kwargs)
if direc is None:
self.direc = np.eye(len(self.x0()), dtype=float) * 0.04
else:
self.direc = np.eye(len(self.x0()), dtype=float) * direc
def call_fmin(self, xtol, ftol, steps):
opt.fmin_powell(self.f,
self.x0(),
# args=(),
xtol=xtol,
ftol=ftol,
maxiter=steps,
# maxfun=None,
# full_output=1,
disp=0,
# retall=0,
callback=self.callback,
direc=self.direc)
|
