diff --git a/code/experiment.py b/code/experiment.py
index 30e0eff..1f97d6d 100644
--- a/code/experiment.py
+++ b/code/experiment.py
@@ -3,6 +3,8 @@ import time
import dill
from tqdm import tqdm
+from collections import defaultdict
+
class Experiment:
@@ -17,6 +19,7 @@ class Experiment:
self.base_dir = self.experiment_name
self.next_iteration = 0
self.log_messages = []
+ self.data_storage = defaultdict(list)
def __enter__(self):
self.dir = os.path.join(self.base_dir, 'experiments', 'exp-{name}-{id}-{it}'.format(
@@ -46,6 +49,10 @@ class Experiment:
with open(os.path.join(self.dir, "{name}.dill".format(name=name)), "wb") as dill_file:
dill.dump(value, dill_file)
+ def add_trajectory_segment(self, run_id, trajectory):
+ self.data_storage[run_id].append(trajectory)
+ return
+
class FixpointExperiment(Experiment):
@@ -54,11 +61,14 @@ class FixpointExperiment(Experiment):
self.counters = dict(divergent=0, fix_zero=0, fix_other=0, fix_sec=0, other=0)
self.interesting_fixpoints = []
- def run_net(self, net, step_limit=100):
+ def run_net(self, net, step_limit=100, run_id=0):
i = 0
while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
net.self_attack()
i += 1
+ if run_id:
+ weights = net.get_weights()
+ self.add_trajectory_segment(run_id, weights)
self.count(net)
def count(self, net):
@@ -78,7 +88,11 @@ class FixpointExperiment(Experiment):
class MixedFixpointExperiment(FixpointExperiment):
- def run_net(self, net, trains_per_application=100, step_limit=100):
+ def run_net(self, net, trains_per_application=100, step_limit=100, run_id=0):
+ # TODO Where to place the trajectory storage ?
+ # weights = net.get_weights()
+ # self.add_trajectory_segment(run_id, weights)
+
i = 0
while i < step_limit and not net.is_diverged() and not net.is_fixpoint():
net.self_attack()
diff --git a/code/network.py b/code/network.py
index 15306eb..31be760 100644
--- a/code/network.py
+++ b/code/network.py
@@ -136,11 +136,11 @@ class NeuralNetwork(PrintingObject):
return False
return True
- def repr_weights(self):
- return self.__class__.weights_to_string(self.get_weights())
+ def repr_weights(self, weights=None):
+ return self.weights_to_string(weights or self.get_weights())
- def print_weights(self):
- print(self.repr_weights())
+ def print_weights(self, weights=None):
+ print(self.repr_weights(weights))
class WeightwiseNeuralNetwork(NeuralNetwork):
@@ -605,7 +605,7 @@ class TrainingNeuralNetworkDecorator(NeuralNetwork):
if __name__ == '__main__':
if False:
with FixpointExperiment() as exp:
- for run_id in tqdm(range(100)):
+ for run_id in tqdm(range(1)):
# net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')
# net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2)\
net = FFTNeuralNetwork(aggregates=4, width=2, depth=2) \
@@ -613,9 +613,10 @@ if __name__ == '__main__':
# net = RecurrentNeuralNetwork(width=2, depth=2).with_keras_params(activation='linear')\
# .with_params(print_all_weight_updates=True)
# net.print_weights()
- exp.run_net(net, 100)
- exp.log(exp.counters)
+ # INFO Run_ID needs to be more than 0, so that exp stores the trajectories!
+ exp.run_net(net, 100, run_id=run_id+1)
+ exp.log(exp.counters)
if False:
# is_fixpoint was wrong because it trivially returned the old weights
with IdentLearningExperiment() as exp:
@@ -679,15 +680,16 @@ if __name__ == '__main__':
print("Fixpoint? " + str(net.is_fixpoint()))
print("Loss " + str(loss))
print()
- if True:
+ if False:
# and this gets somewhat interesting... we can still achieve non-trivial fixpoints
# over multiple applications when training enough in-between
with MixedFixpointExperiment() as exp:
- for run_id in range(100):
- net = TrainingNeuralNetworkDecorator(WeightwiseNeuralNetwork(width=2, depth=2))\
- .with_params(epsilon=0.0001)
+ for run_id in range(10):
+ net = TrainingNeuralNetworkDecorator(FFTNeuralNetwork(2, width=2, depth=2))\
+ .with_params(epsilon=0.0001, activation='sigmoid')
exp.run_net(net, 500, 10)
+
net.print_weights()
+
print("Fixpoint? " + str(net.is_fixpoint()))
- print()
exp.log(exp.counters)
diff --git a/code/visualization.py b/code/visualization.py
index 0a128d1..35604cc 100644
--- a/code/visualization.py
+++ b/code/visualization.py
@@ -1,19 +1,17 @@
import os
-import re
-from collections import defaultdict
-from tqdm import tqdm
-from argparse import ArgumentParser
-from distutils.util import strtobool
+from argparse import ArgumentParser
import numpy as np
-import tensorflow as tf
import plotly as pl
-from plotly import tools
import plotly.graph_objs as go
+import colorlover as cl
+
import dill
+from sklearn.manifold.t_sne import TSNE
+
def build_args():
arg_parser = ArgumentParser()
@@ -22,95 +20,174 @@ def build_args():
return arg_parser.parse_args()
-def numberFromStrings(string) -> list:
- numberfromstring = [int(x) for x in re.findall('\d+', string)]
- return numberfromstring
+def plot_latent_trajectories(data_dict, filename='latent_trajectory_plot'):
+ # TODO Fist and Last Position Markers
-def visulize_as_tiled_subplot(plotting_tuple, filename='plot'):
def norm(val, a=0, b=0.25):
return (val - a) / (b - a)
- data = np.asarray(plotting_tuple)
+ bupu = cl.scales['9']['seq']['BuPu']
+ scale = cl.interp(bupu, len(data_dict)) # Map color scale to N bins
- fig = tools.make_subplots(rows=1, cols=3,
- subplot_titles=('Layers: 1', 'Layers: 2', 'Layers: 3'),
- horizontal_spacing=0.05)
+ # Fit the mebedding space
+ transformer = TSNE()
+ for trajectory in data_dict:
+ transformer.fit(trajectory)
- for x in range(1, 4):
- # Only select Plots with x Layers
- scatter_slice = data[np.where(data[:, 2] == x)]
- # Only Select Plots with x Cells
- scatter_slice = scatter_slice[np.where(scatter_slice[:, 1] <= 10)]
- # Normalize colors
- colors = scatter_slice[:, 4]
- # colors = np.apply_along_axis(norm, 0, scatter_slice[:, 4])
- scatter = go.Scatter(x=scatter_slice[:, 3],
- y=scatter_slice[:, 1],
- hoverinfo='text',
- text=['Absolute Loss:
{}'.format(val) for val in colors],
- mode='markers',
- showlegend=False,
- marker=dict(size=10, color=colors, colorscale='Jet',
- # Only plot the colorscale once, use one for all
- showscale=True if x == 1 else False,
- cmax=0.25, cmin=0,
- colorbar=dict(y=0.5, x=1, tickmode='array', ticks='outside',
- tickvals=[0, 0.05, 0.10, 0.15, 0.20, 0.25],
- ticktext=["0.00", "0.05", "0.10", "0.15", "0.20", "0.25"]
- )
- )
- )
- fig.append_trace(scatter, 1, x,)
- # TODO: Layout Loop
- if x == 1:
- fig['layout']['yaxis{}'.format(x)].update(tickwidth=1, title='Number of Cells')
- if x == 2:
- fig['layout']['xaxis{}'.format(x)].update(tickwidth=1, title='Position -X')
+ # Transform data accordingly and plot it
+ data = []
+ for t_id, trajectory in enumerate(data_dict):
+ transformed = transformer.fit(trajectory)
+ line_trace = go.Scatter(
+ x=transformed[:, 0],
+ y=transformed[:, 1],
+ text='Hovertext goes here'.format(),
+ line=dict(color=scale[t_id]),
+ # legendgroup='Position -{}'.format(pos),
+ # name='Position -{}'.format(pos),
+ showlegend=False,
+ # hoverinfo='text',
+ mode='lines')
+ line_start = go.Scatter(mode='markers', x=transformed[0, 0], y=transformed[0, 1],
+ marker=dict(
+ color='rgb(255, 0, 0)',
+ size=2
+ ),
+ showlegend=False
+ )
+ line_end = go.Scatter(mode='markers', x=transformed[-1, 0], y=transformed[-1, 1],
+ marker=dict(
+ color='rgb(0, 0, 0)',
+ size=2
+ ),
+ showlegend=False
+ )
+ data.extend([line_trace, line_start, line_end])
- fig['layout'].update(title='{} - Mean Absolute Loss'.format(os.path.split('DESTINATION_OR_EXPERIMENT_NAME')[-1].upper()),
- height=300, width=800, margin=dict(l=50, r=0, t=60, b=50))
+ layout = dict(title='{} - Latent Trajectory Movement'.format('Penis'),
+ height=800, width=800, margin=dict(l=0, r=0, t=0, b=0))
# import plotly.io as pio
# pio.write_image(fig, filename)
- pl.offline.plot(fig, filename=filename)
+ fig = go.Figure(data=data, layout=layout)
+ pl.offline.plot(fig, auto_open=True, filename=filename)
pass
-def visulize_as_splatter3d(plotting_tuple, filename='plot'):
- # timesteps, cells, layers, positions, val
- _ , cells, layers, position, val = zip(*plotting_tuple)
- text = ['Cells: {}
Layers: {}
Position: {}
Mean(Min()): {}'.format(cells, layers, position, val)
- for _, cells, layers, position, val in plotting_tuple]
+def plot_latent_trajectories_3D(param_dict, filename='plot'):
+ def norm(val, a=0, b=0.25):
+ return (val - a) / (b - a)
+
+ bupu = cl.scales['9']['seq']['BuPu']
+ scale = cl.interp(bupu, len(param_dict.get('trajectories', []))) # Map color scale to N bins
+
+ max_len = max([len(trajectory) for trajectory in param_dict.get('trajectories', [])])
+
+ # Fit the mebedding space
+ transformer = TSNE()
+ for trajectory in param_dict.get('trajectories', []):
+ transformer.fit(trajectory)
+
+ # Transform data accordingly and plot it
+ data = []
+ for t_id, trajectory in enumerate(param_dict.get('trajectories', [])):
+ transformed = transformer.fit(trajectory)
+ trace = go.Scatter3d(
+ x=transformed[:, 0],
+ y=transformed[:, 1],
+ z=np.arange(max(max_len)),
+ text='Hovertext goes here'.format(),
+ line=dict(color=scale[t_id]),
+ # legendgroup='Position -{}'.format(pos),
+ # name='Position -{}'.format(pos),
+ showlegend=False,
+ # hoverinfo='text',
+ mode='lines')
+ data.append(trace)
- data = [go.Scatter3d(x=cells, y=layers, z=position, text=text, hoverinfo='text', mode='markers',
- marker=dict(color=val, colorscale='Jet', opacity=0.8,
- colorbar=dict(y=0.5, x=0.9, title="Mean(Min(Seeds))"))
- )]
layout = go.Layout(scene=dict(aspectratio=dict(x=2, y=2, z=1),
- xaxis=dict(tickwidth=1, title='Number of Cells'),
- yaxis=dict(tickwidth=1, title='Number of Layers'),
- zaxis=dict(tickwidth=1, title='Position -pX')),
+ xaxis=dict(tickwidth=1, title='Number of Cells'),
+ yaxis=dict(tickwidth=1, title='Number of Layers'),
+ zaxis=dict(tickwidth=1, title='Position -pX')),
+ title='{} - Latent Trajectory Movement'.format('Penis'),
+ width=800, height=800,
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) # filename='3d-scatter_plot'
+ pl.offline.plot(fig, auto_open=True, filename=filename)
+ pass
-def compile_run_name(path: str) -> dict:
- """
- Retrieve all names, extract index positions and group by seeds.
+def plot_histogram(bars_dict_list, filename='histogram_plot'):
+ # catagorical
+ ryb = cl.scales['10']['div']['RdYlBu']
- :param path: Path to the current TB folder of a sinle NN configuration
- :return: List of foldernames to filter for.
- """
- config_keys = ['run_seed', 'timesteps', 'index_position', 'cell_count', 'layers', 'cell_type']
- found_configurations = defaultdict(list)
- for dname in os.listdir(path):
- if os.path.isdir(os.path.join(path, dname)):
- this_config = {key: value for key, value in zip(config_keys, dname.split("_"))}
- found_configurations[this_config['index_position']].append(dname)
+ data = []
+ for bar_id, bars_dict in bars_dict_list:
+ hist = go.Histogram(
+ histfunc="count",
+ y=bars_dict.get('value', 14),
+ x=bars_dict.get('name', 'gimme a name'),
+ showlegend=False,
+ marker=dict(
+ color=ryb[bar_id]
+ ),
+ )
+ data.append(hist)
- return found_configurations
+ layout=dict(title='{} Histogram Plot'.format('Experiment Name Penis'),
+ height=400, width=400, 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 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
if __name__ == '__main__':
@@ -118,7 +195,4 @@ if __name__ == '__main__':
in_file = args.in_file[0]
out_file = args.out_file
- with open(in_file, 'rb') as dill_file:
- experiment = dill.load(dill_file)
-
- print('hi')
+ print('aha')