/
run_dsin.py
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/
run_dsin.py
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import numpy as np
import tensorflow as tf
from deepctr.models import DSIN
from deepctr.inputs import SparseFeat,VarLenSparseFeat,DenseFeat,get_feature_names
def get_xy_fd(hash_flag=False):
feature_columns = [SparseFeat('user', 3, embedding_dim=10,use_hash=hash_flag),
SparseFeat('gender', 2, embedding_dim=4,use_hash=hash_flag),
SparseFeat('item', 3 + 1, embedding_dim=4,use_hash=hash_flag),
SparseFeat('item_gender', 2 + 1, embedding_dim=4,use_hash=hash_flag),
DenseFeat('score', 1)]
feature_columns += [VarLenSparseFeat('sess_0_item',maxlen=4,vocabulary_size=3+1,embedding_dim=4,use_hash=hash_flag,embedding_name='item'),VarLenSparseFeat('sess_0_item_gender',maxlen=4,vocabulary_size=2+1,embedding_dim=4,use_hash=hash_flag,embedding_name='item_gender')]
feature_columns += [VarLenSparseFeat('sess_1_item', maxlen= 4,vocabulary_size=3 + 1,embedding_dim=4, use_hash=hash_flag, embedding_name='item'),VarLenSparseFeat('sess_1_item_gender', maxlen= 4,vocabulary_size=2 + 1, embedding_dim=4,use_hash=hash_flag,embedding_name='item_gender')]
behavior_feature_list = ["item", "item_gender"]
uid = np.array([0, 1, 2])
ugender = np.array([0, 1, 0])
iid = np.array([1, 2, 3]) # 0 is mask value
igender = np.array([1, 2, 1]) # 0 is mask value
score = np.array([0.1, 0.2, 0.3])
sess1_iid = np.array([[1, 2, 3, 0], [1, 2, 3, 0], [0, 0, 0, 0]])
sess1_igender = np.array([[1, 1, 2, 0], [2, 1, 1, 0], [0, 0, 0, 0]])
sess2_iid = np.array([[1, 2, 3, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
sess2_igender = np.array([[1, 1, 2, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
sess_number = np.array([2, 1, 0])
feature_dict = {'user': uid, 'gender': ugender, 'item': iid, 'item_gender': igender,
'sess_0_item': sess1_iid, 'sess_0_item_gender': sess1_igender, 'score': score,
'sess_1_item': sess2_iid, 'sess_1_item_gender': sess2_igender, }
x = {name:feature_dict[name] for name in get_feature_names(feature_columns)}
x["sess_length"] = sess_number
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(True)
model = DSIN(feature_columns, behavior_feature_list, sess_max_count=2,
dnn_hidden_units=[4, 4, 4], dnn_dropout=0.5, )
model.compile('adam', 'binary_crossentropy',
metrics=['binary_crossentropy'])
history = model.fit(x, y, verbose=1, epochs=10, validation_split=0.5)