324 lines
14 KiB
Python
324 lines
14 KiB
Python
import os
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from pathlib import Path
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import pickle
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from torch import mean
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from tqdm import tqdm
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import random
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import copy
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from functionalities_test import is_identity_function, test_status, test_for_fixpoints
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from network import Net
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from visualization import plot_3d_self_train, plot_loss, plot_3d_soup
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import numpy as np
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from tabulate import tabulate
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from sklearn.metrics import mean_absolute_error as MAE
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from sklearn.metrics import mean_squared_error as MSE
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import pandas as pd
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import seaborn as sns
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from matplotlib import pyplot as plt
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def prng():
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return random.random()
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def l1(tup):
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a, b = tup
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return abs(a - b)
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def mean_invariate_manhattan_distance(x, y):
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# One of these one-liners that might be smart or really dumb. Goal is to find pairwise
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# distances of ascending values, ie. sum (abs(min1_X-min1_Y), abs(min2_X-min2Y) ...) / mean.
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# Idea was to find weight sets that have same values but just in different positions, that would
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# make this distance 0.
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return np.mean(list(map(l1, zip(sorted(x.numpy()), sorted(y.numpy())))))
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def distance_matrix(nets, distance="MIM", print_it=True):
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matrix = [[0 for _ in range(len(nets))] for _ in range(len(nets))]
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for net in range(len(nets)):
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weights = nets[net].input_weight_matrix()[:, 0]
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for other_net in range(len(nets)):
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other_weights = nets[other_net].input_weight_matrix()[:, 0]
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if distance in ["MSE"]:
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matrix[net][other_net] = MSE(weights, other_weights)
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elif distance in ["MAE"]:
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matrix[net][other_net] = MAE(weights, other_weights)
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elif distance in ["MIM"]:
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matrix[net][other_net] = mean_invariate_manhattan_distance(weights, other_weights)
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if print_it:
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print(f"\nDistance matrix (all to all) [{distance}]:")
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headers = [i.name for i in nets]
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print(tabulate(matrix, showindex=headers, headers=headers, tablefmt='orgtbl'))
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return matrix
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def distance_from_parent(nets, distance="MIM", print_it=True):
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list_of_matrices = []
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parents = list(filter(lambda x: "clone" not in x.name and is_identity_function(x), nets))
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distance_range = range(10)
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for parent in parents:
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parent_weights = parent.create_target_weights(parent.input_weight_matrix())
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clones = list(filter(lambda y: parent.name in y.name and parent.name != y.name, nets))
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matrix = [[0 for _ in distance_range] for _ in range(len(clones))]
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for dist in distance_range:
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for idx, clone in enumerate(clones):
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clone_weights = clone.create_target_weights(clone.input_weight_matrix())
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if distance in ["MSE"]:
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matrix[idx][dist] = MSE(parent_weights, clone_weights) < pow(10, -dist)
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elif distance in ["MAE"]:
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matrix[idx][dist] = MAE(parent_weights, clone_weights) < pow(10, -dist)
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elif distance in ["MIM"]:
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matrix[idx][dist] = mean_invariate_manhattan_distance(parent_weights, clone_weights) < pow(10,
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-dist)
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if print_it:
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print(f"\nDistances from parent {parent.name} [{distance}]:")
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col_headers = [str(f"10e-{d}") for d in distance_range]
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row_headers = [str(f"clone_{i}") for i in range(len(clones))]
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print(tabulate(matrix, showindex=row_headers, headers=col_headers, tablefmt='orgtbl'))
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list_of_matrices.append(matrix)
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return list_of_matrices
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class SoupSpawnExperiment:
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@staticmethod
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def apply_noise(network, noise: int):
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""" Changing the weights of a network to values + noise """
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for layer_id, layer_name in enumerate(network.state_dict()):
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for line_id, line_values in enumerate(network.state_dict()[layer_name]):
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for weight_id, weight_value in enumerate(network.state_dict()[layer_name][line_id]):
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# network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
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if prng() < 0.5:
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network.state_dict()[layer_name][line_id][weight_id] = weight_value + noise
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else:
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network.state_dict()[layer_name][line_id][weight_id] = weight_value - noise
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return network
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def __init__(self, population_size, log_step_size, net_input_size, net_hidden_size, net_out_size, net_learning_rate,
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epochs, st_steps, attack_chance, nr_clones, noise, directory) -> None:
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self.population_size = population_size
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self.log_step_size = log_step_size
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self.net_input_size = net_input_size
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self.net_hidden_size = net_hidden_size
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self.net_out_size = net_out_size
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self.net_learning_rate = net_learning_rate
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self.epochs = epochs
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self.ST_steps = st_steps
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self.attack_chance = attack_chance
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self.loss_history = []
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self.nr_clones = nr_clones
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self.noise = noise or 10e-5
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print("\nNOISE:", self.noise)
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self.directory = Path(directory)
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self.directory.mkdir(parents=True, exist_ok=True)
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# Populating environment & evolving entities
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self.nets = []
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self.id_functions = []
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self.clone_soup = []
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self.populate_environment()
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self.spawn_and_continue()
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self.weights_evolution_3d_experiment(self.nets, "parents")
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self.weights_evolution_3d_experiment(self.clone_soup, "clones")
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# self.visualize_loss()
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self.distance_matrix = distance_matrix(self.nets, print_it=False)
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self.parent_clone_distances = distance_from_parent(self.nets, print_it=False)
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self.save()
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def populate_environment(self):
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loop_population_size = tqdm(range(self.population_size))
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for i in loop_population_size:
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loop_population_size.set_description("Populating experiment %s" % i)
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net_name = f"parent_net_{str(i)}"
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net = Net(self.net_input_size, self.net_hidden_size, self.net_out_size, net_name)
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for _ in range(self.ST_steps):
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net.self_train(1, self.log_step_size, self.net_learning_rate)
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self.nets.append(net)
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if is_identity_function(net):
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self.id_functions.append(net)
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def evolve(self, population):
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print(f"Clone soup has a population of {len(population)} networks")
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loop_epochs = tqdm(range(self.epochs-1))
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for i in loop_epochs:
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loop_epochs.set_description("\nEvolving clone soup %s" % i)
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# A network attacking another network with a given percentage
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if random.randint(1, 100) <= self.attack_chance:
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random_net1, random_net2 = random.sample(range(len(population)), 2)
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random_net1 = population[random_net1]
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random_net2 = population[random_net2]
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print(f"\n Attack: {random_net1.name} -> {random_net2.name}")
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random_net1.attack(random_net2)
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# Self-training each network in the population
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for j in range(len(population)):
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net = population[j]
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for _ in range(self.ST_steps):
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net.self_train(1, self.log_step_size, self.net_learning_rate)
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def spawn_and_continue(self, number_clones: int = None):
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number_clones = number_clones or self.nr_clones
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df = pd.DataFrame(
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columns=['parent', 'MAE_pre', 'MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise',
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'status_post'])
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# MAE_pre, MSE_pre, MIM_pre = 0, 0, 0
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# For every initial net {i} after populating (that is fixpoint after first epoch);
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for i in range(len(self.id_functions)):
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net = self.nets[i]
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# We set parent start_time to just before this epoch ended, so plotting is zoomed in. Comment out to
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# to see full trajectory (but the clones will be very hard to see).
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# Make one target to compare distances to clones later when they have trained.
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net.start_time = self.ST_steps - 150
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net_input_data = net.input_weight_matrix()
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net_target_data = net.create_target_weights(net_input_data)
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print(f"\nNet {i} is fixpoint")
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# Clone the fixpoint x times and add (+-)self.noise to weight-sets randomly;
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# To plot clones starting after first epoch (z=ST_steps), set that as start_time!
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# To make sure PCA will plot the same trajectory up until this point, we clone the
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# parent-net's weight history as well.
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for j in range(number_clones):
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clone = Net(net.input_size, net.hidden_size, net.out_size,
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f"net_{str(i)}_clone_{str(j)}", start_time=self.ST_steps)
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clone.load_state_dict(copy.deepcopy(net.state_dict()))
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rand_noise = prng() * self.noise
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clone = self.apply_noise(clone, rand_noise)
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clone.s_train_weights_history = copy.deepcopy(net.s_train_weights_history)
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clone.number_trained = copy.deepcopy(net.number_trained)
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# Pre Training distances (after noise application of course)
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clone_pre_weights = clone.create_target_weights(clone.input_weight_matrix())
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MAE_pre = MAE(net_target_data, clone_pre_weights)
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MSE_pre = MSE(net_target_data, clone_pre_weights)
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MIM_pre = mean_invariate_manhattan_distance(net_target_data, clone_pre_weights)
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net.children.append(clone)
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self.clone_soup.append(clone)
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self.evolve(self.clone_soup)
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for i in range(len(self.id_functions)):
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net = self.nets[i]
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net_input_data = net.input_weight_matrix()
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net_target_data = net.create_target_weights(net_input_data)
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for j in range(len(net.children)):
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clone = net.children[j]
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# Post Training distances for comparison
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clone_post_weights = clone.create_target_weights(clone.input_weight_matrix())
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MAE_post = MAE(net_target_data, clone_post_weights)
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MSE_post = MSE(net_target_data, clone_post_weights)
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MIM_post = mean_invariate_manhattan_distance(net_target_data, clone_post_weights)
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# .. log to data-frame and add to nets for 3d plotting if they are fixpoints themselves.
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test_status(clone)
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if is_identity_function(clone):
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print(f"Clone {j} (of net_{i}) is fixpoint."
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f"\nMSE({i},{j}): {MSE_post}"
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f"\nMAE({i},{j}): {MAE_post}"
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f"\nMIM({i},{j}): {MIM_post}\n")
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self.nets.append(clone)
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df.loc[clone.name] = [net.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise,
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clone.is_fixpoint]
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# Finally take parent net {i} and finish it's training for comparison to clone development.
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for _ in range(self.epochs - 1):
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for _ in range(self.ST_steps):
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net.self_train(1, self.log_step_size, self.net_learning_rate)
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net_weights_after = net.create_target_weights(net.input_weight_matrix())
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print(f"Parent net's distance to original position."
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f"\nMSE(OG,new): {MAE(net_target_data, net_weights_after)}"
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f"\nMAE(OG,new): {MSE(net_target_data, net_weights_after)}"
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f"\nMIM(OG,new): {mean_invariate_manhattan_distance(net_target_data, net_weights_after)}\n")
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self.df = df
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def weights_evolution_3d_experiment(self, nets_population, suffix):
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exp_name = f"soup_basins_{str(len(self.nets))}_nets_3d_weights_PCA_{suffix}"
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return plot_3d_soup(nets_population, exp_name, self.directory)
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def visualize_loss(self):
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for i in range(len(self.nets)):
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net_loss_history = self.nets[i].loss_history
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self.loss_history.append(net_loss_history)
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plot_loss(self.loss_history, self.directory)
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def save(self):
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pickle.dump(self, open(f"{self.directory}/experiment_pickle.p", "wb"))
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print(f"\nSaved experiment to {self.directory}.")
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if __name__ == "__main__":
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NET_INPUT_SIZE = 4
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NET_OUT_SIZE = 1
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# Define number of runs & name:
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ST_runs = 1
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ST_runs_name = "test-27"
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soup_ST_steps = 1500
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soup_epochs = 2
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soup_log_step_size = 10
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# Define number of networks & their architecture
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nr_clones = 2
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soup_population_size = 2
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soup_net_hidden_size = 2
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soup_net_learning_rate = 0.04
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soup_attack_chance = 10
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soup_name_hash = random.getrandbits(32)
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print(f"Running the Soup-Spawn experiment:")
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exp_list = []
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for noise_factor in range(2, 5):
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exp = SoupSpawnExperiment(
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population_size=soup_population_size,
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log_step_size=soup_log_step_size,
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net_input_size=NET_INPUT_SIZE,
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net_hidden_size=soup_net_hidden_size,
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net_out_size=NET_OUT_SIZE,
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net_learning_rate=soup_net_learning_rate,
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epochs=soup_epochs,
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st_steps=soup_ST_steps,
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attack_chance=soup_attack_chance,
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nr_clones=nr_clones,
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noise=pow(10, -noise_factor),
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directory=Path('output') / 'soup_spawn_basin' / f'{soup_name_hash}' / f'10e-{noise_factor}'
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)
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exp_list.append(exp)
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# Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis
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df = pd.concat([exp.df for exp in exp_list])
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sns.countplot(data=df, x="noise", hue="status_post")
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plt.savefig(f"output/soup_spawn_basin/{soup_name_hash}/fixpoint_status_countplot.png")
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# Catplot (either kind="point" or "box") that shows before-after training distances to parent
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mlt = df[["MIM_pre", "MIM_post", "noise"]].melt("noise", var_name="time", value_name='Average Distance')
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sns.catplot(data=mlt, x="time", y="Average Distance", col="noise", kind="point", col_wrap=5, sharey=False)
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plt.savefig(f"output/soup_spawn_basin/{soup_name_hash}/clone_distance_catplot.png")
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