Please note the minor changes to the optimiser call signature since 0.0.11, details below.
Freelunch is a convenient python implementation of a number of meta-heuristic optimisation (with an 's') algorithms.
--NEW and EXCITING---
- Particle attractor optimistion
freelunch.PAO(pronounced POW)
Your favourite not in the list? Feel free to add it.
- Differential evolution
freelunch.DE - Simulated Annealing
freelunch.SA - Particle Swarm
freelunch.PSO - Krill Herd
freelunch.KrillHerd - Self-adapting Differential Evolution
freelunch.SADE - Quantum Particle Swarm
freelunch.QPSO
--Coming soon to 0.1.0--
- Quantum Bees
- Grenade Explosion Method
- The Penguin one
Tier list: TBA
- N-dimensional Ackley function
- N-dimensional Periodic function
- N-dimensional Happy Cat function
- N-dimensional Exponential function
Install with pip (req. numpy).
pip install freelunch
Create instances of your favourite meta-heuristics!
import freelunch
opt = freelunch.DE(my_objective_function, bounds=my_bounds) # Differential evolutionWhere,
-
obj: objective function that excepts a single argument, the trial vectorx, and returnsfloatorNone. i.e.obj(x) -> float or None -
bounds: Iterable bounds for elements ofxi.e.bounds [[lower, upper]]*len(sol)where:(sol[i] <= lower) -> booland(sol[i] >= upper) -> bool.
Run by calling the instance. There are several different calling signatures. Use any combination of the arguments below to suit your needs!
To return the best solution only:
quick_result = opt() # (D,)To return optimum after n_runs:
best_of_nruns = opt(n_runs=n) # (D,)To return optimum after n_runs in parallel (uses multiprocessing.Pool), see note below.:
best_of_nruns = opt(n_runs=n, n_workers=w, pool_args={}, chunks=1) # (D,)Return best m solutions in np.ndarray:
best_m = opt(n_return=m) # (D, m)Return json friendly dict with fun metadata!
full_output = opt(full_output=True)
# {
# 'optimiser':'DE',
# 'hypers':...,
# 'bounds':...,
# 'nruns':nruns,
# 'nfe':1234,
# 'solutions':[sol1, sol2, ..., solm*n_runs], # All solutions from all runs sorted by fitness
# 'scores':[fit1, fit2, ..., fitm*n_runs]
# }Want to change things around?
Custom initialisation strategies can be supplied by altering the optimiser.initialiser attribute of any optimiser instance. For example:
opt = fr.DE(obj, ...)
def my_initialiser(bounds, N, **hypers):
'Custom population initialisation'
# Remember to return and interable of length N
population = ... # custum logic
return populationAdditionally, some examples of common initialisation strategies can be found in the freelunch.tech module.
The simplest way to do this is to overwrite the optimiser.bounder attribute. There are a number of ready made strategies in freelunch.tech or alternatively define a custom method with the following call signature.
opt = fr.DE(obj, ...)
def my_bounder(p, bounds, **hypers):
'''custom bounding method'''
p.dna = ... # custom bounding logic
opt.bounder = my_bounder # overwrite the bounder attribute
# and then call as before
x_optimised = opt()Check out the hyperparameters and set your own, (defaults set automatically):
print(opt.hyper_definitions)
# {
# 'N':'Population size (int)',
# 'G':'Number of generations (int)',
# 'F':'Mutation parameter (float in [0,1])',
# 'Cr':'Crossover probability (float in [0,1])'
# }
print(opt.hyper_defaults)
# {
# 'N':100,
# 'G':100,
# 'F':0.5,
# 'Cr':0.2
# }
opt.hypers.update({'N':300})
print(opt.hypers)
# {
# 'N':300,
# 'G':100,
# 'F':0.5,
# 'Cr':0.2
# }Access from freelunch.benchmarks for example:
bench = freelunch.benchmarks.ackley(n=2) # Instanciate a 2D ackley benchmark function
fit = bench(sol) # evaluate by calling
bench.bounds # [[-10, 10],[-10, 10]]
bench.optimum # [0, 0]
bench.f0 # 0.0Because multiprocessing.Pool relies on multiprocessing.forking.pickle to send code to parallel processes, it is imperative that anything passed to the freelunch optimisers can be pickled. For example, the following common python pattern for producing an objective function with a single argument,
method = ... # some methods / args that are requred by the objective function
args =
def wrap_my_obj(method, args):
def _obj(x):
return method(args, x)
return _obj
obj = wrap_my_obj(method, args)cannot be pickled because _obj is not importable from the top level module scope and will raise freelunch.util.UnpicklableObjectiveFunction . Instead consider using functools.partial i.e.
from functools import partial
method = ... # some methods / args that are requred by the objective function
args = ...
def _obj(method, args, x):
return method(args, x)
obj = partial(_obj, method, args)
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent