W = initializeW_opt(userFeatureVectors, SparsityLevel) # Generate user relation matrix
GW = initializeGW(W , epsilon)
articles_random = randomStruct()
algorithms = {}
runCoLinUCB = runGOBLin = runLinUCB = run_M_LinUCB = run_Uniform_LinUCB= run_CFUCB = run_CFEgreedy = run_SGDEgreedy = run_PTS = run_TCF = False
if args.alg:
if args.alg == 'CoLinUCB' or args.alg == 'CoLin':
runCoLinUCB = True
algorithms['CoLin'] = AsyCoLinUCBAlgorithm(dimension=context_dimension, alpha = alpha, lambda_ = lambda_, n = userNum, W = W)
elif args.alg == 'GOBLin':
runGOBLin = True
algorithms['GOBLin'] = GOBLinAlgorithm(dimension=context_dimension, alpha = alpha, lambda_ = lambda_, n = userNum, W = GW)
elif args.alg == 'LinUCB':
runLinUCB = True
algorithms['LinUCB'] = N_LinUCBAlgorithm(dimension = context_dimension, alpha = alpha, lambda_ = lambda_, n = clusterNum)
elif args.alg =='M_LinUCB':
run_M_LinUCB = True
elif args.alg == 'Uniform_LinUCB':
run_Uniform_LinUCB = True
elif args.alg == 'factorLinUCB':
run_factorLinUCB = True
if not args.dimension:
dimension = 5
else:
dimension = int(args.dimension)
algorithms['FactorLinUCBAlgorithm'] = FactorLinUCBAlgorithm(context_dimension = context_dimension, latent_dimension = dimension, alpha = 0.2, alpha2 = 0.1, lambda_ = lambda_, n = clusterNum, itemNum=itemNum, W = W, init='random', window_size = 3)
elif args.alg == 'CFUCB':
run_CFUCB = True
if not args.dimension:
dimension = 5
matrixNoise = lambda : np.random.normal(scale = matrixNoise),
batchSize = batchSize,
type_ = "UniformTheta",
signature = AM.signature,
sparseLevel = sparseLevel,
poolArticleSize = poolSize, NoiseScale = NoiseScale, epsilon = epsilon, Gepsilon =Gepsilon)
print "Starting for ", simExperiment.simulation_signature
algorithms = {}
# algorithms['CFContextual'] = EgreedyContextualStruct(Tu= 200, m=10, lambd=0.1, alpha=0, userNum=n_users, itemNum=n_articles, k=context_dimension+latent_dimension, feature_dim = context_dimension, init='zero')
# algorithms['EgreedyNonContextual'] = EgreedyStruct(Tu= 200, m=10, lambd=0.1, alpha=100, userNum=n_users, itemNum=n_articles, k=context_dimension+latent_dimension, init='random')
# algorithms['CF'] = EgreedyStruct(Tu= 200, m=10, lambd=0.1, alpha=0, userNum=n_users, itemNum=n_articles, k=context_dimension+latent_dimension, init='random')
algorithms['LinUCB'] = N_LinUCBAlgorithm(dimension = context_dimension, alpha = alpha, lambda_ = lambda_, n = n_users)
# algorithms['LinEgreedy'] = N_LinEgreedyAlgorithm(dimension = context_dimension, alpha = alpha, lambda_ = lambda_, n = n_users)
#algorithms['LinUCBRandom'] = N_LinUCBAlgorithm(dimension = context_dimension, alpha = alpha, lambda_ = lambda_, n = n_users, init="random")
#algorithms['GOBLin'] = GOBLinAlgorithm( dimension= dimension, alpha = G_alpha, lambda_ = G_lambda_, n = n_users, W = simExperiment.getGW() )
#algorithms['syncCoLinUCB'] = syncCoLinUCBAlgorithm(dimension=dimension, alpha = alpha, lambda_ = lambda_, n = n_users, W = simExperiment.getW())
#algorithms['AsyncCoLinUCB'] = AsyCoLinUCBAlgorithm(dimension=dimension, alpha = alpha, lambda_ = lambda_, n = n_users, W = simExperiment.getW())
algorithms['EgreedySGDLrConstant'] = EgreedyContextualStruct(epsilon_init=200, userNum=n_users, itemNum=n_articles, k=context_dimension+latent_dimension, feature_dim = context_dimension, lambda_ = lambda_, init='zero', learning_rate='constant')
# algorithms['EgreedySGDLrDecay'] = EgreedyContextualStruct(epsilon_init=200, userNum=n_users, itemNum=n_articles, k=context_dimension+latent_dimension, feature_dim = context_dimension, lambda_ = lambda_, init='zero', learning_rate='decay')
#algorithms['UniformLinUCB'] = Uniform_LinUCBAlgorithm(dimension = dimension, alpha = alpha, lambda_ = lambda_)
#algorithms['WCoLinUCB'] = WAlgorithm(dimension = dimension, alpha = alpha, lambda_ = lambda_, eta_ = eta_, n = n_users)
#algorithms['WknowTheta'] = WknowThetaAlgorithm(dimension = dimension, alpha = alpha, lambda_ = lambda_, eta_ = eta_, n = n_users, theta = simExperiment.getTheta())
#algorithms['W_W0'] = W_W0_Algorithm(dimension = dimension, alpha = alpha, lambda_ = lambda_, eta_ = eta_, n = n_users, W0 = simExperiment.getW0())
#algorithms['eGreedy'] = eGreedyAlgorithm(epsilon = 0.1)
signature=AM.signature,
sparseLevel=sparseLevel,
poolArticleSize=poolSize,
NoiseScale=NoiseScale,
epsilon=epsilon,
Gepsilon=Gepsilon)
selectUser_Algorithms = {}
selectUser_Algorithms['LinUCB_SelectUser'] = LinUCB_SelectUserAlgorithm(
dimension=dimension, alpha=alpha, lambda_=lambda_, n=n_users)
selectUser_Algorithms[
'AsyncCoLin_SelectUser'] = CoLinUCB_SelectUserAlgorithm(
dimension=dimension,
alpha=alpha,
lambda_=lambda_,
n=n_users,
W=simExperiment_SelectUser.getW())
selectUser_Algorithms['GOBLin_SelectUser'] = GOBLin_SelectUserAlgorithm(
dimension=dimension,
alpha=alpha,
lambda_=lambda_,
n=n_users,
W=simExperiment_SelectUser.getGW())
selectUser_Algorithms['LinUCB_RandomUser'] = N_LinUCBAlgorithm(
dimension=dimension, alpha=alpha, lambda_=lambda_, n=n_users)
#selectUser_Algorithms['AsyncCoLin_RandomUser'] = AsyCoLinUCBAlgorithm(dimension=dimension, alpha = alpha, lambda_ = lambda_, n = n_users, W = simExperiment_SelectUser.getW0())
#selectUser_Algorithms['GOBLin_RandomUser'] = GOBLinAlgorithm( dimension= dimension, alpha = G_alpha, lambda_ = G_lambda_, n = n_users, W = simExperiment_SelectUser.getGW() )
simExperiment_SelectUser.runAlgorithms(selectUser_Algorithms)
