def test_model(sparse, exp_recall_20, exp_recall_50, exp_ndcg_100):
data_dir = 'tests/data/'
model_dir = '/tmp/'
train_df = pd.read_csv(data_dir + 'train.csv')
val_df = pd.read_csv(data_dir + 'val.csv')
# keep the items that exist in the training dataset
val_df = val_df[val_df.sid.isin(train_df.sid.unique())]
train_matrix, item_id_map, user_id_map = dataframe_to_csr_matrix(train_df, user_col='uid',
item_col='sid', inter_col='watched')
val_matrix, _, _ = dataframe_to_csr_matrix(val_df, user_col='uid',
item_col='sid', inter_col='watched',
item_id_map=item_id_map, user_id_map=user_id_map)
train_dataset = RecommendationDataset(train_matrix)
val_dataset = RecommendationDataset(val_matrix, train_matrix)
use_cuda = False
model = DynamicAutoencoder(hidden_layers=[200], activation_type='tanh',
noise_prob=0.5, sparse=sparse)
trainer = Recoder(model=model, use_cuda=use_cuda, optimizer_type='adam',
loss='logloss')
trainer.train(train_dataset=train_dataset, val_dataset=val_dataset,
batch_size=500, lr=1e-3, weight_decay=2e-5,
num_epochs=30, negative_sampling=True)
# assert model metrics
recall_20 = Recall(k=20, normalize=True)
recall_50 = Recall(k=50, normalize=True)
ndcg_100 = NDCG(k=100)
results = trainer._evaluate(eval_dataset=val_dataset, num_recommendations=100,
metrics=[recall_20, recall_50, ndcg_100], batch_size=500)
for metric, value in list(results.items()):
results[metric] = np.mean(results[metric])
assert np.isclose(results[recall_20], exp_recall_20, atol=0.01, rtol=0)
assert np.isclose(results[recall_50], exp_recall_50, atol=0.01, rtol=0)
assert np.isclose(results[ndcg_100], exp_ndcg_100, atol=0.01, rtol=0)
# Save the model and evaluate again
model_checkpoint = model_dir + 'test_model.model'
state_file = trainer.save_state(model_checkpoint)
model = DynamicAutoencoder(sparse=sparse)
trainer = Recoder(model=model, use_cuda=use_cuda,
optimizer_type='adam', loss='logloss')
trainer.init_from_model_file(state_file)
results = trainer._evaluate(eval_dataset=val_dataset, num_recommendations=100,
metrics=[recall_20, recall_50, ndcg_100], batch_size=500)
for metric, value in list(results.items()):
results[metric] = np.mean(results[metric])
assert np.isclose(results[recall_20], exp_recall_20, atol=0.01, rtol=0)
assert np.isclose(results[recall_50], exp_recall_50, atol=0.01, rtol=0)
assert np.isclose(results[ndcg_100], exp_ndcg_100, atol=0.01, rtol=0)
os.remove(state_file)
user_based=False,
index_ids=False)
# trainer.init_from_model_file(model_dir + 'bce_ns_d_0.0_n_0.5_200_epoch_50.model')
model_checkpoint = model_dir + 'bce_ns_d_0.0_n_0.5_200'
metrics = [
Recall(k=20, normalize=True),
Recall(k=50, normalize=True),
NDCG(k=100)
]
try:
trainer.train(train_dataset=train_dataset,
val_dataset=val_tr_dataset,
batch_size=500,
lr=1e-3,
weight_decay=2e-5,
num_epochs=100,
num_neg_samples=0,
lr_milestones=[60, 80],
num_data_workers=mp.cpu_count() if use_cuda else 0,
model_checkpoint_prefix=model_checkpoint,
checkpoint_freq=10,
eval_num_recommendations=100,
metrics=metrics,
eval_freq=10)
except (KeyboardInterrupt, SystemExit):
trainer.save_state(model_checkpoint)
raise