262 lines
12 KiB
Python
262 lines
12 KiB
Python
from collections import namedtuple
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from tensorflow import keras
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import pandas as pd
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import numpy as np
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from sklearn.preprocessing import LabelEncoder
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from DeepCrossing import DeepCrossing
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from DeepFM import DeepFM
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from NFM import NFM
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from WideNDeep import WideNDeep
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from DIN import DIN
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from NCF import NCF
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from AFM import AFM
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from DCN import DCN
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from PNN import PNN
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from DIEN import DIEN
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from utils import DenseFeat, SparseFeat, VarLenSparseFeat
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# 简单处理特征，包括填充缺失值，数值处理，类别编码
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def data_process(data_df, dense_features, sparse_features):
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data_df[dense_features] = data_df[dense_features].fillna(0.0)
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for f in dense_features:
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data_df[f] = data_df[f].apply(lambda x: np.log(x+1) if x > -1 else -1)
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data_df[sparse_features] = data_df[sparse_features].fillna("-1")
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for f in sparse_features:
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lbe = LabelEncoder()
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data_df[f] = lbe.fit_transform(data_df[f])
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return data_df[dense_features + sparse_features]
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# 读取criteo数据
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def read_criteo_data():
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# 读取数据
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data = pd.read_csv('./data/criteo_sample.txt')
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# 划分dense和sparse特征
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columns = data.columns.values
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dense_features = [feat for feat in columns if 'I' in feat]
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sparse_features = [feat for feat in columns if 'C' in feat]
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return data, dense_features, sparse_features
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def plot_deepcrossing():
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data, dense_features, sparse_features = read_criteo_data()
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dense_features = dense_features[:3]
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sparse_features = sparse_features[:3]
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# 将特征分组，分成linear部分和dnn部分(根据实际场景进行选择)，并将分组之后的特征做标记（使用DenseFeat, SparseFeat）
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dnn_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for feat in sparse_features] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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# 构建DeepCrossing模型
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history = DeepCrossing(dnn_feature_columns)
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keras.utils.plot_model(history, to_file="./imgs/DeepCrossing.png", show_shapes=True)
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def plot_deepfm():
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# 读取数据
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data, dense_features, sparse_features = read_criteo_data()
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dense_features = dense_features[:3]
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sparse_features = sparse_features[:2]
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# 将特征分组，分成linear部分和dnn部分(根据实际场景进行选择)，并将分组之后的特征做标记（使用DenseFeat, SparseFeat）
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linear_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for feat in sparse_features] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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dnn_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for feat in sparse_features] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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# 构建DeepFM模型
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history = DeepFM(linear_feature_columns, dnn_feature_columns)
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keras.utils.plot_model(history, to_file="./imgs/DeepFM.png", show_shapes=True)
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def plot_nfm():
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# 读取数据
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data, dense_features, sparse_features = read_criteo_data()
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dense_features = dense_features[:3]
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sparse_features = sparse_features[:2]
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# 将特征分组，分成linear部分和dnn部分(根据实际场景进行选择)，并将分组之后的特征做标记（使用DenseFeat, SparseFeat）
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linear_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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dnn_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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# 构建NFM模型
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history = NFM(linear_feature_columns, dnn_feature_columns)
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keras.utils.plot_model(history, to_file="./imgs/NFM.png", show_shapes=True)
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def plot_widendeep():
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# 读取数据
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data, dense_features, sparse_features = read_criteo_data()
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dense_features = dense_features[:3]
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sparse_features = sparse_features[:2]
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# 将特征分组，分成linear部分和dnn部分(根据实际场景进行选择)，并将分组之后的特征做标记（使用DenseFeat, SparseFeat）
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linear_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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dnn_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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# 构建WideNDeep模型
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history = WideNDeep(linear_feature_columns, dnn_feature_columns)
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keras.utils.plot_model(history, to_file="./imgs/Wide&Deep.png", show_shapes=True)
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def plot_din():
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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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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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keras.utils.plot_model(history, to_file="./imgs/DIN.png", show_shapes=True)
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def plot_pnn():
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data, dense_features, sparse_features = read_criteo_data()
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dense_features = dense_features[:3]
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sparse_features = sparse_features[:3]
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# 将特征分组，分成linear部分和dnn部分(根据实际场景进行选择)，并将分组之后的特征做标记（使用DenseFeat, SparseFeat）
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dnn_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for feat in sparse_features] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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# 构建DeepCrossing模型
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history = PNN(dnn_feature_columns)
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keras.utils.plot_model(history, to_file="./imgs/PNN.png", show_shapes=True)
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def plot_ncf():
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# 读取数据，NCF使用的特征只有user_id和item_id
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rnames = ['user_id','movie_id','rating','timestamp']
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data = pd.read_csv('./data/ml-1m/ratings.dat', sep='::', engine='python', names=rnames)
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lbe = LabelEncoder()
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data['user_id'] = lbe.fit_transform(data['user_id'])
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data['movie_id'] = lbe.fit_transform(data['movie_id'])
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dnn_feature_columns = [SparseFeat('user_id', data['user_id'].nunique(), 8),
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SparseFeat('movie_id', data['movie_id'].nunique(), 8)]
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# 构建FM模型
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history = NCF(dnn_feature_columns)
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keras.utils.plot_model(history, to_file="./imgs/NCF.png", show_shapes=True)
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def plot_dcn():
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# 读取数据
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data, dense_features, sparse_features = read_criteo_data()
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dense_features = dense_features[:3]
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sparse_features = sparse_features[:2]
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# 将特征分组，分成linear部分和dnn部分(根据实际场景进行选择)，并将分组之后的特征做标记（使用DenseFeat, SparseFeat）
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linear_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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dnn_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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# 构建AFM模型
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history = DCN(linear_feature_columns, dnn_feature_columns)
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keras.utils.plot_model(history, to_file="./imgs/DCN.png", show_shapes=True)
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def plot_afm():
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# 读取数据
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data, dense_features, sparse_features = read_criteo_data()
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dense_features = dense_features[:3]
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sparse_features = sparse_features[:2]
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# 将特征分组，分成linear部分和dnn部分(根据实际场景进行选择)，并将分组之后的特征做标记（使用DenseFeat, SparseFeat）
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linear_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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dnn_feature_columns = [SparseFeat(feat, vocabulary_size=data[feat].nunique(),embedding_dim=4)
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for i,feat in enumerate(sparse_features)] + [DenseFeat(feat, 1,)
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for feat in dense_features]
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# 构建AFM模型
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history = AFM(linear_feature_columns, dnn_feature_columns)
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keras.utils.plot_model(history, to_file="./imgs/AFM.png", show_shapes=True)
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def plot_dien():
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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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"""数据集"""
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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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"""特征封装"""
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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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feature_columns += [VarLenSparseFeat('neg_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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# 负采样序列特征
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neg_seq_feature_list = ['neg_hist_movie_id']
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"""构建DIN模型"""
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history = DIEN(feature_columns, behavior_feature_list, behavior_seq_feature_list, neg_seq_feature_list, use_neg_sample=True)
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keras.utils.plot_model(history, to_file="./imgs/DIEN.png", show_shapes=True)
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if __name__ == '__main__':
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# plot_deepcrossing()
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# plot_deepfm()
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# plot_nfm()
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# plot_widendeep()
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# plot_din()
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# plot_ncf()
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# plot_afm()
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# plot_dcn()
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# plot_pnn()
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plot_dien()
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