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Fixed exp pickle save. Added exp results. Fixed soup plot issue.

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This commit is contained in:
Maximilian Zorn
2021-06-20 17:09:17 +02:00
parent 1da5bd95d6
commit cf6eec639f
20 changed files with 64 additions and 47 deletions
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11
journal_basin_linspace_clones.py
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@ -2,7 +2,7 @@ import copy
import itertools import itertools
from pathlib import Path from pathlib import Path
import random import random
import pickle
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import torch import torch
@ -113,7 +113,7 @@ if __name__ == '__main__':
ST_name_hash = random.getrandbits(32) ST_name_hash = random.getrandbits(32)
print(f"Running the Spawn experiment:") print(f"Running the Spawn experiment:")
df = SpawnLinspaceExperiment( exp = SpawnLinspaceExperiment(
population_size=ST_population_size, population_size=ST_population_size,
log_step_size=ST_log_step_size, log_step_size=ST_log_step_size,
net_input_size=NET_INPUT_SIZE, net_input_size=NET_INPUT_SIZE,
@ -125,7 +125,12 @@ if __name__ == '__main__':
nr_clones=nr_clones, nr_clones=nr_clones,
noise=None, noise=None,
directory=Path('output') / 'spawn_basin' / f'{ST_name_hash}' / f'linage' directory=Path('output') / 'spawn_basin' / f'{ST_name_hash}' / f'linage'
).df )
df = exp.df
directory = Path('output') / 'spawn_basin' / f'{ST_name_hash}' / 'linage'
pickle.dump(exp, open(f"{directory}/experiment_pickle_{ST_name_hash}.p", "wb"))
print(f"\nSaved experiment to {directory}.")
# Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis # Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis
sns.countplot(data=df, x="noise", hue="status_post") sns.countplot(data=df, x="noise", hue="status_post")

51
journal_basins.py
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@ -126,7 +126,7 @@ class SpawnExperiment:
# self.visualize_loss() # self.visualize_loss()
self.distance_matrix = distance_matrix(self.nets, print_it=False) self.distance_matrix = distance_matrix(self.nets, print_it=False)
self.parent_clone_distances = distance_from_parent(self.nets, print_it=False) self.parent_clone_distances = distance_from_parent(self.nets, print_it=False)
self.save()
def populate_environment(self): def populate_environment(self):
loop_population_size = tqdm(range(self.population_size)) loop_population_size = tqdm(range(self.population_size))
@ -155,7 +155,7 @@ class SpawnExperiment:
# We set parent start_time to just before this epoch ended, so plotting is zoomed in. Comment out to # 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). # 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. # Make one target to compare distances to clones later when they have trained.
net.start_time = self.ST_steps - 150 net.start_time = self.ST_steps - 350
net_input_data = net.input_weight_matrix() net_input_data = net.input_weight_matrix()
net_target_data = net.create_target_weights(net_input_data) net_target_data = net.create_target_weights(net_input_data)
@ -169,7 +169,7 @@ class SpawnExperiment:
for j in range(number_clones): for j in range(number_clones):
clone = Net(net.input_size, net.hidden_size, net.out_size, clone = Net(net.input_size, net.hidden_size, net.out_size,
f"ST_net_{str(i)}_clone_{str(j)}", start_time=self.ST_steps) f"ST_net_{str(i)}_clone_{str(j)}", start_time=self.ST_steps)
clone.load_state_dict(copy.deepcopy(net.state_dict())) clone.load_state_dict(copy.deepcopy(net.state_dict()))
rand_noise = prng() * self.noise rand_noise = prng() * self.noise
clone = self.apply_noise(clone, rand_noise) clone = self.apply_noise(clone, rand_noise)
clone.s_train_weights_history = copy.deepcopy(net.s_train_weights_history) clone.s_train_weights_history = copy.deepcopy(net.s_train_weights_history)
@ -225,9 +225,6 @@ class SpawnExperiment:
self.loss_history.append(net_loss_history) self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory) plot_loss(self.loss_history, self.directory)
def save(self):
pickle.dump(self, open(f"{self.directory}/experiment_pickle.p", "wb"))
print(f"\nSaved experiment to {self.directory}.")
if __name__ == "__main__": if __name__ == "__main__":
@ -243,15 +240,15 @@ if __name__ == "__main__":
ST_log_step_size = 10 ST_log_step_size = 10
# Define number of networks & their architecture # Define number of networks & their architecture
nr_clones = 5 nr_clones = 10
ST_population_size = 2 ST_population_size = 1
ST_net_hidden_size = 2 ST_net_hidden_size = 2
ST_net_learning_rate = 0.04 ST_net_learning_rate = 0.04
ST_name_hash = random.getrandbits(32) ST_name_hash = random.getrandbits(32)
print(f"Running the Spawn experiment:") print(f"Running the Spawn experiment:")
exp_list = [] exp_list = []
for noise_factor in range(2, 4): for noise_factor in range(2, 3):
exp = SpawnExperiment( exp = SpawnExperiment(
population_size=ST_population_size, population_size=ST_population_size,
log_step_size=ST_log_step_size, log_step_size=ST_log_step_size,
@ -267,18 +264,30 @@ if __name__ == "__main__":
) )
exp_list.append(exp) exp_list.append(exp)
# Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis directory = Path('output') / 'spawn_basin' / f'{ST_name_hash}'
pickle.dump(exp_list, open(f"{directory}/experiment_pickle_{ST_name_hash}.p", "wb"))
print(f"\nSaved experiment to {directory}.")
# Concat all dataframes, and add columns depending on where clone weights end up after training (rel. to parent)
df = pd.concat([exp.df for exp in exp_list]) df = pd.concat([exp.df for exp in exp_list])
sns.countplot(data=df, x="noise", hue="status_post") df = df.dropna().reset_index()
plt.savefig(f"output/spawn_basin/{ST_name_hash}/fixpoint_status_countplot.png") df["relative_distance"] = [ (df.loc[i]["MAE_pre"] - df.loc[i]["MAE_post"])/df.loc[i]["noise"] for i in range(len(df))]
df["class"] = [ "approaching" if df.loc[i]["relative_distance"] > 0 else "distancing" if df.loc[i]["relative_distance"] < 0 else "stationary" for i in range(len(df))]
# Catplot (either kind="point" or "box") that shows before-after training distances to parent # Countplot of all fixpoint clone after training per class. Uncomment and manually adjust xticklabels if x-ax size gets too small.
mlt = df[["MIM_pre", "MIM_post", "noise"]].melt("noise", var_name="time", value_name='Average Distance') ax = sns.catplot(kind="count", data=df, x="noise", hue="class", height=5.27, aspect=11.7/5.27)
sns.catplot(data=mlt, x="time", y="Average Distance", col="noise", kind="point", col_wrap=5, sharey=False) ax.set_axis_labels("Noise Levels", "Clone Fixpoints After Training Count ", fontsize=15)
plt.savefig(f"output/spawn_basin/{ST_name_hash}/clone_distance_catplot.png") #ax.set_xticklabels(labels=('10e-10', '10e-9', '10e-8', '10e-7', '10e-6', '10e-5', '10e-4', '10e-3', '10e-2', '10e-1'), fontsize=15)
plt.savefig(f"{directory}/clone_status_after_countplot_{ST_name_hash}.png")
mlt = df.melt(id_vars=["name", "noise"], value_vars=["MAE_pre", "MAE_post"], var_name="State", value_name="Distance") plt.clf()
ax = sns.catplot(data=mlt, x="State", y="Distance", col="noise", hue="name", kind="point", sharey=False, palette="Greens", legend=False)
ax.map(sns.boxplot, "State", "Distance", "noise", linewidth=0.8, order=["MAE_pre", "MAE_post"])
plt.savefig(f"output/spawn_basin/{ST_name_hash}/before_after_distance_catplot.png")
# Catplot of before-after comparison of the clone's weights. Colors links depending on class (approaching, distancing, stationary (i.e., MAE=0)). Blue, orange and green are based on countplot above, should be save for colorblindness (see https://gist.github.com/mwaskom/b35f6ebc2d4b340b4f64a4e28e778486)-
mlt = df.melt(id_vars=["name", "noise", "class"], value_vars=["MAE_pre", "MAE_post"], var_name="State", value_name="Distance")
P = ["blue" if mlt.loc[i]["class"] == "approaching" else "orange" if mlt.loc[i]["class"] == "distancing" else "green" for i in range(len(mlt))]
P = sns.color_palette(P, as_cmap=False)
ax = sns.catplot(data=mlt, x="State", y="Distance", col="noise", hue="name", kind="point", palette=P, col_wrap=min(5, len(exp_list)), sharey=False, legend=False)
ax.map(sns.boxplot, "State", "Distance", "noise", linewidth=0.8, order=["MAE_pre", "MAE_post"], whis=[0, 100])
ax.set_axis_labels("", "Manhattan Distance To Parent Weights", fontsize=15)
ax.set_xticklabels(labels=('after noise application', 'after training'), fontsize=15)
plt.savefig(f"{directory}/before_after_distance_catplot_{ST_name_hash}.png")
plt.clf()

10
journal_robustness.py
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@ -91,7 +91,6 @@ class RobustnessComparisonExperiment:
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) seeds=population_size if self.is_synthetic else 1)
self.save()
def populate_environment(self): def populate_environment(self):
nets = [] nets = []
@ -211,9 +210,6 @@ class RobustnessComparisonExperiment:
self.loss_history.append(net_loss_history) self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory) plot_loss(self.loss_history, self.directory)
def save(self):
pickle.dump(self, open(f"{self.directory}/experiment_pickle.p", "wb"))
print(f"\nSaved experiment to {self.directory}.")
if __name__ == "__main__": if __name__ == "__main__":
@ -230,7 +226,7 @@ if __name__ == "__main__":
ST_synthetic = True ST_synthetic = True
print(f"Running the robustness comparison experiment:") print(f"Running the robustness comparison experiment:")
RobustnessComparisonExperiment( exp = RobustnessComparisonExperiment(
population_size=ST_population_size, population_size=ST_population_size,
log_step_size=ST_log_step_size, log_step_size=ST_log_step_size,
net_input_size=NET_INPUT_SIZE, net_input_size=NET_INPUT_SIZE,
@ -242,3 +238,7 @@ if __name__ == "__main__":
synthetic=ST_synthetic, synthetic=ST_synthetic,
directory=Path('output') / 'journal_robustness' / f'{ST_name_hash}' directory=Path('output') / 'journal_robustness' / f'{ST_name_hash}'
) )
directory = Path('output') / 'journal_robustness' / f'{ST_name_hash}'
pickle.dump(exp, open(f"{directory}/experiment_pickle_{ST_name_hash}.p", "wb"))
print(f"\nSaved experiment to {directory}.")

21
journal_soup_basins.py
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@ -231,6 +231,8 @@ class SoupSpawnExperiment:
MSE_pre = MSE(net_target_data, clone_pre_weights) MSE_pre = MSE(net_target_data, clone_pre_weights)
MIM_pre = mean_invariate_manhattan_distance(net_target_data, clone_pre_weights) MIM_pre = mean_invariate_manhattan_distance(net_target_data, clone_pre_weights)
df.loc[len(df)] = [clone.name, net.name, MAE_pre, 0, MSE_pre, 0, MIM_pre, 0, self.noise, ""]
net.children.append(clone) net.children.append(clone)
self.clones.append(clone) self.clones.append(clone)
self.parents_with_clones.append(clone) self.parents_with_clones.append(clone)
@ -260,10 +262,9 @@ class SoupSpawnExperiment:
f"\nMSE({i},{j}): {MSE_post}" f"\nMSE({i},{j}): {MSE_post}"
f"\nMAE({i},{j}): {MAE_post}" f"\nMAE({i},{j}): {MAE_post}"
f"\nMIM({i},{j}): {MIM_post}\n") f"\nMIM({i},{j}): {MIM_post}\n")
self.parents_clones_id_functions.append(clone) self.parents_clones_id_functions.append(clone):
df.loc[clone.name] = [net.name, MAE_pre, MAE_post, MSE_pre, MSE_post, MIM_pre, MIM_post, self.noise, df.loc[df.name==clone.name, ["MAE_post", "MSE_post", "MIM_post", "status_post"]] = [MAE_post, MSE_post, MIM_post, clone.is_fixpoint]
clone.is_fixpoint]
# Finally take parent net {i} and finish it's training for comparison to clone development. # 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.epochs - 1):
@ -287,9 +288,6 @@ class SoupSpawnExperiment:
self.loss_history.append(net_loss_history) self.loss_history.append(net_loss_history)
plot_loss(self.loss_history, self.directory) plot_loss(self.loss_history, self.directory)
def save(self):
pickle.dump(self, open(f"{self.directory}/experiment_pickle.p", "wb"))
print(f"\nSaved experiment to {self.directory}.")
if __name__ == "__main__": if __name__ == "__main__":
@ -331,12 +329,19 @@ if __name__ == "__main__":
) )
exp_list.append(exp) exp_list.append(exp)
directory = Path('output') / 'soup_spawn_basin' / f'{soup_name_hash}'
pickle.dump(exp_list, open(f"{directory}/experiment_pickle_{soup_name_hash}.p", "wb"))
print(f"\nSaved experiment to {directory}.")
# Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis # Boxplot with counts of nr_fixpoints, nr_other, nr_etc. on y-axis
df = pd.concat([exp.df for exp in exp_list]) df = pd.concat([exp.df for exp in exp_list])
sns.countplot(data=df, x="noise", hue="status_post") sns.countplot(data=df, x="noise", hue="status_post")
plt.savefig(f"output/soup_spawn_basin/{soup_name_hash}/fixpoint_status_countplot.png") plt.savefig(f"output/soup_spawn_basin/{soup_name_hash}/fixpoint_status_countplot.png")
# Catplot (either kind="point" or "box") that shows before-after training distances to parent # 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') mlt = df.melt(id_vars=["name", "noise"], value_vars=["MAE_pre", "MAE_post"], var_name="State", value_name="Distance")
sns.catplot(data=mlt, x="time", y="Average Distance", col="noise", kind="point", col_wrap=5, sharey=False) ax = sns.catplot(data=mlt, x="State", y="Distance", col="noise", hue="name", kind="point", col_wrap=min(5, len(exp_list)), sharey=False, legend=False)
ax.map(sns.boxplot, "State", "Distance", "noise", linewidth=0.8, order=["MAE_pre", "MAE_post"], whis=[0, 100])
ax.set_axis_labels("", "Manhattan Distance To Parent Weights", fontsize=15)
ax.set_xticklabels(labels=('after noise application', 'after training'), fontsize=15)
plt.savefig(f"output/soup_spawn_basin/{soup_name_hash}/clone_distance_catplot.png") plt.savefig(f"output/soup_spawn_basin/{soup_name_hash}/clone_distance_catplot.png")

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18
visualization.py
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@ -92,7 +92,6 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
wm = np.array(wh) wm = np.array(wh)
n, x, y = wm.shape n, x, y = wm.shape
wm = wm.reshape(n, x * y) wm = wm.reshape(n, x * y)
#print(wm.shape, wm)
weight_histories.append(wm) weight_histories.append(wm)
weight_data = np.array(weight_histories) weight_data = np.array(weight_histories)
@ -104,7 +103,6 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
for transformed_trajectory, start_time in zip(np.split(weight_data_pca, n), start_times): for transformed_trajectory, start_time in zip(np.split(weight_data_pca, n), start_times):
start_log_time = int(start_time / batch_size) start_log_time = int(start_time / batch_size)
#print(start_time, start_log_time)
xdata = transformed_trajectory[start_log_time:, 0] xdata = transformed_trajectory[start_log_time:, 0]
ydata = transformed_trajectory[start_log_time:, 1] ydata = transformed_trajectory[start_log_time:, 1]
zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size).tolist() zdata = np.arange(start_time, len(ydata)*batch_size+start_time, batch_size).tolist()
@ -139,7 +137,7 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
else: else:
ax.scatter(np.asarray(xdata), np.asarray(ydata), zdata, s=3) ax.scatter(np.asarray(xdata), np.asarray(ydata), zdata, s=3)
steps = mpatches.Patch(color="white", label=f"{z_axis_legend}: {len(matrices_weights_history)} steps") #steps = mpatches.Patch(color="white", label=f"{z_axis_legend}: {len(matrices_weights_history)} steps")
population_size = mpatches.Patch(color="white", label=f"Population: {population_size} networks") population_size = mpatches.Patch(color="white", label=f"Population: {population_size} networks")
if z_axis_legend == "Self-application": if z_axis_legend == "Self-application":
@ -147,14 +145,14 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
trained = mpatches.Patch(color="white", label=f"Trained: true") trained = mpatches.Patch(color="white", label=f"Trained: true")
else: else:
trained = mpatches.Patch(color="white", label=f"Trained: false") trained = mpatches.Patch(color="white", label=f"Trained: false")
ax.legend(handles=[steps, population_size, trained]) ax.legend(handles=[population_size, trained])
else: else:
ax.legend(handles=[steps, population_size]) ax.legend(handles=[population_size])
ax.set_title(f"PCA Weights history") ax.set_title(f"PCA Transformed Weight Trajectories")
ax.set_xlabel("PCA X") ax.set_xlabel("PCA Transformed X-Axis")
ax.set_ylabel("PCA Y") ax.set_ylabel("PCA Transformed Y-Axis")
ax.set_zlabel(f"Epochs") ax.set_zlabel(f"Self Training Steps")
# FIXME: Replace this kind of operation with pathlib.Path() object interactions # FIXME: Replace this kind of operation with pathlib.Path() object interactions
directory = Path(directory) directory = Path(directory)
@ -168,7 +166,7 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
else: else:
plt.savefig(str(filepath)) plt.savefig(str(filepath))
# plt.show() plt.show()
def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Path], batch_size: int, plot_pca_together: bool): def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Path], batch_size: int, plot_pca_together: bool):