2021-05-03 08:37:31 +02:00

121 lines
4.1 KiB
Python

import os
import time
import dill
from tqdm import tqdm
import copy
class Experiment:
@staticmethod
def from_dill(path):
with open(path, "rb") as dill_file:
return dill.load(dill_file)
def __init__(self, name=None, ident=None):
self.experiment_id = '{}_{}'.format(ident or '', time.time())
self.experiment_name = name or 'unnamed_experiment'
self.next_iteration = 0
self.log_messages = []
self.historical_particles = {}
def __enter__(self):
self.dir = os.path.join('experiments', 'exp-{name}-{id}-{it}'.format(
name=self.experiment_name, id=self.experiment_id, it=self.next_iteration)
)
os.makedirs(self.dir)
print("** created {dir} **".format(dir=self.dir))
return self
def __exit__(self, exc_type, exc_value, traceback):
self.save(experiment=self.without_particles())
self.save_log()
self.next_iteration += 1
def log(self, message, **kwargs):
self.log_messages.append(message)
print(message, **kwargs)
def save_log(self, log_name="log"):
with open(os.path.join(self.dir, "{name}.txt".format(name=log_name)), "w") as log_file:
for log_message in self.log_messages:
print(str(log_message), file=log_file)
def __copy__(self):
copy_ = Experiment(name=self.experiment_name,)
copy_.__dict__ = {attr: self.__dict__[attr] for attr in self.__dict__ if
attr not in ['particles', 'historical_particles']}
return copy_
def without_particles(self):
self_copy = copy.copy(self)
# self_copy.particles = [particle.states for particle in self.particles]
self_copy.historical_particles = {key: val.states for key, val in self.historical_particles.items()}
return self_copy
def save(self, **kwargs):
for name, value in kwargs.items():
with open(os.path.join(self.dir, "{name}.dill".format(name=name)), "wb") as dill_file:
dill.dump(value, dill_file)
class FixpointExperiment(Experiment):
def __init__(self, **kwargs):
kwargs['name'] = self.__class__.__name__ if 'name' not in kwargs else kwargs['name']
super().__init__(**kwargs)
self.counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
self.interesting_fixpoints = []
def run_net(self, net, step_limit=100, run_id=0):
i = 0
while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
net.self_attack()
i += 1
if run_id:
net.save_state(time=i)
self.count(net)
def count(self, net):
if net.is_diverged():
self.counters['divergent'] += 1
elif net.is_fixpoint():
if net.is_zero():
self.counters['fix_zero'] += 1
else:
self.counters['fix_other'] += 1
self.interesting_fixpoints.append(net.get_weights())
elif net.is_fixpoint(2):
self.counters['fix_sec'] += 1
else:
self.counters['other'] += 1
class MixedFixpointExperiment(FixpointExperiment):
def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0):
i = 0
while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
net.self_attack()
with tqdm(postfix=["Loss", dict(value=0)]) as bar:
for _ in range(trains_per_application):
loss = net.compiled().train()
bar.postfix[1]["value"] = loss
bar.update()
i += 1
if run_id:
net.save_state()
self.count(net)
class SoupExperiment(Experiment):
pass
class IdentLearningExperiment(Experiment):
def __init__(self):
super(IdentLearningExperiment, self).__init__(name=self.__class__.__name__)
pass