Work on MCMC algorithm, transition probabilities

MCMC_profile.txt

@@ -0,0 +1,92 @@
+Name:
+Type: DiGraph
+Number of nodes: 2001
+Number of edges: 3932
+Average in degree: 1.9650
+Average out degree: 1.9650
+ 9019172280 function calls (9019159907 primitive calls) in 4591.231 seconds
+
+ Ordered by: cumulative time
+
+ ncalls tottime percall cumtime percall filename:lineno(function)
+ 2 0.000 0.000 4591.252 2295.626 core.py:1(<module>)
+ 1052/1 0.017 0.000 4591.233 4591.233 {built-in method builtins.exec}
+ 1 0.026 0.026 4547.025 4547.025 simulation.py:68(run)
+ 2000 0.004 0.000 4546.924 2.273 simulation.py:100(tip_selection)
+ 2000 0.040 0.000 4546.920 2.273 simulation.py:218(weighted_MCMC)
+ 2000 24.496 0.012 4515.675 2.258 simulation.py:234(calc_weights)
+ 2003000 112.210 0.000 4490.570 0.002 dag.py:74(ancestors)
+ 2003000 9.370 0.000 4294.554 0.002 generic.py:149(shortest_path_length)
+ 2003000 165.348 0.000 4265.964 0.002 unweighted.py:25(single_source_shortest_path_length)
+668813765 654.194 0.000 4097.642 0.000 unweighted.py:69(_single_shortest_path_length)
+666816845 1432.373 0.000 2899.202 0.000 {method 'update' of 'dict' objects}
+1796600942 506.662 0.000 813.392 0.000 _collections_abc.py:719(__iter__)
+666810765 436.412 0.000 544.249 0.000 coreviews.py:81(__getitem__)
+666810765 324.471 0.000 434.178 0.000 _collections_abc.py:672(keys)
+666810765 197.035 0.000 306.731 0.000 coreviews.py:51(__iter__)
+1129790177 219.260 0.000 219.260 0.000 coreviews.py:54(__getitem__)
+680028033 111.715 0.000 111.715 0.000 {built-in method builtins.iter}
+666810765 109.707 0.000 109.707 0.000 _collections_abc.py:698(__init__)
+668813765 108.416 0.000 108.416 0.000 coreviews.py:45(__init__)
+668813765 82.347 0.000 82.347 0.000 dag.py:90(<genexpr>)
+ 1 0.001 0.001 43.328 43.328 simulation.py:82(print_graph)
+ 1 0.000 0.000 43.239 43.239 pyplot.py:236(show)
+ 1 0.000 0.000 43.239 43.239 backend_bases.py:178(show)
+ 1 0.000 0.000 43.110 43.110 backend_macosx.py:208(mainloop)
+ 1 37.035 37.035 43.110 43.110 {built-in method matplotlib.backends._macosx.show}
+ 4000 0.262 0.000 28.158 0.007 simulation.py:240(weighted_random_walk)
+ 4000 8.186 0.002 24.058 0.006 simulation.py:153(get_valid_tips)
+ 3671332 9.170 0.000 11.382 0.000 simulation.py:171(all_approvers_not_visible)
+ 2003050 3.923 0.000 7.215 0.000 contextlib.py:85(__exit__)
+ 4006592 2.002 0.000 7.171 0.000 {built-in method builtins.next}
+ 2003050 2.174 0.000 7.169 0.000 contextlib.py:157(helper)
+ 13207612 4.441 0.000 6.458 0.000 digraph.py:788(predecessors)
+ 1 0.000 0.000 5.953 5.953 backend_macosx.py:170(save_figure)
+ 1 5.682 5.682 5.682 5.682 {built-in method matplotlib.backends._macosx.choose_save_file}
+ 4006000 4.763 0.000 5.162 0.000 contextmanagers.py:10(reversed)
+ 2003050 4.107 0.000 4.994 0.000 contextlib.py:59(__init__)
+ 2003050 0.964 0.000 4.841 0.000 contextlib.py:79(__enter__)
+ 4000 2.703 0.001 3.058 0.001 simulation.py:134(get_visible_transactions)
+ 2000 1.848 0.001 3.031 0.002 simulation.py:284(get_tips)
+ 2003000 1.719 0.000 2.297 0.000 digraph.py:319(adj)
+11569771/11569178 1.509 0.000 1.509 0.000 {built-in method builtins.len}
+ 2003000 1.047 0.000 1.047 0.000 graph.py:797(has_node)
+ 2020984 0.898 0.000 0.899 0.000 {built-in method builtins.getattr}
+ 632/4 0.005 0.000 0.866 0.216 <frozen importlib._bootstrap>:966(_find_and_load)
+ 632/4 0.003 0.000 0.866 0.216 <frozen importlib._bootstrap>:936(_find_and_load_unlocked)
+ 596/4 0.004 0.000 0.865 0.216 <frozen importlib._bootstrap>:651(_load_unlocked)
+ 535/4 0.002 0.000 0.865 0.216 <frozen importlib._bootstrap_external>:672(exec_module)
+ 731/4 0.001 0.000 0.864 0.216 <frozen importlib._bootstrap>:211(_call_with_frames_removed)
+ 2003000 0.677 0.000 0.677 0.000 graph.py:385(__contains__)
+ 32 0.001 0.000 0.619 0.019 __init__.py:1(<module>)
+ 1 0.000 0.000 0.548 0.548 __init__.py:54(<module>)
+ 3671332 0.496 0.000 0.496 0.000 {method 'issubset' of 'set' objects}
+ 407/50 0.001 0.000 0.495 0.010 {built-in method builtins.__import__}
+ 2004276 0.412 0.000 0.412 0.000 {method 'items' of 'dict' objects}
+ 51263 0.084 0.000 0.407 0.000 simulation.py:261(<listcomp>)
+2227/1400 0.002 0.000 0.400 0.000 <frozen importlib._bootstrap>:997(_handle_fromlist)
+ 2003001 0.399 0.000 0.399 0.000 digraph.py:1062(is_directed)
+ 4497465 0.397 0.000 0.397 0.000 {method 'append' of 'list' objects}
+ 309501 0.324 0.000 0.324 0.000 {built-in method builtins.max}
+ 1 0.000 0.000 0.317 0.317 simulation.py:1(<module>)
+ 1 0.000 0.000 0.270 0.270 figure.py:1929(savefig)
+ 1 0.000 0.000 0.269 0.269 backend_bases.py:2107(print_figure)
+ 1 0.000 0.000 0.268 0.268 backend_agg.py:512(print_png)
+ 1 0.238 0.238 0.238 0.238 {built-in method matplotlib._png.write_png}
+ 1 0.000 0.000 0.234 0.234 pyplot.py:19(<module>)
+ 3 0.000 0.000 0.184 0.061 __init__.py:4(<module>)
+ 4 0.000 0.000 0.181 0.045 __init__.py:3(<module>)
+ 1 0.000 0.000 0.168 0.168 nx_pydot.py:15(<module>)
+ 1 0.000 0.000 0.166 0.166 __init__.py:16(<module>)
+ 535 0.005 0.000 0.156 0.000 <frozen importlib._bootstrap_external>:743(get_code)
+ 51263 0.101 0.000 0.146 0.000 simulation.py:262(<listcomp>)
+ 51263 0.102 0.000 0.138 0.000 simulation.py:269(weigthed_choice)
+ 1 0.000 0.000 0.135 0.135 intersection.py:4(<module>)
+ 1 0.064 0.064 0.129 0.129 {method 'show' of '_macosx.FigureManager' objects}
+ 1 0.000 0.000 0.123 0.123 algebraicconnectivity.py:9(<module>)
+ 1 0.000 0.000 0.120 0.120 __init__.py:106(<module>)
+ 583/581 0.002 0.000 0.120 0.000 <frozen importlib._bootstrap>:564(module_from_spec)
+1449/1388 0.035 0.000 0.117 0.000 {built-in method builtins.__build_class__}
+ 131 0.002 0.000 0.113 0.001 backend_bases.py:1955(motion_notify_event)
+ 43/42 0.000 0.000 0.100 0.002 <frozen importlib._bootstrap_external>:919(create_module)
+ 43/42 0.097 0.002 0.100 0.002 {built-in method _imp.create_dynamic}

requirements.txt

@@ -1,27 +1,64 @@
+appnope==0.1.0
 atomicwrites==1.1.5
 attrs==18.1.0
+bleach==2.1.3
 coverage==4.5.1
 cycler==0.10.0
 decorator==4.3.0
+entrypoints==0.2.3
 flake8==2.6.2
 hacking==1.1.0
+html5lib==1.0.1
 hypothesis==3.57.0
+ipykernel==4.8.2
+ipython==5.0.0
+ipython-genutils==0.2.0
+ipywidgets==7.2.1
+Jinja2==2.10
+jsonschema==2.6.0
+jupyter==1.0.0
+jupyter-client==5.2.3
+jupyter-console==5.2.0
+jupyter-core==4.4.0
 kiwisolver==1.0.1
+MarkupSafe==1.0
 matplotlib==2.2.2
 mccabe==0.5.3
+mistune==0.8.3
 more-itertools==4.2.0
+nbconvert==5.3.1
+nbformat==4.4.0
 networkx==2.1
 nose==1.3.7
+notebook==5.5.0
 numpy==1.14.3
+pandocfilters==1.4.2
 pbr==4.0.3
+pexpect==4.6.0
+pickleshare==0.7.4
 pluggy==0.6.0
+prompt-toolkit==1.0.15
+ptyprocess==0.5.2
 py==1.5.3
 pycodestyle==2.0.0
 pyflakes==1.2.3
+Pygments==2.2.0
 pyparsing==2.2.0
+pyprof2calltree==1.4.3
 pysha3==1.0.2
 pytest==3.6.0
 python-dateutil==2.7.3
 pytz==2018.4
+pyzmq==17.0.0
+qtconsole==4.3.1
+Send2Trash==1.5.0
+simplegeneric==0.8.1
 six==1.11.0
+terminado==0.8.1
+testpath==0.3.1
+tornado==5.0.2
+traitlets==4.3.2
 utils==0.9.0
+wcwidth==0.1.7
+webencodings==0.5.1
+widgetsnbextension==3.2.1

simulation/core.py

@@ -1,16 +1,17 @@
 import helpers
 import constants
+import networkx as nx
 from simulation import Simulation
 
 #Parameters: no_of_transactions, lambda, no_of_agents, alpha, latency, distance, tip_selection_algo
 #Tip selection algorithms are "random", "weighted", "unweighted"
 
-simu = Simulation(10000, 50, 1, 0.05, 1, 0, "weighted")
+simu = Simulation(20, 2, 1, 0, 1, 0, "weighted")
 
 simu.setup()
 
 simu.run()
 
-simu.print_graph()
-
+print(nx.info(simu.DG))
 
+simu.print_graph()

simulation/simulation.py

@@ -81,18 +81,18 @@ def run(self):
 
  def print_graph(self):
 
- # Plotting number of tips
- lens = []
- for i in self.record_tips:
- lens.append(len(i))
- plt.plot(self.arrival_times, lens)
- plt.show()
+ #Plotting number of tips
+ # lens = []
+ # for i in self.record_tips:
+ #  lens.append(len(i))
+ # plt.plot(self.arrival_times, lens)
+ # plt.show()
 
  #Plot the graph
- # pos = nx.get_node_attributes(self.DG, 'pos')
- # nx.draw_networkx(self.DG, pos, with_labels=True)
- # plt.title("Transactions = " + str(self.no_of_transactions) + ", " + r'$\lambda$' + " = " + str(self.lam))
- # plt.show()
+ pos = nx.get_node_attributes(self.DG, 'pos')
+ nx.draw_networkx(self.DG, pos, with_labels=True)
+ plt.title("Transactions = " + str(self.no_of_transactions) + ", " + r'$\lambda$' + " = " + str(self.lam))
+ plt.show()
 
  #Save the graph
  #plt.savefig('graph.png')
@@ -187,7 +187,7 @@ def unweighted_MCMC(self, transaction):
  if(tip1 != tip2):
  self.DG.add_edge(transaction,tip2)
 
- #self.record_tips.append(self.get_tips())
+ self.record_tips.append(self.get_tips())
 
  def random_walk(self, start, transaction):
 
@@ -220,7 +220,7 @@ def weighted_MCMC(self, transaction):
  #Start at genesis
  start = self.transactions[0]
 
- self.calc_weights()
+ #self.calc_weights(list(self.DX.nodes))
 
  tip1 = self.weighted_random_walk(start, transaction)
  tip2 = self.weighted_random_walk(start, transaction)
@@ -231,21 +231,32 @@ def weighted_MCMC(self, transaction):
 
  self.record_tips.append(self.get_tips())
 
- def calc_weights(self):
+ def calc_weights(self, transactions):
 
- for transaction in self.DG.nodes:
+ for transaction in transactions:
  transaction.cum_weight = len(list(nx.ancestors(self.DG, transaction))) + 1
  #print("Transaction " + str(transaction) + " has cum_weight " + str(transaction.cum_weight))
 
+ # sorted = nx.topological_sort(self.DG)
+ #
+ # for transaction in sorted:
+ # children = self.DG.successors(transaction)
+ #
+ # for child in children:
+ # child.ancestors = child.ancestors.union(transaction.ancestors)
+ # set1 = set()
+ # set1.add(transaction)
+ # child.ancestors = child.ancestors.union(set1)
+ #
+ # transaction.cum_weight = len(transaction.ancestors) + 1
+
  def weighted_random_walk(self, start, transaction):
 
  walker_on = start
  visible_transactions, not_visible_transactions = self.get_visible_transactions(transaction)
  valid_tips = self.get_valid_tips(visible_transactions, not_visible_transactions)
 
- #print("Valid tips: " + str(valid_tips))
-
- # If only genesis a valid tip, approve genesis
+ #If only genesis a valid tip, approve genesis
  if (valid_tips == [walker_on]):
  #print("Return early: " + str(walker_on))
  return walker_on
@@ -255,30 +266,35 @@ def weighted_random_walk(self, start, transaction):
  approvers = list(self.DG.predecessors(walker_on))
  if approvers == []:
  return walker_on
- #print("Approvers: " + str(approvers))
 
+ self.calc_weights(approvers)
+
+ #Calculate transition probabilities
  weights = [approver.cum_weight for approver in approvers]
  normalized_weights = [weight - max(weights) for weight in weights]
  weights = [math.exp(self.alpha * weight) for weight in normalized_weights]
+ sum_of_weights = sum(weights)
+ transition_probabilities = [math.exp(self.alpha * approver.cum_weight)/sum_of_weights for approver in approvers]
 
- walker_on = self.weigthed_choice(approvers, weights)
+ walker_on = np.random.choice(approvers, p=transition_probabilities)
+ #walker_on = self.weigthed_choice(approvers, weights)
 
  #print("Return after loop: " + str(walker_on))
  return walker_on
 
- def weigthed_choice(self, approvers, weights):
-
- sum_of_weights = sum(weights)
- rand = random.random() * sum_of_weights
-
- cum_sum = weights[0]
-
- for i in range(1, len(weights)):
- if rand < cum_sum:
- return approvers[i-1]
- cum_sum += weights[i]
-
- return approvers[-1]
+ # def weigthed_choice(self, approvers, weights):
+ #
+ #  sum_of_weights = sum(weights)
+ #  rand = random.random() * sum_of_weights
+ #
+ #  cum_sum = weights[0]
+ #
+ #  for i in range(1, len(weights)):
+ #  if rand < cum_sum:
+ #  return approvers[i-1]
+ #  cum_sum += weights[i]
+ #
+ #  return approvers[-1]
 
  #For printing number of tips as function of time
  def get_tips(self):

simulation/transaction.py

@@ -7,6 +7,7 @@ def __init__(self, _arrival_time, counter):
  self.arrival_time = _arrival_time
  self.agent, self.tip1, self.tip2 = None, None, None
  self.cum_weight = 0
+ self.ancestors = set()
 
  #Give transaction a random hash as identifier? For now just using numbers
  self.id = counter