#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) #algorithms['UCB1'] = UCB1Algorithm() algorithms['CFUCB-window1'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0.3, alpha2 = 0.2, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random', window_size = 1) algorithms['CFUCB-window10'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0.3, alpha2 = 0.2, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random', window_size = 10) algorithms['CFUCB-window50'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0.3, alpha2 = 0.2, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random', window_size = 10) # algorithms['CFUCB-0.3-0.2'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0.3, alpha2 = 0.2, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random') # algorithms['CFUCB-0-0.2'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0, alpha2 = 0.2, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random') # algorithms['CFUCB-0.3-0'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0.3, alpha2 = 0, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random') # algorithms['CFUCB-0-0'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = 0, alpha2 = 0, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random') algorithms['CFEgreedy'] = CFEgreedyAlgorithm(context_dimension = context_dimension, latent_dimension = latent_dimension, alpha = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random') # algorithms['CFUCB10'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = 10, alpha = alpha, alpha2 = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random') # algorithms['CFEgreedy10'] = CFEgreedyAlgorithm(context_dimension = context_dimension, latent_dimension = 10, alpha = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random', epsilon_init=200) # algorithms['CFUCB2'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = 2, alpha = alpha, alpha2 = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random') # algorithms['CFEgreedy2'] = CFEgreedyAlgorithm(context_dimension = context_dimension, latent_dimension = 2, alpha = alpha, lambda_ = lambda_, n = n_users, itemNum=n_articles, init='random', epsilon_init=200) simExperiment.runAlgorithms(algorithms)
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
else:
dimension = int(args.dimension)
algorithms['CFUCB'] = CFUCBAlgorithm(context_dimension = context_dimension, latent_dimension = dimension, alpha = 0.2, alpha2 = 0.1, lambda_ = lambda_, n = clusterNum, itemNum=itemNum, init='random')
elif args.alg == 'CFEgreedy':
run_CFEgreedy = True
if not args.dimension:
dimension = 5
else:
dimension = int(args.dimension)
algorithms['CFEgreedy'] = CFEgreedyAlgorithm(context_dimension = context_dimension, latent_dimension = dimension, alpha = 200, lambda_ = lambda_, n = clusterNum, itemNum=itemNum, init='random')
elif args.alg == 'SGDEgreedy':
run_SGDEgreedy = True
if not args.dimension:
dimension = 5
else:
dimension = int(args.dimension)
algorithms['SGDEgreedy'] = EgreedyContextualStruct(epsilon_init=200, userNum=clusterNum, itemNum=itemNum, k=dimension, feature_dim = context_dimension, lambda_ = lambda_, init='random', learning_rate='constant')
elif args.alg == 'PTS':
run_PTS = True
if not args.particle_num:
particle_num = 10
else:
particle_num = int(args.particle_num)
if not args.dimension:
dimension = 5