def experiment_fsa(of, maxeval, num_runs, T0, n0, alpha, r):
results = []
for i in tqdm(
range(num_runs),
'Testing T0={}, n0={}, alpha={}, r={}'.format(T0, n0, alpha, r)):
mut = CauchyMutation(r=r, correction=Correction(of))
result = FastSimulatedAnnealing(of,
maxeval=maxeval,
T0=T0,
n0=n0,
alpha=alpha,
mutation=mut).search()
result['run'] = i
result['heur'] = 'FSA_{}_{}_{}_{}'.format(T0, n0, alpha,
r) # name of the heuristic
result['T0'] = T0
result['n0'] = n0
result['alpha'] = alpha
result['r'] = r
results.append(result)
return pd.DataFrame(results,
columns=[
'heur', 'run', 'T0', 'n0', 'alpha', 'r', 'best_x',
'best_y', 'neval'
])
def get_correction_mutation(of):
correction_mutations = []
for correction in [Correction(of), MirrorCorrection(of)]:
for r in [1, 1.5, 2, 5]:
correction_mutations.append(
CauchyMutation(r, correction=Correction(of)))
return correction_mutations
def get_overview_mutation(of):
overview_mutations = []
for correction in [Correction(of), MirrorCorrection(of)]:
for r in [0.1, 0.5, 0.75]:
overview_mutations.append(
CauchyMutation(r, correction=Correction(of)))
return overview_mutations
{'crossover': Crossover(), 'name': 'mix'},
{'crossover': RandomCombination(), 'name': 'rnd'}]
for multipoint in multipoints:
crossover = {'crossover': UniformMultipoint(multipoint), 'name': 'uni{}'.format(multipoint)}
crossovers.append(crossover)
corrections = [
{'correction': Correction(tsp), 'name': 'vanilla'},
{'correction': MirrorCorrection(tsp), 'name': 'mirror'},
{'correction': ExtensionCorrection(tsp), 'name': 'extension'}]
parameters = [1, 3, 5]
mutations = []
for parameter in parameters:
for correction in corrections:
mutation = {'mutation': CauchyMutation(r=parameter, correction = correction['correction']), 'name': 'cauchy{}_'
.format(parameter)+correction['name']}
mutations.append(mutation)
mutation = {'mutation': LevyMutation(scale=parameter, correction = correction['correction']), 'name': 'levy{}_'
.format(parameter)+correction['name']}
mutations.append(mutation)
# In[8]:
results = pd.DataFrame()
for crossover in crossovers:
for mutation in mutations:
heur_name = 'GO_mut:({})_cro:{}'.format(mutation['name'], crossover['name'])
runs = []
heur_name = 'GO_{}'.format(N)
for i in tqdm(range(num_runs), 'Testing {}'.format(heur_name)):
result = GeneticOptimization(of, maxeval, N=N, M=M, Tsel1=Tsel1, Tsel2=Tsel2,
mutation=mutation, crossover=crossover).search()
result['run'] = i
result['heur'] = heur_name
result['N'] = N
results.append(result)
return pd.DataFrame(results, columns=['heur', 'run', 'N', 'best_x', 'best_y', 'neval'])
# In[16]:
results = pd.DataFrame()
mutation = CauchyMutation(r=0.75, correction=Correction(tsp))
crossover = UniformMultipoint(1)
for N in [1, 2, 3, 5, 10, 20, 30, 100]:
res = experiment_go(of=tsp, maxeval=maxeval, num_runs=NUM_RUNS, N=N, M=N*3, Tsel1=0.1, Tsel2=1,
mutation=mutation, crossover=crossover)
results = pd.concat([results, res], axis=0)
# In[17]:
# (well-known performance criteria from previous classes)
def rel(x):
return len([n for n in x if n < np.inf])/len(x)
def mne(x):
return np.mean([n for n in x if n < np.inf])
# # Optimization demonstration
# In[12]:
from heur_aux import MirrorCorrection, CauchyMutation
from heur_fsa import FastSimulatedAnnealing
# In[13]:
heur = FastSimulatedAnnealing(of,
maxeval=10000,
T0=10,
n0=10,
alpha=2,
mutation=CauchyMutation(
r=0.1, correction=MirrorCorrection(of)))
res = heur.search()
print('x_best = {}'.format(res['best_x']))
print('y_best = {}'.format(res['best_y']))
print('neval = {}'.format(res['neval']))
# In[14]:
visualize_solution(res['best_x'], of)
# ## Excercises
#
# * ~~Implement~~ fix the implementation of this objective function (especially the random solution generator)
# * Tune heuristics for this objective function
# * Tune this objective function, e.g. by penalization for smaller number of clusters than $h$ (and make sure you understand why this is possible)
# * Compare heuristic approach to the original _k_-means
MirrorCorrection(dj).correct(np.array([6.12, -4.38, 2.96]))
# In[20]:
# extension correction in R^n (mind x[1])
ExtensionCorrection(dj).correct(np.array([6.12, -4.38, 2.96]))
# I.e. corrections work also in the continuous case, as expected...
# In[21]:
from heur_aux import CauchyMutation
# In[22]:
cauchy = CauchyMutation(r=.1, correction=MirrorCorrection(dj))
cauchy.mutate(np.array([6.12, -4.38, 2.96]))
# ## De Jong 1 optimization via FSA
#
# Thanks to current state of the framework, no modification to FSA is needed.
# In[23]:
from heur_fsa import FastSimulatedAnnealing
# In[24]:
heur = FastSimulatedAnnealing(dj,
maxeval=10000,
T0=10,
crossovers = [
{'crossover': Crossover(), 'name': 'mix'},
{'crossover': UniformMultipoint(1), 'name': 'uni'}, # test for other n as well!
{'crossover': RandomCombination(), 'name': 'rnd'},
]
# In[12]:
results = pd.DataFrame()
for crossover in crossovers:
heur_name = 'GO_{}'.format(crossover['name'])
runs = []
for i in tqdm_notebook(range(NUM_RUNS), 'Testing {}'.format(heur_name)):
run = GeneticOptimization(tsp, maxeval, N=5, M=15, Tsel1=1.0, Tsel2=0.5,
mutation=CauchyMutation(r=1.0, correction=Correction(tsp)),
crossover=crossover['crossover']).search()
run['run'] = i
run['heur'] = heur_name
run['crossover'] = crossover['name']
runs.append(run)
res_df = pd.DataFrame(runs, columns=['heur', 'run', 'crossover', 'best_x', 'best_y', 'neval'])
results = pd.concat([results, res_df], axis=0)
# In[13]:
def rel(x):
return len([n for n in x if n < np.inf])/len(x)
