diff --git a/code/bar_plot.py b/code/bar_plot.py
new file mode 100644
index 0000000..ebefa73
--- /dev/null
+++ b/code/bar_plot.py
@@ -0,0 +1,139 @@
+import os
+
+from experiment import Experiment
+# noinspection PyUnresolvedReferences
+from soup import Soup
+from typing import List
+
+from collections import defaultdict
+
+from argparse import ArgumentParser
+import numpy as np
+
+import plotly as pl
+import plotly.graph_objs as go
+
+import colorlover as cl
+
+import dill
+
+
+def build_args():
+ arg_parser = ArgumentParser()
+ arg_parser.add_argument('-i', '--in_file', nargs=1, type=str)
+ arg_parser.add_argument('-o', '--out_file', nargs='?', default='out', type=str)
+ return arg_parser.parse_args()
+
+
+def plot_histogram(bars_dict_list: List[dict], filename='histogram_plot'):
+ # catagorical
+ ryb = cl.scales['10']['div']['RdYlBu']
+
+ data = []
+
+ if bars_dict_list:
+ keys = bars_dict_list[0].keys()
+ keyDict = defaultdict(list)
+ else:
+ raise IOError('This List is empty, is this intended?')
+
+ for key in keys:
+ keyDict[key] = np.mean([bars_dict[key] for bars_dict in bars_dict_list])
+
+ hist = go.Bar(
+ y=[keyDict.get(key, 0) for key in keys],
+ x=[key for key in keys],
+ showlegend=False,
+ marker=dict(
+ color=[ryb[bar_id] for bar_id in range(len(keys))]
+ ),
+ )
+ data.append(hist)
+
+ layout = dict(title='{} Histogram Plot'.format('Experiment Name Penis'),
+ # height=400, width=400,
+ # margin=dict(l=20, r=20, t=20, b=20)
+ )
+
+ fig = go.Figure(data=data, layout=layout)
+ pl.offline.plot(fig, auto_open=True, filename=filename)
+ pass
+
+
+def line_plot(line_dict_list, filename='lineplot'):
+ # lines with standard deviation
+ # Transform data accordingly and plot it
+ data = []
+ rdylgn = cl.scales['10']['div']['RdYlGn']
+ rdylgn_background = [scale + (0.4,) for scale in cl.to_numeric(rdylgn)]
+ for line_id, line_dict in enumerate(line_dict_list):
+ name = line_dict.get('name', 'gimme a name')
+
+ upper_bound = go.Scatter(
+ name='Upper Bound',
+ x=line_dict['x'],
+ y=line_dict['upper_y'],
+ mode='lines',
+ marker=dict(color="#444"),
+ line=dict(width=0),
+ fillcolor=rdylgn_background[line_id],
+ )
+
+ trace = go.Scatter(
+ x=line_dict['x'],
+ y=line_dict['main_y'],
+ mode='lines',
+ name=name,
+ line=dict(color=line_id),
+ fillcolor=rdylgn_background[line_id],
+ fill='tonexty')
+
+ lower_bound = go.Scatter(
+ name='Lower Bound',
+ x=line_dict['x'],
+ y=line_dict['lower_y'],
+ marker=dict(color="#444"),
+ line=dict(width=0),
+ mode='lines')
+
+ data.extend([upper_bound, trace, lower_bound])
+
+ layout=dict(title='{} Line Plot'.format('Experiment Name Penis'),
+ height=800, width=800, margin=dict(l=0, r=0, t=0, b=0))
+
+ fig = go.Figure(data=data, layout=layout)
+ pl.offline.plot(fig, auto_open=True, filename=filename)
+ pass
+
+
+def search_and_apply(absolut_file_or_folder, plotting_function, files_to_look_for=[]):
+ if os.path.isdir(absolut_file_or_folder):
+ for sub_file_or_folder in os.scandir(absolut_file_or_folder):
+ search_and_apply(sub_file_or_folder.path, plotting_function, files_to_look_for=files_to_look_for)
+ elif absolut_file_or_folder.endswith('.dill'):
+ file_or_folder = os.path.split(absolut_file_or_folder)[-1]
+ if file_or_folder in files_to_look_for and not os.path.exists('{}.html'.format(file_or_folder[:-5])):
+ print('Apply Plotting function "{func}" on file "{file}"'.format(func=plotting_function.__name__,
+ file=absolut_file_or_folder)
+ )
+
+ with open(absolut_file_or_folder, 'rb') as in_f:
+ exp = dill.load(in_f)
+
+ plotting_function(exp, filename='{}.html'.format(absolut_file_or_folder[:-5]))
+
+ else:
+ pass
+ # This was not a file i should look for.
+ else:
+ # This was either another FilyType or Plot.html alerady exists.
+ pass
+
+
+if __name__ == '__main__':
+ args = build_args()
+ in_file = args.in_file[0]
+ out_file = args.out_file
+
+ search_and_apply(in_file, plot_histogram, files_to_look_for=['all_counters.dill'])
+ # , 'all_names.dill', 'all_notable_nets.dill'])
diff --git a/code/latent_trajectory_plot.html b/code/latent_trajectory_plot.html
deleted file mode 100644
index 1433788..0000000
--- a/code/latent_trajectory_plot.html
+++ /dev/null
@@ -1,7 +0,0 @@
-
\ No newline at end of file
diff --git a/code/plot.html b/code/plot.html
deleted file mode 100644
index e28ba6b..0000000
--- a/code/plot.html
+++ /dev/null
@@ -1,7 +0,0 @@
-
\ No newline at end of file
diff --git a/code/setups/fixpoint-density.py b/code/setups/fixpoint-density.py
index abb9bcc..d126efd 100644
--- a/code/setups/fixpoint-density.py
+++ b/code/setups/fixpoint-density.py
@@ -1,6 +1,7 @@
import sys
-
-sys.path += ['../', './']
+import os
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
from util import *
from experiment import *
@@ -26,7 +27,7 @@ def count(counters, net, notable_nets=[]):
return counters, notable_nets
with Experiment('fixpoint-density') as exp:
- exp.trials = 1000
+ exp.trials = 100
exp.epsilon = 1e-4
net_generators = []
for activation in ['linear', 'sigmoid', 'relu']:
@@ -43,6 +44,7 @@ with Experiment('fixpoint-density') as exp:
net = net_generator().with_params(epsilon=exp.epsilon)
name = str(net.__class__.__name__) + " activiation='" + str(net.get_keras_params().get('activation')) + "' use_bias='" + str(net.get_keras_params().get('use_bias')) + "'"
count(counters, net, notable_nets)
+ K.clear_session()
all_counters += [counters]
all_notable_nets += [notable_nets]
all_names += [name]
@@ -52,4 +54,6 @@ with Experiment('fixpoint-density') as exp:
for exp_id, counter in enumerate(all_counters):
exp.log(all_names[exp_id])
exp.log(all_counters[exp_id])
- exp.log('\n')
\ No newline at end of file
+ exp.log('\n')
+
+print('Done')
diff --git a/code/setups/known-fixpoint-variation.py b/code/setups/known-fixpoint-variation.py
index e28fa80..ca3b066 100644
--- a/code/setups/known-fixpoint-variation.py
+++ b/code/setups/known-fixpoint-variation.py
@@ -1,6 +1,10 @@
import sys
-sys.path += ['../', './']
+import os
+
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
+
from util import *
from experiment import *
diff --git a/code/setups/mixed-self-fixpoints.py b/code/setups/mixed-self-fixpoints.py
index beecc0f..0ee3c0a 100644
--- a/code/setups/mixed-self-fixpoints.py
+++ b/code/setups/mixed-self-fixpoints.py
@@ -1,6 +1,7 @@
import sys
+import os
-sys.path += ['../', './']
+from typing import Tuple
from util import *
from experiment import *
@@ -9,10 +10,32 @@ from network import *
import keras.backend
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
+
+
def generate_counters():
+ """
+ Initial build of the counter dict, to store counts.
+
+ :rtype: dict
+ :return: dictionary holding counter for: 'divergent', 'fix_zero', 'fix_sec', 'other'
+ """
return {'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
+
def count(counters, net, notable_nets=[]):
+ """
+ Count the occurences ot the types of weight trajectories.
+
+ :param counters: A counter dictionary.
+ :param net: A Neural Network
+ :param notable_nets: A list to store and save intersting candidates
+
+ :rtype Tuple[dict, list]
+ :return: Both the counter dictionary and the list of interessting nets.
+ """
+
if net.is_diverged():
counters['divergent'] += 1
elif net.is_fixpoint():
@@ -28,6 +51,7 @@ def count(counters, net, notable_nets=[]):
counters['other'] += 1
return counters, notable_nets
+
with Experiment('training_fixpoint') as exp:
exp.trials = 20
exp.selfattacks = 4
@@ -69,4 +93,4 @@ with Experiment('training_fixpoint') as exp:
for exp_id, name in enumerate(all_names):
exp.log(all_names[exp_id])
exp.log(all_data[exp_id])
- exp.log('\n')
\ No newline at end of file
+ exp.log('\n')
diff --git a/code/setups/training-fixpoints.py b/code/setups/training-fixpoints.py
index 9018c8d..8fe3df2 100644
--- a/code/setups/training-fixpoints.py
+++ b/code/setups/training-fixpoints.py
@@ -1,6 +1,8 @@
import sys
+import os
-sys.path += ['../', './']
+# Concat top Level dir to system environmental variables
+sys.path += os.path.join('..', '.')
from util import *
from experiment import *