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_dkn_component_definition(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec",
"dkn")
yaml_file = os.path.join(data_path, "dkn.yaml")
wordEmb_file = os.path.join(data_path, "word_embeddings_100.npy")
entityEmb_file = os.path.join(data_path, "TransE_entity2vec_100.npy")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/deeprec/",
data_path,
"dknresources.zip",
)
hparams = prepare_hparams(
yaml_file,
wordEmb_file=wordEmb_file,
entityEmb_file=entityEmb_file,
epochs=1,
learning_rate=0.0001,
)
assert hparams is not None
model = DKN(hparams, DKNTextIterator)
assert model.logit is not None
assert model.update is not None
assert model.iterator is not None
def test_model_dkn(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "dkn")
yaml_file = os.path.join(data_path, "dkn.yaml")
train_file = os.path.join(data_path, "final_test_with_entity.txt")
valid_file = os.path.join(data_path, "final_test_with_entity.txt")
wordEmb_file = os.path.join(data_path, "word_embeddings_100.npy")
entityEmb_file = os.path.join(data_path, "TransE_entity2vec_100.npy")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/deeprec/",
data_path,
"dknresources.zip",
)
hparams = prepare_hparams(
yaml_file,
wordEmb_file=wordEmb_file,
entityEmb_file=entityEmb_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_model_dkn(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec",
"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.blob.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_model_dkn(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec",
"dkn")
yaml_file = os.path.join(data_path, "dkn.yaml")
train_file = os.path.join(data_path, "final_test_with_entity.txt")
valid_file = os.path.join(data_path, "final_test_with_entity.txt")
wordEmb_file = os.path.join(data_path, "word_embeddings_100.npy")
entityEmb_file = os.path.join(data_path, "TransE_entity2vec_100.npy")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/deeprec/",
data_path,
"dknresources.zip",
)
hparams = prepare_hparams(
yaml_file,
wordEmb_file=wordEmb_file,
entityEmb_file=entityEmb_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_dkn_component_definition(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec",
"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.blob.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)
assert hparams is not None
model = DKN(hparams, DKNTextIterator)
assert model.logit is not None
assert model.update is not None
assert model.iterator is not None
def test_dkn_component_definition(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "dkn")
yaml_file = os.path.join(data_path, "dkn.yaml")
wordEmb_file = os.path.join(data_path, "word_embeddings_100.npy")
entityEmb_file = os.path.join(data_path, "TransE_entity2vec_100.npy")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/deeprec/",
data_path,
"dknresources.zip",
)
hparams = prepare_hparams(
yaml_file,
wordEmb_file=wordEmb_file,
entityEmb_file=entityEmb_file,
epochs=5,
learning_rate=0.0001,
)
assert hparams is not None
model = DKN(hparams, DKNTextIterator)
assert model.logit is not None
assert model.update is not None
assert model.iterator is not None
def test_naml_component_definition(tmp):
wordEmb_file = os.path.join(tmp, "utils", "embedding_all.npy")
userDict_file = os.path.join(tmp, "utils", "uid2index.pkl")
wordDict_file = os.path.join(tmp, "utils", "word_dict_all.pkl")
vertDict_file = os.path.join(tmp, "utils", "vert_dict.pkl")
subvertDict_file = os.path.join(tmp, "utils", "subvert_dict.pkl")
yaml_file = os.path.join(tmp, "utils", r"naml.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
r"https://recodatasets.blob.core.windows.net/newsrec/",
os.path.join(tmp, "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_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_prepare_hparams(must_exist_attributes, resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
hparams = prepare_hparams(yaml_file)
assert hasattr(hparams, must_exist_attributes)
def test_dkn_component_definition(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",
)
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,
)
assert hparams is not None
model = DKN(hparams, DKNTextIterator)
assert model.logit is not None
assert model.update is not None
assert model.iterator is not None
### test DKN's item2item version
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
assert hparams is not None
model_item2item = DKNItem2Item(hparams, DKNItem2itemTextIterator)
assert model_item2item.pred_logits is not None
assert model_item2item.update is not None
assert model_item2item.iterator is not None
def test_load_yaml_file(tmp):
yaml_file = os.path.join(tmp, "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(tmp, "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_load_yaml_file(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/deeprec/",
data_path,
"xdeepfmresources.zip",
)
config = load_yaml(yaml_file)
assert config is not None
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_DKN_iterator(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "dkn")
data_file = os.path.join(data_path, "final_test_with_entity.txt")
yaml_file = os.path.join(data_path, "dkn.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.core.windows.net/deeprec/",
data_path,
"dknresources.zip",
)
hparams = prepare_hparams(yaml_file, wordEmb_file="", entityEmb_file="")
iterator = DKNTextIterator(hparams, tf.Graph())
assert iterator is not None
for res in iterator.load_data_from_file(data_file):
assert isinstance(res, dict)
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_lstur_component_definition(tmp):
yaml_file = os.path.join(tmp, "lstur.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,
"lstur.zip")
hparams = prepare_hparams(yaml_file, wordEmb_file=wordEmb_file, epochs=1)
iterator = NewsIterator
model = LSTURModel(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_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_FFM_iterator(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "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.blob.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, dict)
def test_xdeepfm_component_definition(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "xdeepfm")
yaml_file = os.path.join(data_path, "xDeepFM.yaml")
if not os.path.exists(yaml_file):
download_deeprec_resources(
"https://recodatasets.blob.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_npa(tmp):
yaml_file = os.path.join(tmp, "npa.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,
"npa.zip")
hparams = prepare_hparams(yaml_file, wordEmb_file=wordEmb_file, epochs=1)
assert hparams is not None
iterator = NewsIterator
model = NPAModel(hparams, iterator)
assert model.run_eval(valid_file) is not None
assert isinstance(model.fit(train_file, valid_file), BaseModel)
def test_model_xdeepfm(resource_path):
data_path = os.path.join(resource_path, '../resources/deeprec/xdeepfm')
yaml_file = os.path.join(data_path, r'xDeepFM.yaml')
data_file = os.path.join(data_path, r'sample_FFM_data.txt')
output_file = os.path.join(data_path, r'output.txt')
if not os.path.exists(yaml_file):
download_deeprec_resources(
r'https://recodatasets.blob.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 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 test_model_xdeepfm(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "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.blob.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 download_data(mind_type="small"):
mind_url, mind_train_dataset, mind_dev_dataset, mind_utils = get_mind_data_set(mind_type)
data_path = get_data_path(mind_type)
train_news_file, _ = get_path("train", mind_type)
valid_news_file, _ = get_path("valid", mind_type)
test_news_file, _ = get_path("test", mind_type)
if not os.path.exists(train_news_file):
download_deeprec_resources(mind_url, os.path.join(data_path, 'train'), mind_train_dataset)
if not os.path.exists(valid_news_file):
download_deeprec_resources(mind_url, os.path.join(data_path, 'valid'), mind_dev_dataset)
if mind_type == "large":
if not os.path.exists(test_news_file):
download_deeprec_resources(mind_url, os.path.join(data_path, 'test'), mind_dev_dataset)
if not os.path.exists(get_yaml_path()):
utils_url = r'https://recodatasets.blob.core.windows.net/newsrec/'
download_deeprec_resources(utils_url, os.path.join(get_root_path(), 'utils'), mind_utils)
def test_model_dkn(resource_path):
data_path = os.path.join(resource_path, '../resources/deeprec/dkn')
yaml_file = os.path.join(data_path, r'dkn.yaml')
train_file = os.path.join(data_path, r'final_test_with_entity.txt')
valid_file = os.path.join(data_path, r'final_test_with_entity.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')
if not os.path.exists(yaml_file):
download_deeprec_resources(
r'https://recodatasets.blob.core.windows.net/deeprec/', data_path,
'dknresources.zip')
hparams = prepare_hparams(yaml_file,
wordEmb_file=wordEmb_file,
entityEmb_file=entityEmb_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_naml_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_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,
batch_size=1024,
)
train_iterator = MINDAllIterator(hparams, hparams.npratio)
test_iterator = MINDAllIterator(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) == 11
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) == 11
break
def test_DKN_iterator(resource_path):
data_path = os.path.join(resource_path, "..", "resources", "deeprec", "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")
yaml_file = os.path.join(data_path, "dkn.yaml")
download_deeprec_resources(
"https://recodatasets.blob.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)
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(tmp):
train_news_file = os.path.join(tmp, "train", r"news.tsv")
train_behaviors_file = os.path.join(tmp, "train", r"behaviors.tsv")
valid_news_file = os.path.join(tmp, "valid", r"news.tsv")
valid_behaviors_file = os.path.join(tmp, "valid", r"behaviors.tsv")
wordEmb_file = os.path.join(tmp, "utils", "embedding.npy")
userDict_file = os.path.join(tmp, "utils", "uid2index.pkl")
wordDict_file = os.path.join(tmp, "utils", "word_dict.pkl")
yaml_file = os.path.join(tmp, "utils", r"nrms.yaml")
if not os.path.exists(train_news_file):
download_deeprec_resources(
r"https://recodatasets.blob.core.windows.net/newsrec/",
os.path.join(tmp, "train"),
"MINDdemo_train.zip",
)
if not os.path.exists(valid_news_file):
download_deeprec_resources(
r"https://recodatasets.blob.core.windows.net/newsrec/",
os.path.join(tmp, "valid"),
"MINDdemo_dev.zip",
)
if not os.path.exists(yaml_file):
download_deeprec_resources(
r"https://recodatasets.blob.core.windows.net/newsrec/",
os.path.join(tmp, "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
def test_model_nrms(tmp):
train_news_file = os.path.join(tmp, "train", r"news.tsv")
train_behaviors_file = os.path.join(tmp, "train", r"behaviors.tsv")
valid_news_file = os.path.join(tmp, "valid", r"news.tsv")
valid_behaviors_file = os.path.join(tmp, "valid", r"behaviors.tsv")
wordEmb_file = os.path.join(tmp, "utils", "embedding.npy")
userDict_file = os.path.join(tmp, "utils", "uid2index.pkl")
wordDict_file = os.path.join(tmp, "utils", "word_dict.pkl")
yaml_file = os.path.join(tmp, "utils", r"nrms.yaml")
if not os.path.exists(train_news_file):
download_deeprec_resources(
r"https://recodatasets.blob.core.windows.net/newsrec/",
os.path.join(tmp, "train"),
"MINDdemo_train.zip",
)
if not os.path.exists(valid_news_file):
download_deeprec_resources(
r"https://recodatasets.blob.core.windows.net/newsrec/",
os.path.join(tmp, "valid"),
"MINDdemo_dev.zip",
)
if not os.path.exists(yaml_file):
download_deeprec_resources(
r"https://recodatasets.blob.core.windows.net/newsrec/",
os.path.join(tmp, "utils"),
"MINDdemo_utils.zip",
)
hparams = prepare_hparams(
yaml_file,
wordEmb_file=wordEmb_file,
wordDict_file=wordDict_file,
userDict_file=userDict_file,
epochs=1,
)
assert hparams is not None
iterator = MINDIterator
model = NRMSModel(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,
)
train_news_file = os.path.join(data_path, 'train', r'news.tsv')
train_behaviors_file = os.path.join(data_path, 'train', r'behaviors.tsv')
valid_news_file = os.path.join(data_path, 'valid', r'news.tsv')
fast_valid_behaviors_file = os.path.join(data_path, 'valid',
r'behaviors.small.tsv')
wordEmb_file = os.path.join(data_path, "utils", "embedding.npy")
userDict_file = os.path.join(data_path, "utils", "uid2index.pkl")
wordDict_file = os.path.join(data_path, "utils", "word_dict.pkl")
yaml_file = os.path.join(data_path, "utils", r'npa.yaml')
model_dir = os.path.join(data_path, "nrms")
mind_url, mind_train_dataset, mind_dev_dataset, mind_utils = get_mind_data_set(
MIND_type)
if not os.path.exists(train_news_file):
download_deeprec_resources(mind_url, os.path.join(data_path, 'train'),
mind_train_dataset)
if not os.path.exists(valid_news_file):
download_deeprec_resources(mind_url, \
os.path.join(data_path, 'valid'), mind_dev_dataset)
if not os.path.exists(yaml_file):
download_deeprec_resources(r'https://recodatasets.blob.core.windows.net/newsrec/', \
os.path.join(data_path, 'utils'), mind_utils)
hparams = prepare_hparams(yaml_file, wordEmb_file=wordEmb_file, \
wordDict_file=wordDict_file, userDict_file=userDict_file, \
epochs=epochs,
show_step=10)
print("[NRMS] Config,", hparams)
iterator = MINDIterator
data_path = os.path.abspath(os.path.join(os.getcwd(), "../../data/mind-demo-dkn"))
data_path1 = os.path.abspath(os.path.join(os.getcwd(), "../../data"))
print('data_path:', data_path)
print('data_path1:', data_path1)
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')
if not os.path.exists(yaml_file):
download_deeprec_resources(r'https://recodatasets.blob.core.windows.net/deeprec/', data_path1, 'mind-demo-dkn.zip')
## Create hyper-parameters
epochs = 10
history_size = 50
batch_size = 100
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=epochs,
history_size=history_size,
batch_size=batch_size)
print('There are in total {0} fields and {1} features.'.format(
converter.field_count, converter.feature_count))
tmpdir = TemporaryDirectory()
data_path = tmpdir.name
yaml_file = os.path.join(data_path, YAML_FILE_NAME)
train_file = os.path.join(data_path, TRAIN_FILE_NAME)
valid_file = os.path.join(data_path, VALID_FILE_NAME)
test_file = os.path.join(data_path, TEST_FILE_NAME)
model_file = os.path.join(data_path, MODEL_FILE_NAME)
output_file = os.path.join(data_path, OUTPUT_FILE_NAME)
if not os.path.exists(yaml_file):
download_deeprec_resources(
r'https://recodatasets.blob.core.windows.net/deeprec/', data_path,
'xdeepfmresources.zip')
# Training task
ffm_model = xl.create_ffm() # Use field-aware factorization machine (ffm)
ffm_model.setTrain(train_file) # Set the path of training dataset
ffm_model.setValidate(valid_file) # Set the path of validation dataset
# Parameters:
# 0. task: binary classification
# 1. learning rate: 0.2
# 2. regular lambda: 0.002
# 3. evaluation metric: auc
# 4. number of epochs: 10
# 5. optimization method: sgd
param = {
