else:
use_coUsers = 'collaborative' in user and user['collaborative']
reward_type = reco['type'] if 'type' in reco else 'linear'
#if user.has_key('collaborative') and user['collaborative']:
if use_coUsers:
UM = CoUserManager(context_dimension + latent_dimension,
user,
argv={
'l2_limit': 1,
'sparseLevel': n_users,
'matrixNoise': rewardManagerDict['matrixNoise']
})
else:
UM = UserManager(context_dimension + latent_dimension,
user,
argv={'l2_limit': 1})
UM.CoTheta()
rewardManagerDict['W'] = UM.getW()
rewardManagerDict['users'] = UM.getUsers()
articlesFilename = os.path.join(
sim_files_folder, "articles_" + str(n_articles) + "context_" +
str(context_dimension) + "latent_" + str(latent_dimension) +
"Agroups" + str(ArticleGroups) + ".json")
AM = ArticleManager(context_dimension + latent_dimension,
n_articles=n_articles,
ArticleGroups=ArticleGroups,
FeatureFunc=featureUniform,
argv={'l2_limit': 1})
# Matrix parameters
matrixNoise = 0.01
sparseLevel = n_users # 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)
#PCA
pca_articles(articles, 'random')