soup pictures

.gitignore

@@ -3,7 +3,8 @@
 
 
 ### Local Datasets ###
-
+/experiments
+/setups/experiments
 
 ### Data ###
 *.csv

code/experiment.py

@@ -13,7 +13,7 @@ class Experiment:
             return dill.load(dill_file)
     
     def __init__(self, name=None, ident=None):
-        self.experiment_id = '{}_{}'.format(ident or '', time.time().as_integer_ratio()[0])
+        self.experiment_id = '{}_{}'.format(ident or '', time.time())
         self.experiment_name = name or 'unnamed_experiment'
         self.next_iteration = 0
         self.log_messages = []

code/network.py

@@ -167,8 +167,8 @@ class ParticleDecorator:
     next_uid = 0
 
     def __init__(self, net):
-        self.uid = self.next_uid
-        self.next_uid += 1
+        self.uid = self.__class__.next_uid
+        self.__class__.next_uid += 1
         self.net = net
         self.states = []
 

code/setups/experiments/Aggregating/log.txt

@@ -1 +0,0 @@
-{'divergent': 43, 'fix_zero': 57, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/experiments/Aggregating_Soup/log.txt

@@ -1 +0,0 @@
-{'divergent': 0, 'fix_zero': 1, 'fix_other': 0, 'fix_sec': 0, 'other': 9}

code/setups/experiments/FFTN/log.txt

@@ -1 +0,0 @@
-{'divergent': 3, 'fix_zero': 97, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/experiments/Weightwise_Learning/log.txt

@@ -1 +0,0 @@
-{'divergent': 0, 'fix_zero': 100, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/experiments/Weightwise_Soup/log.txt

@@ -1 +0,0 @@
-{'divergent': 0, 'fix_zero': 0, 'fix_other': 10, 'fix_sec': 0, 'other': 0}

code/setups/experiments/exp-FixpointExperiment-_814031087631283-0/log.txt

@@ -1 +0,0 @@
-{'divergent': 0, 'fix_zero': 10, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/experiments/exp-soup-_1552643542.3005245-0/log.txt

@@ -0,0 +1 @@
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 13, 'fix_sec': 0, 'other': 7}

code/setups/network_trajectorys.py

@@ -26,8 +26,9 @@ if __name__ == '__main__':
                 run_exp(net)
                 K.clear_session()
             exp.log(exp.counters)
+            exp.save(trajectorys=exp.without_particles())
 
-    if True:
+    if False:
         # Aggregating Neural Network
         with FixpointExperiment() as exp:
             for run_id in tqdm(range(10)):
@@ -36,8 +37,9 @@ if __name__ == '__main__':
                 run_exp(net)
                 K.clear_session()
             exp.log(exp.counters)
+            exp.save(trajectorys=exp.without_particles())
 
-    if True:
+    if False:
         #FFT Neural Network
         with FixpointExperiment() as exp:
             for run_id in tqdm(range(10)):
@@ -46,8 +48,9 @@ if __name__ == '__main__':
                 run_exp(net)
                 K.clear_session()
             exp.log(exp.counters)
+            exp.save(trajectorys=exp.without_particles())
 
-    if True:
+    if False:
         # ok so this works quite realiably
         with FixpointExperiment() as exp:
             for i in range(10):
@@ -60,8 +63,9 @@ if __name__ == '__main__':
                     if run_id % 10 == 0:
                         run_exp(net)
                 K.clear_session()
+            exp.save(trajectorys=exp.without_particles())
 
-    if True:
+    if False:
         # ok so this works quite realiably
         with FixpointExperiment() as exp:
             for i in range(10):
@@ -74,6 +78,7 @@ if __name__ == '__main__':
                     if run_id % 10 == 0:
                         run_exp(net)
                 K.clear_session()
+            exp.save(trajectorys=exp.without_particles())
 
     if False:
         # this explodes in our faces completely... NAN everywhere

code/setups/soup_trajectorys.py

@@ -11,15 +11,17 @@ from experiment import *
 if __name__ == '__main__':
     if True:
         with SoupExperiment("soup") as exp:
-            for run_id in range(10):
-                # net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
-                #     .with_keras_params(activation='linear').with_params(epsilon=0.0001)
+            for run_id in range(1):
+                net_generator = lambda: TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(2, 2)) \
+                     .with_keras_params(activation='linear').with_params(epsilon=0.0001)
                 # net_generator = lambda: TrainingNeuralNetworkDecorator(AggregatingNeuralNetwork(4, 2, 2))\
                 #     .with_keras_params(activation='linear')
-                net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
-                    .with_keras_params(activation='linear')
+                # net_generator = lambda: TrainingNeuralNetworkDecorator(FFTNeuralNetwork(4, 2, 2))\
+                #    .with_keras_params(activation='linear')
                 # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
-                soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=20)
+                soup = Soup(20, net_generator).with_params(remove_divergent=True, remove_zero=True,
+                                                           train=30,
+                                                           learn_from_rate=-1)
                 soup.seed()
                 for _ in tqdm(range(100)):
                     soup.evolve()

code/soup.py

@@ -136,7 +136,7 @@ if __name__ == '__main__':
                 #     .with_keras_params(activation='linear')\
                 # .with_params(shuffler=AggregatingNeuralNetwork.shuffle_random)
                 # net_generator = lambda: RecurrentNeuralNetwork(2, 2).with_keras_params(activation='linear').with_params()
-                soup = Soup(10, net_generator).with_params(remove_divergent=True, remove_zero=True, train=20)
+                soup = Soup(100, net_generator).with_params(remove_divergent=True, remove_zero=True, train=20)
                 soup.seed()
                 for _ in tqdm(range(100)):
                     soup.evolve()

code/visualization.py

@@ -101,8 +101,9 @@ def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
     if not data_list:
         return
 
-    bupu = cl.scales['11']['div']['RdYlGn']
-    scale = cl.interp(bupu, len(data_list)+1)  # Map color scale to N bins
+    base_scale = cl.scales['9']['div']['RdYlGn']
+    # base_scale = cl.scales['9']['qual']['Set1']
+    scale = cl.interp(base_scale, len(data_list)+1)  # Map color scale to N bins
 
     # Fit the embedding space
     transformer = PCA(n_components=2)
@@ -122,10 +123,13 @@ def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
             y=transformed[:, 1],
             z=np.asarray(particle_dict['time']),
             text='Particle: {}<br> It had {} lifes.'.format(p_id, len(particle_dict['trajectory'])),
-            line=dict(color=scale[p_id]),
+            line=dict(
+                color=scale[p_id],
+                width=4
+            ),
             # legendgroup='Particle - {}'.format(p_id),
             name='Particle -{}'.format(p_id),
-            # showlegend=True,
+            showlegend=False,
             hoverinfo='text',
             mode='lines')
 
@@ -144,18 +148,27 @@ def plot_latent_trajectories_3D(soup_or_experiment, filename='plot'):
                                   color='rgb(0, 0, 0)',
                                   size=4
                               ),
-                              showlegend=False
-                              )
+                                showlegend=False
+                                )
 
         data.extend([line_trace, line_start, line_end])
 
-    layout = go.Layout(scene=dict(aspectratio=dict(x=2, y=2, z=2),
-                                  xaxis=dict(tickwidth=1, title='Transformed X'),
-                                  yaxis=dict(tickwidth=1, title='transformed Y'),
-                                  zaxis=dict(tickwidth=1, title='Epoch')),
-                       title='{} - Latent Trajectory Movement'.format('Penis'),
-                       # width=0, height=0,
-                       margin=dict(l=0, r=0, b=0, t=0))
+    axis_layout = dict(gridcolor='rgb(255, 255, 255)',
+                       zerolinecolor='rgb(255, 255, 255)',
+                       showbackground=True,
+                       backgroundcolor='rgb(230, 230,230)'
+                       )
+
+    layout = go.Layout(scene=dict(
+        # aspectratio=dict(x=2, y=2, z=2),
+        xaxis=dict(tickwidth=1, title='Transformed X', **axis_layout),
+        yaxis=dict(tickwidth=1, title='Transformed Y', **axis_layout),
+        zaxis=dict(tickwidth=1, title='Epoch', **axis_layout)),
+        # title='{} - Latent Trajectory Movement'.format('Soup'),
+
+        width=1024, height=1024,
+        margin=dict(l=0, r=0, b=0, t=0)
+    )
 
     fig = go.Figure(data=data, layout=layout)
     pl.offline.plot(fig, auto_open=True, filename=filename)
@@ -240,7 +253,7 @@ def search_and_apply(absolut_file_or_folder, plotting_function, files_to_look_fo
             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])):
+        if file_or_folder in files_to_look_for and not os.path.exists('{}.html'.format(absolut_file_or_folder[:-5])):
             print('Apply Plotting function "{func}" on file "{file}"'.format(func=plotting_function.__name__,
                                                                              file=absolut_file_or_folder)
                   )
@@ -262,4 +275,4 @@ if __name__ == '__main__':
     in_file = args.in_file[0]
     out_file = args.out_file
 
-    search_and_apply(in_file, plot_latent_trajectories_3D, ["experiment.dill", "soup.dill"])
+    search_and_apply(in_file, plot_latent_trajectories_3D, ["trajectorys.dill", "soup.dill"])