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journal linspace basins

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steffen-illium
2021-06-14 11:55:11 +02:00
parent e156540e2c
commit 0ba109c083
3 changed files with 154 additions and 14 deletions
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142
journal_basin_linspace_clones.py Normal file
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import copy
import itertools
from pathlib import Path
import random
import pandas as pd
import numpy as np
from functionalities_test import is_identity_function, test_status
from journal_basins import SpawnExperiment, prng, mean_invariate_manhattan_distance
from network import Net
from sklearn.metrics import mean_absolute_error as MAE
from sklearn.metrics import mean_squared_error as MSE
import seaborn as sns
from matplotlib import pyplot as plt
class SpawnLinspaceExperiment(SpawnExperiment):
def spawn_and_continue(self, number_clones: int = None):
number_clones = number_clones or self.nr_clones
df = pd.DataFrame(
columns=['parent', 'MAE_pre', 'MAE_post', 'MSE_pre', 'MSE_post', 'MIM_pre', 'MIM_post', 'noise',
'status_post'])
# For every initial net {i} after populating (that is fixpoint after first epoch);
pairwise_net_list = itertools.permutations(self.nets, 2)
for net1, net2 in pairwise_net_list:
# We set parent start_time to just before this epoch ended, so plotting is zoomed in. Comment out to
# to see full trajectory (but the clones will be very hard to see).
# Make one target to compare distances to clones later when they have trained.
net1.start_time = self.ST_steps - 150
net1_input_data = net1.input_weight_matrix()
net1_target_data = net1.create_target_weights(net1_input_data)
net2.start_time = self.ST_steps - 150
net2_input_data = net2.input_weight_matrix()
net2_target_data = net2.create_target_weights(net2_input_data)
if is_identity_function(net1) and is_identity_function(net2):
# Clone the fixpoint x times and add (+-)self.noise to weight-sets randomly;
# To plot clones starting after first epoch (z=ST_steps), set that as start_time!
# To make sure PCA will plot the same trajectory up until this point, we clone the
# parent-net's weight history as well.
in_between_weights = np.linspace(net2_target_data, net2_target_data, number_clones)
for in_between_weight in in_between_weights:
clone = Net(net1.input_size, net1.hidden_size, net1.out_size, start_time=self.ST_steps)
clone.apply_weights(in_between_weight)
clone.s_train_weights_history = copy.deepcopy(net1.s_train_weights_history)
clone.number_trained = copy.deepcopy(net1.number_trained)
# Pre Training distances (after noise application of course)
clone_pre_weights = clone.create_target_weights(clone.input_weight_matrix())
MAE_pre = MAE(net1_target_data, clone_pre_weights)
MSE_pre = MSE(net1_target_data, clone_pre_weights)
MIM_pre = mean_invariate_manhattan_distance(net1_target_data, clone_pre_weights)
# Then finish training each clone {j} (for remaining epoch-1 * ST_steps) ..
for _ in range(self.epochs - 1):
for _ in range(self.ST_steps):
clone.self_train(1, self.log_step_size, self.net_learning_rate)
# Post Training distances for comparison
clone_post_weights = clone.create_target_weights(clone.input_weight_matrix())
MAE_post = MAE(net1_target_data, clone_post_weights)
MSE_post = MSE(net1_target_data, clone_post_weights)
MIM_post = mean_invariate_manhattan_distance(net1_target_data, clone_post_weights)
# .. log to data-frame and add to nets for 3d plotting if they are fixpoints themselves.
test_status(clone)
if is_identity_function(clone):
#print(f"Clone {j} (of net_{i}) is fixpoint."
# f"\nMSE({i},{j}): {MSE_post}"
# f"\nMAE({i},{j}): {MAE_post}"
# f"\nMIM({i},{j}): {MIM_post}\n")
self.nets.append(clone)
df.loc[clone.name] = [net1.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise,
clone.is_fixpoint]
# Finally take parent net {i} and finish it's training for comparison to clone development.
for _ in range(self.epochs - 1):
for _ in range(self.ST_steps):
net1.self_train(1, self.log_step_size, self.net_learning_rate)
net_weights_after = net1.create_target_weights(net1.input_weight_matrix())
print(f"Parent net's distance to original position."
f"\nMSE(OG,new): {MAE(net1_target_data, net_weights_after)}"
f"\nMAE(OG,new): {MSE(net1_target_data, net_weights_after)}"
f"\nMIM(OG,new): {mean_invariate_manhattan_distance(net1_target_data, net_weights_after)}\n")
self.df = df
if __name__ == '__main__':
NET_INPUT_SIZE = 4
NET_OUT_SIZE = 1
# Define number of runs & name:
ST_runs = 1
ST_runs_name = "test-27"
ST_steps = 2000
ST_epochs = 2
ST_log_step_size = 10
# Define number of networks & their architecture
nr_clones = 5
ST_population_size = 2
ST_net_hidden_size = 2
ST_net_learning_rate = 0.04
ST_name_hash = random.getrandbits(32)
print(f"Running the Spawn experiment:")
exp_list = []
for noise_factor in range(2, 5):
exp = SpawnExperiment(
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,
nr_clones=nr_clones,
noise=pow(10, -noise_factor),
directory=Path('output') / 'spawn_basin' / f'{ST_name_hash}' / f'10e-{noise_factor}'
)
exp_list.append(exp)
# Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis
df = pd.concat([exp.df for exp in exp_list])
sns.countplot(data=df, x="noise", hue="status_post")
plt.savefig(f"output/spawn_basin/{ST_name_hash}/fixpoint_status_countplot.png")
# Catplot (either kind="point" or "box") that shows before-after training distances to parent
mlt = df[["MIM_pre", "MIM_post", "noise"]].melt("noise", var_name="time", value_name='Average Distance')
sns.catplot(data=mlt, x="time", y="Average Distance", col="noise", kind="point", col_wrap=5, sharey=False)
plt.savefig(f"output/spawn_basin/{ST_name_hash}/clone_distance_catplot.png")

4
journal_basins.py
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@@ -1,8 +1,6 @@
import os
from pathlib import Path
import pickle
from torch import mean
from tqdm import tqdm
import random
import copy
@@ -17,11 +15,9 @@ import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
def prng():
return random.random()
def l1(tup):
a, b = tup
return abs(a - b)

22
journal_robustness.py
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@@ -126,7 +126,8 @@ 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', '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)
@@ -160,21 +161,22 @@ class RobustnessComparisonExperiment:
# When this raises a Type Error, we found a second order fixpoint!
steps += 1
df.loc[df.shape[0]] = [setting, noise_level, steps, absolute_loss,
df.loc[df.shape[0]] = [setting, f'10e-{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('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'],
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')
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.')
plt.tight_layout()
# sns.set(rc={'figure.figsize': (10, 50)})
@@ -221,9 +223,9 @@ if __name__ == "__main__":
ST_steps = 1000
ST_epochs = 5
ST_log_step_size = 10
ST_population_size = 100
ST_population_size = 2
ST_net_hidden_size = 2
ST_net_learning_rate = 0.04
ST_net_learning_rate = 0.004
ST_name_hash = random.getrandbits(32)
ST_synthetic = True