def test_model_naml(mind_resource_path):
train_news_file = os.path.join(mind_resource_path, "train", r"news.tsv")
train_behaviors_file = os.path.join(mind_resource_path, "train",
r"behaviors.tsv")
valid_news_file = os.path.join(mind_resource_path, "valid", r"news.tsv")
valid_behaviors_file = os.path.join(mind_resource_path, "valid",
r"behaviors.tsv")
wordEmb_file = os.path.join(mind_resource_path, "utils",
"embedding_all.npy")
userDict_file = os.path.join(mind_resource_path, "utils", "uid2index.pkl")
wordDict_file = os.path.join(mind_resource_path, "utils",
"word_dict_all.pkl")
vertDict_file = os.path.join(mind_resource_path, "utils", "vert_dict.pkl")
subvertDict_file = os.path.join(mind_resource_path, "utils",
"subvert_dict.pkl")
yaml_file = os.path.join(mind_resource_path, "utils", r"naml.yaml")
if not os.path.exists(train_news_file):
download_deeprec_resources(
r"https://recodatasets.z20.web.core.windows.net/newsrec/",
os.path.join(mind_resource_path, "train"),
"MINDdemo_train.zip",
)
if not os.path.exists(valid_news_file):
download_deeprec_resources(
r"https://recodatasets.z20.web.core.windows.net/newsrec/",
os.path.join(mind_resource_path, "valid"),
"MINDdemo_dev.zip",
)
if not os.path.exists(yaml_file):
download_deeprec_resources(
r"https://recodatasets.z20.web.core.windows.net/newsrec/",
os.path.join(mind_resource_path, "utils"),
"MINDdemo_utils.zip",
)
hparams = prepare_hparams(
yaml_file,
wordEmb_file=wordEmb_file,
wordDict_file=wordDict_file,
userDict_file=userDict_file,
vertDict_file=vertDict_file,
subvertDict_file=subvertDict_file,
epochs=1,
)
iterator = MINDAllIterator
model = NAMLModel(hparams, iterator)
assert model.run_eval(valid_news_file, valid_behaviors_file) is not None
assert isinstance(
model.fit(train_news_file, train_behaviors_file, valid_news_file,
valid_behaviors_file),
BaseModel,
)
def test_naml_component_definition(mind_resource_path):
wordEmb_file = os.path.join(mind_resource_path, "utils",
"embedding_all.npy")
userDict_file = os.path.join(mind_resource_path, "utils", "uid2index.pkl")
wordDict_file = os.path.join(mind_resource_path, "utils",
"word_dict_all.pkl")
vertDict_file = os.path.join(mind_resource_path, "utils", "vert_dict.pkl")
subvertDict_file = os.path.join(mind_resource_path, "utils",
"subvert_dict.pkl")
yaml_file = os.path.join(mind_resource_path, "utils", r"naml.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
r"https://recodatasets.z20.web.core.windows.net/newsrec/",
os.path.join(mind_resource_path, "utils"),
"MINDdemo_utils.zip",
)
hparams = prepare_hparams(
yaml_file,
wordEmb_file=wordEmb_file,
wordDict_file=wordDict_file,
userDict_file=userDict_file,
vertDict_file=vertDict_file,
subvertDict_file=subvertDict_file,
epochs=1,
)
iterator = MINDAllIterator
model = NAMLModel(hparams, iterator)
assert model.model is not None
assert model.scorer is not None
assert model.loss is not None
assert model.train_optimizer is not None
def test_model_naml(tmp):
yaml_file = os.path.join(tmp, "naml.yaml")
train_file = os.path.join(tmp, "train.txt")
valid_file = os.path.join(tmp, "test.txt")
wordEmb_file = os.path.join(tmp, "embedding.npy")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/newsrec/", tmp,
"naml.zip")
hparams = prepare_hparams(yaml_file, wordEmb_file=wordEmb_file, epochs=1)
assert hparams is not None
iterator = NAMLIterator
model = NAMLModel(hparams, iterator)
assert model.run_eval(valid_file) is not None
assert isinstance(model.fit(train_file, valid_file), BaseModel)
def test_naml_component_definition(tmp):
yaml_file = os.path.join(tmp, "naml.yaml")
wordEmb_file = os.path.join(tmp, "embedding.npy")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/newsrec/", tmp,
"naml.zip")
hparams = prepare_hparams(yaml_file, wordEmb_file=wordEmb_file, epochs=1)
iterator = NAMLIterator
model = NAMLModel(hparams, iterator)
assert model.model is not None
assert model.scorer is not None
assert model.loss is not None
assert model.train_optimizer is not None
wordDict_file = os.path.join(data_path, "utils", "word_dict_all.pkl")
subvertDict_file = os.path.join(data_path, "utils", "subvert_dict.pkl")
vertDict_file = os.path.join(data_path, "utils", "vert_dict.pkl")
yaml_file = os.path.join(data_path, "utils", '{}.yaml'.format(opt.model_name))
hparams = prepare_hparams(yaml_file,
wordEmb_file=wordEmb_file,
wordDict_file=wordDict_file,
userDict_file=userDict_file,
vertDict_file=vertDict_file,
subvertDict_file=subvertDict_file,
batch_size=batch_size,
epochs=epochs)
print(hparams)
iterator = iterator = MINDAllIterator
model = NAMLModel(hparams, iterator, seed=seed)
# print(model.run_slow_eval(news_file, valid_behaviors_file))
model.fit(news_file, train_behaviors_file, news_file, valid_behaviors_file)
# model_path = os.path.join(model_path, "model")
# os.makedirs(model_path, exist_ok=True)
# model.model.save_weights(os.path.join(model_path, "nrms_ckpt"))
# group_impr_indexes, group_labels, group_preds = model.run_slow_eval(test_news_file, test_behaviors_file)
# res = cal_metric(group_labels, group_preds, hparams.metrics)
subvertDict_file=subvertDict_file,
batch_size=batch_size,
epochs=epochs,
show_step=10)
logging.info(hparams)
# ## Train the NRMS model
if model_type == 'nrms':
iterator = MINDIterator
model = NRMSModel(hparams, iterator, seed=seed)
elif model_type == 'naml':
iterator = MINDAllIterator
model = NAMLModel(hparams, iterator, seed=seed)
elif model_type == 'npa':
iterator = MINDIterator
model = NPAModel(hparams, iterator, seed=seed)
elif model_type == 'nrmma':
iterator = MINDAllIterator
model = NRMMAModel(hparams, iterator, seed=seed)
else:
raise NotImplementedError(f"{exp_name} is not implemented")
# In[8]:
model_path = os.path.join(exp_path, model_type)
model_name = model_type + '_ckpt'
model.fit(train_news_file, train_behaviors_file, valid_news_file, valid_behaviors_file,
model_path=model_path, model_name=model_name)