/usr/share/pyshared/openopt/kernel/MOP.py is in python-openopt 0.38+svn1589-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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from numpy import inf
from openopt.kernel.setDefaultIterFuncs import SMALL_DELTA_X, SMALL_DELTA_F
class MOP(NonLinProblem):
_optionalData = ['lb', 'ub', 'A', 'Aeq', 'b', 'beq', 'c', 'h']
showGoal = True
goal = 'weak Pareto front'
probType = 'MOP'
allowedGoals = ['weak Pareto front', 'strong Pareto front', 'wpf', 'spf']
isObjFunValueASingleNumber = False
expectedArgs = ['f', 'x0']
_frontLength = 0
_nIncome = 0
_nOutcome = 0
iprint = 1
def __init__(self, *args, **kwargs):
NonLinProblem.__init__(self, *args, **kwargs)
self.nSolutions = 'all'
self.kernelIterFuncs.pop(SMALL_DELTA_X, None)
self.kernelIterFuncs.pop(SMALL_DELTA_F, None)
self.data4TextOutput = ['front length', 'income', 'outcome', 'log10(maxResidual)']
f = self.f
i = 0
targets = []
while True:
if len(f[i:]) == 0: break
func = f[i]
if type(func) in (list, tuple):
F, tol, val = func
i += 1
else:
F, tol, val = f[i], f[i+1], f[i+2]
i += 3
t = target()
t.func, t.tol = F, tol
t.val = val if type(val) != str \
else inf if val in ('max', 'maximum') \
else -inf if val in ('min', 'minimum') \
else self.err('incorrect MOP func target')
targets.append(t)
self.targets = targets
self.f = [t.func for t in targets]
self.user.f = self.f
def objFuncMultiple2Single(self, fv):
return 0#(fv ** 2).sum()
def solve(self, *args, **kw):
# if self.plot or kw.get('plot', False):
# self.warn('\ninteractive graphic output for MOP is unimplemented yet and will be turned off')
# kw['plot'] = False
self.graphics.drawFuncs = [mop_iterdraw]
r = NonLinProblem.solve(self, *args, **kw)
r.plot = lambda *args, **kw: self._plot(**kw)
r.__call__ = lambda *args, **kw: self.err('evaluation of MOP result on arguments is unimplemented yet, use r.solutions')
r.export = lambda *args, **Kw: _export_to_xls(self, r, *args, **kw)
T0 = self.targets[0]
if T0.val == -inf:
keyfunc = lambda elem: elem[T0.func]
elif T0.val == inf:
keyfunc = lambda elem: -elem[T0.func]
else:
keyfunc = lambda elem: abs(T0.val - elem[T0.func])
r.solutions.sort(key=keyfunc)
for v in self._categoricalVars:
for elem in r.solutions:
elem.useAsMutable = True
elem[v] = v.aux_domain[elem[v]]
elem.useAsMutable = False
return r
def _plot(self, **kw):
from numpy import asarray, atleast_1d, array_equal
S = self.solutions
if type(S)==list and len(S) == 0: return
tmp = asarray(self.solutions.F if 'F' in dir(self.solutions) else self.solutions.values)
from copy import deepcopy
kw2 = deepcopy(kw)
useShow = kw2.pop('show', True)
if not useShow and hasattr(self, '_prev_mop_solutions') and array_equal(self._prev_mop_solutions, tmp):
return
self._prev_mop_solutions = tmp.copy()
if tmp.size == 0:
if self.isFinished:
self.disp('no solutions, nothing to plot')
return
try:
import pylab
except:
self.err('you should have matplotlib installed')
pylab.ion()
if self.nf != 2:
self.err('MOP plotting is implemented for problems with only 2 goals, while you have %d' % self.nf)
X, Y = atleast_1d(tmp[:, 0]), atleast_1d(tmp[:, 1])
useGrid = kw2.pop('grid', 'on')
if 'marker' not in kw2:
kw2['marker'] = (5, 1, 0)
if 's' not in kw2:
kw2['s']=[150]
if 'edgecolor' not in kw2:
kw2['edgecolor'] = 'b'
if 'facecolor' not in kw2:
kw2['facecolor'] = '#FFFF00'#'y'
pylab.scatter(X, Y, **kw2)
pylab.grid(useGrid)
t0_goal = 'min' if self.targets[0].val == -inf else 'max' if self.targets[0].val == inf else str(self.targets[0].val)
t1_goal = 'min' if self.targets[1].val == -inf else 'max' if self.targets[1].val == inf else str(self.targets[1].val)
pylab.xlabel(self.user.f[0].name + ' (goal: %s tolerance: %s)' %(t0_goal, self.targets[0].tol))
pylab.ylabel(self.user.f[1].name + ' (goal: %s tolerance: %s)' %(t1_goal, self.targets[1].tol))
pylab.title('problem: %s goal: %s' %(self.name, self.goal))
figure = pylab.gcf()
from openopt import __version__ as ooversion
figure.canvas.set_window_title('OpenOpt ' + ooversion)
pylab.hold(0)
pylab.draw()
if useShow:
pylab.ioff()
pylab.show()
def mop_iterdraw(p):
p._plot(show=False)
TkinterIsInstalled = True
import platform
if platform.python_version()[0] == '2':
# Python2
try:
from Tkinter import Tk
from tkFileDialog import asksaveasfilename
except:
TkinterIsInstalled = False
else:
# Python3
try:
from tkinter import Tk
from tkinter.filedialog import asksaveasfilename
except:
TkinterIsInstalled = False
def _export_to_xls(p, r, *args, **kw):
try:
import xlwt
except:
s = '''
To export OpenOpt MOP result into xls file
you should have Python module "xlwt" installed,
(http://www.python-excel.org),
available via easy_install xlwt
or Linux apt-get python-xlwt
'''
p.err(s)
if len(args) != 0:
xls_file = args[0]
elif TkinterIsInstalled:
root = Tk()
root.withdraw()
import os
hd = os.getenv("HOME")
xls_file = asksaveasfilename(defaultextension='.xls', initialdir = hd, filetypes = [('xls files', '.xls')])
root.destroy()
if xls_file in (None, ''):
return
else:
p.err('''
you should either provide xls file name for data output
or have tkinter installed to set it via GUI window''')
# xls_file = asksaveasfilename(defaultextension='.xls', initialdir = self.hd, filetypes = [('xls files', '.xls')])
# if xls_file in (None, ''):
# return
nf = p.nf
target_funcs = [t.func for t in p.targets]
vars4export = set(p._freeVarsList).difference(target_funcs)
vars4export = list(vars4export)
vars4export.sort(key = lambda v: v._id)
nv = len(vars4export)
R = [[] for i in range(nv + nf)]
Names = [t.name for t in target_funcs] + [v.name for v in vars4export]
Keys = target_funcs + vars4export
for elem in r.solutions:
for i, key in enumerate(Keys):
R[i].append(elem[key])
from numpy import asarray
R = asarray(R)
L = len(r.solutions)
wb = xlwt.Workbook()
ws = wb.add_sheet('OpenOpt MOP result')
from openopt import __version__ as ver
i = 0
ws.write(i, 0, 'OpenOpt ver')
ws.write(i, 1, ver)
i += 1
ws.write(i, 0, 'Solver')
ws.write(i, 1, p.solver.__name__)
i += 1
ws.write(i, 0, 'Prob name')
ws.write(i, 1, p.name)
i += 1
ws.write(i, 0, 'Prob type')
ws.write(i, 1, p.probType)
i += 1
ws.write(i, 0, 'Time, s')
ws.write(i, 1, str(int(r.elapsed['solver_time'])))
i += 1
ws.write(i, 0, 'CPU Time, s')
ws.write(i, 1, str(int(r.elapsed['solver_cputime'])))
i += 1
ws.write(i, 0, 'N solutions')
ws.write(i, 1, str(L))
style1 = xlwt.easyxf("""
font:
name Times New Roman,
colour_index black;
pattern:
back_colour yellow,
pattern thick_forward_diag,
fore-colour yellow
""")
for i in range(nf):
ws.write(0, 3+i, Names[i], style1)
for j in range(L):
ws.write(1+j, 3+i, R[i, j], style1)
for i in range(nf, nf + nv):
ws.write(0, 3+i, Names[i])
for j in range(L):
ws.write(1+j, 3+i, R[i, j])
wb.save(xls_file)
p.disp('export MOP %s result to xls file finished' % p.name)
class target:
pass
|