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,
latent_dimension = latent_dimension,
training_iterations = training_iterations,
testing_iterations = testing_iterations,
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)
# Here, the [0.15, 0.9] means that the user has a 0.15 chance of clicking on an article given that it is irrelevant,
# and 0.9 chance of clicking on an article given that it is relevant
users = UM.loadUsers(userFilename, [0.15, 0.9])
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)
