大佬已经做出来了高Baseline的代码：https://github.com/YouChouNoBB/2018-tencent-ad-competition-baseline
无奈渣渣电脑基本跑不出来大神的结果，所以只好默默尝试别的出路
考虑interest kw topic此类特征太多，one-hot直接维数爆掉，所以采用了word2vec方法降维；没有调参的情况下基本可以达到one-hot的得分
- baseline :
- baseline_topk: 选择在interest kw topic等特征中出现频率topk的值，删除剩余的低频值
len_appIdAction len_appIdInstall len_interest1 len_interest2
count 1.106480e+07 1.106480e+07 1.106480e+07 1.106480e+07
mean 1.137803e+00 3.306016e+00 1.294338e+01 4.164523e+00
std 1.732410e+00 2.864749e+01 8.972224e+00 4.244111e+00
min 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
25% 1.000000e+00 1.000000e+00 6.000000e+00 1.000000e+00
50% 1.000000e+00 1.000000e+00 1.200000e+01 2.000000e+00
75% 1.000000e+00 1.000000e+00 1.900000e+01 6.000000e+00
max 5.370000e+02 9.200000e+02 3.800000e+01 3.200000e+01
len_interest3 len_interest4 len_interest5 len_kw1
count 1.106480e+07 1.106480e+07 1.106480e+07 1.106480e+07
mean 1.168589e+00 1.050987e+00 1.515969e+01 4.392344e+00
std 1.136084e+00 4.851396e-01 1.185373e+01 1.350022e+00
min 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
25% 1.000000e+00 1.000000e+00 1.000000e+00 5.000000e+00
50% 1.000000e+00 1.000000e+00 1.500000e+01 5.000000e+00
75% 1.000000e+00 1.000000e+00 2.300000e+01 5.000000e+00
max 1.000000e+01 1.000000e+01 8.600000e+01 5.000000e+00
len_kw2 len_kw3 len_topic1 len_topic2 len_topic3
count 1.106480e+07 1.106480e+07 1.106480e+07 1.106480e+07 1.106480e+07
mean 4.792818e+00 1.181388e+00 4.657463e+00 4.855681e+00 1.183553e+00
std 8.417202e-01 8.301784e-01 1.117917e+00 7.452962e-01 8.366755e-01
min 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00 1.000000e+00
25% 5.000000e+00 1.000000e+00 5.000000e+00 5.000000e+00 1.000000e+00
50% 5.000000e+00 1.000000e+00 5.000000e+00 5.000000e+00 1.000000e+00
75% 5.000000e+00 1.000000e+00 5.000000e+00 5.000000e+00 1.000000e+00
max 5.000000e+00 5.000000e+00 5.000000e+00 5.000000e+00 5.000000e+00
word_vec: count top_20%
this is feature: appIdAction
word_vec: 6215 1243
this is feature: appIdInstall
word_vec: 64856 12971
this is feature: interest1
word_vec: 123 24
this is feature: interest2
word_vec: 81 16
this is feature: interest3
word_vec: 11 2
this is feature: interest4
word_vec: 11 2
this is feature: interest5
word_vec: 137 27
this is feature: kw1
word_vec: 259909 51981
this is feature: kw2
word_vec: 49197 9839
this is feature: kw3
word_vec: 11922 2384
this is feature: topic1
word_vec: 10001 2000
this is feature: topic2
word_vec: 9980 1996
this is feature: topic3
word_vec: 5873 1174
this is feature: interest1
word_vec: 123 24
count 1.142004e+07
mean 8.807826e+00
std 5.239412e+00
min 1.000000e+00
25% 4.000000e+00
50% 9.000000e+00
75% 1.300000e+01
max 2.400000e+01
Name: interest1, dtype: float64
this is feature: interest2
word_vec: 81 16
count 1.142004e+07
mean 2.676273e+00
std 2.391842e+00
min 1.000000e+00
25% 1.000000e+00
50% 2.000000e+00
75% 4.000000e+00
max 1.500000e+01
Name: interest2, dtype: float64
this is feature: interest5
word_vec: 137 27
count 1.142004e+07
mean 9.560934e+00
std 6.553343e+00
min 1.000000e+00
25% 1.000000e+00
50% 1.000000e+01
75% 1.500000e+01
max 2.600000e+01
Name: interest5, dtype: float64
this is feature: kw1
word_vec: 263311 52662
count 1.142004e+07
mean 4.227658e+00
std 1.328209e+00
min 1.000000e+00
25% 4.000000e+00
50% 5.000000e+00
75% 5.000000e+00
max 5.000000e+00
Name: kw1, dtype: float64
this is feature: kw2
word_vec: 49779 9955
count 1.142004e+07
mean 4.680158e+00
std 9.268412e-01
min 1.000000e+00
25% 5.000000e+00
50% 5.000000e+00
75% 5.000000e+00
max 5.000000e+00
Name: kw2, dtype: float64
this is feature: topic1
word_vec: 10001 2000
count 1.142004e+07
mean 3.636819e+00
std 1.328687e+00
min 1.000000e+00
25% 3.000000e+00
50% 4.000000e+00
75% 5.000000e+00
max 5.000000e+00
Name: topic1, dtype: float64
this is feature: topic2
word_vec: 9983 1996
count 1.142004e+07
mean 3.840774e+00
std 1.245283e+00
min 1.000000e+00
25% 3.000000e+00
50% 4.000000e+00
75% 5.000000e+00
max 5.000000e+00
Name: topic2, dtype: float64