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journal linspace basins

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steffen-illium
2021-06-25 10:25:25 +02:00
parent cf6eec639f
commit 14d9a533cb
8 changed files with 69 additions and 100 deletions
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27
journal_robustness.py
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@@ -91,7 +91,6 @@ class RobustnessComparisonExperiment:
self.time_to_vergence, self.time_as_fixpoint = self.test_robustness(
seeds=population_size if self.is_synthetic else 1)
def populate_environment(self):
nets = []
if self.is_synthetic:
@@ -125,8 +124,8 @@ class RobustnessComparisonExperiment:
# This checks wether to use synthetic setting with multiple seeds
# or multi network settings with a singlee seed
df = pd.DataFrame(columns=['setting', 'Noise Level', 'steps', 'absolute_loss',
'time_to_vergence', 'time_as_fixpoint'])
df = pd.DataFrame(columns=['setting', 'Noise Level', 'Self Train Steps', 'absolute_loss',
'Time to vergence', 'Time as fixpoint'])
with tqdm(total=max(len(self.id_functions), seeds)) as pbar:
for i, fixpoint in enumerate(self.id_functions): # 1 / n
row_headers.append(fixpoint.name)
@@ -138,8 +137,7 @@ class RobustnessComparisonExperiment:
clone = Net(fixpoint.input_size, fixpoint.hidden_size, fixpoint.out_size,
f"{fixpoint.name}_clone_noise10e-{noise_level}")
clone.load_state_dict(copy.deepcopy(fixpoint.state_dict()))
rand_noise = prng() * pow(10, -noise_level) # n / 1
clone = self.apply_noise(clone, rand_noise)
clone = clone.apply_noise(pow(10, -noise_level))
while not is_zero_fixpoint(clone) and not is_divergent(clone):
# -> before
@@ -154,7 +152,6 @@ class RobustnessComparisonExperiment:
absolute_loss = F.l1_loss(target_data_pre_application, target_data_post_application).item()
if is_identity_function(clone):
time_as_fixpoint[setting][noise_level] += 1
# When this raises a Type Error, we found a second order fixpoint!
@@ -166,26 +163,24 @@ class RobustnessComparisonExperiment:
pbar.update(1)
# Get the measuremts at the highest time_time_to_vergence
df_sorted = df.sort_values('Steps', ascending=False).drop_duplicates(['setting', 'Noise Level'])
df_melted = df_sorted.reset_index().melt(id_vars=['setting', 'Noise Level', 'Steps'],
df_sorted = df.sort_values('Self Train Steps', ascending=False).drop_duplicates(['setting', 'Noise Level'])
df_melted = df_sorted.reset_index().melt(id_vars=['setting', 'Noise Level', 'Self Train Steps'],
value_vars=['Time to vergence', 'Time as fixpoint'],
var_name="Measurement",
value_name="Steps")
value_name="Steps").sort_values('Noise Level')
# Plotting
sns.set(style='whitegrid', font_scale=2)
bf = sns.boxplot(data=df_melted, y='Steps', x='Noise Level', hue='Measurement', palette=PALETTE)
synthetic = 'synthetic' if self.is_synthetic else 'natural'
bf.set_title(f'Robustness as self application steps per noise level for {synthetic} fixpoints.')
# bf.set_title(f'Robustness as self application steps per noise level for {synthetic} fixpoints.')
plt.tight_layout()
# sns.set(rc={'figure.figsize': (10, 50)})
# bx = sns.catplot(data=df[df['absolute_loss'] < 1], y='absolute_loss', x='application_step', kind='box',
# col='noise_level', col_wrap=3, showfliers=False)
directory = Path('output') / 'robustness'
directory.mkdir(parents=True, exist_ok=True)
filename = f"absolute_loss_perapplication_boxplot_grid.png"
filepath = directory / filename
filename = f"absolute_loss_perapplication_boxplot_grid_{'synthetic' if self.is_synthetic else 'wild'}.png"
filepath = self.directory / filename
plt.savefig(str(filepath))
if print_it:
@@ -219,11 +214,11 @@ if __name__ == "__main__":
ST_steps = 1000
ST_epochs = 5
ST_log_step_size = 10
ST_population_size = 2
ST_population_size = 500
ST_net_hidden_size = 2
ST_net_learning_rate = 0.004
ST_name_hash = random.getrandbits(32)
ST_synthetic = True
ST_synthetic = False
print(f"Running the robustness comparison experiment:")
exp = RobustnessComparisonExperiment(