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add DIEN.py
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TFRecModel/src/com/sparrowrecsys/offline/tensorflow/DIEN.py
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| ''' | ||
| Author: | ||
| zcxia23, [email protected] | ||
| Diff with DIN: | ||
| 1、GRU with attentional update gate (AUGRU) | ||
| 2、auxiliary loss function with click or not click movie(negetive sampleming) | ||
| Reference: | ||
| [1] Zhou G , Mou N , Fan Y , et al. Deep Interest Evolution Network for Click-Through Rate Prediction[J]. 2018. | ||
| ''' | ||
| import pandas as pd | ||
| import tensorflow as tf | ||
| from tensorflow.python.ops import math_ops | ||
| import numpy as np | ||
| import random | ||
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| # Training samples path, change to your local path | ||
| training_samples_file_path = tf.keras.utils.get_file("trainingSamples.csv", | ||
| "file:///Users/zhewang/Workspace/SparrowRecSys/src/main" | ||
| "/resources/webroot/sampledata/trainingSamples.csv") | ||
| # Test samples path, change to your local path | ||
| test_samples_file_path = tf.keras.utils.get_file("testSamples.csv", | ||
| "file:///Users/zhewang/Workspace/SparrowRecSys/src/main" | ||
| "/resources/webroot/sampledata/testSamples.csv") | ||
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| def get_dataset_with_negtive_movie(path,batch_size,seed_num): | ||
| tmp_df = pd.read_csv(path) | ||
| 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) | ||
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| for i in tmp_df.select_dtypes('O').columns: | ||
| tmp_df[i] = tmp_df[i].astype('str') | ||
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| if tf.__version__<'2.3.0': | ||
| tmp_df = tmp_df.sample( n= batch_size*( len(tmp_df)//batch_size ) ,random_state=seed_num ) | ||
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| dataset = tf.data.Dataset.from_tensor_slices( ( dict(tmp_df)) ) | ||
| dataset = dataset.batch(batch_size) | ||
| return dataset | ||
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| 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) | ||
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| # Config | ||
| RECENT_MOVIES = 5 # userRatedMovie{1-5} | ||
| EMBEDDING_SIZE = 10 | ||
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| # define input for keras model | ||
| inputs = { | ||
| 'movieAvgRating': tf.keras.layers.Input(name='movieAvgRating', shape=(), dtype='float32'), | ||
| 'movieRatingStddev': tf.keras.layers.Input(name='movieRatingStddev', shape=(), dtype='float32'), | ||
| 'movieRatingCount': tf.keras.layers.Input(name='movieRatingCount', shape=(), dtype='int32'), | ||
| 'userAvgRating': tf.keras.layers.Input(name='userAvgRating', shape=(), dtype='float32'), | ||
| 'userRatingStddev': tf.keras.layers.Input(name='userRatingStddev', shape=(), dtype='float32'), | ||
| 'userRatingCount': tf.keras.layers.Input(name='userRatingCount', shape=(), dtype='int32'), | ||
| 'releaseYear': tf.keras.layers.Input(name='releaseYear', shape=(), dtype='int32'), | ||
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| 'movieId': tf.keras.layers.Input(name='movieId', shape=(), dtype='int32'), | ||
| 'userId': tf.keras.layers.Input(name='userId', shape=(), dtype='int32'), | ||
| 'userRatedMovie1': tf.keras.layers.Input(name='userRatedMovie1', shape=(), dtype='int32'), | ||
| 'userRatedMovie2': tf.keras.layers.Input(name='userRatedMovie2', shape=(), dtype='int32'), | ||
| 'userRatedMovie3': tf.keras.layers.Input(name='userRatedMovie3', shape=(), dtype='int32'), | ||
| 'userRatedMovie4': tf.keras.layers.Input(name='userRatedMovie4', shape=(), dtype='int32'), | ||
| 'userRatedMovie5': tf.keras.layers.Input(name='userRatedMovie5', shape=(), dtype='int32'), | ||
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| 'userGenre1': tf.keras.layers.Input(name='userGenre1', shape=(), dtype='string'), | ||
| 'userGenre2': tf.keras.layers.Input(name='userGenre2', shape=(), dtype='string'), | ||
| 'userGenre3': tf.keras.layers.Input(name='userGenre3', shape=(), dtype='string'), | ||
| 'userGenre4': tf.keras.layers.Input(name='userGenre4', shape=(), dtype='string'), | ||
| 'userGenre5': tf.keras.layers.Input(name='userGenre5', shape=(), dtype='string'), | ||
| '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'), | ||
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| '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'), | ||
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| 'label':tf.keras.layers.Input(name='label', shape=(), dtype='int32') | ||
| } | ||
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| # user id embedding feature | ||
| user_col = tf.feature_column.categorical_column_with_identity(key='userId', num_buckets=30001) | ||
| user_emb_col = tf.feature_column.embedding_column(user_col, EMBEDDING_SIZE) | ||
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| # genre features vocabulary | ||
| genre_vocab = ['Film-Noir', 'Action', 'Adventure', 'Horror', 'Romance', 'War', 'Comedy', 'Western', 'Documentary', | ||
| 'Sci-Fi', 'Drama', 'Thriller', | ||
| 'Crime', 'Fantasy', 'Animation', 'IMAX', 'Mystery', 'Children', 'Musical'] | ||
| # user genre embedding feature | ||
| user_genre_col = tf.feature_column.categorical_column_with_vocabulary_list(key="userGenre1", | ||
| vocabulary_list=genre_vocab) | ||
| user_genre_emb_col = tf.feature_column.embedding_column(user_genre_col, EMBEDDING_SIZE) | ||
| # item genre embedding feature | ||
| item_genre_col = tf.feature_column.categorical_column_with_vocabulary_list(key="movieGenre1", | ||
| vocabulary_list=genre_vocab) | ||
| item_genre_emb_col = tf.feature_column.embedding_column(item_genre_col, EMBEDDING_SIZE) | ||
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| candidate_movie_col = [ tf.feature_column.numeric_column(key='movieId', default_value=0), ] | ||
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| # user behaviors | ||
| recent_rate_col = [ | ||
| tf.feature_column.numeric_column(key='userRatedMovie1', default_value=0), | ||
| tf.feature_column.numeric_column(key='userRatedMovie2', default_value=0), | ||
| tf.feature_column.numeric_column(key='userRatedMovie3', default_value=0), | ||
| tf.feature_column.numeric_column(key='userRatedMovie4', default_value=0), | ||
| tf.feature_column.numeric_column(key='userRatedMovie5', default_value=0), | ||
| ] | ||
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| 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), | ||
| ] | ||
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| # user profile | ||
| user_profile = [ | ||
| user_emb_col, | ||
| user_genre_emb_col, | ||
| tf.feature_column.numeric_column('userRatingCount'), | ||
| tf.feature_column.numeric_column('userAvgRating'), | ||
| tf.feature_column.numeric_column('userRatingStddev'), | ||
| ] | ||
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| # context features | ||
| context_features = [ | ||
| item_genre_emb_col, | ||
| tf.feature_column.numeric_column('releaseYear'), | ||
| tf.feature_column.numeric_column('movieRatingCount'), | ||
| tf.feature_column.numeric_column('movieAvgRating'), | ||
| tf.feature_column.numeric_column('movieRatingStddev'), | ||
| ] | ||
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| label =[ tf.feature_column.numeric_column(key='label', default_value=0), ] | ||
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| candidate_layer = tf.keras.layers.DenseFeatures(candidate_movie_col)(inputs) | ||
| user_behaviors_layer = tf.keras.layers.DenseFeatures(recent_rate_col)(inputs) | ||
| negtive_movie_layer = tf.keras.layers.DenseFeatures(negtive_movie_col)(inputs) | ||
| user_profile_layer = tf.keras.layers.DenseFeatures(user_profile)(inputs) | ||
| context_features_layer = tf.keras.layers.DenseFeatures(context_features)(inputs) | ||
| y_true = tf.keras.layers.DenseFeatures(label)(inputs) | ||
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| # Activation Unit | ||
| movie_emb_layer = tf.keras.layers.Embedding(input_dim=1001,output_dim=EMBEDDING_SIZE,mask_zero=True)# mask zero | ||
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| 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) | ||
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| candidate_emb_layer = tf.squeeze(candidate_emb_layer,axis=1) | ||
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| user_behaviors_hidden_state=tf.keras.layers.GRU(EMBEDDING_SIZE, return_sequences=True)(user_behaviors_emb_layer) | ||
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| class attention(tf.keras.layers.Layer): | ||
| def __init__(self, embedding_size=EMBEDDING_SIZE, time_length=5, ): | ||
| super().__init__() | ||
| 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)) | ||
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| def build(self, input_shape): | ||
| pass | ||
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| def call(self, 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_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) | ||
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| return Repeat_attention_s | ||
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| attention_score=attention()( [candidate_emb_layer, user_behaviors_hidden_state]) | ||
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| class GRU_gate_parameter(tf.keras.layers.Layer): | ||
| 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' ) | ||
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| 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) | ||
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| 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]) )) | ||
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| class AUGRU(tf.keras.layers.Layer): | ||
| 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() | ||
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| def build(self, input_shape): | ||
| self.R_t = GRU_gate_parameter() | ||
| self.Z_t = GRU_gate_parameter() | ||
| self.H_t_next = GRU_gate_parameter() | ||
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| 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]) | ||
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| AUGRU_hidden_state = self.Add( [self.Multiply([(1-Rt_attention),AUGRU_hidden_state ] ), self.Multiply([Rt_attention ,h_t_next ] )]) | ||
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| return AUGRU_hidden_state | ||
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| augru_emb=AUGRU()( [ user_behaviors_hidden_state ,attention_score ] ) | ||
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| concat_layer = tf.keras.layers.concatenate([ augru_emb, candidate_emb_layer,user_profile_layer,context_features_layer]) | ||
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| output_layer = tf.keras.layers.Dense(128)(concat_layer) | ||
| output_layer = tf.keras.layers.PReLU()(output_layer) | ||
| output_layer = tf.keras.layers.Dense(64)(output_layer) | ||
| output_layer = tf.keras.layers.PReLU()(output_layer) | ||
| y_pred = tf.keras.layers.Dense(1, activation='sigmoid')(output_layer) | ||
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| class auxiliary_loss_layer(tf.keras.layers.Layer): | ||
| 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() | ||
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| def build(self, input_shape): | ||
| pass | ||
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| 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) | ||
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| 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 | ||
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| final_loss = tf.keras.losses.binary_crossentropy( y_true, y_pred )-alpha* tf.reduce_mean( tf.reduce_sum( auxiliary_loss_values,axis=1 )) | ||
| 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") | ||
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| return final_loss | ||
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| auxiliary_loss_value=auxiliary_loss_layer()( [ negtive_movie_emb_layer,user_behaviors_emb_layer,user_behaviors_hidden_state,y_true,y_pred] ) | ||
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| model = tf.keras.Model(inputs=inputs, outputs=[y_pred,auxiliary_loss_value]) | ||
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| model.compile(optimizer="adam") | ||
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| # train the model | ||
| model.fit(train_dataset, epochs=5) | ||
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| # 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)) | ||
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| model.summary() | ||
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| # 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")) |