actionset = args.actionset
else:
actionset = "basis_vector" # "basis_vector" or "random"
testing_iterations = 500
NoiseScale = 0.1 # standard deviation of Gaussian noise
n_articles = 25
n_users = 10
poolArticleSize = None
if actionset == "basis_vector":
n_articles = context_dimension # there can be at most context_dimension number of basis vectors
## Set Up Simulation ##
UM = UserManager(context_dimension,
n_users,
thetaFunc=gaussianFeature,
argv={'l2_limit': 1})
users = UM.simulateThetafromUsers()
AM = ArticleManager(context_dimension,
n_articles=n_articles,
argv={'l2_limit': 1})
articles = AM.simulateArticlePool(actionset)
simExperiment = simulateOnlineData(
context_dimension=context_dimension,
testing_iterations=testing_iterations,
plot=True,
articles=articles,
users=users,
noise=lambda: np.random.normal(scale=NoiseScale),
signature=AM.signature,
# Matrix parameters
matrixNoise = 0.01
sparseLevel = 10 # if smaller or equal to 0 or larger or enqual to usernum, matrix is fully connected
# Parameters for GOBLin
G_alpha = alpha
G_lambda_ = lambda_
Gepsilon = 1
userFilename = os.path.join(sim_files_folder, "users_"+str(n_users)+"context_"+str(context_dimension)+"latent_"+str(latent_dimension)+ "Ugroups" + str(UserGroups)+".json")
#"Run if there is no such file with these settings; if file already exist then comment out the below funciton"
# we can choose to simulate users every time we run the program or simulate users once, save it to 'sim_files_folder', and keep using it.
UM = UserManager(context_dimension+latent_dimension, n_users, UserGroups = UserGroups, thetaFunc=featureUniform, argv={'l2_limit':1})
# users = UM.simulateThetafromUsers()
# UM.saveUsers(users, userFilename, force = False)
users = UM.loadUsers(userFilename)
articlesFilename = os.path.join(sim_files_folder, "articles_"+str(n_articles)+"context_"+str(context_dimension)+"latent_"+str(latent_dimension)+ "Agroups" + str(ArticleGroups)+".json")
# Similarly, we can choose to simulate articles every time we run the program or simulate articles once, save it to 'sim_files_folder', and keep using it.
AM = ArticleManager(context_dimension+latent_dimension, n_articles=n_articles, ArticleGroups = ArticleGroups,
FeatureFunc=featureUniform, argv={'l2_limit':1})
# articles = AM.simulateArticlePool()
# AM.saveArticles(articles, articlesFilename, force=False)
articles = AM.loadArticles(articlesFilename)
for i in range(len(articles)):
articles[i].contextFeatureVector = articles[i].featureVector[:context_dimension]
simExperiment = simulateOnlineData(context_dimension = context_dimension,
config["delta_1"] = 1e-1
config["delta_2"] = 1e-1
config["tilde_delta_1"] = config[
"delta_1"] #/ 5.0 # tilde_delta_1 should be a number between 0 and self.delta_1
config["dLinUCB_alpha"] = 0.6
#CoDBand
config["memory_size"] = 70
# Generate user and item vectors
userFilename = os.path.join(
sim_files_folder, "users_" + str(config["n_users"]) + "context_" +
str(config["context_dimension"]) + "Ugroups" +
str(config["UserGroups"]) + ".json")
UM = UserManager(config["context_dimension"],
config["n_users"],
config["gamma"],
UserGroups=config["UserGroups"],
thetaFunc=gaussianFeature,
argv={'l2_limit': 1})
users, global_parameter_set, parameter_index_for_users = UM.simulateThetafromUsers(
)
articlesFilename = os.path.join(
sim_files_folder, "articles_" + str(config["n_articles"]) +
"context_" + str(config["context_dimension"]) + "Agroups" +
str(config["ArticleGroups"]) + ".json")
AM = ArticleManager(config["context_dimension"],
n_articles=config["n_articles"],
ArticleGroups=config["ArticleGroups"],
FeatureFunc=gaussianFeature,
argv={'l2_limit': 1})
articles = AM.simulateArticlePool()
# Generate simulated users dataset
#-----------------------------------------------------------------------------------------------------------------------
# Filename to save to if first run or read from.
userFilename = os.path.join(
sim_files_folder, "users_" + str(n_users) + "context_" +
str(context_dimension) + "latent_" + str(latent_dimension) +
"Ugroups" + str(UserGroups) + ".json")
# Run if there is no such file with these settings
# if file already exist then comment out the below function
# We can choose to simulate users every time we run the program or simulate users once,
# save it to 'sim_files_folder', and keep using it.
UM = UserManager(
context_dimension + latent_dimension,
n_users, # 10
UserGroups=UserGroups, # 5 => 10/5 = 2 => 2 users per group
thetaFunc=
featureUniform, # initialization function for your parameters is uniform
argv={'l2_limit': 1})
# Get the users from simulation with the mask and user groups
users = UM.simulateThetafromUsers()
# Save the simulated users onto the file
UM.saveUsers(users, userFilename,
force=True) # Force overwrite the simulated users.
#UM.saveUsers(users, userFilename, force = False) # Don't overwrite if the file already exist
# Load from the saved file
users = UM.loadUsers(userFilename)
articlesFilename = os.path.join(
sim_files_folder, "articles_" + str(n_articles) + "context_" +
def __init__(self, dimension, user_dict, argv=None):
UserManager.__init__(self, dimension, user_dict, argv=None)
self.matrixNoise = argv['matrixNoise']
self.sparseLevel = argv['sparseLevel']
self.W, self.W0 = self.constructAdjMatrix(self.sparseLevel)