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1"Scripts for generating, solving and sweeping programs"
2from time import time
3import warnings as pywarnings
4import numpy as np
5from ad import adnumber
6from ..nomials import parse_subs
7from ..solution_array import SolutionArray
8from ..keydict import KeyDict
9from ..small_scripts import maybe_flatten
10from ..small_classes import FixedScalar
11from ..exceptions import Infeasible
12from ..globals import SignomialsEnabled
15def evaluate_linked(constants, linked):
16 "Evaluates the values and gradients of linked variables."
17 kdc = KeyDict({k: adnumber(maybe_flatten(v), k)
18 for k, v in constants.items()})
19 kdc_plain = None
20 array_calulated = {}
21 for key in constants: # remove gradients from constants
22 if key.gradients:
23 del key.descr["gradients"]
24 for v, f in linked.items():
25 try:
26 if v.veckey and v.veckey.vecfn:
27 if v.veckey not in array_calulated:
28 with SignomialsEnabled(): # to allow use of gpkit.units
29 vecout = v.veckey.vecfn(kdc)
30 if not hasattr(vecout, "shape"):
31 vecout = np.array(vecout)
32 array_calulated[v.veckey] = vecout
33 out = array_calulated[v.veckey][v.idx]
34 else:
35 with SignomialsEnabled(): # to allow use of gpkit.units
36 out = f(kdc)
37 if isinstance(out, FixedScalar): # to allow use of gpkit.units
38 out = out.value
39 if hasattr(out, "units"):
40 out = out.to(v.units or "dimensionless").magnitude
41 elif out != 0 and v.units:
42 pywarnings.warn(
43 "Linked function for %s did not return a united value."
44 "Modifying it to do so (e.g. by using `()` instead of `[]`"
45 " to access variables) will reduce errors." % v)
46 if not hasattr(out, "x"):
47 constants[v] = out
48 continue # a new fixed variable, not a calculated one
49 constants[v] = out.x
50 gradients = {adn.tag:
51 grad for adn, grad in out.d().items() if adn.tag}
52 if gradients:
53 v.descr["gradients"] = gradients
54 except Exception as exception: # pylint: disable=broad-except
55 from .. import settings
56 if settings.get("ad_errors_raise", None):
57 raise
58 print("Warning: skipped auto-differentiation of linked variable"
59 " %s because %s was raised. Set `gpkit.settings"
60 "[\"ad_errors_raise\"] = True` to raise such Exceptions"
61 " directly.\n" % (v, repr(exception)))
62 if kdc_plain is None:
63 kdc_plain = KeyDict(constants)
64 constants[v] = f(kdc_plain)
65 v.descr.pop("gradients", None)
68def progify(program, return_attr=None):
69 """Generates function that returns a program() and optionally an attribute.
71 Arguments
72 ---------
73 program: NomialData
74 Class to return, e.g. GeometricProgram or SequentialGeometricProgram
75 return_attr: string
76 attribute to return in addition to the program
77 """
78 def programfn(self, constants=None, **initargs):
79 "Return program version of self"
80 if not constants:
81 constants, _, linked = parse_subs(self.varkeys, self.substitutions)
82 if linked:
83 evaluate_linked(constants, linked)
84 prog = program(self.cost, self, constants, **initargs)
85 prog.model = self # NOTE SIDE EFFECTS
86 if return_attr:
87 return prog, getattr(prog, return_attr)
88 return prog
89 return programfn
92def solvify(genfunction):
93 "Returns function for making/solving/sweeping a program."
94 def solvefn(self, solver=None, *, verbosity=1, skipsweepfailures=False,
95 **solveargs):
96 """Forms a mathematical program and attempts to solve it.
98 Arguments
99 ---------
100 solver : string or function (default None)
101 If None, uses the default solver found in installation.
102 verbosity : int (default 1)
103 If greater than 0 prints runtime messages.
104 Is decremented by one and then passed to programs.
105 skipsweepfailures : bool (default False)
106 If True, when a solve errors during a sweep, skip it.
107 **solveargs : Passed to solve() call
109 Returns
110 -------
111 sol : SolutionArray
112 See the SolutionArray documentation for details.
114 Raises
115 ------
116 ValueError if the program is invalid.
117 RuntimeWarning if an error occurs in solving or parsing the solution.
118 """
119 constants, sweep, linked = parse_subs(self.varkeys, self.substitutions)
120 solution = SolutionArray()
121 solution.modelstr = str(self)
123 # NOTE SIDE EFFECTS: self.program and self.solution set below
124 if sweep:
125 run_sweep(genfunction, self, solution, skipsweepfailures,
126 constants, sweep, linked, solver, verbosity, **solveargs)
127 else:
128 self.program, progsolve = genfunction(self)
129 result = progsolve(solver, verbosity=verbosity, **solveargs)
130 if solveargs.get("process_result", True):
131 self.process_result(result)
132 solution.append(result)
133 solution.to_arrays()
134 self.solution = solution
135 return solution
136 return solvefn
139# pylint: disable=too-many-locals,too-many-arguments,too-many-branches
140def run_sweep(genfunction, self, solution, skipsweepfailures,
141 constants, sweep, linked, solver, verbosity, **solveargs):
142 "Runs through a sweep."
143 # sort sweeps by the eqstr of their varkey
144 sweepvars, sweepvals = zip(*sorted(list(sweep.items()),
145 key=lambda vkval: vkval[0].eqstr))
146 if len(sweep) == 1:
147 sweep_grids = np.array(list(sweepvals))
148 else:
149 sweep_grids = np.meshgrid(*list(sweepvals))
151 N_passes = sweep_grids[0].size
152 sweep_vects = {var: grid.reshape(N_passes)
153 for (var, grid) in zip(sweepvars, sweep_grids)}
155 if verbosity > 0:
156 print("Sweeping with %i solves:" % N_passes)
157 tic = time()
159 self.program = []
160 last_error = None
161 for i in range(N_passes):
162 constants.update({var: sweep_vect[i]
163 for (var, sweep_vect) in sweep_vects.items()})
164 if linked:
165 evaluate_linked(constants, linked)
166 program, solvefn = genfunction(self, constants)
167 self.program.append(program) # NOTE: SIDE EFFECTS
168 try:
169 if verbosity > 1:
170 print("\nSolve %i:" % i)
171 result = solvefn(solver, verbosity=verbosity-1, **solveargs)
172 if solveargs.get("process_result", True):
173 self.process_result(result)
174 solution.append(result)
175 except Infeasible as e:
176 last_error = e
177 if not skipsweepfailures:
178 raise RuntimeWarning(
179 "Solve %i was infeasible; progress saved to m.program."
180 " To continue sweeping after failures, solve with"
181 " skipsweepfailures=True." % i) from e
182 if verbosity > 0:
183 print("Solve %i was %s." % (i, e.__class__.__name__))
184 if not solution:
185 raise RuntimeWarning("All solves were infeasible.") from last_error
187 solution["sweepvariables"] = KeyDict()
188 ksweep = KeyDict(sweep)
189 for var, val in list(solution["constants"].items()):
190 if var in ksweep:
191 solution["sweepvariables"][var] = val
192 del solution["constants"][var]
193 elif linked: # if any variables are linked, we check all of them
194 if hasattr(val[0], "shape"):
195 differences = ((l != val[0]).any() for l in val[1:])
196 else:
197 differences = (l != val[0] for l in val[1:])
198 if not any(differences):
199 solution["constants"][var] = [val[0]]
200 else:
201 solution["constants"][var] = [val[0]]
203 if verbosity > 0:
204 soltime = time() - tic
205 print("Sweeping took %.3g seconds." % (soltime,))