138 lines
6.2 KiB
Python
138 lines
6.2 KiB
Python
import copy
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import itertools
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from pathlib import Path
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import random
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import pandas as pd
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import numpy as np
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import torch
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from functionalities_test import is_identity_function, test_status
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from journal_basins import SpawnExperiment, prng, mean_invariate_manhattan_distance
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from network import Net
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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 seaborn as sns
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from matplotlib import pyplot as plt
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class SpawnLinspaceExperiment(SpawnExperiment):
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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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# For every initial net {i} after populating (that is fixpoint after first epoch);
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pairwise_net_list = itertools.combinations(self.parents, 2)
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for net1, net2 in pairwise_net_list:
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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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net1.start_time = self.ST_steps - 150
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net1_input_data = net1.input_weight_matrix()
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net1_target_data = net1.create_target_weights(net1_input_data)
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net2.start_time = self.ST_steps - 150
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net2_input_data = net2.input_weight_matrix()
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net2_target_data = net2.create_target_weights(net2_input_data)
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if is_identity_function(net1) and is_identity_function(net2):
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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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in_between_weights = np.linspace(net1_target_data, net2_target_data, number_clones,endpoint=False)
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for j, in_between_weight in enumerate(in_between_weights):
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clone = Net(net1.input_size, net1.hidden_size, net1.out_size,
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name=f"{net1.name}_clone_{str(j)}", start_time=self.ST_steps)
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clone.apply_weights(torch.as_tensor(in_between_weight))
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clone.s_train_weights_history = copy.deepcopy(net1.s_train_weights_history)
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clone.number_trained = copy.deepcopy(net1.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(net1_target_data, clone_pre_weights)
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MSE_pre = MSE(net1_target_data, clone_pre_weights)
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MIM_pre = mean_invariate_manhattan_distance(net1_target_data, clone_pre_weights)
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# Then finish training each clone {j} (for remaining epoch-1 * ST_steps) ..
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for _ in range(self.epochs - 1):
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for _ in range(self.ST_steps):
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clone.self_train(1, self.log_step_size, self.net_learning_rate)
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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(net1_target_data, clone_post_weights)
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MSE_post = MSE(net1_target_data, clone_post_weights)
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MIM_post = mean_invariate_manhattan_distance(net1_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_{net1.name}) is fixpoint."
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f"\nMSE({net1.name},{j}): {MSE_post}"
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f"\nMAE({net1.name},{j}): {MAE_post}"
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f"\nMIM({net1.name},{j}): {MIM_post}\n")
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self.nets.append(clone)
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df.loc[clone.name] = [net1.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post,
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self.noise, clone.is_fixpoint]
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for parent in self.parents:
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for _ in range(self.epochs - 1):
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for _ in range(self.ST_steps):
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parent.self_train(1, self.log_step_size, self.net_learning_rate)
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self.df = df
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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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ST_steps = 2000
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ST_epochs = 2
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ST_log_step_size = 10
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# Define number of networks & their architecture
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nr_clones = 3
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ST_population_size = 3
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ST_net_hidden_size = 2
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ST_net_learning_rate = 0.04
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ST_name_hash = random.getrandbits(32)
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print(f"Running the Spawn experiment:")
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df = SpawnLinspaceExperiment(
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population_size=ST_population_size,
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log_step_size=ST_log_step_size,
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net_input_size=NET_INPUT_SIZE,
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net_hidden_size=ST_net_hidden_size,
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net_out_size=NET_OUT_SIZE,
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net_learning_rate=ST_net_learning_rate,
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epochs=ST_epochs,
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st_steps=ST_steps,
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nr_clones=nr_clones,
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noise=None,
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directory=Path('output') / 'spawn_basin' / f'{ST_name_hash}' / f'linage'
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).df
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# Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis
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sns.countplot(data=df, x="noise", hue="status_post")
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plt.savefig(f"output/spawn_basin/{ST_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/spawn_basin/{ST_name_hash}/clone_distance_catplot.png")
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