diff --git a/journal_robustness.py b/journal_robustness.py index ad7224c..c6c6ff6 100644 --- a/journal_robustness.py +++ b/journal_robustness.py @@ -4,7 +4,6 @@ import pandas as pd import torch import random import copy -import numpy as np from pathlib import Path from tqdm import tqdm @@ -21,6 +20,7 @@ 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], @@ -28,15 +28,32 @@ def generate_perfekt_synthetic_fixpoint_weights(): ], 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 """ + # 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 + # 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: @@ -55,7 +72,7 @@ class RobustnessComparisonExperiment: self.epochs = epochs self.ST_steps = st_steps self.loss_history = [] - self.synthetic = synthetic + self.is_synthetic = synthetic self.fixpoint_counters = { "identity_func": 0, "divergent": 0, @@ -71,14 +88,14 @@ class RobustnessComparisonExperiment: self.id_functions = [] self.nets = self.populate_environment() self.count_fixpoints() - self.time_to_vergence, self.time_as_fixpoint = self.test_robustness() + self.time_to_vergence, self.time_as_fixpoint = self.test_robustness( + seeds=population_size if self.is_synthetic else 1) self.save() def populate_environment(self): - loop_population_size = tqdm(range(self.population_size)) nets = [] - if self.synthetic: + 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) @@ -86,6 +103,7 @@ class RobustnessComparisonExperiment: 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) @@ -99,58 +117,61 @@ class RobustnessComparisonExperiment: 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) - is_synthetic = True if len(self.id_functions) > 1 and seeds == 1 else False - avg_time_to_vergence = [[0 for _ in range(noise_levels)] for _ in - range(seeds if is_synthetic else len(self.id_functions))] - avg_time_as_fixpoint = [[0 for _ in range(noise_levels)] for _ in - range(seeds if is_synthetic else len(self.id_functions))] + 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 = [] - data_pos = 0 + # 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', 'application_step', 'absolute_loss', 'time_to_vergence']) - for i, fixpoint in enumerate(self.id_functions): #1 / n - row_headers.append(fixpoint.name) - for seed in range(seeds): #n / 1 - for noise_level in range(noise_levels): - self_application_steps = 0 - 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) + df = pd.DataFrame(columns=['setting', 'noise_level', '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) + for seed in range(seeds): # n / 1 + for noise_level in range(noise_levels): + steps = 0 + 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) - 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) + 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) - avg_time_to_vergence[i][noise_level] += 1 - # -> after - clone_weight_post_application = clone.input_weight_matrix() - target_data_post_application = clone.create_target_weights(clone_weight_post_application) + clone.self_application(1, self.log_step_size) + time_to_vergence[i][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() + absolute_loss = F.l1_loss(target_data_pre_application, target_data_post_application).item() - setting = i if is_synthetic else seed + setting = seed if self.is_synthetic else i - if is_identity_function(clone): - avg_time_as_fixpoint[i][noise_level] += 1 - # When this raises a Type Error, we found a second order fixpoint! - self_application_steps += 1 - else: - self_application_steps = pd.NA # Not a Number! + if is_identity_function(clone): + time_as_fixpoint[i][noise_level] += 1 + # When this raises a Type Error, we found a second order fixpoint! + steps += 1 - df.loc[df.shape[0]] = [setting, noise_level, self_application_steps, - absolute_loss, avg_time_to_vergence[i][noise_level]] + df.loc[df.shape[0]] = [setting, noise_level, steps, absolute_loss, + time_to_vergence[i][noise_level], time_as_fixpoint[i][noise_level]] + pbar.update(1) - - # calculate the average: - # df = df.replace([np.inf, -np.inf], np.nan) - # df = df.dropna() - bf = sns.boxplot(data=df, y='self_application_steps', x='noise_level', ) + # 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'], + value_vars=['time_to_vergence', 'time_as_fixpoint'], + var_name="Measurement", + value_name="Steps") + # Plotting + sns.set(style='whitegrid') + bf = sns.boxplot(data=df_melted, y='Steps', x='noise_level', hue='Measurement', palette=PALETTE) bf.set_title('Robustness as self application steps per noise level') plt.tight_layout() @@ -158,6 +179,7 @@ class RobustnessComparisonExperiment: # 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 @@ -167,12 +189,12 @@ class RobustnessComparisonExperiment: col_headers = [str(f"10e-{d}") for d in range(noise_levels)] print(f"\nAppplications steps until divergence / zero: ") - print(tabulate(avg_time_to_vergence, showindex=row_headers, headers=col_headers, tablefmt='orgtbl')) + print(tabulate(time_to_vergence, showindex=row_headers, headers=col_headers, tablefmt='orgtbl')) print(f"\nTime as fixpoint: ") - print(tabulate(avg_time_as_fixpoint, showindex=row_headers, headers=col_headers, tablefmt='orgtbl')) + print(tabulate(time_as_fixpoint, showindex=row_headers, headers=col_headers, tablefmt='orgtbl')) - return avg_time_as_fixpoint, avg_time_to_vergence + return time_as_fixpoint, time_to_vergence def count_fixpoints(self): exp_details = f"ST steps: {self.ST_steps}" @@ -198,7 +220,7 @@ if __name__ == "__main__": ST_steps = 1000 ST_epochs = 5 ST_log_step_size = 10 - ST_population_size = 5 + ST_population_size = 100 ST_net_hidden_size = 2 ST_net_learning_rate = 0.04 ST_name_hash = random.getrandbits(32)