import numpy as np
from scipy.optimize import minimize
from iwopy.core import Optimizer
from iwopy.core import SingleObjOptResults
[docs]class Optimizer_scipy(Optimizer):
"""
Interface to the scipy optimizers.
Note that these solvers do not support
vectorized evaluation.
Attributes
----------
scipy_pars: dict
Additional parameters for
scipy.optimze.minimize()
mem_size: int
The memory size, number of
stored obj, cons evaluations
:group: interfaces.scipy
"""
[docs] def __init__(self, problem, scipy_pars={}, mem_size=100, **kwargs):
"""
Constructor
Parameters
----------
problem: iwopy.Problem
The problem to optimize
scipy_pars: dict
Additional parameters for
scipy.optimze.minimize()
mem_size: int
The memory size, number of
stored obj, cons evaluations
kwargs: dict, optional
Additional parameters for base class
"""
super().__init__(problem, **kwargs)
self.scipy_pars = scipy_pars
self.mem_size = mem_size
self._mem = None
[docs] def print_info(self):
"""
Print solver info, called before solving
"""
super().print_info()
if len(self.scipy_pars):
print("\nScipy parameters:")
print("-----------------")
for k, v in self.scipy_pars.items():
if isinstance(v, int) or isinstance(v, float) or isinstance(v, str):
print(f" {k}: {v}")
print()
[docs] def initialize(self, verbosity=1):
"""
Initialize the object.
Parameters
----------
verbosity: int
The verbosity level, 0 = silent
"""
# Check objectives:
if self.problem.n_objectives > 1:
raise RuntimeError(
"Scipy minimize does not support multi-objective optimization."
)
# Define constraints:
cons = list()
for i in range(self.problem.n_constraints):
cons.append({"type": "ineq", "fun": self._constraints, "args": (i,)})
self.scipy_pars["constraints"] = cons
if verbosity:
print(f"Using optimizer memory, size: {self.mem_size}")
self._mem = {}
super().initialize(verbosity)
def _get_results(self, x):
"""
Evaluate obj and cons
Parameters
----------
x: numpy array
Array containing design variables
Returns
-------
objs: np.array
The objective function values, shape: (n_objectives,)
cons: np.array
The constraints values, shape: (n_constraints,)
prob_results: object
The problem results
"""
key = tuple(x)
if key not in self._mem:
i0 = self.problem.n_vars_int
vars_int = x[:i0].astype(np.int32)
vars_float = x[i0:]
data = self.problem.evaluate_individual(
vars_int, vars_float, ret_prob_res=True
)
if len(self._mem) > self.mem_size:
key0 = next(iter(self._mem.keys()))
del self._mem[key0]
self._mem[key] = data
return self._mem[key]
def _objective(self, x):
"""
Function which converts array from scipy
to readable variables for the problem and
evaluates the objective function.
Parameters
----------
x: numpy array
Array containing design variables
Returns
-------
float:
Current objective function value
"""
objs, __, __ = self._get_results(x)
return objs[0]
def _constraints(self, x, ci):
"""
Function which converts array from scipy
to readable variables for the problem and
evaluates the constraints.
Parameters
----------
x: numpy array
Array containing design variables
ci: int
Index for constraint component
Returns
-------
float:
Value of constraint component
"""
__, cons, __ = self._get_results(x)
return cons[ci]
[docs] def solve(self, verbosity=1):
"""
Run the optimization solver.
Parameters
----------
verbosity: int
The verbosity level, 0 = silent
Returns
-------
results: iwopy.SingleObjOptResults
The optimization results object
"""
# check problem initialization:
super().solve()
# Initial values:
x0 = np.array(self.problem.initial_values_int(), dtype=np.float64)
x0 = np.append(
x0, np.array(self.problem.initial_values_float(), dtype=np.float64)
)
# Find bounds:
mini = [
x if x != -self.problem.INT_INF else None
for x in self.problem.min_values_int()
]
maxi = [
x if x != self.problem.INT_INF else None
for x in self.problem.max_values_int()
]
bounds = [(mini[i], maxi[i]) for i in range(len(mini))]
minf = [x if x != -np.inf else None for x in self.problem.min_values_float()]
maxf = [x if x != np.inf else None for x in self.problem.max_values_float()]
bounds = [(minf[i], maxf[i]) for i in range(len(minf))]
# Run minimization:
results = minimize(self._objective, x0, bounds=bounds, **self.scipy_pars)
# final evaluation:
if results.success:
x = results.x
i0 = self.problem.n_vars_int
vars_int = x[:i0].astype(np.int32)
vars_float = x[i0:]
prob_res, objs, cons = self.problem.finalize_individual(
vars_int, vars_float, verbosity=verbosity
)
else:
prob_res = None
vars_int = None
vars_float = None
objs = None
cons = None
return SingleObjOptResults(
self.problem, results.success, vars_int, vars_float, objs, cons, prob_res
)