max_item=train_dataset.max_item(),
dim_embed=50,
opt='Adam',
sess_config=None,
l2_reg=0.1)
sampler = PairwiseSampler(batch_size=batch_size,
dataset=train_dataset,
num_process=5)
model_trainer = ImplicitModelTrainer(batch_size=batch_size,
test_batch_size=test_batch_size,
train_dataset=train_dataset,
model=cml_model,
sampler=sampler,
eval_save_prefix="cml-citeulike")
auc_evaluator = AUC()
recall_evaluator = Recall(recall_at=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
precision_evaluator = Precision(
precision_at=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
ndcg_evaluator = NDCG(ndcg_at=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
cml_model.load("cml-citeulike")
model_trainer._eval_manager = ImplicitEvalManager(evaluators=[
auc_evaluator, recall_evaluator, ndcg_evaluator, precision_evaluator
])
model_trainer._num_negatives = 200
model_trainer._exclude_positives([test_dataset])
model_trainer._sample_negatives(seed=10)
model_trainer._eval_save_prefix = "cml-citeulike-test"
model_trainer._evaluate_partial(test_dataset)
wcml_model = WCML(batch_size=batch_size,
max_user=train_dataset.max_user(),
max_item=train_dataset.max_item(),
dim_embed=50,
neg_num=5,
l2_reg=0.001,
opt='Adam',
sess_config=None)
sampler = NPairwiseSampler(batch_size=batch_size,
dataset=train_dataset,
negativenum=5,
num_process=4)
model_trainer = ImplicitModelTrainer(batch_size=batch_size,
test_batch_size=test_batch_size,
train_dataset=train_dataset,
model=wcml_model,
sampler=sampler,
eval_save_prefix="wcml-yahoo",
item_serving_size=500)
auc_evaluator = AUC()
wcml_model.load("wcml-yahoo")
model_trainer._eval_manager = ImplicitEvalManager(evaluators=[auc_evaluator])
model_trainer._num_negatives = 300
model_trainer._exclude_positives([train_dataset, val_dataset])
model_trainer._sample_negatives(seed=10)
model_trainer._eval_save_prefix = "wcml-yahoo-val"
model_trainer._evaluate_partial(val_dataset)