import pickle
import pandas as pd
import torch
import random
import copy
from pathlib import Path
from matplotlib.ticker import ScalarFormatter
from tqdm import tqdm
from tabulate import tabulate
from functionalities_test import is_identity_function, is_zero_fixpoint, test_for_fixpoints, is_divergent
from network import Net
from torch.nn import functional as F
from visualization import plot_loss, bar_chart_fixpoints
import seaborn as sns
from matplotlib import pyplot as plt
def prng():
return random.random()
def generate_perfekt_synthetic_fixpoint_weights():
return torch.tensor([[1.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0],
[1.0], [0.0], [0.0], [0.0],
[1.0], [0.0]
], dtype=torch.float32)
PALETTE = 10 * (
"#377eb8",
"#4daf4a",
"#984ea3",
"#e41a1c",
"#ff7f00",
"#a65628",
"#f781bf",
"#888888",
"#a6cee3",
"#b2df8a",
"#cab2d6",
"#fb9a99",
"#fdbf6f",
)
class RobustnessComparisonExperiment:
@staticmethod
def apply_noise(network, noise: int):
# Changing the weights of a network to values + noise
for layer_id, layer_name in enumerate(network.state_dict()):
for line_id, line_values in enumerate(network.state_dict()[layer_name]):
for weight_id, weight_value in enumerate(network.state_dict()[layer_name][line_id]):
# network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
if prng() < 0.5:
network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
else:
network.state_dict()[layer_name][line_id][weight_id] = weight_value - noise
return network
def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
epochs, st_steps, synthetic, directory) -> None:
self.population_size = population_size
self.log_step_size = log_step_size
self.net_input_size = net_input_size
self.net_hidden_size = net_hidden_size
self.net_out_size = net_out_size
self.net_learning_rate = net_learning_rate
self.epochs = epochs
self.ST_steps = st_steps
self.loss_history = []
self.is_synthetic = synthetic
self.fixpoint_counters = {
"identity_func": 0,
"divergent": 0,
"fix_zero": 0,
"fix_weak": 0,
"fix_sec": 0,
"other_func": 0
}
self.directory = Path(directory)
self.directory.mkdir(parents=True, exist_ok=True)
self.id_functions = []
self.nets = self.populate_environment()
self.count_fixpoints()
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:
''' Either use perfect / hand-constructed fixpoint ... '''
net_name = f"net_{str(0)}_synthetic"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
net.apply_weights(generate_perfekt_synthetic_fixpoint_weights())
nets.append(net)
else:
loop_population_size = tqdm(range(self.population_size))
for i in loop_population_size:
loop_population_size.set_description("Populating experiment %s" % i)
''' .. or use natural approach to train fixpoints from random initialisation. '''
net_name = f"net_{str(i)}"
net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
for _ in range(self.epochs):
net.self_train(self.ST_steps, self.log_step_size, self.net_learning_rate)
nets.append(net)
return nets
def test_robustness(self, print_it=True, noise_levels=10, seeds=10):
assert (len(self.id_functions) == 1 and seeds > 1) or (len(self.id_functions) > 1 and seeds == 1)
time_to_vergence = [[0 for _ in range(noise_levels)] for _ in
range(seeds if self.is_synthetic else len(self.id_functions))]
time_as_fixpoint = [[0 for _ in range(noise_levels)] for _ in
range(seeds if self.is_synthetic else len(self.id_functions))]
row_headers = []
# 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', 'Self Train Steps', 'absolute_loss',
'Time to convergence', '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)
for seed in range(seeds): # n / 1
setting = seed if self.is_synthetic else i
for noise_level in range(noise_levels):
steps = 0
clone = Net(fixpoint.input_size, fixpoint.hidden_size, fixpoint.out_size,
f"{fixpoint.name}_clone_noise_1e-{noise_level}")
clone.load_state_dict(copy.deepcopy(fixpoint.state_dict()))
clone = clone.apply_noise(pow(10, -noise_level))
while not is_zero_fixpoint(clone) and not is_divergent(clone):
# -> before
clone_weight_pre_application = clone.input_weight_matrix()
target_data_pre_application = clone.create_target_weights(clone_weight_pre_application)
clone.self_application(1, self.log_step_size)
time_to_vergence[setting][noise_level] += 1
# -> after
clone_weight_post_application = clone.input_weight_matrix()
target_data_post_application = clone.create_target_weights(clone_weight_post_application)
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!
steps += 1
df.loc[df.shape[0]] = [setting, f'$\mathregular{{10^{{-{noise_level}}}}}$',
steps, absolute_loss,
time_to_vergence[setting][noise_level],
time_as_fixpoint[setting][noise_level]]
pbar.update(1)
# Get the measuremts at the highest time_time_to_vergence
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 convergence', 'Time as fixpoint'],
var_name="Measurement",
value_name="Steps").sort_values('Noise Level')
# Plotting
# plt.rcParams.update({
# "text.usetex": True,
# "font.family": "sans-serif",
# "font.size": 12,
# "font.weight": 'bold',
# "font.sans-serif": ["Helvetica"]})
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'
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)
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:
col_headers = [str(f"1e-{d}") for d in range(noise_levels)]
print(f"\nAppplications steps until divergence / zero: ")
# print(tabulate(time_to_vergence, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
print(f"\nTime as fixpoint: ")
# print(tabulate(time_as_fixpoint, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
return time_as_fixpoint, time_to_vergence
def count_fixpoints(self):
exp_details = f"ST steps: {self.ST_steps}"
self.id_functions = test_for_fixpoints(self.fixpoint_counters, self.nets)
bar_chart_fixpoints(self.fixpoint_counters, self.population_size, self.directory, self.net_learning_rate,
exp_details)
def visualize_loss(self):
for i in range(len(self.nets)):
net_loss_history = self.nets[i].loss_history
self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory)
if __name__ == "__main__":
NET_INPUT_SIZE = 4
NET_OUT_SIZE = 1
ST_steps = 1000
ST_epochs = 5
ST_log_step_size = 10
ST_population_size = 1000
ST_net_hidden_size = 2
ST_net_learning_rate = 0.004
ST_name_hash = random.getrandbits(32)
ST_synthetic = False
print(f"Running the robustness comparison experiment:")
exp = RobustnessComparisonExperiment(
population_size=ST_population_size,
log_step_size=ST_log_step_size,
net_input_size=NET_INPUT_SIZE,
net_hidden_size=ST_net_hidden_size,
net_out_size=NET_OUT_SIZE,
net_learning_rate=ST_net_learning_rate,
epochs=ST_epochs,
st_steps=ST_steps,
synthetic=ST_synthetic,
directory=Path('output') / 'journal_robustness' / f'{ST_name_hash}'
)
directory = Path('output') / 'journal_robustness' / f'{ST_name_hash}'
pickle.dump(exp, open(f"{directory}/experiment_pickle_{ST_name_hash}.p", "wb"))
print(f"\nSaved experiment to {directory}.")