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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Import standard library
import inspect
# Import modules
import numpy as np
import pytest
# Import from pyswarms
import pyswarms.backend.topology as t
from pyswarms.single import GeneralOptimizerPSO
from pyswarms.utils.functions.single_obj import sphere
from .abc_test_optimizer import ABCTestOptimizer
def istopology(x):
"""Helper predicate to check if it's a subclass"""
return inspect.isclass(x) and not inspect.isabstract(x)
# Get all classes in the topology module, then
# Instatiate topologies, no need to suppy static param
topologies = [topo() for _, topo in inspect.getmembers(t, istopology)]
class TestGeneralOptimizer(ABCTestOptimizer):
@pytest.fixture(params=topologies)
def optimizer(self, request, options):
x_max = 10 * np.ones(2)
x_min = -1 * x_max
bounds = (x_min, x_max)
return GeneralOptimizerPSO(
n_particles=10,
dimensions=2,
options=options,
bounds=bounds,
topology=request.param,
)
@pytest.fixture(params=topologies)
def optimizer_history(self, request, options):
opt = GeneralOptimizerPSO(
n_particles=10,
dimensions=2,
options=options,
topology=request.param,
)
opt.optimize(sphere, 1000)
return opt
@pytest.fixture(params=topologies)
def optimizer_reset(self, request, options):
opt = GeneralOptimizerPSO(
n_particles=10,
dimensions=2,
options=options,
topology=request.param,
)
opt.optimize(sphere, 1000)
opt.reset()
return opt
def test_ftol_effect(self, optimizer):
"""Test if setting the ftol breaks the optimization process"""
# Set optimizer tolerance
optimizer.ftol = 1e-1
optimizer.optimize(sphere, 2000)
assert np.array(optimizer.cost_history).shape != (2000,)
def test_parallel_evaluation(self, obj_without_args, optimizer):
"""Test if parallelization breaks the optimization process"""
import multiprocessing
optimizer.optimize(
obj_without_args, 2000, n_processes=multiprocessing.cpu_count()
)
assert np.array(optimizer.cost_history).shape == (2000,)
@pytest.mark.skip(reason="Some topologies converge too slowly")
def test_obj_with_kwargs(self, obj_with_args, optimizer):
"""Test if kwargs are passed properly in objfunc"""
cost, pos = optimizer.optimize(obj_with_args, 1000, a=1, b=100)
assert np.isclose(cost, 0, rtol=1e-03)
assert np.isclose(pos[0], 1.0, rtol=1e-03)
assert np.isclose(pos[1], 1.0, rtol=1e-03)
def test_obj_unnecessary_kwargs(self, obj_without_args, optimizer):
"""Test if error is raised given unnecessary kwargs"""
with pytest.raises(TypeError):
# kwargs `a` should not be supplied
cost, pos = optimizer.optimize(obj_without_args, 1000, a=1)
def test_obj_missing_kwargs(self, obj_with_args, optimizer):
"""Test if error is raised with incomplete kwargs"""
with pytest.raises(TypeError):
# kwargs `b` is missing here
cost, pos = optimizer.optimize(obj_with_args, 1000, a=1)
def test_obj_incorrect_kwargs(self, obj_with_args, optimizer):
"""Test if error is raised with wrong kwargs"""
with pytest.raises(TypeError):
# Wrong kwargs
cost, pos = optimizer.optimize(obj_with_args, 1000, c=1, d=100)
def test_general_correct_pos(self, options, optimizer):
""" Test to check general optimiser returns the correct position corresponding to the best cost """
cost, pos = optimizer.optimize(sphere, iters=5)
# find best pos from history
min_cost_idx = np.argmin(optimizer.cost_history)
min_pos_idx = np.argmin(sphere(optimizer.pos_history[min_cost_idx]))
assert np.array_equal(
optimizer.pos_history[min_cost_idx][min_pos_idx], pos
)
def test_ftol_iter_effect(self, optimizer):
"""Test if setting the ftol breaks the optimization process after the set number of iterations"""
# Set optimizer tolerance
optimizer.ftol = 1e-1
optimizer.ftol_iter = 5
optimizer.optimize(sphere, 2000)
assert np.array(optimizer.cost_history).shape[0] >= optimizer.ftol_iter
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