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Update DIEN.py #59

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241 changes: 119 additions & 122 deletions TFRecModel/src/com/sparrowrecsys/offline/tensorflow/DIEN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,33 +26,31 @@
"/resources/webroot/sampledata/testSamples.csv")



def get_dataset_with_negtive_movie(path,batch_size,seed_num):
def get_dataset_with_negtive_movie(path, batch_size, seed_num):
tmp_df = pd.read_csv(path)
tmp_df.fillna(0,inplace=True)
tmp_df.fillna(0, inplace=True)
random.seed(seed_num)
negtive_movie_df=tmp_df.loc[:,'userRatedMovie2':'userRatedMovie5'].applymap( lambda x: random.sample( set(range(0, 1001))-set([int(x)]), 1)[0] )
negtive_movie_df.columns = ['negtive_userRatedMovie2','negtive_userRatedMovie3','negtive_userRatedMovie4','negtive_userRatedMovie5']
tmp_df=pd.concat([tmp_df,negtive_movie_df],axis=1)
negtive_movie_df = tmp_df.loc[:, 'userRatedMovie2':'userRatedMovie5'].applymap(
lambda x: random.sample(set(range(0, 1001)) - set([int(x)]), 1)[0])
negtive_movie_df.columns = ['negtive_userRatedMovie2', 'negtive_userRatedMovie3', 'negtive_userRatedMovie4',
'negtive_userRatedMovie5']
tmp_df = pd.concat([tmp_df, negtive_movie_df], axis=1)

for i in tmp_df.select_dtypes('O').columns:
tmp_df[i] = tmp_df[i].astype('str')

if tf.__version__<'2.3.0':
tmp_df = tmp_df.sample( n= batch_size*( len(tmp_df)//batch_size ) ,random_state=seed_num )


dataset = tf.data.Dataset.from_tensor_slices( ( dict(tmp_df)) )

dataset = tf.data.Dataset.from_tensor_slices((dict(tmp_df)))
dataset = dataset.batch(batch_size)
return dataset

train_dataset = get_dataset_with_negtive_movie(training_samples_file_path,12,seed_num=2020)
test_dataset = get_dataset_with_negtive_movie(test_samples_file_path,12,seed_num=2021)

batch_size = 12
train_dataset = get_dataset_with_negtive_movie(training_samples_file_path, batch_size, seed_num=2020)
test_dataset = get_dataset_with_negtive_movie(test_samples_file_path, batch_size, seed_num=2021)

# Config
RECENT_MOVIES = 5 # userRatedMovie{1-5}
EMBEDDING_SIZE = 10

# define input for keras model
inputs = {
'movieAvgRating': tf.keras.layers.Input(name='movieAvgRating', shape=(), dtype='float32'),
Expand All @@ -79,15 +77,14 @@ def get_dataset_with_negtive_movie(path,batch_size,seed_num):
'movieGenre1': tf.keras.layers.Input(name='movieGenre1', shape=(), dtype='string'),
'movieGenre2': tf.keras.layers.Input(name='movieGenre2', shape=(), dtype='string'),
'movieGenre3': tf.keras.layers.Input(name='movieGenre3', shape=(), dtype='string'),

'negtive_userRatedMovie2': tf.keras.layers.Input(name='negtive_userRatedMovie2', shape=(), dtype='int32'),
'negtive_userRatedMovie3': tf.keras.layers.Input(name='negtive_userRatedMovie3', shape=(), dtype='int32'),
'negtive_userRatedMovie4': tf.keras.layers.Input(name='negtive_userRatedMovie4', shape=(), dtype='int32'),
'negtive_userRatedMovie5': tf.keras.layers.Input(name='negtive_userRatedMovie5', shape=(), dtype='int32'),

'label':tf.keras.layers.Input(name='label', shape=(), dtype='int32')
}
'negtive_userRatedMovie5': tf.keras.layers.Input(name='negtive_userRatedMovie5', shape=(), dtype='int32'),

'label': tf.keras.layers.Input(name='label', shape=(), dtype='int32')
}

# user id embedding feature
user_col = tf.feature_column.categorical_column_with_identity(key='userId', num_buckets=30001)
Expand All @@ -106,9 +103,7 @@ def get_dataset_with_negtive_movie(path,batch_size,seed_num):
vocabulary_list=genre_vocab)
item_genre_emb_col = tf.feature_column.embedding_column(item_genre_col, EMBEDDING_SIZE)



candidate_movie_col = [ tf.feature_column.numeric_column(key='movieId', default_value=0), ]
candidate_movie_col = [tf.feature_column.numeric_column(key='movieId', default_value=0), ]

# user behaviors
recent_rate_col = [
Expand All @@ -119,16 +114,13 @@ def get_dataset_with_negtive_movie(path,batch_size,seed_num):
tf.feature_column.numeric_column(key='userRatedMovie5', default_value=0),
]


negtive_movie_col = [
tf.feature_column.numeric_column(key='negtive_userRatedMovie2', default_value=0),
tf.feature_column.numeric_column(key='negtive_userRatedMovie3', default_value=0),
tf.feature_column.numeric_column(key='negtive_userRatedMovie4', default_value=0),
tf.feature_column.numeric_column(key='negtive_userRatedMovie5', default_value=0),
]



# user profile
user_profile = [
user_emb_col,
Expand All @@ -147,8 +139,7 @@ def get_dataset_with_negtive_movie(path,batch_size,seed_num):
tf.feature_column.numeric_column('movieRatingStddev'),
]

label =[ tf.feature_column.numeric_column(key='label', default_value=0), ]

label = [tf.feature_column.numeric_column(key='label', default_value=0), ]

candidate_layer = tf.keras.layers.DenseFeatures(candidate_movie_col)(inputs)
user_behaviors_layer = tf.keras.layers.DenseFeatures(recent_rate_col)(inputs)
Expand All @@ -158,98 +149,106 @@ def get_dataset_with_negtive_movie(path,batch_size,seed_num):
y_true = tf.keras.layers.DenseFeatures(label)(inputs)

# Activation Unit
movie_emb_layer = tf.keras.layers.Embedding(input_dim=1001,output_dim=EMBEDDING_SIZE,mask_zero=True)# mask zero
movie_emb_layer = tf.keras.layers.Embedding(input_dim=1001, output_dim=EMBEDDING_SIZE, mask_zero=True) # mask zero

user_behaviors_emb_layer = movie_emb_layer(user_behaviors_layer)
candidate_emb_layer = movie_emb_layer(candidate_layer)
negtive_movie_emb_layer = movie_emb_layer(negtive_movie_layer)

user_behaviors_emb_layer = movie_emb_layer(user_behaviors_layer)
candidate_emb_layer = movie_emb_layer(candidate_layer)
negtive_movie_emb_layer = movie_emb_layer(negtive_movie_layer)
candidate_emb_layer = tf.squeeze(candidate_emb_layer, axis=1)

candidate_emb_layer = tf.squeeze(candidate_emb_layer,axis=1)
user_behaviors_hidden_state = tf.keras.layers.GRU(EMBEDDING_SIZE, return_sequences=True)(user_behaviors_emb_layer)

user_behaviors_hidden_state=tf.keras.layers.GRU(EMBEDDING_SIZE, return_sequences=True)(user_behaviors_emb_layer)

class attention(tf.keras.layers.Layer):
def __init__(self, embedding_size=EMBEDDING_SIZE, time_length=5, ):
super().__init__()
self.time_length = time_length
self.time_length = time_length
self.embedding_size = embedding_size
self.RepeatVector_time = tf.keras.layers.RepeatVector(self.time_length)
self.RepeatVector_emb = tf.keras.layers.RepeatVector(self.embedding_size)
self.Multiply = tf.keras.layers.Multiply()
self.Dense32 = tf.keras.layers.Dense(32,activation='sigmoid')
self.Dense1 = tf.keras.layers.Dense(1,activation='sigmoid')
self.Flatten = tf.keras.layers.Flatten()
self.Permute = tf.keras.layers.Permute((2, 1))
self.RepeatVector_emb = tf.keras.layers.RepeatVector(self.embedding_size)
self.Multiply = tf.keras.layers.Multiply()
self.Dense32 = tf.keras.layers.Dense(32, activation='sigmoid')
self.Dense1 = tf.keras.layers.Dense(1, activation='sigmoid')
self.Flatten = tf.keras.layers.Flatten()
self.Permute = tf.keras.layers.Permute((2, 1))

def build(self, input_shape):
pass

def call(self, inputs):
candidate_inputs,gru_hidden_state=inputs
candidate_inputs, gru_hidden_state = inputs
repeated_candidate_layer = self.RepeatVector_time(candidate_inputs)
activation_product_layer = self.Multiply([gru_hidden_state,repeated_candidate_layer])
activation_product_layer = self.Multiply([gru_hidden_state, repeated_candidate_layer])
activation_unit = self.Dense32(activation_product_layer)
activation_unit = self.Dense1(activation_unit)
Repeat_attention_s=tf.squeeze(activation_unit,axis=2)
Repeat_attention_s=self.RepeatVector_emb(Repeat_attention_s)
Repeat_attention_s=self.Permute(Repeat_attention_s)
activation_unit = self.Dense1(activation_unit)
Repeat_attention_s = tf.squeeze(activation_unit, axis=2)
Repeat_attention_s = self.RepeatVector_emb(Repeat_attention_s)
Repeat_attention_s = self.Permute(Repeat_attention_s)

return Repeat_attention_s

attention_score=attention()( [candidate_emb_layer, user_behaviors_hidden_state])

attention_score = attention()([candidate_emb_layer, user_behaviors_hidden_state])


class GRU_gate_parameter(tf.keras.layers.Layer):
def __init__(self,embedding_size=EMBEDDING_SIZE):
def __init__(self, embedding_size=EMBEDDING_SIZE):
super().__init__()
self.embedding_size = embedding_size
self.Multiply = tf.keras.layers.Multiply()
self.Dense_sigmoid = tf.keras.layers.Dense( self.embedding_size,activation='sigmoid' )
self.Dense_tanh =tf.keras.layers.Dense( self.embedding_size,activation='tanh' )
self.embedding_size = embedding_size
self.Multiply = tf.keras.layers.Multiply()
# self.Dense_sigmoid = tf.keras.layers.Dense( self.embedding_size,activation='sigmoid' ) #这里只作为一个激活函数，应该直接用tf.sigmoid,不应该引入线性层的参数
# self.Dense_tanh =tf.keras.layers.Dense( self.embedding_size,activation='tanh' ) #这里只作为一个激活函数，应该直接用tf.tanh,不应该引入线性层的参数

def build(self, input_shape):
self.input_w = tf.keras.layers.Dense(self.embedding_size,activation=None,use_bias=True)
self.hidden_w = tf.keras.layers.Dense(self.embedding_size,activation=None,use_bias=False)
self.input_w = tf.keras.layers.Dense(self.embedding_size, activation=None, use_bias=True)
self.hidden_w = tf.keras.layers.Dense(self.embedding_size, activation=None, use_bias=False)

def call(self, inputs, Z_t_inputs=None):
gru_inputs, hidden_inputs = inputs
if Z_t_inputs == None:
return tf.sigmoid(self.input_w(gru_inputs) + self.hidden_w(hidden_inputs))
else:
# return self.Dense_tanh( self.input_w(gru_inputs) + self.hidden_w(self.Multiply([hidden_inputs,Z_t_inputs]) ))
return tf.tanh(
self.input_w(gru_inputs) + self.Multiply([Z_t_inputs, self.hidden_w(hidden_inputs)])) # 他之前的公式打错了

def call(self, inputs,Z_t_inputs=None ):
gru_inputs,hidden_inputs = inputs
if Z_t_inputs==None:
return self.Dense_sigmoid( self.input_w(gru_inputs) + self.hidden_w(hidden_inputs) )
else:
return self.Dense_tanh( self.input_w(gru_inputs) + self.hidden_w(self.Multiply([hidden_inputs,Z_t_inputs]) ))


class AUGRU(tf.keras.layers.Layer):
def __init__(self,embedding_size=EMBEDDING_SIZE, time_length=5):
def __init__(self, embedding_size=EMBEDDING_SIZE, time_length=5):
super().__init__()
self.time_length = time_length
self.embedding_size = embedding_size
self.Multiply = tf.keras.layers.Multiply()
self.Add=tf.keras.layers.Add()

self.embedding_size = embedding_size
self.Multiply = tf.keras.layers.Multiply()
self.Add = tf.keras.layers.Add()
# self.RepeatVector = tf.keras.layers.RepeatVector(batch_size)

def build(self, input_shape):
self.R_t = GRU_gate_parameter()
self.Z_t = GRU_gate_parameter()
self.H_t_next = GRU_gate_parameter()
self.Z_t = GRU_gate_parameter()
self.H_t_next = GRU_gate_parameter()

def call(self, inputs ):
gru_hidden_state_inputs,attention_s=inputs
def call(self, inputs):
gru_hidden_state_inputs, attention_s = inputs
initializer = tf.keras.initializers.GlorotUniform()
AUGRU_hidden_state = tf.reshape(initializer(shape=(1,self.embedding_size )),shape=(-1,self.embedding_size ))
for t in range(self.time_length):
r_t= self.R_t( [gru_hidden_state_inputs[:,t,:], AUGRU_hidden_state] )
z_t= self.Z_t( [gru_hidden_state_inputs[:,t,:], AUGRU_hidden_state] )
h_t_next= self.H_t_next( [gru_hidden_state_inputs[:,t,:], AUGRU_hidden_state] , z_t )
Rt_attention =self.Multiply([attention_s[:,t,:] , r_t])

AUGRU_hidden_state = self.Add( [self.Multiply([(1-Rt_attention),AUGRU_hidden_state ] ), self.Multiply([Rt_attention ,h_t_next ] )])

# 原来的代码AUGRU_hidden_state一开始维数:(1,embedding_size),后面就变成(batch,embedding_size)
AUGRU_hidden_state = initializer(shape=(1, self.embedding_size))
# AUGRU_hidden_state=self.RepeatVector(AUGRU_hidden_state)
# AUGRU_hidden_state=tf.reshape(AUGRU_hidden_state,[-1,self.embedding_size]) #所以直接变成(batch,embedding_size)

for t in range(self.time_length):
r_t = self.R_t([gru_hidden_state_inputs[:, t, :], AUGRU_hidden_state])
z_t = self.Z_t([gru_hidden_state_inputs[:, t, :], AUGRU_hidden_state])
h_t_next = self.H_t_next([gru_hidden_state_inputs[:, t, :], AUGRU_hidden_state], z_t)
Rt_attention = self.Multiply([attention_s[:, t, :], r_t])
AUGRU_hidden_state = self.Add(
[self.Multiply([(1 - Rt_attention), AUGRU_hidden_state]), self.Multiply([Rt_attention, h_t_next])])
return AUGRU_hidden_state

augru_emb=AUGRU()( [ user_behaviors_hidden_state ,attention_score ] )

concat_layer = tf.keras.layers.concatenate([ augru_emb, candidate_emb_layer,user_profile_layer,context_features_layer])
augru_emb = AUGRU()([user_behaviors_hidden_state, attention_score])

concat_layer = tf.keras.layers.concatenate([augru_emb, candidate_emb_layer, user_profile_layer, context_features_layer])

output_layer = tf.keras.layers.Dense(128)(concat_layer)
output_layer = tf.keras.layers.PReLU()(output_layer)
Expand All @@ -259,58 +258,56 @@ def call(self, inputs ):


class auxiliary_loss_layer(tf.keras.layers.Layer):
def __init__(self,time_length=5 ):
def __init__(self, time_length=5):
super().__init__()
self.time_len = time_length-1
self.Dense_sigmoid_positive32 = tf.keras.layers.Dense(32,activation='sigmoid')
self.Dense_sigmoid_positive1 = tf.keras.layers.Dense(1,activation='sigmoid')
self.Dense_sigmoid_negitive32 = tf.keras.layers.Dense(32,activation='sigmoid')
self.Dense_sigmoid_negitive1 = tf.keras.layers.Dense(1,activation='sigmoid')
self.Dot = tf.keras.layers.Dot(axes=(1, 1))
self.auc =tf.keras.metrics.AUC()

self.time_len = time_length - 1
self.Multiply = tf.keras.layers.Multiply()
self.auc = tf.keras.metrics.AUC()

def build(self, input_shape):
pass

def call(self, inputs,alpha=0.5):
negtive_movie_t1,postive_movie_t0,movie_hidden_state,y_true,y_pred=inputs
#auxiliary_loss_values = []
positive_concat_layer=tf.keras.layers.concatenate([ movie_hidden_state[:,0:4,:], postive_movie_t0[:,1:5,:] ])
positive_concat_layer=self.Dense_sigmoid_positive32( positive_concat_layer )
positive_loss = self.Dense_sigmoid_positive1(positive_concat_layer)

negtive_concat_layer=tf.keras.layers.concatenate([ movie_hidden_state[:,0:4,:], negtive_movie_t1[:,:,:] ])
negtive_concat_layer=self.Dense_sigmoid_negitive32( negtive_concat_layer )
negtive_loss = self.Dense_sigmoid_negitive1(negtive_concat_layer)
auxiliary_loss_values = positive_loss + negtive_loss

final_loss = tf.keras.losses.binary_crossentropy( y_true, y_pred )-alpha* tf.reduce_mean( tf.reduce_sum( auxiliary_loss_values,axis=1 ))

def call(self, inputs, alpha=0.5):
negtive_movie_t1, postive_movie_t0, movie_hidden_state, y_true, y_pred = inputs

positive_loss = 1 - tf.sigmoid(
tf.reduce_sum(self.Multiply([movie_hidden_state[:, 0:4, :], postive_movie_t0[:, 1:5, :]]),
axis=-1)) # dim:(batchsize,4)
negtive_loss = tf.sigmoid(
tf.reduce_sum(self.Multiply([movie_hidden_state[:, 0:4, :], negtive_movie_t1[:, :, :]]),
axis=-1)) # dim:(batchsize,4)

auxiliary_loss_values = positive_loss + negtive_loss # 原来代码中positive_loss，negtive_loss 处理的方式一样，没有体现出负采样，向量越接近点积的结果越大，sigmoid越大,1-sigmoid作为损失值越小，负采样相反

final_loss = tf.keras.losses.binary_crossentropy(y_true, y_pred) + alpha * tf.reduce_mean(
tf.reduce_sum(auxiliary_loss_values, axis=1)) # alpha前面应该是+，之前的损失函数值可能为负
self.add_loss(final_loss, inputs=True)
self.auc.update_state(y_true, y_pred )
self.add_metric(self.auc.result(), aggregation="mean", name="auc_value")
return final_loss
self.auc.update_state(y_true, y_pred)
self.add_metric(self.auc.result(), aggregation="mean", name="auc_value")

return final_loss

auxiliary_loss_value=auxiliary_loss_layer()( [ negtive_movie_emb_layer,user_behaviors_emb_layer,user_behaviors_hidden_state,y_true,y_pred] )

model = tf.keras.Model(inputs=inputs, outputs=[y_pred,auxiliary_loss_value])
auxiliary_loss_value = auxiliary_loss_layer()(
[negtive_movie_emb_layer, user_behaviors_emb_layer, user_behaviors_hidden_state, y_true, y_pred])

model = tf.keras.Model(inputs=inputs, outputs=[y_pred, auxiliary_loss_value])

model.compile(optimizer="adam")

# train the model
model.fit(train_dataset, epochs=5)
with tf.device(
'/cpu:0'): # 如果用GPU可能会报错 Error polling for event status: failed to query event: CUDA_ERROR_ILLEGAL_ADDRESS
model.fit(train_dataset, epochs=5)

# evaluate the model
test_loss, test_roc_auc = model.evaluate(test_dataset)
print('\n\nTest Loss {}, Test ROC AUC {},'.format(test_loss, test_roc_auc))



model.summary()
test_loss, test_accuracy, test_roc_auc = model.evaluate(test_dataset)
print('\n\nTest Loss {}, Test Accuracy {}, Test ROC AUC {}'.format(test_loss, test_accuracy,
test_roc_auc))

# print some predict results
predictions = model.predict(test_dataset)
for prediction, goodRating in zip(predictions[0][:12], list(test_dataset)[0]):
print("Predicted good rating: {:.2%}".format(prediction[0]),
" | Actual rating label: ",
("Good Rating" if bool(goodRating) else "Bad Rating"))
("Good Rating" if bool(goodRating) else "Bad Rating"))