NoiseScale=NoiseScale,
poolArticleSize=poolArticleSize)
## Initiate Bandit Algorithms ##
algorithms = {}
# algorithms['EpsilonGreedyMultiArmedBandit'] = EpsilonGreedyMultiArmedBandit(num_arm=n_articles, epsilon=None)
# ## my implementation
# algorithms['UppperConfidenceBound'] = UpperConfidenceBound(num_arm=n_articles, c=0.01)
# algorithms['ThompsonSampling'] = ThompsonSampling(num_arm=n_articles, c=0.001)
# algorithms['PHE'] = PHE(num_arm=n_articles, c=0, a = 0.001, p = 0.5)
algorithms['EpsilonGreedyLinearBandit'] = EpsilonGreedyLinearBandit(
dimension=context_dimension, lambda_=0.1, epsilon=None)
##
algorithms['LinUCB'] = LinUCB(dimension=context_dimension,
lambda_=0.1,
c=0.1)
algorithms['LinTS'] = LinTS(dimension=context_dimension,
lambda_=0.01,
c=0.00001)
algorithms['LinPHE'] = LinPHE(dimension=context_dimension,
lambda_=0.1,
c=0,
a=0.01,
p=0.5)
## Run Simulation ##
print("Starting for ", simExperiment.simulation_signature)
simExperiment.runAlgorithms(algorithms)
poolArticleSize=config["poolSize"],
NoiseScale=config["NoiseScale"],
Plot=Plot,
Write_to_File=Write_to_File)
print("Starting for ", simExperiment.simulation_signature)
algorithms = {}
algorithms['oracleLinUCB'] = oracleLinUCB(
dimension=config["context_dimension"],
alpha=config["alpha"],
lambda_=config["lambda_"],
NoiseScale=config["NoiseScale"],
delta_1=config["delta_1"])
algorithms['LinUCB'] = LinUCB(dimension=config["context_dimension"],
alpha=config["alpha"],
lambda_=config["lambda_"],
NoiseScale=config["NoiseScale"])
algorithms['adTS'] = AdaptiveThompson(
dimension=config["context_dimension"],
AdTS_Window=config["AdTS_Window"],
AdTS_CheckInter=50,
v=config["v"])
algorithms['dLinUCB'] = dLinUCB(dimension=config["context_dimension"],
alpha=config["dLinUCB_alpha"],
lambda_=config["lambda_"],
NoiseScale=config["NoiseScale"],
tau=config["tau"],
delta_1=config["delta_1"],
delta_2=config["delta_2"],
tilde_delta_1=config["tilde_delta_1"])
algorithms['CLUB'] = CLUBAlgorithm(dimension=config["context_dimension"],
## Initiate Bandit Algorithms ##
algorithms = {}
# algorithms['EpsilonGreedyMultiArmedBandit'] = EpsilonGreedyMultiArmedBandit(num_arm=n_articles, epsilon=None)
# algorithms['UCB'] = UCB(num_arm=n_articles, NoiseScale=NoiseScale)
# algorithms['TS'] = TS(num_arm=n_articles, NoiseScale=NoiseScale)
# algorithms['PHE'] = PHE(num_arm=n_articles, perturbationScale=0.1)
lambda_ = 0.1
delta = 1e-1
algorithms['EpsilonGreedyLinearBandit'] = EpsilonGreedyLinearBandit(
dimension=context_dimension, lambda_=lambda_, epsilon=None)
algorithms['LinUCB'] = LinUCB(dimension=context_dimension,
alpha=-1,
lambda_=lambda_,
delta_=delta,
NoiseScale=NoiseScale)
algorithms['LinTS'] = LinTS(dimension=context_dimension,
NoiseScale=NoiseScale,
lambda_=lambda_)
algorithms['LinPHE'] = LinPHE(dimension=context_dimension,
lambda_=lambda_,
perturbationScale=1)
algorithms['NeuralPHE'] = NeuralPHE(dimension=context_dimension,
lambda_=lambda_,
perturbationScale=1)
## Run Simulation ##
print("Starting for ", simExperiment.simulation_signature)
simExperiment.runAlgorithms(algorithms)
articles=articles,
users=users,
noise=lambda: np.random.normal(scale=NoiseScale),
batchSize=batchSize,
type_="UniformTheta",
signature=AM.signature,
poolArticleSize=poolSize,
NoiseScale=NoiseScale,
Write_to_File=False)
print "Starting for ", simExperiment.simulation_signature
algorithms = {}
if not args.alg:
algorithms['LinUCB'] = LinUCB(dimension=context_dimension,
alpha=alpha,
lambda_=lambda_,
NoiseScale=NoiseScale)
#algorithms['adTS'] = AdaptiveThompson(dimension = context_dimension, AdTS_Window = 200, AdTS_CheckInter = 50, sample_num = 1000, v = 0.1)
algorithms['dLinUCB'] = dLinUCB(dimension=context_dimension,
alpha=alpha,
lambda_=lambda_,
NoiseScale=NoiseScale,
tau=tau)
elif algName == 'LinUCB':
algorithms['LinUCB'] = LinUCB(dimension=context_dimension,
alpha=alpha,
lambda_=lambda_,
NoiseScale=NoiseScale)
#elif algName == 'adTS':
#algorithms['adTS'] = AdaptiveThompson(dimension = context_dimension, AdTS_Window = 200, AdTS_CheckInter = 50, sample_num = 1000, v = 0.1)