271 lines
10 KiB
Python
271 lines
10 KiB
Python
import warnings
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warnings.filterwarnings("ignore")
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import itertools
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import pandas as pd
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import numpy as np
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from tqdm import tqdm
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from collections import namedtuple
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import tensorflow as tf
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from tensorflow.keras.layers import *
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from tensorflow.keras.models import *
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler, LabelEncoder
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from utils import SparseFeat, DenseFeat, VarLenSparseFeat
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# 构建输入层
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# 将输入的数据转换成字典的形式，定义输入层的时候让输入层的name和字典中特征的key一致，就可以使得输入的数据和对应的Input层对应
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def build_input_layers(feature_columns):
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input_layer_dict = {}
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for fc in feature_columns:
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if isinstance(fc, SparseFeat):
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input_layer_dict[fc.name] = Input(shape=(1,), name=fc.name)
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elif isinstance(fc, DenseFeat):
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input_layer_dict[fc.name] = Input(shape=(fc.dimension, ), name=fc.name)
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elif isinstance(fc, VarLenSparseFeat):
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input_layer_dict[fc.name] = Input(shape=(fc.maxlen, ), name=fc.name)
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return input_layer_dict
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# 构建embedding层
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def build_embedding_layers(feature_columns, input_layer_dict):
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embedding_layer_dict = {}
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for fc in feature_columns:
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if isinstance(fc, SparseFeat):
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embedding_layer_dict[fc.name] = Embedding(fc.vocabulary_size, fc.embedding_dim, name='emb_' + fc.name)
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elif isinstance(fc, VarLenSparseFeat):
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embedding_layer_dict[fc.name] = Embedding(fc.vocabulary_size + 1, fc.embedding_dim, name='emb_' + fc.name, mask_zero=True)
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return embedding_layer_dict
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def embedding_lookup(feature_columns, input_layer_dict, embedding_layer_dict):
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embedding_list = []
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for fc in feature_columns:
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_input = input_layer_dict[fc]
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_embed = embedding_layer_dict[fc]
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embed = _embed(_input)
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embedding_list.append(embed)
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return embedding_list
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class Dice(Layer):
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def __init__(self):
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super(Dice, self).__init__()
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self.bn = BatchNormalization(center=False, scale=False)
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def build(self, input_shape):
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self.alpha = self.add_weight(shape=(input_shape[-1],), dtype=tf.float32, name='alpha')
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def call(self, x):
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x_normed = self.bn(x)
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x_p = tf.sigmoid(x_normed)
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return self.alpha * (1.0-x_p) * x + x_p * x
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class LocalActivationUnit(Layer):
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def __init__(self, hidden_units=(256, 128, 64), activation='prelu'):
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super(LocalActivationUnit, self).__init__()
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self.hidden_units = hidden_units
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self.linear = Dense(1)
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self.dnn = [Dense(unit, activation=PReLU() if activation == 'prelu' else Dice()) for unit in hidden_units]
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def call(self, inputs):
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# query: B x 1 x emb_dim keys: B x len x emb_dim
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query, keys = inputs
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# 获取序列长度
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keys_len = keys.get_shape()[1]
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queries = tf.tile(query, multiples=[1, keys_len, 1]) # (None, len, emb_dim)
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# 将特征进行拼接
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att_input = tf.concat([queries, keys, queries - keys, queries * keys], axis=-1) # B x len x 4*emb_dim
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# 将原始向量与外积结果拼接后输入到一个dnn中
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att_out = att_input
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for fc in self.dnn:
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att_out = fc(att_out) # B x len x att_out
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att_out = self.linear(att_out) # B x len x 1
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att_out = tf.squeeze(att_out, -1) # B x len
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return att_out
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class AttentionPoolingLayer(Layer):
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def __init__(self, att_hidden_units=(256, 128, 64)):
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super(AttentionPoolingLayer, self).__init__()
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self.att_hidden_units = att_hidden_units
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self.local_att = LocalActivationUnit(self.att_hidden_units)
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def call(self, inputs):
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# keys: B x len x emb_dim, queries: B x 1 x emb_dim
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queries, keys = inputs
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# 获取行为序列embedding的mask矩阵，将Embedding矩阵中的非零元素设置成True，
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key_masks = tf.not_equal(keys[:,:,0], 0) # B x len
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# key_masks = keys._keras_mask # tf的有些版本不能使用这个属性，2.1是可以的，2.4好像不行
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# 获取行为序列中每个商品对应的注意力权重
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attention_score = self.local_att([queries, keys]) # B x len
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# 去除最后一个维度，方便后续理解与计算
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# outputs = attention_score
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# 创建一个padding的tensor, 目的是为了标记出行为序列embedding中无效的位置
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paddings = tf.zeros_like(attention_score) # B x len
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# outputs 表示的是padding之后的attention_score
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outputs = tf.where(key_masks, attention_score, paddings) # B x len
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# 将注意力分数与序列对应位置加权求和，这一步可以在
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outputs = tf.expand_dims(outputs, axis=1) # B x 1 x len
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# keys : B x len x emb_dim
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outputs = tf.matmul(outputs, keys) # B x 1 x dim
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outputs = tf.squeeze(outputs, axis=1)
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return outputs
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def get_dnn_logits(dnn_input, hidden_units=(200, 80), activation='prelu'):
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dnns = [Dense(unit, activation=PReLU() if activation == 'prelu' else Dice()) for unit in hidden_units]
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dnn_out = dnn_input
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for dnn in dnns:
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dnn_out = dnn(dnn_out)
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# 获取logits
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dnn_logits = Dense(1, activation='sigmoid')(dnn_out)
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return dnn_logits
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# 输入层拼接成列表
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def concat_input_list(input_list):
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feature_nums = len(input_list)
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if feature_nums > 1:
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return Concatenate(axis=1)(input_list)
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elif feature_nums == 1:
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return input_list[0]
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else:
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return None
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# 将所有的sparse特征embedding拼接
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def concat_embedding_list(feature_columns, input_layer_dict, embedding_layer_dict, flatten=False):
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embedding_list = []
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for fc in feature_columns:
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_input = input_layer_dict[fc.name] # 获取输入层
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_embed = embedding_layer_dict[fc.name] # B x 1 x dim 获取对应的embedding层
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embed = _embed(_input) # B x dim 将input层输入到embedding层中
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# 是否需要flatten, 如果embedding列表最终是直接输入到Dense层中，需要进行Flatten，否则不需要
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if flatten:
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embed = Flatten()(embed)
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embedding_list.append(embed)
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return embedding_list
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def DIN(feature_columns, behavior_feature_list, behavior_seq_feature_list):
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# 构建Input层
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input_layer_dict = build_input_layers(feature_columns)
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# 将Input层转化成列表的形式作为model的输入
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input_layers = list(input_layer_dict.values())
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# 筛选出特征中的sparse特征和dense特征，方便单独处理
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sparse_feature_columns = list(filter(lambda x: isinstance(x, SparseFeat), feature_columns))
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dense_feature_columns = list(filter(lambda x: isinstance(x, DenseFeat), feature_columns))
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# 获取dense
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dnn_dense_input = []
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for fc in dense_feature_columns:
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dnn_dense_input.append(input_layer_dict[fc.name])
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# 将所有的dense特征拼接
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dnn_dense_input = concat_input_list(dnn_dense_input)
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# 构建embedding字典
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embedding_layer_dict = build_embedding_layers(feature_columns, input_layer_dict)
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# 因为这里最终需要将embedding拼接后直接输入到全连接层(Dense)中, 所以需要Flatten
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dnn_sparse_embed_input = concat_embedding_list(sparse_feature_columns, input_layer_dict, embedding_layer_dict, flatten=True)
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# 将所有sparse特征的embedding进行拼接
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dnn_sparse_input = concat_input_list(dnn_sparse_embed_input)
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# 获取当前的行为特征(movie)的embedding，这里有可能有多个行为产生了行为序列，所以需要使用列表将其放在一起
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query_embed_list = embedding_lookup(behavior_feature_list, input_layer_dict, embedding_layer_dict)
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# 获取行为序列(movie_id序列, hist_movie_id) 对应的embedding，这里有可能有多个行为产生了行为序列，所以需要使用列表将其放在一起
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keys_embed_list = embedding_lookup(behavior_seq_feature_list, input_layer_dict, embedding_layer_dict)
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# 使用注意力机制将历史movie_id序列进行池化
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dnn_seq_input_list = []
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for i in range(len(keys_embed_list)):
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seq_emb = AttentionPoolingLayer()([query_embed_list[i], keys_embed_list[i]])
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dnn_seq_input_list.append(seq_emb)
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# 将多个行为序列attention poolint 之后的embedding进行拼接
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dnn_seq_input = concat_input_list(dnn_seq_input_list)
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# 将dense特征，sparse特征，及通过注意力加权的序列特征拼接
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dnn_input = Concatenate(axis=1)([dnn_dense_input, dnn_sparse_input, dnn_seq_input])
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# 获取最终dnn的logits
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dnn_logits = get_dnn_logits(dnn_input, activation='prelu')
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model = Model(input_layers, dnn_logits)
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return model
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if __name__ == "__main__":
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# 读取数据
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samples_data = pd.read_csv("./data/movie_sample.txt", sep="\t", header = None)
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samples_data.columns = ["user_id", "gender", "age", "hist_movie_id", "hist_len", "movie_id", "movie_type_id", "label"]
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# samples_data = shuffle(samples_data)
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X = samples_data[["user_id", "gender", "age", "hist_movie_id", "hist_len", "movie_id", "movie_type_id"]]
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y = samples_data["label"]
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X_train = {"user_id": np.array(X["user_id"]), \
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"gender": np.array(X["gender"]), \
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"age": np.array(X["age"]), \
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"hist_movie_id": np.array([[int(i) for i in l.split(',')] for l in X["hist_movie_id"]]), \
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"hist_len": np.array(X["hist_len"]), \
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"movie_id": np.array(X["movie_id"]), \
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"movie_type_id": np.array(X["movie_type_id"])}
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y_train = np.array(y)
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feature_columns = [SparseFeat('user_id', max(samples_data["user_id"])+1, embedding_dim=8),
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SparseFeat('gender', max(samples_data["gender"])+1, embedding_dim=8),
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SparseFeat('age', max(samples_data["age"])+1, embedding_dim=8),
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SparseFeat('movie_id', max(samples_data["movie_id"])+1, embedding_dim=8),
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SparseFeat('movie_type_id', max(samples_data["movie_type_id"])+1, embedding_dim=8),
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DenseFeat('hist_len', 1)]
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feature_columns += [VarLenSparseFeat('hist_movie_id', vocabulary_size=max(samples_data["movie_id"])+1, embedding_dim=8, maxlen=50)]
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# 行为特征列表，表示的是基础特征
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behavior_feature_list = ['movie_id']
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# 行为序列特征
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behavior_seq_feature_list = ['hist_movie_id']
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history = DIN(feature_columns, behavior_feature_list, behavior_seq_feature_list)
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history.compile('adam', 'binary_crossentropy')
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history.fit(X_train, y_train, batch_size=64, epochs=5, validation_split=0.2, )
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