def rs_recommend(proc_dir, model_path, item_list):
with open(path.join(proc_dir, 'x2i.pickle'), 'rb') as handle:
x2i = pickle.load(handle)
model = DynamicAutoencoder()
recoder = Recoder(model)
recoder.init_from_model_file(model_path)
interactions = load_item_list(x2i, recoder.num_items, item_list)
out = recoder.predict(interactions)
with open(path.join(proc_dir, 'recommendations.pickle'), 'wb') as handle:
pickle.dump(out[0].detach().squeeze(0).numpy(), handle)
def load_models() -> Dict[str, Recoder]:
model_paths = {}
model_re = re.compile(r'^(?P<ds>.*)\.model$')
for f in os.listdir(MODELS_DIR):
match = model_re.match(f)
if match:
model_paths[match.group('ds')] = os.path.join(MODELS_DIR, f)
recorders = {}
for ds, path in model_paths.items():
model = DynamicAutoencoder()
recoder = Recoder(model)
recoder.init_from_model_file(path)
recorders[ds] = recoder
return recorders
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)
model_dir = root_dir + 'models/ml-20m/'
common_params = {
'user_col': 'uid',
'item_col': 'sid',
'inter_col': 'watched',
}
method = 'inference'
model_file = model_dir + 'bce_ns_d_0.0_n_0.5_200_epoch_100.model'
index_file = model_dir + 'bce_ns_d_0.0_n_0.5_200_epoch_100.model.index'
num_recommendations = 100
if method == 'inference':
model = DynamicAutoencoder()
recoder = Recoder(model)
recoder.init_from_model_file(model_file)
recommender = InferenceRecommender(recoder, num_recommendations)
elif method == 'similarity':
embeddings_index = AnnoyEmbeddingsIndex()
embeddings_index.load(index_file=index_file)
cache_embeddings_index = MemCacheEmbeddingsIndex(embeddings_index)
recommender = SimilarityRecommender(cache_embeddings_index,
num_recommendations,
scale=1,
n=50)
train_df = pd.read_csv(data_dir + 'train.csv')
val_te_df = pd.read_csv(data_dir + 'test_te.csv')
val_tr_df = pd.read_csv(data_dir + 'test_tr.csv')
# uncomment it to train with MatrixFactorization
# train_df = train_df.append(val_tr_df)
train_matrix, item_id_map, _ = dataframe_to_csr_matrix(train_df, **common_params)
val_tr_matrix, _, user_id_map = dataframe_to_csr_matrix(val_tr_df, item_id_map=item_id_map,
**common_params)
val_te_matrix, _, _ = dataframe_to_csr_matrix(val_te_df, item_id_map=item_id_map,
user_id_map=user_id_map, **common_params)
train_dataset = RecommendationDataset(train_matrix)
val_tr_dataset = RecommendationDataset(val_tr_matrix, val_te_matrix)
use_cuda = True
model = DynamicAutoencoder(hidden_layers=[200], activation_type='tanh',
noise_prob=0.5, sparse=False)
# NOTE(keshav2): Don't remove in case we want to try a different model
# model = MatrixFactorization(embedding_size=200, activation_type='tanh',
# dropout_prob=0.5, sparse=False)
trainer = Recoder(model=model, use_cuda=use_cuda, optimizer_type='adam',
loss='logistic', user_based=False,
gavel_dir=(args.checkpoint_dir if args.enable_gavel_iterator else None))
metrics = [Recall(k=20, normalize=True), Recall(k=50, normalize=True),
NDCG(k=100)]
try:
trainer.train(args.local_rank, train_dataset=train_dataset, val_dataset=val_tr_dataset,
batch_size=args.batch_size, lr=1e-3, weight_decay=2e-5,
def autoencoder():
autoencoder = DynamicAutoencoder([300, 200])
autoencoder.init_model(num_items=500)
return autoencoder
def train_rs(proc_dir: str, model_dir: str, model_name: str, lr: float,
lr_milestones: List[int], wd: float, epochs: int, emb_size: int,
batch_size: int, valid_users_pct: float, valid_items_pct: float,
wo_eval: bool):
print('Reading data...')
ds = pd.read_csv(path.join(proc_dir, 'ds.csv'))
ds['inter'] = 1
item_identity = {i: i for i in ds['item']}
if wo_eval:
train = ds
else:
print('Train test split...')
train, valid = train_test_split(ds, valid_users_pct)
valid_t, valid_e = train_eval_split(valid, valid_items_pct)
del valid
del ds
print('Making sparse matrices...')
common_params = {
'user_col': 'user',
'item_col': 'item',
'inter_col': 'inter',
}
train_matrix, _, _ = dataframe_to_csr_matrix(train,
item_id_map=item_identity,
**common_params)
train_dataset = RecommendationDataset(train_matrix)
del train
if wo_eval:
valid_dataset = None
else:
# noinspection PyUnboundLocalVariable
val_t_matrix, _, user_id_map = dataframe_to_csr_matrix(
valid_t, item_id_map=item_identity, **common_params)
# noinspection PyUnboundLocalVariable
val_e_matrix, _, _ = dataframe_to_csr_matrix(valid_e,
item_id_map=item_identity,
user_id_map=user_id_map,
**common_params)
valid_dataset = RecommendationDataset(val_t_matrix, val_e_matrix)
del valid_t, valid_e
use_cuda = True
print('Training model...')
model = DynamicAutoencoder(hidden_layers=[emb_size],
activation_type='tanh',
noise_prob=0.5,
sparse=False)
trainer = Recoder(model=model,
use_cuda=use_cuda,
optimizer_type='adam',
loss='logistic',
user_based=False)
metrics = [
Recall(k=20, normalize=True),
Recall(k=50, normalize=True),
NDCG(k=100)
]
model_prefix = path.join(model_dir, model_name)
eval_num_recs = 100
trainer.train(train_dataset=train_dataset,
val_dataset=valid_dataset,
batch_size=batch_size,
lr=lr,
weight_decay=wd,
num_epochs=epochs,
negative_sampling=True,
lr_milestones=lr_milestones,
num_data_workers=mp.cpu_count(),
model_checkpoint_prefix=model_prefix,
checkpoint_freq=0,
eval_num_recommendations=eval_num_recs,
metrics=metrics,
eval_freq=5)
actual_path = "{}_epoch_{}.model".format(model_prefix, epochs)
shutil.move(actual_path, model_prefix + '.model')
results = trainer._evaluate(valid_dataset, eval_num_recs, metrics,
batch_size)
with open(model_prefix + '_metrics.json', 'w') as f:
json.dump(
{str(metric): np.mean(results[metric])
for metric in metrics}, f)
