model = VisualCML(batch_size=batch_size,
max_user=raw_data['max_user'],
max_item=raw_data['max_item'],
l2_reg=0.001,
l2_reg_mlp=0.001,
dropout_rate=0.5,
dim_embed=50,
item_f_source=raw_data['item_features'],
dims=[1028, 128, 50],
sess_config=sess_config,
opt='Adam')
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,
item_serving_size=item_serving_size,
train_dataset=train_dataset,
model=model,
sampler=sampler)
auc_evaluator = AUC()
recall_evaluator = Recall(recall_at=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
model_trainer.train(num_itr=int(1e5),
display_itr=display_itr,
eval_datasets=[val_dataset, test_dataset],
evaluators=[auc_evaluator, recall_evaluator],
num_negatives=1000)
dim_embed=20, opt='Adam', sess_config=config)
sampler = PairwiseSamplerWithExplicitConversion(
dataset=train_dataset, batch_size=batch_size, num_process=3)
model_trainer = ImplicitModelTrainer(batch_size=batch_size, test_batch_size=test_batch_size,
train_dataset=train_dataset, model=model, sampler=sampler)
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])
print(((str(datetime.datetime.now())).split('.')[0]).split(' ')[
1] + ' ' + ((str(datetime.datetime.now())).split('.')[0]).split(' ')[0])
print(fileToLoad)
model_trainer.train(num_itr=int(10), display_itr=display_itr, eval_datasets=[val_dataset],
evaluators=[auc_evaluator, recall_evaluator, precision_evaluator, ndcg_evaluator])
print("Save")
model.save("./model", 3)
print("Saved")
# big_bpr = BPR(batch_size=batch_size, max_user=max_user,
# max_item=max_item, dim_embed=20)
# Recommender.load(big_bpr, "model-1")
# print("model loaded")
# print(big_bpr)
# print(big_bpr.serve(pythonsucks))
name='Val')
test_dataset = ImplicitDataset(raw_data=csv,
max_user=max_users,
max_item=max_items,
name='Test')
bpr_model = BPR(batch_size=1000,
max_user=train_dataset.max_user(),
max_item=train_dataset.max_item(),
dim_embed=20,
opt='Adam')
print("before sampler")
sampler = PairwiseSampler(batch_size=1000, dataset=train_dataset)
print("after sampler")
auc_evaluator = AUC()
print("after evaluator")
model_trainer = ImplicitModelTrainer(batch_size=1000,
test_batch_size=100,
train_dataset=train_dataset,
model=bpr_model,
sampler=sampler)
print("after implicit")
model_trainer.train(num_itr=10,
display_itr=10,
eval_datasets=[val_dataset, test_dataset],
evaluators=[auc_evaluator])
test_batch_size = 100
display_itr = 10000
print 'yuck'
train_dataset = ImplicitDataset(raw_data['train_data'], raw_data['max_user'], raw_data['max_item'], name='Train')
print 'h'
val_dataset = ImplicitDataset(raw_data['val_data'], raw_data['max_user'], raw_data['max_item'], name='Val')
print 'e'
test_dataset = ImplicitDataset(raw_data['test_data'], raw_data['max_user'], raw_data['max_item'], name='Test')
print 'here 1'
youtube_model = YoutubeRec(batch_size=batch_size, max_user=train_dataset.max_user(), max_item=train_dataset.max_item(),
dim_embed=20, sess_config=sess_config, opt='Adam')
# bpr_model = FeatureBasedBPR(batch_size=batch_size, max_user=train_dataset.max_user(), max_item=train_dataset.max_item(),
# dim_embed=20, opt='Adam', sess_config=sess_config)
print 'here 2'
sampler = GeneralSampler(batch_size=batch_size, dataset=train_dataset, num_process=1, genre_f = raw_data['song_to_genre'])
print 'here 3'
model_trainer = ImplicitModelTrainer(batch_size=batch_size, test_batch_size=test_batch_size,
train_dataset=train_dataset, model=youtube_model, sampler=sampler)
print 'here 4'
auc_evaluator = AUC()
print 'here 5'
model_trainer.train(num_itr=int(1e6), display_itr=display_itr, eval_datasets=[val_dataset, test_dataset],
evaluators=[auc_evaluator])
print 'here 6'
if dataset_name == "amazon_book":
eval_save_prefix = None
model_trainer = ImplicitModelTrainer(batch_size=batch_size,
test_batch_size=test_batch_size,
train_dataset=train_dataset,
model=fhmf_model,
sampler=sampler,
eval_save_prefix=eval_save_prefix)
#Evaluators
auc_evaluator = AUC()
recall_evaluator = Recall(recall_at=[10, 50, 100, 300])
#mse_evaluator = MSE()
#Train
model_trainer.train(num_itr=num_itr,
display_itr=display_itr,
eval_datasets=[focused_val_set, focused_test_set],
evaluators=[auc_evaluator, recall_evaluator],
num_negatives=num_negatives)
elif recommender == "PMF":
model = PMF(batch_size=batch_size,
max_user=train_dataset.max_user(),
max_item=train_dataset.max_item(),
dim_embed=50,
opt='Adam',
sess_config=sess_config,
l2_reg=l2_reg)
sampler = PointwiseSampler(batch_size=batch_size,
dataset=train_dataset,
pos_ratio=pos_ratio,