def __init__(self, nth_gen, n_plots=4) -> None:
super().__init__()
self.nth_gen = nth_gen
self.term = MultiObjectiveSpaceToleranceTerminationWithRenormalization(
n_last=30, all_to_current=True, sliding_window=False)
self.hist = []
self.n_hist = n_plots
class RunningMetric(AnimationCallback):
def __init__(self, nth_gen, n_plots=4) -> None:
super().__init__()
self.nth_gen = nth_gen
self.term = MultiObjectiveSpaceToleranceTerminationWithRenormalization(n_last=100000,
all_to_current=True,
sliding_window=False)
self.hist = []
self.n_hist = n_plots
def notify(self, algorithm):
self.term.do_continue(algorithm)
def press(event):
if event.key == 'q':
algorithm.termination.force_termination = True
fig = plt.figure()
fig.canvas.mpl_connect('key_press_event', press)
metric = self.term.get_metric()
metrics = self.term.metrics
tau = len(metrics)
if metric is not None and tau % self.nth_gen == 0:
for k, f in self.hist:
plt.plot(np.arange(len(f)), f, label="t=%s" % k, alpha=0.6, linewidth=3)
_delta_f = metric["delta_f"]
plt.plot(np.arange(len(_delta_f)), _delta_f, label="t=%s (*)" % tau, alpha=0.9, linewidth=3)
_delta_ideal = [m['delta_ideal'] > 0.005 for m in metrics]
_delta_nadir = [m['delta_nadir'] > 0.005 for m in metrics]
for k in range(len(_delta_ideal)):
if _delta_ideal[k] or _delta_nadir[k]:
plt.plot([k, k], [0, _delta_f[k]], color="black", linewidth=0.5, alpha=0.5)
plt.plot([k], [_delta_f[k]], "o", color="black", alpha=0.5, markersize=2)
self.hist.append((tau, _delta_f))
if self.n_hist is not None:
self.hist = self.hist[-(self.n_hist - 1):]
plt.yscale("symlog")
plt.legend()
plt.xlabel("Generation")
plt.ylabel("$\Delta \, f$", rotation=0)
plt.draw()
plt.waitforbuttonpress()
fig.clf()
plt.close('all')
def __init__(self,
delta_gen,
n_plots=4,
only_if_n_plots=False,
key_press=True,
**kwargs) -> None:
super().__init__(**kwargs)
self.delta_gen = delta_gen
self.key_press = key_press
self.only_if_n_plots = only_if_n_plots
self.term = MultiObjectiveSpaceToleranceTerminationWithRenormalization(
n_last=100000, all_to_current=True, sliding_window=False)
self.hist = []
self.n_plots = n_plots
class RunningMetric(AnimationCallback):
def __init__(self,
delta_gen,
n_plots=4,
only_if_n_plots=False,
key_press=True,
**kwargs) -> None:
super().__init__(**kwargs)
self.delta_gen = delta_gen
self.key_press = key_press
self.only_if_n_plots = only_if_n_plots
self.term = MultiObjectiveSpaceToleranceTerminationWithRenormalization(
n_last=100000, all_to_current=True, sliding_window=False)
self.hist = []
self.n_plots = n_plots
def do(self, _, algorithm, force_plot=False, **kwargs):
self.term.do_continue(algorithm)
metric = self.term.get_metric()
metrics = self.term.metrics
tau = len(metrics)
# if for whatever reason the metric is not written yet
if metric is None:
return
if (tau + 1) % self.delta_gen == 0 or force_plot:
_delta_f = metric["delta_f"]
_delta_ideal = [m['delta_ideal'] > 0.005 for m in metrics]
_delta_nadir = [m['delta_nadir'] > 0.005 for m in metrics]
if force_plot or not self.only_if_n_plots or (self.only_if_n_plots
and len(self.hist)
== self.n_plots - 1):
fig, ax = plt.subplots()
if self.key_press:
def press(event):
if event.key == 'q':
algorithm.termination.force_termination = True
fig.canvas.mpl_connect('key_press_event', press)
for k, f in self.hist:
ax.plot(np.arange(len(f)) + 1,
f,
label="t=%s" % (k + 1),
alpha=0.6,
linewidth=3)
ax.plot(np.arange(len(_delta_f)) + 1,
_delta_f,
label="t=%s (*)" % (tau + 1),
alpha=0.9,
linewidth=3)
for k in range(len(_delta_ideal)):
if _delta_ideal[k] or _delta_nadir[k]:
ax.plot([k + 1, k + 1], [0, _delta_f[k]],
color="black",
linewidth=0.5,
alpha=0.5)
ax.plot([k + 1], [_delta_f[k]],
"o",
color="black",
alpha=0.5,
markersize=2)
ax.set_yscale("symlog")
ax.legend()
ax.set_xlabel("Generation")
ax.set_ylabel("$\Delta \, f$", rotation=0)
if self.key_press:
plt.draw()
plt.waitforbuttonpress()
plt.close('all')
self.hist.append((tau, _delta_f))
self.hist = self.hist[-(self.n_plots - 1):]