def job(model_type, learning_rate, eta, gamma, reward_method, f, train_set, test_set, num_features, output_fold):
if model_type == "perfect":
pc = [0.0, 0.2, 0.4, 0.8, 1.0]
ps = [0.0, 0.0, 0.0, 0.0, 0.0]
elif model_type == "navigational":
pc = [0.05, 0.3, 0.5, 0.7, 0.95]
ps = [0.2, 0.3, 0.5, 0.7, 0.9]
elif model_type == "informational":
pc = [0.4, 0.6, 0.7, 0.8, 0.9]
ps = [0.1, 0.2, 0.3, 0.4, 0.5]
cm = PBM(pc, 2)
for r in range(1, 16):
# np.random.seed(r)
ranker = MDPRankerV2(256, num_features, learning_rate)
print("MDP Adam MSLR10K fold{} {} eta{} reward {} run{} start!".format(f, model_type, eta, reward_method, r))
ndcg_scores, cndcg_scores = run(train_set, test_set, ranker, eta, gamma, reward_method, NUM_INTERACTION, cm)
os.makedirs(os.path.dirname("{}/fold{}/".format(output_fold, f)),
exist_ok=True) # create directory if not exist
with open(
"{}/fold{}/{}_run{}_ndcg.txt".format(output_fold, f, model_type, r),
"wb") as fp:
pickle.dump(ndcg_scores, fp)
with open(
"{}/fold{}/{}_run{}_cndcg.txt".format(output_fold, f, model_type, r),
"wb") as fp:
pickle.dump(cndcg_scores, fp)
def job(model_type, f, train_set, test_set, tau, output_fold):
if model_type == "perfect":
pc = [0.0, 0.2, 0.4, 0.8, 1.0]
ps = [0.0, 0.0, 0.0, 0.0, 0.0]
elif model_type == "navigational":
pc = [0.05, 0.3, 0.5, 0.7, 0.95]
ps = [0.2, 0.3, 0.5, 0.7, 0.9]
elif model_type == "informational":
pc = [0.4, 0.6, 0.7, 0.8, 0.9]
ps = [0.1, 0.2, 0.3, 0.4, 0.5]
# if model_type == "perfect":
# pc = [0.0, 0.5, 1.0]
# ps = [0.0, 0.0, 0.0]
# elif model_type == "navigational":
# pc = [0.05, 0.5, 0.95]
# ps = [0.2, 0.5, 0.9]
# elif model_type == "informational":
# pc = [0.4, 0.7, 0.9]
# ps = [0.1, 0.3, 0.5]
cm = PBM(pc, 1)
for r in range(16, 26):
# np.random.seed(r)
ranker = PDGDLinearRanker(FEATURE_SIZE, Learning_rate, tau)
print("PDGD tau{} fold{} {} run{} start!".format(
tau, f, model_type, r))
ndcg_scores, cndcg_scores, final_weight = run(train_set, test_set,
ranker, NUM_INTERACTION,
cm)
os.makedirs(os.path.dirname("{}/fold{}/".format(output_fold, f)),
exist_ok=True) # create directory if not exist
with open(
"{}/fold{}/{}_run{}_ndcg.txt".format(output_fold, f,
model_type, r),
"wb") as fp:
pickle.dump(ndcg_scores, fp)
with open(
"{}/fold{}/{}_run{}_cndcg.txt".format(output_fold, f,
model_type, r),
"wb") as fp:
pickle.dump(cndcg_scores, fp)
with open(
"../results/exploration/mq2007/PDGD/fold{}/{}_tau{}_run{}_final_weight.txt"
.format(f, model_type, tau, r), "wb") as fp:
pickle.dump(final_weight, fp)
print("PDGD tau{} fold{} {} run{} finished!".format(
tau, f, model_type, r))
def job(model_type, learning_rate, eta, reward_method, f, train_set, test_set, num_features, output_fold):
if model_type == "perfect":
pc = [0.0, 0.5, 1.0]
ps = [0.0, 0.0, 0.0]
elif model_type == "navigational":
pc = [0.05, 0.5, 0.95]
ps = [0.2, 0.5, 0.9]
elif model_type == "informational":
pc = [0.4, 0.7, 0.9]
ps = [0.1, 0.3, 0.5]
cm = PBM(pc, 1)
for r in range(1, 16):
# np.random.seed(r)
ranker = ActorCriticLinearRankerV2(num_features, learning_rate, 256)
print("ActorCritic mq2007 fold{} {} eta{} reward{} run{} start!".format(f, model_type, eta, reward_method, r))
ndcg_scores, cndcg_scores = run(train_set, test_set, ranker, eta, reward_method, NUM_INTERACTION, cm)
def job(model_type, f, train_set, test_set, tau, step_size, gamma, num_rankers, learning_rate_decay, output_fold):
# if model_type == "perfect":
# pc = [0.0, 0.5, 1.0]
# ps = [0.0, 0.0, 0.0]
# elif model_type == "navigational":
# pc = [0.05, 0.5, 0.95]
# ps = [0.2, 0.5, 0.9]
# elif model_type == "informational":
# pc = [0.4, 0.7, 0.9]
# ps = [0.1, 0.3, 0.5]
# click setting for 4-grade relevance label datasets.
if model_type == "perfect":
pc = [0.0, 0.2, 0.4, 0.8, 1.0]
ps = [0.0, 0.0, 0.0, 0.0, 0.0]
elif model_type == "navigational":
pc = [0.05, 0.3, 0.5, 0.7, 0.95]
ps = [0.2, 0.3, 0.5, 0.7, 0.9]
elif model_type == "informational":
pc = [0.4, 0.6, 0.7, 0.8, 0.9]
ps = [0.1, 0.2, 0.3, 0.4, 0.5]
# using PBM click model to simulate clicks.
# cm = SDBN(pc, ps)
cm = PBM(pc, 1)
for r in range(16, 26):
# np.random.seed(r)
ranker = COLTRLinearRanker(FEATURE_SIZE, Learning_rate, step_size, tau, gamma, learning_rate_decay=learning_rate_decay)
print("COTLR {} tau{} fold{} {} run{} start!".format(output_fold, tau, f, model_type, r))
ndcg_scores, cndcg_scores = run(train_set, test_set, ranker, NUM_INTERACTION, cm, num_rankers)
os.makedirs(os.path.dirname("{}/fold{}/".format(output_fold, f)),
exist_ok=True) # create directory if not exist
# store the results.
with open(
"{}/fold{}/{}_run{}_ndcg.txt".format(output_fold, f, model_type, r),
"wb") as fp:
pickle.dump(ndcg_scores, fp)
with open(
"{}/fold{}/{}_run{}_cndcg.txt".format(output_fold, f, model_type, r),
"wb") as fp:
pickle.dump(cndcg_scores, fp)
def job(model_type, f, train_set, test_set, delta, alpha, FEATURE_SIZE, num_rankers, output_fold):
if model_type == "perfect":
pc = [0.0, 0.2, 0.4, 0.8, 1.0]
ps = [0.0, 0.0, 0.0, 0.0, 0.0]
elif model_type == "navigational":
pc = [0.05, 0.3, 0.5, 0.7, 0.95]
ps = [0.2, 0.3, 0.5, 0.7, 0.9]
elif model_type == "informational":
pc = [0.4, 0.6, 0.7, 0.8, 0.9]
ps = [0.1, 0.2, 0.3, 0.4, 0.5]
#
# if model_type == "perfect":
# pc = [0.0, 0.5, 1.0]
# ps = [0.0, 0.0, 0.0]
# elif model_type == "navigational":
# pc = [0.05, 0.5, 0.95]
# ps = [0.2, 0.5, 0.9]
# elif model_type == "informational":
# pc = [0.4, 0.7, 0.9]
# ps = [0.1, 0.3, 0.5]
cm = PBM(pc, 1)
for r in range(1, 26):
# np.random.seed(r)
ranker = ProbabilisticRanker(delta, alpha, FEATURE_SIZE)
print("DBGD fold{} run{} start!".format(f, model_type, r))
ndcg_scores, cndcg_scores = run(train_set, test_set, ranker, NUM_INTERACTION, cm, num_rankers)
os.makedirs(os.path.dirname("{}/fold{}/".format(output_fold, f)),
exist_ok=True) # create directory if not exist
with open(
"{}/fold{}/{}_run{}_ndcg.txt".format(output_fold, f, model_type, r),
"wb") as fp:
pickle.dump(ndcg_scores, fp)
with open(
"{}/fold{}/{}_run{}_cndcg.txt".format(output_fold, f, model_type, r),
"wb") as fp:
pickle.dump(cndcg_scores, fp)