def point4(true_graph: Graph, budget, repetitions, simulations):
# Copy the original graph
graph = Graph(copy=true_graph)
graph.adj_matrix = np.where(true_graph.adj_matrix > 0, 0.5, 0)
x_list = []
x2_list = []
y_list = []
y2_list = []
total_error = 0.0
# Main procedure
for r in range(repetitions):
print("Iteration: " + str(r + 1) + "/" + str(repetitions), end="")
#epsilon = (1 - r / repetitions) ** 2
seeds = choose_seeds_from_sampling(graph, budget, simulations)
graph.influence_episode(seeds, true_graph.adj_matrix)
# in this case where return only of the indices of the non-zero value
indices = np.where(graph.adj_matrix > 0)
error = get_total_error(graph, true_graph)
total_error += error
x_list.append(r)
x2_list.append(r)
y_list.append(total_error)
y2_list.append(0)
print("", end="\r")
print("", end="")
plt.plot(x_list,
y_list,
label='Bandit Approximation',
color='tab:blue',
linestyle='-')
plt.plot(x2_list,
y2_list,
label='Ideal 0 Value',
color='tab:orange',
linestyle='--')
plt.title("Unknown Activation Probabilities - Approximation Error")
plt.ylabel("Approximation Error")
plt.xlabel("Time")
plt.legend()
plt.show()
def point7(graphs, prices, conv_rates, n_phases, k, budget, n_experiments, T,
simulations):
window_size = 2 * int((np.sqrt(T)))
# init revenue and n_customer for each graph, expeeriment and day
revenue = np.zeros([len(graphs), n_experiments, T])
n_customers = np.zeros([len(graphs), n_experiments, T])
phases_lens = np.zeros([len(graphs), n_phases], dtype=int)
best_graphs_seeds = []
for g in range(len(graphs)):
seeds, _ = greedy_algorithm(graphs[g], budget, k)
best_graphs_seeds.append(seeds)
for exper in range(n_experiments):
for g in range(len(graphs)):
learner = SWTS_Learner(len(prices), prices, window_size, T)
env = Non_Stationary_Environment(len(prices), conv_rates[g], T)
# init the graph for point 4
graph = Graph(copy=graphs[g])
graph.adj_matrix = np.where(graphs[g].adj_matrix > 0, 0.5, 0)
print(
f'Experiment : {exper+1}/{n_experiments} Graph : {g+1}/{len(graphs)}'
)
for t in tqdm(range(T)):
r = 0
# every day the sellers make social influence
seeds = choose_seeds_from_sampling(graph, budget, simulations)
potential_customers = graph.influence_episode(
seeds, graphs[g].adj_matrix)
best_potential_customers = graph.influence_episode(
best_graphs_seeds[g], graphs[g].adj_matrix, sampling=False)
indeces = np.where(graph.adj_matrix > 0)
curr_phase = int(t / (T / n_phases))
phases_lens[g][curr_phase] += potential_customers
# Retrieve for each of them alpha and beta, compute the deviation and update probability
for i in range(len(indeces[0])):
x = indeces[0][i]
y = indeces[1][i]
alpha = graph.beta_parameters_matrix[x][y].a
beta = graph.beta_parameters_matrix[x][y].b
mu = alpha / (alpha + beta)
graph.adj_matrix[x][y] = mu
n_customers[g][exper][t] = best_potential_customers
for _ in range(potential_customers):
pulled_arm = learner.pull_arm()
reward = env.round(pulled_arm, t)
learner.update(pulled_arm, reward, t)
r += prices[pulled_arm] * reward
# revenue of the day
revenue[g, exper, t] = r
# average over experiments
avg_revenue = np.average(revenue, 1)
avg_customers = np.average(n_customers, 1)
# compute the true expected revenue
true_expect_revenue = np.zeros([len(graphs), n_phases, len(prices)])
for g, conv_rate in enumerate(conv_rates):
for phase in range(n_phases):
true_expect_revenue[g][phase] = conv_rate[phase] * prices
time = range(T)
for g in range(len(graphs)):
opt_revenue = []
actual_revenue = []
regret = []
for day in range(T):
phase_size = T / n_phases
curr_phase = int(day / phase_size)
# compute the clairvoyant revenue
avg_customers_per_graph = np.mean(avg_customers, 1)
opt = np.max(true_expect_revenue[g]
[curr_phase]) * avg_customers_per_graph[g]
# revenue of the algorithm
actual = avg_revenue[g][day]
# compute the instantaneous regret
regret.append(opt - actual)
opt_revenue.append(opt)
actual_revenue.append(actual)
# print the instantaneous revenue
plt.figure(1)
ax1 = plt.subplot(221)
ax1.set_title(f'Graph {g}: Instantaneous Revenue')
plt.plot(time, actual_revenue, label='TS_SW')
plt.plot(time, opt_revenue, '--', label='clairvoyant')
plt.ylabel('revenue')
plt.xlabel('Time Horizon')
plt.legend(loc="lower right")
# print the cumulative revenue
ax2 = plt.subplot(222)
ax2.set_title(f'Graph {g}: Cumulative Revenue')
plt.plot(time, np.cumsum(actual_revenue), label='TS_SW')
plt.plot(time, np.cumsum(opt_revenue), '--', label='clairvoyant')
plt.xlabel('Time Horizon')
plt.ylabel('revenue')
plt.legend(loc="lower right")
# print the cumulative regret
ax3 = plt.subplot(223)
ax3.set_title(f'Graph {g}: Cumulative Regret')
plt.plot(time, np.cumsum(regret), label='TS_SW')
plt.legend(loc="lower right")
plt.xlabel('Time Horizon')
plt.ylabel('regret')
# plt.savefig(f'results/point5 graph{g+1}')
plt.show()
