def test_model_dkn(deeprec_resource_path):
data_path = os.path.join(deeprec_resource_path, "dkn")
yaml_file = os.path.join(data_path, r"dkn.yaml")
train_file = os.path.join(data_path, r"train_mind_demo.txt")
valid_file = os.path.join(data_path, r"valid_mind_demo.txt")
test_file = os.path.join(data_path, r"test_mind_demo.txt")
news_feature_file = os.path.join(data_path, r"doc_feature.txt")
user_history_file = os.path.join(data_path, r"user_history.txt")
wordEmb_file = os.path.join(data_path, r"word_embeddings_100.npy")
entityEmb_file = os.path.join(data_path, r"TransE_entity2vec_100.npy")
contextEmb_file = os.path.join(data_path, r"TransE_context2vec_100.npy")
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"mind-demo.zip",
)
hparams = prepare_hparams(
yaml_file,
news_feature_file=news_feature_file,
user_history_file=user_history_file,
wordEmb_file=wordEmb_file,
entityEmb_file=entityEmb_file,
contextEmb_file=contextEmb_file,
epochs=1,
learning_rate=0.0001,
)
input_creator = DKNTextIterator
model = DKN(hparams, input_creator)
assert isinstance(model.fit(train_file, valid_file), BaseModel)
assert model.run_eval(valid_file) is not None
def test_npa_component_definition(mind_resource_path):
wordEmb_file = os.path.join(mind_resource_path, "utils", "embedding.npy")
userDict_file = os.path.join(mind_resource_path, "utils", "uid2index.pkl")
wordDict_file = os.path.join(mind_resource_path, "utils", "word_dict.pkl")
yaml_file = os.path.join(mind_resource_path, "utils", r"npa.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,
epochs=1,
)
iterator = MINDIterator
model = NPAModel(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_prepare_hparams(must_exist_attributes, deeprec_resource_path):
wordEmb_file = os.path.join(deeprec_resource_path, "mind", "utils", "embedding.npy")
userDict_file = os.path.join(
deeprec_resource_path, "mind", "utils", "uid2index.pkl"
)
wordDict_file = os.path.join(
deeprec_resource_path, "mind", "utils", "word_dict.pkl"
)
yaml_file = os.path.join(deeprec_resource_path, "mind", "utils", r"nrms.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
r"https://recodatasets.z20.web.core.windows.net/newsrec/",
os.path.join(deeprec_resource_path, "mind", "utils"),
"MINDdemo_utils.zip",
)
hparams = prepare_hparams(
yaml_file,
wordEmb_file=wordEmb_file,
wordDict_file=wordDict_file,
userDict_file=userDict_file,
epochs=1,
)
assert hasattr(hparams, must_exist_attributes)
def test_load_yaml_file(deeprec_resource_path):
yaml_file = os.path.join(deeprec_resource_path, "mind", "utils", r"nrms.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/newsrec/",
os.path.join(deeprec_resource_path, "mind", "utils"),
"MINDdemo_utils.zip",
)
config = load_yaml(yaml_file)
assert config is not None
def test_prepare_hparams(deeprec_resource_path, must_exist_attributes):
data_path = os.path.join(deeprec_resource_path, "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
hparams = prepare_hparams(yaml_file)
assert hasattr(hparams, must_exist_attributes)
def test_DKN_iterator(deeprec_resource_path):
data_path = os.path.join(deeprec_resource_path, "dkn")
data_file = os.path.join(data_path, r"train_mind_demo.txt")
news_feature_file = os.path.join(data_path, r"doc_feature.txt")
user_history_file = os.path.join(data_path, r"user_history.txt")
wordEmb_file = os.path.join(data_path, "word_embeddings_100.npy")
entityEmb_file = os.path.join(data_path, "TransE_entity2vec_100.npy")
contextEmb_file = os.path.join(data_path, "TransE_context2vec_100.npy")
yaml_file = os.path.join(data_path, "dkn.yaml")
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"mind-demo.zip",
)
hparams = prepare_hparams(
yaml_file,
news_feature_file=news_feature_file,
user_history_file=user_history_file,
wordEmb_file="",
entityEmb_file="",
contextEmb_file="",
)
iterator = DKNTextIterator(hparams, tf.Graph())
assert iterator is not None
for res, impression, data_size in iterator.load_data_from_file(data_file):
assert isinstance(res, dict)
# test DKN item2item iterator
hparams = prepare_hparams(
yaml_file,
news_feature_file=news_feature_file,
wordEmb_file=wordEmb_file,
entityEmb_file=entityEmb_file,
contextEmb_file=contextEmb_file,
epochs=1,
is_clip_norm=True,
max_grad_norm=0.5,
his_size=20,
MODEL_DIR=os.path.join(data_path, "save_models"),
use_entity=True,
use_context=True,
)
hparams.neg_num = 9
iterator_item2item = DKNItem2itemTextIterator(hparams, tf.Graph())
assert iterator_item2item is not None
test_round = 3
for res, impression, data_size in iterator_item2item.load_data_from_file(
os.path.join(data_path, "doc_list.txt")):
assert isinstance(res, dict)
test_round -= 1
if test_round <= 0:
break
def test_load_yaml_file(deeprec_resource_path):
data_path = os.path.join(deeprec_resource_path, "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
config = load_yaml(yaml_file)
assert config is not None
def test_FFM_iterator(deeprec_resource_path):
data_path = os.path.join(deeprec_resource_path, "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
data_file = os.path.join(data_path, "sample_FFM_data.txt")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
hparams = prepare_hparams(yaml_file)
iterator = FFMTextIterator(hparams, tf.Graph())
assert iterator is not None
for res in iterator.load_data_from_file(data_file):
assert isinstance(res, tuple)
def test_xdeepfm_component_definition(deeprec_resource_path):
data_path = os.path.join(deeprec_resource_path, "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
hparams = prepare_hparams(yaml_file)
model = XDeepFMModel(hparams, FFMTextIterator)
assert model.logit is not None
assert model.update is not None
assert model.iterator is not None
def test_model_xdeepfm(deeprec_resource_path):
data_path = os.path.join(deeprec_resource_path, "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
data_file = os.path.join(data_path, "sample_FFM_data.txt")
output_file = os.path.join(data_path, "output.txt")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
hparams = prepare_hparams(yaml_file, learning_rate=0.01)
assert hparams is not None
input_creator = FFMTextIterator
model = XDeepFMModel(hparams, input_creator)
assert model.run_eval(data_file) is not None
assert isinstance(model.fit(data_file, data_file), BaseModel)
assert model.predict(data_file, output_file) is not None
def dkn_files(deeprec_resource_path):
data_path = os.path.join(deeprec_resource_path, "dkn")
yaml_file = os.path.join(data_path, "dkn.yaml")
news_feature_file = os.path.join(data_path, r"doc_feature.txt")
user_history_file = os.path.join(data_path, r"user_history.txt")
wordEmb_file = os.path.join(data_path, r"word_embeddings_100.npy")
entityEmb_file = os.path.join(data_path, r"TransE_entity2vec_100.npy")
contextEmb_file = os.path.join(data_path, r"TransE_context2vec_100.npy")
download_deeprec_resources(
"https://recodatasets.z20.web.core.windows.net/deeprec/",
data_path,
"mind-demo.zip",
)
return (
data_path,
yaml_file,
news_feature_file,
user_history_file,
wordEmb_file,
entityEmb_file,
contextEmb_file,
)
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_news_iterator(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.npy")
userDict_file = os.path.join(mind_resource_path, "utils", "uid2index.pkl")
wordDict_file = os.path.join(mind_resource_path, "utils", "word_dict.pkl")
yaml_file = os.path.join(mind_resource_path, "utils", r"nrms.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,
epochs=1,
)
train_iterator = MINDIterator(hparams, hparams.npratio)
test_iterator = MINDIterator(hparams, -1)
assert train_iterator is not None
for res in train_iterator.load_data_from_file(train_news_file,
train_behaviors_file):
assert isinstance(res, dict)
assert len(res) == 5
break
assert test_iterator is not None
for res in test_iterator.load_data_from_file(valid_news_file,
valid_behaviors_file):
assert isinstance(res, dict)
assert len(res) == 5
break
