import copy
import itertools
from pathlib import Path
import random
import pickle
import pandas as pd
import numpy as np
import torch
from functionalities_test import is_identity_function, test_status
from journal_basins import SpawnExperiment, 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
class SpawnLinspaceExperiment(SpawnExperiment):
def spawn_and_continue(self, number_clones: int = None):
number_clones = number_clones or self.nr_clones
df = pd.DataFrame(
columns=['clone', 'parent', 'parent2',
'MAE_pre', 'MAE_post',
'MSE_pre', 'MSE_post',
'MIM_pre', 'MIM_post',
'noise', 'status_pst'])
# For every initial net {i} after populating (that is fixpoint after first epoch);
# parent = self.parents[0]
# parent_clone = clone = Net(parent.input_size, parent.hidden_size, parent.out_size,
# name=f"{parent.name}_clone_{0}", start_time=self.ST_steps)
# parent_clone.apply_weights(torch.as_tensor(parent.create_target_weights(parent.input_weight_matrix())))
# parent_clone = parent_clone.apply_noise(self.noise)
# self.parents.append(parent_clone)
pairwise_net_list = list(itertools.combinations(self.parents, 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().detach()
net1_target_data = net1.create_target_weights(net1_input_data).detach()
net2.start_time = self.ST_steps - 150
net2_input_data = net2.input_weight_matrix().detach()
net2_target_data = net2.create_target_weights(net2_input_data).detach()
if is_identity_function(net1) and is_identity_function(net2):
# if True:
# 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(net1_target_data, net2_target_data, number_clones, endpoint=False)
# in_between_weights = np.logspace(net1_target_data, net2_target_data, number_clones, endpoint=False)
for j, in_between_weight in enumerate(in_between_weights):
clone = Net(net1.input_size, net1.hidden_size, net1.out_size,
name=f"{net1.name}_{net2.name}_clone_{str(j)}", start_time=self.ST_steps + 100)
clone.apply_weights(torch.as_tensor(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()).detach()
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)
try:
# 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)
if any([torch.isnan(x).any() for x in clone.parameters()]):
raise ValueError
except ValueError:
print("Ran into nan in 'in beetween weights' array.")
df.loc[len(df)] = [j, net1.name, net2.name,
MAE_pre, 0,
MSE_pre, 0,
MIM_pre, 0,
self.noise, clone.is_fixpoint]
continue
# Post Training distances for comparison
clone_post_weights = clone.create_target_weights(clone.input_weight_matrix()).detach()
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} (between {net1.name} and {net2.name}) is fixpoint."
f"\nMSE({net1.name},{j}): {MSE_post}"
f"\nMAE({net1.name},{j}): {MAE_post}"
f"\nMIM({net1.name},{j}): {MIM_post}\n")
self.nets.append(clone)
df.loc[len(df)] = [j, net1.name, net2.name,
MAE_pre, MAE_post,
MSE_pre, MSE_post,
MIM_pre, MIM_post,
self.noise, clone.is_fixpoint]
for net1, net2 in pairwise_net_list:
try:
value = 'MAE'
c_selector = [f'{value}_pre', f'{value}_post']
values = df.loc[(df['parent'] == net1.name) & (df['parent2'] == net2.name)][c_selector]
this_min, this_max = values.values.min(), values.values.max()
df.loc[(df['parent'] == net1.name) &
(df['parent2'] == net2.name), c_selector] = (values - this_min) / (this_max - this_min)
except ValueError:
pass
for parent in self.parents:
for _ in range(self.epochs - 1):
for _ in range(self.ST_steps):
parent.self_train(1, self.log_step_size, self.net_learning_rate)
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 = 25
ST_population_size = 10
ST_net_hidden_size = 2
ST_net_learning_rate = 0.04
ST_name_hash = random.getrandbits(32)
print(f"Running the Spawn experiment:")
exp = SpawnLinspaceExperiment(
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=1e-8,
directory=Path('output') / 'spawn_basin' / f'{ST_name_hash}' / f'linage'
)
df = exp.df
directory = Path('output') / 'spawn_basin' / f'{ST_name_hash}' / 'linage'
with (directory / f"experiment_pickle_{ST_name_hash}.p").open('wb') as f:
pickle.dump(exp, f)
print(f"\nSaved experiment to {directory}.")
# Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis
# 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")
# Pointplot with pre and after parent Distances
import seaborn as sns
from matplotlib import pyplot as plt, ticker
# ptplt = sns.pointplot(data=exp.df, x='MAE_pre', y='MAE_post', join=False)
ptplt = sns.scatterplot(x=exp.df['MAE_pre'], y=exp.df['MAE_post'])
# ptplt.set(xscale='log', yscale='log')
x0, x1 = ptplt.axes.get_xlim()
y0, y1 = ptplt.axes.get_ylim()
lims = [max(x0, y0), min(x1, y1)]
# This is the x=y line using transforms
ptplt.plot(lims, lims, 'w', linestyle='dashdot', transform=ptplt.axes.transData)
ptplt.plot([0, 1], [0, 1], ':k', transform=ptplt.axes.transAxes)
ptplt.set(xlabel='Mean Absolute Distance before Self-Training',
ylabel='Mean Absolute Distance after Self-Training')
# ptplt.axes.xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: round(float(x), 2)))
# ptplt.xticks(rotation=45)
#for ind, label in enumerate(ptplt.get_xticklabels()):
# if ind % 10 == 0: # every 10th label is kept
# label.set_visible(True)
# else:
# label.set_visible(False)
filepath = exp.directory / 'mim_dist_plot.pdf'
plt.tight_layout()
plt.savefig(filepath, dpi=600, format='pdf', bbox_inches='tight')