diff --git a/README.md b/README.md
index 5d5cb7ff42781eb619729d93a6766709b57efa7d..672f1ccfa358a772672d2d0b7ada290a177ef219 100644
--- a/README.md
+++ b/README.md
@@ -1,92 +1,4 @@
# ContinuousIM
+Code for experiments of Chapter 3 of the thesis 'Influence Maximisation and Adaptive Control of Opinion Dynamics on Complex Networks'.
-
-## Getting started
-
-To make it easy for you to get started with GitLab, here's a list of recommended next steps.
-
-Already a pro? Just edit this README.md and make it your own. Want to make it easy? [Use the template at the bottom](#editing-this-readme)!
-
-## Add your files
-
-- [ ] [Create](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#create-a-file) or [upload](https://docs.gitlab.com/ee/user/project/repository/web_editor.html#upload-a-file) files
-- [ ] [Add files using the command line](https://docs.gitlab.com/ee/gitlab-basics/add-file.html#add-a-file-using-the-command-line) or push an existing Git repository with the following command:
-
-```
-cd existing_repo
-git remote add origin https://git.soton.ac.uk/grm1g17/continuousim.git
-git branch -M main
-git push -uf origin main
-```
-
-## Integrate with your tools
-
-- [ ] [Set up project integrations](https://git.soton.ac.uk/grm1g17/continuousim/-/settings/integrations)
-
-## Collaborate with your team
-
-- [ ] [Invite team members and collaborators](https://docs.gitlab.com/ee/user/project/members/)
-- [ ] [Create a new merge request](https://docs.gitlab.com/ee/user/project/merge_requests/creating_merge_requests.html)
-- [ ] [Automatically close issues from merge requests](https://docs.gitlab.com/ee/user/project/issues/managing_issues.html#closing-issues-automatically)
-- [ ] [Enable merge request approvals](https://docs.gitlab.com/ee/user/project/merge_requests/approvals/)
-- [ ] [Set auto-merge](https://docs.gitlab.com/ee/user/project/merge_requests/merge_when_pipeline_succeeds.html)
-
-## Test and Deploy
-
-Use the built-in continuous integration in GitLab.
-
-- [ ] [Get started with GitLab CI/CD](https://docs.gitlab.com/ee/ci/quick_start/index.html)
-- [ ] [Analyze your code for known vulnerabilities with Static Application Security Testing(SAST)](https://docs.gitlab.com/ee/user/application_security/sast/)
-- [ ] [Deploy to Kubernetes, Amazon EC2, or Amazon ECS using Auto Deploy](https://docs.gitlab.com/ee/topics/autodevops/requirements.html)
-- [ ] [Use pull-based deployments for improved Kubernetes management](https://docs.gitlab.com/ee/user/clusters/agent/)
-- [ ] [Set up protected environments](https://docs.gitlab.com/ee/ci/environments/protected_environments.html)
-
-***
-
-# Editing this README
-
-When you're ready to make this README your own, just edit this file and use the handy template below (or feel free to structure it however you want - this is just a starting point!). Thank you to [makeareadme.com](https://www.makeareadme.com/) for this template.
-
-## Suggestions for a good README
-Every project is different, so consider which of these sections apply to yours. The sections used in the template are suggestions for most open source projects. Also keep in mind that while a README can be too long and detailed, too long is better than too short. If you think your README is too long, consider utilizing another form of documentation rather than cutting out information.
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-## Name
-Choose a self-explaining name for your project.
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-## Description
-Let people know what your project can do specifically. Provide context and add a link to any reference visitors might be unfamiliar with. A list of Features or a Background subsection can also be added here. If there are alternatives to your project, this is a good place to list differentiating factors.
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-## Badges
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-## Visuals
-Depending on what you are making, it can be a good idea to include screenshots or even a video (you'll frequently see GIFs rather than actual videos). Tools like ttygif can help, but check out Asciinema for a more sophisticated method.
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-## Installation
-Within a particular ecosystem, there may be a common way of installing things, such as using Yarn, NuGet, or Homebrew. However, consider the possibility that whoever is reading your README is a novice and would like more guidance. Listing specific steps helps remove ambiguity and gets people to using your project as quickly as possible. If it only runs in a specific context like a particular programming language version or operating system or has dependencies that have to be installed manually, also add a Requirements subsection.
-
-## Usage
-Use examples liberally, and show the expected output if you can. It's helpful to have inline the smallest example of usage that you can demonstrate, while providing links to more sophisticated examples if they are too long to reasonably include in the README.
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-## Support
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-
-## Roadmap
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-
-## Contributing
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-
-For people who want to make changes to your project, it's helpful to have some documentation on how to get started. Perhaps there is a script that they should run or some environment variables that they need to set. Make these steps explicit. These instructions could also be useful to your future self.
-
-You can also document commands to lint the code or run tests. These steps help to ensure high code quality and reduce the likelihood that the changes inadvertently break something. Having instructions for running tests is especially helpful if it requires external setup, such as starting a Selenium server for testing in a browser.
-
-## Authors and acknowledgment
-Show your appreciation to those who have contributed to the project.
-
-## License
-For open source projects, say how it is licensed.
-
-## Project status
-If you have run out of energy or time for your project, put a note at the top of the README saying that development has slowed down or stopped completely. Someone may choose to fork your project or volunteer to step in as a maintainer or owner, allowing your project to keep going. You can also make an explicit request for maintainers.
diff --git a/constructive_model.py b/constructive_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d6e710c15fb45305f61a45d191fe60318e3dae4
--- /dev/null
+++ b/constructive_model.py
@@ -0,0 +1,795 @@
+import warnings
+import pickle
+import json
+import random
+import numpy as np
+import networkx as nx
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+from python.networks import get_highest_nodes
+from python.general_GA_avoidance import load_data, compute_a_analytical_small_budget, print_multi_axes, \
+ divide_into_neighbouring_groups, DISCRETE_COLOR, complete_hyper_params, main_text
+from python.utils import DATA_PATH, PLOTS_PATH, create_range
+
+SUB_DATA_PATH = "constructive_model/"
+mpl.rcParams['text.usetex'] = True
+
+
+def create_name(type_of_experiment, l, a_budget, b_budget, N_allocations):
+ fname = "-N{:d}-a{:.1f}-b{:.1f}".format(N_allocations, a_budget, b_budget)
+ if type_of_experiment == "plot":
+ return PLOTS_PATH + "constructive" + fname + ".eps"
+ else:
+ return DATA_PATH + SUB_DATA_PATH + type_of_experiment + str(l) + fname + ".pkl"
+
+
+def binary_search(compute_x_func, a_low, a_high, verbose=False):
+ a_middle = (a_high + a_low) / 2
+ X_low, _ = compute_x_func(a_low)
+ X_middle, _ = compute_x_func(a_middle)
+ X_high, _ = compute_x_func(a_high)
+ diff = a_high - a_low
+ i = 0
+ while diff > 1e-4:
+ new_a_low = (a_low + a_middle) / 2
+ new_a_high = (a_middle + a_high) / 2
+ new_X_low, a_values_low = compute_x_func(new_a_low)
+ new_X_high, a_values_high = compute_x_func(new_a_high)
+ if verbose == True:
+ print(a_low, new_a_low, a_middle, new_a_high, a_high)
+
+ if np.max([X_low, new_X_low]) > np.max([X_middle, X_high, new_X_high]):
+ a_high = a_middle
+ a_middle = new_a_low
+ X_high = X_middle
+ X_middle = new_X_low
+ elif X_middle > np.max([X_low, new_X_low, X_high, new_X_high]):
+ a_high = new_a_high
+ a_low = new_a_low
+ X_high = new_X_high
+ X_low = new_X_low
+ elif np.max([X_high, new_X_high]) > np.max([X_middle, X_low, new_X_low]):
+ a_low = a_middle
+ a_middle = new_a_high
+ X_low = X_middle
+ X_middle = new_X_high
+ diff = a_high - a_low
+ i += 1
+ return i, X_middle, a_middle
+
+
+class Constructor:
+ def __init__(self, a_budget, b, W, X_opt_disc, X_opt_cont, b_allocation="discrete", seed=5, verbose=False):
+ self.W = W
+ self.L = np.diagflat(np.sum(self.W, axis=0)) - self.W
+ self.N = self.W.shape[0]
+ self.seq = np.squeeze(np.array(np.sum(self.W, axis=1)))
+
+ self.a_budget = a_budget
+ if b_allocation == "discrete":
+ b_targets = b[b > 0]
+ assert np.all(b_targets[0] == b_targets)
+ self.b = b
+ self.b_allocation = b_allocation
+
+ self.mask_b, self.mask_b_neighbours, self.mask_rest = divide_into_neighbouring_groups(b, W)
+ self.N_b = np.sum(self.mask_b)
+
+ self.X_degree_disc = None
+ self.X_shadow_disc = None
+ self.X_opt_disc = X_opt_disc
+ self.X_random = None
+
+ self.X_small = None
+ self.a_shadowing_cont = None
+ self.X_uniform = None
+ self.X_opt_cont = X_opt_cont
+
+ self.seed = seed
+ self.verbose = verbose
+
+ def build_strategy(self, strat, l, verbose=False):
+ if strat == "high disc":
+ return self.build_high_degree_disc(verbose=verbose)
+ elif strat == "shadow disc":
+ return self.build_shadowing_disc(l, verbose=verbose)
+ elif strat == "shield disc":
+ return self.build_shielding_disc(l, verbose=verbose)
+ elif strat == "opt disc":
+ return self.X_opt_disc
+ elif strat == "random":
+ if self.X_random is None:
+ a_vec = np.zeros(self.N)
+ np.random.seed(self.seed)
+ indices = np.random.choice(self.N, self.N_b, replace=False)
+ a_vec[indices] = self.a_budget / self.N_b
+ self.X_random, _ = self.compute_x(a_vec)
+ if verbose or self.verbose:
+ print("{:<18s}{:f}".format("X_rand", self.X_random))
+ return self.X_random
+ elif strat == "opt cont":
+ return self.X_opt_cont
+ elif strat == "high cont":
+ return self.build_high_degree_cont(l, verbose=verbose)
+ elif strat == "first order":
+ return self.compute_first_order(verbose=verbose)
+ elif strat == "shield cont num":
+ if self.a_shadowing_cont is None:
+ self.build_shadowing_cont(l, verbose=verbose)
+ return self.build_shielding_cont(l, self.a_shadowing_cont, verbose=verbose)
+ elif strat == "uniform":
+ if self.X_uniform is None:
+ a_max_ent = np.ones(self.N) / self.N * self.a_budget
+ self.X_uniform, _ = self.compute_x(a_max_ent)
+ return self.X_uniform
+ elif strat == "shield cont":
+ return self.build_shielding_cont(l, "first order", verbose=verbose)
+ elif strat == "shadow cont":
+ return self.build_shadowing_cont(l, verbose=verbose)
+ else:
+ raise Exception("Strategy {:s} not understood.".format(strat))
+
+ def compute_first_order(self, verbose=False):
+ if self.X_small is None:
+ a_vec = compute_a_analytical_small_budget(self.seq, self.b, self.a_budget / self.N, np.sum(self.b) / self.N)
+ self.X_small, _ = self.compute_x(a_vec)
+ if self.verbose or verbose:
+ print("X_small {:.4f}".format(self.X_small), "a_small", a_vec[self.mask_b][0], "a_small_rest",
+ a_vec[np.logical_not(self.mask_b)][0])
+ # print("a_with_b (small)", a_small[indices_with_b][0], a_small[indices_without_b][0], X_small)
+ # a_big = compute_a_analytical_big_budget(self.b, self.a_budget/self.N, np.sum(self.b)/self.N)
+ # X_big = self.compute_x(a_small)
+ # print("a_with_b (big)", a_big[indices_with_b][0], a_big[indices_without_b][0], X_big)
+ return self.X_small
+
+ def build_shadowing_cont(self, l, verbose=False):
+ assert self.b_allocation == "discrete", "Shadowing only implemented for discrete bs"
+ # seq = np.squeeze(np.array(np.sum(self.W, axis=1)))
+ # indices_b = np.arange(N)[self.b > 0]
+ # print("indices b", indices_b)
+ # indices_b_wo_b_neighbours = indices_b[np.sum(W[indices_b][:, indices_b], axis=0) == 0]
+ # print("Indices b without b neighbours", indices_b_wo_b_neighbours)
+ # a_with_b = self.a_num[indices_b_wo_b_neighbours]
+ # # a_with_b = self.a_num[indices_b]
+ # sort_ind = np.argsort(seq[indices_b_wo_b_neighbours])
+ # print("a_with_b", a_with_b[sort_ind])
+ # print("degree_b", seq[indices_b_wo_b_neighbours][sort_ind])
+
+ # a_shadowing = np.mean(a_with_b)
+ # std_a_with_b = np.std(a_with_b)
+ # print("a shadowing: {:f} +- {:f}".format(a_shadowing, std_a_with_b))
+
+ indices_without_b = self.b == 0
+ # N_without_b = np.sum(indices_without_b)
+ # a_vec_without_b = self.a_num[indices_without_b]
+ # a_without_b = np.median(a_vec_without_b)
+ # a_with_b = (self.a_budget - a_without_b * N_without_b) / (N - N_without_b)
+
+ fname = create_name("shadowing", l, self.a_budget, np.sum(self.b), np.sum(self.mask_b))
+ try:
+ with open(fname, 'rb') as f:
+ data = pickle.load(f)
+ print("Data loaded from " + fname)
+ best_X, best_a = data['best_X'], data['best_a']
+
+ except FileNotFoundError:
+ def compute_x(a_shadow):
+ a_vec = np.zeros(self.N)
+ a_vec[self.mask_b] = a_shadow
+ a_vec[indices_without_b] = (self.a_budget - a_shadow * self.N_b) / (self.N - self.N_b)
+ X, _ = self.compute_x(a_vec)
+ return X, a_vec
+
+ a_low, a_high = 0, self.a_budget / self.N_b
+ i, best_X, best_a = binary_search(compute_x, a_low, a_high, verbose=verbose or self.verbose)
+
+ data = {'best_X': best_X, 'best_a': best_a}
+ print("i", i, "Saving model in " + fname)
+ with open(fname, 'wb') as f:
+ pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
+
+ # self.a_shadowing = a_with_b
+ # self.a[np.logical_not(indices_without_b)] = a_with_b
+ # self.a[indices_without_b] = a_without_b
+ # assert a_shadowing > 0
+ # self.a = self.a * self.a_budget / np.sum(self.a)
+ # print("a_with_b", a_with_b, "a_without_b", a_without_b)
+ # a_rest = (self.a_budget - best_a * N_with_b) / (self.N - N_with_b)
+ # if a_rest < 0:
+ # a_rest = 0
+ # best_a = self.a_budget / N_with_b
+ # best_X = compute_x(best_a)
+
+ a_vec = np.zeros(self.N)
+ a_vec[self.mask_b] = best_a
+ a_vec[indices_without_b] = (self.a_budget - best_a * self.N_b) / (self.N - self.N_b)
+ if True: # self.verbose or verbose:
+ print("X_shadow_cont_num {:.4f}".format(best_X), "a_shadowing", best_a, "a_rest",
+ a_vec[np.logical_not(self.mask_b)][0])
+
+ self.a_shadowing_cont = a_vec
+ assert np.isclose(np.sum(a_vec), self.a_budget), "{:f} {:f}".format(np.sum(a_vec), self.a_budget)
+ return best_X
+
+ def build_shadowing_disc(self, l, verbose=False):
+ if self.X_shadow_disc is None:
+ if self.a_budget >= 0.9 * np.sum(self.b):
+ a_vec = self.b * self.a_budget / np.sum(self.b)
+ else:
+ indices_not_b = np.arange(self.N)[self.b == 0]
+ indices = random.sample(list(indices_not_b), self.N_b)
+
+ a_vec = np.zeros_like(self.b)
+ a_vec[indices] = self.b[self.mask_b][0] * self.a_budget / np.sum(self.b)
+
+ self.X_shadow_disc, _ = self.compute_x(a_vec)
+ if verbose or self.verbose:
+ print("{:<18s}{:f}".format("X_imitation", self.X_shadow_disc))
+ return self.X_shadow_disc
+
+ def build_shielding_cont(self, l, shadow_strat=None, verbose=False):
+ assert self.b_allocation == "discrete", "Shielding only implemented for discrete bs"
+
+ mask_b_neighbours = np.logical_and(np.sum(self.W[self.mask_b], axis=0) > 0, np.logical_not(self.mask_b))
+ mask_rest = np.logical_not(np.logical_or(self.mask_b, mask_b_neighbours))
+ # print("N neighbours", np.sum(mask_b_neighbours))
+ N_b = np.sum(self.mask_b)
+ N_b_neighbours = np.sum(mask_b_neighbours)
+ N_rest = np.sum(mask_rest)
+ assert N_b + N_b_neighbours + N_rest == self.N
+
+ if type(shadow_strat) == np.ndarray:
+ type_of_experiment = "shielding_nnum"
+ a_ini = shadow_strat
+ elif shadow_strat == "first order":
+ a_ini = compute_a_analytical_small_budget(self.seq, self.b, self.a_budget / self.N, np.sum(self.b) / self.N)
+ type_of_experiment = "shielding"
+ elif shadow_strat == "grid":
+ type_of_experiment = "shielding_grid"
+ else:
+ raise Exception("Initialisation of shielding cont '{:s}' not understood.".format(shadow_strat))
+
+ fname = create_name(type_of_experiment, l, self.a_budget, np.sum(self.b), N_b)
+ try:
+ with open(fname, 'rb') as f:
+ data = pickle.load(f)
+ # print("Data loaded from " + fname)
+ best_X, best_a = data['best_X'], data['best_a']
+
+ except FileNotFoundError:
+ if type(shadow_strat) == str:
+ # TODO: implement proper search in 2D
+ raise Exception("Work (not) in progress.")
+
+ def compute_x(a_shadowing, a_shielding):
+ a_vec = np.zeros_like(self.b)
+ a_vec[self.mask_b] = a_shadowing
+ a_vec[mask_b_neighbours] = a_shielding
+ a_rest = (self.a_budget - a_shadowing * N_b - a_shielding * N_b_neighbours) / N_rest
+ a_vec[mask_rest] = a_rest
+ X, _ = self.compute_x(a_vec)
+ return X, a_rest
+
+ def compute_Xs(a_shadows, a_shieldings):
+ return np.array(
+ [[compute_x(a_shadow, a_shield)[0] for a_shield in a_shieldings] for a_shadow in a_shadows])
+
+ a_shadows = (0, 5)
+ a_shieldings = (0, 5)
+ Xs = compute_Xs(a_shadows, a_shieldings)
+ diff = 10
+ i = 0
+ while diff > 1e-6:
+ new_a = (np.sum(a_shadows) / 2, np.sum(a_shieldings) / 2)
+ new_X, a_rest = compute_x(new_a[0], new_a[1])
+
+ if Xs[0, 0] > Xs[0, 1] and Xs[0, 0] > Xs[1, 0]:
+ a_shadows[1] = new_a[0]
+ a_shieldings[1] = new_a[1]
+ Xs[1, 1]
+ else:
+ X_high = new_X
+ a_high = new_a
+ # diff = a_high - a_low
+ i += 1
+ # best_a, best_X = a_values, new_X
+ if np.isclose(new_a, 0) or np.isclose(new_a, 5):
+ raise Exception("SHIELDING OUT OF RANGE LIMIT")
+ else:
+ def compute_x(a_shielding):
+ a_vec = np.copy(a_ini)
+ a_vec[mask_b_neighbours] = a_shielding
+ a_vec = a_vec / np.sum(a_vec) * self.a_budget
+ X, _ = self.compute_x(a_vec)
+ return X, (a_vec[self.mask_b][0], a_vec[mask_b_neighbours][0], a_vec[mask_rest][0])
+
+ a_low, a_high = 0, 30
+ i, best_X, best_a = binary_search(compute_x, a_low, a_high, verbose=verbose or self.verbose)
+ if np.isclose(best_a, a_high, atol=1e-3):
+ raise Exception("SHIELDING OUT OF RANGE LIMIT")
+
+ data = {'best_X': best_X, 'best_a': best_a}
+ print("i", i, "Saving model in " + fname)
+ with open(fname, 'wb') as f:
+ pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
+ if self.verbose or verbose:
+ print("X_shield_cont {:.4f}".format(best_X), "a_shadowing", best_a[0], "a_shielding", best_a[1],
+ "a_rest", best_a[2])
+
+ return best_X
+
+ def build_shielding_disc(self, l, verbose=False):
+ assert self.b_allocation == "discrete", "Shielding only implemented for discrete bs"
+
+ fname = create_name("shielding_disc", l, self.a_budget, np.sum(self.b), np.sum(self.b > 0))
+ try:
+ with open(fname, 'rb') as f:
+ data = pickle.load(f)
+ # print("Data loaded from " + fname)
+ X, a_vec = data['best_X'], data['best_a']
+
+ except FileNotFoundError:
+ N_b, N_b_neighbours, N_rest = np.sum(self.mask_b), np.sum(self.mask_b_neighbours), np.sum(self.mask_rest)
+
+ indices_neighbours = np.arange(self.N)[self.mask_b_neighbours]
+ indices_rest = np.arange(self.N)[self.mask_rest]
+ if self.a_budget >= 0.9 * np.sum(self.b):
+ if N_b < N_b_neighbours:
+ # TODO: introduce randomness if >1 nodes have the same number of neighbours
+ sort_ind = np.argsort(np.sum(self.W[self.mask_b], axis=0)[self.mask_b_neighbours])[::-1]
+ indices_a = indices_neighbours[sort_ind][:N_b]
+ elif N_b == N_b_neighbours:
+ indices_a = indices_neighbours
+ else:
+ assert N_b < self.N / 2
+ indices_a = np.append(indices_neighbours, random.sample(list(indices_rest), N_b - N_b_neighbours))
+ else:
+ if N_b < N_rest:
+ indices_a = random.sample(list(indices_rest), N_b)
+ elif N_b == N_rest:
+ indices_a = indices_rest
+ else:
+ assert N_b < self.N / 2
+ # TODO: introduce randomness if >1 nodes have the same number of neighbours
+ sort_ind = np.argsort(np.sum(self.W[self.mask_b], axis=0)[self.mask_b_neighbours])
+ indices_a = np.append(indices_rest, indices_neighbours[sort_ind][:N_b - N_rest])
+ assert N_b == len(indices_a), str(N_b) + " != " + str(len(indices_a))
+
+ gain = self.b[self.mask_b][0]
+ a_vec = np.zeros_like(self.b)
+ a_vec[indices_a] = gain * self.a_budget / np.sum(self.b)
+ X, _ = self.compute_x(a_vec)
+
+ data = {'best_X': X, 'best_a': a_vec}
+ print("N_neighbours", N_b_neighbours, "Saving model in " + fname)
+ with open(fname, 'wb') as f:
+ pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
+
+ # print("a_shadowing", best_a[0], "a_shielding", best_a[1], "a_rest", best_a[2])
+ return X
+
+ def build_high_degree_cont(self, l, verbose=False):
+ fname = create_name("high_degree_cont", l, self.a_budget, np.sum(self.b), np.sum(self.b > 0))
+ try:
+ with open(fname, 'rb') as f:
+ data = pickle.load(f)
+ # print("Data loaded from " + fname)
+ best_X, best_a = data['best_X'], data['best_a']
+
+ except FileNotFoundError:
+ def compute_x(a_slope):
+ a_vec = np.array([a_slope * k for k in self.seq])
+ a_vec += (self.a_budget - np.sum(a_vec)) / self.N
+
+ while np.any(a_vec < 0):
+ broken_nodes = a_vec <= 0
+ a_vec += (self.a_budget - np.sum(a_vec[a_vec > 0])) / np.sum(a_vec > 0)
+ a_vec[broken_nodes] = 0
+
+ assert np.isclose(np.sum(a_vec), self.a_budget), str(np.sum(a_vec)) + " " + str(self.a_budget)
+ X, _ = self.compute_x(a_vec)
+ return X, a_slope
+
+ sort_deg = np.sort(np.unique(self.seq))
+ a_low = - self.a_budget / np.sum(self.seq == sort_deg[0]) / (sort_deg[1] - sort_deg[0])
+ a_high = self.a_budget / np.sum(self.seq == sort_deg[-1]) / (sort_deg[-1] - sort_deg[-2])
+ i, best_X, best_a = binary_search(compute_x, a_low, a_high)
+
+ data = {'best_X': best_X, 'best_a': best_a}
+ print("i", i, "Saving model in " + fname)
+ with open(fname, 'wb') as f:
+ pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
+ if self.verbose or verbose:
+ print("X_shield_cont {:.4f}".format(best_X), "a_shadowing", best_a[0], "a_shielding", best_a[1],
+ "a_rest", best_a[2])
+
+ return best_X
+
+ def build_high_degree_disc(self, verbose=False):
+ if self.X_degree_disc is None:
+ def get_X_degree_disc(end):
+ if end == "high":
+ sorted_indices = np.argsort(self.seq)[::-1]
+ d_star = self.seq[sorted_indices][self.N_b - 1]
+ chosen_indices = sorted_indices[self.seq[sorted_indices] > d_star]
+ elif end == "low":
+ sorted_indices = np.argsort(self.seq)
+ d_star = self.seq[sorted_indices][self.N_b - 1]
+ chosen_indices = sorted_indices[self.seq[sorted_indices] < d_star]
+ else:
+ raise Exception("end {:s} not understood.".format(end))
+
+ chosen_indices = np.append(chosen_indices, random.sample(
+ list(sorted_indices[self.seq[sorted_indices] == d_star]), self.N_b - len(chosen_indices)))
+ a_degree = np.zeros(self.N)
+ a_degree[chosen_indices] = self.a_budget / self.N_b
+ X, _ = self.compute_x(a_degree)
+ return X
+
+ X_high = get_X_degree_disc("high")
+ X_low = get_X_degree_disc("low")
+ if X_high > X_low:
+ self.X_degree_disc = X_high
+ else:
+ print("Low degree (fully avoidance) present!")
+ self.X_degree_disc = X_low
+ return self.X_degree_disc
+
+ def compute_x(self, a):
+ if len(a.shape) == 1:
+ a = a[:, None]
+ if len(self.b.shape) == 1:
+ b = self.b[:, None]
+ else:
+ b = self.b
+ inv_Lab = np.linalg.inv(self.L + np.diagflat(a + b))
+ x = np.array(np.matmul(inv_Lab, a).T)
+ X = np.sum(x) / self.N
+
+ return X, x[0, :]
+
+
+def print_X_gap(ax, pars, print_variable="allocations"):
+ n_nets = pars['n_nets']
+
+ n_disc = 5
+ disc_strats = ["random", "high disc", "shadow disc", "shield disc", "opt disc"]
+ cont_strats = ["opt cont", "uniform", "high cont", "shadow cont", "shield cont num", "first order",
+ "shield cont"]
+ n_cont = 5
+ Xs = {name: np.zeros(pars['n_nets']) for name in disc_strats + cont_strats[:n_cont]}
+ NAMES = {"high disc": "deg", "shadow disc": "shadw", "shield disc": "shield", "opt disc": "opt", "random": "rand",
+ "high cont": "deg", "uniform": "unif", "first order": "shadw", "shield cont": "shield\nfo",
+ "shield cont num": "shadw\nshield", "opt cont": "opt", "shadow cont": "shadw"}
+
+ _, _, _, Xs_opt_cont, _, _, _, Xs_opt_disc, _, nets = load_data(pars)
+ print("b gain", np.max(nets[0].b))
+
+ for l in range(n_nets):
+ print("Network", l)
+ model = Constructor(np.sum(nets[l].a), nets[l].b[:, 0], nets[l].W.toarray(), Xs_opt_disc[l], Xs_opt_cont[l])
+ for strat in Xs.keys():
+ Xs[strat][l] = model.build_strategy(strat, l)
+
+ print("X_opt_cont", np.mean(Xs["opt cont"]), "+-", np.std(Xs["opt cont"]) / np.sqrt(n_nets))
+ print("X_opt_disc", np.mean(Xs["opt disc"]), "+-", np.std(Xs["opt disc"]) / np.sqrt(n_nets))
+ print("X_shadow_shield", np.mean(Xs["shield cont num"]), "+-", np.std(Xs["shield cont num"]) / np.sqrt(n_nets))
+ print("")
+
+ x_axis = range(len(Xs) - 2) # -2 because of the opt
+ norm = 1 # np.mean(Xs["opt cont"] - Xs["random"])
+ y_axis = [np.mean(Xs["opt disc"] - Xs[strat]) / norm for strat in disc_strats[:-1]]
+ y_axis += [np.mean(Xs["opt cont"] - Xs[strat]) / norm for strat in cont_strats[1:n_cont]]
+ y_err = [np.std(Xs["opt disc"] - Xs[strat]) / np.sqrt(n_nets) / norm for strat in disc_strats[:-1]]
+ y_err += [np.std(Xs["opt cont"] - Xs[strat]) / np.sqrt(n_nets) / norm for strat in cont_strats[1:n_cont]]
+ x_labels = [NAMES[strat] for strat in disc_strats[:-1] + cont_strats[1:n_cont]]
+
+ print(x_labels, "\n", y_axis, "\n", y_err)
+
+ bars = ax.bar(x_axis, y_axis, yerr=y_err, color=[DISCRETE_COLOR] * (len(disc_strats) - 1) + ["orange"] * n_cont,
+ width=0.6, log=True) # , tick_label=x_labels)
+
+ def autolabel(rects):
+ for i, rect in enumerate(rects):
+ ax.annotate('{}'.format(x_labels[i]), xy=(rect.get_x() + rect.get_width() / 2, rect.get_height()),
+ textcoords="offset points", ha='center', va='bottom', xytext=(0, 3)) # 3 pt vertical offset
+
+ autolabel(bars)
+ ax.xaxis.set(ticks=[], ticklabels=[])
+
+ if len(pars['multiaxesarray']) > 1:
+ ax.set(ylabel=r"$X^{\mathrm{opt}} - X\quad(N=" + "{:d})$".format(pars['N_allocations']))
+ else:
+ ax.set(ylabel=r"Vote share gap to optimal, $\Delta X$") # X^{\mathrm{opt\,cont}} - X^{\mathrm{heur}}$")
+
+ ax.set(title=r"$\mathcal{B}_a/\mathcal{B}_b = " + str(1 / pars['bmul']) + "$")
+ ax.yaxis.grid()
+ ax.margins(x=0.03)
+ # if pars['N_allocations'] == 256:
+ if pars['network'] == "email":
+ ax.set(ylim=[5e-4, 5e-1]) # 4e-2])
+ elif pars['network'] == "ba":
+ ax.set(ylim=[1e-4, 5e-1]) # 4e-2])
+ elif pars['network'] == "conf":
+ ax.set(ylim=[2e-4, 5e-1]) # 4e-2])
+ elif pars['network'] == "advogato":
+ ax.set(ylim=[2e-4, 6e-1])
+ elif pars['network'] == "classroom":
+ ax.set(ylim=[9e-5, 5e-1])
+
+ if pars[pars['axespar']] == pars['axesarray'][0]: # [-1]: # and pars[pars['multiaxespar']] == pars['multiaxesarray'][0]:
+ # if pars[pars['axespar']] == pars['axesarray'][0]: # and pars[pars['multiaxespar']] == pars['multiaxesarray'][0]:
+ from matplotlib.patches import Patch
+ legends = [
+ Patch(facecolor=DISCRETE_COLOR, label="discrete"),
+ Patch(facecolor="orange", label="continuous")
+ ]
+ ax.legend(handles=legends)
+
+ return Xs
+
+
+def print_performance_iters(ax, pars, print_variable="allocations"):
+ assert pars['debug']
+ n_nets = pars['n_nets']
+
+ disc_strats = ["high disc", "shadow disc", "shield disc", "opt disc"]
+ cont_strats = ["uniform", "high cont", "shadow cont", "shield cont num", "opt cont"]
+ strats = disc_strats + cont_strats
+ Xs = {name: np.zeros(pars['n_nets']) for name in disc_strats + cont_strats}
+ NAMES = {"high disc": "degree", "shadow disc": "shadowing", "shield disc": "shielding", "opt disc": "optimal",
+ "random": "random",
+ "high cont": "degree", "uniform": "uniform", "first order": "shadowing", "shield cont": "shield\nfo",
+ "shield cont num": "shadow shield", "opt cont": "opt", "shadow cont": "shadowing"}
+
+ _, _, _, Xs_opt_cont, _, _, _, Xs_opt_disc, _, nets = load_data(pars)
+ print("b gain", np.max(nets[0].b))
+
+ for l, net in enumerate(nets):
+ print("Network", l)
+ model = Constructor(np.sum(net.a), net.b[:, 0], net.W.toarray(), Xs_opt_disc[l], Xs_opt_cont[l], seed=net.seed)
+ for strat in Xs.keys():
+ Xs[strat][l] = model.build_strategy(strat, l)
+
+ print("X_opt_cont", np.mean(Xs["opt cont"]), "+-", np.std(Xs["opt cont"]) / np.sqrt(n_nets))
+ print("X_opt_disc", np.mean(Xs["opt disc"]), "+-", np.std(Xs["opt disc"]) / np.sqrt(n_nets))
+ print("X_shadow_shield", np.mean(Xs["shield cont num"]), "+-", np.std(Xs["shield cont num"]) / np.sqrt(n_nets))
+ print("")
+ # print("X random", Xs["random"])
+ # print("X ini", pars['Xs_v_iters'][:, 0])
+
+ # norm = 1 # np.mean(Xs["opt cont"] - Xs["random"])
+ # y_axis = [np.mean(Xs["opt disc"] - Xs[strat]) / norm for strat in disc_strats[:-1]]
+ # y_err = [np.std(Xs["opt disc"] - Xs[strat]) / np.sqrt(n_nets) / norm for strat in disc_strats[:-1]]
+
+ its = {strat: [np.argmax(np.logical_or(pars['Xs_v_iters'][l] > X_strat, np.isclose(pars['Xs_v_iters'][l], X_strat)))
+ for l, X_strat in enumerate(Xs[strat])] for strat in strats}
+ print(its)
+ y_axis = [np.mean(its[strat]) for strat in strats]
+ y_err = [np.std(its[strat]) for strat in strats]
+ x_labels = [NAMES[strat] for strat in strats]
+ x_axis = range(len(y_axis))
+ print(x_labels, "\n", y_axis, "\n", y_err)
+
+ bars = ax.barh(x_axis[::-1], y_axis, xerr=y_err, height=0.6, log=True,
+ color=[DISCRETE_COLOR] * len(disc_strats) + ["orange"] * len(cont_strats), tick_label=x_labels)
+
+ def autolabel(rects):
+ for i, rect in enumerate(rects):
+ ax.annotate('{}'.format(x_labels[i]),
+ xy=(rect.get_width() + y_err[i], rect.get_y() + rect.get_height() / 2),
+ textcoords="offset points", ha='left', va='center', xytext=(3, 0)) # 5 pt horizontal offset
+
+ autolabel(bars)
+ ax.yaxis.set(ticks=[], ticklabels=[])
+
+ if len(pars['multiaxesarray']) > 1:
+ ax.set(xlabel=r"$X^{\mathrm{opt}} - X\quad(N=" + "{:d})$".format(pars['N_allocations']))
+ else:
+ ax.set(xlabel=r"Iteratios of GA for higher $X$") # X^{\mathrm{opt\,cont}} - X^{\mathrm{heur}}$")
+
+ ax.set(title=r"$\mathcal{B}_a/\mathcal{B}_b = " + str(1 / pars['bmul']) + "$")
+ ax.xaxis.grid()
+ ax.margins(y=0.03)
+ # if pars['N_allocations'] == 256:
+ if pars['network'] == "email":
+ ax.set(xlim=[3e-1, 1e3]) # 4e-2])
+ # elif pars['network'] == "ba":
+ # ax.set(ylim=[1e-4, 5e-1]) # 4e-2])
+ # if pars['network'] == "conf":
+ # ax.set(ylim=[2e-4, 5e-1]) # 4e-2])
+
+ if pars[pars['axespar']] == pars['axesarray'][-2]: # and pars[pars['multiaxespar']] == pars['multiaxesarray'][0]:
+ from matplotlib.patches import Patch
+ legends = [
+ Patch(facecolor=DISCRETE_COLOR, label="discrete"),
+ Patch(facecolor="orange", label="continuous")
+ ]
+ ax.legend(handles=legends, loc="upper right")
+
+
+def print_shadshield_vs_opt(ax, pars, print_variable="allocations"):
+ n_nets = pars['n_nets']
+
+ n_disc = 5
+ disc_strats = ["random", "high disc", "shadow disc", "shield disc", "opt disc"]
+ cont_strats = ["opt cont", "uniform", "high cont", "shadow cont", "shield cont num", "first order",
+ "shield cont"]
+ n_cont = 5
+ Xs = {name: np.zeros(pars['n_nets']) for name in disc_strats + cont_strats[:n_cont]}
+ NAMES = {"high disc": "deg", "shadow disc": "shadw", "shield disc": "shield", "opt disc": "opt", "random": "rand",
+ "high cont": "deg", "uniform": "unif", "first order": "shadw", "shield cont": "shield\nfo",
+ "shield cont num": "shadw\nshield", "opt cont": "opt", "shadow cont": "shadw"}
+
+ _, _, _, Xs_opt_cont, _, _, _, Xs_opt_disc, _, nets = load_data(pars)
+ print("b gain", np.max(nets[0].b))
+
+ for l in range(n_nets):
+ print("Network", l)
+ model = Constructor(np.sum(nets[l].a), nets[l].b[:, 0], nets[l].W.toarray(), Xs_opt_disc[l], Xs_opt_cont[l])
+ for strat in Xs.keys():
+ Xs[strat][l] = model.build_strategy(strat, l)
+
+ print("X_opt_cont", np.mean(Xs["opt cont"]), "+-", np.std(Xs["opt cont"]))
+ print("X_opt_disc", np.mean(Xs["opt disc"]), "+-", np.std(Xs["opt disc"]))
+ print("")
+
+ x_axis = range(len(Xs) - 2) # -2 because of the opt
+ norm = 1 # np.mean(Xs["opt cont"] - Xs["random"])
+ y_axis = [np.mean(Xs["opt disc"] - Xs[strat]) / norm for strat in disc_strats[:-1]]
+ y_axis += [np.mean(Xs["opt cont"] - Xs[strat]) / norm for strat in cont_strats[1:n_cont]]
+ y_err = [np.std(Xs["opt disc"] - Xs[strat]) / np.sqrt(n_nets) / norm for strat in disc_strats[:-1]]
+ y_err += [np.std(Xs["opt cont"] - Xs[strat]) / np.sqrt(n_nets) / norm for strat in cont_strats[1:n_cont]]
+ x_labels = [NAMES[strat] for strat in disc_strats[:-1] + cont_strats[1:n_cont]]
+
+ print(x_labels, "\n", y_axis, "\n", y_err)
+
+
+def print_evolution(ax, pars, print_variable="allocations"):
+ disc_strats = ["high disc", "shadow disc", "shield disc", "opt disc"]
+ cont_strats = ["opt cont", "high cont", "first order", "shield cont num", "uniform", "shadow cont",
+ "shield cont"]
+ n_cont = 5
+ Xs = {name: np.zeros((len(pars['axisarray']), pars['n_nets'])) for name in disc_strats + cont_strats[:n_cont]}
+ NAMES = {"high disc": "hub disc", "shadow disc": "shadow disc", "shield disc": "shield disc",
+ "opt disc": "opt disc", "high cont": "hub cont", "uniform": "max ent", "first order": "shadow cont",
+ "shield cont": "shield cont", "shield cont num": "shield cont", "opt cont": "opt cont"}
+
+ for i, val in enumerate(create_range(pars)):
+ print("\n", pars['axispar'], i, val)
+
+ _, _, _, Xs_opt_cont, _, _, _, Xs_opt_disc, _, nets = load_data(pars)
+ print("b gain", np.max(nets[0].b))
+ assert pars['network'] == "email", "Strategies implemented for email network only!"
+
+ for l in range(pars['n_nets']):
+ print("Network", l)
+ model = Constructor(np.sum(nets[l].a), nets[l].b[:, 0], nets[l].G, Xs_opt_disc[l], Xs_opt_cont[l])
+ for strat in Xs.keys():
+ Xs[strat][i, l] = model.build_strategy(strat, l)
+
+ # [print(names[i], np.mean(comparison_list[i]), "+-", np.std(comparison_list[i])) for i in
+ print("X_opt_cont", np.mean(Xs["opt cont"], axis=1), "+-", np.std(Xs["opt cont"], axis=1))
+ print("")
+ [print(NAMES[strat], np.mean(Xs["opt cont"] - Xs[strat], axis=1), "+-", np.std(Xs["opt cont"] - Xs[strat], axis=1))
+ for strat in Xs.keys()]
+
+ axis_vec = pars['axisarray']
+ linewidth = 1
+ markers, linestyles = ["s", "v", "^", "*"][::-1], ["-", "--", "-.", ":"]
+ color_dist = plt.cm.get_cmap("winter")(np.linspace(0, 0.9, len(disc_strats)))
+ color_cont = plt.cm.get_cmap("autumn")(np.linspace(0, 0.9, n_cont - 1))
+
+ if print_variable == "allocations":
+ for k, strat in enumerate(disc_strats):
+ y_axis = np.mean(Xs["opt cont"] - Xs[strat], axis=1)
+ y_err = np.std(Xs["opt cont"] - Xs[strat], axis=1)
+ ax.errorbar(axis_vec, y_axis, yerr=y_err, label=NAMES[strat], linewidth=linewidth, marker=markers[k],
+ color=color_dist[k], fillstyle="none", ls=linestyles[0]) # ecolor="k")
+ for k, strat in enumerate(cont_strats[1:n_cont]):
+ y_axis = np.mean(Xs["opt cont"] - Xs[strat], axis=1)
+ y_err = np.std(Xs["opt cont"] - Xs[strat], axis=1)
+ ax.errorbar(axis_vec, y_axis, yerr=y_err, label=NAMES[strat], linewidth=linewidth,
+ marker=markers[k], color=color_cont[k], fillstyle="none", ls=linestyles[1]) # , ecolor="k")
+
+ ax.set_yscale("log")
+ ax.set(ylabel=r"Gap to optimal, $X^{opt} - X$",
+ title=r"$\mathcal{B}_a/\mathcal{B}_b = " + str(1 / pars['bmul']) + "$",
+ ylim=[2e-5, 6e-1])
+ ax.yaxis.grid()
+ ax.legend(ncol=2) # loc="right")
+ else:
+ raise ValueError("Print variable '" + print_variable + "' not understood.")
+
+ ax.set(xlabel=r'N of B-targeted nodes (discrete)')
+ ax.set_xscale("log")
+
+ # ax.margins(x=0.0) # , y=0.0)
+
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ # if print_variable == 'allocations':
+ # ax.legend(ncol=2)
+ # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+ # ax.grid()
+
+
+def result_constructive(network, end, n_nets=1, min_iters_since_change=100, figsize=(3, 2.5), **kwargs):
+ pars = {
+ # Experiment setting
+ 'network': network, 'n_nets': n_nets,
+ 'kabmul': 1 / 30, 'b_allocation': 'discrete', 'end': end, # 'N_allocations': 256, # 'bmul': 100,
+ 'axespar': 'bmul', 'axesarray': [0.1, 1, 10],
+ 'multiaxespar': None, 'multiaxesarray': [None], 'N_allocations': 16,
+ # 'multiaxespar': "N_allocations", 'multiaxesarray': [32],
+
+ # Methods parameters
+ 'add_discrete': True, 'disc_iters': 22660, 'min_iters_since_change': min_iters_since_change,
+ 'initial_a': 'random_disc',
+ 'add_continuous': True, 'iters': 60000, 'lrate': 50,
+ }
+ for key, val in kwargs.items():
+ pars[key] = val
+
+ pars = complete_hyper_params(pars)
+
+ axes, outs = print_multi_axes(pars, print_X_gap, figsize=figsize, sharey=True, letter_labels=True)
+ # print_multi_axes(pars, print_performance_iters, figsize=(3, 2.5), sharey=True, letter_labels=True)
+ # print_multi_axes(pars, print_shadshield_vs_opt)
+
+ # plotname = create_name("plot", _, a_budget, a_budget * pars['bmul'])
+ #
+ # fig.tight_layout()
+ # fig.savefig(plotname)
+ # print("\nFigure saved in " + plotname)
+ # fig.show()
+
+ return outs
+
+
+def result_evolution():
+ pars = {
+ # Experiment setting
+ 'network': 'email', 'n_nets': 15, 'seed': 5,
+ 'kabmul': 1 / 30, 'b_allocation': 'discrete', 'end': 'middle', # 'N_allocations': 256, # 'bmul': 100,
+ 'axispar': 'N_allocations', 'axisarray': [4, 8, 16, 32, 64, 128, 256],
+ 'axespar': 'bmul', 'axesarray': [0.1, 1, 10],
+ 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': True, 'disc_iters': 200, 'min_iters_since_change': 100, 'initial_a': 'random',
+ 'add_continuous': True, 'iters': 60000, 'lrate': 50,
+ }
+
+ print_multi_axes(pars, print_evolution, figsize=(4.5, 3.75))
+
+
+if __name__ == "__main__":
+ hyper_pars = {
+ "network": "email",
+ # "network": "random", "p": 0.01,
+ # "network": "ba", "m": 5,
+ # "network": "conf", "gamma": 3, 'dmin':5,
+ # "network": "advogato",
+ # "network": "classroom",
+ "end": "middle", # "low"
+ # "end": "periphery",
+ # "initial_a_disc": "cont",
+ "N": 1133,
+ 'debug': False,
+ }
+
+ result_constructive(figsize=(2.5, 2.5), **hyper_pars)
+ # result_evolution()
+
+ if False:
+ outs = result_constructive(**hyper_pars)
+
+ d = main_text()
+ d["fig6"] = {letter: {key: val.tolist() for key, val in out.items()} for letter, out in
+ zip(["6a", "6b", "6c"], outs)}
+ json.dump(d, open(DATA_PATH + "minimal_data_set.json", "w"))
diff --git a/datasets/classroom/Friendship-network_data_2013.csv b/datasets/classroom/Friendship-network_data_2013.csv
new file mode 100644
index 0000000000000000000000000000000000000000..4cfc91f86057ae90e8ffbe931b2c637314f906bb
--- /dev/null
+++ b/datasets/classroom/Friendship-network_data_2013.csv
@@ -0,0 +1,668 @@
+1 55
+1 205
+1 272
+1 494
+1 779
+1 894
+3 1
+3 28
+3 147
+3 272
+3 407
+3 674
+3 884
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+\citation{Guimera2003}
+\@writefile{toc}{\contentsline {section}{\numberline {1}Details of the network employed}{3}{section.1}\protected@file@percent }
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+\newlabel{fig:perturbations}{{2}{5}{Increase in vote share $\Delta X$ for an optimal active controller when the passive controller deviates from uniform allocation on nodes with degree $d_n$ with $n=40$ perturbations of size $|1-w_{bi}/\langle \boldsymbol {w}_b \rangle |=1$. Error bars represent standard errors over 15 instances of the experiment and are smaller than the symbols. Both controllers hold the same budget $\mathcal {B}=N\langle d\rangle /15$. \relax }{figure.caption.2}{}}
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+\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Comparison of analytical and numerical results for optimal influence allocations on the continuous regime. Dependence of optimal allocations $w_{ai}$ on the allocations of a passive controller $w_{bi}$ resolved by classes of nodes of different degrees, indicated by their colour. The active controller is in budget disadvantage, $\mathcal {B}_a/\mathcal {B}_b=0.1$ and the passive controller targets continuously with weights randomly drawn from a uniform distribution. The value of external allocations (shown on top of each panel) are generally lower (\textit {left}), equal (\textit {middle}), or higher (\textit {right}) than node degrees. Numerical results are given by a cloud of points coloured by node degree and analytical results are given by \emph {curves} that correspond to equation \textup {\hbox {\mathsurround \z@ \normalfont (\ignorespaces \ref {eq:small}\unskip \@@italiccorr )}} (\textit {solid}) or equation \textup {\hbox {\mathsurround \z@ \normalfont (\ignorespaces \ref {eq:large}\unskip \@@italiccorr )}} (\textit {dashed}). Lines from $w_{ai}^L$ are also coloured by node degree, while showing only one line every six degrees (i.e. for degrees 1,7,13,...).\relax }}{5}{figure.caption.3}\protected@file@percent }
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+\citation{Abebe2018}
+\bibdata{sample.bib}
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+\newlabel{fig:corr_conf}{{7c}{10}{scale-free\relax }{figure.caption.8}{}}
+\newlabel{sub@fig:corr_conf}{{c}{10}{scale-free\relax }{figure.caption.8}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Kendall rank correlation coefficients $\tau _{wd}$ between allocation strength $w_{ai}$ and node degree $d_i$ in the continuous and discrete regimes and for different $K$ and on \textbf {a} an email network, \textbf {b} Barabasi-Albert networks and \textbf {c} scale-free networks. The active controller targets nodes optimally (\textit {squares}) or following a shielding strategy (\textit {circles}). The passive controller targets random nodes in the network and the total budget of both controllers sums up to $\mathcal {B}_a+\mathcal {B}_b=N\langle d\rangle /30$. Error bars represent standard errors over 15 experiment samples.\relax }}{10}{figure.caption.8}\protected@file@percent }
+\newlabel{fig:correlations}{{7}{10}{Kendall rank correlation coefficients $\tau _{wd}$ between allocation strength $w_{ai}$ and node degree $d_i$ in the continuous and discrete regimes and for different $K$ and on \textbf {a} an email network, \textbf {b} Barabasi-Albert networks and \textbf {c} scale-free networks. The active controller targets nodes optimally (\textit {squares}) or following a shielding strategy (\textit {circles}). The passive controller targets random nodes in the network and the total budget of both controllers sums up to $\mathcal {B}_a+\mathcal {B}_b=N\langle d\rangle /30$. Error bars represent standard errors over 15 experiment samples.\relax }{figure.caption.8}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {11}Iterations to optimal IM of the proposed heuristics}{10}{section.11}\protected@file@percent }
+\newlabel{sect:iter_perf}{{11}{10}{Iterations to optimal IM of the proposed heuristics}{section.11}{}}
+\newlabel{fig:gap_email}{{8a}{11}{email\relax }{figure.caption.9}{}}
+\newlabel{sub@fig:gap_email}{{a}{11}{email\relax }{figure.caption.9}{}}
+\newlabel{fig:gap_ba}{{8b}{11}{BA\relax }{figure.caption.9}{}}
+\newlabel{sub@fig:gap_ba}{{b}{11}{BA\relax }{figure.caption.9}{}}
+\newlabel{fig:gap_conf}{{8c}{11}{scale-free\relax }{figure.caption.9}{}}
+\newlabel{sub@fig:gap_conf}{{c}{11}{scale-free\relax }{figure.caption.9}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Comparison of various heuristics to optimal allocations. Bars represent the gap in vote share $\Delta X$ of the heuristics with respect to optimal numerical allocations for three different budget scenarios (as indicated on the top of the panels) and on \textbf {a} an email network, \textbf {b} Barabasi-Albert networks and \textbf {c} scale-free networks. Each bar represents one of the following heuristics: random (\textit {rand}), degree-based (\textit {deg}), shadowing-based (\textit {shadw}), shielding-based (\textit {shield}), uniform targeting (\textit {unif}), combination of shadowing and shielding (\textit {shadw shield}). The passive controller targets $K=16$ nodes in the network in a discrete fashion. Error bars represent standard errors for 15 instances of the experiments and the total budget of both controllers sums up to $\mathcal {B}_a+\mathcal {B}_b=N\langle d\rangle /30$.\relax }}{11}{figure.caption.9}\protected@file@percent }
+\newlabel{fig:gaps}{{8}{11}{Comparison of various heuristics to optimal allocations. Bars represent the gap in vote share $\Delta X$ of the heuristics with respect to optimal numerical allocations for three different budget scenarios (as indicated on the top of the panels) and on \textbf {a} an email network, \textbf {b} Barabasi-Albert networks and \textbf {c} scale-free networks. Each bar represents one of the following heuristics: random (\textit {rand}), degree-based (\textit {deg}), shadowing-based (\textit {shadw}), shielding-based (\textit {shield}), uniform targeting (\textit {unif}), combination of shadowing and shielding (\textit {shadw shield}). The passive controller targets $K=16$ nodes in the network in a discrete fashion. Error bars represent standard errors for 15 instances of the experiments and the total budget of both controllers sums up to $\mathcal {B}_a+\mathcal {B}_b=N\langle d\rangle /30$.\relax }{figure.caption.9}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces Alternative assessment of the proposed heuristics. On the x-axis, the number of iterations of the gradient-ascent algorithm required for obtaining a similar level of performance than the corresponding heuristic. On the y-axis, bars representing one of the following heuristics: degree-based (\textit {degree}), shadowing-based (\textit {shadowing}), shielding-based (\textit {shielding}), uniform targeting (\textit {uniform}), combination of shadowing and shielding (\textit {shadow shield}). The passive controller randomly targets $K=16$ nodes in the network in a discrete fashion and the total budget of both controllers sums up to $\mathcal {B}_a+\mathcal {B}_b=N\langle d\rangle /30$. Three budget scenarios are depicted (as indicated on the top of the panels). Error bars represent standard errors for 15 instances of the experiments. The initial allocation of the gradient-ascent algorithm is set at a discrete allocation on $K$ random nodes.\relax }}{12}{figure.caption.10}\protected@file@percent }
+\newlabel{fig:perf_iter}{{9}{12}{Alternative assessment of the proposed heuristics. On the x-axis, the number of iterations of the gradient-ascent algorithm required for obtaining a similar level of performance than the corresponding heuristic. On the y-axis, bars representing one of the following heuristics: degree-based (\textit {degree}), shadowing-based (\textit {shadowing}), shielding-based (\textit {shielding}), uniform targeting (\textit {uniform}), combination of shadowing and shielding (\textit {shadow shield}). The passive controller randomly targets $K=16$ nodes in the network in a discrete fashion and the total budget of both controllers sums up to $\mathcal {B}_a+\mathcal {B}_b=N\langle d\rangle /30$. Three budget scenarios are depicted (as indicated on the top of the panels). Error bars represent standard errors for 15 instances of the experiments. The initial allocation of the gradient-ascent algorithm is set at a discrete allocation on $K$ random nodes.\relax }{figure.caption.10}{}}
+\newlabel{LastPage}{{}{12}{}{page.12}{}}
+\xdef\lastpage@lastpage{12}
+\xdef\lastpage@lastpageHy{12}
+\ttl@finishall
diff --git a/datasets/classroom/output.out b/datasets/classroom/output.out
new file mode 100644
index 0000000000000000000000000000000000000000..cd58acec345eef23b1bb9838d0339a9866b921a1
--- /dev/null
+++ b/datasets/classroom/output.out
@@ -0,0 +1,11 @@
+\BOOKMARK [1][-]{section.1}{Details of the network employed}{}% 1
+\BOOKMARK [1][-]{section.2}{First-order Taylor expansion of the vote share for small allocations}{}% 2
+\BOOKMARK [1][-]{section.3}{Zero-order Taylor expansion of the vote share for large allocations}{}% 3
+\BOOKMARK [1][-]{section.4}{Exploitability of a passive controller when deviating from uniform allocation}{}% 4
+\BOOKMARK [1][-]{section.5}{Comparison of numerical and analytical results for optimal allocations in the continuous regime and budget disadvantage}{}% 5
+\BOOKMARK [1][-]{section.6}{Shadowing, shielding and hub preferences in the discrete regime}{}% 6
+\BOOKMARK [1][-]{section.7}{Proof of uniqueness of Nash Equilibrium}{}% 7
+\BOOKMARK [1][-]{section.8}{Numerical results of an iterative approach to finding the Nash Equilibrium}{}% 8
+\BOOKMARK [1][-]{section.9}{Extension to other network topologies}{}% 9
+\BOOKMARK [1][-]{section.10}{Proof of concavity}{}% 10
+\BOOKMARK [1][-]{section.11}{Iterations to optimal IM of the proposed heuristics}{}% 11
diff --git a/datasets/ia-email-univ/email.txt b/datasets/ia-email-univ/email.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8e7d2807317987b2bccb56d8c62c053a8a592b14
--- /dev/null
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diff --git a/datasets/ia-email-univ/ia-email-univ.mtx b/datasets/ia-email-univ/ia-email-univ.mtx
new file mode 100644
index 0000000000000000000000000000000000000000..120257b6f86486a0c65a926c02b7c6666f0b686e
--- /dev/null
+++ b/datasets/ia-email-univ/ia-email-univ.mtx
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diff --git a/datasets/ia-email-univ/readme.html b/datasets/ia-email-univ/readme.html
new file mode 100644
index 0000000000000000000000000000000000000000..8f63fc6df93495872befe92a05b5f52d9f86bf28
--- /dev/null
+++ b/datasets/ia-email-univ/readme.html
@@ -0,0 +1,44 @@
+<html>
+<link rel="stylesheet" media="screen" href="http://networkrepository.com/assets/css/bootstrap.css">
+<link rel="stylesheet" href="http://networkrepository.com/assets/css/responsive.css">
+<link rel="stylesheet" href="http://networkrepository.com/assets/css/style.css">
+
+<body>
+<br><br>
+<div class="container">
+<div class="row">
+<div class="col-md-12">
+<div class="panel panel-success">
+ <div class="panel-heading">
+ <h3 class="panel-title"><i class="icon-group icon-large"></i> Acknowledgement policy</h3>
+ </div>
+ <div class="panel-body">
+ <h4>Please acknowledge the <a href="http://networkrepository.com" target="_blank">repository</a> in published materials</h4>
+ <p class="lead12">
+ If you publish material based on data obtained from this <a href="http://networkrepository.com" target="_blank">repository</a>, then, in your acknowledgements, please note the assistance you received by using <a href="http://networkrepository.com" target="_blank">network repository</a>.
+ <BR><BR>
+ Please use the following <code>BiBTeX</code> reference:<BR>
+
+ <blockquote class="hero">
+ <p style="font-family:'Lato';">
+ <code style="font-size:1.1em;">
+ @inproceedings{nr-aaai15,<BR>
+ title = {The Network Data Repository with Interactive Graph Analytics and Visualization},<BR>
+ author={Ryan A. Rossi and Nesreen K. Ahmed},<BR>
+ booktitle = {Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence},<BR>
+ url={<i><a href="http://networkrepository.com" target="_blank"><b style="font-weight:bold !important;">http://networkrepository.com</b></a></i>},<BR>
+ year={2015}<BR>
+ }<BR>
+ </code>
+ </p>
+ </blockquote>
+ Many of the datasets have additional citation requests; these can be found on each dataset page.
+ See the <a href="http://networkrepository.com/policy.php" target="_blank">data license and policy</a> for more information.
+ </p>
+ </div>
+</div>
+</div>
+</div>
+</div>
+</body>
+</html>
\ No newline at end of file
diff --git a/datasets/soc-advogato/readme.html b/datasets/soc-advogato/readme.html
new file mode 100644
index 0000000000000000000000000000000000000000..9d0691b601db511db842d4722abdfbd7d2c83a25
--- /dev/null
+++ b/datasets/soc-advogato/readme.html
@@ -0,0 +1,39 @@
+<html>
+<link rel="stylesheet" media="screen" href="http://networkrepository.com/assets/css/bootstrap.css">
+<link rel="stylesheet" href="http://networkrepository.com/assets/css/responsive.css">
+<link rel="stylesheet" href="http://networkrepository.com/assets/css/style.css">
+
+<body>
+<br><br>
+<div class="container">
+<div class="row">
+<div class="col-md-12">
+<div class="panel panel-success">
+ <div class="panel-heading">
+ <h3 class="panel-title"><i class="icon-group icon-large"></i> Acknowledgement policy</h3>
+ </div>
+ <div class="panel-body">
+ <h4>Please cite the following if you use the data:</h4>
+ <p class="lead12">
+
+ <blockquote class="hero">
+ <p style="font-family:'Lato';">
+ <code style="font-size:1.1em;">
+ @inproceedings{nr-aaai15,<BR>
+ title = {The Network Data Repository with Interactive Graph Analytics and Visualization},<BR>
+ author={Ryan A. Rossi and Nesreen K. Ahmed},<BR>
+ booktitle = {AAAI},<BR>
+ url={<i><a href="http://networkrepository.com" target="_blank"><b style="font-weight:bold !important;">http://networkrepository.com</b></a></i>},<BR>
+ year={2015}<BR>
+ }<BR>
+ </code>
+ </p>
+ </blockquote>
+ </p>
+ </div>
+</div>
+</div>
+</div>
+</div>
+</body>
+</html>
\ No newline at end of file
diff --git a/datasets/soc-advogato/soc-advogato.edges b/datasets/soc-advogato/soc-advogato.edges
new file mode 100644
index 0000000000000000000000000000000000000000..42d1d5d302a08f059901b86a603af5ccef31f006
--- /dev/null
+++ b/datasets/soc-advogato/soc-advogato.edges
@@ -0,0 +1,51335 @@
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diff --git a/gascent.py b/gascent.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbe733fa882b9aa5d78b94f852e4d77446a7b541
--- /dev/null
+++ b/gascent.py
@@ -0,0 +1,897 @@
+import time
+import random
+
+import numpy as np
+import networkx as nx
+import matplotlib.pyplot as plt
+import matplotlib.lines as mlines
+
+
+PLOTS_PATH = 'plots/'
+
+
+class GA:
+ def __init__(self, W, b_vec, a_budget=None, initial_a="uniform", iters=1000, lrate=10, seed=5, verbose=False, **pars):
+ self.W = W
+ self.N = W.shape[0]
+
+ self.max_iters, self.learning_rate = iters, lrate * a_budget / self.N
+ assert a_budget is not None
+ self.a_max = a_budget
+ self.backtracking_done = 0
+ self.conv = None
+ self.atol, self.rtol = 1e-10, 1e-8
+ self.always_project = True
+ self.verbose = verbose
+
+ # Initialize allocations
+ if type(initial_a) == np.ndarray:
+ self.a = initial_a[:, None]
+ assert self.a.shape == (self.N, 1)
+ print("Initial 'a' from previous 'a'")
+ elif initial_a == "random":
+ np.random.seed(seed)
+ self.a = np.random.random((self.N, 1)) * self.a_max / self.N
+ elif initial_a == "uniform":
+ self.a = np.ones((self.N, 1)) * self.a_max / self.N
+ elif initial_a == "zeros":
+ self.a = np.zeros((self.N, 1))
+ self.always_project = False
+ else:
+ raise Exception("Initial a-allocation '" + str(initial_a) + "' not understood.")
+
+ # assert np.isclose(np.mean(b_vec), pars['b']), str(np.mean(b_vec)) + " " + str(pars['b'])
+ if len(b_vec.shape) == 1:
+ self.b = b_vec[:, None]
+ elif len(b_vec.shape) == 2 and b_vec.shape[1] == 1:
+ self.b = b_vec
+ else:
+ raise Exception("'b' must be a column vector, but b.shape = "+str(b_vec.shape))
+
+ self.initialize(**pars)
+ self.project_a()
+
+ def initialize(self, **pars):
+ self.L = np.diagflat(np.sum(self.W, axis=0)) - self.W
+
+ def execute(self):
+ """
+ Perform gradient ascent.
+ :return: (optimal allocations, vote share, opinions vector, n of iterations)
+ """
+ X, x = self.compute_x()
+
+ for i in range(self.max_iters):
+ prev_a, prev_X = np.copy(self.a), X
+
+ grad_X = self.compute_gradient()
+ self.a += grad_X * self.learning_rate
+ self.project_a()
+
+ X, x = self.compute_x()
+
+ if np.allclose(prev_a, self.a, rtol=self.rtol, atol=self.atol):
+ break
+ else:
+ self.adapt_step(X, prev_X, prev_a, i)
+
+ if i + 1 >= self.max_iters:
+ self.conv = False
+ print('Max iterations reached: {:d}, backtracking: {:d}'.format(i + 1, self.backtracking_done))
+ else:
+ self.conv = True
+
+ return self.a[:, 0], X, x, i
+
+ def project_a(self):
+ if self.always_project or np.sum(self.a) > self.a_max:
+ # self.a = self.a - (np.sum(self.a) - self.a_max) * np.ones((self.N, 1)) / self.N
+ #
+ # if self.a[self.a < 0].any():
+ # a_subs = np.sum(self.a[self.a < 0])
+ # self.a[self.a >= 0] = self.a[self.a >= 0] + a_subs / len(self.a[self.a >= 0].T)
+ # self.a[self.a < 0] = 0
+
+ n = len(self.a)
+ a_desc = np.sort(self.a[:, 0])[::-1]
+ a_csum = np.cumsum(a_desc) - self.a_max
+ ids = np.arange(n) + 1.0
+ cond = (a_desc - a_csum / ids) > 0.0
+ rho = ids[cond][-1]
+ theta = a_csum[cond][-1] / float(rho)
+ a_proj = np.maximum(self.a - theta, 0.0)
+
+ self.a = a_proj
+ assert np.isclose(self.a_max, np.sum(self.a)), str((self.a_max, np.sum(self.a)))
+
+ def compute_x(self):
+ self.inv_Lab = np.linalg.inv(self.L + np.diagflat(self.a + self.b))
+ x = np.array(np.matmul(self.inv_Lab, self.a).T)
+ X = np.sum(x) / self.N
+
+ return X, x[0, :]
+
+ def compute_gradient(self):
+ return self.a_max * np.sum(np.matmul(self.inv_Lab, np.diagflat(np.matmul(self.inv_Lab, self.b))),
+ axis=0).T / self.N
+
+ def adapt_step(self, X, prev_X, prev_a, i):
+ if X < prev_X:
+ self.learning_rate /= 2
+ self.a = prev_a
+ if i < int(self.max_iters / 2):
+ self.backtracking_done = 1
+ else:
+ self.backtracking_done = 2
+ if self.verbose:
+ print("Backtracking at iteration {:d}, learning rate halved to {:f}".format(i, self.learning_rate))
+ # elif i in (
+ # int(self.iters / 2), int(self.iters * 3 / 4), int(self.iters * 7 / 8), int(self.iters * 15 / 16)):
+ # self.learning_rate *= 2
+
+
+class DebugGA(GA):
+ def initialize(self, **pars):
+ super().initialize(**pars)
+
+ self.start_time = time.time()
+
+ self.X_vec = np.zeros(self.max_iters + 1)
+ self.i = 0
+
+ print("L:\n", self.L, "\na vector:\n", self.a.T, "\nb_vector:\n", self.b)
+
+ self.fig, (self.ax1, self.ax2) = plt.subplots(1, 2, figsize=(10, 5))
+
+ def compute_x(self):
+ print("Step " + str(self.i))
+
+ X, x = super().compute_x()
+ self.X_vec[self.i] = X
+ self.i += 1
+
+ return X, x
+
+ def execute(self):
+ a, X, x, i = super().execute()
+ print('Backtracked: ' + str(self.backtracking_done))
+ fig = self.report()
+ return a, X, x, i
+
+ def report(self):
+ print("total a", np.sum(self.a) / self.N)
+ print("Elapsed time:", time.time() - self.start_time)
+ print("Time per step", (time.time() - self.start_time) / self.i)
+ print("")
+
+ ax1, ax2 = self.ax1, self.ax2
+
+ # Plot individual allocations
+ if self.ax1 is not None:
+ if not np.allclose(self.b[:, 0], 0):
+ ax1.bar(range(len(self.a)), self.b[:, 0], label="b", color="C0")
+ ax1.bar(range(len(self.a)), self.a[:, 0], label="a", color="C1")
+ ax1.set(ylabel=r"$a_n$") # , ylim=[0, max(b)])
+
+ # Plot X_vec
+ ax2.plot(range(1, self.i), self.X_vec[:self.i - 1], marker="v", color="green")
+ ax2.set(
+ title=r"$X_0$:" + " {:.4f}, ".format(self.X_vec[0]) + r"$X_{\mathrm{GA}}$:" + " {:.4f}, ".format(
+ self.X_vec[self.i - 1]),
+ # +r"$X_{\mathrm{ana}}$:"+" {:.4f}, ".format(X_ana_net)+r"$X_{ana\,mf}$" + " {:.4f}".format(X_ana),
+ xlabel="t", ylabel="X")
+
+
+def project_a_and_compute_x(a, b, a_max, L, N):
+ a = a - (np.sum(a) - a_max) * np.ones((N, 1)) / N
+
+ if a[a < 0].any():
+ a_subs = np.sum(a[a < 0])
+ a[a >= 0] = a[a >= 0] + a_subs / len(a[a >= 0].T)
+ a[a < 0] = 0
+
+ inv_Lab = np.linalg.inv(L + np.diagflat(a + b))
+ x = np.array(np.matmul(inv_Lab, a).T)
+ X = np.sum(x) / N
+
+ return a, X, x[0, :], inv_Lab
+
+
+def gascent(W, b, pars):
+ N = W.shape[0]
+ L = np.diagflat(np.sum(W, axis=0)) - W
+ iters, learning_rate = pars['iters'], pars["lrate"] * pars['a']
+ a_max = pars['a'] * N
+
+ a = np.ones((N, 1)) * a_max / N
+ # a = np.random.random((N, 1)) * a_max / N
+ a, X, x, inv_Lab = project_a_and_compute_x(a, b, a_max, L, N)
+
+ backtracking_done = False
+ for i in range(iters):
+ prev_a, prev_X = np.copy(a), X
+
+ grad_X = a_max * np.sum(np.matmul(inv_Lab, np.diagflat(np.matmul(inv_Lab, b))), axis=0) / N
+ a += grad_X.T * learning_rate
+
+ a, X, x, inv_Lab = project_a_and_compute_x(a, b, a_max, L, N)
+
+ if np.allclose(prev_a, a):
+ break
+ else:
+ if X < prev_X:
+ learning_rate /= 2
+ a = prev_a
+ backtracking_done = True
+
+ if i + 1 == iters:
+ print('Max iterations reached: ' + str(iters) + ", pars: " + str(pars))
+ return a[:, 0], X, np.NaN
+
+ print('Backtracked: ' + str(backtracking_done))
+ return a[:, 0], X, x
+
+
+def debug_gascent_bimodal(W, pars, iters=1000, partial_b=None, axes=None):
+ from python.mfield_bimodal import analytical_optimisation
+ start_time = time.time()
+
+ N = W.shape[0]
+ learning_rate = pars['lrate']
+ a_max, b_max = pars['a'] * N, pars['b']
+
+ cut = int(np.ceil(pars['rho'] * N))
+
+ def project_to_2D(vec):
+ return np.array([np.mean(vec[:cut]), np.mean(vec[cut:])])
+
+ if partial_b is None:
+ b = np.ones((N, 1))
+ b[:cut] *= pars['beta'] * b_max / pars['rho']
+ b[cut:] *= (1 - pars['beta']) * b_max / (1 - pars['rho'])
+ else:
+ b = np.zeros((N, 1))
+ b[:partial_b] = np.full((partial_b, 1), b_max * N / partial_b)
+
+ if partial_b == 2:
+ w = W.toarray()
+ plt.plot(w[0])
+ plt.plot(w[1])
+ plt.show()
+
+ L = np.diagflat(np.sum(W, axis=0)) - W
+
+ a = np.zeros((N, 1))
+ # a = np.random.random((N, 1)) * a_max / N # np.ones((N, 1))
+ # a = np.ones((N, 1)) * a_max / N / (1 - pars['rho'])
+
+ a_vec = np.zeros((iters + 1, 2))
+ a_cut = np.zeros((iters + 1, 2))
+ X_vec = np.zeros(iters + 1)
+ grad_X_vec = np.zeros((iters, 2))
+
+ a_vec[0] = project_to_2D(a)
+ a, X_vec[0], x, inv_Lab = project_a_and_compute_x(a, b, a_max, L, N)
+ a_cut[0] = project_to_2D(a)
+
+ print("L:\n", L, "\ninv_Lab:\n", inv_Lab, "\na vector:\n", a.T, "\nb_vector:\n", b.T, "\nx vector:\n", x)
+
+ for i in range(iters):
+ print("Step " + str(i))
+ prev_a = np.copy(a)
+
+ grad_X = np.sum(np.matmul(inv_Lab, np.diagflat(np.matmul(inv_Lab, b))), axis=0) / N * a_max
+ a += grad_X.T * learning_rate
+
+ grad_X_vec[i] = project_to_2D(grad_X)
+
+ a_vec[i + 1] = project_to_2D(a)
+ a, X_vec[i + 1], x, inv_Lab = project_a_and_compute_x(a, b, a_max, L, N)
+ a_cut[i + 1] = project_to_2D(a)
+
+ if np.allclose(prev_a, a):
+ print("Convergence with ", a_cut[i + 1])
+ break
+ else:
+ if X_vec[i + 1] < X_vec[i]:
+ learning_rate /= 2
+ a = prev_a
+
+ if i + 1 == iters:
+ print('Max iterations reached: ' + str(iters))
+ # print(a.T)
+
+ a1, a1_var = np.mean(a[:cut]), np.std(a[:cut])
+ alpha_exp = a1 * pars['rho'] / pars['a']
+ alpha_exp_var = a1_var * pars['rho'] / pars['a']
+ a_var = (np.std(a[:cut]) + np.std(a[cut:])) / 2 # / pars['a']
+
+ print("total a", np.sum(a) / N, "std", a_var)
+ print("alpha_exp", alpha_exp, "pm", alpha_exp_var)
+ print("alpha_exp_norm", alpha_exp / pars['rho'])
+ print("grad direction", learning_rate * np.mean(grad_X[0, :int(np.ceil(pars['rho'] * N))]),
+ learning_rate * np.mean(grad_X[0, int(np.ceil(pars['rho'] * N)):]))
+
+ # ANALYTIC
+ alpha_ana, X_ana = analytical_optimisation(pars)
+
+ L = np.diagflat(np.sum(W, axis=0)) - W
+ ana = np.ones((N, 1))
+ ana[:cut] = ana[:cut] * alpha_ana * pars['a'] / pars['rho']
+ ana[cut:] = ana[cut:] * (1 - alpha_ana) * pars['a'] / (1 - pars['rho'])
+ b = np.ones((N, 1))
+ b[:cut] *= pars['beta'] * pars['b'] / pars['rho']
+ b[cut:] *= (1 - pars['beta']) * pars['b'] / (1 - pars['rho'])
+ # print(a.shape,b.shape,L.shape)
+ inv_Lab = np.linalg.inv(L + np.diagflat(ana + b))
+ X_ana_net = 1 / N * np.sum(np.matmul(inv_Lab, ana))
+ print("alpha_ana/rho", alpha_ana / pars['rho'], "X_ana", X_ana_net)
+ print("")
+ print("Elapsed time:", time.time() - start_time)
+ print("Time per step", (time.time() - start_time) / i)
+ print("X_GA^0:", X_vec[0])
+ print("X_GA^*:", X_vec[i])
+
+ # ------------------------
+ # -------- PLOTS
+ # ------------------------
+ if axes is None:
+ fig, (ax2, ax1) = plt.subplots(1, 2, figsize=(2 * 3.5, 3))
+ else:
+ fig, ax = plt.subplots(1, 2, figsize=(2 * 2.75, 2))
+ (ax2, ax1) = axes
+ fig2, ax3 = plt.subplots(figsize=(3.5, 3))
+ fig3, ax4 = plt.subplots(figsize=(20, 5))
+ # fig, ((ax2, ax1), (ax3, ax4)) = plt.subplots(2, 2, figsize=(5.5, 5.5))
+ # fig, (ax2, ax1, ax3) = plt.subplots(1, 3, figsize=(11, 2.75))
+
+ print("a1 std:", np.std(a[:cut]), "a2 std:", np.std(a[cut:]))
+ ax4.boxplot([a[:cut, 0], a[cut:, 0]],
+ labels=[r'$d_1$', r'$d_2$']) # r'Low degree nodes', r'High degree nodes']) # , $d_2$'])
+ ax4.grid(), ax4.set(
+ ylabel=r"Node allocations within the same $k$") # Distribution of node allocations for $k\in K$")
+
+ # a[::2], b[1::2] = 0, 0
+ ax1.bar(range(len(a)), a[:, 0], label=r"$w_{ai}^{\mathrm{GA}}$", color="C1")
+ # ax1.bar(range(len(a)), b[:, 0], label=r"$b_i$")
+ ax1.plot(ana, label=r"$w_{ai}^{\mathrm{ana}}$", color="green")
+ ax1.set(xlabel=r"low degree nodes $\qquad$ high degree nodes",
+ ylabel=r"Node allocations, $w_{ai}$") # , ylabel=r"Individual node allocations, $w_{ai}$")
+ ax1.legend()
+
+ ax2.semilogx(range(1, i + 1), X_vec[1:i + 1], marker=".")
+ ax2.set( # title= # r"$X^0_{\mathrm{GA}}$:" + " {:.4f}, ".format(X_vec[0]) +
+ # r"$X_{\mathrm{GA}}^*$:" + " {:.4f}, ".format(
+ # X_vec[i]) + r"$X_{\mathrm{ana}}$:" + " {:.4f}, ".format(X_ana_net) + r"$X_{\mathrm{ana}}^{mf}:$" + " {:.4f}".format(
+ # X_ana),
+ xlabel="t",
+ ylabel=r"Vote share, $X_{\mathrm{GA}}$") # , ylabel=r"Evolving vote share, $X_{\mathrm{GA}}^t$")
+ # ax2.plot(X_vec[:i + 1], marker=".")
+ # ax2.set(title=r"$X_{\mathrm{GA}}$:" + " {:.4f}, ".format(X_vec[i]) + r"$X_{\mathrm{ana}}$:" + " {:.4f}, ".format(
+ # X_ana_net) + r"$X_{ana\,mf}$" + " {:.4f}".format(X_ana), xlabel="t", ylabel="X")
+
+ for j in range(i):
+ # ax3.arrow(0, 0, a_vec[j, 0], a_vec[j, 1])
+ ax3.arrow(a_vec[j, 0], a_vec[j, 1], a_cut[j, 0] - a_vec[j, 0], a_cut[j, 1] - a_vec[j, 1], color="red")
+ ax3.arrow(a_cut[j, 0], a_cut[j, 1], a_vec[j + 1, 0] - a_cut[j, 0], a_vec[j + 1, 1] - a_cut[j, 1])
+ ax3.plot(a_vec[:i + 1, 0], a_vec[:i + 1, 1], marker="X", ls='')
+ ax3.plot(a_cut[:i + 1, 0], a_cut[:i + 1, 1], marker="X", ls='')
+ ax3.plot([0, pars['a'] / pars['rho']], [pars['a'] / (1 - pars['rho']), 0])
+ # ax3.set(title=r"$\alpha_{\mathrm{GA}}$:" + " {:.4f}, ".format(alpha_exp) + r"$\alpha_{\mathrm{ana}}$:" + " {:.4f}, ".format(
+ # alpha_ana) + r"$\beta$:" + " {:.1f}".format(pars["beta"]))
+ ax3.set(
+ # title=r"$\overline{a1}$:" + " {:.4f}, ".format(a_cut[i, 0]) + r"$\overline{a2}$:" + " {:.4f}".format(a_cut[i, 1]),
+ xlim=[0, 1.1 * max(np.max(a_vec), np.max(a_cut))],
+ ylim=[0, 1.1 * max(np.max(a_vec), np.max(a_cut))], # xlim=[0, max(a_vec[:, 0]) + 0.05],
+ xlabel=r'Mean allocation to $d_1$', # low degree nodes', #$\overline{a}_{i:d_i=d_1}$',
+ ylabel=r'Mean allocation to $d_2$') # high degree nodes')#, $\overline{a}_{i:d_i=d_2}$')
+ # ax3.set_aspect("equal")
+
+ legends = []
+ legends.append(mlines.Line2D([], [], color='C1', marker='X', label=r"$w_a^{t}$", ls=""))
+ legends.append(mlines.Line2D([], [], color='C0', marker='X', label=r"$w_a+\mu\nabla_{w_a}X$", ls=""))
+ legends.append(mlines.Line2D([], [], color='black', marker='', label=r"$\mu\nabla_{w_a}X$", ls="solid"))
+ legends.append(mlines.Line2D([], [], color='red', marker='', label=r"projection", ls="solid"))
+ ax3.legend(handles=legends)
+
+ # for j in range(i):
+ # ax4.arrow(0, 0, learning_rate * grad_X_vec[j, 0], learning_rate * grad_X_vec[j, 1])
+ # ax4.plot(learning_rate * grad_X_vec[:i, 0], learning_rate * grad_X_vec[:i, 1], marker="X")
+ # ax4.plot([learning_rate * np.min(grad_X_vec), learning_rate * np.max(grad_X_vec)],
+ # [learning_rate * np.min(grad_X_vec), learning_rate * np.max(grad_X_vec)])
+ # ax4.set(title=r"$\nabla_{a1}X$" + ": {:.4f}, ".format(grad_X_vec[i, 0]) + r"$\nabla_{a2}X$" + ": {:.4f}".format(
+ # grad_X_vec[i, 1]), xlabel=r"$\nabla_{a1}X$", ylabel=r"$\nabla_{a2}X$")
+ # # ax4.set_xlim(left=0), ax4.set_ylim(bottom=0)
+ # ax4.set_aspect("equal")
+
+ if axes is None:
+ fig.tight_layout()
+ fig.savefig(PLOTS_PATH + "debug_bimodal.eps")
+ fig.show()
+
+ fig2.tight_layout()
+ fig2.savefig(PLOTS_PATH + "debug_bimodal2.eps")
+ fig2.show()
+
+
+def debug_gascent_regular(W, pars):
+ iters = pars['iters']
+ start_time = time.time()
+
+ N = W.shape[0]
+ cut = random.sample(range(N), int(np.ceil(pars['rho'] * N)))
+ rest = set(range(N)).difference(set(cut))
+ rest, cut = np.array(list(rest)), np.array(cut)
+
+ learning_rate = pars['lrate']
+ a_max, b_max = pars['a'] * N, pars['b']
+ b = np.zeros((N, 1))
+ b[cut] = b_max / pars['rho']
+
+ L = np.diagflat(np.sum(W, axis=0)) - W
+ print("L")
+ print(L)
+
+ a_vec = np.zeros((iters + 1, 2))
+ a = np.random.random((N, 1)) * a_max / N
+ a[a < 0] = 0
+ a_vec[0] = np.array([np.mean(a[cut]), np.mean(a[rest])])
+
+ a_cut = np.zeros((iters + 1, 2))
+ a = a - (np.sum(a) - a_max) * np.ones((N, 1)) / N
+ a_cut[0] = np.array([np.mean(a[cut]), np.mean(a[rest])])
+
+ inv_Lab = np.linalg.inv(L + np.diagflat(a + b))
+ print(inv_Lab)
+ print("a vector")
+ print(a.T)
+ print("b_vector")
+ print(b.T)
+ print("x vector")
+ print(np.matmul(inv_Lab, a).T)
+
+ X_vec = np.zeros(iters + 1)
+ X_vec[0] = 1 / N * np.sum(np.matmul(inv_Lab, a))
+
+ grad_X_vec = np.zeros((iters, 2))
+
+ for i in range(iters):
+ print("Step " + str(i))
+ prev_a = np.copy(a)
+
+ grad_X = np.sum(np.matmul(inv_Lab, np.diagflat(np.matmul(inv_Lab, b))), axis=0) / N * a_max
+ grad_X_vec[i] = [np.mean(grad_X[0, cut]), np.mean(grad_X[0, rest])]
+ a += grad_X.T * learning_rate
+ a_vec[i + 1] = [np.mean(a[cut]), np.mean(a[rest])]
+
+ # Plane projection
+ a = a - (np.sum(a) - a_max) * np.ones((N, 1)) / N
+
+ # Non-negativity constraint
+ a_subs = np.sum(a[a < 0])
+ a[a >= 0] = a[a >= 0] + a_subs / len(a[a >= 0].T)
+ a[a < 0] = 0
+
+ a_cut[i + 1] = np.array([np.mean(a[cut]), np.mean(a[rest])])
+
+ inv_Lab = np.linalg.inv(L + np.diagflat(a + b))
+ X_vec[i + 1] = 1 / N * np.sum(np.matmul(inv_Lab, a))
+
+ if np.allclose(prev_a, a):
+ print("Convergence with ", a_cut[i + 1])
+ break
+ else:
+ if X_vec[i + 1] < X_vec[i]:
+ learning_rate /= 2
+ a = prev_a
+
+ if i + 1 == iters:
+ print('Max iterations reached: ' + str(iters))
+ # print(a.T)
+
+ a1 = np.mean(a[rest])
+ alpha_exp = a1 * pars['rho'] / pars['a']
+ print("total a", np.sum(a) / N)
+ print("alpha_exp", alpha_exp)
+ print("alpha_exp_norm", alpha_exp / pars['rho'])
+ print("grad direction", learning_rate * np.mean(grad_X[0, cut]), learning_rate * np.mean(grad_X[0, rest]))
+
+ # ANALYTIC
+ # alpha_ana, X_ana = analytical_optimisation(pars)
+ #
+ # L = np.diagflat(np.sum(W, axis=0)) - W
+ # ana = np.ones((N, 1))
+ # ana[rest] = ana[rest] * alpha_ana * pars['a'] / pars['rho']
+ # ana[cut] = ana[cut] * (1 - alpha_ana) * pars['a'] / (1 - pars['rho'])
+ # b = np.ones((N, 1))
+ # b[:int(np.ceil(pars['rho'] * N))] *= pars['beta'] * pars['b'] / pars['rho']
+ # b[int(np.ceil(pars['rho'] * N)):] *= (1 - pars['beta']) * pars['b'] / (1 - pars['rho'])
+ # # print(a.shape,b.shape,L.shape)
+ # inv_Lab = np.linalg.inv(L + np.diagflat(ana + b))
+ # X_ana_net = 1 / N * np.sum(np.matmul(inv_Lab, ana))
+ # print("alpha_ana/rho", alpha_ana / pars['rho'], "X_ana", X_ana_net)
+ print("Elapsed time:", time.time() - start_time)
+ print("Time per step", (time.time() - start_time) / i)
+
+ # PLOTS
+ fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10, 10))
+ ax1.bar(range(len(a)), b[:, 0], label="b")
+ ax1.bar(range(len(a)), a[:, 0], label="a")
+ ax1.set(title=r"$\alpha_{\mathrm{GA}}$:" + " {:.4f}, ".format(alpha_exp),
+ # +r"$\alpha_{\mathrm{ana}}$:"+" {:.4f}".format(alpha_ana),
+ ylabel=r"$a_n$", ylim=[0, max(a)])
+
+ ax2.semilogx(range(1, i + 2), X_vec[:i + 1], marker=".")
+ ax2.set(title=r"$X_{\mathrm{GA}}-X_0$:" + " {:.4f}, ".format(X_vec[i] - X_vec[0]),
+ # +r"$X_{\mathrm{ana}}$:"+" {:.4f}, ".format(X_ana_net)+r"$X_{ana\,mf}$" + " {:.4f}".format(X_ana),
+ xlabel="t", ylabel="X")
+
+ for j in range(i):
+ ax3.arrow(a_vec[j, 0], a_vec[j, 1], a_cut[j, 0] - a_vec[j, 0], a_cut[j, 1] - a_vec[j, 1], color="red")
+ ax3.arrow(a_cut[j, 0], a_cut[j, 1], a_vec[j + 1, 0] - a_cut[j, 0], a_vec[j + 1, 1] - a_cut[j, 1])
+ ax3.plot(a_vec[:i + 1, 0], a_vec[:i + 1, 1], marker="X", ls='')
+ ax3.plot(a_cut[:i + 1, 0], a_cut[:i + 1, 1], marker="X", ls='')
+ ax3.plot([0, pars['a'] / pars['rho']], [pars['a'] / (1 - pars['rho']), 0])
+ ax3.set(xlim=[0, max(np.max(a_vec), np.max(a_cut))], ylim=[0, max(np.max(a_vec), np.max(a_cut))],
+ xlabel=r'$\overline{a_1}$', ylabel=r'$\overline{a_2}$')
+ ax3.set_aspect("equal")
+
+ if False:
+ w = W.toarray()
+ w_neighbours = np.sum(w[cut], axis=0)[rest]
+ a_neighbours = []
+ for i in range(N - 1):
+ a_i = a[rest]
+ a_i = a_i[w_neighbours == i]
+ if len(a_i) != 0:
+ a_neighbours.append(a_i)
+ else:
+ break
+ w_neighbours = np.sum(w[cut], axis=0)[cut]
+ a_neighboursB = []
+ for j in range(N - 1):
+ a_i = a[cut]
+ a_i = a_i[w_neighbours == j]
+ if len(a_i) != 0:
+ a_neighboursB.append(a_i)
+ else:
+ break
+ ax4.boxplot(a_neighboursB[::-1] + a_neighbours[::-1],
+ labels=["B" + str(j) for j in range(j - 1, -1, -1)] + list(range(i - 1, -1, -1)))
+ ax4.set(xlabel=r"n of B-controlled neighbours", ylabel=r"$w_{ai}$")
+ else:
+ lengths = nx.single_source_shortest_path_length(G, 0)
+ box_data = np.empty(list(lengths.values())[-1] + 1, dtype=list)
+ for i, el in enumerate(box_data):
+ box_data[i] = []
+ for key in lengths.keys():
+ box_data[lengths[key]].append(a[key])
+ ax4.boxplot(box_data, labels=range(len(box_data))) # ,
+ # labels=["B" + str(j) for j in range(j - 1, -1, -1)] + list(range(i - 1, -1, -1)))
+ ax4.set(xlabel=r"Distance to B-controlled node", ylabel=r"$w_{ai}$", ylim=0)
+
+ fig.savefig(PLOTS_PATH + "debug_regular.eps")
+ fig.show()
+
+ # plt.plot(np.sum(w[cut], axis=0))
+ # plt.show()
+
+
+def debug_gascent_powerlaw(W, b, pars, seq):
+ start_time = time.time()
+
+ N = W.shape[0]
+ learning_rate, iters = pars['lrate'] * pars['a'], pars['iters']
+ a_max, b_max = pars['a'] * N, pars['b']
+
+ L = np.diagflat(np.sum(W, axis=0)) - W
+ print("L")
+ print(L)
+
+ a = np.random.random((N, 1)) * a_max / N
+ # a[a < 0] = 0
+ # a = np.zeros((N, 1))
+ a = a - (np.sum(a) - a_max) * np.ones((N, 1)) / N
+
+ inv_Lab = np.linalg.inv(L + np.diagflat(a + b))
+ print(inv_Lab)
+ print("a vector")
+ print(a.T)
+ print("b_vector")
+ print(b.T)
+ print("x vector")
+ print(np.matmul(inv_Lab, a).T)
+
+ X_vec = np.zeros(iters + 1)
+ X_vec[0] = 1 / N * np.sum(np.matmul(inv_Lab, a))
+
+ backtracked = False
+ for i in range(iters):
+ print("Step " + str(i))
+ prev_a = np.copy(a)
+
+ grad_X = np.sum(np.matmul(inv_Lab, np.diagflat(np.matmul(inv_Lab, b))), axis=0) / N # * a_max
+ a += grad_X.T * learning_rate
+ print(grad_X[:, :2])
+
+ # Plane projection
+ a = a - (np.sum(a) - a_max) * np.ones((N, 1)) / N
+
+ # Non-negativity constraint
+ a_subs = np.sum(a[a < 0])
+ a[a >= 0] = a[a >= 0] + a_subs / len(a[a >= 0].T)
+ a[a < 0] = 0
+
+ inv_Lab = np.linalg.inv(L + np.diagflat(a + b))
+ X_vec[i + 1] = 1 / N * np.sum(np.matmul(inv_Lab, a))
+
+ if np.allclose(prev_a, a):
+ print("Convergence")
+ break
+ else:
+ if X_vec[i + 1] < X_vec[i]:
+ learning_rate /= 2
+ a = prev_a
+ backtracked = True
+
+ if i + 1 == iters:
+ print('Max iterations reached: ' + str(iters))
+ if backtracked:
+ print('Backtracked')
+ # print(a.T)
+
+ print("total a", np.sum(a) / N)
+ print("Elapsed time:", time.time() - start_time)
+ print("Time per step", (time.time() - start_time) / i)
+
+ # PLOTS
+
+ seq = np.array(seq)
+ sort_ind = np.argsort(seq)
+ seq = seq[sort_ind]
+ a = a[sort_ind]
+ b = b[sort_ind]
+ # print(a)
+ print(seq)
+
+ fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(10, 10))
+ ax1.bar(range(len(a)), b[:, 0], label="b")
+ ax1.bar(range(len(a)), a[:, 0], label="a")
+ ax1.set(ylabel=r"$a_n$") # , ylim=[0, max(b)])
+ ax3.bar(range(len(a)), a[:, 0], label="a", color="C1")
+ ax3.bar(range(len(a)), b[:, 0], label="b", color="C0")
+ ax3.set(ylabel=r"$a_n$") # , ylim=[0, max(b)])
+
+ ax2.semilogx(range(1, i + 1), X_vec[1:i + 1], marker=".")
+ ax2.set(title=r"$X_0$:" + " {:.4f}, ".format(X_vec[0]) + r"$X_{\mathrm{GA}}$:" + " {:.4f}, ".format(X_vec[i]),
+ # +r"$X_{\mathrm{ana}}$:"+" {:.4f}, ".format(X_ana_net)+r"$X_{ana\,mf}$" + " {:.4f}".format(X_ana),
+ xlabel="t", ylabel="X")
+
+ ax4.scatter(seq, a, color="C1")
+ ax4.scatter(seq, b, color="C0")
+ ax4.set(xlabel='k', ylabel=r'$a_n$', title="beta = {:f} k".format(pars['beta']))
+
+ fig.savefig(PLOTS_PATH + "debug_powerlaw.eps")
+ fig.show()
+
+ return a
+
+
+def test_debug_regular():
+ pars = {
+ "rho": 0.005,
+ 'k0': 3,
+ 'k1': 30,
+ 'a': 2,
+ 'b': 8,
+ 'beta': 1,
+
+ 'N': 50,
+ 'lrate': 25,
+ }
+
+ N = pars['N']
+ if False:
+ seq = [pars['k0'] for _ in range(int(N * pars['rho']))] + [pars['k1'] for _ in
+ range(int(N * (1 - pars['rho'])))]
+ G = nx.random_degree_sequence_graph(seq, tries=20)
+ else:
+ # G = nx.fast_gnp_random_graph(N, np.log(N) / N * 2)
+ G = nx.random_regular_graph(pars['k0'], pars['N'])
+
+ W = nx.adj_matrix(G, range(N))
+ print("W.shape", W.shape)
+
+ debug_gascent_regular(W, pars)
+
+
+def test_path():
+ pars = {
+ 'a': 1, 'b': 1,
+ 'N': 91,
+ 'iters': 10000,
+ 'lrate': 5,
+ }
+ # 'N': 151,
+ # 'iters': 15000,
+ # 'lrate': 5,
+ # }
+
+ N = pars['N']
+ # G = nx.fast_gnp_random_graph(N, np.log(N) / N / 2)
+ G = nx.path_graph(N)
+ W = nx.adj_matrix(G)
+
+ b = np.zeros((N, 1))
+ b[int(N / 2)] = pars['b'] * N
+ # b = np.ones((N, 1)) * pars['b']
+ # b = np.ones((N, 1)) * pars['b']/2
+ # b[int(N / 2)] += pars['b'] * N/2
+
+ a = debug_gascent(W, b, pars)
+
+ # now = time.time()
+ # a = gascent(W, b, pars)
+ # print("Elapsed time: " + str(time.time() - now))
+
+
+def test_debug_bimodal(axes=None):
+ import matplotlib
+ matplotlib.rcParams['text.usetex'] = True
+
+ pars = {
+ 'N': 200, 'k0': 2, 'k1': 6, 'rho': 0.5,
+ 'beta': 0.8, 'a': 1, 'b': 4,
+ 'iters': 1000, 'lrate': 3,
+ 'seed': 3,
+ }
+ seq = [pars['k0'] for _ in range(int(pars['N'] * pars['rho']))] + [pars['k1'] for _ in
+ range(int(pars['N'] * (1 - pars['rho'])))]
+ G = nx.random_degree_sequence_graph(seq, tries=20, seed=pars['seed'])
+ if not nx.is_connected(G):
+ raise Exception("Graph not connected")
+ W = nx.adj_matrix(G)
+
+ debug_gascent_bimodal(W, pars, axes=axes)
+
+
+def test_debu_star():
+ pars = {
+ 'a': 2, 'b': 1,
+ 'N': 10000,
+ 'iters': 10,
+ 'lrate': 5,
+ }
+
+ N = pars['N']
+ G = nx.star_graph(N - 1)
+ W = nx.adj_matrix(G)
+ # print(W)
+ # raise Exception("caca")
+
+ # b = np.zeros((N, 1))
+ # b[0] = pars['b'] * N
+
+ b = np.ones((N, 1)) * pars['b'] * N / (N - 1)
+ b[0] = 0
+
+ # b = np.ones((N, 1)) * pars['b']/2
+ # b[int(N / 2)] += pars['b'] * N/2
+
+ a = debug_gascent(W, b, pars)
+
+ # now = time.time()
+ # a = gascent(W, b, pars)
+ # print("Elapsed time: " + str(time.time() - now))
+
+ return a
+
+
+def test_debu_powerlaw():
+ pars = {
+ 'a': 1, 'b': 1,
+ 'N': 500, 'gamma': 2.5, 'beta': -1,
+ 'iters': 10000, 'lrate': 50,
+ }
+
+ # W, seq, rhos, b = create_power_network(pars)
+ N = W.shape[0]
+
+ rhos = np.bincount(seq) / N
+
+ # def beta_allocation(k, beta):
+ # return k ** -beta / rhos[k]
+ # # return beta*k / rhos[k]
+ # beta_allocations = [0 if n == 0 else beta_allocation(k, pars['beta']) for k, n in enumerate(rhos)]
+ # norm_beta = sum(beta_allocations)
+ #
+ # b = np.zeros((N, 1))
+ # for i, k in enumerate(seq):
+ # b[i] = beta_allocations[k] * pars['b'] / norm_beta
+
+ b = b * pars['b']
+ a = debug_gascent_powerlaw(W, b, pars, seq)
+
+ # now = time.time()
+ # a = gascent(W, b, pars)
+ # print("Elapsed time: " + str(time.time() - now))
+
+
+def test_zealot_GA():
+ N = 100
+ pars = {
+ 'a': 1 / 8,
+ 'q': 0.45, 'rho': 0.2, "q_periphery": 0,
+ 'N': N, 'network': 'complete',
+ 'iters': 200, 'lrate': 2000,
+ }
+
+ N = pars['N']
+ G = nx.complete_graph(N)
+ W = nx.adj_matrix(G)
+
+ b = np.zeros((N, 1))
+
+ # initial_a = np.zeros(N)
+ # initial_a[:int(N*pars['rho'])] = 15.625
+ ga = DebugZealotGA(W, b, a_budget=pars['a'] * pars['N'], **pars) # , initial_a="zeros") # , initial_a=initial_a)
+
+ print("Running gradient ascent...")
+ fig = ga.execute()
+
+ fig.savefig(PLOTS_PATH + "debug_zealots.eps")
+ fig.show()
+
+ print("Allocations to zealots:", np.mean(ga.a[ga.q > 0]))
+ print("Allocation to normal agents:", np.mean(ga.a[ga.q == 0]))
+
+ return ga
+
+ # now = time.time()
+ # a = gascent(W, b, pars)
+ # print("Elapsed time: " + str(time.time() - now))
+
+
+def test_biased_A_B(a_rel_budget=1/8):
+ N = 100
+ pars = {
+ 'a': a_rel_budget,
+ 'q': 0.45, 'rho': (0.2, 0.7, 0.1), "q_periphery": 0.6,
+ 'N': N, 'network': 'complete',
+ 'iters': 200, 'lrate': 2000,
+ }
+
+ G = nx.complete_graph(N)
+ W = nx.adj_matrix(G)
+ b = np.ones((N, 1)) / 8
+
+ # initial_a = np.zeros(N)
+ # initial_a[:int(N*pars['rho'])] = 15.625
+ # ga = DebugZealotGA(W, b, pars) # , initial_a="zeros") # , initial_a=initial_a)
+ ga = ZealotGA(W, b, a_budget=pars['a'] * pars['N'], **pars)
+
+ print("Running gradient ascent...")
+ fig = ga.execute()
+
+ # fig.savefig(PLOTS_PATH + "debug_zealots.eps")
+ # fig.show()
+
+ print("Allocation to B-biased:", np.mean(ga.a[ga.q > 0]))
+ print("Allocation to normal agents:", np.mean(ga.a[ga.q == 0]))
+ print("Allocation to A-biased:", np.mean(ga.a[ga.q < 0]))
+
+ return ga
+
+ # now = time.time()
+ # a = gascent(W, b, pars)
+ # print("Elapsed time: " + str(time.time() - now))
+
+
+if __name__ == "__main__":
+ # test_debug_bimodal()
+ # test_debug_regular()
+ # test_debug_path()
+ # a = test_debu_star()
+ # a = test_debu_powerlaw()
+ # ga = test_zealot_GA()
+ ga = test_biased_A_B(a_rel_budget=0.006)
diff --git a/general_GA_avoidance.py b/general_GA_avoidance.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b5c217a5f2a4d6553960308fe7de754ffba1467
--- /dev/null
+++ b/general_GA_avoidance.py
@@ -0,0 +1,2943 @@
+"""File for generating all experiments, results and plots for the paper:
+'Shadowing, shielding and degree dependence: A comparison between discrete and continuous influence maximisation'."""
+
+import warnings
+import pickle
+import json
+
+import numpy as np
+import networkx as nx
+from scipy.stats import entropy, kendalltau, spearmanr, pearsonr, linregress, gmean
+import pandas as pd
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import seaborn as sns
+# import matplotlib.lines as mlines
+# from sklearn.linear_model import LinearRegression
+
+from python.utils import DATA_PATH, PLOTS_PATH, create_range, std_errors
+from python.networks import Network
+from python.hpc_experiments import SUB_DATA_PATH, create_name, load_data, load_networks
+
+MULTIPLIER = 8
+ALLO_GROUP_LABELS = (r"$\mathbf{H} \; (w_{ai} \geq " + str(MULTIPLIER) + r"\langle w_a\rangle)$",
+ r"$\mathbf{A} \; (w_{ai} \geq \langle w_a\rangle)$",
+ r"$\mathbf{V} \; (w_{ai} < \langle w_a\rangle)$",
+ r"$\mathbf{Z} \; (w_{ai} = 0$)",)
+NEIGH_GROUP_LABELS = (r"$\mathbf{T}_b$", r"$\mathbf{N}_b$", r"$\mathbf{R}$")
+ALLO_GROUP_COLOURS = ("red", "orange", "yellow", "green")
+NEIGH_GROUP_COLOURS = plt.cm.get_cmap("viridis")(np.linspace(0, 0.95, 3))
+DISCRETE_COLOR = "purple"
+K_X_LABEL = r'$K$ (for a discrete controller)'
+
+FI = 1.618
+ELINEWIDTH = 1
+mpl.rcParams['text.usetex'] = True
+
+
+def get_allo_label(group, short=False):
+ labels = ((r"\mathbf{H}", r"(w_{ai} \geq " + str(MULTIPLIER) + r"\langle w_a\rangle)"),
+ (r"\mathbf{A}", r"(w_{ai} \geq \langle w_a\rangle)"),
+ (r"\mathbf{V}", r"(w_{ai} < \langle w_a\rangle)"),
+ (r"\mathbf{Z}", r"(w_{ai} = 0)"),)
+ if short:
+ return "$" + labels[group][0] + "$"
+ else:
+ return "$" + labels[group][0] + r" \; " + labels[group][1] + "$"
+
+
+def get_neigh_label(group, short=False):
+ labels = ((r"\mathbf{T}_b",),
+ (r"\mathbf{N}_b", r"(i \notin \mathbf{T}_b \;|\; (\exists j \in \mathbf{T}_b \;|\; w_{ij} > 0))"),
+ # \notin \mathbf{T}_b \;|\; (\exists j \in \mathbf{T}_b \;|\; w_{ij} > 0))"),
+ (r"\mathbf{R}",))
+ if short:
+ return "$" + labels[group][0] + "$"
+ else:
+ return "$" + labels[group][0] + r" \; " + labels[group][1] + "$"
+
+
+def compute_average_targeted_degree(allocations, degrees):
+ return sum([allocation * degrees[i] for i, allocation in enumerate(allocations)]) / sum(allocations)
+
+
+def flatten(list_of_lists):
+ return [item for sublist in list_of_lists for item in sublist]
+
+
+def compute_a_analytical_small_budget(seq, b_vec, a, b, verbose=False):
+ intercept = (a + b) / 2
+ slope = (a / b - 1) / 2
+ a_vec = intercept + slope * b_vec
+ # print("a_vec =", intercept, "+", slope, "b_vec")
+ if slope >= 0:
+ return a_vec
+ elif slope < 0:
+ if verbose:
+ print("Analytical first-order approximation is degree-dependent.")
+ while np.any(a_vec < 0):
+ broken_nodes = a_vec <= 0
+ # print(np.sum(a_vec > 0), b, np.sum(b_vec[a_vec > 0])/np.sum(a_vec > 0), np.sum(seq[a_vec > 0])/np.sum(a_vec > 0))
+ k_slope = (-(a + b) / 2 * np.sum(a_vec > 0) + a * len(b_vec) - slope * np.sum(b_vec[a_vec > 0])) / np.sum(
+ seq[a_vec > 0])
+ # print("k_intercept", k_intercept)
+ a_vec = intercept + slope * b_vec + seq * k_slope
+ a_vec[broken_nodes] = 0
+ assert np.isclose(a, np.mean(a_vec))
+ return a_vec
+
+
+def compute_a_analytical_big_budget(b_vec, a, b, verbose=False):
+ a_vec = (a + b) * np.sqrt(b_vec) / np.sum(np.sqrt(b_vec)) * len(b_vec) - b_vec
+ while np.any(a_vec < 0):
+ broken_nodes = a_vec <= 0
+ a_vec = (a + np.sum(b_vec[a_vec > 0]) / len(b_vec)) * np.sqrt(b_vec) / np.sum(np.sqrt(b_vec[a_vec > 0])) * len(
+ b_vec) - b_vec
+ a_vec[broken_nodes] = 0
+ assert np.isclose(a, np.mean(a_vec)), str(a) + " != " + str(np.mean(a_vec))
+ return a_vec
+
+
+def compute_distances(G, b, seq, a=None):
+ node_list = np.array(G.nodes)
+ N = len(node_list)
+ max_degree = int(np.max(seq))
+
+ unweighted_dists = []
+ unweighted_dists_plus_degrees = []
+ if a is not None:
+ a_mean = np.mean(a)
+ weighted_dists = []
+ weighted_dists_plus_repetitions = [[], []]
+ weighted_dists_plus_degrees = []
+
+ for node in range(N):
+ closest_distance = 9999
+ closest_b_node = None
+ distances = nx.single_source_shortest_path_length(G, node_list[node])
+ for b_node in range(N):
+ if b[b_node] > 0:
+ if distances[node_list[b_node]] < closest_distance:
+ closest_distance = distances[node_list[b_node]]
+ repetitions = 1
+ closest_b_node = b_node
+ if closest_distance == 0:
+ break
+ elif distances[node_list[b_node]] == closest_distance:
+ repetitions += 1
+
+ while len(unweighted_dists) < closest_distance + 1:
+ unweighted_dists.append(0)
+ unweighted_dists[closest_distance] += 1
+
+ while len(unweighted_dists_plus_degrees) < closest_distance + 1:
+ unweighted_dists_plus_degrees.append(np.zeros(max_degree + 1))
+ unweighted_dists_plus_degrees[closest_distance][int(seq[node])] += 1
+
+ if a is not None:
+ a_value = a[node]
+ if closest_distance > 1 and a_value > 10 * a_mean:
+ print("Outlier! Allocation {:f}, distance {:d},".format(a_value, closest_distance),
+ " degree {:.0f}, repetitions {:d}.".format(seq[node], repetitions))
+ while len(weighted_dists) < closest_distance + 1:
+ weighted_dists.append([])
+ weighted_dists[closest_distance].append(a_value)
+
+ if a_value > 0:
+ while len(weighted_dists_plus_degrees) < closest_distance + 2:
+ weighted_dists_plus_degrees.append(np.zeros(max_degree + 1))
+ weighted_dists_plus_degrees[closest_distance + 1][int(seq[node])] += 1
+ weighted_dists_plus_degrees[0][int(seq[closest_b_node])] += 1
+
+ while len(weighted_dists_plus_repetitions[0]) < closest_distance + 1:
+ weighted_dists_plus_repetitions[0].append([])
+ while len(weighted_dists_plus_repetitions[1]) < closest_distance + 1:
+ weighted_dists_plus_repetitions[1].append([])
+ if repetitions == 1:
+ weighted_dists_plus_repetitions[0][closest_distance].append(a_value)
+ else:
+ weighted_dists_plus_repetitions[1][closest_distance].append(a_value)
+
+ return_values = (unweighted_dists, unweighted_dists_plus_degrees)
+ if a is not None:
+ return_values += (weighted_dists, weighted_dists_plus_degrees, weighted_dists_plus_repetitions)
+ return return_values
+
+ # avg_distances[i] = 0
+ # for b_node in range(1, pars['N']):
+ # weighted_distance = 0
+ # if b[b_node - 1] > 0:
+ # distances = nx.single_source_shortest_path_length(net.G, b_node)
+ # for node in distances:
+ # weighted_distance += a[node - 1] * distances[node]
+ # weighted_distance /= np.sum(a)
+ # avg_distances[i] += b[b_node - 1] * weighted_distance
+ # avg_distances[i] /= np.sum(b)
+
+
+def divide_into_allocation_groups(allocations, n_groups=4, group_criterion="mean",
+ **pars): # criterion = (mean, std, N_allo, cutoff)
+ N = len(allocations)
+ if group_criterion == "mean":
+ a_mean = np.mean(allocations)
+ mask_H = allocations >= MULTIPLIER * a_mean
+ mask_A = np.logical_and(allocations >= a_mean, allocations < a_mean * MULTIPLIER)
+ if n_groups == 3:
+ mask_V = allocations < a_mean
+ masks = (mask_H, mask_A, mask_V)
+ elif n_groups == 4:
+ # mask_3 = np.logical_and(allocations < a_mean, allocations > a_mean / MULTIPLIER)
+ # mask_4 = allocations < a_mean / MULTIPLIER
+ mask_V = np.logical_and(allocations < a_mean, allocations > 0)
+ mask_Z = allocations == 0
+ masks = (mask_H, mask_A, mask_V, mask_Z)
+ else:
+ raise Exception("n_groups not understood: " + str(n_groups))
+
+ elif group_criterion == "std":
+ a_mean = np.mean(allocations)
+ a_std = np.std(allocations)
+
+ mask_H = allocations >= a_mean + MULTIPLIER * a_std
+ mask_A = np.logical_and(allocations >= a_mean, allocations < a_mean + a_std * MULTIPLIER)
+ if n_groups == 3:
+ mask_V = allocations < a_mean
+ masks = (mask_H, mask_A, mask_V)
+ elif n_groups == 4:
+ mask_V = np.logical_and(allocations < a_mean, allocations >= a_mean - a_std * MULTIPLIER)
+ mask_Z = allocations < a_mean - a_std * MULTIPLIER
+ masks = (mask_H, mask_A, mask_V, mask_Z)
+ else:
+ raise Exception("n_groups not understood: " + str(n_groups))
+
+ elif group_criterion == "cutoff":
+ a_mean = gmean(allocations[allocations > 0])
+ # a_mean = np.e**np.mean(np.log(allocations[allocations>0]))
+
+ sorted_indices = np.argsort(allocations)[::-1]
+ sorted_allocations = allocations[sorted_indices]
+ # relative_diff = 1 - allocations[1:] / allocations[:-1]
+ for i in range(N - 5):
+ if 1 - sorted_allocations[i + 10] / sorted_allocations[i] < 0.1:
+ break
+ for j in range(N - 5):
+ if 1 - sorted_allocations[j + 10] / sorted_allocations[j] < 0.01:
+ break
+ # mask_1 = np.logical_and(np.arange(N)[np.argsort(sorted_indices)] < i, allocations >= a_mean)
+ mask_H = np.arange(N)[np.argsort(sorted_indices)] < i
+ # mask_1 = np.logical_and(np.arange(N) < i, allocations >= a_mean)
+ mask_A = np.logical_and(np.arange(N)[np.argsort(sorted_indices)] >= i,
+ np.arange(N)[np.argsort(sorted_indices)] < j)
+ if n_groups == 3:
+ # mask_3 = allocations < a_mean
+ mask_V = np.arange(N)[np.argsort(sorted_indices)] >= j
+ masks = (mask_H, mask_A, mask_V)
+ elif n_groups == 4:
+ # mask_3 = np.logical_and(allocations < a_mean, allocations > 0)
+ mask_V = np.logical_and(np.arange(N)[np.argsort(sorted_indices)] >= j, allocations > 0)
+ mask_Z = allocations == 0
+ masks = (mask_H, mask_A, mask_V, mask_Z)
+ else:
+ raise Exception("n_groups not understood: " + str(n_groups))
+
+ else:
+ raise Exception("criterion not understood: " + str(group_criterion))
+
+ group_sizes = [np.sum(mask) for mask in masks]
+ # print("group sizes", group_sizes)
+ assert np.sum(group_sizes) == len(allocations), "{:d} != {:d}".format(np.sum(group_sizes), len(
+ allocations)) + "\ngroups 1-2: {:s}" + str(
+ np.arange(N)[np.logical_and(mask_H, mask_A)]) + "\ngroups 2-3: {:s}" + str(
+ np.arange(N)[np.logical_and(mask_A, mask_V)]) + "\ngroups 3-4: {:s}" + str(
+ np.arange(N)[np.logical_and(mask_V, mask_Z)])
+ return masks
+
+ # a_cumsum = np.cumsum(a_agg_mean)
+ # indices_1 = a_cumsum < pars['N'] * a_agg_mean_mean * 0.1
+ # print(np.sum(indices_1))
+ # indices_1 = np.arange(int(pars['N'] * 0.03)) # pars['N_allocations'])
+ # indices_2 = np.logical_and(pars['N'] * a_agg_mean_mean * 0.1 < a_cumsum, a_cumsum < pars['N'] * a_agg_mean_mean * 0.5)
+ # indices_2 = np.arange(int(pars['N'] * 0.03), int(pars['N'] * 0.3)) # pars['N_allocations'], int(pars['N'] / 2))
+ # indices_3 = a_cumsum > pars['N'] * a_agg_mean_mean * 0.5
+ # indices_3 = np.arange(int(pars['N'] * 0.3), pars['N']) # int(pars['N'] / 2), pars['N'])
+
+
+def divide_into_neighbouring_groups(allocations, W, n_groups=3):
+ if type(W) != np.ndarray:
+ W = W.toarray()
+ mask_b = allocations > 0
+ n_neighbours = np.squeeze(np.sum(W[mask_b], axis=0))
+ if n_groups == 3:
+ mask_Nb = np.logical_and(n_neighbours > 0, np.logical_not(mask_b))
+ mask_R = np.logical_not(mask_b + mask_Nb)
+ masks = (mask_b, mask_Nb, mask_R)
+ elif n_groups == 4:
+ mask_NNb = np.logical_and(n_neighbours > 1, np.logical_not(mask_b))
+ mask_Nb = np.logical_and(n_neighbours == 1, np.logical_not(mask_b))
+ # mask_Nb = np.logical_and(np.logical_and(n_neighbours > 0, n_neighbours <= 2), np.logical_not(mask_b))
+ mask_R = np.logical_not(mask_b + mask_NNb + mask_Nb)
+ masks = (mask_b, mask_NNb, mask_Nb, mask_R)
+ else:
+ raise Exception("n_groups not understood: " + str(n_groups))
+
+ group_sizes = [np.sum(mask) for mask in masks]
+ # print("group sizes", group_sizes)
+ assert np.sum(group_sizes) == len(allocations), "{:d} != {:d}".format(np.sum(group_sizes), len(allocations))
+ return masks
+
+
+# PRINT FUNCTIONS
+def print_topology(ax, pars, print_variable="allocations"):
+ nets = load_networks(**pars)
+
+ degree_stats = {"max": [], "mean": [], "assor": []}
+ for net in nets:
+ print("degree seq", np.unique(net.seq, return_counts=True))
+ degree_stats["max"].append(max(net.seq))
+ degree_stats["mean"].append(np.mean(net.seq))
+ degree_stats["assor"].append(nx.degree_assortativity_coefficient(net.G))
+
+ for key, val in degree_stats.items():
+ print("mean {:s}: {:f} +- {:f}".format(key, np.mean(val), np.std(val) / np.sqrt(len(val))))
+
+
+def print_a_disc_vs_deg(ax, pars, print_variable="degrees"): # ("degrees", "nodes")
+ aes, bs, seqs, Xs, xs, convs, aes_disc, Xs_disc, xs_disc, networks = load_data(pars)
+
+ if print_variable == "degrees":
+ for l in range(pars['n_nets']):
+ gain = networks[l].mean_a / np.sum(aes_disc[l] > 0)
+ if False:
+ def get_props_vs_k(seq, a):
+ seq_vals, idx, counts = np.unique(seq, return_inverse=True, return_counts=True)
+ sum_aes = np.bincount(idx, weights=a)
+ return seq_vals, sum_aes / counts
+ # print(seq_vals) : ['User0' 'User1' 'User2' 'User3' 'User4']
+ # print(mean_aes) : [ 0.45 0.55 0.55 0.435 0.81666667]
+ # ax2.bar(seq_vals, np.ones_like(seq_vals) * pars['gain'], color="C4", alpha=0.2)
+
+ seq, a = get_props_vs_k(seqs[l][bs[l] > 0], aes_disc[l][bs[l] > 0])
+ ax.bar(range(len(a)), a / gain / pars['N'], color="purple",
+ # ax.plot(seq, a / gain / pars['N'], color="purple",
+ label=r"$b_i\neq0$") # , ls='', marker="o", markersize=2) # alpha=0.3)
+ print("Average targeted degrees: (a_k:b_k>0)", compute_average_targeted_degree(a, seq))
+
+ seq, a = get_props_vs_k(seqs[l][bs[l] == 0], aes_disc[l][bs[l] == 0])
+ ax.bar(range(len(a)), a / gain / pars['N'], color="C2",
+ # ax.plot(seq, a / gain / pars['N'], color="C2",
+ label=r"$b_i=0$") # , ls='', marker="o", markersize=2) # alpha=0.3)
+ print("Average targeted degrees: (a_k:b_k=0)", compute_average_targeted_degree(a, seq))
+ ax.set(ylim=[0, 1])
+ ax.margins(x=0.0)
+ else:
+ seq_vals, idx, counts = np.unique(seqs[l], return_inverse=True, return_counts=True)
+ sum_aes = np.bincount(idx, weights=aes_disc[l])
+ ax.bar(seq_vals, counts, color="grey")
+ ax.bar(seq_vals, sum_aes / gain / pars['N'], color="green")
+ ax.margins(x=0.0)
+ ax.set_yscale("log")
+ elif print_variable == "nodes":
+ if pars['network'] != "email":
+ raise Exception("Discrete allocations only implemented for email network")
+ if False:
+ for l in range(len(bs)):
+ seq = seqs[l][aes_disc[l] > 0]
+ b = bs[l][aes_disc[l] > 0]
+ seq_with = np.copy(seq)
+ seq_with[b == 0] = 0
+ seq_without = np.copy(seq)
+ seq_without[b > 0] = 0
+ gain = nets[l].mean_b / pars['N_allocations']
+ step = pars['N'] / pars['N_allocations']
+ ax.bar(np.arange(0, pars['N'], step), seq_with, color="C2", label=r"$b_i={:.1e}$".format(gain),
+ width=step)
+ ax.bar(np.arange(0, pars['N'], step), seq_without, color="C3", label=r"$b_i=0$", width=step)
+ ax.plot(seqs[l])
+ # seq_with = seq[b<0]
+ # seq_without = seq[b==0]
+ # ax.bar(range(len(seq_with)), seq_with, color="C2", label=r"$a_k:b_k>0$") # alpha=0.3))
+ # ax.bar(range(len(seq_with), len(seq_with) + len(seq_without)), seq_without, color="C3",
+ # label=r"$a_k:b_k=0$") # alpha=0.3)
+ n_ticks = 10
+ tick_step = step * pars['N_allocations'] / n_ticks
+ ax.xaxis.set(
+ ticks=[]) # ticks=np.arange(0, pars['N'], tick_step), ticklabels=np.arange(0, pars['N_allocations'], int(pars['N_allocations']/n_ticks)))
+ else:
+ seqs_with = np.zeros(pars['N'])
+ seqs_without = np.zeros(pars['N'])
+ for l in range(len(bs)):
+ # seq = np.copy(seqs[l])
+ # seq[aes_disc[l] == 0] = 0
+ # seq_with = np.copy(seq)
+ # seq_without = np.copy(seq)
+ # seq_with[bs[l] == 0] = 0
+ # seq_without[bs[l] > 0] = 0
+
+ # seq_with = np.zeros(pars['N'])
+ # seq_with[aes_disc[l]]
+ seqs_with[np.logical_and(aes_disc[l] > 0, bs[l] > 0)] += 1
+ seqs_without[np.logical_and(aes_disc[l] > 0, bs[l] == 0)] += 1
+ print(np.argmax(bs[l]), np.argmax(aes_disc[l]))
+ # seq_without = np.zeros(pars['N'])
+
+ # ax.bar(np.arange(pars['N']), seq_with, color="purple", label=r"$b_i\neq0$") # , width=step)
+ # ax.bar(np.arange(pars['N']), seq_without, color="C1", label=r"$b_i=0$") # , width=step)
+ alphas = seqs_with + seqs_without
+ colours = 1 - seqs_with / alphas
+ alphas /= np.max(alphas)
+ # norm = mpl.colors.Normalize(vmin=0, vmax=k_max)
+ # colors = [plt.cm.cool(el) for el in colours]
+ for i in range(pars['N']):
+ # ax.bar(i, seqs[l][i], color=plt.cm.winter(colours[i]), alpha=alphas[i])
+ ax.bar(i, seqs[l][i], alpha=alphas[i])
+ ax.plot(seqs[l], linewidth=0.4)
+
+ # for i, k in enumerate(seq):
+ # if k != 0:
+ # x_min = i
+ # break
+ # for i, k in enumerate(seq[::-1]):
+ # if k != 0:
+ # x_max = pars['N'] - 1 - i
+ # break
+ # ax.set(xlim=[x_min-1, x_max+1])
+ # ax.xaxis.set(ticks=[])
+ # ax.margins(x=0)
+ else:
+ raise Exception("print variable " + print_variable + " not understood.")
+
+ # ax.set(xlabel='Node degree, k', title="")
+ ax.set(xlabel='Nodes targeted by the A-controller', title="")
+
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ if pars['axespar'] is not None:
+ if pars[pars['axespar']] == pars['axesarray'][0]:
+ ax.set(ylabel='Node degree, k')
+
+ if pars['multiaxespar'] is not None:
+ if pars[pars['multiaxespar']] == pars['multiaxesarray'][0]:
+ ax.set(title=r"$a= {:s}\, b$".format(str(pars['bmul'])))
+ # ax.legend()
+ # pass
+ # ax.grid()
+ # plt.show()
+
+
+def print_a_vs_deg(ax, pars, print_variable="allocations"): # (allocations, x, )
+ ls = ("--", "-.", "solid", (0, (3, 5, 1, 5, 1, 5)), ":")
+ lw = 0.7
+
+ aes, bs, seqs, Xs, xs, convs, aes_disc, Xs_disc, xs_disc, nets = load_data(pars)
+
+ n_nets = len(nets)
+ n_deg = int(np.max(seqs)) + 1
+ if print_variable == "groups":
+ n_groups = 4
+ boxes = np.zeros((n_nets, n_deg, n_groups))
+ for l, net in enumerate(nets):
+ if convs[l]:
+ masks = divide_into_allocation_groups(aes[l], n_groups, **pars)
+ for i in range(net.N):
+ for k, mask in enumerate(masks):
+ if mask[i]:
+ boxes[l, int(seqs[l][i]), k] += 1
+ else:
+ print(l, "NOT CONVERGED")
+
+ boxes0 = np.sum(boxes, axis=0)
+ tots = np.sum(boxes0, axis=1)
+ boxes = boxes0 / tots[:, None]
+
+ for k in range(n_groups):
+ k = n_groups - k - 1
+
+ errors = np.zeros(n_deg)
+ for j in range(n_deg):
+ if not np.isnan(boxes0[j, k]):
+ count_j = int(boxes0[j, k])
+ errors[j] = np.std([1] * count_j + [0] * (int(tots[j]) - count_j)) / np.sqrt(tots[j])
+
+ # boxes[:, k] + np.sum(boxes[:, k + 1:]
+ ax.bar(range(n_deg), boxes[:, k] + np.sum(boxes[:, :k], axis=1), color=ALLO_GROUP_COLOURS[k],
+ label=get_allo_label(k), yerr=errors, error_kw={"elinewidth": 0.5})
+
+ ax.set(ylabel=r"$P(\mathbf{G} \;|\; d_i)$", xlim=[0.5, n_deg + 0.5], ylim=[0, 1])
+ ax.legend(loc="upper left")
+ if pars['network'] == "conf":
+ ax.set_xscale("log")
+
+ elif print_variable == "boxplot":
+ boxes = [[] for _ in range(int(np.max(seqs[0])) + 1)]
+ for l in range(pars['n_nets']):
+ for i in range(pars['N']):
+ boxes[int(seqs[l][i])].append(aes[l][i])
+
+ # labels = np.array([str(i) for i in np.arange(len(boxes))])
+ # labels[::2] = ""
+ # ax.boxplot(boxes, sym=".", labels=labels)
+
+ labels = np.array([str(i) for i, el in enumerate(boxes) if len(el) > 1])
+ labels[:40:2] = ""
+ # ax.boxplot([el for el in boxes if len(el) > 1], labels=labels, flierprops=dict(marker='.', markersize=1),
+ # positions=[i for i, el in enumerate(boxes) if len(el) > 1])
+
+ # ax.violinplot([el for el in boxes if len(el)>1])
+ ax.errorbar(range(len(boxes)), [np.mean(box) if box else np.nan for box in boxes], markersize=3,
+ yerr=[np.std(box) / np.sqrt(len(box)) if box else np.nan for box in boxes], ls="", marker="o")
+ # ax.errorbar(range(len(boxes)), [np.median(box) if box else 0 for box in boxes],
+ # yerr=[[np.quantile(box, quant) if box else 0 for box in boxes] for quant in (0.25, 0.75)])
+ # ax.plot(range(len(boxes)), [np.median(box) if box else None for box in boxes])
+ # ax.plot(range(len(boxes)), [np.quantile(box, 0.25) if box else None for box in boxes])
+ # ax.plot(range(len(boxes)), [np.quantile(box, 0.75) if box else None for box in boxes])
+
+ ax.set(ylim=-0.001, ylabel=r"Allocation strength, $w_{ai}$")
+
+ elif print_variable == "correlations":
+ n_groups = 4
+ boxes = np.zeros((n_nets, n_deg, n_groups))
+ for l, net in enumerate(nets):
+ if convs[l]:
+ masks = divide_into_allocation_groups(aes[l], n_groups, **pars)
+ for i in range(net.N):
+ for k, mask in enumerate(masks):
+ if mask[i]:
+ boxes[l, int(seqs[l][i]), k] += 1
+ else:
+ print(l, "NOT CONVERGED")
+
+ boxes1 = boxes / np.sum(boxes, axis=2)[..., None] # For Kendall Tau
+
+ # correlation coefficients
+ print("\n\nCORRELATIONS OF PROBABILITIES")
+ mask = np.logical_not(np.isnan(boxes[:, 0]))
+ size = len(boxes[:, 0][mask])
+ for k in range(n_groups):
+ print("")
+ print(get_allo_label(k))
+ print(kendalltau(boxes[:, k][mask], range(size), nan_policy="omit"))
+ taus = np.array([kendalltau(box[:, k][mask], range(size), nan_policy="omit") for box in boxes1])
+ print(taus)
+ print("Kendall taus:", np.mean(taus, axis=0), np.std(taus, axis=0))
+ print("Weighted taus:", np.sum(taus[:, 0] * np.log(taus[:, 1])) / np.sum(np.log(taus[:, 1])))
+ print(spearmanr(boxes[:, k][mask], range(size), nan_policy="omit"))
+ # print("pearson", pearsonr(boxes[:, k][mask], np.unique(seqs[0])))
+ # print(boxes[:, k][mask], np.unique(seqs[0]))
+ print("linregress", linregress(np.unique(seqs), boxes[:, k][mask]))
+
+ # With error-bars
+ # boxes /= np.sum(boxes, axis=2)[:, :, None]
+ # boxes_mean = np.mean(boxes, axis=0)
+ # boxes_std = np.std(boxes, axis=0)
+ # ax.bar(range(len(boxes_mean)), boxes_mean[:, 0], yerr=boxes_std[:, 0])
+ # ax.bar(range(len(boxes_mean)), boxes_mean[:, 1], yerr=boxes_std[:, 1], bottom=boxes_mean[:, 0])
+ # ax.bar(ind, boxes_mean[:, 2], yerr=boxes_std[:, 2], bottom=boxes_mean[:, 0]+boxes_mean[:, 1])
+
+ print("\nINTRA-GROUP CORRELATIONS")
+ boxes = [[] for _ in range(n_groups + 1)]
+ for l, net in enumerate(nets):
+ if convs[l]:
+ a_mean = np.mean(aes[l])
+ # indices = np.argsort(aes[l])[::-1]
+ for i in range(net.N):
+ if aes[l, i] > MULTIPLIER * a_mean:
+ boxes[0].append((int(seqs[l][i]), aes[l, i]))
+ elif a_mean < aes[l, i] < MULTIPLIER * a_mean:
+ boxes[1].append((int(seqs[l][i]), aes[l, i]))
+ elif 0 < aes[l, i] < a_mean:
+ boxes[2].append((int(seqs[l][i]), aes[l, i]))
+ else:
+ boxes[3].append((int(seqs[l][i]), aes[l, i]))
+ boxes[4].append((int(seqs[l][i]), aes_disc[l, i]))
+ else:
+ print(l, "NOT CONVERGED")
+
+ all_points = np.array(flatten([box for box in boxes[:-1]]))
+ boxes.append(all_points)
+ boxes.append([(k, np.mean(all_points[:, 1][all_points[:, 0] == k])) for k in np.unique(all_points[:, 0])])
+ legend = [get_allo_label(k) for k in range(n_groups)] + ["DISC", "ALL", "ALL_K"]
+ for i in range(len(boxes)):
+ box = np.array(boxes[i])
+ print("")
+ print(legend[i])
+ print(kendalltau(box[:, 0], box[:, 1]))
+ print(spearmanr(box[:, 0], box[:, 1]))
+ print("pearson", pearsonr(box[:, 0], box[:, 1]))
+
+ elif print_variable == "discrete":
+ n_deg = int(np.max(seqs)) + 1
+ boxes = np.zeros((n_nets, n_deg, 4))
+ if True:
+ for l, net in enumerate(nets):
+ masks = divide_into_allocation_groups(aes[l], **pars)
+ for i in range(net.N):
+ for k, mask in enumerate(masks[:2]):
+ if mask[i]:
+ boxes[l, int(seqs[l][i]), k] += 1
+ if aes_disc[l, i] > 0:
+ boxes[l, int(seqs[l][i]), 3] += 1
+
+ masks = divide_into_neighbouring_groups(net.b[:, 0], net.W)
+ for degree in nets[l].seq[masks[1]]:
+ boxes[l, degree, 2] += 1
+
+ boxes = np.sum(boxes, axis=0)
+ # boxes /= boxes[..., 0][:, None]
+ # boxes /= np.sum(boxes, axis=1)[:, None]
+ boxes /= np.sum(boxes, axis=0)[None, :]
+
+ ind = np.arange(n_deg)
+ # width = 0.45
+ # ax.bar(ind + width / 2, boxes[:, 1], width, color="red", label=r"Continuous IM, "+ALLO_GROUP_LABELS[0])
+ # ax.bar(ind - width / 2, boxes[:, 3], width, color=DISCRETE_COLOR, label=r"Discrete IM, $w_{ai}=g$")
+
+ ax.plot(ind, boxes[:, 0], color=ALLO_GROUP_COLOURS[0],
+ label=r"Continuous IM, " + get_allo_label(0, short=True), ls=ls[0], linewidth=lw)
+ ax.plot(ind, boxes[:, 1], color=ALLO_GROUP_COLOURS[1],
+ label=r"Continuous IM, " + get_allo_label(1, short=True), ls=ls[2], linewidth=lw)
+ ax.plot(ind, boxes[:, 3], color=DISCRETE_COLOR, label=r"Discrete IM, $\mathbf{T}_a$", ls=ls[1],
+ linewidth=lw)
+ ax.plot(ind, boxes[:, 2], color="b", label=r"Neighbours of $\mathbf{T}_b$ ($\mathbf{N}_b$)", ls=ls[-1],
+ linewidth=lw)
+
+ ax.set(ylim=0)
+
+ for i in (1, 3):
+ print("group", i, np.sum([d * p for d, p in enumerate(boxes[:, i])]))
+
+ else: # For including the three groups at once in this plot
+ for l in range(pars['n_nets']):
+ a_mean = np.mean(aes[l])
+ # indices = np.argsort(aes[l])[::-1]
+ for i in range(N):
+ if aes[l, i] < a_mean:
+ # if i in indices[:int(N*0.03)]: # pars['N_allocations']]:
+ boxes[l, int(seqs[l][i]), 2] += 1
+ elif aes[l, i] < 3 * a_mean:
+ # elif i in indices[int(N*0.03):int(N*0.3)]: # pars['N_allocations']:int(N/2)]:
+ boxes[l, int(seqs[l][i]), 1] += 1
+ else:
+ boxes[l, int(seqs[l][i]), 0] += 1
+ if aes_disc[l, i] > 0:
+ boxes[l, int(seqs[l][i]), 3] += 1
+ # if i in indices[:pars['N_allocations']]:
+ # boxes[l, int(seqs[l][i]), 1] += 1
+
+ boxes = np.sum(boxes, axis=0)
+ # boxes /= np.sum(boxes, axis=1)[:, None]
+ # boxes /= np.sum(boxes, axis=0)[None, :]
+ boxes[:, :3] /= np.sum(boxes[:, :3])
+ boxes[:, 3] /= np.sum(boxes[:, 3])
+ width = 0.45
+ ind = np.arange(n_deg)
+ ax.bar(ind - width / 2, boxes[..., 0], width, color="red", label=r"Continuous IM, $w_{ai}>3 \bar{w_a}$")
+ ax.bar(ind - width / 2, boxes[..., 1], width, bottom=boxes[:, 0], color="orange",
+ label=r"Continuous IM, $w_{ai}> \bar{w_a}$")
+ ax.bar(ind - width / 2, boxes[..., 2], width, bottom=boxes[:, 0] + boxes[:, 1], color="yellow",
+ label=r"Continuous IM, $w_{ai}< \bar{w_a}$")
+ ax.bar(ind + width / 2, boxes[..., 3], width, color="green", label=r"Discrete IM, $w_{ai}>0$")
+
+ ax.set(ylabel=r"$P(d_i)$", xlim=[0.5, n_deg + 0.5])
+ ax.legend()
+
+ # boxes /= np.sum(boxes, axis=2)[:, :, None]
+ # boxes_mean = np.mean(boxes, axis=0)
+ # boxes_std = np.std(boxes, axis=0)
+ # ax.bar(range(len(boxes_mean)), boxes_mean[:, 0], yerr=boxes_std[:, 0])
+ # ax.bar(range(len(boxes_mean)), boxes_mean[:, 1], yerr=boxes_std[:, 1], bottom=boxes_mean[:, 0])
+ # ax.bar(range(len(boxes_mean)), boxes_mean[:, 2], yerr=boxes_std[:, 2], bottom=boxes_mean[:, 0]+boxes_mean[:, 1])
+
+ elif print_variable == "discrete_degree":
+ n_deg = int(np.max(seqs)) + 1
+ boxes = np.zeros((pars['n_nets'], n_deg, 4))
+ for l, net in enumerate(nets):
+ masks = np.argsort(aes[l])[::-1]
+ for i in range(net.N):
+ boxes[l, int(seqs[l][i]), 0] += 1
+ if aes_disc[l, i] > 0:
+ boxes[l, int(seqs[l][i]), 3] += 1
+ if i in masks[:pars['N_allocations']]:
+ boxes[l, int(seqs[l][i]), 1] += 1
+
+ boxes = np.sum(boxes, axis=0)
+ boxes /= boxes[..., 0][:, None]
+ # boxes /= np.sum(boxes, axis=1)[:, None]
+ # boxes /= np.sum(boxes, axis=0)[None, :]
+ ind = np.arange(n_deg)
+ ax.bar(ind, boxes[..., 0], color="lightgrey")
+ ax.bar(ind, boxes[..., 3], color=DISCRETE_COLOR, label=r"Discrete IM, $w_{ai}>0$")
+ ax.set(ylabel=r"$P(w_{ai}=g \;|\; d_i)$", xlim=[0.5, n_deg + 0.5], ylim=[0, 1])
+ # ax.legend()
+
+ elif print_variable == "shielding":
+ boxes = np.zeros((n_nets, n_deg, 4))
+ for l, net in enumerate(nets):
+ seq = net.seq
+ neigh_masks = divide_into_neighbouring_groups(net.b[:, 0], net.W)
+ allo_masks = divide_into_allocation_groups(net.a)
+ for i in range(net.N):
+ boxes[l, int(seqs[l][i]), 0] += 1
+ if neigh_masks[1][i]:
+ boxes[l, int(seq[i]), 1] += 1
+ if allo_masks[1][i]:
+ boxes[l, int(seq[i]), 3] += 1
+ # if aes_disc[l, i] > 0:
+ # boxes[l, int(seqs[l][i]), 2] += 1
+
+ boxes0 = np.sum(boxes, axis=0)
+ boxes = boxes0 / boxes0[..., 0][:, None]
+ # boxes = np.nan_to_num(boxes)
+ ind = np.arange(n_deg)[np.logical_not(np.isnan(boxes[:, 0]))]
+ # ax.bar(range(n_deg), np.sum(boxes, axis=1), color="lightgrey")
+ # ax.plot(ind, boxes[:, 1][ind], color="blue", label=get_neigh_label(1), linewidth=linewidth, ls=ls[0])
+
+ errors = np.zeros((n_deg, 2))
+ for j in range(n_deg):
+ if not np.isnan(boxes0[j, 0]) and not np.isnan(boxes0[j, 1]):
+ errors[j, 0] = np.std(
+ [1] * int(boxes0[j, 1]) + [0] * (int(boxes0[j, 0]) - int(boxes0[j, 1]))) / np.sqrt(boxes0[j, 0])
+ errors[j, 1] = np.std(
+ [1] * int(boxes0[j, 3]) + [0] * (int(boxes0[j, 0]) - int(boxes0[j, 3]))) / np.sqrt(boxes0[j, 0])
+
+ style_dic = {"linewidth": lw, "elinewidth": 0.01, "capsize": 0.01} # , "ecolor": "grey"}
+ ax.errorbar(ind, boxes[:, 1][ind], yerr=errors[:, 0][ind], color=NEIGH_GROUP_COLOURS[1],
+ label=get_neigh_label(1, short=True), ls=ls[0], **style_dic)
+ # ax.plot(ind, boxes[:, 2][ind], color=DISCRETE_COLOR, label=r"$\mathbf{T}_a$", linewidth=lw, ls=ls[1])
+ # ax.plot(ind, boxes[:, 3][ind], color=ALLO_GROUP_COLOURS[1], label=get_allo_label(1), linewidth=lw, ls=ls[2])
+ ax.errorbar(ind, boxes[:, 3][ind], yerr=errors[:, 1][ind], color=ALLO_GROUP_COLOURS[1],
+ label=get_allo_label(1, short=True), ls=ls[2], **style_dic)
+ # ax.fill_between(ind, boxes[:,1][ind], color="blue", alpha=0.5)
+ # ax.fill_between(ind, boxes[:,2][ind], color=DISCRETE_COLOR, alpha=0.5)
+
+ ax.set(ylabel=r"$P(\; \cdot \;|\; d_i)$", xlim=[0.5, n_deg + 0.5], ylim=[0, 1])
+ if pars["network"] == "ba":
+ # ax.set_xscale("log")
+ ax.set(xlim=[5, 100])
+ # ax.set(ylabel=r"$P(" + NEIGH_GROUP_LABELS[1][1:-1] + " \;|\; d_i)$", xlim=[0.5, n_deg + 0.5], ylim=[0, 1])
+ ax.legend(loc="upper right", ncol=2)
+
+ elif print_variable == "allocations":
+ # FITTING
+ alphas = np.zeros((pars['n_nets'], 2))
+ for l in range(pars['n_nets']):
+ if all((print_variable == "allocations", aes is not None, aes_disc is not None)):
+ print("Average targeted degrees: (A)", compute_average_targeted_degree(aes[l], seqs[l]),
+ # "(A_disc)", compute_average_targeted_degree(aes_disc[l], seqs[l]),
+ "(B)", compute_average_targeted_degree(bs[l], seqs[l]))
+ if pars['b_allocation'] == "linear":
+ fitting_a = {}
+ for i, k in enumerate(seqs[l]):
+ if aes[l, i] >= 0:
+ if int(k) not in fitting_a:
+ fitting_a[int(k)] = [aes[l, i]]
+ else:
+ fitting_a[int(k)].append(aes[l, i])
+
+ sort_k = np.sort(list(fitting_a.keys()))
+ fitting_a = [np.mean(fitting_a[k]) for k in sort_k] # if k < len(fitting_a)]
+ alphas[l] = np.polyfit(sort_k, fitting_a, 1)
+ elif pars['b_allocation'] == "powerlaw":
+ # reg = LinearRegression().fit(np.log(seqs[l]).reshape(-1, 1), np.log(aes[l]))
+ # alphas[l] = (reg.coef_, reg.intercept_)
+ # print(reg.score(np.log(seqs[l]), np.log(aes[l])))
+ alphas[l] = np.polyfit(np.log(seqs[l]), np.log(aes[l]), 1)
+
+ for l in range(pars['n_nets']):
+ if aes_disc is not None:
+ seq_vals, idx, counts = np.unique(seqs[l], return_inverse=True, return_counts=True)
+ sum_aes = np.bincount(idx, weights=aes_disc[l])
+ mean_aes = sum_aes / counts
+ # print(seq_vals) : ['User0' 'User1' 'User2' 'User3' 'User4']
+ # print(mean_aes) : [ 0.45 0.55 0.55 0.435 0.81666667]
+ # ax2.bar(seq_vals, np.ones_like(seq_vals) * pars['gain'], color="C4", alpha=0.2)
+ if pars['b_allocation'] == "discrete":
+ ax.plot(seq_vals, mean_aes, color="C2", label=r"$a_{disc}$", ls='', marker="o",
+ markersize=2) # alpha=0.3)
+ else:
+ ax.plot(seq_vals, mean_aes, color="C2", label=r"$a_{disc}$", ls='', marker="o",
+ markersize=2) # alpha=0.3)
+ if convs is not None and aes is not None:
+ if convs[l]:
+ if pars['b_allocation'] == "powerlaw":
+ # ax.loglog([1, np.max(seqs[l])],
+ # [np.e ** alphas[l, 1], np.e ** alphas[l, 1] * seqs[l, -1] ** alphas[l, 0]],
+ # color="C1", ls="solid", marker="", linewidth=0.4)
+ ax.plot(seqs[l], aes[l], color="C1", ls="", marker="o", markersize=2, label=r"$a_i$")
+ # ax.loglog(seqs[l], bs[l], color="C0", ls="", marker="o", markersize=2, label=r"$b_i$")
+ # ax.loglog(seqs[l], bs[l] / pars['bmul'], color="C0", ls="", marker="o", markersize=2)
+ else:
+ if pars['b_allocation'] == "linear":
+ # ax.plot([0, seqs[l, i_star - 1]], [alphas[l, 1], alphas[l, 1] + seqs[l, i_star - 1] * alphas[l, 0]],
+ # color="C1", ls="solid", marker="", linewidth=0.4)
+ ax.plot([0, sort_k[-1]], [alphas[l, 1], alphas[l, 1] + sort_k[-1] * alphas[l, 0]],
+ color="C1", ls="solid", marker="", linewidth=0.4)
+ elif pars['b_allocation'] in ("sin", "Amajor"):
+ if nets[l].mean_a + nets[l].mean_b > np.mean(seqs[l]):
+ a_vec = compute_a_analytical_big_budget(bs[l], a=nets.mean_a, b=nets.mean_b)
+ else:
+ a_vec = compute_a_analytical_small_budget(seqs[l], bs[l], a=nets.mean_a, b=nets.mean_b)
+ ax.plot(seqs[l], a_vec, color="C1", ls="solid", marker="", linewidth=0.4)
+ ax.plot(seqs[l], bs[l], color="C0", ls="solid", marker="", linewidth=0.4)
+ ax.plot(seqs[l], aes[l], color="C1", ls="", marker="o", markersize=0.5, label=r"$a_i$")
+
+ # ax.plot(seqs[l], bs[l] / pars['bmul'], color="C0", ls="", marker="o", markersize=0.5, label=r"$b_i \cdot r$")
+ if pars['b_allocation'] != "discrete":
+ ax.plot(seqs[l], bs[l], color="C0", ls="", marker="o", markersize=0.5, label=r"$b_i$")
+
+ elif print_variable == "x":
+ for l, net in enumerate(nets):
+ ax.plot(seqs[l], xs[l], ls="", marker="o", markersize=2)
+ else:
+ raise Exception("Print variable '" + print_variable + "' not understood.")
+
+ title = ""
+ if pars['b_allocation'] == 'powerlaw' and aes is not None:
+ title = r"$a_k \propto k^{" + "{:.3f}".format(np.mean(alphas[:, 0])) + r"}, b_k \propto k^{" + "{:.3f}".format(
+ pars['beta']) + r"}$" # + ", r = {:.1f}".format(1 / pars['bmul'])
+ elif pars['b_allocation'] == "linear":
+ title = r"$\beta\!= {:.3f}$".format(pars['beta'])
+ if print_variable == "allocations":
+ # title += r", $\alpha\!= " + "{:.3f}$".format(np.mean(alphas[:, 0]))
+ # corr = np.corrcoef(aes[l], bs[l])
+ # print(corr)
+ # title += r", $R_{ab}\!= $" + " {:.2f}".format(corr[0, 1])
+ # if Xs is not None:
+ # title += ", X = {:.3f} ".format(np.mean(Xs)) # + r"$\pm$" + "{:.3f}".format(np.std(Xs))
+ # if "add_discrete" in pars:
+ # if pars['add_discrete']:
+ # title += r", $X_{disc}$" + " = {:.3f} ".format(
+ # np.mean(Xs_disc)) # + r"$\pm$" + "{:.3f}".format(np.std(Xs))
+ ax.set(ylabel="Per-node allocations")
+ elif print_variable == "x":
+ ax.set(ylabel=r"Per-node vote share, $x_i$")
+ if Xs is not None:
+ title += r", $X_{GA}\!=$" + "{:.4f} ".format(np.mean(Xs)) # + r"$\pm$" + "{:.3f}".format(np.std(Xs))
+ elif pars['b_allocation'] in ("sin", "Amajor"):
+ ax.set(ylim=0)
+
+ ax.set(xlabel='Node degree, $d_i$', title=title)
+
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ if print_variable == 'allocations' and pars['bmul'] == 10:
+ ax.legend()
+ # ax.grid()
+
+ return boxes[:, (1, 3)]
+
+
+def print_a_vs_b(ax, pars, print_variable=None):
+ aes, bs, seqs, Xs, xs, convs, _, _, _, nets = load_data(pars)
+ assert len(nets) == 1
+ a, b = nets[0].mean_a, nets[0].mean_b
+
+ k_max = seqs[0, -1]
+ if pars['network'] in ('email', 'ba'):
+ norm = mpl.colors.LogNorm(vmin=1, vmax=k_max)
+ else:
+ norm = mpl.colors.Normalize(vmin=1, vmax=k_max)
+
+ # alphas = np.zeros((len(aes), 2))
+ for l in range(len(aes)):
+ a_vec, b_vec = aes[l], bs[l]
+ if convs[l]:
+ # b_max = np.max(bs[l])
+ # b_star = b_max
+ # for i, a_i in enumerate(aes[l]):
+ # if np.isclose(a_i, 0) and b[i] < b_star:
+ # b_star = b[i]
+ # print("Cut point at b* = {:.2f}".format(b_star))
+ # fitting_a = a[b < b_star]
+ # fitting_b = b[b < b_star]
+ # fitting_a = a_vec[a_vec > 0]
+ # fitting_b = b_vec[a_vec > 0]
+ # b_star = np.max(fitting_b)
+ # alphas[l] = np.polyfit(fitting_b, fitting_a, 1)
+
+ # colors = np.array([plt.cm.cool(np.log(int(k)) / np.log(k_max)) for k in seqs[l]])
+ ax.scatter(b_vec, a_vec, c=seqs[l], marker="o", s=0.5, norm=norm, cmap=plt.cm.get_cmap("cool"))
+ # ax.plot([0, b_star], [alphas[l, 1], alphas[l, 1] + b_star * alphas[l, 0]], color="C1", ls="solid",
+ # marker="", linewidth=0.4)
+
+ if pars['b_allocation'] == "random":
+ b_max = 2 * b
+ elif pars['b_allocation'] == "discrete":
+ b_max = np.max(bs)
+
+ low_budget_label = r"$w^L_{ai} \; (w_{ai}\!+\!w_{bi}\!\ll\!d_i)$"
+ high_budget_label = r"$w^H_{ai} \; (w_{ai}\!+\!w_{bi}\!\gg\!d_i)$"
+ lw = 1.5
+ color = "k"
+ assert len(bs) == 1
+
+ ana = []
+ # Low budget limit
+ if (a + b) < np.mean(seqs[0]) * 1.1:
+ intercept_ana = (a + b) / 2
+ bi_coeff_ana = (a / b - 1) / 2
+ if a >= 0.99 * b:
+ ax.plot([0, b_max], [intercept_ana, intercept_ana + b_max * bi_coeff_ana], color=color, ls="solid",
+ marker="", linewidth=lw, label=low_budget_label)
+ ana.append([intercept_ana, intercept_ana + b_max * bi_coeff_ana])
+ elif pars['b_allocation'] == "random":
+ b_artif = np.array([])
+ seq_artif = np.array([])
+ for d in seqs[0]:
+ b_artif = np.append(b_artif, np.linspace(0, b_max, 100))
+ seq_artif = np.append(seq_artif, [d] * 100)
+ a_ana = compute_a_analytical_small_budget(seq_artif, b_artif, a, b)
+ ana.append(a_ana)
+ for d in np.unique(seqs[0]):
+ if d % 6 == 1:
+ mask = np.logical_and(seq_artif == d, a_ana > 0)
+ color2 = plt.cm.get_cmap("cool")(norm(d))
+ ax.plot(b_artif[mask], a_ana[mask], color=color2, ls="solid", marker="", linewidth=1.0)
+ ax.plot([0], [0], color=plt.cm.get_cmap("cool")(norm(25)), ls="solid", marker="", linewidth=1.0,
+ label=low_budget_label)
+ elif pars['b_allocation'] == "discrete":
+ a_ana = compute_a_analytical_small_budget(seqs[0], bs[0], a, b)
+ ax.plot(bs[0], a_ana, color=color, ls="solid", marker="", linewidth=1.0, label=low_budget_label)
+
+ # High budget limit
+ if (a + b) > np.mean(seqs[0]) * 0.9:
+ if pars['b_allocation'] == "random":
+ b_ana = np.linspace(0, b_max, 100)
+ a_ana = compute_a_analytical_big_budget(b_ana, a, b)
+ ax.plot(b_ana, a_ana, color=color, ls="--", marker="", linewidth=lw, label=high_budget_label)
+ elif pars['b_allocation'] == "discrete":
+ b_ana = bs[0]
+ a_ana = compute_a_analytical_big_budget(b_ana, a, b)
+ ax.plot([0, b_max], [np.min(a_ana), np.max(a_ana)], color=color, ls="--", marker="", linewidth=lw,
+ label=high_budget_label)
+ ana.append(a_ana)
+
+ if pars['b_allocation'] == "random":
+ if pars['bmul'] < 1.1:
+ ax.set(ylim=[0, 2 * a], xlim=[0, b_max])
+ else:
+ ax.set(ylim=0)
+ ax.margins(x=0.0, y=0.0)
+ ax.legend(columnspacing=0.01, handletextpad=0.3, loc="lower right" if pars["bmul"] == 0.1 or pars["kabmul"]==10 else "upper right")
+
+ # title = r"$a={:.2f},\ b={:.2f}$".format(a, b)
+ title = ""
+ xlabel = r"$w_{bi}$"
+ if pars['axespar'] == 'kabmul':
+ kabmul = pars['kabmul']
+ if kabmul == 1:
+ kabmul = ""
+ title += r"$\langle w_{a} \rangle+\langle w_b \rangle= " + str(kabmul) + r"\,\langle d \rangle$"
+ elif pars['axespar'] == "bva":
+ xlabel += r"$b_i$" + r"$\quad(a= {:.1f}\, b)$".format(a / b)
+
+ ylabel = r"$w_{ai}$"
+ if pars['multiaxespar'] == "kabmul":
+ ylabel += r"$\quad(a+b= {:.1f})$".format(a + b)
+ elif pars['multiaxespar'] == "bmul":
+ # ylabel += r"$\quad(\,\mathcal{B}_a/\mathcal{B}_b=" + str(1 / pars['bmul']) + r"\,)$"
+ bmul = 1 / pars['bmul']
+ if np.isclose(bmul, int(bmul)):
+ bmul = int(bmul)
+ ylabel = r"$\mathcal{B}_a/\mathcal{B}_b=" + str(bmul) + r"$" + "\n" + r" $w_{ai}$"
+
+ ax.set(title=title, xlabel=xlabel, ylabel=ylabel)
+ # ax.grid()
+
+ if pars['axespar'] is not None and pars[pars['axespar']] == pars['axesarray'][-1]:
+ from mpl_toolkits.axes_grid1 import make_axes_locatable
+ # cax, _ = matplotlib.colorbar.make_axes(ax)
+ divider = make_axes_locatable(ax)
+ cax = divider.append_axes("right", size="5%", pad=0.05)
+ cb = mpl.colorbar.ColorbarBase(cax, cmap=plt.cm.get_cmap('cool'), norm=norm)
+ cb.set_label('$d_i$')
+
+ return aes[0], bs[0], seqs[0], ana
+
+
+def print_Fa_vs_a(ax, pars, print_variable=None):
+ from scipy.stats import gaussian_kde, entropy
+
+ aes, bs, seqs, Xs, xs, convs, aes_disc, Xs_disc, xs_disc, nets = load_data(pars)
+
+ # factor = 0.2
+
+ for l in range(len(aes)):
+ a, b = aes[l], bs[l]
+
+ if pars['b_allocation'] != "discrete":
+ x = np.linspace(0., np.max([np.max(a), np.max(aes_disc[l]), np.max(b)]), 200)
+
+ # density = gaussian_kde(aes_disc, bw_method=factor)
+ density = gaussian_kde(aes_disc)
+ # density.covariance_factor = lambda: .01
+ # density._compute_covariance()
+ ax.plot(x, density(x), label=r"$a_{disc}$", color="C2")
+ # a_disc_index = entropy(density(x))
+
+ # density = gaussian_kde(b, bw_method=factor)
+ density = gaussian_kde(b)
+ ax.plot(x, density(x), label="b", color="C0")
+ # a_disc_index = entropy(density(x))
+
+ title = ""
+ else:
+ x = np.linspace(0., np.max(a), 200)
+ n_a_nodes = int(nets.mean_a * pars['N'] / pars['gain'])
+ n_b_nodes = int(nets.mean_b * pars['N'] / pars['gain'])
+ title = r"$N_{a} = " + "{:d}, ".format(n_a_nodes) + r"N_{b} = " + "{:d}$\n".format(n_b_nodes)
+
+ if convs[l]:
+ # density = gaussian_kde(a, bw_method=factor)
+ density = gaussian_kde(a)
+ ax.plot(x, density(x), label="a", color="C1")
+ # a_index = entropy(density(x))
+ else:
+ ax.plot([0], [0], label="Not converged")
+
+ a_index = entropy(a)
+ a_disc_index = entropy(aes_disc[l])
+ b_index = entropy(b)
+ title += r"$H_{a}:" + "{:.2f}, ".format(a_index) + "H_{adisc}:" + "{:.2f}, ".format(
+ a_disc_index) + "H_{b}:" + "{:.2f}$".format(b_index) # + "\ncacafuti"
+
+ ax.set(title=title, xlabel=r"$a$", ylabel=r"$F(a)$")
+ # ax.grid()
+ ax.legend()
+
+ # xs = np.linspace(0, 8, 200)
+ # density.covariance_factor = lambda: .25
+ # density._compute_covariance()
+ # plt.plot(xs, density(xs))
+ # plt.show()
+
+
+def print_a_vs_nodes(ax, pars, print_variable=None):
+ aes, bs, seqs, Xs, xs, convs, aes_disc, Xs_disc, xs_disc, networks = load_data(pars)
+
+ aes_sorted = np.zeros((pars['n_nets'], len(aes[0])))
+ for l in range(pars['n_nets']):
+ print("deg of B-targets", seqs[l][~np.isclose(bs[l], 0)])
+ indices = np.argsort(aes[l])[::-1]
+ # indices = np.arange(pars['N'])
+ # indices = np.arange(len(b))[np.logical_or(aes[l] > np.mean(aes[l]), aes_disc[l] > 0)]
+ aes_sorted[l] = aes[l][indices]
+ if not convs[l]:
+ warnings.warn(str(l) + " NOT CONVERGED")
+
+ x_axis = np.arange(pars['N'])
+ if False: # pars['n_nets'] == 1:
+ for l in range(pars['n_nets']):
+ ax.bar(x_axis, bs[l][indices], color="C0", label=r"$w_{bi}$") # ,\; H:{:.2f}$".format(b_index))
+ # if pars['add_discrete']:
+ # ax.bar(x_axis, aes_disc[l][indices], color="C2", label=r"$w_{ai}^{\mathrm{disc}}$")
+
+ aes_agg = np.mean(aes_sorted, axis=0) # a_agg_std = np.std(a_agg, axis=0)
+ masks = divide_into_allocation_groups(aes_agg, n_groups=4, **pars) # , criterion="std")
+ sizes = [np.sum(mask) for mask in masks]
+
+ # ax.bar(x_axis[indices_1], a_agg_mean[indices_1], yerr=a_agg_std[indices_1], color="red", label=r"$w_{ai}$")
+ for k, mask in enumerate(masks):
+ if np.any(mask):
+ ax.bar(x_axis[mask], aes_agg[mask], color=ALLO_GROUP_COLOURS[k],
+ label="$|$" + get_allo_label(k, short=True) + "$|={:d}$".format(sizes[k]))
+ else:
+ ax.bar(-100, aes_agg[0], color=ALLO_GROUP_COLOURS[k],
+ label="$|$" + get_allo_label(k, short=True) + "$|={:d}$".format(sizes[k]))
+
+ # ax.plot(x_axis[:-1], 1 - a_agg_mean[1:] / a_agg_mean[:-1], color="k", label="difference")
+
+ # ax.plot(x_axis, a_agg_mean + a_agg_std, color="grey", linewidth=0.1)
+ # ax.plot(x_axis, a_agg_mean - a_agg_std, color="grey", linewidth=0.1)
+ # ax.plot(x_axis, a_agg_mean, color="k", linewidth=0.1)
+
+ if True: # pars['N_allocations'] < 40:
+ ax.set_yscale("log")
+ ax.set(xlim=[x_axis[0] - int(len(x_axis) / 50), x_axis[-1]], ylim=[0.03, 10])
+ print("lowest values", aes_agg[-10:])
+ # ax.set(xlim=[1100, 1133]) # [0, 30]) # [0, max(aes[0])])
+ # ax.margins(x=0.0)
+
+ if len(pars['axesarray']) > 1:
+ ax.set(title="N allocations = " + str(pars['N_allocations']))
+ # ax.set(xlabel=r"Nodes (sorted by degree, ascending)", ylabel=r"Allocation strength, $w_{ai},\; w_{bi}$")
+ ax.set(xlabel=r"$i$ (sorted by $w_{ai}$, descending)", ylabel=r"Allocation strength, $w_{ai}$") # ,\; w_{bi}$")
+ # ax.grid()
+ ax.legend()
+
+ # ax.margins(x=0.0)
+
+ def compute_mean_std(vec):
+ print("a_mean", np.mean(vec), "a_std", np.std(vec), "a_std/a_mean", np.std(vec) / np.mean(vec))
+
+ compute_mean_std(aes_sorted)
+ compute_mean_std(aes_agg[masks[2]])
+
+ return aes_agg
+
+
+def print_graph(ax, pars, print_variable=None):
+ aes, bs, seqs, Xs, xs, convs, aes_disc, Xs_disc, xs_disc, nets = load_data(pars)
+
+ for l in range(pars['n_nets']):
+ if convs[l]:
+ net = nets[l]
+ node_list = np.array(net.G.nodes)
+ a, b = net.a, net.b[:, 0]
+
+ if pars['b_allocation'] == "discrete":
+ pos = nx.spring_layout(net.G)
+ opponent_true = np.flatnonzero(b)
+ opponent_false = np.where(b < 0.1)[0]
+ norm = mpl.colors.Normalize(vmin=0, vmax=np.max(a))
+ nx.draw_networkx_nodes(net.G, pos, nodelist=node_list(opponent_true), node_shape="s",
+ cmap=plt.cm.get_cmap("Oranges"), norm=norm, node_color=a[opponent_true], ax=ax)
+ nx.draw_networkx_nodes(net.G, pos, nodelist=node_list(opponent_false), node_shape="o",
+ cmap=plt.cm.get_cmap("Oranges"), norm=norm, node_color=a[opponent_false], ax=ax)
+ nx.draw_networkx_edges(net.G, pos, ax=ax, edge_color="grey", cmap=plt.cm.get_cmap("Oranges"), vmin=0,
+ vmax=2)
+ ax.set_axis_off()
+ else:
+ if False:
+ log_a = np.array([np.log(a_el) if a_el > 0 else np.log(0.002) for a_el in a])
+ nx.draw(net.G, node_color=log_a, ax=ax, edge_color="grey", cmap=plt.cm.get_cmap("Oranges"))
+ else:
+ nx.draw(net.G, node_color=a, ax=ax, edge_color="grey", cmap=plt.cm.get_cmap("Oranges"))
+ # vmin=np.log(0.002), vmax=np.log(2))
+ else:
+ ax.plot([0], [0], label="Not converged")
+
+ # title += r"$H_{a}:" + "{:.2f}, ".format(a_index) + "H_{b}:" + "{:.2f}$".format(
+ # b_index) # + "H_{adisc}:" + "{:.2f}, ".format(a_disc_index)
+
+
+def print_avg_vs_r(ax, pars, print_variable="allocations"):
+ colors = plt.cm.get_cmap("winter")(np.linspace(0, 1, len(pars['linesarray'])))
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ avg_degrees = np.zeros(len(pars['axisarray']))
+ for i, val in enumerate(create_range(pars)):
+ print(pars['axispar'], i, val)
+ aes, bs, seqs, Xs, xs, convs, nets = load_data(pars)
+
+ for l in range(len(aes)):
+ if i == 0 and l == 0:
+ b_avg_degree = compute_average_targeted_degree(bs[l], seqs[l])
+ if convs is not None:
+ if convs[l]:
+ avg_degrees[i] = compute_average_targeted_degree(aes[l], seqs[l])
+ else:
+ print("NOT CONVERGED")
+
+ axis_vec = np.array(list(create_range(pars)))[::-1]
+
+ if print_variable == "allocations":
+ if pars['b_allocation'] == "powerlaw":
+ ax.semilogx(axis_vec, avg_degrees, color=colors[j], marker=".", markersize=2,
+ label=r"$\beta\!=\!{:.1f}".format(linesval) + r", \langle k\rangle_b =" + "{:.1f}".format(
+ b_avg_degree) + "$")
+ else:
+ if pars['b_allocation'] == "linear":
+ pass
+ # ax.plot([0, sort_k[-1]], [alphas[l, 1], alphas[l, 1] + sort_k[-1] * alphas[l, 0]],
+ # color="C1", ls="solid", marker="", linewidth=0.4)
+ elif pars['b_allocation'] == "sin":
+ # ax.plot(seqs[l], ((nets.mean_a / nets.mean_b - 1) * bs[l] + nets[l].mean_a + nets[l].mean_b) / 2,
+ ax.plot(seqs[l],
+ ((nets[l].mean_a / nets[l].mean_b - 1) * bs[l] + nets[l].mean_a + nets[l].mean_b) / 2,
+ color="C1", ls="solid", marker="", linewidth=0.4)
+ ax.plot(seqs[l], aes[l], color="C1", ls="", marker="o", markersize=0.5, label=r"$a_i$")
+ # ax.plot(seqs[l], bs[l] / pars['bmul'], color="C0", ls="", marker="o", markersize=2, label=r"$b_i \cdot r$")
+ ax.plot(seqs[l], bs[l], color="C0", ls="", marker="o", markersize=0.5, label=r"$b_i$")
+ elif print_variable == "x":
+ ax.plot(seqs[l], xs[l], ls="", marker="o", markersize=2)
+ else:
+ raise Exception("Print variable '" + print_variable + "' not understood.")
+
+ if pars['b_allocation'] == 'powerlaw':
+ # title = r"$\langle k\rangle_b =" + "{:.3f}".format(b_avg_degree) + "$"
+ ax.set(ylabel=r"Average A-targeted node degree, $\langle k\rangle_a$") # $\sum_i a_i\,k_i / \sum_i a_i$")
+ # elif pars['b_allocation'] == "linear":
+ # title = r"$\beta\!= {:.3f}$".format(pars['beta'])
+ # if print_variable == "allocations":
+ # title += r", $\alpha\!= " + "{:.3f}$".format(np.mean(alphas[:, 0]))
+ # # corr = np.corrcoef(aes[l], bs[l])
+ # # print(corr)
+ # # title += r", $R_{ab}\!= $" + " {:.2f}".format(corr[0, 1])
+ # # if Xs is not None:
+ # # title += ", X = {:.3f} ".format(np.mean(Xs)) # + r"$\pm$" + "{:.3f}".format(np.std(Xs))
+ # ax.set(ylabel="Per-node allocations")
+ # elif print_variable == "x":
+ # ax.set(ylabel=r"Per-node vote share, $x_i$")
+ # if Xs is not None:
+ # title += r", $X_{GA}\!=$" + "{:.4f} ".format(
+ # np.mean(Xs)) # + r"$\pm$" + "{:.3f}".format(np.std(Xs))
+ # elif pars['b_allocation'] == "sin":
+ # title = ""
+ # ax.set(ylim=0)
+ # else:
+ # raise Exception("B allocation '" + str(pars['b_allocation']) + "' not understood.")
+ #
+ ax.set(xlabel='Budget ratio, $r=a_{max}/b_{max}$')
+ ax.margins(x=0.0, y=0.0)
+
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ if print_variable == 'allocations':
+ ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+ # ax.grid()
+
+
+def print_avg_vs_beta(ax, pars, print_variable="allocations"):
+ colors = plt.cm.get_cmap("winter")(np.linspace(0, 1, len(pars['linesarray'])))[::-1]
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ print("\nXXXX bmul ", j, linesval)
+ avg_a_degrees = np.zeros(len(pars['axisarray']))
+ avg_b_degrees = np.zeros(len(pars['axisarray']))
+ for i, val in enumerate(create_range(pars)):
+ print(pars['axispar'], i, val)
+ aes, bs, seqs, Xs, xs, convs, _, _, nets = load_data(pars)
+
+ for l in range(len(aes)):
+ avg_b_degrees[i] = compute_average_targeted_degree(bs[l], seqs[l])
+ if convs is not None:
+ if convs[l]:
+ avg_a_degrees[i] = compute_average_targeted_degree(aes[l], seqs[l])
+ else:
+ print("NOT CONVERGED")
+
+ axis_vec = np.array(list(create_range(pars)))
+
+ if print_variable == "allocations":
+ if pars['b_allocation'] == "powerlaw":
+ ax.plot(axis_vec, avg_a_degrees, color=colors[j], marker=".", markersize=3,
+ label=r"$\langle k\rangle_a,r\!=\!" + str(1 / linesval) + r"$")
+ if j == len(colors) - 1:
+ ax.plot(axis_vec, avg_b_degrees, color="orange", marker=".", markersize=3, ls="--",
+ label=r"$\langle k\rangle_b$") # ,
+ else:
+ if pars['b_allocation'] == "linear":
+ pass
+ # ax.plot([0, sort_k[-1]], [alphas[l, 1], alphas[l, 1] + sort_k[-1] * alphas[l, 0]],
+ # color="C1", ls="solid", marker="", linewidth=0.4)
+ elif pars['b_allocation'] == "sin":
+ # ax.plot(seqs[l], ((nets[l].mean_a / nets[l].mean_b - 1) * bs[l] + nets[l].mean_a + nets[l].mean_b) / 2,
+ ax.plot(seqs[l],
+ ((nets[l].mean_a / nets[l].mean_a - 1) * bs[l] + nets[l].mean_a + nets[l].mean_b) / 2,
+ color="C1", ls="solid", marker="", linewidth=0.4)
+ ax.plot(seqs[l], aes[l], color="C1", ls="", marker="o", markersize=0.5, label=r"$a_i$")
+ # ax.plot(seqs[l], bs[l] / pars['bmul'], color="C0", ls="", marker="o", markersize=2, label=r"$b_i \cdot r$")
+ ax.plot(seqs[l], bs[l], color="C0", ls="", marker="o", markersize=0.5, label=r"$b_i$")
+ elif print_variable == "x":
+ ax.plot(seqs[l], xs[l], ls="", marker="o", markersize=2)
+ else:
+ raise Exception("Print variable '" + print_variable + "' not understood.")
+
+ if pars['b_allocation'] == 'powerlaw':
+ # title = r"$\langle k\rangle_b =" + "{:.3f}".format(b_avg_degree) + "$"
+ ax.set(ylabel=r"Average targeted node degree") # $\sum_i a_i\,k_i / \sum_i a_i$")
+ # elif pars['b_allocation'] == "linear":
+ # title = r"$\beta\!= {:.3f}$".format(pars['beta'])
+ # if print_variable == "allocations":
+ # title += r", $\alpha\!= " + "{:.3f}$".format(np.mean(alphas[:, 0]))
+ # # corr = np.corrcoef(aes[l], bs[l])
+ # # print(corr)
+ # # title += r", $R_{ab}\!= $" + " {:.2f}".format(corr[0, 1])
+ # # if Xs is not None:
+ # # title += ", X = {:.3f} ".format(np.mean(Xs)) # + r"$\pm$" + "{:.3f}".format(np.std(Xs))
+ # ax.set(ylabel="Per-node allocations")
+ # elif print_variable == "x":
+ # ax.set(ylabel=r"Per-node vote share, $x_i$")
+ # if Xs is not None:
+ # title += r", $X_{GA}\!=$" + "{:.4f} ".format(
+ # np.mean(Xs)) # + r"$\pm$" + "{:.3f}".format(np.std(Xs))
+ # elif pars['b_allocation'] == "sin":
+ # title = ""
+ # ax.set(ylim=0)
+ # else:
+ # raise Exception("B allocation '" + str(pars['b_allocation']) + "' not understood.")
+ #
+ ax.set(xlabel=r'Power-law exponent of B-allocations, $\beta$', ylim=0)
+ ax.margins(x=0.0) # , y=0.0)
+
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ if print_variable == 'allocations':
+ # ax.legend(ncol=2)
+ ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+ # ax.grid()
+
+
+def print_entropy_vs_N_b(ax, pars, print_variable="entropy"):
+ a_entropies = np.zeros((len(pars['linesarray']), len(pars['axisarray']), 2))
+ a_disc_entropies = np.zeros(len(pars['axisarray']))
+ X_improvements = np.zeros((len(pars['linesarray']), len(pars['axisarray']), 2))
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ for i, val in enumerate(create_range(pars)):
+ print("\n", pars['axispar'], i, val)
+ aes, bs, seqs, Xs, xs, convs, aes_disc, Xs_disc, _, nets = load_data(pars)
+
+ a_disc_entropies[i] = np.mean([entropy(a_disc) for a_disc in aes_disc])
+ if convs is not None:
+ if np.all(convs):
+ entropies = [entropy(a) for a in aes]
+ a_entropies[j, i] = (np.mean(entropies), np.std(entropies))
+ improvements = (Xs - Xs_disc) / Xs_disc * 100
+ # improvements = (Xs - Xs_disc) / (Xs_disc - nets[l].mean_a/ (nets[l].mean_a + nets[l].mean_b)) * 100
+ # improvements = (Xs - Xs_disc) / nets[l].mean_b * (nets[l].mean_a + nets[l].mean_b)
+ # improvements = [(x - xd) / xd if xd < 0.5 else (x - xd) / (1-xd) for x, xd in zip(Xs, Xs_disc)]
+ X_improvements[j, i] = (np.mean(improvements), np.std(improvements))
+ print("Xs", np.mean(Xs), "+-", np.std(Xs))
+ print("Xdisc", np.mean(Xs_disc), "+-", np.std(Xs_disc))
+ else:
+ print("NOT CONVERGED", convs)
+
+ axis_vec = np.array(pars['axisarray'])
+ colors = plt.cm.get_cmap("winter")(np.linspace(0, 0.8, len(pars['linesarray'])))
+ markers = ["s", "D", "o", "x"]
+ labels = ["unif", r"$\mathcal{B}_a/\mathcal{B}_b=$", "discrete"]
+ elinewidth = 1
+ if print_variable == "entropy":
+ max_ent = entropy(np.ones(pars['N']))
+ # ax.plot(axis_vec, np.ones_like(axis_vec) * entropy(np.ones(pars['N'])), label=labels[0], color="purple")
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ if np.isclose(1 / linesval, int(1 / linesval)):
+ label = str(int(1 / linesval))
+ else:
+ label = str(1 / linesval)
+ # ax.errorbar(axis_vec, a_entropies[:, 0], yerr=a_entropies[:, 1], label=labels[1] + " " + label,
+ print("\n", list(zip(axis_vec, a_entropies[j, :, 0] / max_ent)))
+ ax.errorbar(axis_vec, a_entropies[j, :, 0] / max_ent, label=labels[1] + label, color=colors[j], ls="",
+ yerr=a_entropies[j, :, 1] / max_ent / np.sqrt(pars['n_nets']), marker=markers[j],
+ ecolor="k", elinewidth=elinewidth)
+ ax.plot(axis_vec, a_disc_entropies / max_ent, label=labels[2], color=DISCRETE_COLOR, ls="--")
+ ax.set(ylabel=r"Entropy of allocations, $H$", ylim=[0, 1])
+ ax.legend() # loc="right")
+ elif print_variable == "improvement":
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ linesval = 1 / linesval
+ if np.isclose(linesval, int(linesval)):
+ linesval = int(linesval)
+ ax.errorbar(axis_vec, X_improvements[j, :, 0], yerr=X_improvements[j, :, 1] / np.sqrt(pars['n_nets']),
+ color=colors[j], ecolor="k", ls='', marker=markers[j], label=labels[1] + str(linesval),
+ elinewidth=elinewidth)
+ ax.set_ylabel(r"$(X^{\mathrm{con}}-X^{\mathrm{dis}})/X^{\mathrm{dis}} \; [\%]$")
+ ax.set(ylim=[0, 27.5])
+ ax.legend() # loc="upper center")
+ else:
+ raise ValueError("Print variable '" + print_variable + "' not understood.")
+
+ ax.set(xlabel=K_X_LABEL)
+ ax.set_xscale("log")
+
+ # ax.margins(x=0.0) # , y=0.0)
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ # if print_variable == 'allocations':
+ # ax.legend(ncol=2)
+ # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+ # ax.grid()
+
+ return a_entropies, a_disc_entropies, X_improvements
+
+
+def print_perturbations(ax, pars, print_variable="allocations"):
+ # colors = plt.cm.get_cmap("winter")(np.linspace(0, 0.8, len(pars['linesarray'])))
+ n_nets = pars['n_nets']
+ axis_vec = np.array(pars['axisarray'])
+ print("x axis:", axis_vec)
+
+ ls = ["", "solid"] # [":", "solid"]
+ cs = ["C0", "C1"]
+ # markers = ["s", "D", "o"], markersize = 5
+ markers = [",", "v", "o", "s", "D"]
+ markersize = 6
+
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ Xs_num = np.zeros((len(axis_vec), n_nets))
+ Xs_ana = np.zeros((len(axis_vec), n_nets))
+ for i, axisval in enumerate(create_range(pars)):
+ aes, bs, seqs, Xs, xs, convs, _, _, _, nets = load_data(pars)
+
+ Xs_num[i] = Xs - 0.5
+ Xs_ana[i] = nets[0].mean_b / 4 / nets[0].N * np.sum((1 - bs / nets[0].mean_b) ** 2 / seqs, axis=1)
+ # print(Xs_num[i], Xs_ana[i])
+
+ # if np.isclose(1 / linesval, int(1 / linesval)):
+ # label = str(int(1 / linesval))
+ # else:
+ # label = str(1 / linesval)
+ linesval = ""
+
+ ax.errorbar(axis_vec, np.mean(Xs_ana, axis=1), markersize=markersize, marker=markers[0], ls=ls[1], ecolor="k",
+ fillstyle='none', yerr=std_errors(Xs_ana, axis=1), label="analytical", color=cs[0])
+ ax.errorbar(axis_vec, np.mean(Xs_num, axis=1), markersize=markersize, marker=markers[1], ls=ls[0],
+ yerr=std_errors(Xs_num, axis=1), ecolor="k", label="numerical", color=cs[1])
+
+ # ax.set(title="end: {:s}".format(pars['end']),
+ # ax.set(xlabel="Number of perturbations, $n$", ylabel=r"$\Delta X$")
+ ax.set(xlabel="Degree of perturbated nodes, $d_p$", ylabel=r"$\Delta X$", ylim=1e-4)
+ ax.legend() # ncol=2)
+ ax.set_xscale("log"), ax.set_yscale("log")
+ # ax.set_facecolor("whitesmoke")
+
+ # ax.margins(x=0.0) # , y=0.0)
+
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+ # ax.grid()
+
+
+def print_correlations(ax, pars, print_variable="allocations"):
+ if pars['end'] == "periphery" and pars["network"] == "email":
+ warnings.warn(
+ "Periphery targeting on email network, only one network is used as there is no randomness in the process. NOTE THAT THE NETWORK NUMBER REMAIN AT 1 FROM ON (this should be corrected in future versions)")
+ pars['n_nets'] = 1
+ else:
+ print(pars['end'], pars['network'])
+ corrs_cont = np.zeros((len(pars['linesarray']), len(pars['axisarray']), pars['n_nets'], 3))
+ corrs_disc = np.zeros((len(pars['linesarray']), len(pars['axisarray']), pars['n_nets'], 3))
+ corrs_shield = np.zeros((len(pars['linesarray']), len(pars['axisarray']), pars['n_nets'], 3))
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ for i, val in enumerate(create_range(pars)):
+ print("\n", pars['axispar'], i, val)
+ aes, bs, seqs, Xs, xs, convs, aes_disc, Xs_disc, _, nets = load_data(pars)
+
+ aes_shield = np.zeros_like(aes)
+ for l, net in enumerate(nets):
+ mask_neigh = divide_into_neighbouring_groups(net.b[:, 0], net.W.toarray())
+ mask_shield = mask_neigh[1][net.sort_ind]
+ aes_shield[l][mask_shield] = net.mean_a * net.N / np.sum(mask_shield)
+
+ degrees = np.unique(seqs[0])
+
+ def obtain_correlations(points):
+ points = [np.mean(points[seqs[0] == k]) for k in degrees]
+ return kendalltau(degrees, points)[0], spearmanr(degrees, points)[0], pearsonr(degrees, points)[0]
+
+ # corrs_disc[j, i] = [obtain_correlations(a_disc) for a_disc in aes_disc]
+ if np.all(convs):
+ corrs_cont[j, i] = [obtain_correlations(a) for a in aes]
+ corrs_shield[j, i] = [obtain_correlations(a) for a in aes_shield]
+ else:
+ corrs_cont[j, i] = [obtain_correlations(a) for a in aes]
+ corrs_shield[j, i] = [obtain_correlations(a) for a in aes_shield]
+ warnings.warn("NOT CONVERGED")
+
+ axis_vec = np.array(pars['axisarray'])
+ colors = plt.cm.get_cmap("winter")(np.linspace(0, 0.8, len(pars['linesarray'])))
+ markers = ["s", "D", "o", "x", "v"]
+ labels = ["unif", r"$\mathcal{B}_a/\mathcal{B}_b=$", "discrete"]
+ elinewidth = 1
+
+ ax.axhline(color="k", linewidth=0.8)
+ plot_pars = {"elinewidth": elinewidth, "ls": "", "ecolor": "k"}
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ corr_cont_mean = np.mean(corrs_cont[j, :, :, 0], axis=1)
+ print("Correlations continuous regime", corr_cont_mean)
+ if len(colors) == 1:
+ if pars['n_nets'] == 1:
+ ax.plot(axis_vec, corr_cont_mean, label="optimal", color="orange", marker=markers[4], ls="")
+ ax.plot(axis_vec, np.mean(corrs_shield[j, :, :, 0], axis=1), marker=markers[2], label="shielding",
+ color=NEIGH_GROUP_COLOURS[1], ls="")
+ else:
+ ax.errorbar(axis_vec, corr_cont_mean, label="optimal", color="orange",
+ yerr=std_errors(corrs_cont[j, :, :, 0], axis=1), marker=markers[4], **plot_pars)
+ # ax.errorbar(axis_vec, np.mean(corrs_disc[j, :, :, 0], axis=1), marker=markers[1], label="discrete IM",
+ # yerr=std_errors(corrs_disc[j, :, :, 0], axis=1), color=DISCRETE_COLOR, **plot_pars)
+ ax.errorbar(axis_vec, np.mean(corrs_shield[j, :, :, 0], axis=1), marker=markers[2], label="shielding",
+ yerr=std_errors(corrs_shield[j, :, :, 0], axis=1), color=NEIGH_GROUP_COLOURS[1],
+ **plot_pars)
+ else:
+ linesval = 1 / linesval
+ if np.isclose(linesval, int(linesval)):
+ linesval = int(linesval)
+ ax.errorbar(axis_vec, corr_cont_mean, color=colors[j], marker=markers[j],
+ yerr=std_errors(corrs_cont[j, :, :, 0], axis=1), label=labels[1] + str(linesval), **plot_pars)
+
+ # ax.plot(axis_vec, corrs_cont[j, :, 1], ls="", marker=markers[1], label="spearman", color="orange")
+ # ax.plot(axis_vec, corrs_cont[j, :, 2], ls="", marker=markers[2], label="pearson", color="orange")
+ # ax.plot(axis_vec, corrs_disc[j, :, 0], ls="", marker=markers[1], label="discrete IM", color=DISCRETE_COLOR)
+ # ax.plot(axis_vec, corrs_disc[j, :, 1], ls="", marker=markers[1], color="green")
+ # ax.plot(axis_vec, corrs_disc[j, :, 2], ls="", marker=markers[2], color="green")
+
+ ax.set(xlabel=K_X_LABEL, ylabel=r"Correlation $\tau\,(w_{ai},d_i)$")
+ if pars['end'] == "middle":
+ ax.legend(loc="lower right") # ncol=2) # loc="right")
+ else:
+ ax.legend() # ncol=2) # loc="right")
+
+ if len(colors) == 1:
+ ax.set(ylim=[-0.55, 1])
+ else:
+ ax.set(ylim=[-1, 1])
+ if pars['end'] == "periphery":
+ ax.legend(loc="upper left")
+
+ ax.set_xscale("log")
+ if pars["end"] == "middle":
+ ax.set(title="Opponent targets randomly")
+ elif pars["end"] in ("low", "periphery"):
+ ax.set(title="Opponent targets periphery")
+
+ # ax.margins(x=0.0) # , y=0.0)
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ # ax.legend(ncol=2, loc='center left', bbox_to_anchor=(1, 0.5))
+ ax.yaxis.grid()
+
+ return corrs_cont, corrs_shield
+
+
+def print_specific_distance_to_b(ax, pars, print_variable="disc"): # (discrete, discrete_degree, continuous)
+ aes, bs, seqs, Xs, xs, convs, aes_disc, _, _, nets = load_data(pars)
+
+ weighted_distances = []
+ for l in range(pars['n_nets']):
+ if all((not pars['add_continuous'], pars['add_discrete'], print_variable not in (
+ "discrete", "discrete_degree"))):
+ print_variable = "discrete"
+
+ net, b, seq = nets[l], nets[l].b[:, 0], nets[l].seq
+
+ if print_variable in ("continuous", "allocations") or not pars['add_discrete']:
+ if print_variable not in ("continuous", "allocations"):
+ print_variable = "continuous"
+ if convs is not None:
+ if convs[l]:
+ a = net.a
+ else:
+ raise Exception("GA has not converged.")
+ elif print_variable in ("discrete", "discrete_degree"):
+ a = net.a_disc
+ else:
+ raise Exception("Print variable " + print_variable + " not understood.")
+
+ uw_dists, uw_dists_degree, w_dists, w_dists_degree, w_dists_repetitions = compute_distances(net.G, b, seq, a)
+ weighted_distances.append(w_dists)
+
+ print("Degree of B-allocations: ", seq[b > 0])
+ if print_variable == "discrete":
+ n_nodes = len(a > 0)
+ distances = [np.sum(np.array(el) > 0) for el in weighted_distances]
+ print(distances[1], uw_dists[1], n_nodes)
+ order_para = distances[1] / uw_dists[1]
+ order_potential = min([n_nodes / uw_dists[1], 1.0])
+ print("Shielding done:", order_para, "Shielding potential:", order_potential)
+
+ if print_variable == 'discrete':
+ ax.bar(range(len(uw_dists)), uw_dists, color="grey")
+ ax.bar(range(len(weighted_distances)), distances, color="green")
+ ax.set_yscale("log")
+ elif print_variable == "discrete_degree":
+ # print(np.array(distances_plus_degrees))
+ # norm = mpl.colors.LogNorm()
+ print("Degrees of first-neighbours", uw_dists_degree[1])
+ ticks = np.arange(-1, len(w_dists_degree) - 1)
+ if pars['N_allocations'] > 1:
+ w_dists_degree = [dist / np.max(dist) if np.max(dist) > 0 else dist for dist in w_dists_degree]
+
+ ax.imshow(np.array(w_dists_degree).T, cmap='Blues') # , norm=norm)
+ ax.xaxis.set(ticks=range(len(ticks)), ticklabels=ticks)
+ ax.set(ylabel="Degree of target")
+ elif print_variable == "continuous":
+ if True:
+ max_dist = np.max([len(el) for el in weighted_distances])
+
+ mean_dist = [np.mean(flatten([wd_l[d] if d < len(wd_l) else [] for wd_l in weighted_distances])) for d in
+ range(max_dist)]
+ std_dist = [np.std(flatten([wd_l[d] if d < len(wd_l) else [] for wd_l in weighted_distances])) for d in
+ range(max_dist)]
+ # mean_dist = [np.mean(el) for el in weighted_distances]
+ # std_dist = [np.std(el) for el in weighted_distances]
+ ax.bar(range(1, len(mean_dist[1:]) + 1), mean_dist[1:], yerr=std_dist[1:], color="C1")
+ ax.set(ylabel=r"Link strength, $w_{ai}$", ylim=0)
+ else:
+ weighted_distances = flatten(
+ zip(w_dists_repetitions[0], w_dists_repetitions[1]))
+ labels = flatten(zip(range(len(w_dists_repetitions[0])),
+ range(len(w_dists_repetitions[1]))))
+ ax.boxplot(weighted_distances, labels=labels)
+ elif print_variable == "allocations":
+ max_dist = np.max([len(el) for el in weighted_distances])
+ weighted_dists = [flatten([wd_l[d] if d < len(wd_l) else [] for wd_l in weighted_distances]) for d in
+ range(max_dist)][1:]
+
+ x = flatten([[k] * len(box) for k, box in enumerate(weighted_dists)])
+ y = flatten(weighted_dists)
+ sns.boxenplot(x, y, ax=ax, showfliers=False) # jitter=0.45, size=1
+
+ ax.xaxis.set(ticklabels=range(1, max_dist + 1))
+ # ax.xaxis.set(ticks=range(-1, max_dist+1), ticklabels=)
+ ax.set(ylabel=r"$w_{ai}$")
+
+ # ax.violinplot(weighted_dists[1:], widths=1, showextrema=False) # showmeans=True
+ if pars['bmul'] == 1:
+ ax.set(ylim=[0.04, 10.1]) # [0, 10]
+ ax.set_yscale("log")
+ else:
+ raise Exception("Print variable " + print_variable + " not understood.")
+
+ ax.set(xlabel=r'Distance to opponent',
+ title="$K={:d}$".format(pars['N_allocations'])) # + "(for a discrete controller)")
+ ax.margins(x=0.0)
+
+ return x, y
+
+
+def print_general_distance_to_b(ax, pars, print_variable="allocations"):
+ ax.axhline(color="k")
+ colors = plt.cm.get_cmap("winter")(np.linspace(0, 1, len(pars['linesarray'])))
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ if pars['add_discrete']:
+ type_of_exp = "HillClimb"
+ else:
+ type_of_exp = "GA"
+ fname = create_name(level="data", type_of_experiment=type_of_exp, distances=True, **pars)
+ try:
+ with open(fname, 'rb') as f:
+ data = pickle.load(f)
+ print("Distance data loaded from " + fname)
+
+ avg_distances = data['avg_distances']
+ unweighted_distances = data['unweighted_distances']
+
+ except FileNotFoundError:
+ print("Distances data " + fname + " not found. Running experiments...")
+
+ avg_distances = np.zeros((len(pars['axisarray']), pars['n_nets']))
+ unweighted_distances = np.empty((len(pars['axisarray']), pars['n_nets']), dtype=list)
+ for i, val in enumerate(create_range(pars)):
+ # print(pars['axispar'])
+ # pars['gain'] *= pars['bmul']
+ # if np.isclose(pars['gain'], int(pars['gain'])):
+ # pars['gain'] = int(pars['gain'])
+ print("\n", pars['axispar'], i, pars[pars['axispar']])
+
+ aes, bs, seqs, Xs, xs, convs, aes_disc, _, _, networks = load_data(pars)
+
+ for l in range(len(bs)):
+ net = networks[l]
+ node_list = np.array(net.G.nodes)
+ _, sort_ind = net.sort_by_degree(type_of_experiment=None)
+ unsort_ind = np.argsort(sort_ind)
+
+ if not pars['add_discrete']:
+ if convs is not None:
+ if convs[l]:
+ a, b = aes[l][unsort_ind], bs[l][unsort_ind]
+ else:
+ a, b = aes_disc[l][unsort_ind], bs[l][unsort_ind]
+
+ # avg_distances[i] = 0
+ # for b_node in range(1, pars['N']):
+ # weighted_distance = 0
+ # if b[b_node - 1] > 0:
+ # distances = nx.single_source_shortest_path_length(net.G, b_node)
+ # for node in distances:
+ # weighted_distance += a[node - 1] * distances[node]
+ # weighted_distance /= np.sum(a)
+ # avg_distances[i] += b[b_node - 1] * weighted_distance
+ # avg_distances[i] /= np.sum(b)
+
+ avg_distances[i, l] = 0
+ tot_a = 0
+ # print("In!!!")
+ unweighted_distances[i, l] = []
+ # tot_dist = 0
+ for a_node, a_value in enumerate(a):
+ closest_distance = 9999
+ distances = nx.single_source_shortest_path_length(net.G, node_list[a_node])
+ for b_node in range(pars['N']):
+ if b[b_node] > 0 and distances[node_list[b_node]] < closest_distance:
+ closest_distance = distances[node_list[b_node]]
+ if closest_distance == 0:
+ break
+ if closest_distance > 0:
+ # if not pars['add_discrete']:
+ avg_distances[i, l] += a_value * closest_distance
+ tot_a += a_value
+ # elif a_value > 0:
+ # avg_distances += closest_distance
+ # tot_a += 1
+ unweighted_distances[i, l].append(closest_distance)
+ # tot_dist += 1
+ # unweighted_distances[i, l] /= tot_dist # len(a)
+ avg_distances[i, l] /= tot_a # np.sum(a)
+ # avg_distances[i, l] /= unweighted_distance
+
+ data = {'avg_distances': avg_distances, 'unweighted_distances': unweighted_distances}
+
+ print("Saving model in " + fname + "\n----------------------------------------------\n")
+ with open(fname, 'wb') as f:
+ pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
+
+ mean_unweighted = np.zeros_like(unweighted_distances)
+ std_unweighted = np.zeros_like(unweighted_distances)
+ for i in range(unweighted_distances.shape[0]):
+ for l in range(unweighted_distances.shape[1]):
+ if unweighted_distances[i, l] is not None:
+ mean_unweighted[i, l] = np.mean(unweighted_distances[i, l])
+ std_unweighted[i, l] = np.std(unweighted_distances[i, l])
+ # print(unweighted_distances)
+ # print(avg_distances)
+ # print(mean_unweighted)
+ # print(std_unweighted)
+ expressions = (avg_distances - mean_unweighted) / std_unweighted
+
+ # axis_vec = np.array(list(create_range(pars)))
+ axis_vec = pars[
+ 'axisarray'] # [int(pars['N'] * compute_a_from_amul(net, pars) / gain) for gain in pars['axisarray']]
+
+ if np.isclose(1 / linesval, int(1 / linesval)):
+ label = str(int(1 / linesval))
+ else:
+ label = str(1 / linesval)
+ # ax.semilogx(axis_vec, avg_distances, color=colors[j], marker=".", markersize=2,
+ ax.semilogx(axis_vec, np.mean(expressions, axis=1), color=colors[j], marker=".", markersize=2,
+ label=r"$a/b\!=\!" + label + "$") # r"$b\!=\!" + str(linesval) + "\,a$")
+
+ # ax.semilogx(axis_vec, unweighted_distances, color="C1", label="avg")
+
+ ax.set(ylabel=r"Average distance to opponent, $D$")
+ ax.legend() # loc="right")
+ ax.set(xlabel=r'N of B-targeted nodes (discrete)') # , ylim=0)
+ ax.margins(x=0.0)
+
+
+def print_shialdowing_disc(ax, pars, print_variable="allocations"):
+ n_nets = pars['n_nets']
+ axis_vec = np.array(pars['axisarray'])
+ colors = plt.cm.get_cmap("winter")(np.linspace(0, 0.8, len(pars['linesarray'])))
+ symbols = ["s", "D", "o", "x", "v"]
+ markersize = 5
+ elinewidth = 1
+
+ normalised_degree = True
+
+ reference_strat = np.zeros((3, len(pars['linesarray']), len(pars['axisarray']), n_nets))
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ shialdowing = np.zeros((len(pars['axisarray']), 2))
+ for i, val in enumerate(create_range(pars)):
+ print("\n", pars['axispar'], i, val)
+ N_allo = pars['N_allocations']
+
+ _, bs, seqs, Xs, xs, convs, aes_d, Xs_disc, _, nets = load_data(pars)
+
+ shadowings = [np.sum(np.logical_and(aes_d[l] > 0, bs[l] > 0)) / N_allo for l in range(n_nets)]
+ if print_variable == "degrees":
+ degrees = [seqs[l][aes_d[l] > 0] for l in range(n_nets)]
+ shialdowing[i, 0] = np.mean(degrees)
+ shialdowing[i, 1] = np.std(degrees)
+
+ assert pars['network'] == "email", "High/lo-degree nodes only implemented for email network!"
+ sorted_seq, _ = nets[0].sort_by_degree(type_of_experiment=None)
+ reference_strat[0, :, i] = np.mean(sorted_seq[::-1][:N_allo])
+ reference_strat[1, :, i] = np.mean(sorted_seq[:N_allo])
+
+ if normalised_degree:
+ shialdowing[i, 0] = (shialdowing[i, 0] - reference_strat[1, 0, i, 0]) / (
+ reference_strat[0, 0, i, 0] - reference_strat[1, 0, i, 0])
+ shialdowing[i, 1] = shialdowing[i, 1] / (reference_strat[0, 0, i, 0] - reference_strat[1, 0, i, 0])
+
+ def compute_overlap(array1, array2):
+ assert len(array1) == len(array2)
+ overlap = 0
+ for el in array1:
+ founds = array2 == el
+ if np.any(founds):
+ overlap += 1
+ array2 = np.delete(array2, founds.argmax())
+ return overlap / len(array1)
+
+ high_overlaps = [compute_overlap(degs, sorted_seq[::-1][:N_allo]) for degs in degrees]
+ low_overlaps = [compute_overlap(degs, sorted_seq[:N_allo]) for degs in degrees]
+ print("high overlaps: ", np.mean(high_overlaps), np.std(high_overlaps) / np.sqrt(n_nets))
+ print("low overlaps: ", np.mean(low_overlaps), np.std(low_overlaps) / np.sqrt(n_nets))
+ elif print_variable == "shadowing":
+ shialdowing[i, 0] = np.mean(shadowings)
+ shialdowing[i, 1] = np.std(shadowings)
+ elif print_variable == "shielding":
+ shieldings = np.zeros(n_nets)
+ for l, net in enumerate(nets):
+ b, a, W, N = net.b[:, 0], net.a_disc, net.W.toarray(), net.N
+
+ mask_b, mask_b_neighbours, mask_rest = divide_into_neighbouring_groups(b, W)
+
+ n_nodes = int(N_allo * (1 - shadowings[l]))
+ assert np.isclose(n_nodes, N_allo * (1 - shadowings[l]))
+ a_b_neighbours = np.sum(np.logical_and(a > 0, mask_b_neighbours))
+
+ # Shielding in targeting high- and low-degree nodes (baselines)
+ high_b_neighbours, m = 0, 0
+ indices_b, indices_neighbours = np.arange(N)[mask_b], np.arange(N)[mask_b_neighbours]
+ for index in net.sort_ind[::-1]:
+ if m == n_nodes:
+ break
+ if index in indices_neighbours:
+ high_b_neighbours += 1
+ if index not in indices_b:
+ m += 1
+ low_b_neighbours, m = 0, 0
+ for index in net.sort_ind:
+ if m == n_nodes:
+ break
+ if index in indices_neighbours:
+ low_b_neighbours += 1
+ if index not in indices_b:
+ m += 1
+
+ # print("a_b_neighbours", a_b_neighbours, "N_b_neighbours", np.sum(mask_b_neighbours), "n_nodes",
+ # n_nodes, "high degree", int(high_b_neighbours * np.sum(mask_b_neighbours)))
+
+ if shadowings[l] < 1:
+ # den = min([n_nodes, N_b_neighbours])
+ den = N_allo * (1 - shadowings[l])
+ shieldings[l] = a_b_neighbours / den
+ reference_strat[0, j, i, l] = high_b_neighbours / den
+ reference_strat[1, j, i, l] = low_b_neighbours / den
+ else:
+ shieldings[l] = np.NaN
+ reference_strat[:, j, i, l] = np.NaN
+
+ shialdowing[i, 0] = np.nanmean(shieldings)
+ shialdowing[i, 1] = np.nanstd(shieldings)
+ else:
+ raise Exception("Print variable {:s} not understood.".format(print_variable))
+
+ if np.isclose(1 / linesval, int(1 / linesval)):
+ label = str(int(1 / linesval))
+ else:
+ label = str(1 / linesval)
+
+ ax.errorbar(axis_vec, shialdowing[:, 0], yerr=shialdowing[:, 1] / np.sqrt(n_nets), color=colors[j], ecolor="k",
+ ls='', label=r"$r\!=\!" + label + "$", marker=symbols[j], elinewidth=elinewidth,
+ markersize=markersize) # , fillstyle='none') label=r"$\mathcal{B}_a/\mathcal{B}_b\!=\!"
+ print(shialdowing[:, 0])
+
+ if print_variable == "degrees":
+ low_deg = reference_strat[1, 0, :, 0]
+ print("Mean degree", np.mean(shialdowing[:, 0] * (reference_strat[0, 0, :, 0] - low_deg) + low_deg))
+
+ if print_variable == "shadowing":
+ ax.errorbar(axis_vec, axis_vec / pars['N'], color="purple", ls='', markersize=markersize,
+ marker=symbols[len(pars['linesarray'])], label=r"$\phi_R$")
+ ax.set_ylabel(r"$\phi = P(i\in\mathbf{T}_a \;|\; i\in\mathbf{T}_b)$")
+ ax.legend(ncol=2, columnspacing=0.01, handletextpad=0.01)
+ ax.set(ylim=[0, 1])
+ elif print_variable == "shielding":
+ ax.errorbar(axis_vec, np.nanmean(reference_strat[0], axis=(0, 2)), marker=symbols[len(pars['linesarray']) + 1],
+ yerr=std_errors(reference_strat[0], axis=(0, 2), nan=True), color="purple",
+ elinewidth=elinewidth, label=r"$\Pi_H$", ecolor="k", ls='', markersize=markersize)
+ # ax.errorbar(axis_vec, np.nanmean(reference_strat[1], axis=(0, 2)), elinewidth=elinewidth, label=r"$\Pi_H$",
+ # yerr=np.nanstd(reference_strat[1], axis=(0, 2)) / np.sqrt(n_nets), color="purple",
+ # marker=symbols[len(pars['linesarray']) + 1], ecolor="k", ls='', markersize=markersize)
+ # ax.errorbar(axis_vec, np.nanmean(reference_strat[3], axis=(0, 2)), marker=symbols[len(pars['linesarray'])],
+ # yerr=np.std_errors(reference_strat[3], axis=(0, 2), nan=True), color="purple",
+ # elinewidth=elinewidth, label=r"$\Pi_S$", ecolor="k", ls='', markersize=markersize)
+ ax.set_ylabel(r"$\Pi=P(i\in\mathbf{N}_b \;|\; i\in(\mathbf{T}_a\cup \neg\mathbf{T}_b))$")
+ ax.legend(loc='upper left', columnspacing=0.01, handletextpad=0.01)
+ ax.set(ylim=[0, 1])
+ elif print_variable == "degrees":
+ if not normalised_degree:
+ ax.errorbar(axis_vec, reference_strat[0, 0, :, 0], color="purple", ls='', markersize=markersize,
+ marker=symbols[len(pars['linesarray'])], label=r"high degree")
+ ax.errorbar(axis_vec, reference_strat[1, 0, :, 0], color="purple", ls='', markersize=markersize,
+ marker=symbols[len(pars['linesarray']) + 1], label=r"low degree")
+ else:
+ ax.set(ylim=[0, 1])
+ ax.set_ylabel(r"$\delta$")
+ # r"$\tilde{\langle d\rangle}_{\mathcal{T}_a}$") # (\langle d \rangle_{\mathcal{T}_a} - \langle d \rangle_L)/(\langle d \rangle_H - \langle d \rangle_L)$")
+ ax.legend(loc="lower left", ncol=2, columnspacing=0.01, handletextpad=0.01)
+
+ ax.set(xlabel=K_X_LABEL)
+ ax.set_xscale("log")
+
+ # ax.margins(x=0.0) # , y=0.0)
+
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+ # ax.grid()
+
+
+def print_general_shielding_cont(ax, pars, print_variable="allocations"):
+ n_nets = pars['n_nets']
+ axis_vec = np.array(pars['axisarray'])
+ # colors = plt.cm.get_cmap("winter")(np.linspace(0, 0.8, len(pars['linesarray'])))
+ # markers = ["x", "+", ".", "s", "D"], markersize = 8
+ markers = ["s", "D", "o"]
+ markersize = 5
+
+ for j, linesval in enumerate(create_range(pars, "lines")):
+ shialdowing = np.zeros((len(pars['axisarray']), n_nets, 3))
+ for i, val in enumerate(create_range(pars)):
+ print("\n", pars['axispar'], i, val)
+
+ aes, bs, seqs, Xs, xs, convs, _, _, _, nets = load_data(pars)
+
+ for l, net in enumerate(nets):
+ masks = divide_into_neighbouring_groups(net.b[:, 0], net.W.toarray())
+ shialdowing[i, l] = np.array([np.mean(net.a[mask]) for mask in masks]) / np.mean(net.a)
+
+ if False:
+ if np.isclose(1 / linesval, int(1 / linesval)):
+ label = str(int(1 / linesval))
+ else:
+ label = str(1 / linesval)
+
+ ax.errorbar(axis_vec, np.mean(shialdowing[..., 1] / shialdowing[..., 2], axis=1), markersize=markersize,
+ yerr=np.std(shialdowing[..., 1] / shialdowing[..., 2], axis=1) / np.sqrt(n_nets),
+ marker=markers[j],
+ color=colors[j], ecolor="k", ls='', label=r"$\mathcal{B}_a/\mathcal{B}_b\!=\!" + label + "$")
+ else:
+ for k, (g_label, color) in enumerate(
+ zip(NEIGH_GROUP_LABELS, plt.cm.get_cmap("viridis")(np.linspace(0, 0.95, 3)))):
+ ax.errorbar(axis_vec, np.mean(shialdowing[..., k], axis=1), marker=markers[k], markersize=markersize,
+ yerr=np.std(shialdowing[..., k], axis=1) / np.sqrt(n_nets), color=color, ecolor="k", ls='',
+ label=g_label, elinewidth=ELINEWIDTH) # , fillstyle='none')
+
+ # ax.set_ylabel(r"$\langle w_{a} \rangle$")
+ ax.set_ylabel(r"$\langle w_{a} \rangle_{\mathbf{S}} \;/\; \langle w_{a} \rangle$")
+ ax.set(xlabel=K_X_LABEL, ylim=[1e-2, 4e2])
+ ax.set(title=r"$\mathcal{B}_a/\mathcal{B}_b =" + str(1 / pars['bmul']) + "$")
+ if pars['bmul'] == 1:
+ ax.legend() # ncol=2)
+ ax.set_xscale("log")
+ ax.set_yscale("log")
+ # ax.set_facecolor("whitesmoke")
+ print(np.mean(shialdowing, axis=1))
+
+ # ax.margins(x=0.0) # , y=0.0)
+
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+ # ax.grid()
+
+ return shialdowing
+
+
+def print_cont_shielding(ax, pars, print_variable="allocations"):
+ n_nets = pars['n_nets']
+ n_neigh_groups = 3
+ n_allo_groups = 4
+
+ if n_neigh_groups == 3:
+ group_labels = NEIGH_GROUP_LABELS
+ elif n_neigh_groups == 4:
+ group_labels = (r"$w_{bi}>0$", r"$\mathbf{N}_b+$", r"$\mathbf{N}_b$", r"Rest")
+ else:
+ raise Exception("Number of neighbouring groups not understood: " + str(n_neigh_groups))
+
+ if print_variable == "boxplot":
+ boxes = np.zeros((pars['n_nets'], n_neigh_groups, n_allo_groups))
+
+ aes, bs, seqs, Xs, xs, convs, _, _, _, nets = load_data(pars)
+
+ group_allo_sizes = np.zeros((n_nets, n_allo_groups))
+ group_neigh_sizes = np.zeros((n_nets, n_neigh_groups))
+ for l in range(n_nets):
+ b, a, W = nets[l].b[:, 0], nets[l].a, nets[l].W.toarray()
+
+ masks_neighbouring_groups = divide_into_neighbouring_groups(b, W, n_groups=n_neigh_groups)
+ group_neigh_sizes[l] = np.array([np.sum(mask) for mask in masks_neighbouring_groups])
+
+ if convs[l]:
+ masks_allo = divide_into_allocation_groups(a, n_allo_groups, **pars)
+ group_allo_sizes = np.array([np.sum(mask) for mask in masks_allo])
+
+ for m, mask_neigh in enumerate(masks_neighbouring_groups):
+ for k, mask_allo in enumerate(masks_allo):
+ boxes[l, m, k] = np.sum(np.logical_and(mask_allo, mask_neigh))
+ else:
+ print(l, "NOT CONVERGED")
+
+ print("group allo sizes", group_allo_sizes)
+ print("aggregaed group allo sizes", np.mean(group_allo_sizes, axis=0), np.mean(group_neigh_sizes, axis=0))
+
+ if False:
+ boxes /= np.sum(boxes, axis=1)[:, None]
+
+ for k in range(n_allo_groups):
+ ax.bar(range(3), boxes[:, k], bottom=np.sum(boxes[:, k + 1:], axis=1), color=ALLO_GROUP_COLOURS[k],
+ label=get[k][:12] + "$")
+
+ ax.xaxis.set(ticks=[0, 1, 2], ticklabels=group_labels)
+ else:
+ # boxes = np.sum(boxes, axis=0)
+ for l in range(len(boxes)):
+ boxes[l] /= np.sum(boxes[l], axis=0)[None:]
+
+ for k in range(n_neigh_groups):
+ means = np.mean(boxes, axis=0)
+ errors = std_errors(boxes, axis=0)
+ print(boxes.shape, means.shape, errors.shape)
+ ax.bar(range(n_allo_groups), means[k, :], bottom=np.sum(means[:k, :], axis=0), yerr=errors[k, :],
+ color=NEIGH_GROUP_COLOURS[k], label=group_labels[k])
+
+ ax.xaxis.set(ticks=range(n_allo_groups), ticklabels=[label[:12] + "$" for label in ALLO_GROUP_LABELS])
+
+ ax.set(ylim=[0, 1], ylabel=r"$P(\mathbf{S} \;|\; \mathbf{G})$") # \;|\; \mathbf{F})$")
+ ax.legend(loc="center right")
+
+ return boxes
+
+ elif print_variable == "allocations":
+ boxes = [[] for _ in range(n_neigh_groups)]
+ boxes_allo = [[] for _ in range(n_neigh_groups)]
+
+ aes, bs, seqs, Xs, xs, convs, _, _, _, nets = load_data(pars)
+
+ group_neigh_sizes = np.zeros((n_nets, n_neigh_groups))
+ for l in range(n_nets):
+ b, a, W = nets[l].b[:, 0], nets[l].a, nets[l].W.toarray()
+
+ masks_neighbouring_groups = divide_into_neighbouring_groups(b, W, n_groups=n_neigh_groups)
+ group_neigh_sizes[l] = np.array([np.sum(mask) for mask in masks_neighbouring_groups])
+
+ if convs[l]:
+ masks_allo_groups = divide_into_allocation_groups(a, n_groups=n_allo_groups)
+ allo_groups = np.full(len(a), "")
+ for k, mask_allo in enumerate(masks_allo_groups):
+ allo_groups[mask_allo] = ALLO_GROUP_LABELS[k][10] # GROUP_LABELS[k][:12] + "$"
+ for m, mask_neigh in enumerate(masks_neighbouring_groups):
+ boxes[m].extend(a[mask_neigh])
+ boxes_allo[m].extend(allo_groups[mask_neigh])
+ else:
+ print(l, "NOT CONVERGED")
+
+ print("aggregated group sizes", np.mean(group_neigh_sizes, axis=0))
+
+ # ax.bar(range(3), [np.mean(box) for box in boxes], yerr=[np.std(box) for box in boxes])
+ # ax.violinplot(boxes, widths=1, showmedians=True, showextrema=False)
+
+ x = flatten([[k] * len(box) for k, box in enumerate(boxes)])
+ y = flatten(boxes)
+ hue = flatten(boxes_allo)
+ categories = ["H", "A", "V", "Z"]
+ data = pd.DataFrame({"x": x, "y": y, "G": pd.Categorical(hue, categories=categories, ordered=True)})
+ # sns.catplot(x="x", y="y", hue="G", kind="strip", size=1, jitter=0.45, ax=ax, data=data)
+ sns.stripplot(x=x, y="y", hue="G", size=1, jitter=0.4, ax=ax, data=data,
+ palette={categories[k]: ALLO_GROUP_COLOURS[k] for k in range(n_allo_groups)})
+ # sns.stripplot(x, y, hue=hue, size=1, jitter=0.45, ax=ax)
+
+ ax.xaxis.set(ticks=range(n_neigh_groups), ticklabels=group_labels)
+ # ax.set(ylabel=r"$\langle w_{ai} \rangle_{\mathbf{C}}$") # , ylim=[0, 3]) # \;|\; \mathbf{F})$")
+ ax.set(ylabel=r"$w_{ai}$") # , ylim=[0, 3]) # \;|\; \mathbf{F})$")
+ ax.set_facecolor("whitesmoke")
+
+ # ax.margins(x=0.0) # , y=0.0)
+
+ # legends = [
+ # mlines.Line2D([], [], color='C1', marker="o", label=r"$a_i$", ls="", markersize=2),
+ # mlines.Line2D([], [], color='C0', marker="o", label=r"$b_i \cdot r$", ls="", markersize=2),
+ # ]
+ # ax.legend(handles=legends)
+ # ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
+ # ax.grid()
+
+
+def print_multi_axes(pars, print_func, print_variable="allocations", figsize=(2.5, 2.5), create_name_func=create_name,
+ letter_labels=False, **sharing):
+ if pars['network'] == "email":
+ print(pars['N'])
+ G = Network(pars['network'], **pars)
+ pars['N'] = G.N
+
+ n_subplots = (len(pars['multiaxesarray']), len(pars['axesarray']))
+ print("\n\nXXXXXXXXXXXXXXXX\n\n", print_func.__name__, n_subplots, "n nets", pars['n_nets'])
+
+ fig, axes = plt.subplots(n_subplots[0], n_subplots[1], squeeze=False,
+ figsize=(figsize[0] * n_subplots[1], figsize[1] * n_subplots[0]), **sharing)
+ labels = "abcdefghijklmnopqrstuvwxyz"
+ outs = []
+ for i, el_multi in enumerate(create_range(pars, level="multiaxes")):
+ for j, el_axes in enumerate(create_range(pars, level="axes")):
+ print(f"\n\n*** Experiments with {pars['axespar']} = {el_axes} ({i}, {j})")
+ ax = axes[i][j]
+
+ outs.append(print_func(ax, pars, print_variable=print_variable))
+
+ if j > 0 or (n_subplots[0] == 1 and i > 0):
+ ax.set(ylabel="")
+
+ if i != 0:
+ ax.set(title="")
+ # if i < n_subplots[0] - 1:
+ # ax.set(xlabel="")
+
+ if letter_labels:
+ if print_func == print_a_vs_b:
+ x, y = 0.1, 0.9
+ if j == 2 and i == 1:
+ x = 0.2
+ elif print_func == print_specific_distance_to_b:
+ x, y = 0.05, 0.9
+ if j == 0:
+ x = 0.15
+ elif print_func.__name__ == "print_X_gap":
+ x, y = 0.07, 0.95
+ if j == 1 and pars['network'] in ("email", "advogato"):
+ x, y = 0.17, 0.9
+ elif print_func.__name__ == "print_performance_iters":
+ x, y = 0.46, 0.94
+ if j == 2:
+ x = 0.6
+ else:
+ raise Exception("letter labels not implemented for this printing function")
+
+ ax.text(x, y, r"\textbf{" + labels[i * n_subplots[1] + j] + r"}", horizontalalignment='center',
+ verticalalignment='center', transform=ax.transAxes)
+
+ plotname = create_name_func(level="plot", print_func=print_func, print_variable=print_variable, **pars)
+
+ fig.tight_layout()
+ fig.savefig(plotname)
+ print("\nFigure saved in " + plotname)
+ fig.show()
+
+ return axes, outs
+
+
+# RESULT FUNCTIONS
+def result_bpower_avg_degree():
+ pars = {
+ 'amul': 'akdegree%3',
+ 'axispar': 'beta', 'axisarray': np.linspace(-2, 2.5, 37),
+ # 'axispar': 'beta', 'axisarray': np.linspace(-1.5, 1.5, 25),
+ 'linespar': 'bmul', 'linesarray': [0.01, 0.1, 0.5, 1.0, 2.0, 10.0, 100],
+ 'axespar': None, 'axesarray': [None],
+ 'multiaxespar': None, 'multiaxesarray': [None],
+ 'b_allocation': 'powerlaw', # in 'random', # 'sin', # 'powerlaw', 'linear'
+
+ 'network': 'email',
+ 'iters': 60000, 'lrate': 50,
+ }
+ for i, val in enumerate(pars['axisarray']):
+ if val == -0.5:
+ pars['axisarray'][i] = -0.49
+ if val == 0.5:
+ pars['axisarray'][i] = 0.49
+ if val == 1.0:
+ pars['axisarray'][i] = 0.99
+ if val == 1.5:
+ pars['axisarray'][i] = 1.49
+
+ if pars['network'] != "email":
+ pars['N'] = 200
+ pars['n_nets'] = 1
+ pars['net'] = 'ba' # 'conf'
+ pars['gamma'] = 2.1 # For scale-free conf model
+ pars['m'] = 1 # For Barabasi-Albert network
+
+ if pars['b_allocation'] in ["random", "randn"]:
+ pars['axespar'] = 'bmul'
+ pars['axesarray'] = np.power(20, np.array([1, 0, -1.0])) # 0, -1])),
+ pars['multiaxespar'] = 'beta'
+ pars['multiaxesarray'] = [0]
+
+ # print_multi_axes(pars, print_avg_targeted_degree_vs_bmul)
+ print_multi_axes(pars, print_avg_vs_beta, figsize=(4.5, 2.5))
+
+
+def result_network_topology(network="email", n_nets=1, end="middle", **kwargs):
+ pars = {'network': network, 'n_nets': n_nets,
+ 'kabmul': 1 / 30, 'bmul': 1.0, 'b_allocation': 'discrete', 'end': end, 'N_allocations': 16,
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+ }
+ for key, val in kwargs.items():
+ pars[key] = val
+
+ print_multi_axes(pars, print_topology, figsize=(FI * 2.5, 2.5))
+
+
+def result_introductory_example(network="email", end="middle", n_nets=1, min_iters_since_change=100, N_allocations=16,
+ iters=200000, include_a_vs_nodes=True, include_shielding=False, include_G_vs_S=True,
+ include_a_vs_deg=False, **kwargs):
+ pars = {
+ # Experiment setting
+ 'network': network, 'n_nets': n_nets,
+ 'kabmul': 1 / 30, 'bmul': 1.0, 'b_allocation': 'discrete', 'end': end, 'N_allocations': N_allocations,
+
+ # Parameter ranges
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+ # 'axespar': 'N_allocations', 'axesarray': [1, 2, 4, 8, 16, 32, 64], 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': False, 'disc_iters': 200, 'min_iters_since_change': min_iters_since_change,
+ 'add_continuous': True, 'iters': iters, 'lrate': 50, 'initial_a': 'random',
+
+ # Plotting parameters
+ 'group_criterion': "mean",
+ }
+ for key, val in kwargs.items():
+ pars[key] = val
+
+ # print_multi_axes(pars, print_graph, figsize=(30, 30))
+ # print_multi_axes(pars, print_specific_distance_to_b, figsize=(3, 2.5), print_variable="continuous")
+
+ if include_a_vs_deg:
+ print_multi_axes(pars, print_a_vs_deg, figsize=(4.5, 2.5), print_variable="boxplot")
+ if include_G_vs_S:
+ print_multi_axes(pars, print_cont_shielding, figsize=(3, 2.5), print_variable="boxplot")
+ if include_a_vs_nodes:
+ pars['n_nets'] = 1
+ print_multi_axes(pars, print_a_vs_nodes, figsize=(3, 2.5))
+ pars['n_nets'] = n_nets
+ if pars['network'] == "email" and pars["end"] == "middle":
+ pars['n_nets'] = 60
+ # print_multi_axes(pars, print_a_vs_deg, figsize=(4.5, 2.5), print_variable="groups")
+ if include_shielding:
+ print_multi_axes(pars, print_a_vs_deg, figsize=(3, 2.5), print_variable="shielding")
+
+ pars['n_nets'] = n_nets
+ # print_multi_axes(pars, print_cont_shielding, figsize=(3, 2.5))
+ pars['add_discrete'] = True
+ # print_multi_axes(pars, print_a_vs_deg, figsize=(4.5, 2.5), print_variable="correlations")
+ # print_multi_axes(pars, print_a_vs_deg, figsize=(4.5, 2.5), print_variable="discrete")
+ # print_multi_axes(pars, print_a_vs_deg, figsize=(4.5, 2.5), print_variable="discrete_degree")
+
+
+def result_entropy_vs_N(network="email", end="middle", n_nets=1, min_iters_since_change=100, **kwargs):
+ pars = {
+ # Experiment setting
+ 'network': network, 'n_nets': n_nets,
+ 'axispar': 'N_allocations',
+ 'axisarray': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512], # 1024],
+ 'kabmul': 1 / 30, 'b_allocation': 'discrete', 'end': end, # 'bmul': 1.0,
+ 'linespar': 'bmul', 'linesarray': [0.1, 1, 10],
+ # 'linespar': 'bmul', 'linesarray': [0.01, 1, 100],
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': True, 'disc_iters': 2, 'initial_a': 'random', 'min_iters_since_change': min_iters_since_change,
+ 'add_continuous': True, 'iters': 200000, 'lrate': 50,
+ }
+ for key, val in kwargs.items():
+ pars[key] = val
+
+ if pars['network'] == "classroom":
+ pars['axisarray'] = [round(el / 1133 * pars['N']) for el in (8, 16, 32, 64, 128, 256, 512)]
+
+ # print_multi_axes(pars, print_entropy_vs_N_b, figsize=(3, 2.5), print_variable="entropy")
+ print_multi_axes(pars, print_entropy_vs_N_b, figsize=(3, 2.5), print_variable="improvement")
+
+
+def result_correlations(network="email", n_nets=1, min_iters_since_change=100, initial_a_disc="random",
+ plot_array=False, **kwargs):
+ pars = {
+ # Experiment setting
+ 'network': network, 'n_nets': n_nets,
+ 'axispar': 'N_allocations', 'axisarray': [1, 2, 4, 8, 16, 32, 64, 128, 256],
+ 'kabmul': 1 / 30, 'b_allocation': 'discrete', 'bmul': 1,
+ 'linespar': 'None', 'linesarray': [None], # 'linespar': 'bmul', 'linesarray': [0.1, 1, 10],
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': False, 'disc_iters': 200, 'initial_a_disc': initial_a_disc,
+ 'min_iters_since_change': min_iters_since_change,
+ 'add_continuous': True, 'iters': 200000, 'lrate': 50,
+ }
+ for key, val in kwargs.items():
+ pars[key] = val
+
+ if pars['network'] == "classroom":
+ pars['axisarray'] = [round(el / 1133 * pars['N']) for el in (8, 16, 32, 64, 128, 256, 512)]
+ elif pars['network'] == "advogato":
+ pars['axisarray'] = [round(el / 1133 * pars['N']) for el in (1, 2, 4, 8, 16, 32, 64, 256)]
+
+ if plot_array:
+ pars['axespar'] = "end"
+ pars['axesarray'] = ["middle", "periphery"]
+ print_multi_axes(pars, print_correlations, figsize=(3, 2.5), sharey=True)
+ else:
+ # print_multi_axes(pars, print_general_distance_to_b, figsize=(3, 2.5))
+ pars['end'] = "middle"
+ print_multi_axes(pars, print_correlations, figsize=(3, 2.5), sharey=True)
+ if pars['network'] != "advogato":
+ pars['end'] = "periphery"
+ print_multi_axes(pars, print_correlations, figsize=(3, 2.5))
+
+
+def result_specific_shielding_continuous(network="email", end="middle", n_nets=1, **kwargs):
+ pars = {
+ # Experiment setting
+ 'network': network, 'n_nets': n_nets,
+ # 'N_allocations_a': "over_shielding",
+ 'kabmul': 1 / 30, 'bmul': 1,
+ 'b_allocation': 'discrete', 'end': end, 'N_allocations': 16,
+ 'axespar': None, 'axesarray': [None], # [4, 3, 2, 1], # [32], # [256, 32, 4],
+ 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': False, 'add_continuous': True, 'iters': 60000, 'lrate': 50,
+ }
+ for key, val in kwargs.items():
+ if key not in pars:
+ pars[key] = val
+
+ print_multi_axes(pars, print_cont_shielding, figsize=(3, 2.5), print_variable="boxplot")
+
+
+def result_general_shielding_continuous(network, end, n_nets=1, min_iters_since_change=100, plot_array=False, **kwargs):
+ pars = {
+ # Experiment setting
+ 'network': network, 'n_nets': n_nets,
+ 'axispar': 'N_allocations', 'axisarray': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512],
+ 'linespar': 'None', 'linesarray': [None], # [0.01, 1, 100],
+ 'kabmul': 1 / 30, 'b_allocation': 'discrete', 'end': end,
+ 'axespar': None, 'axesarray': [None], # 0.1], # 1, 10],
+ 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': False, 'initial_a': 'random', 'min_iters_since_change': min_iters_since_change,
+ 'add_continuous': True, 'iters': 30000, 'lrate': 50,
+ }
+ for key, val in kwargs.items():
+ if key not in pars:
+ pars[key] = val
+
+ if pars['network'] == "classroom":
+ pars['axisarray'] = [round(el / 1133 * pars['N']) for el in (8, 16, 32, 64, 128, 256, 512)]
+ elif pars['network'] == "advogato":
+ pars['axisarray'] = [round(el / 1133 * pars['N']) for el in (1, 2, 4, 8, 16, 32, 64, 256)]
+
+ if plot_array:
+ pars['axespar'] = "bmul"
+ pars['axesarray'] = [1, 10]
+ print_multi_axes(pars, print_general_shielding_cont, figsize=(3, 2.5), sharey=True)
+ else:
+ # print_multi_axes(pars, print_general_distance_to_b, figsize=(3, 2.5))
+ pars['bmul'] = 1
+ print_multi_axes(pars, print_general_shielding_cont, figsize=(3, 2.5), sharey=True)
+ if pars['network'] != "advogato":
+ pars['bmul'] = 10
+ print_multi_axes(pars, print_general_shielding_cont, figsize=(3, 2.5))
+
+
+def result_linear_nodes(network="email", letter_labels=False, bmul=0.1, multiaxes=False, figsize=(3, 2.5), **kwargs):
+ pars = {
+ 'network': network, 'n_nets': 1, # random
+ 'axespar': 'kabmul', 'axesarray': [0.1, 1, 10],
+ 'multiaxespar': 'bmul' if multiaxes else None, 'multiaxesarray': [0.1, 10] if multiaxes else None, 'bmul': bmul,
+ # 'multiaxespar': 'bmul', 'multiaxesarray': [0.01, 100],
+ 'b_allocation': 'random', # in ['random', 'sin', 'powerlaw', 'linear', 'discrete', 'randn']
+ # 'b_allocation': 'discrete', 'end': 'middle', 'N_allocations': 32,
+
+ 'add_continuous': True, 'iters': 60000, 'lrate': 50, 'add_discrete': False,
+ }
+ for key, val in kwargs.items():
+ if key not in pars:
+ pars[key] = val
+
+ if pars['b_allocation'] == "powerlaw":
+ pars['axespar'] = 'beta'
+ pars['axesarray'] = [-1.5, 1.5]
+ # pars['axesarray'] = [-2.0, -1.0, -0.25, 0.25, 1.0, 2.0]
+
+ axes, outs, = print_multi_axes(pars, print_a_vs_b, figsize=figsize, letter_labels=letter_labels)
+
+ return outs, axes
+
+
+def result_perturbation_exploitation():
+ pars = {
+ 'network': 'email', 'n_nets': 15, 'N': 1133,
+ 'kabmul': 1 / 30, 'bmul': 1.0,
+ # 'axispar': 'n_perturbations', 'axisarray': [4, 8, 16, 32, 64, 128, 256],
+ 'axispar': 'end', 'axisarray': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
+ # 'linespar': 'end', 'linesarray': ["low", "high"],
+ 'linespar': 'n_perturbations', 'linesarray': [20],
+ 'axespar': None, 'axesarray': [None],
+ 'multiaxespar': None, 'multiaxesarray': [None],
+ 'b_allocation': 'randlin', # 'n_perturbations': 100, # 'end': 'high',
+
+ 'add_continuous': True, 'iters': 60000, 'lrate': 50,
+ 'add_discrete': False,
+ }
+
+ print_multi_axes(pars, print_perturbations, figsize=(FI * 3, 2.5), sharey=True)
+
+
+def result_discrete():
+ pars = {
+ # Experiment setting
+ 'kabmul': 1 / 30, "end": "middle",
+ # 'axespar': None, 'axesarray': [None], 'bmul': 1.0,
+ 'axespar': 'bmul', 'axesarray': np.array([10, 1.0, 0.1]),
+ 'multiaxespar': 'N_allocations', "multiaxesarray": [363, 36, 3],
+ # 'multiaxespar': None, "multiaxesarray": [None], 'N_allocations': 363,
+ 'b_allocation': 'discrete', # in ['random', 'sin', 'powerlaw', 'linear', 'discrete', 'randn']
+
+ # Network parameters
+ 'network': 'email', 'n_nets': 1,
+
+ # Experiment parameters
+ 'add_discrete': True, 'disc_iters': 2000, 'min_iters_since_change': 1000,
+ 'add_continuous': False, 'initial_a': 'random',
+ }
+
+ print_multi_axes(pars, print_a_disc_vs_deg, figsize=(3, 2.5), print_variable="nodes")
+
+
+def result_general_shielding_discrete():
+ pars = {
+ # Experiment setting
+ 'network': 'email', 'n_nets': 1,
+ 'N_allocations_a': 363,
+ 'axispar': 'gain', 'axisarray': [2, 100],
+ # 'axisarray': [2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 32, 35, 40, 45, 50, 60, 70, 85, 100],
+ # 'axisarray': [1, 2, 3, 4, 5, 6, 7, 25, 35, 45, 50, 60, 70, 100],
+ 'amul': 'akdegree%30', 'b_allocation': 'discrete', 'end': 'high',
+ 'linespar': 'bmul', 'linesarray': [0.01, 1, 100], # [0.01, 0.1, 0.5, 1.0, 2.0, 10.0, 100],
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': True, 'disc_iters': 2000, 'initial_a': 'random',
+ # 'add_discrete': True, 'disc_iters': 100, 'initial_a': 'random', 'gain_adjustment': True, 'iters': 60000, 'lrate': 50,
+ }
+ # pars = {
+ # # Experiment setting
+ # 'N_allocations_a': 363, "end": "middle",
+ # 'axespar': 'bmul', 'axesarray': np.array([10, 1.0, 0.1]),
+ # 'multiaxespar': 'N_allocations', "multiaxesarray": [363, 36],
+ # # 'multiaxespar': None, "multiaxesarray": [None], 'N_allocations': 363,
+ # 'b_allocation': 'discrete', # in ['random', 'sin', 'powerlaw', 'linear', 'discrete', 'randn']
+ #
+ # # Network parameters
+ # 'network': 'email',
+ # 'N': 200, 'n_nets': 1,
+ # 'net': 'ba', # 'conf' # 'gamma': 2.1, # For scale-free conf model # 'm': 1, # For Barabasi-Albert network
+
+ # Experiment parameters
+ # 'add_discrete': True, 'disc_iters': 200, 'initial_a': 'random',
+ # }
+
+ print_multi_axes(pars, print_general_distance_to_b, figsize=(3, 2.5), print_variable="nodes")
+
+
+def results_distance_continuous(network="email", end="middle", n_nets=1, letter_labels=True, figsize=(3,2.5), **kwargs):
+ pars = {
+ # Experiment setting
+ 'network': network, 'n_nets': n_nets,
+ # 'N_allocations_a': "over_shielding",
+ 'kabmul': 1 / 30, 'bmul': 1,
+ 'b_allocation': 'discrete', 'end': end,
+ 'axespar': None, 'axesarray': [None], # [1], # [4, 3, 2, 1], # [4, 32, 256],
+ 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': False, 'disc_iters': 20000, 'initial_a': 'random', 'min_iters_since_change': 10000,
+ 'add_continuous': True, 'iters': 60000, 'lrate': 50,
+ }
+ for key, val in kwargs.items():
+ if key not in pars:
+ pars[key] = val
+
+ # print_multi_axes(pars, print_a_vs_nodes, figsize=(3, 2.5))
+ # print_multi_axes(pars, print_a_disc_vs_deg, figsize=(3, 2.5), print_variable="degrees")
+ # print_multi_axes(pars, print_specific_distance_to_b, figsize=(3, 2.5), print_variable="continuous")
+ # print_multi_axes(pars, print_specific_distance_to_b, figsize=(3, 2.5), print_variable="discrete")
+ # print_multi_axes(pars, print_specific_distance_to_b, figsize=(3, 15), print_variable="discrete_degree")
+ # print_multi_axes(pars, print_cont_shielding, figsize=(3, 2.5))
+
+ # pars['N_allocations'] = 4
+ # print_multi_axes(pars, print_specific_distance_to_b, figsize=(3, 2.5), print_variable="allocations")
+ # pars['N_allocations'] = 32
+ # print_multi_axes(pars, print_specific_distance_to_b, figsize=(3, 2.5), print_variable="allocations")
+ # pars['N_allocations'] = 256
+ # print_multi_axes(pars, print_specific_distance_to_b, figsize=(3, 2.5), print_variable="allocations")
+
+ pars['axespar'] = "N_allocations"
+ pars['axesarray'] = [4, 32, 256]
+ axes, outs = print_multi_axes(pars, print_specific_distance_to_b, figsize=figsize, print_variable="allocations",
+ sharey=True, letter_labels=letter_labels)
+ return outs, axes
+
+
+def result_shielding_prop_to_k():
+ pars = {
+ # Experiment setting
+ 'kabmul': 1 / 30, "end": "middle",
+ 'axespar': None, 'axesarray': [None], 'bmul': 1.0,
+ # 'axespar': 'bmul', 'axesarray': np.array([10, 1.0, 0.1]),
+ # 'multiaxespar': 'N_allocations', "multiaxesarray": [363, 36, 3],
+ 'multiaxespar': None, "multiaxesarray": [None], 'N_allocations': 1,
+ 'b_allocation': 'discrete', # in ['random', 'sin', 'powerlaw', 'linear', 'discrete', 'randn']
+
+ # Network parameters
+ 'network': 'email',
+ 'N': 200, 'n_nets': 10,
+ 'net': 'ba', # 'conf' # 'gamma': 2.1, # For scale-free conf model # 'm': 1, # For Barabasi-Albert network
+
+ # Experiment parameters
+ 'add_discrete': True, 'disc_iters': 20000, 'initial_a': 'random', 'min_iters_since_change': 10000,
+ 'add_continuous': False,
+ }
+
+ # print_multi_axes(pars, print_a_disc_vs_deg, figsize=(3, 2.5), print_variable="nodes")
+ print_multi_axes(pars, print_specific_distance_to_b, figsize=(3, 10))
+
+
+def result_shialdowing(network="email", end="middle", n_nets=1, min_iters_since_change=100, **kwargs):
+ pars = {
+ # Experiment setting
+ 'network': network, 'n_nets': n_nets,
+ 'axispar': 'N_allocations', 'axisarray': [1, 2, 4, 8, 16, 32, 64, 128, 256],
+ 'linespar': 'bmul', 'linesarray': [0.1, 1, 10],
+ 'kabmul': 1 / 30, 'b_allocation': 'discrete', 'end': end, # 'bmul': 1.0,
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': True, 'disc_iters': 200, 'initial_a': 'random',
+ 'min_iters_since_change': min_iters_since_change,
+ 'add_continuous': False, 'iters': 60000, 'lrate': 50,
+ }
+ for key, val in kwargs.items():
+ if key not in pars:
+ pars[key] = val
+ else:
+ warnings.warn("Parameter {:s} multiply defined.".format(key))
+
+ figsize = (3, 2.5) # (FI * 2.5, 2.5)
+ print(n_nets)
+ # print_multi_axes(pars, print_shialdowing_disc, figsize=figsize, print_variable="shadowing")
+ print_multi_axes(pars, print_shialdowing_disc, figsize=figsize, print_variable="shielding")
+ print_multi_axes(pars, print_shialdowing_disc, figsize=figsize, print_variable="degrees")
+
+
+def result_nash():
+ fig, axes = plt.subplots(1, 2, figsize=(2 * FI * 2.5, 2.5))
+ colors = {"a": "orange", "b": "blue"}
+ markers = {"a": "x", "b": "+"}
+
+ def print_entropy(ax, case, controller):
+ # fname = DATA_PATH + "Nash_Schuh/NashResults_Case{:d}{:s}.csv".format(case, controller)
+ # vec = pd.read_csv(fname, header=None, sep=";").values
+ fname = DATA_PATH + "Nash_Schuh/Case{:d}_1_P{:s}.csv".format(case, controller)
+ vec = pd.read_csv(fname, header=None, sep=",").values
+ print(case, controller, vec.shape)
+ # max_ent = entropy(np.ones(len(vec)))
+ # ax.semilogx(np.ones(vec.shape[1]) * max_ent)
+ # ax.semilogx([entropy(iter_vec) for iter_vec in vec.T]) # max_ent -
+ # ax.set(ylim=[6.95, 7.035])
+ # ax.loglog(range(1, vec.shape[1] + 1), [max_ent - entropy(iter_vec) for iter_vec in vec.T],
+ # label=r"$w^{iter}_" + controller + "$", color=colors[controller])
+ # ax.semilogx(range(1,vec.shape[1]+1), vec.T, label=r"$w^{iter}_" + controller + "$", color=colors[controller], linewidth=0.2)
+ ax.semilogy(range(1, vec.shape[1] + 1), np.std(vec, axis=0) / np.mean(vec, axis=0), ls="",
+ marker=markers[controller],
+ label=r"$w^{iter}_" + controller + "$", color=colors[controller])
+ # ax.set(ylim=1e-8)
+ ax.legend()
+ if case == 2:
+ xlabel = ''
+ elif case == 1:
+ xlabel = str(5)
+ ax.set(xlabel=r"iterations", title=r"$\mathcal{B}_a = " + xlabel + r"\;\mathcal{B}_b$")
+ # ax.semilogx([max_ent - entropy(iter_vec) for iter_vec in vec.T])
+
+ # axes[0].set(ylim=[0, 0.005])
+ # axes[1].set(ylim=[0, 0.09])
+ # axes[0].set(ylabel=r"$H(unif) - H(w)$")
+ axes[0].set(ylabel=r"std($w$) / $\langle w\rangle$")
+
+ print_entropy(axes[1], 1, "a")
+ print_entropy(axes[1], 1, "b")
+ print_entropy(axes[0], 2, "a")
+ print_entropy(axes[0], 2, "b")
+
+ plotname = PLOTS_PATH + SUB_DATA_PATH + "print_nash.eps"
+
+ fig.tight_layout()
+ fig.savefig(plotname)
+ print("\nFigure saved in " + plotname)
+ fig.show()
+
+ return axes
+
+
+def fig1_scirep(**kwargs):
+ pars = {
+ # Experiments setting
+ 'network': 'email', 'n_nets': 1, 'N': 1133,
+ 'kabmul': 1 / 30, 'bmul': 1.0, 'b_allocation': 'discrete', 'end': "middle", 'N_allocations': 16,
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': True, 'disc_iters': 2, 'min_iters_since_change': 11330,
+ 'add_continuous': True, 'iters': 200000, 'lrate': 50, 'initial_a': 'random',
+
+ # Plotting parameters
+ 'group_criterion': "mean",
+ }
+
+ for key, val in kwargs.items():
+ if key not in pars:
+ pars[key] = val
+
+ G = Network(pars['network'], **pars)
+ pars['N'] = G.N
+
+ fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(9, 2.5))
+
+ aes = print_a_vs_nodes(ax1, pars)
+
+ pars['n_nets'] = 15
+ pars['axispar'] = 'N_allocations'
+ pars['axisarray'] = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]
+ pars['linespar'] = 'bmul'
+ pars['linesarray'] = [0.1, 1, 10]
+
+ print_entropy_vs_N_b(ax2, pars, print_variable="entropy")
+ a_entropies, a_disc_entropies, X_improvements = print_entropy_vs_N_b(ax3, pars, print_variable="improvement")
+
+ x, y = 0.06, 0.9
+ ax1.text(x, y, r"\textbf{a}", horizontalalignment='center', verticalalignment='center', transform=ax1.transAxes)
+ ax2.text(x, y, r"\textbf{b}", horizontalalignment='center', verticalalignment='center', transform=ax2.transAxes)
+ ax3.text(x, y, r"\textbf{c}", horizontalalignment='center', verticalalignment='center', transform=ax3.transAxes)
+
+ plotname = PLOTS_PATH + SUB_DATA_PATH + "Fig_1.eps"
+ fig.tight_layout()
+ fig.savefig(plotname)
+ print("\nFigure saved in " + plotname)
+ fig.show()
+
+ return {'1a': aes.tolist(), '1b': {"cont": a_entropies.tolist(), "disc": a_disc_entropies.tolist()},
+ '1c': {"mean": X_improvements[:, :, 0].tolist(), "std": X_improvements[:, :, 1].tolist()}}, ax1
+
+
+def fig4_scirep(figsize=(9,2.5), **kwargs):
+ pars = {
+ # Experiments setting
+ 'n_nets': 1, 'N': 1133,
+ 'kabmul': 1 / 30, 'bmul': 1.0, 'b_allocation': 'discrete', 'end': "middle", 'N_allocations': 16,
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': False, 'add_continuous': True, 'iters': 200000, 'lrate': 50, 'initial_a': 'random',
+
+ # Plotting parameters
+ 'group_criterion': "mean",
+ }
+
+ for key, val in kwargs.items():
+ if key not in pars:
+ pars[key] = val
+
+ G = Network(pars['network'], **pars)
+ pars['N'] = G.N
+
+ fig = plt.figure(figsize=figsize)
+ ax1 = fig.add_subplot(1, 3, 1)
+ ax2 = fig.add_subplot(1, 3, 2)
+ ax3 = fig.add_subplot(1, 3, 3, sharey=ax2)
+
+ boxes = print_cont_shielding(ax1, pars, print_variable="boxplot")
+
+ pars['n_nets'] = 15
+ pars['axispar'] = 'N_allocations'
+ pars['axisarray'] = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]
+ pars['linespar'] = None
+ pars['linesarray'] = [None]
+
+ shialdowing1 = print_general_shielding_cont(ax2, pars)
+ pars['bmul'] = 10
+ shialdowing10 = print_general_shielding_cont(ax3, pars)
+
+ x, y = 0.15, 0.9
+ ax1.text(x, y, r"\textbf{a}", horizontalalignment='center', verticalalignment='center', transform=ax1.transAxes)
+ ax2.text(x, y, r"\textbf{b}", horizontalalignment='center', verticalalignment='center', transform=ax2.transAxes)
+ ax3.text(x, y, r"\textbf{c}", horizontalalignment='center', verticalalignment='center', transform=ax3.transAxes)
+
+ plotname = PLOTS_PATH + SUB_DATA_PATH + "Fig_4.eps"
+ fig.tight_layout()
+ fig.savefig(plotname)
+ print("\nFigure saved in " + plotname)
+ fig.show()
+
+ return {'4a': boxes.tolist(), '4b': shialdowing1.tolist(), "4c": shialdowing10.tolist()}, ax1
+
+
+def fig5_scirep(figsize=(9,2.5)): # **kwargs):
+ pars = {
+ # Experiments setting
+ 'network': 'email', 'n_nets': 60, 'N': 1133,
+ 'kabmul': 1 / 30, 'bmul': 1.0, 'b_allocation': 'discrete', 'end': "middle", 'N_allocations': 16,
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': False, 'add_continuous': True, 'iters': 200000, 'lrate': 50, 'initial_a': 'random',
+
+ # Plotting parameters
+ 'group_criterion': "mean",
+ }
+
+ if pars['network'] == "email":
+ # pars['n_nets'] = 60
+ pars['n_nets'] = 15
+ G = Network(pars['network'], **pars)
+ pars['N'] = G.N
+ else:
+ pars['n_nets'] = 125
+
+ fig = plt.figure(figsize=figsize)
+ ax1 = fig.add_subplot(1, 3, 1)
+ ax2 = fig.add_subplot(1, 3, 2)
+ ax3 = fig.add_subplot(1, 3, 3, sharey=ax2)
+
+ boxes = print_a_vs_deg(ax1, pars, print_variable="shielding")
+
+ pars['n_nets'] = 15
+ pars['axispar'] = 'N_allocations'
+ pars['axisarray'] = [1, 2, 4, 8, 16, 32, 64, 128, 256]
+ pars['linespar'] = None
+ pars['linesarray'] = [None]
+
+ corrs_cont, corrs_shield = print_correlations(ax2, pars)
+ pars['end'] = "periphery"
+ per_cont, per_shield = print_correlations(ax3, pars)
+ ax3.set(ylabel="")
+ # ax3.yaxis.set(ticklabels=[])
+
+ x, y = 0.1, 0.9
+ ax1.text(0.1, 0.7, r"\textbf{a}", horizontalalignment='center', verticalalignment='center', transform=ax1.transAxes)
+ ax2.text(x, y, r"\textbf{b}", horizontalalignment='center', verticalalignment='center', transform=ax2.transAxes)
+ ax3.text(x, y, r"\textbf{c}", horizontalalignment='center', verticalalignment='center', transform=ax3.transAxes)
+
+ plotname = PLOTS_PATH + SUB_DATA_PATH + "Fig_5.eps"
+ fig.tight_layout()
+ fig.savefig(plotname)
+ print("\nFigure saved in " + plotname)
+ fig.show()
+
+ return {"5a": boxes.tolist(), "5b": {"corr_cont": corrs_cont.tolist(), "corr_shield": corrs_shield.tolist()},
+ "5c": {"corr_cont": per_cont.tolist(), "corr_shield": per_shield.tolist()}}
+
+
+def main_text():
+ hyper_pars = {"network": "email", "end": "middle", "N": 1133}
+ pars = complete_hyper_params(hyper_pars)
+ d = {}
+
+ d["fig1"], _ = fig1_scirep()
+
+ if True: # w_ai vs w_bi
+ outs, _ = result_linear_nodes(letter_labels=True, **pars)
+ d2 = {}
+ for letter, out in zip(["a", "b", "c"], outs):
+ d2["2" + letter] = {"w_ai": out[0].tolist(), "w_bi": out[1].tolist(), "d_i": out[2].tolist(),
+ "w_ai^ana": [wana.tolist() if type(wana) == np.ndarray else wana for wana in out[3]]}
+ d["fig2"] = d2
+
+ if True: # dist continuous
+ outs, _ = results_distance_continuous(letter_labels=True, **pars)
+ d["fig3"] = {"3" + letter: {"x": out[0], "y": out[1]} for letter, out in zip(["a", "b", "c"], outs)}
+
+ d["fig4"], _ = fig4_scirep(**pars)
+ d["fig5"] = fig5_scirep()
+
+ return d
+
+
+def suppl_materials():
+ hyper_pars = {"network": "email", "end": "middle", "N": 1133, }
+ pars = complete_hyper_params({"network": "email", "end": "middle", "N": 1133, })
+
+ # S2: Allocation distribution for other K
+ if False:
+ result_introductory_example(include_G_vs_S=False, N_allocations=8, **pars)
+ result_introductory_example(include_G_vs_S=False, N_allocations=16, **pars)
+ result_introductory_example(include_G_vs_S=False, N_allocations=32, **pars)
+
+ # S5: Optimal allocations vs weighted degree
+ if False:
+ result_introductory_example(include_a_vs_nodes=False, include_G_vs_S=False, include_a_vs_deg=True, **pars)
+ hyper_pars["end"] = "periphery"
+ pars = complete_hyper_params(hyper_pars)
+ result_introductory_example(include_a_vs_nodes=False, include_G_vs_S=False, include_a_vs_deg=True, **pars)
+
+ # result_linear_nodes(letter_labels=True, bmul=10, **hyper_pars)
+ # result_shialdowing(**hyper_pars)
+
+ # Other networks
+ for network in ("classroom",): # "advogato", "classroom", "ba", "conf"):
+ hyper_pars["network"] = network
+
+ hyper_pars["N_allocations"] = 16
+ if hyper_pars["network"] == "advogato":
+ hyper_pars["seed"] = 1
+ elif "seed" in hyper_pars:
+ del hyper_pars["seed"]
+ pars = complete_hyper_params(hyper_pars)
+ result_introductory_example(include_G_vs_S=False, **pars)
+
+ if 'seed' in pars:
+ del pars['seed']
+ # result_entropy_vs_N(**pars)
+ # result_general_shielding_continuous(**pars)
+ # result_correlations(**pars)
+
+
+def abstract():
+ hyper_pars = {"network": "email", "end": "middle", "N": 1133}
+ pars1 = complete_hyper_params(hyper_pars)
+
+ pars = {
+ # Experiments setting
+ 'network': 'email', 'n_nets': 1, 'N': 1133,
+ 'kabmul': 1 / 30, 'bmul': 1.0, 'b_allocation': 'discrete', 'end': "middle", 'N_allocations': 16,
+ 'axespar': None, 'axesarray': [None], 'multiaxespar': None, 'multiaxesarray': [None],
+
+ # Methods parameters
+ 'add_discrete': False, 'add_continuous': True, 'iters': 200000, 'lrate': 50, 'initial_a': 'random',
+
+ # Plotting parameters
+ 'group_criterion': "mean",
+ }
+
+ for key, val in pars1.items():
+ if key not in pars:
+ pars[key] = val
+
+ G = Network(pars['network'], **pars)
+ pars['N'] = G.N
+
+ fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(9, 2.5))
+
+ print_a_vs_nodes(ax1, pars)
+ print_cont_shielding(ax3, pars, print_variable="boxplot")
+
+ # pars['N_allocations'] = 4
+ # print_specific_distance_to_b(ax3, pars, print_variable="allocations")
+
+ pars['kabmul'], pars['bmul'] = 0.1, 0.1
+ pars['b_allocation'] = 'random'
+ print_a_vs_b(ax2, pars)
+
+ plotname = PLOTS_PATH + SUB_DATA_PATH + "abstract.png"
+ fig.tight_layout()
+ fig.savefig(plotname)
+ print("\nFigure saved in " + plotname)
+ fig.show()
+
+
+def thesis():
+ hyper_pars = {"network": "email", "end": "middle", "N": 1133}
+ pars = complete_hyper_params(hyper_pars)
+
+ result_linear_nodes(letter_labels=True, multiaxes=True, figsize=(2.7, 2.5), **pars)
+ results_distance_continuous(letter_labels=True, figsize=(2.5, 2.5), **pars)
+ fig4_scirep(figsize=(7.5, 2.5), **pars)
+ fig5_scirep(figsize=(7.5, 2.5))
+ result_perturbation_exploitation()
+
+
+def other_experiments():
+ hyper_params = {
+ "network": "email",
+ # "network": "random", "p": 0.01,
+ # "network": "ba", "m": 5,
+ # "network": "conf", "gamma": 3, 'dmin':5,
+ # "end": "middle", # "low"
+ # "end": "low",
+ "end": "periphery",
+ # "initial_a_disc": "cont",
+ "N": 1133,
+ }
+
+ hyper_params = complete_hyper_params(hyper_params)
+
+ # result_network_topology(**hyper_pars)
+ # result_introductory_example(**hyper_pars)
+ # result_entropy_vs_N(**hyper_pars)
+
+ # result_bsin()
+ # result_linear_nodes(**hyper_pars)
+ # result_perturbation_exploitation()
+
+ # results_distance_continuous(**hyper_pars)
+ # result_specific_shielding_continuous(**hyper_pars)
+ # result_general_shielding_continuous(plot_array=True, **hyper_pars)
+
+ result_correlations(plot_array=True, **hyper_params)
+
+ # result_discrete()
+ # result_shialdowing(**hyper_pars)
+ # result_shielding_prop_to_k()
+ # result_general_shielding_discrete()
+
+ # result_nash()
+
+ # result_bpower_avg_degree()
+
+
+def complete_hyper_params(hyper_pars):
+ if hyper_pars["network"] == "advogato":
+ hyper_pars['N'] = 5054
+ hyper_pars['iters'] = 2000
+ elif hyper_pars["network"] == "classroom":
+ hyper_pars["N"] = 128
+ elif hyper_pars["network"] == "ba":
+ hyper_pars["m"] = 5
+ hyper_pars["N"] = 1133
+ elif hyper_pars["network"] == "conf":
+ hyper_pars["gamma"] = 3
+ hyper_pars['dmin'] = 5
+ hyper_pars["N"] = 1133
+
+ if hyper_pars['network'] in ("classroom", "advogato") and "N_allocations" in hyper_pars:
+ hyper_pars["N_allocations"] = round(hyper_pars["N_allocations"] / 1133 * hyper_pars['N'])
+
+ if hyper_pars['end'] == "periphery":
+ hyper_pars["n_nets"] = 1
+ if "initial_a_disc" not in hyper_pars:
+ hyper_pars["min_iters_since_change"] = 11330
+ elif hyper_pars["initial_a_disc"] == "cont":
+ hyper_pars["min_iters_since_change"] = 1133
+ elif hyper_pars["network"] in ("random",) or hyper_pars["end"] == "low":
+ hyper_pars["n_nets"] = 10
+ hyper_pars["min_iters_since_change"] = 1000
+ elif hyper_pars["network"] == "advogato":
+ hyper_pars["n_nets"] = 5
+ hyper_pars["iters"] = 2000
+ else:
+ hyper_pars["n_nets"] = 15
+ hyper_pars["min_iters_since_change"] = 11330
+
+ return hyper_pars
+
+
+if __name__ == "__main__":
+ # main_text()
+ # suppl_materials()
+ # abstract()
+ thesis()
+
+ # other_experiments()
diff --git a/hillclimb.py b/hillclimb.py
new file mode 100644
index 0000000000000000000000000000000000000000..6350346c551aaa2cccb2961d9ade8f74949aec0a
--- /dev/null
+++ b/hillclimb.py
@@ -0,0 +1,150 @@
+import time
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+PLOTS_PATH = 'plots/'
+
+
+class HillClimber:
+ def __init__(self, W, b_vec, n_nodes, gain, initial_a_disc="random", **pars):
+ self.W = W
+ self.N = W.shape[0]
+ self.L = np.diagflat(np.sum(self.W, axis=0)) - self.W
+ self.seq = np.squeeze(np.array(np.sum(self.W, axis=1)))
+
+ self.gain = gain
+ self.n_nodes = n_nodes
+ if self.n_nodes > self.N:
+ raise Exception(
+ "There are more nodes for discrete allocation ({:d}) than nodes in the network ({:d}).".format(
+ self.n_nodes, self.N))
+ self.best_degrees = [] # For the greedy initialization
+
+ # Initialize allocations
+ self.b = b_vec
+ if type(initial_a_disc) == np.ndarray:
+ if len(np.unique(initial_a_disc)) == 2:
+ assert sum(initial_a_disc > 0) == n_nodes
+ self.a_ind = set(np.arange(self.N)[initial_a_disc > 0])
+ print("Initial 'a' from previous 'a'")
+ else:
+ assert np.isclose(np.sum(initial_a_disc), n_nodes * gain)
+ self.a_ind = set(np.argsort(initial_a_disc)[:n_nodes])
+ print("Initial 'a' from continuous 'a'")
+ elif initial_a_disc == "random":
+ self.a_ind = set(np.random.choice(self.N, self.n_nodes, replace=False))
+ elif initial_a_disc == "zeros":
+ X = self.greedy()
+ print("Hill climbing initialized with greedy algorithm (X={:f})".format(X))
+ else:
+ raise Exception("Initial a-allocation '" + str(initial_a_disc) + "' not understood.")
+
+ def produce_a_vec(self):
+ # self.a = np.zeros((self.N, 1))
+ self.a = np.zeros(self.N)
+ self.a[list(self.a_ind)] = self.gain
+ self.a = self.a[:, None]
+
+ def greedy(self):
+ self.a_ind = set()
+ self.produce_a_vec()
+
+ degree_dic = {}
+ for j, degree in enumerate(self.seq):
+ if degree not in degree_dic:
+ degree_dic[degree] = []
+ degree_dic[degree].append(j)
+
+ for k in range(self.n_nodes):
+ best_i, best_X, best_degree = None, -1, None
+ # for i, a_i in enumerate(self.a):
+ # if np.isclose(a_i, 0):
+ # self.a[i] = self.gain
+ # X, x = self.compute_x()
+ # if X > best_X:
+ # best_X = X
+ # best_i = i
+ # self.a[i] = 0
+ for degree, indices in degree_dic.items():
+ i = np.random.choice(indices)
+ self.a[i] = self.gain
+ X, x = self.compute_x(produce_a_vec=False)
+ if X > best_X:
+ best_X = X
+ best_i = i
+ best_degree = degree
+ self.a[i] = 0
+
+ self.a[best_i] = self.gain
+ self.a_ind.add(best_i)
+ degree_dic[best_degree].remove(best_i)
+ print(k, best_degree)
+ self.best_degrees.append(best_degree)
+ if not degree_dic[best_degree]:
+ del degree_dic[best_degree]
+
+ assert len(self.a_ind) == self.n_nodes
+ fig, ax = plt.subplots()
+ ax.plot(self.best_degrees)
+ fig.show()
+ return best_X
+
+ def execute(self, min_iters, min_iters_since_change=1, starting_iters=0, starting_iters_since_change=0, **pars):
+ X, x = self.compute_x()
+ no_a_ind = set(np.arange(self.N)).difference(self.a_ind)
+
+ if self.n_nodes > 1:
+ i = starting_iters
+ iters_since_change = starting_iters_since_change
+ # weights = self.seq / np.sum(self.seq)
+ while i < min_iters or iters_since_change < min_iters_since_change:
+ # new_ind = np.random.randint(self.N)
+ # while new_ind in self.a_ind:
+ # new_ind = np.random.choice(self.N, replace=False, p=weights)
+
+ ind_untargeted = np.random.choice(list(no_a_ind))
+ ind_targeted = np.random.choice(list(self.a_ind))
+ self.a_ind.remove(ind_targeted)
+ self.a_ind.add(ind_untargeted)
+
+ new_X, new_x = self.compute_x()
+
+ if new_X > X:
+ X, x = new_X, new_x
+ no_a_ind.remove(ind_untargeted)
+ no_a_ind.add(ind_targeted)
+ iters_since_change = 0
+ else:
+ self.a_ind.remove(ind_untargeted)
+ self.a_ind.add(ind_targeted)
+ iters_since_change += 1
+
+ i += 1
+
+ print("Hill climbing finished after {:.0f} iterations. Iterations since last change: {:.0f}".format(i,
+ iters_since_change))
+ else:
+ print("Discrete setting with a single node, performing greedy algorithm.")
+ max_j = -1
+ for j in range(self.N):
+ self.a_ind = set([j])
+ new_X, new_x = self.compute_x()
+ if new_X > X:
+ X, x = new_X, new_x
+ max_j = j
+ self.a_ind = set([max_j])
+ i, iters_since_change = 999999, 999999
+
+ self.produce_a_vec()
+ return self.a[:, 0], X, x, i, iters_since_change
+
+ def compute_x(self, produce_a_vec=True):
+ if produce_a_vec:
+ self.produce_a_vec()
+ inv_Lab = np.linalg.inv(self.L + np.diagflat(self.a + self.b))
+ x = np.array(np.matmul(inv_Lab, self.a).T)
+ X = np.sum(x) / self.N
+
+ return X, x[0, :]
+
diff --git a/hpc_experiments.py b/hpc_experiments.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d751118f0dab5db66382d1b9bf47de212310932
--- /dev/null
+++ b/hpc_experiments.py
@@ -0,0 +1,582 @@
+import warnings
+import pickle
+import ast
+import argparse
+from functools import reduce
+
+import numpy as np
+import pandas as pd
+
+from python.utils import DATA_PATH, PLOTS_PATH
+from python.networks import Network, compute_gain_nnodes
+
+SUB_DATA_PATH = "general_GA_avoidance/"
+
+
+def create_name(level=None, type_of_experiment=None, print_func=None, distances=False,
+ print_variable=None, **pars): # level= {data, deprec, plot}
+ if level is None:
+ fname = ""
+ elif level in ("data", "deprec"):
+ fname = DATA_PATH + SUB_DATA_PATH
+ elif level == "plot":
+ fname = PLOTS_PATH + SUB_DATA_PATH
+ else:
+ raise Exception("level value '" + level + "' not understood.")
+
+ if level in ("data", "deprec"):
+ if type_of_experiment is not None:
+ fname += type_of_experiment + "-"
+ else:
+ raise Exception("'type of experiment' not defined.")
+
+ if level is not None:
+ fname += pars['network']
+
+ if level == "plot":
+ fname += "-" + print_func.__name__
+
+ print_variable_dict = {"allocations": "", "x": "x", "entropy": "H", "improvement": "X", "degrees": "deg",
+ "nodes": "node", "discrete": "disc", "continuous": "cont", "discrete_degree": "discdeg",
+ "boxplot": "box", "shielding": "shield"}
+ if print_variable in print_variable_dict:
+ fname += print_variable_dict[print_variable]
+ else:
+ warnings.warn("Print variable " + print_variable + " not in name dictionary.")
+
+ if pars['network'] == "conf":
+ fname += "-gamma{:.2f}".format(pars['gamma'])
+
+ allocation_dict = {"powerlaw": '-bpower', "sin": "-bsin", "Amajor": "-bAmajor", "random": "-brand",
+ "randn": "-brandn", "discrete": "-bdisc", "linear": "", "randlin": "-brandlin"}
+ fname += allocation_dict[pars['b_allocation']]
+
+ if level in ("data", "deprec") or (level == "plot" and 'bmul' not in pars.values()):
+ fname += "-bmul{:.1f}".format(pars['bmul'])
+
+ if pars['b_allocation'] in ['linear', 'powerlaw', 'sin'] and (level != "plot" or 'beta' not in pars.values()):
+ fname += "-beta{:.5f}".format(pars['beta'])
+ elif pars['b_allocation'] in ("discrete", "randlin") and (level != "plot" or 'end' not in pars.values()):
+ fname += "-end" + str(pars["end"])
+
+ if pars['b_allocation'] == "randlin" and (level != "plot" or 'n_perturbations' not in pars.values()):
+ fname += "-n{:d}".format(pars['n_perturbations'])
+
+ if level == "deprec" and type_of_experiment == "GA":
+ fname += "-iters{:d}".format(pars['iters'])
+
+ if level in ("data", "deprec"):
+ if distances:
+ fname += "-dist_to_b"
+ if type_of_experiment == "HillClimb" or pars['b_allocation'] == "discrete":
+ if "initial_a_disc" in pars and type_of_experiment == "HillClimb":
+ if pars['initial_a_disc'] != "random":
+ fname += "-" + pars['initial_a_disc']
+ if "gain" in pars:
+ fname += "-gain" + str(pars["gain"])
+ elif "N_allocations" in pars:
+ fname += "-N_allo{:d}".format(pars['N_allocations'])
+ if "N_allocations_a" in pars:
+ fname += "-N_allo_a" + str(pars['N_allocations_a'])
+ elif not distances:
+ raise Exception("Discrete allocations not properly defined.")
+ elif level == "plot":
+ if "add_discrete" in pars:
+ if pars['add_discrete'] or pars['b_allocation'] == "discrete":
+ if "gain" in pars:
+ fname += "-gain" + str(pars["gain"])
+ elif "N_allocations" in pars and 'N_allocations' not in pars.values():
+ fname += "-N_allo{:d}".format(pars['N_allocations'])
+ if "N_allocations_a" in pars:
+ fname += "-N_allo_a" + str(pars['N_allocations_a'])
+ elif 'gain' not in pars.values() and 'N_allocations' not in pars.values():
+ raise Exception("Discrete allocations not properly defined.")
+ if pars['add_discrete']:
+ if "initial_a_disc" in pars:
+ if pars['initial_a_disc'] != "random":
+ fname += "-" + pars['initial_a_disc']
+ fname += "-disc_iters_change{:d}".format(pars["min_iters_since_change"])
+ else:
+ raise Exception("Level '" + level + "' not understood.")
+
+ if level == "plot" and pars['n_nets'] > 1:
+ fname += "-n_nets{:d}".format(pars['n_nets'])
+
+ if pars['network'] not in ("email", "advogato", "classroom"):
+ fname += "-N{:d}".format(pars['N'])
+ if pars['network'] == "random":
+ fname += "-p{:.2f}".format(pars["p"])
+ elif pars['network'] == "scale-free":
+ if pars['net'] == "ba":
+ fname += "-m{:d}".format(pars['m'])
+ elif pars['net'] == "conf":
+ fname += "-gamm{:.1f}".format(pars['gamma'])
+ else:
+ raise Exception("Network type '" + pars['net'] + "' not understood.")
+
+ if 'kabmul' in pars:
+ if level != "plot" or pars['kabmul'] not in pars.values():
+ fname += "-kabmul{:.3f}".format(pars['kabmul'])
+ else:
+ if level in ["data", "deprec"] or (level == "plot" and pars['amul'][8] != '%'):
+ fname += "-{:s}".format(pars['amul'])
+ else:
+ fname += "-akdegree{:d}".format(int(pars['amul'][9:]))
+
+ if 'seed' in pars:
+ fname += "-seed{:d}".format(pars['seed'])
+
+ if level is None:
+ return fname
+ elif level in ("data", "deprec"):
+ return fname + ".pkl"
+ elif level == "plot":
+ return fname + ".eps"
+ else:
+ return Exception("level value '" + level + "' not understood.")
+
+
+def compute_a_from_amul(mean_degree, amul):
+ if type(amul) in (float, np.float, np.float64):
+ return mean_degree * amul
+ elif amul == 'akdegree':
+ return mean_degree
+ elif amul[8] == '%':
+ return mean_degree / int(amul[9:])
+ elif amul[8] == "X":
+ return mean_degree * int(amul[9:])
+ else:
+ raise Exception("Pars 'amul' " + amul + " not understood.")
+
+
+def load_networks(seed=5, **pars):
+ fname = DATA_PATH + "network_DB"
+ columns = ["type", "net_pars", "N", "edge_list", "seed"]
+ try:
+ network_DB = pd.read_csv(fname)
+ network_DB["net_pars"] = network_DB["net_pars"].apply(lambda x: ast.literal_eval(x))
+ network_DB["edge_list"] = network_DB["edge_list"].apply(lambda x: ast.literal_eval(x))
+ network_DB["node_order"] = network_DB["node_order"].apply(lambda x: ast.literal_eval(x))
+ except FileNotFoundError:
+ print("Network database not found at '" + fname + "', creating new one...")
+ network_DB = pd.DataFrame(columns=columns)
+
+ networks = []
+ for l in range(pars['n_nets']):
+ if pars['network'] != "conf":
+ net = Network(network_class=pars['network'], seed=seed + l, **pars)
+ if pars['network'] == "email":
+ assert pars['N'] == net.N == 1133
+ elif pars['network'] == "advogato":
+ assert pars['N'] == net.N == 5054
+ elif pars['network'] == "classroom":
+ assert pars['N'] == net.N == 128
+ else:
+ row_vals = {"type": pars['network'], "N": pars['N'], "seed": seed + l,
+ "net_pars": Network.extract_network_pars(pars['network'], **pars)}
+ conditions = reduce(np.logical_and, [network_DB[key] == value for key, value in row_vals.items()])
+ row = network_DB[conditions]
+ if len(row) > 1:
+ raise Exception("Error: Network values duplicated.")
+ elif len(row) == 1:
+ print("Network loaded from DB")
+ adjlist = row["edge_list"].values[0]
+ net = Network(network_class=pars['network'], seed=row_vals["seed"], adjlist=adjlist,
+ node_order=row["node_order"].values[0], **pars)
+ else:
+ net = Network(network_class=pars['network'], seed=row_vals["seed"], **pars)
+
+ row_vals["edge_list"] = net.get_edge_list()
+ row_vals["construction_data"] = net.construction_graph_data
+ row_vals["node_order"] = list(net.G.nodes) # TODO: this is a horrible fix
+ for col in row_vals.keys():
+ if col not in network_DB.columns:
+ network_DB[col] = None
+ for col in network_DB.columns:
+ if col not in row_vals:
+ row_vals[col] = None
+ network_DB.loc[len(network_DB)] = row_vals
+ network_DB.to_csv(fname)
+ # if pars['n_nets'] > 1:
+ # print("-----\nNetwork " + str(l))
+
+ if 'kabmul' in pars:
+ pars['amul'] = pars['kabmul'] / (1 + pars['bmul'])
+ net.mean_a = compute_a_from_amul(np.mean(net.seq), pars['amul'])
+ # print(net.mean_a, np.mean(net.seq), pars['amul'])
+ net.mean_b = net.mean_a * pars['bmul']
+ output = net.allocate_b(pars['b_allocation'], **pars)
+ networks.append(net)
+
+ return networks
+
+
+def run_experiments(networks, type_of_experiment, **pars): # type_of_experiment = {"GA", "HillClimb"}
+ N, n_nets = networks[0].N, len(networks)
+ aes, bs, seqs = np.zeros((n_nets, N)), np.zeros((n_nets, N)), np.zeros((n_nets, N))
+ Xs, xs = np.zeros(n_nets), np.zeros((n_nets, N))
+
+ if type_of_experiment == "GA":
+ convs = np.full(n_nets, False)
+ if "debug" in pars:
+ if pars['debug']:
+ Xs_v_iters = np.zeros((n_nets, pars['iters'] + 1))
+ elif type_of_experiment == "HillClimb":
+ best_degrees = []
+ iters_since_changes = []
+ iterations = []
+ if "initial_a_disc" in pars:
+ a_ini = pars['initial_a_disc']
+ else:
+ a_ini = None
+
+ for l, net in enumerate(networks):
+ if n_nets > 1:
+ print("-----\nNetwork " + str(l))
+
+ if type_of_experiment == "GA":
+ print("Performing gradient ascent...")
+ _, Xs[l], convs[l], i = net.gradient_ascent(**pars)
+ if "debug" in pars:
+ if pars['debug']:
+ Xs_v_iters[l] = net.ga.X_vec
+ print("Gradient ascent converged after {:d} iterations.".format(i))
+
+ elif type_of_experiment == "HillClimb":
+ print("Performing hill climbing algorithm. Min iterations:", pars["disc_iters"],
+ ", min iterations since change:", pars['min_iters_since_change'])
+
+ gain, n_nodes = compute_gain_nnodes(net, **pars)
+ if a_ini == "cont":
+ pars['initial_a_disc'] = net.a
+ assert pars['add_continuous'], "HillClimb initial 'a' from GA but GA experiment not set."
+
+ _, Xs[l], _, iters, iters_since_change = net.hill_climbing(n_nodes, gain=gain, min_iters=pars['disc_iters'],
+ **pars)
+ best_degrees.append(net.hc.best_degrees)
+ iters_since_changes.append(iters_since_change)
+ iterations.append(iters)
+ else:
+ raise Exception("Type of experiment '" + type_of_experiment + "' not understood.")
+
+ seqs[l], aes[l], bs[l], xs[l] = net.sort_by_degree(type_of_experiment)
+
+ if type_of_experiment == "GA":
+ extras = {"convs": convs}
+ if "debug" in pars:
+ if pars['debug']:
+ extras["Xs_v_iters"] = Xs_v_iters
+ elif type_of_experiment == "HillClimb":
+ extras = {"best_degrees": best_degrees, "iters_since_change": iters_since_changes, "iterations": iterations}
+ else:
+ raise Exception("Type of experiment '" + type_of_experiment + "' not understood.")
+
+ return aes, bs, seqs, Xs, xs, extras
+
+
+def extend_experiments(aes, bs, seqs, Xs, xs, others, networks, type_of_experiment, previous_iters, **pars):
+ n_nets, previous_n_nets = len(networks), len(seqs)
+ if type_of_experiment == "HillClimb":
+ i_since_changes, iters = others["iters_since_change"], others["iterations"]
+ print("Iterations:", iters, "Iterations since last change:", i_since_changes)
+ disc_iters, min_i_since_change = pars['disc_iters'], pars['min_iters_since_change']
+
+ # Rerunning previous experiments with more iterations
+ rerun = False
+ for l in range(np.min([previous_n_nets, n_nets])):
+ net = networks[l]
+ _, sort_ind = net.sort_by_degree(type_of_experiment=None)
+ unsort_ind = np.argsort(sort_ind) # For unordering ordered seqs, aes, and bs
+
+ if type_of_experiment == "GA":
+ # TODO: fix extension of GA algorithm to include previous max iterations
+ if False: # not others['convs'][l] and (pars1['iters'] < pars['iters']):
+ print("Re-running GA on net {:d} (iterations: {:.0f})".format(l, previous_iters))
+ rerun = True
+ pars['initial_a'] = aes[l][unsort_ind]
+
+ _, Xs[l], others['convs'][l], i = net.gradient_ascent(**pars)
+ seqs[l], aes[l], bs[l], xs[l] = net.sort_by_degree(type_of_experiment)
+
+ elif type_of_experiment == "HillClimb":
+ if iters[l] < disc_iters or i_since_changes[l] < min_i_since_change:
+ print("Rerunning hill climbing algorithm on net {:d}".format(l) +
+ " (iterations: {:.0f} <= {:.0f},".format(iters[l], disc_iters) +
+ " iters since change: {:.0f} <= {:.0f})".format(i_since_changes[l], min_i_since_change))
+ rerun = True
+ a_ini = aes[l][unsort_ind]
+ gain, n_nodes = compute_gain_nnodes(net, **pars)
+ _, Xs[l], _, iters[l], i_since_changes[l] = net.hill_climbing(n_nodes, gain, min_iters=disc_iters,
+ initial_a_disc=a_ini, iters=iters[l],
+ i_since_change=i_since_changes[l],
+ min_iters_since_change=min_i_since_change)
+ seqs[l], aes[l], bs[l], xs[l] = net.sort_by_degree(type_of_experiment)
+ else:
+ raise Exception("Type of experiment {:s} not understood.".format(type_of_experiment))
+ if rerun and type_of_experiment == "HillClimb":
+ others["iters_since_change"], others["iterations"] = i_since_changes, iters
+
+ # Extending number of networks
+ if previous_n_nets < n_nets:
+ print("Extending number of networks from {:d} to {:d}...".format(previous_n_nets, n_nets))
+
+ aes2, bs2, seqs2, Xs2, xs2, others2 = run_experiments(networks[previous_n_nets:], type_of_experiment, **pars)
+ Xs = np.append(Xs, Xs2)
+ aes, bs, xs, seqs = [np.concatenate([el1, el2]) for el1, el2 in
+ zip([aes, bs, xs, seqs], [aes2, bs2, xs2, seqs2])]
+ for key in others.keys():
+ if key in others2:
+ others[key] = np.append(others[key], others2[key])
+
+ return aes, bs, seqs, Xs, xs, others, rerun
+
+
+def load_experiments(networks, type_of_experiment, **pars):
+ fname = create_name(level="data", type_of_experiment=type_of_experiment, **pars)
+
+ def save_data(aes, bs, seqs, Xs, xs, others, **pars):
+ data = {'pars': pars, 'allocations': (aes, bs, seqs), 'Xs': Xs, 'xs': xs, 'others': others}
+ print("Saving model in " + fname + "\n----------------------------------------------\n")
+ with open(fname, 'wb') as f:
+ pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
+
+ try:
+ with open(fname, 'rb') as f:
+ data = pickle.load(f)
+ print(type_of_experiment + " data loaded from " + fname)
+
+ pars1, (aes, bs, seqs), Xs, xs, others = data['pars'], data['allocations'], data["Xs"], data['xs'], data[
+ 'others']
+
+ if "debug" in pars and type_of_experiment == "GA":
+ if pars['debug'] and "Xs_v_iters" not in others:
+ print("Problem with data not run with enough level of detail!")
+ raise FileNotFoundError
+
+ # Check that previous network settings are the same as the new ones (as network loading is not implemented)
+ n_nets, previous_n_nets = len(networks), len(seqs)
+ for l in range(np.min([n_nets, previous_n_nets])):
+ net = networks[l]
+ _, sort_ind = net.sort_by_degree(type_of_experiment=None)
+ unsort_ind = np.argsort(sort_ind)
+ net.allocate_a(aes[l][unsort_ind], type_of_experiment=type_of_experiment)
+ networks[l].allocate_b(bs[l][unsort_ind], **pars)
+ assert np.allclose(networks[l].seq[sort_ind], seqs[l])
+ # assert np.allclose(networks[l].b[sort_ind][:, 0], bs[l]), str(np.arange(net.N)[networks[l].b[:, 0] > 0]) + " " + str(np.arange(net.N)[bs[l][unsort_ind] > 0])
+ # seqs[l][networks[l].b[sort_ind][:, 0] > 0]) + " " + str(seqs[l][bs[l] > 0])
+ # networks[l].seq[networks[l].b[:, 0] > 0]) + " " + str(seqs[l][bs[l] > 0])
+
+ aes, bs, seqs, Xs, xs, others, rerun = extend_experiments(aes, bs, seqs, Xs, xs, others, networks,
+ type_of_experiment, pars1['iters'], **pars)
+
+ if rerun or previous_n_nets < n_nets:
+ save_data(aes, bs, seqs, Xs, xs, others, **pars)
+
+ # Reduce extension if previous_nets > pars['n_nets']
+ aes, bs, seqs, Xs, xs = aes[:n_nets], bs[:n_nets], seqs[:n_nets], Xs[:n_nets], xs[:n_nets]
+ for key in others.keys():
+ others[key] = others[key][:n_nets]
+
+ except FileNotFoundError:
+ print(type_of_experiment + " data " + fname + " not found. Running experiments...")
+ aes, bs, seqs, Xs, xs, others = run_experiments(networks, type_of_experiment, **pars)
+ save_data(aes, bs, seqs, Xs, xs, others, **pars)
+
+ return aes, bs, seqs, Xs, xs, others
+
+
+def load_data(pars):
+ networks = load_networks(**pars)
+
+ if pars['add_continuous']:
+ aes, bs, seqs, Xs, xs, others = load_experiments(networks, "GA", **pars)
+ if "debug" in pars:
+ if pars["debug"]:
+ # TODO: This is very dirty. This data should be returned via 'return', which should include either a dictionary or a bespoke object
+ pars["Xs_v_iters"] = others["Xs_v_iters"]
+ else:
+ aes, Xs, xs, others = None, None, None, {}
+ others['convs'] = [None] * pars['n_nets']
+
+ if "add_discrete" in pars:
+ if pars["add_discrete"]:
+ aes_disc, bs_disc, seqs, Xs_disc, xs_disc, _ = load_experiments(networks, "HillClimb", **pars)
+ if aes is not None:
+ pass
+ # assert np.all(bs == bs_disc), str(bs)+str(bs_disc)
+ else:
+ bs = bs_disc
+ else:
+ aes_disc, Xs_disc, xs_disc = None, None, None
+ else:
+ aes_disc, Xs_disc, xs_disc = None, None, None
+
+ return aes, bs, seqs, Xs, xs, others['convs'], aes_disc, Xs_disc, xs_disc, networks
+
+
+def run_HPC_experiments():
+ parser = argparse.ArgumentParser(description='Perform experiment')
+ parser.add_argument('--bmul', type=float, default=1.0, metavar='bmul', help="multiplication factor of B's budget.")
+ # 'linespar': 'bmul', 'linesarray': [0.01, 1, 100],
+ parser.add_argument('--N_allo', type=int, default=1, metavar='N_allo',
+ help='number of nodes targeted by discrete controllers')
+ parser.add_argument('--network', choices=["email", "ba", "conf", "random", "advogato"], default="email",
+ metavar='network', help='Network topology.')
+ parser.add_argument('--n_nets', type=int, default=1, metavar='n_nets',
+ help='Number of instances of the experiment.')
+ # 'axispar': 'N_allocations', 'axisarray': [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024],
+ parser.add_argument("--end", choices=["low", "middle", "high", "periphery"], default="middle", metavar="end",
+ help="Nodes targeted by a discrete passive opponent.")
+ parser.add_argument("--add_disc", type=bool, default=True, metavar="add_disc", help="Perform hill-climbing.")
+ parser.add_argument("--seed", type=int, default=-1, metavar="seed", help="Seed (-1 == ignore).")
+ args = parser.parse_args()
+
+ pars = {
+ # Experiment setting
+ 'network': args.network, 'kabmul': 1 / 30, 'b_allocation': 'discrete', 'N': 1133,
+ 'n_nets': args.n_nets, 'bmul': args.bmul, 'N_allocations': args.N_allo, 'end': args.end,
+
+ # Methods parameters
+ 'add_discrete': args.add_disc, 'disc_iters': 22660, 'initial_a': 'random', 'min_iters_since_change': 1133,
+ 'add_continuous': True, 'iters': 2000, 'lrate': 50,
+ }
+
+ if pars["network"] == "random":
+ pars["p"] = 0.01
+ elif pars['network'] == "ba":
+ pars["m"] = 5
+ elif pars['network'] == "conf":
+ pars["gamma"] = 3
+ pars["dmin"] = 5
+ elif pars['network'] == "advogato":
+ pars['N'] = 5054
+
+ if pars['network'] in ("classroom", "advogato"):
+ pars["N_allocations"] = round(pars['N_allocations'] / 1133 * pars['N'])
+
+ if args.seed != -1:
+ pars['seed'] = args.seed
+
+ aes, bs, seqs, Xs, xs, convs, aes_disc, Xs_disc, xs_disc, _ = load_data(pars)
+
+
+def test_HPC_experiments():
+ pars = {
+ # Experiment setting
+ 'network': "advogato", 'kabmul': 1 / 30, 'b_allocation': 'discrete', 'N': 1133, 'seed': 8,
+ 'n_nets': 1, 'bmul': 1.0, 'N_allocations': 16, 'end': "middle",
+
+ # Methods parameters
+ 'add_discrete': False, 'disc_iters': 22660, 'initial_a': 'random', 'min_iters_since_change': 11330,
+ 'add_continuous': False, 'iters': 20000, 'lrate': 50,
+ }
+
+ if pars["network"] == "random":
+ pars["p"] = 0.01
+ elif pars['network'] == "ba":
+ pars["m"] = 5
+ elif pars['network'] == "conf":
+ pars["gamma"] = 3
+ pars["dmin"] = 5
+ elif pars['network'] == "advogato":
+ pars['N'] = 5054
+
+ aes, bs, seqs, Xs, xs, convs, aes_disc, Xs_disc, xs_disc, _ = load_data(pars)
+
+
+def join_HPC_experiments(type_of_experiment, N_allo, bmul=1.0):
+ pars = {
+ # Experiment setting
+ 'network': "advogato", 'kabmul': 1 / 30, 'b_allocation': 'discrete', 'N': 5054,
+ 'n_nets': 5, 'bmul': bmul, 'N_allocations': N_allo, 'end': "middle",
+
+ # Methods parameters
+ 'add_discrete': False, 'disc_iters': 22660, 'initial_a': 'random', 'min_iters_since_change': 1133,
+ 'add_continuous': False, 'iters': 2000, 'lrate': 50,
+ }
+
+ if type_of_experiment == "GA":
+ pars['add_continuous'] = True
+ elif type_of_experiment == "HillClimb":
+ pars['add_discrete'] = True
+
+ fname = create_name(level="data", type_of_experiment=type_of_experiment, **pars)
+
+ def save_data(aes, bs, seqs, Xs, xs, others, **pars):
+ data = {'pars': pars, 'allocations': (aes, bs, seqs), 'Xs': Xs, 'xs': xs, 'others': others}
+ print("Saving model in ", fname, "\n----------------------------------------------\n")
+ with open(fname, 'wb') as f:
+ pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
+
+ try:
+ with open(fname, 'rb') as f:
+ data = pickle.load(f)
+ print(type_of_experiment + " data loaded from " + fname)
+
+ if "debug" in pars:
+ if pars['debug'] and "Xs_v_iters" not in data['others']:
+ print("Problem with data not run with enough level of detail!")
+ raise FileNotFoundError
+
+ except FileNotFoundError:
+ print(type_of_experiment + " data " + fname + " not found. Gathering seeds...")
+
+ n_nets = pars['n_nets']
+ pars['n_nets'] = 1
+
+ aes, bs, seqs, Xs, xs, others = [], [], [], [], [], []
+ for seed in range(1, n_nets + 1):
+ pars['seed'] = seed
+ fname = create_name(level="data", type_of_experiment=type_of_experiment, **pars)
+
+ with open(fname, 'rb') as f:
+ data = pickle.load(f)
+ print(type_of_experiment + " data loaded from", fname)
+
+ for key in pars:
+ if (type_of_experiment == "GA" and key not in ("add_discrete", "min_iters_since_change")) or (
+ type_of_experiment == "HillClimb" and key not in ("add_continuous", "iters")):
+ assert pars[key] == data['pars'][key], str(key) + ": " + str(pars[key]) + ", " + str(
+ data['pars'][key])
+
+ aes.append(data['allocations'][0][0, :])
+ assert aes[-1].ndim == 1
+ bs.append(data['allocations'][1][0, :])
+ assert bs[-1].ndim == 1
+ seqs.append(data['allocations'][2][0, :])
+ assert seqs[-1].ndim == 1
+ Xs.append(data['Xs'])
+ xs.append(data['xs'])
+ others.append(data['others'])
+
+ pars['n_nets'] = n_nets
+ del pars['seed']
+ aes, bs, seqs, Xs, xs = np.array(aes), np.array(bs), np.array(seqs), np.array(Xs), np.array(xs)
+
+ all_others = {}
+ for key in others[1].keys():
+ for other in others:
+ if key not in all_others:
+ all_others[key] = [other[key]]
+ else:
+ all_others[key].append(other[key])
+ all_others[key] = np.array(all_others[key])
+
+ fname = create_name(level="data", type_of_experiment=type_of_experiment, **pars)
+ save_data(aes, bs, seqs, Xs, xs, all_others, **pars)
+
+
+if __name__ == "__main__":
+ # run_HPC_experiments()
+ # test_HPC_experiments()
+
+ # For continuous experiments with bmul=1.0
+ # for n_allo in (4, 9, 18, 36, 71, 143, 285, 571, 1142):
+ # join_HPC_experiments("GA", n_allo)
+
+ # For constructive
+ # join_HPC_experiments("GA", N_allo=71, bmul=0.1)
+ # join_HPC_experiments("GA", N_allo=71, bmul=10.0)
+ join_HPC_experiments("HillClimb", N_allo=71, bmul=0.1)
+ join_HPC_experiments("HillClimb", N_allo=71, bmul=1.0)
+ join_HPC_experiments("HillClimb", N_allo=71, bmul=10.0)
diff --git a/networks.py b/networks.py
new file mode 100644
index 0000000000000000000000000000000000000000..9378bd14f339e212d650101318768c8aff3cabf5
--- /dev/null
+++ b/networks.py
@@ -0,0 +1,473 @@
+import warnings
+import random
+import numpy as np
+import networkx as nx
+import pandas as pd
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+from python.utils import PLOTS_PATH
+from python.gascent import GA, DebugGA
+from python.hillclimb import HillClimber
+
+import os
+
+dirname = os.path.dirname(__file__)
+EMAIL_PATH = os.path.join(dirname, "datasets/ia-email-univ/email.txt")
+ADVOGATO_PATH = os.path.join(dirname, "datasets/soc-advogato/soc-advogato.edges")
+CLASSROOM_PATH = os.path.join(dirname, "datasets/classroom/Friendship-network_data_2013.csv")
+
+mpl.rcParams['text.usetex'] = True
+
+
+def get_highest_nodes(seq, n, end):
+ if end == "high":
+ sorted_indices = np.argsort(seq)[::-1]
+ d_star = seq[sorted_indices][n - 1]
+ chosen_indices = sorted_indices[seq[sorted_indices] > d_star]
+ elif end == "low":
+ sorted_indices = np.argsort(seq)
+ d_star = seq[sorted_indices][n - 1]
+ chosen_indices = sorted_indices[seq[sorted_indices] < d_star]
+ else:
+ raise Exception("end {:s} not understood.".format(end))
+
+ chosen_indices = np.append(chosen_indices, random.sample(
+ list(sorted_indices[seq[sorted_indices] == d_star]), n - len(chosen_indices)))
+
+ return chosen_indices
+
+
+def build_graph(network_class, seed, **pars):
+ if "N" in pars:
+ N = pars['N']
+ else:
+ raise Exception("Number of nodes N not specified!")
+
+ net_pars = {}
+ if network_class == "complete":
+ G = nx.complete_graph(N)
+ elif network_class == "bipartite":
+ G = nx.complete_bipartite_graph(int(N * pars['rho']), int(N * (1 - pars['rho'])))
+ elif network_class == 'ba':
+ G = nx.barabasi_albert_graph(N, pars['m'], seed=seed)
+ elif network_class == 'conf':
+ fails = 0
+ while True:
+ try:
+ # seq = np.array(nx.utils.powerlaw_sequence(N, pars['gamma'], seed=seed), dtype=int)
+
+ # nx.utils.zipf_rv(alpha=pars['gamma'], xmin=pars['dmin'], seed=seed)
+ # print([k for k in np.arange(np.sum(seq == 1))])
+
+ seq = [nx.utils.zipf_rv(alpha=pars['gamma'], xmin=pars['dmin'], seed=seed + k) for k in range(N)]
+ print(max(seq), np.mean(seq), np.unique(seq, return_counts=True))
+ # G = nx.random_degree_sequence_graph(seq) # , tries=50)
+ G = nx.configuration_model(seq, seed=seed)
+ if not nx.is_connected(G):
+ raise nx.exception.NetworkXError("Graph not connected")
+ break
+ except (nx.exception.NetworkXError, nx.exception.NetworkXUnfeasible) as err:
+ print(err)
+ seed += 100
+ fails += 1
+ G = nx.Graph(G) # remove parallel edges
+ G.remove_edges_from(nx.selfloop_edges(G))
+ net_pars["fails"] = fails
+ elif network_class == "regular":
+ G = nx.random_regular_graph(pars['kdegree'], N, seed=seed)
+ # G = nx.fast_gnp_random_graph(pars['N'], 2 * pars['kdegree'] / (pars['N'] - 1))
+ elif network_class == "small-world":
+ G = nx.connected_watts_strogatz_graph(N, pars['kdegree'], pars['p'], seed=seed)
+ elif network_class == "email":
+ edges = pd.read_csv(EMAIL_PATH, sep='\s+').values.tolist()
+ G = nx.Graph(edges)
+ for (u, v) in nx.selfloop_edges(G):
+ G.remove_edge(u, v)
+ # print("N of nodes: " + str(G.number_of_nodes()) + ", N of edges: " + str(G.number_of_edges()))
+ elif network_class == "advogato":
+ G = nx.read_weighted_edgelist(ADVOGATO_PATH, comments="%") # , create_using=nx.DiGraph)
+ for i, (u, v) in enumerate(nx.selfloop_edges(G)):
+ G.remove_edge(u, v)
+ G = G.subgraph(max(nx.connected_components(G), key=len))
+ elif network_class == "classroom":
+ G = nx.read_edgelist(CLASSROOM_PATH) # , create_using=nx.DiGraph)
+ G = G.subgraph(max(nx.connected_components(G), key=len))
+ elif network_class == "random":
+ G = nx.fast_gnp_random_graph(N, pars['p'], seed=seed)
+ else:
+ raise Exception("Network class '" + network_class + "' not understood.")
+
+ assert nx.is_connected(G), "Graph not connected"
+ return G, net_pars
+
+
+def compute_gain_nnodes(net, controller="A", **pars):
+ if controller == "A":
+ budget = net.mean_a * net.N
+ elif controller == "B":
+ budget = net.mean_b * net.N
+ else:
+ raise Exception("Controller {:s} not understood.".format(controller))
+
+ if 'N_allocations_a' in pars and controller == "A":
+ if type(pars['N_allocations_a']) == int:
+ n_nodes = pars['N_allocations_a']
+ elif pars['N_allocations_a'] in ("full_shielding", "over_shielding"):
+ n_neighbours = np.squeeze(np.sum(net.W[net.b[:, 0] > 0], axis=0))
+ mask_Nb = np.logical_and(n_neighbours > 0, net.b[:, 0] == 0)
+ n_nodes = np.sum(mask_Nb)
+ if pars['N_allocations_a'] == "over_shielding":
+ n_nodes = int(n_nodes * 1.5)
+ else:
+ raise Exception("Parameter N_allocations_a not understood.")
+ gain = budget / n_nodes
+ print("Allocations from the A-controller:", n_nodes, "gain:", gain)
+ elif 'gain' in pars:
+ n_nodes = int(budget / pars['gain'])
+ if 'gain_adjustment' in pars:
+ if pars['gain_adjustment']:
+ gain = budget / n_nodes
+ print("Gain adjusted to the new value", gain)
+ else:
+ gain = pars['gain']
+ else:
+ warnings.warn("Gain adjustment not defined for these experiments (!)")
+ gain = pars['gain']
+ elif 'N_allocations' in pars:
+ n_nodes = pars['N_allocations']
+ gain = budget / n_nodes
+ else:
+ raise Exception("Discrete allocations not completely defined.")
+
+ return gain, n_nodes
+
+
+# TODO: THERE SHOULD BE AN ABSTRACT NETWORK CLASS AND CHILDREN CORRESPONDING TO SPECIFIC NETWORK TYPES
+class Network:
+ def __init__(self, network_class, seed=5, adjlist=None, node_order=None, **pars):
+ if adjlist is None:
+ self.G, self.construction_graph_data = build_graph(network_class, seed, **pars)
+ else:
+ self.G = nx.parse_adjlist(adjlist)
+ if network_class != "conf":
+ self.W = nx.adjacency_matrix(self.G) # , range(1, pars['N']+1))
+ else:
+ # print(type(node_order), len(node_order), node_order)
+ # self.W = nx.adjacency_matrix(self.G, node_order)
+ # print("nodes", self.G.nodes)
+ # print("edges", self.G.edges)
+ self.W = nx.adjacency_matrix(self.G) # , nodelist=np.arange(pars['N']))
+ self.L = np.diagflat(np.sum(self.W, axis=0)) - self.W
+ self.N = self.W.shape[0]
+ if "N" in pars:
+ assert self.N == pars['N'], "{:d} != {:d}".format(self.N, pars['N'])
+ self.seq = np.squeeze(np.array(np.sum(self.W, axis=1)))
+ self.sort_ind = None
+ if network_class != "email":
+ print(network_class, " network with mean degree", np.mean(self.seq), "max degree", np.max(self.seq))
+
+ self.sample = None
+ self.seed = seed
+ self.mean_b = None
+ self.b = None
+
+ self.mean_a = None
+ self.a = None
+ self.ga = None
+ self.x, self.X = None, None
+
+ self.a_disc, self.x_disc = None, None
+ self.hc = None
+
+ def allocate_a(self, a_vec, type_of_experiment):
+ assert type(a_vec) == np.ndarray
+ assert len(a_vec) == self.N
+ assert np.isclose(np.mean(a_vec), self.mean_a), "{:f} {:f}".format(np.mean(a_vec), self.mean_a)
+ # if len(a_vec.shape) == 1:
+ # a_vec = a_vec[:, None]
+ if type_of_experiment == "GA":
+ self.a = a_vec
+ elif type_of_experiment == "HillClimb":
+ self.a_disc = a_vec
+ else:
+ raise Exception("Type of experiment {:s} not understood.".format(type_of_experiment))
+
+ def allocate_b(self, allocation_type, **pars): # 'b' is the mean allocation given by the B-controller
+ assert self.mean_b is not None
+ random.seed(self.seed)
+ np.random.seed(self.seed)
+
+ if type(allocation_type) == np.ndarray:
+ self.b = allocation_type
+ assert len(self.b) == self.N
+ assert np.isclose(np.mean(self.b), self.mean_b), "{:f} {:f}".format(np.mean(self.b), self.mean_b)
+ if len(self.b.shape) == 1:
+ self.b = self.b[:, None]
+ else:
+ if allocation_type in ("linear", "powerlaw", "sin", "Amajor", "random", "randn"):
+ self.allocate_b_wrt_func(allocation_type, **pars)
+ elif allocation_type == 'discrete':
+ if 'gain' in pars:
+ n_nodes = int(self.mean_b * self.N / pars['gain'])
+ elif 'N_allocations' in pars:
+ n_nodes = pars['N_allocations']
+ else:
+ raise Exception("Discrete allocations not completely defined.")
+ self.allocate_b_discrete(n_nodes=n_nodes, end=pars['end'])
+ elif allocation_type == "randlin":
+ self.allocate_uniform_b_with_perturbations(pars['n_perturbations'], pars['end'])
+
+ else:
+ raise Exception("B allocation '" + str(allocation_type) + "' not understood.")
+ if 'share_of_uniform' in pars:
+ self.partially_allocate_b_uniformly(pars['share_of_uniform'])
+
+ assert np.isclose(np.mean(self.b), 1), str(np.mean(self.b)) + " != 1"
+ assert np.all(self.b >= 0), str(self.b[self.b < 0])
+ self.b = self.b * self.mean_b
+
+ def allocate_b_wrt_func(self, allocation_type, beta=None, **pars):
+ assert beta is not None or allocation_type in ("random", "randn")
+
+ def beta_allocation(k):
+ if allocation_type == "linear":
+ return k * beta
+ if allocation_type == "powerlaw":
+ return k ** beta
+ elif allocation_type == "sin":
+ assert 0 < beta <= 1
+ return 1 + beta * np.sin(k / np.max(self.seq) * 8 * np.pi)
+ elif allocation_type == "Amajor":
+ assert 0 < beta <= 1
+ return 1 + beta * (np.sin(k / np.max(self.seq) * 8.8 * np.pi) + np.sin(
+ k / np.max(self.seq) * 11 * np.pi) + np.sin(k / np.max(self.seq) * 13.2 * np.pi)) / 3
+ elif allocation_type == "random":
+ return random.random()
+ elif allocation_type == "randn":
+ allocation = 1 + np.random.rand()
+ elif allocation_type == "randlin":
+ allocation = 1 + 0.005 * np.abs(k - beta) * np.random.randn()
+ else:
+ raise Exception("B allocation '" + str(allocation_type) + "' not understood.")
+ if allocation < 0:
+ allocation = 0
+ elif allocation > 2:
+ allocation = 2
+ return allocation
+
+ b = np.zeros((self.N, 1))
+ random.seed(self.seed)
+ for j, k in enumerate(self.seq):
+ b[j] = beta_allocation(k)
+
+ # Re-scaling
+ if allocation_type in ("linear",): # "randlin"):
+ # Shift (keeping slope)
+ self.b = b + (self.N - np.sum(b)) / self.N
+
+ while np.any(self.b < 0):
+ broken_nodes = self.b <= 0
+ self.b += (self.N - np.sum(self.b[self.b > 0])) / np.sum(self.b > 0)
+ self.b[broken_nodes] = 0
+ else:
+ # Re-scale
+ self.b = b / np.sum(b) * self.N
+
+ def allocate_uniform_b_with_perturbations(self, n_perturbations, degree):
+ if type(degree) == "string":
+ if degree in ("low, high"):
+ indices = get_highest_nodes(self.seq, 2 * n_perturbations, degree)
+ else:
+ raise ValueError("value not understood: " + degree)
+ elif type(degree) == int:
+ indices = np.arange(self.N)[self.seq == degree]
+ else:
+ ValueError("Error with degree of type: " + str(type(degree)))
+ assert 2 * n_perturbations <= len(indices), "{:d} {:d}".format(n_perturbations, len(indices))
+
+ self.b = np.ones((self.N, 1))
+ for j in range(n_perturbations):
+ i1, i2 = np.random.choice(indices, 2, replace=False)
+ indices = indices[np.logical_not(np.logical_or(indices == i1, indices == i2))]
+
+ perturbation = 1 # 0.3 * np.random.randn()
+ assert perturbation <= 1, "Perturbation too big: " + str(perturbation)
+ self.b[i1] += perturbation
+ self.b[i2] -= perturbation
+
+ def partially_allocate_b_uniformly(self, share):
+ self.sample = np.random.choice(self.N, int(share * self.N), replace=False)
+ bs = np.sum(self.b[self.sample])
+ self.b[self.sample] = bs / int(share * self.N)
+
+ def allocate_b_discrete(self, n_nodes, end): # end= {'low' degree, 'high' degree, 'middle' = random}
+ b = np.zeros((self.N, 1))
+
+ if end == "middle":
+ b[random.sample(range(self.N), n_nodes)] = 1
+ else:
+ if end == "high":
+ sorted_indices = np.argsort(self.seq)[::-1]
+ d_star = self.seq[sorted_indices][n_nodes - 1]
+ chosen_indices = sorted_indices[self.seq[sorted_indices] > d_star]
+ elif end == "low":
+ sorted_indices = np.argsort(self.seq)
+ d_star = self.seq[sorted_indices][n_nodes - 1]
+ chosen_indices = sorted_indices[self.seq[sorted_indices] < d_star]
+
+ if end in ("high", "low"):
+ chosen_indices = np.append(chosen_indices, random.sample(
+ list(sorted_indices[self.seq[sorted_indices] == d_star]), n_nodes - len(chosen_indices)))
+ elif end == "periphery":
+ max_neigh_degs = np.zeros(self.N)
+ for i in range(self.N):
+ max_neigh_degs[i] = np.max(self.W[i] * self.seq)
+ sorted_ind = np.argsort(max_neigh_degs)
+ # sorted_neigh_degs = max_neigh_degs[sorted_ind]
+ chosen_indices = sorted_ind[:n_nodes]
+ # print("degree of nodes", self.seq[chosen_indices])
+ print("max degree of neighbours", max_neigh_degs[chosen_indices])
+ d_star = np.max(self.seq[chosen_indices])
+ else:
+ raise Exception("end {:s} not understood.".format(end))
+
+ b[chosen_indices] = 1
+ print("Discrete b allocation with {:d} nodes, cut degree {:d} ({:s})".format(n_nodes, d_star, end))
+
+ self.b = b / np.sum(b) * self.N
+
+ def gradient_ascent(self, debug=False, **pars):
+ if 'seed' not in pars:
+ pars['seed'] = self.seed
+
+ if not debug:
+ self.ga = GA(self.W, self.b, a_budget=self.mean_a * self.N, **pars)
+ else:
+ if pars['initial_a'] == "random_disc":
+ gain, n_nodes = compute_gain_nnodes(self, **pars)
+ a = np.zeros(self.N)
+ np.random.seed(self.seed)
+ a[np.random.choice(self.N, n_nodes, replace=False)] = gain
+ pars['initial_a'] = a
+ self.ga = DebugGA(self.W, self.b, a_budget=self.mean_a * self.N, **pars)
+ self.a, self.X, self.x, i = self.ga.execute()
+ return self.a, self.X, self.ga.conv, i
+
+ def hill_climbing(self, n_nodes, gain, min_iters, **pars):
+ self.hc = HillClimber(self.W, self.b, n_nodes, gain, **pars)
+ self.a_disc, X, self.x_disc, iters, i_since_change = self.hc.execute(min_iters, **pars)
+ assert np.isclose(np.mean(self.a_disc), self.mean_a)
+ return self.a_disc, X, self.x_disc, iters, i_since_change
+
+ def sort_by_degree(self, type_of_experiment="GA"):
+ if self.sort_ind is None:
+ self.sort_ind = np.argsort(self.seq)
+ if type_of_experiment == "GA":
+ return self.seq[self.sort_ind], self.a[self.sort_ind], self.b[self.sort_ind, 0], self.x[self.sort_ind]
+ elif type_of_experiment == "HillClimb":
+ return self.seq[self.sort_ind], self.a_disc[self.sort_ind], self.b[self.sort_ind, 0], self.x_disc[
+ self.sort_ind]
+ else:
+ return self.seq[self.sort_ind], self.sort_ind
+
+ def get_edge_list(self):
+ return list(nx.generate_adjlist(self.G))
+
+ @staticmethod
+ def extract_network_pars(network_class, **pars):
+ net_pars = {}
+ if network_class == 'ba':
+ net_pars["m"] = pars['m']
+ elif network_class == 'conf':
+ net_pars["gamma"] = pars['gamma']
+ elif network_class == "regular":
+ net_pars["kdegree"] = pars["kdegree"]
+ elif network_class == "small-world":
+ net_pars["kdegree"] = pars['kdegree']
+ net_pars["p"] = pars['p']
+ elif network_class == "random":
+ net_pars["p"] = pars['p']
+ elif network_class != "email":
+ raise Exception("Network class '" + network_class + "' not understood.")
+
+ return net_pars
+
+
+def create_regular_network(pars):
+ G = nx.random_regular_graph(pars['kdegree'], pars['N'])
+ # G = nx.fast_gnp_random_graph(pars['N'], 2 * pars['kdegree'] / (pars['N'] - 1))
+ if not nx.is_connected(G):
+ raise Exception("Graph not connected")
+ W = nx.adjacency_matrix(G)
+ L = np.diagflat(np.sum(W, axis=0)) - W
+ N = W.shape[0]
+
+ return W, L, N
+
+
+if __name__ == "__main__":
+ pars = {
+ 'N': 1133,
+ 'p': 0.01, # For random graph
+ 'gamma': 3, 'dmin': 5, # For scale-free conf model
+ 'm': 5, # For Barabasi-Albert network
+ }
+
+ # network_type = "email"
+ # network_type = "advogato"
+ network_type = "classroom"
+
+ # network_type = "random"
+ # network_type = "bamean"
+ # network_type = "conf"
+
+ if network_type in ("random", "ba", "conf"):
+ n_seeds = 15
+ else:
+ n_seeds = 1
+
+ if network_type == "advogato":
+ pars['N'] = 5054
+ elif network_type == "classroom":
+ pars["N"] = 128
+
+ for k in range(n_seeds):
+ seed = 6 + k
+ net = Network(network_type, seed=seed, **pars)
+ print(net.N)
+ print("degrees (max, mean, seq)", max(net.seq), np.mean(net.seq), np.unique(net.seq, return_counts=True))
+ print("assortativity", nx.degree_assortativity_coefficient(net.G))
+
+ if True:
+ if False:
+ fig, ax = plt.subplots(figsize=(1.618 * 2.5, 2.5))
+ ax.hist(net.seq, bins=range(int(np.max(net.seq)) + 1), color="purple")
+ ax.set_yscale("log")
+ ax.set(xlabel="Node degree", ylabel="Count")
+ ax.margins(x=0.0)
+
+ fig.tight_layout()
+ fig.savefig(PLOTS_PATH + network_type + "_degree_dist.eps")
+ else:
+ print("Directed?", nx.is_directed(net.G))
+ fig, ax = plt.subplots(figsize=(3, 2.5))
+ ax.bar(range(net.N), sorted(net.seq)[::-1], color="purple")
+ ax.set_yscale("log")
+ ax.set(xlabel=r"$i$ (sorted by $d_{i}$, descending)", ylabel=r"Weighted degree, $d_{i}$")
+ ax.set(xlim=[-10, net.N + 9])
+ fig.tight_layout()
+ fig.savefig(PLOTS_PATH + network_type + "_degree_dist_nodes.png")
+
+ fig.show()
+ else:
+ fig, ax = plt.subplots(figsize=(10, 5))
+
+ net.mean_b = 1
+ net.allocate_b("randlin", n_perturbations=10, end=1)
+ # print(np.unique())
+
+ ax.scatter(net.seq, net.b)
+
+ ax.set(ylim=0)
+ fig.show()
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..edc3e4f124fc4d8ba31c89004659cab689913f2e
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,15 @@
+cycler==0.10.0
+decorator==4.4.2
+kiwisolver==1.2.0
+matplotlib==3.2.2
+mpmath==1.1.0
+networkx==2.4
+numpy==1.19.0
+pandas==1.0.5
+pyparsing==2.4.7
+python-dateutil==2.8.1
+pytz==2020.1
+scipy==1.5.1
+seaborn==0.10.1
+six==1.15.0
+sympy==1.6.2
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..f6f8157ad2c944e457e2c570492ad140df62c8c7
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,74 @@
+import numpy as np
+import networkx as nx
+
+import os
+dirname = os.path.dirname(__file__)
+DATA_PATH = os.path.join(dirname, 'simulation_data/')
+PLOTS_PATH = os.path.join(dirname, 'plots/')
+
+
+def std_errors(array, axis=0, confidence=False, nan=False):
+ if type(axis) in (tuple, list):
+ n_dim_averaged = sum(array.shape[ax] for ax in axis)
+ else:
+ n_dim_averaged = array.shape[axis]
+
+ if nan:
+ errors = np.nanstd(array, axis=axis) / np.sqrt(n_dim_averaged)
+ else:
+ errors = np.std(array, axis=axis) / np.sqrt(n_dim_averaged)
+
+ if confidence:
+ return 2 * errors
+ else:
+ return errors
+
+
+def create_range(pars, level='axis'):
+ if level == "axis":
+ if pars[level + "par"] in ("rho", "beta") and level + "array" not in pars:
+ for el in np.linspace(pars[level + "min"], pars[level + "max"], pars[level + 'length']):
+ pars[pars[level + 'par']] = el
+ yield el
+ elif pars[level + 'par'] == "k1":
+ for rho in np.linspace(1 / pars['N'], (pars['N'] - 1) / pars['N'], pars['N'] - 1):
+ num = (pars['kdegree'] - rho * pars['k0']) / (1 - rho)
+ if num < 28 + 7 * pars['k0'] and np.isclose(np.mod(num, 1), 0):
+ seq = [pars['k0'] for _ in range(int(pars['N'] * rho))] + [int(num) for _ in
+ range(int(pars['N'] * (1 - rho)))]
+ if nx.is_graphical(seq):
+ pars['k1'] = int(num)
+ pars['rho'] = rho
+ if 'betamul' in list(pars):
+ pars['alpha'] = rho + (1 - rho) * (1 - pars['betamul'])
+ pars['beta'] = pars['betamul'] * rho
+ yield (int(num), rho)
+ elif level + "array" in pars:
+ for el in pars[level + 'array']:
+ pars[pars[level + 'par']] = el
+ yield el
+ else:
+ raise Exception("Axis parameter not understood: " + pars[level + "par"])
+ elif level == "lines":
+ if level + "array" in pars:
+ for el in pars[level + 'array']:
+ if pars[level + 'par'] is not None:
+ pars[pars[level + 'par']] = el
+ yield el
+ else:
+ raise Exception("Lines parameter '{:s}' not understood.".format(pars[level + 'par']))
+ elif level == "axes" or level == "multiaxes":
+ if pars[level + "par"] == "ab":
+ for el in np.power(10.0, np.array([-1, 0, 1])):
+ pars['a'], pars['b'] = el, el
+ yield el
+ elif level + "array" in pars:
+ for el in pars[level + 'array']:
+ if pars[level + 'par'] is not None:
+ pars[pars[level + 'par']] = el
+ yield el
+ else:
+ raise Exception("Axes par '{:s}' not understood".format(pars[level + "par"]))
+ else:
+ raise Exception("Level '{:s}' not understood.".format(level))
+