Source code for deepctr.models.sequence.dsin

# coding: utf-8
    Weichen Shen,

    [1] Feng Y, Lv F, Shen W, et al. Deep Session Interest Network for Click-Through Rate Prediction[J]. arXiv preprint arXiv:1905.06482, 2019.(


from collections import OrderedDict

from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import (Concatenate, Dense, Embedding,
                                            Flatten, Input)
from tensorflow.python.keras.regularizers import l2

from ...feature_column import SparseFeat, VarLenSparseFeat, DenseFeat, build_input_features
from ...inputs import (get_embedding_vec_list, get_inputs_list, embedding_lookup, get_dense_input)
from ...layers.core import DNN, PredictionLayer
from ...layers.sequence import (AttentionSequencePoolingLayer, BiasEncoding,
                                BiLSTM, Transformer)
from ...layers.utils import concat_func, combined_dnn_input

[docs]def DSIN(dnn_feature_columns, sess_feature_list, sess_max_count=5, bias_encoding=False, att_embedding_size=1, att_head_num=8, dnn_hidden_units=(256, 128, 64), dnn_activation='sigmoid', dnn_dropout=0, dnn_use_bn=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, seed=1024, task='binary', ): """Instantiates the Deep Session Interest Network architecture. :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. :param sess_feature_list: list,to indicate sequence sparse field :param sess_max_count: positive int, to indicate the max number of sessions :param sess_len_max: positive int, to indicate the max length of each session :param bias_encoding: bool. Whether use bias encoding or postional encoding :param att_embedding_size: positive int, the embedding size of each attention head :param att_head_num: positive int, the number of attention head :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net :param dnn_activation: Activation function to use in deep net :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate. :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in deep net :param l2_reg_dnn: float. L2 regularizer strength applied to DNN :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector :param seed: integer ,to use as random seed. :param task: str, ``"binary"`` for binary logloss or ``"regression"`` for regression loss :return: A Keras model instance. """ hist_emb_size = sum( map(lambda fc: fc.embedding_dim, filter(lambda fc: in sess_feature_list, dnn_feature_columns))) if (att_embedding_size * att_head_num != hist_emb_size): raise ValueError( "hist_emb_size must equal to att_embedding_size * att_head_num ,got %d != %d *%d" % ( hist_emb_size, att_embedding_size, att_head_num)) features = build_input_features(dnn_feature_columns) sparse_feature_columns = list( filter(lambda x: isinstance(x, SparseFeat), dnn_feature_columns)) if dnn_feature_columns else [] dense_feature_columns = list( filter(lambda x: isinstance(x, DenseFeat), dnn_feature_columns)) if dnn_feature_columns else [] varlen_sparse_feature_columns = list( filter(lambda x: isinstance(x, VarLenSparseFeat), dnn_feature_columns)) if dnn_feature_columns else [] sparse_varlen_feature_columns = [] history_fc_names = list(map(lambda x: "sess" + x, sess_feature_list)) for fc in varlen_sparse_feature_columns: feature_name = if feature_name in history_fc_names: continue else: sparse_varlen_feature_columns.append(fc) inputs_list = list(features.values()) user_behavior_input_dict = {} for idx in range(sess_max_count): sess_input = OrderedDict() for i, feat in enumerate(sess_feature_list): sess_input[feat] = features["sess_" + str(idx) + "_" + feat] user_behavior_input_dict["sess_" + str(idx)] = sess_input user_sess_length = Input(shape=(1,), name='sess_length') embedding_dict = {feat.embedding_name: Embedding(feat.vocabulary_size, feat.embedding_dim, embeddings_initializer=feat.embeddings_initializer, embeddings_regularizer=l2( l2_reg_embedding), name='sparse_emb_' + str(i) + '-' +, mask_zero=( in sess_feature_list)) for i, feat in enumerate(sparse_feature_columns)} query_emb_list = embedding_lookup(embedding_dict, features, sparse_feature_columns, sess_feature_list, sess_feature_list, to_list=True) dnn_input_emb_list = embedding_lookup(embedding_dict, features, sparse_feature_columns, mask_feat_list=sess_feature_list, to_list=True) dense_value_list = get_dense_input(features, dense_feature_columns) query_emb = concat_func(query_emb_list, mask=True) dnn_input_emb = Flatten()(concat_func(dnn_input_emb_list)) tr_input = sess_interest_division(embedding_dict, user_behavior_input_dict, sparse_feature_columns, sess_feature_list, sess_max_count, bias_encoding=bias_encoding) Self_Attention = Transformer(att_embedding_size, att_head_num, dropout_rate=0, use_layer_norm=False, use_positional_encoding=(not bias_encoding), seed=seed, supports_masking=True, blinding=True) sess_fea = sess_interest_extractor( tr_input, sess_max_count, Self_Attention) interest_attention_layer = AttentionSequencePoolingLayer(att_hidden_units=(64, 16), weight_normalization=True, supports_masking=False)( [query_emb, sess_fea, user_sess_length]) lstm_outputs = BiLSTM(hist_emb_size, layers=2, res_layers=0, dropout_rate=0.2, )(sess_fea) lstm_attention_layer = AttentionSequencePoolingLayer(att_hidden_units=(64, 16), weight_normalization=True)( [query_emb, lstm_outputs, user_sess_length]) dnn_input_emb = Concatenate()( [dnn_input_emb, Flatten()(interest_attention_layer), Flatten()(lstm_attention_layer)]) dnn_input_emb = combined_dnn_input([dnn_input_emb], dense_value_list) output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input_emb) output = Dense(1, use_bias=False)(output) output = PredictionLayer(task)(output) sess_input_list = [] for i in range(sess_max_count): sess_name = "sess_" + str(i) sess_input_list.extend(get_inputs_list( [user_behavior_input_dict[sess_name]])) model = Model(inputs=inputs_list + [user_sess_length], outputs=output) return model
def sess_interest_division(sparse_embedding_dict, user_behavior_input_dict, sparse_fg_list, sess_feture_list, sess_max_count, bias_encoding=True): tr_input = [] for i in range(sess_max_count): sess_name = "sess_" + str(i) keys_emb_list = get_embedding_vec_list(sparse_embedding_dict, user_behavior_input_dict[sess_name], sparse_fg_list, sess_feture_list, sess_feture_list) keys_emb = concat_func(keys_emb_list, mask=True) tr_input.append(keys_emb) if bias_encoding: tr_input = BiasEncoding(sess_max_count)(tr_input) return tr_input def sess_interest_extractor(tr_input, sess_max_count, TR): tr_out = [] for i in range(sess_max_count): tr_out.append(TR( [tr_input[i], tr_input[i]])) sess_fea = concat_func(tr_out, axis=1) return sess_fea