def test_DIEN(gru_type):
model_name = "DIEN_"+gru_type
x, y, feature_dim_dict, behavior_feature_list = get_xy_fd(hash_flag=True)
model = DIEN(feature_dim_dict, behavior_feature_list, hist_len_max=4, embedding_size=8,
dnn_hidden_units=[4, 4, 4], dnn_dropout=0.5, gru_type=gru_type)
check_model(model,model_name,x,y,check_model_io=(gru_type=="GRU"))#TODO:fix bugs when load model in other type
def test_DIEN_neg():
model_name = "DIEN_neg"
x, y, feature_dim_dict, behavior_feature_list = get_xy_fd(use_neg=True)
model = DIEN(feature_dim_dict, behavior_feature_list, hist_len_max=4, embedding_size=8,
dnn_hidden_units=[4, 4, 4], dnn_dropout=0.5, gru_type="AUGRU", use_negsampling=True)
check_model(model,model_name,x,y)
def test_DIEN_neg():
model_name = "DIEN_neg"
if version.parse(tf.__version__) >= version.parse("1.14.0"):
return
x, y, feature_dim_dict, behavior_feature_list = get_xy_fd(use_neg=True)
model = DIEN(feature_dim_dict, behavior_feature_list,
dnn_hidden_units=[4, 4, 4], dnn_dropout=0.5, gru_type="AUGRU", use_negsampling=True)
check_model(model,model_name,x,y)
def test_DIEN(gru_type):
if tf.__version__ >= '2.0.0':
tf.compat.v1.disable_eager_execution() #todo
model_name = "DIEN_"+gru_type
x, y, feature_columns, behavior_feature_list = get_xy_fd(hash_flag=True)
model = DIEN(feature_columns, behavior_feature_list, hist_len_max=4, embedding_size=8,
dnn_hidden_units=[4, 4, 4], dnn_dropout=0.5, gru_type=gru_type)
check_model(model,model_name,x,y,check_model_io=(gru_type=="GRU"))#TODO:fix bugs when load model in other type
def test_DIEN(gru_type):
if version.parse(tf.__version__) >= version.parse('2.0.0'):
tf.compat.v1.disable_eager_execution() # todo
return
model_name = "DIEN_" + gru_type
x, y, feature_columns, behavior_feature_list = get_xy_fd(hash_flag=True)
model = DIEN(feature_columns,
behavior_feature_list,
dnn_hidden_units=[4, 4, 4],
dnn_dropout=0.5,
gru_type=gru_type)
check_model(model, model_name, x, y, check_model_io=(
gru_type == "GRU")) # TODO:fix bugs when load model in other type
embedding_dim=4,
embedding_name='cate_id'),
maxlen=4,
length_name="seq_length")
]
x = {
name: feature_dict[name]
for name in get_feature_names(feature_columns)
}
y = [1, 0, 1]
return x, y, feature_columns, behavior_feature_list
if __name__ == "__main__":
if tf.__version__ >= '2.0.0':
tf.compat.v1.disable_eager_execution()
x, y, feature_columns, behavior_feature_list = get_xy_fd(use_neg=True)
model = DIEN(feature_columns,
behavior_feature_list,
dnn_hidden_units=[4, 4, 4],
dnn_dropout=0.6,
gru_type="AUGRU",
use_negsampling=True)
model.compile('adam',
'binary_crossentropy',
metrics=['binary_crossentropy'])
history = model.fit(x, y, verbose=1, epochs=10, validation_split=0.5)
if use_neg:
feature_dict['neg_hist_item'] = np.array([[1, 2, 3, 0], [1, 2, 3, 0],
[1, 2, 0, 0]])
feature_dict['neg_hist_item_gender'] = np.array([[1, 1, 2, 0],
[2, 1, 1, 0],
[2, 1, 0, 0]])
x += [
feature_dict['neg_hist_' + feat] for feat in behavior_feature_list
]
x += [behavior_length]
y = [1, 0, 1]
return x, y, feature_dim_dict, behavior_feature_list
if __name__ == "__main__":
x, y, feature_dim_dict, behavior_feature_list = get_xy_fd(use_neg=True)
model = DIEN(feature_dim_dict,
behavior_feature_list,
hist_len_max=4,
embedding_size=8,
hidden_size=[4, 4, 4],
keep_prob=0.6,
gru_type="AUGRU",
use_negsampling=True)
model.compile('adam',
'binary_crossentropy',
metrics=['binary_crossentropy'])
history = model.fit(x, y, verbose=1, epochs=10, validation_split=0.5)
if sys.argv[1] == 'DeepFM_UDG':
model = DeepFM_UDG(linear_feature_columns, dnn_feature_columns, untrainable_features_columns,
(200, 80), uid_feature_name=udg_features, udg_embedding_size=int(sys.argv[5]))
elif sys.argv[1] == 'DeepFM':
model = DeepFM(linear_feature_columns, dnn_feature_columns, [], (200, 80))
elif sys.argv[1] == 'PNN_UDG':
model = PNN_UDG(dnn_feature_columns, untrainable_features_columns, (200, 80), uid_feature_name=udg_features,
udg_embedding_size=int(sys.argv[5]))
elif sys.argv[1] == 'PNN':
model = PNN(dnn_feature_columns, untrainable_features_columns, (200, 80))
elif sys.argv[1] == 'WDL':
model = WDL(linear_feature_columns, dnn_feature_columns, [], (200, 80))
elif sys.argv[1] == 'WDL_UDG':
model = WDL_UDG(linear_feature_columns, dnn_feature_columns, untrainable_features_columns, (200, 80), uid_feature_name=udg_features, udg_embedding_size=int(sys.argv[5]))
elif sys.argv[1] == 'DIEN':
model = DIEN(fixlen_feature_columns, behavior_feature_list,
dnn_hidden_units=[200, 80], dnn_dropout=0, gru_type="AUGRU", use_negsampling=True)
elif sys.argv[1] == 'DIEN_UDG':
model = DIEN_UDG(fixlen_feature_columns, untrainable_features_columns, behavior_feature_list, dnn_hidden_units=[200, 80], dnn_dropout=0, gru_type="AUGRU", use_negsampling=True, uid_feature_name=udg_features, udg_embedding_size=int(sys.argv[5]))
elif sys.argv[1] == 'DIN':
model = DIN(fixlen_feature_columns, behavior_feature_list, dnn_hidden_units=[200, 80], dnn_dropout=0)
elif sys.argv[1] == 'DIN_UDG':
model = DIN_UDG(fixlen_feature_columns, untrainable_features_columns, behavior_feature_list, dnn_hidden_units=[200, 80], dnn_dropout=0, uid_feature_name=udg_features, udg_embedding_size=int(sys.argv[5]))
if sys.argv[4] == 'focal':
model.compile("adam", loss=focal_loss, metrics=['binary_crossentropy'], )
else:
model.compile("adam", "binary_crossentropy", metrics=['binary_crossentropy'], )
init_lr = float(tf.keras.backend.get_value(model.optimizer.learning_rate))
lr = [init_lr, init_lr/2, init_lr/4]
history_all = {}
max_auc, min_log, min_rmse, max_rig = 0, 0, 0, 0
