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MAINT: slsqp PEP8 formatting fixes, typos, etc. #7841

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merged 1 commit into from Sep 9, 2017
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MAINT: slsqp PEP8 formatting fixes, typos, etc. #7841

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73 changes: 38 additions & 35 deletions scipy/optimize/slsqp.py
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Expand Up @@ -15,20 +15,20 @@

from __future__ import division, print_function, absolute_import

__all__ = ['approx_jacobian','fmin_slsqp']
__all__ = ['approx_jacobian', 'fmin_slsqp']

import numpy as np
from scipy.optimize._slsqp import slsqp
from numpy import zeros, array, linalg, append, asfarray, concatenate, finfo, \
sqrt, vstack, exp, inf, where, isfinite, atleast_1d
from numpy import (zeros, array, linalg, append, asfarray, concatenate, finfo,
sqrt, vstack, exp, inf, isfinite, atleast_1d)
from .optimize import wrap_function, OptimizeResult, _check_unknown_options

__docformat__ = "restructuredtext en"

_epsilon = sqrt(finfo(float).eps)


def approx_jacobian(x,func,epsilon,*args):
def approx_jacobian(x, func, epsilon, *args):
"""
Approximate the Jacobian matrix of a callable function.

Expand Down Expand Up @@ -56,7 +56,7 @@ def approx_jacobian(x,func,epsilon,*args):
"""
x0 = asfarray(x)
f0 = atleast_1d(func(*((x0,)+args)))
jac = zeros([len(x0),len(f0)])
jac = zeros([len(x0), len(f0)])
dx = zeros(len(x0))
for i in range(len(x0)):
dx[i] = epsilon
Expand Down Expand Up @@ -189,11 +189,11 @@ def fmin_slsqp(func, x0, eqcons=(), f_eqcons=None, ieqcons=(), f_ieqcons=None,

# Build the constraints as a tuple of dictionaries
cons = ()
# 1. constraints of the 1st kind (eqcons, ieqcons); no jacobian; take
# 1. constraints of the 1st kind (eqcons, ieqcons); no Jacobian; take
# the same extra arguments as the objective function.
cons += tuple({'type': 'eq', 'fun': c, 'args': args} for c in eqcons)
cons += tuple({'type': 'ineq', 'fun': c, 'args': args} for c in ieqcons)
# 2. constraints of the 2nd kind (f_eqcons, f_ieqcons) and their jacobian
# 2. constraints of the 2nd kind (f_eqcons, f_ieqcons) and their Jacobian
# (fprime_eqcons, fprime_ieqcons); also take the same extra arguments
# as the objective function.
if f_eqcons:
Expand Down Expand Up @@ -225,7 +225,7 @@ def _minimize_slsqp(func, x0, args=(), jac=None, bounds=None,
ftol : float
Precision goal for the value of f in the stopping criterion.
eps : float
Step size used for numerical approximation of the jacobian.
Step size used for numerical approximation of the Jacobian.
disp : bool
Set to True to print convergence messages. If False,
`verbosity` is ignored and set to 0.
Expand All @@ -242,7 +242,7 @@ def _minimize_slsqp(func, x0, args=(), jac=None, bounds=None,
if not disp:
iprint = 0

# Constraints are triaged per type into a dictionnary of tuples
# Constraints are triaged per type into a dictionary of tuples
if isinstance(constraints, dict):
constraints = (constraints, )

Expand All @@ -266,10 +266,10 @@ def _minimize_slsqp(func, x0, args=(), jac=None, bounds=None,
if 'fun' not in con:
raise ValueError('Constraint %d has no function defined.' % ic)

# check jacobian
# check Jacobian
cjac = con.get('jac')
if cjac is None:
# approximate jacobian function. The factory function is needed
# approximate Jacobian function. The factory function is needed
# to keep a reference to `fun`, see gh-4240.
def cjac_factory(fun):
def cjac(x, *args):
Expand All @@ -283,16 +283,16 @@ def cjac(x, *args):
'args': con.get('args', ())}, )

exit_modes = {-1: "Gradient evaluation required (g & a)",
0: "Optimization terminated successfully.",
1: "Function evaluation required (f & c)",
2: "More equality constraints than independent variables",
3: "More than 3*n iterations in LSQ subproblem",
4: "Inequality constraints incompatible",
5: "Singular matrix E in LSQ subproblem",
6: "Singular matrix C in LSQ subproblem",
7: "Rank-deficient equality constraint subproblem HFTI",
8: "Positive directional derivative for linesearch",
9: "Iteration limit exceeded"}
0: "Optimization terminated successfully.",
1: "Function evaluation required (f & c)",
2: "More equality constraints than independent variables",
3: "More than 3*n iterations in LSQ subproblem",
4: "Inequality constraints incompatible",
5: "Singular matrix E in LSQ subproblem",
6: "Singular matrix C in LSQ subproblem",
7: "Rank-deficient equality constraint subproblem HFTI",
8: "Positive directional derivative for linesearch",
9: "Iteration limit exceeded"}

# Wrap func
feval, func = wrap_function(func, args)
Expand All @@ -308,8 +308,10 @@ def cjac(x, *args):

# Set the parameters that SLSQP will need
# meq, mieq: number of equality and inequality constraints
meq = sum(map(len, [atleast_1d(c['fun'](x, *c['args'])) for c in cons['eq']]))
mieq = sum(map(len, [atleast_1d(c['fun'](x, *c['args'])) for c in cons['ineq']]))
meq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
for c in cons['eq']]))
mieq = sum(map(len, [atleast_1d(c['fun'](x, *c['args']))
for c in cons['ineq']]))
# m = The total number of constraints
m = meq + mieq
# la = The number of constraints, or 1 if there are no constraints
Expand Down Expand Up @@ -351,25 +353,26 @@ def cjac(x, *args):
xl[infbnd[:, 0]] = np.nan
xu[infbnd[:, 1]] = np.nan

# Clip initial guess to bounds (SLSQP may fail with bounds-infeasible initial point)
# Clip initial guess to bounds (SLSQP may fail with bounds-infeasible
# initial point)
have_bound = np.isfinite(xl)
x[have_bound] = np.clip(x[have_bound], xl[have_bound], np.inf)
have_bound = np.isfinite(xu)
x[have_bound] = np.clip(x[have_bound], -np.inf, xu[have_bound])

# Initialize the iteration counter and the mode value
mode = array(0,int)
acc = array(acc,float)
majiter = array(iter,int)
mode = array(0, int)
acc = array(acc, float)
majiter = array(iter, int)
majiter_prev = 0

# Print the header if iprint >= 2
if iprint >= 2:
print("%5s %5s %16s %16s" % ("NIT","FC","OBJFUN","GNORM"))
print("%5s %5s %16s %16s" % ("NIT", "FC", "OBJFUN", "GNORM"))

while 1:

if mode == 0 or mode == 1: # objective and constraint evaluation requird
if mode == 0 or mode == 1: # objective and constraint evaluation required

# Compute objective function
fx = func(x)
Expand All @@ -380,7 +383,7 @@ def cjac(x, *args):
# Compute the constraints
if cons['eq']:
c_eq = concatenate([atleast_1d(con['fun'](x, *con['args']))
for con in cons['eq']])
for con in cons['eq']])
else:
c_eq = zeros(0)
if cons['ineq']:
Expand All @@ -396,7 +399,7 @@ def cjac(x, *args):

# Compute the derivatives of the objective function
# For some reason SLSQP wants g dimensioned to n+1
g = append(fprime(x),0.0)
g = append(fprime(x), 0.0)

# Compute the normals of the constraints
if cons['eq']:
Expand All @@ -416,7 +419,7 @@ def cjac(x, *args):
a = zeros((la, n))
else:
a = vstack((a_eq, a_ieq))
a = concatenate((a,zeros([la,1])),1)
a = concatenate((a, zeros([la, 1])), 1)

# Call SLSQP
slsqp(m, meq, x, xl, xu, fx, c, g, a, acc, majiter, mode, w, jw)
Expand All @@ -428,8 +431,8 @@ def cjac(x, *args):
# Print the status of the current iterate if iprint > 2 and the
# major iteration has incremented
if iprint >= 2 and majiter > majiter_prev:
print("%5i %5i % 16.6E % 16.6E" % (majiter,feval[0],
fx,linalg.norm(g)))
print("%5i %5i % 16.6E % 16.6E" % (majiter, feval[0],
fx, linalg.norm(g)))

# If exit mode is not -1 or 1, slsqp has completed
if abs(mode) != 1:
Expand Down Expand Up @@ -482,7 +485,7 @@ def jieqcon(x, c=10):

# constraints dictionaries
cons = ({'type': 'eq', 'fun': feqcon, 'jac': jeqcon, 'args': (1, )},
{'type': 'ineq', 'fun': fieqcon, 'jac': jieqcon, 'args': (10,)})
{'type': 'ineq', 'fun': fieqcon, 'jac': jieqcon, 'args': (10,)})

# Bounds constraint problem
print(' Bounds constraints '.center(72, '-'))
Expand Down