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journal_robustness.py redone, now is sensitive to seeds and plots

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steffen-illium 2021-05-23 15:49:48 +02:00
parent 55bdd706b6
commit 5e5511caf8
2 changed files with 10 additions and 8 deletions
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9
journal_robustness.py
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@ -9,10 +9,7 @@ import numpy as np
from pathlib import Path
from tqdm import tqdm
from tabulate import tabulate
from sklearn.metrics import mean_absolute_error as MAE
from sklearn.metrics import mean_squared_error as MSE
from journal_basins import mean_invariate_manhattan_distance as MIM
from functionalities_test import is_identity_function, is_zero_fixpoint, test_for_fixpoints, is_divergent
from network import Net
from torch.nn import functional as F
@ -153,7 +150,11 @@ class RobustnessComparisonExperiment:
# sns.set(rc={'figure.figsize': (10, 50)})
bx = sns.catplot(data=df[df['absolute_loss'] < 1], y='absolute_loss', x='application_step', kind='box',
col='noise_level', col_wrap=3, showfliers=False)
plt.show()
directory = Path('output') / 'robustness'
filename = f"absolute_loss_perapplication_boxplot_grid.png"
filepath = directory / filename
plt.savefig(str(filepath))
if print_it:
col_headers = [str(f"10e-{d}") for d in range(noise_levels)]

9
visualization.py
View File

@ -131,10 +131,10 @@ def plot_3d(matrices_weights_history, directory: Union[str, Path], population_si
for j in range(start_log_time, len(weight_matrix_pca)):
xdata.append(weight_matrix_pca[j][0])
ydata.append(weight_matrix_pca[j][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)
ax.plot3D(xdata, ydata, zdata, label=f"net {i}")
ax.scatter(np.array(xdata), np.array(ydata), np.array(zdata), s=7)
ax.scatter(np.asarray(xdata), np.asarray(ydata), zdata, s=7)
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")
@ -181,7 +181,8 @@ def plot_3d_self_train(nets_array: List, exp_name: str, directory: Union[str, Pa
z_axis_legend = "epochs"
return plot_3d(matrices_weights_history, directory, len(nets_array), z_axis_legend, exp_name, "", batch_size, plot_pca_together=plot_pca_together)
return plot_3d(matrices_weights_history, directory, len(nets_array), z_axis_legend, exp_name, "", batch_size,
plot_pca_together=plot_pca_together)
def plot_3d_self_application(nets_array: List, exp_name: str, directory_name: Union[str, Path], batch_size: int) -> None:
@ -212,7 +213,7 @@ def plot_3d_soup(nets_list, exp_name, directory: Union[str, Path]):
# will send forward the number "1" for batch size with the variable <irrelevant_batch_size>.
irrelevant_batch_size = 1
plot_3d_self_train(nets_list, exp_name, directory, irrelevant_batch_size)
plot_3d_self_train(nets_list, exp_name, directory, irrelevant_batch_size, False)
def line_chart_fixpoints(fixpoint_counters_history: list, epochs: int, ST_steps_between_SA: int,