2021-03-18：WideNDeep模型

task2主要学习了wideNdeep的tf的实现,仍是改为了没有字典和nametuple的形式并打印了每行的显示结果。先学习模型的编写及内容，在本次组队学习后再掌握将其改为字典形式。

模型部分内容

1. wide部分

   • wide部分的输入包括数值型特征的输入和类别型特征的输入.数值型特征的输入后,经过concat->dense

   • concat将各个数值型特征的tensor拼接起来,经过Dense后相当于做了加和,Dense的过程中出现的参数体现了不同数值型特征的权重

   • 如果直接对数值特征进行add()就忽视了不同特征之间的权重

   • 类别型特征在Embedding后,相当于给其分配了不同的权重,所以可以直接add

   • wide部分输入的特征,应该是对结果相关性较为密切的特征,这样由于线性的记忆能力,可以使得线性部分的特征输入能有一个比较好的结果

2. deep部分
   • deep就是将特惠总能输入到三层神经网络中。。。

代码展示

import warnings
warnings.filterwarnings("ignore")
import itertools
import pandas as pd
import numpy as np
from tqdm import tqdm
from collections import namedtuple

import tensorflow as tf
from tensorflow.keras.layers import *
from tensorflow.keras.models import *

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import  MinMaxScaler, LabelEncoder

from utils import SparseFeat, DenseFeat, VarLenSparseFeat

# 读取数据
data = pd.read_csv('./data/criteo_sample.txt')

data.head()

	label	I1	I2	I3	I4	I5	I6	I7	I8	I9	...	C17	C18	C19	C20	C21	C22	C23	C24	C25	C26
0	0	NaN	3	260.0	NaN	17668.0	NaN	NaN	33.0	NaN	...	e5ba7672	87c6f83c	NaN	NaN	0429f84b	NaN	3a171ecb	c0d61a5c	NaN	NaN
1	0	NaN	-1	19.0	35.0	30251.0	247.0	1.0	35.0	160.0	...	d4bb7bd8	6fc84bfb	NaN	NaN	5155d8a3	NaN	be7c41b4	ded4aac9	NaN	NaN
2	0	0.0	0	2.0	12.0	2013.0	164.0	6.0	35.0	523.0	...	e5ba7672	675c9258	NaN	NaN	2e01979f	NaN	bcdee96c	6d5d1302	NaN	NaN
3	0	NaN	13	1.0	4.0	16836.0	200.0	5.0	4.0	29.0	...	e5ba7672	52e44668	NaN	NaN	e587c466	NaN	32c7478e	3b183c5c	NaN	NaN
4	0	0.0	0	104.0	27.0	1990.0	142.0	4.0	32.0	37.0	...	e5ba7672	25c88e42	21ddcdc9	b1252a9d	0e8585d2	NaN	32c7478e	0d4a6d1a	001f3601	92c878de

5 rows × 40 columns

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 200 entries, 0 to 199
Data columns (total 40 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   label   200 non-null    int64  
 1   I1      110 non-null    float64
 2   I2      200 non-null    int64  
 3   I3      166 non-null    float64
 4   I4      165 non-null    float64
 5   I5      194 non-null    float64
 6   I6      149 non-null    float64
 7   I7      190 non-null    float64
 8   I8      200 non-null    float64
 9   I9      190 non-null    float64
 10  I10     110 non-null    float64
 11  I11     190 non-null    float64
 12  I12     43 non-null     float64
 13  I13     165 non-null    float64
 14  C1      200 non-null    object 
 15  C2      200 non-null    object 
 16  C3      191 non-null    object 
 17  C4      191 non-null    object 
 18  C5      200 non-null    object 
 19  C6      168 non-null    object 
 20  C7      200 non-null    object 
 21  C8      200 non-null    object 
 22  C9      200 non-null    object 
 23  C10     200 non-null    object 
 24  C11     200 non-null    object 
 25  C12     191 non-null    object 
 26  C13     200 non-null    object 
 27  C14     200 non-null    object 
 28  C15     200 non-null    object 
 29  C16     191 non-null    object 
 30  C17     200 non-null    object 
 31  C18     200 non-null    object 
 32  C19     118 non-null    object 
 33  C20     118 non-null    object 
 34  C21     191 non-null    object 
 35  C22     41 non-null     object 
 36  C23     200 non-null    object 
 37  C24     191 non-null    object 
 38  C25     118 non-null    object 
 39  C26     118 non-null    object 
dtypes: float64(12), int64(2), object(26)
memory usage: 62.6+ KB

# 将数据划分为sparse_feature和dense_feature
columns = data.columns.values
columns

array(['label', 'I1', 'I2', 'I3', 'I4', 'I5', 'I6', 'I7', 'I8', 'I9',
       'I10', 'I11', 'I12', 'I13', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6',
       'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'C13', 'C14', 'C15', 'C16',
       'C17', 'C18', 'C19', 'C20', 'C21', 'C22', 'C23', 'C24', 'C25',
       'C26'], dtype=object)

dense_features = [ feat for feat in columns if 'I' in feat]
sparse_features = [feat for feat in columns if 'C' in feat]

dense_features

['I1',
 'I2',
 'I3',
 'I4',
 'I5',
 'I6',
 'I7',
 'I8',
 'I9',
 'I10',
 'I11',
 'I12',
 'I13']

# 数据处理的函数
def data_process(data_df,dense_features,sparse_features):
    """
    简单处理特征，包括填充缺失值，数值处理，类别编码
    param data_df: DataFrame格式的数据
    param dense_features: 数值特征名称列表
    param sparse_features: 类别特征名称列表
    """
    data_df[dense_features] = data_df[dense_features].fillna(0.0)
    for f in dense_features: # 对数值型特征的处理
        data_df[f] = data_df[f].apply(lambda x: np.log(x+1) if x > -1 else -1)
    # 将类别型特征的空值填上-1    
    data_df[sparse_features] = data_df[sparse_features].fillna("-1")
    for f in sparse_features:
        lbe = LabelEncoder() # 对类别型特征进行了one-hot
        data_df[f] = lbe.fit_transform(data_df[f]) # 传入的是个列表
    
    return data_df[dense_features + sparse_features]

train_data = data_process(data, dense_features, sparse_features)

train_data

<

	I1	I2	I3	I4	I5	I6	I7	I8	I9	I10	...	C17	C18	C19	C20	C21	C22	C23	C24	C25	C26
0	0.000000	1.386294	5.564520	0.000000	9.779567	0.000000	0.000000	3.526361	0.000000	0.000000	...	8	66	0	0	3	0	1	96	0	0
1	0.000000	-1.000000	2.995732	3.583519	10.317318	5.513429	0.693147	3.583519	5.081404	0.000000	...	7	52	0	0	47	0	7	112	0	0
2	0.000000	0.000000	1.098612	2.564949	7.607878	5.105945	1.945910	3.583519	6.261492	0.000000	...	8	49	0	0	25	0	6	53	0	0
3	0.000000	2.639057	0.693147	1.609438	9.731334	5.303305	1.791759	1.609438	3.401197	0.000000	...	8	37	0	0	156	0	0	32	0	0
4	0.000000	0.000000	4.653960	3.332205	7.596392	4.962845	1.609438	3.496508	3.637586	0.000000	...	8	14	5	3	9	0	0	5	1	47
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
195	0.000000	0.000000	4.736198	1.386294	8.018625	6.356108	1.098612	1.386294	5.370638	0.000000	...	0	74	5	1	30	5	0	118	17	48
196	0.000000	0.693147	0.693147	0.693147	7.382746	2.564949	0.693147	2.564949	2.772589	0.000000	...	1	25	0	0	138	0	0	68	0	0
197	0.693147	0.000000	1.945910	1.386294	0.000000	0.000000	2.995732	1.386294	1.386294	0.693147	...	4	40	17	2	41	0	0	12	16	11
198	0.000000	3.135494	1.945910	3.135494	5.318120	5.036953	4.394449	2.944439	6.232448	0.000000	...	4	7	18	1	123	0	0	10	16	49