I will like to evaluate the features and CF_based features a liitle more from different angles. As well as test the idea that combine rank of features and rank of similarity together.


In [1]:
import pandas as pd
import numpy as np
import os
import scipy as sp
from sklearn.metrics.pairwise import cosine_similarity
import operator
import cv2
import glob
from keras.preprocessing import image
from matplotlib import pyplot as plt
import seaborn as sns
%matplotlib inline


Using TensorFlow backend.

In [2]:
os.chdir('/Users/Walkon302/Desktop/deep-learning-models-master/view2buy')

In [96]:
df = pd.read_pickle('view2buy_url_CF_user_buy.pkl')

In [97]:
df.head()


Out[97]:
0 user_id buy_spu buy_sn buy_ct3 view_spu view_sn view_ct3 time_interval view_cnt view_secondes view_features buy_features spu url user_buy CF_item
0 4209887493\t453532580309307392\t10004616\t334\... 4209887493 453532580309307392 10004616 334 14150170026959126 10010102 334 21114 1 11 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... 4209887493-453532580309307392 [82267097285177879, 12742773141631009, 8883989...
1 529805243\t103096245561765919\t10010102\t334\t... 529805243 103096245561765919 10010102 334 14150170026959126 10010102 334 37794 4 66 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.467, 0.385, 0.0, 0.043, 0.292, 0.0, 0.448, ... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... 529805243-103096245561765919 [82267097285177879, 12742773141631009, 8883989...
2 3748045464\t446777176556679168\t10005711\t334\... 3748045464 446777176556679168 10005711 334 14150170026959126 10010102 334 18820 1 34 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.018, 0.161, 0.088, 0.141, 0.231, 0.0, 0.036... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... 3748045464-446777176556679168 [82267097285177879, 12742773141631009, 8883989...
3 4209887493\t438895881520357521\t10004616\t334\... 4209887493 438895881520357521 10004616 334 14150170026959126 10010102 334 13978 1 11 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... 4209887493-438895881520357521 [82267097285177879, 12742773141631009, 8883989...
4 4209887493\t74104320184119307\t10004616\t334\t... 4209887493 74104320184119307 10004616 334 14150170026959126 10010102 334 14313 1 11 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... 4209887493-74104320184119307 [82267097285177879, 12742773141631009, 8883989...

In [89]:
df2 = pd.read_pickle('view2buy_url_CF.pkl')

In [90]:
df2.head()


Out[90]:
0 user_id buy_spu buy_sn buy_ct3 view_spu view_sn view_ct3 time_interval view_cnt view_secondes view_features buy_features spu url CF_item
0 4209887493\t453532580309307392\t10004616\t334\... 4209887493 453532580309307392 10004616 334 14150170026959126 10010102 334 21114 1 11 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... [82267097285177879, 12742773141631009, 3341747...
1 529805243\t103096245561765919\t10010102\t334\t... 529805243 103096245561765919 10010102 334 14150170026959126 10010102 334 37794 4 66 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.467, 0.385, 0.0, 0.043, 0.292, 0.0, 0.448, ... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... [82267097285177879, 12742773141631009, 3341747...
2 3748045464\t446777176556679168\t10005711\t334\... 3748045464 446777176556679168 10005711 334 14150170026959126 10010102 334 18820 1 34 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.018, 0.161, 0.088, 0.141, 0.231, 0.0, 0.036... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... [82267097285177879, 12742773141631009, 3341747...
3 4209887493\t438895881520357521\t10004616\t334\... 4209887493 438895881520357521 10004616 334 14150170026959126 10010102 334 13978 1 11 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... [82267097285177879, 12742773141631009, 3341747...
4 4209887493\t74104320184119307\t10004616\t334\t... 4209887493 74104320184119307 10004616 334 14150170026959126 10010102 334 14313 1 11 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... [82267097285177879, 12742773141631009, 3341747...

In [86]:
df[df['user_id']== 4209887493].columns


Out[86]:
Index([u'0', u'user_id', u'buy_spu', u'buy_sn', u'buy_ct3', u'view_spu',
       u'view_sn', u'view_ct3', u'time_interval', u'view_cnt',
       u'view_secondes', u'view_features_x', u'buy_features', u'spu', u'url',
       u'CF_item_x', u'CF_item_y', u'ave_CF_fea_x', u'user_features',
       u'CF_sim', u'CF_rank', u'ori_user_features', u'ori_sim', u'ori_rank'],
      dtype='object')

In [77]:
df.drop(['0', 'CF_item_y', 'user_features', 'CF_sim', 'CF_rank', 'ori_user_features', 'ori_sim', 'ori_rank'], axis = 1)


Out[77]:
user_id buy_spu buy_sn buy_ct3 view_spu view_sn view_ct3 time_interval view_cnt view_secondes view_features_x buy_features spu url CF_item_x ave_CF_fea_x
0 4209887493 453532580309307392 10004616 334 14150170026959126 10010102 334 21114 1 11 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... [82267097285177879, 12742773141631009, 3341747... [0.5528, 1.3589, 0.0329, 0.2652, 0.1121, 0.070...
1 4209887493 438895881520357521 10004616 334 14150170026959126 10010102 334 13978 1 11 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... [82267097285177879, 12742773141631009, 3341747... [0.5528, 1.3589, 0.0329, 0.2652, 0.1121, 0.070...
2 4209887493 74104320184119307 10004616 334 14150170026959126 10010102 334 14313 1 11 [0.135, 1.078, 0.06, 0.241, 0.213, 0.22, 0.039... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 14150170026959126 http://a.vpimg2.com/upload/merchandise/pdcvis/... [82267097285177879, 12742773141631009, 3341747... [0.5528, 1.3589, 0.0329, 0.2652, 0.1121, 0.070...
3 4209887493 453532580309307392 10004616 334 99155636687355977 10023064 334 18202 1 22 [0.349, 0.394, 0.007, 2.666, 0.009, 0.0, 0.0, ... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 99155636687355977 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
4 4209887493 438895881520357521 10004616 334 99155636687355977 10023064 334 11066 1 22 [0.349, 0.394, 0.007, 2.666, 0.009, 0.0, 0.0, ... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 99155636687355977 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
5 4209887493 74104320184119307 10004616 334 99155636687355977 10023064 334 11401 1 22 [0.349, 0.394, 0.007, 2.666, 0.009, 0.0, 0.0, ... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 99155636687355977 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
6 4209887493 453532580309307392 10004616 334 82548613061427358 10023064 334 18053 2 10 [0.261, 1.139, 0.074, 2.173, 0.081, 0.0, 0.158... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 82548613061427358 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
7 4209887493 438895881520357521 10004616 334 82548613061427358 10023064 334 10917 2 10 [0.261, 1.139, 0.074, 2.173, 0.081, 0.0, 0.158... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 82548613061427358 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
8 4209887493 74104320184119307 10004616 334 82548613061427358 10023064 334 11252 2 10 [0.261, 1.139, 0.074, 2.173, 0.081, 0.0, 0.158... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 82548613061427358 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
9 4209887493 453532580309307392 10004616 334 74104320184119307 10004616 334 6801 10 62 [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 74104320184119307 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
10 4209887493 74104320184119307 10004616 334 74104320184119307 10004616 334 0 10 62 [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 74104320184119307 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
11 4209887493 453532580309307392 10004616 334 443118006171230222 10004616 334 15671 2 10 [0.349, 0.113, 0.013, 0.515, 0.542, 0.0, 0.097... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 443118006171230222 http://a.vpimg2.com/upload/merchandise/pdc/222... [455221430169571357, 453532580309307392, 44311... [0.3929, 0.919, 0.1094, 0.4559, 0.2379, 0.0193...
12 4209887493 438895881520357521 10004616 334 443118006171230222 10004616 334 8535 2 10 [0.349, 0.113, 0.013, 0.515, 0.542, 0.0, 0.097... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 443118006171230222 http://a.vpimg2.com/upload/merchandise/pdc/222... [455221430169571357, 453532580309307392, 44311... [0.3929, 0.919, 0.1094, 0.4559, 0.2379, 0.0193...
13 4209887493 74104320184119307 10004616 334 443118006171230222 10004616 334 8870 2 10 [0.349, 0.113, 0.013, 0.515, 0.542, 0.0, 0.097... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 443118006171230222 http://a.vpimg2.com/upload/merchandise/pdc/222... [455221430169571357, 453532580309307392, 44311... [0.3929, 0.919, 0.1094, 0.4559, 0.2379, 0.0193...
14 4209887493 453532580309307392 10004616 334 455221430169571357 10004616 334 15829 2 25 [0.128, 1.575, 0.0, 0.03, 0.0, 0.0, 0.013, 0.0... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 455221430169571357 http://a.vpimg2.com/upload/merchandise/pdcvis/... [443118006171230222, 453532580309307392, 99155... [0.166, 0.8309, 0.0245, 0.5715, 0.1694, 0.0303...
15 4209887493 438895881520357521 10004616 334 455221430169571357 10004616 334 8693 2 25 [0.128, 1.575, 0.0, 0.03, 0.0, 0.0, 0.013, 0.0... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 455221430169571357 http://a.vpimg2.com/upload/merchandise/pdcvis/... [443118006171230222, 453532580309307392, 99155... [0.166, 0.8309, 0.0245, 0.5715, 0.1694, 0.0303...
16 4209887493 74104320184119307 10004616 334 455221430169571357 10004616 334 9028 2 25 [0.128, 1.575, 0.0, 0.03, 0.0, 0.0, 0.013, 0.0... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 455221430169571357 http://a.vpimg2.com/upload/merchandise/pdcvis/... [443118006171230222, 453532580309307392, 99155... [0.166, 0.8309, 0.0245, 0.5715, 0.1694, 0.0303...
17 4209887493 453532580309307392 10004616 334 453532580309307392 10004616 334 0 13 149 [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 453532580309307392 http://a.vpimg2.com/upload/merchandise/pdc/392... [82548613061427358, 443680956124434457, 872045... [0.1436, 0.5797, 0.0125, 0.6813, 0.1741, 0.016...
18 4209887493 453532580309307392 10004616 334 438895881520357521 10004616 334 7136 10 77 [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 438895881520357521 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
19 4209887493 438895881520357521 10004616 334 438895881520357521 10004616 334 0 10 77 [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 438895881520357521 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
20 4209887493 453532580309307392 10004616 334 461976829617950723 10004616 334 15660 1 10 [0.006, 0.131, 0.0, 0.44, 0.406, 0.002, 0.198,... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 461976829617950723 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
21 4209887493 438895881520357521 10004616 334 461976829617950723 10004616 334 8524 1 10 [0.006, 0.131, 0.0, 0.44, 0.406, 0.002, 0.198,... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 461976829617950723 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
22 4209887493 74104320184119307 10004616 334 461976829617950723 10004616 334 8859 1 10 [0.006, 0.131, 0.0, 0.44, 0.406, 0.002, 0.198,... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 461976829617950723 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
23 4209887493 453532580309307392 10004616 334 74104337169281026 10004616 334 15606 1 4 [0.054, 0.782, 0.0, 0.269, 0.0, 0.0, 0.164, 0.... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 74104337169281026 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
24 4209887493 438895881520357521 10004616 334 74104337169281026 10004616 334 8470 1 4 [0.054, 0.782, 0.0, 0.269, 0.0, 0.0, 0.164, 0.... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 74104337169281026 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
25 4209887493 74104320184119307 10004616 334 74104337169281026 10004616 334 8805 1 4 [0.054, 0.782, 0.0, 0.269, 0.0, 0.0, 0.164, 0.... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 74104337169281026 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
26 4209887493 453532580309307392 10004616 334 93807568553869425 10004616 334 15517 1 28 [0.01, 0.688, 0.033, 0.075, 0.525, 0.0, 0.084,... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 93807568553869425 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
27 4209887493 438895881520357521 10004616 334 93807568553869425 10004616 334 8381 1 28 [0.01, 0.688, 0.033, 0.075, 0.525, 0.0, 0.084,... [0.036, 0.439, 0.0, 0.074, 0.194, 0.0, 0.331, ... 93807568553869425 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
28 4209887493 74104320184119307 10004616 334 93807568553869425 10004616 334 8716 1 28 [0.01, 0.688, 0.033, 0.075, 0.525, 0.0, 0.084,... [0.078, 2.304, 0.132, 0.191, 0.0, 0.087, 0.341... 93807568553869425 http://a.vpimg2.com/upload/merchandise/pdcvis/... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
29 4209887493 453532580309307392 10004616 334 3061398180775350287 10004616 334 15788 1 2 [0.071, 0.918, 0.0, 0.001, 0.109, 0.076, 0.18,... [0.1, 1.804, 0.049, 0.883, 0.092, 0.053, 0.042... 3061398180775350287 http://a.vpimg2.com/upload/merchandise/pdc/287... [438895881520357521, 74104320184119307, 443680... [0.146, 0.7319, 0.0257, 0.6735, 0.1741, 0.0252...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
17430 3673391202 450999316113584132 10021264 334 233700618554818563 10012320 334 84949 5 45 [0.223, 0.956, 0.0, 0.048, 0.061, 0.007, 0.562... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... 233700618554818563 http://a.vpimg2.com/upload/merchandise/pdc/563... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17431 3673391202 4995412772592787655 10012320 334 233700618554818563 10012320 334 84352 5 45 [0.223, 0.956, 0.0, 0.048, 0.061, 0.007, 0.562... [0.545, 1.177, 0.063, 0.793, 0.013, 0.055, 0.2... 233700618554818563 http://a.vpimg2.com/upload/merchandise/pdc/563... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
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17434 3673391202 4995412772592787655 10012320 334 453532627729321984 10021264 334 3416 2 24 [0.117, 0.0, 0.126, 0.838, 0.32, 0.083, 0.117,... [0.545, 1.177, 0.063, 0.793, 0.013, 0.055, 0.2... 453532627729321984 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17435 3673391202 433266429406154772 10021264 334 456910280733184030 10012320 334 83174 4 51 [0.448, 2.32, 0.0, 0.182, 0.087, 0.0, 0.376, 1... [0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0.1... 456910280733184030 http://a.vpimg2.com/upload/merchandise/pdcvis/... [5144594509986807823, 439740353870500018, 4535... [0.2653, 0.9639, 0.1314, 0.9796, 0.1666, 0.129...
17436 3673391202 450999316113584132 10021264 334 456910280733184030 10012320 334 83863 4 51 [0.448, 2.32, 0.0, 0.182, 0.087, 0.0, 0.376, 1... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... 456910280733184030 http://a.vpimg2.com/upload/merchandise/pdcvis/... [5144594509986807823, 439740353870500018, 4535... [0.2653, 0.9639, 0.1314, 0.9796, 0.1666, 0.129...
17437 3673391202 4995412772592787655 10012320 334 456910280733184030 10012320 334 83266 4 51 [0.448, 2.32, 0.0, 0.182, 0.087, 0.0, 0.376, 1... [0.545, 1.177, 0.063, 0.793, 0.013, 0.055, 0.2... 456910280733184030 http://a.vpimg2.com/upload/merchandise/pdcvis/... [5144594509986807823, 439740353870500018, 4535... [0.2653, 0.9639, 0.1314, 0.9796, 0.1666, 0.129...
17438 3673391202 433266429406154772 10021264 334 455221477589586090 10021264 334 3362 1 4 [0.023, 0.31, 0.271, 0.444, 0.353, 0.0, 1.217,... [0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0.1... 455221477589586090 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17439 3673391202 450999316113584132 10021264 334 455221477589586090 10021264 334 4051 1 4 [0.023, 0.31, 0.271, 0.444, 0.353, 0.0, 1.217,... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... 455221477589586090 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17440 3673391202 4995412772592787655 10012320 334 455221477589586090 10021264 334 3454 1 4 [0.023, 0.31, 0.271, 0.444, 0.353, 0.0, 1.217,... [0.545, 1.177, 0.063, 0.793, 0.013, 0.055, 0.2... 455221477589586090 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17441 3673391202 433266429406154772 10021264 334 9083613970694320 10012320 334 35886 2 8 [0.317, 0.707, 0.068, 1.711, 0.429, 0.101, 0.8... [0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0.1... 9083613970694320 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17442 3673391202 450999316113584132 10021264 334 9083613970694320 10012320 334 36575 2 8 [0.317, 0.707, 0.068, 1.711, 0.429, 0.101, 0.8... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... 9083613970694320 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17443 3673391202 4995412772592787655 10012320 334 9083613970694320 10012320 334 35978 2 8 [0.317, 0.707, 0.068, 1.711, 0.429, 0.101, 0.8... [0.545, 1.177, 0.063, 0.793, 0.013, 0.055, 0.2... 9083613970694320 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17444 3673391202 433266429406154772 10021264 334 28223939643359401 10012320 334 569644 2 13 [0.162, 1.513, 0.044, 0.483, 0.018, 0.608, 0.1... [0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0.1... 28223939643359401 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17445 3673391202 450999316113584132 10021264 334 28223939643359401 10012320 334 570333 2 13 [0.162, 1.513, 0.044, 0.483, 0.018, 0.608, 0.1... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... 28223939643359401 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17446 3673391202 4995412772592787655 10012320 334 28223939643359401 10012320 334 569736 2 13 [0.162, 1.513, 0.044, 0.483, 0.018, 0.608, 0.1... [0.545, 1.177, 0.063, 0.793, 0.013, 0.055, 0.2... 28223939643359401 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17447 3673391202 433266429406154772 10021264 334 11335441040720040 10012320 334 58881 1 6 [0.358, 1.042, 0.195, 3.888, 0.099, 0.124, 0.3... [0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0.1... 11335441040720040 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17448 3673391202 450999316113584132 10021264 334 11335441040720040 10012320 334 59570 1 6 [0.358, 1.042, 0.195, 3.888, 0.099, 0.124, 0.3... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... 11335441040720040 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17449 3673391202 4995412772592787655 10012320 334 11335441040720040 10012320 334 58973 1 6 [0.358, 1.042, 0.195, 3.888, 0.099, 0.124, 0.3... [0.545, 1.177, 0.063, 0.793, 0.013, 0.055, 0.2... 11335441040720040 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17450 3673391202 433266429406154772 10021264 334 439740353870500018 10021264 334 33150 2 10 [0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.11,... [0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0.1... 439740353870500018 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17451 3673391202 450999316113584132 10021264 334 439740353870500018 10021264 334 33839 2 10 [0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.11,... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... 439740353870500018 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17452 3673391202 4995412772592787655 10012320 334 439740353870500018 10021264 334 33242 2 10 [0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.11,... [0.545, 1.177, 0.063, 0.793, 0.013, 0.055, 0.2... 439740353870500018 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17453 3673391202 433266429406154772 10021264 334 15839007868620828 10012320 334 83074 4 47 [0.499, 0.832, 0.099, 0.362, 0.062, 0.077, 0.2... [0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0.1... 15839007868620828 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17454 3673391202 450999316113584132 10021264 334 15839007868620828 10012320 334 83763 4 47 [0.499, 0.832, 0.099, 0.362, 0.062, 0.077, 0.2... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... 15839007868620828 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17455 3673391202 4995412772592787655 10012320 334 15839007868620828 10012320 334 83166 4 47 [0.499, 0.832, 0.099, 0.362, 0.062, 0.077, 0.2... [0.545, 1.177, 0.063, 0.793, 0.013, 0.055, 0.2... 15839007868620828 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
17456 3673391202 433266429406154772 10021264 334 5144594509986807823 10021264 334 33371 2 15 [0.109, 0.653, 0.021, 1.039, 0.0, 0.298, 0.213... [0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0.1... 5144594509986807823 http://a.vpimg2.com/upload/merchandise/pdc/823... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...
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17459 3673391202 450999316113584132 10021264 334 450999316113584132 10021264 334 0 11 66 [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... 450999316113584132 http://a.vpimg2.com/upload/merchandise/pdcvis/... [439740353870500018, 15839007868620828, 282239... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252...

17460 rows × 16 columns


In [119]:
df_buy_user = df.groupby(['user_id', 'buy_spu', 'spu']).count().reset_index()[['user_id', 'buy_spu', 'spu']]

In [120]:
df_buy_user.shape


Out[120]:
(15785, 3)

In [124]:
df_buy_user.head()


Out[124]:
user_id buy_spu spu
0 3440325 2898705571343994880 952306123355361280
1 3440325 2898705571343994880 1374237151951519744
2 3440325 2898705571343994880 1392814500414361602
3 3440325 2898705571343994880 2462700831731142656
4 3440325 2898705571343994880 2462982307401474048

In [30]:
df_spu = pd.read_pickle('spu_CF_features.pkl')

In [31]:
df_spu.head()


Out[31]:
spu CF_item spu_features CF_features ave_CF_fea
0 357872333107204 [8952950888272863232, 1664156381170176000, 284... [0.035, 0.385, 0.112, 0.014, 0.0, 0.123, 0.438... [[[0.462, 0.551, 0.068, 0.833, 0.0, 0.0, 0.0, ... [0.4269, 0.6321, 0.101, 0.9695, 0.2211, 0.131,...
1 357875526680651 [2046769978417582, 461413925257830545, 3255052... [0.132, 1.678, 0.061, 0.918, 0.462, 0.342, 0.4... [[[0.357, 2.503, 0.0, 0.641, 0.143, 0.0, 0.104... [0.4727, 0.8078, 0.0697, 0.5796, 0.3075, 0.009...
2 357882254983171 [459725075493814272, 8582811288237465674, 1976... [0.026, 0.936, 0.056, 0.614, 0.139, 0.0, 0.302... [[[1.884, 0.52, 0.0, 3.98, 0.175, 0.008, 0.663... [0.6206, 0.8754, 0.1066, 0.7549, 0.2387, 0.186...
3 357901107539985 [2466922956389351424, 8459806721299259392, 780... [0.229, 0.543, 0.132, 0.144, 0.295, 0.018, 0.0... [[[0.124, 0.819, 0.0, 0.596, 0.306, 0.043, 0.2... [0.4016, 0.6379, 0.2088, 0.7764, 0.3335, 0.012...
4 639360131194904 [451843765076328474, 81141212316332372, 320394... [1.113, 0.5, 0.758, 0.218, 0.0, 0.0, 0.335, 1.... [[[0.501, 0.12, 0.0, 0.0, 0.23, 0.108, 0.377, ... [0.2916, 0.5296, 0.0964, 0.3321, 0.1873, 0.022...

In [14]:
def dot(K, L):
    if len(K) != len(L): return 0
    return sum(i[0]*i[1] for i in zip(K, L))

def similarity(item_1, item_2):
    return dot(item_1, item_2) / np.sqrt(dot(item_1, item_1)*dot(item_2, item_2))

def average(lists):
    return [np.mean(i) for i in zip(*[l for l in lists])]

In [138]:
CF_user_fea = df_cal.groupby(['user_id', 'buy_spu'])['ave_CF_fea'].apply(lambda x: average(x))
CF_user_fea = pd.DataFrame(CF_user_fea)
CF_user_fea = CF_user_fea.reset_index()

In [139]:
Ori_user_fea = df_cal.groupby(['user_id', 'buy_spu'])['spu_features'].apply(lambda x: average(x))
Ori_user_fea = pd.DataFrame(Ori_user_fea)
Ori_user_fea = Ori_user_fea.reset_index()

In [158]:
CF_user_fea.head()


Out[158]:
user_id buy_spu ave_CF_fea
0 3440325 2898705571343994880 [0.327609375, 0.573775, 0.11804375, 0.87390625...
1 7052311 7957699990364366 [0.307052173913, 0.738013043478, 0.06435217391...
2 7052311 299847271351230626 [0.306652173913, 0.7439, 0.0624434782609, 0.60...
3 7052311 448747521265074449 [0.306817391304, 0.735182608696, 0.06435217391...
4 9254280 3925244893201649664 [0.385093939394, 0.623651515152, 0.12763030303...

Now I got the correct user_features either based on original features or CF_based features for each user per perchase.


In [5]:
user_fea = pd.read_pickle('user_buy_CF_Ori_fea.pkl')
df_spu = pd.read_pickle('spu_CF_features.pkl')

In [169]:
user_fea.head()


Out[169]:
user_id buy_spu ave_CF_fea ave_view_fea
0 3440325 2898705571343994880 [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ...
1 7052311 7957699990364366 [0.307052173913, 0.738013043478, 0.06435217391... [0.320739130435, 0.577217391304, 0.088, 0.6661...
2 7052311 299847271351230626 [0.306652173913, 0.7439, 0.0624434782609, 0.60... [0.314217391304, 0.620695652174, 0.08865217391...
3 7052311 448747521265074449 [0.306817391304, 0.735182608696, 0.06435217391... [0.323086956522, 0.60552173913, 0.088, 0.653, ...
4 9254280 3925244893201649664 [0.385093939394, 0.623651515152, 0.12763030303... [0.356484848485, 0.575484848485, 0.11351515151...

In [171]:
df_spu.head()


Out[171]:
spu CF_item spu_features CF_features ave_CF_fea
0 357872333107204 [8952950888272863232, 1664156381170176000, 284... [0.035, 0.385, 0.112, 0.014, 0.0, 0.123, 0.438... [[[0.462, 0.551, 0.068, 0.833, 0.0, 0.0, 0.0, ... [0.4269, 0.6321, 0.101, 0.9695, 0.2211, 0.131,...
1 357875526680651 [2046769978417582, 461413925257830545, 3255052... [0.132, 1.678, 0.061, 0.918, 0.462, 0.342, 0.4... [[[0.357, 2.503, 0.0, 0.641, 0.143, 0.0, 0.104... [0.4727, 0.8078, 0.0697, 0.5796, 0.3075, 0.009...
2 357882254983171 [459725075493814272, 8582811288237465674, 1976... [0.026, 0.936, 0.056, 0.614, 0.139, 0.0, 0.302... [[[1.884, 0.52, 0.0, 3.98, 0.175, 0.008, 0.663... [0.6206, 0.8754, 0.1066, 0.7549, 0.2387, 0.186...
3 357901107539985 [2466922956389351424, 8459806721299259392, 780... [0.229, 0.543, 0.132, 0.144, 0.295, 0.018, 0.0... [[[0.124, 0.819, 0.0, 0.596, 0.306, 0.043, 0.2... [0.4016, 0.6379, 0.2088, 0.7764, 0.3335, 0.012...
4 639360131194904 [451843765076328474, 81141212316332372, 320394... [1.113, 0.5, 0.758, 0.218, 0.0, 0.0, 0.335, 1.... [[[0.501, 0.12, 0.0, 0.0, 0.23, 0.108, 0.377, ... [0.2916, 0.5296, 0.0964, 0.3321, 0.1873, 0.022...

In [47]:
user_fea['ave_Ori_sim_rank']= user_fea.apply(lambda x: sim_cal(x['buy_spu'],x['ave_view_fea']), axis = 1)


2898705571343994880
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3925244893201649664
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4420359388939563031
25690665644495446
4766573611946106883
2823270277605580800
2909964570461929472
4411070714708987927
6926894069614792705
2482122605837119488
2886883622319759360
2913060795180240896
2823270277605580800
2832277476150169600
24001769421647872
2896735246507962368
314483964450287810
1392814500414406660
4397841390802718742
2463545257346113536
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In [4]:
# Calculate similarity and ranking, return the final result for each buy item.
def sim_cal(buy_item, item):
    df_spu['sim'] = df_spu.apply(lambda x: similarity(x['spu_features'], item), axis = 1)
    df_spu['rank'] = df_spu['sim'].rank(ascending=False)
    print user
    return (df_spu[df_spu['spu'] == buy_item]['sim'], df_spu[df_spu['spu'] == buy_item]['rank'])

In [38]:
df_spu.head()


Out[38]:
spu CF_item spu_features CF_features ave_CF_fea sim rank
0 357872333107204 [8952950888272863232, 1664156381170176000, 284... [0.035, 0.385, 0.112, 0.014, 0.0, 0.123, 0.438... [[[0.462, 0.551, 0.068, 0.833, 0.0, 0.0, 0.0, ... [0.4269, 0.6321, 0.101, 0.9695, 0.2211, 0.131,... 0.787449 632.0
1 357875526680651 [2046769978417582, 461413925257830545, 3255052... [0.132, 1.678, 0.061, 0.918, 0.462, 0.342, 0.4... [[[0.357, 2.503, 0.0, 0.641, 0.143, 0.0, 0.104... [0.4727, 0.8078, 0.0697, 0.5796, 0.3075, 0.009... 0.690612 1561.0
2 357882254983171 [459725075493814272, 8582811288237465674, 1976... [0.026, 0.936, 0.056, 0.614, 0.139, 0.0, 0.302... [[[1.884, 0.52, 0.0, 3.98, 0.175, 0.008, 0.663... [0.6206, 0.8754, 0.1066, 0.7549, 0.2387, 0.186... 0.754448 965.0
3 357901107539985 [2466922956389351424, 8459806721299259392, 780... [0.229, 0.543, 0.132, 0.144, 0.295, 0.018, 0.0... [[[0.124, 0.819, 0.0, 0.596, 0.306, 0.043, 0.2... [0.4016, 0.6379, 0.2088, 0.7764, 0.3335, 0.012... 0.692096 1544.0
4 639360131194904 [451843765076328474, 81141212316332372, 320394... [1.113, 0.5, 0.758, 0.218, 0.0, 0.0, 0.335, 1.... [[[0.501, 0.12, 0.0, 0.0, 0.23, 0.108, 0.377, ... [0.2916, 0.5296, 0.0964, 0.3321, 0.1873, 0.022... 0.665405 1752.0

In [34]:
df_spu.head()


Out[34]:
spu CF_item spu_features CF_features ave_CF_fea sim rank
0 357872333107204 [8952950888272863232, 1664156381170176000, 284... [0.035, 0.385, 0.112, 0.014, 0.0, 0.123, 0.438... [[[0.462, 0.551, 0.068, 0.833, 0.0, 0.0, 0.0, ... [0.4269, 0.6321, 0.101, 0.9695, 0.2211, 0.131,... 0.787449 632.0
1 357875526680651 [2046769978417582, 461413925257830545, 3255052... [0.132, 1.678, 0.061, 0.918, 0.462, 0.342, 0.4... [[[0.357, 2.503, 0.0, 0.641, 0.143, 0.0, 0.104... [0.4727, 0.8078, 0.0697, 0.5796, 0.3075, 0.009... 0.690612 1561.0
2 357882254983171 [459725075493814272, 8582811288237465674, 1976... [0.026, 0.936, 0.056, 0.614, 0.139, 0.0, 0.302... [[[1.884, 0.52, 0.0, 3.98, 0.175, 0.008, 0.663... [0.6206, 0.8754, 0.1066, 0.7549, 0.2387, 0.186... 0.754448 965.0
3 357901107539985 [2466922956389351424, 8459806721299259392, 780... [0.229, 0.543, 0.132, 0.144, 0.295, 0.018, 0.0... [[[0.124, 0.819, 0.0, 0.596, 0.306, 0.043, 0.2... [0.4016, 0.6379, 0.2088, 0.7764, 0.3335, 0.012... 0.692096 1544.0
4 639360131194904 [451843765076328474, 81141212316332372, 320394... [1.113, 0.5, 0.758, 0.218, 0.0, 0.0, 0.335, 1.... [[[0.501, 0.12, 0.0, 0.0, 0.23, 0.108, 0.377, ... [0.2916, 0.5296, 0.0964, 0.3321, 0.1873, 0.022... 0.665405 1752.0

In [221]:
a = sim_cal(3440325, user_fea['ave_CF_fea'][0])


3440325

In [18]:
df_spu


Out[18]:
user_id view_spu spu CF_item spu_features_x CF_features ave_CF_fea_x CF_user_features Ori_user_features
0 3440325 952306123355361280 952306123355361280 [2626800816051441664, 107318408860868627, 8886... [0.631, 1.654, 0.0, 0.253, 0.041, 0.0, 0.103, ... [[[0.392, 0.326, 0.0, 0.076, 0.171, 0.009, 0.0... [0.4213, 0.6232, 0.2287, 0.7885, 0.1946, 0.047... [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ...
1 3440325 1374237151951519744 1374237151951519744 [3773248405777813504, 2452849208253480960, 570... [0.276, 0.458, 0.0, 0.234, 0.062, 0.023, 0.458... [[[0.166, 0.366, 0.0, 0.075, 0.058, 0.294, 0.5... [0.2671, 0.4109, 0.0948, 0.6352, 0.1639, 0.120... [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ...
2 3440325 1392814500414361602 1392814500414361602 [18935228309520387, 2531099323969781760, 24694... [1.066, 0.992, 0.004, 0.868, 0.169, 0.389, 0.1... [[[0.902, 0.444, 0.0, 0.236, 0.075, 0.259, 0.3... [0.4135, 0.4454, 0.0279, 1.3677, 0.2129, 0.093... [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ...
3 3440325 2462700831731142656 2462700831731142656 [2825803552328679424, 1397036625065005056, 290... [0.2, 0.231, 0.302, 0.536, 0.064, 0.0, 0.261, ... [[[0.318, 0.811, 0.003, 0.248, 0.016, 0.016, 0... [0.5295, 0.539, 0.1045, 0.9142, 0.1462, 0.0491... [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ...
4 3440325 2462982307401474048 2462982307401474048 [2478463431161782272, 2813981603399778304, 284... [0.177, 0.029, 0.036, 0.558, 0.393, 0.059, 0.2... [[[0.146, 0.576, 0.498, 1.403, 0.082, 0.009, 0... [0.1984, 0.5322, 0.296, 1.4349, 0.4927, 0.0944... [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ...
5 3440325 2467767382049587200 2467767382049587200 [4048530939349401602, 4418107589125877775, 439... [0.0, 0.345, 0.464, 1.119, 0.452, 0.0, 0.162, ... [[[0.173, 0.102, 0.096, 0.694, 0.017, 0.062, 0... [0.4394, 1.0768, 0.1263, 0.7698, 0.3109, 0.124... [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ...
6 3440325 2474241305768906752 2474241305768906752 [7016121635473047552, 1764924422095110144, 116... [0.146, 0.976, 0.109, 0.529, 0.063, 0.0, 0.151... [[[0.479, 0.745, 0.0, 2.47, 0.097, 0.066, 0.01... [0.2691, 0.6029, 0.0289, 0.5591, 0.0743, 0.071... [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ...
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12769 3673391202 233700618554818563 233700618554818563 [439740353870500018, 15839007868620828, 282239... [0.223, 0.956, 0.0, 0.048, 0.061, 0.007, 0.562... [[[0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.1... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252... [0.287178571429, 0.962092857143, 0.13320714285... [0.324642857143, 1.05078571429, 0.139, 0.8925,...
12770 3673391202 433266429406154772 433266429406154772 [437488556136837122, 296188100116926918, 46732... [0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0.1... [[[0.898, 0.571, 0.107, 0.862, 0.459, 0.376, 0... [0.3493, 0.6946, 0.1288, 1.2265, 0.2617, 0.103... [0.287178571429, 0.962092857143, 0.13320714285... [0.324642857143, 1.05078571429, 0.139, 0.8925,...
12771 3673391202 437488556136837122 437488556136837122 [433266429406154772, 296188100116926918, 46732... [0.898, 0.571, 0.107, 0.862, 0.459, 0.376, 0.0... [[[0.001, 1.004, 0.462, 0.862, 0.259, 0.192, 0... [0.2596, 0.7379, 0.1643, 1.2265, 0.2417, 0.085... [0.287178571429, 0.962092857143, 0.13320714285... [0.324642857143, 1.05078571429, 0.139, 0.8925,...
12772 3673391202 439740353870500018 439740353870500018 [439740353870500018, 15839007868620828, 282239... [0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.11,... [[[0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.1... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252... [0.287178571429, 0.962092857143, 0.13320714285... [0.324642857143, 1.05078571429, 0.139, 0.8925,...
12773 3673391202 450999316113584132 450999316113584132 [439740353870500018, 15839007868620828, 282239... [0.791, 1.814, 0.013, 0.438, 0.037, 0.0, 0.412... [[[0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.1... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252... [0.287178571429, 0.962092857143, 0.13320714285... [0.324642857143, 1.05078571429, 0.139, 0.8925,...
12774 3673391202 453532627729321984 453532627729321984 [439740353870500018, 15839007868620828, 282239... [0.117, 0.0, 0.126, 0.838, 0.32, 0.083, 0.117,... [[[0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.1... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252... [0.287178571429, 0.962092857143, 0.13320714285... [0.324642857143, 1.05078571429, 0.139, 0.8925,...
12775 3673391202 455221477589586090 455221477589586090 [439740353870500018, 15839007868620828, 282239... [0.023, 0.31, 0.271, 0.444, 0.353, 0.0, 1.217,... [[[0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.1... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252... [0.287178571429, 0.962092857143, 0.13320714285... [0.324642857143, 1.05078571429, 0.139, 0.8925,...
12776 3673391202 456910280733184030 456910280733184030 [5144594509986807823, 439740353870500018, 4535... [0.448, 2.32, 0.0, 0.182, 0.087, 0.0, 0.376, 1... [[[0.109, 0.653, 0.021, 1.039, 0.0, 0.298, 0.2... [0.2653, 0.9639, 0.1314, 0.9796, 0.1666, 0.129... [0.287178571429, 0.962092857143, 0.13320714285... [0.324642857143, 1.05078571429, 0.139, 0.8925,...
12777 3673391202 5144594509986807823 5144594509986807823 [439740353870500018, 15839007868620828, 282239... [0.109, 0.653, 0.021, 1.039, 0.0, 0.298, 0.213... [[[0.054, 1.812, 0.477, 0.545, 0.287, 0.0, 1.1... [0.2881, 1.0109, 0.1309, 0.8878, 0.125, 0.1252... [0.287178571429, 0.962092857143, 0.13320714285... [0.324642857143, 1.05078571429, 0.139, 0.8925,...

12778 rows × 9 columns


In [177]:
df_spu.head()


Out[177]:
spu CF_item spu_features CF_features ave_CF_fea sim
0 357872333107204 [8952950888272863232, 1664156381170176000, 284... [0.035, 0.385, 0.112, 0.014, 0.0, 0.123, 0.438... [[[0.462, 0.551, 0.068, 0.833, 0.0, 0.0, 0.0, ... [0.4269, 0.6321, 0.101, 0.9695, 0.2211, 0.131,... 0.746659
1 357875526680651 [2046769978417582, 461413925257830545, 3255052... [0.132, 1.678, 0.061, 0.918, 0.462, 0.342, 0.4... [[[0.357, 2.503, 0.0, 0.641, 0.143, 0.0, 0.104... [0.4727, 0.8078, 0.0697, 0.5796, 0.3075, 0.009... 0.739002
2 357882254983171 [459725075493814272, 8582811288237465674, 1976... [0.026, 0.936, 0.056, 0.614, 0.139, 0.0, 0.302... [[[1.884, 0.52, 0.0, 3.98, 0.175, 0.008, 0.663... [0.6206, 0.8754, 0.1066, 0.7549, 0.2387, 0.186... 0.764982
3 357901107539985 [2466922956389351424, 8459806721299259392, 780... [0.229, 0.543, 0.132, 0.144, 0.295, 0.018, 0.0... [[[0.124, 0.819, 0.0, 0.596, 0.306, 0.043, 0.2... [0.4016, 0.6379, 0.2088, 0.7764, 0.3335, 0.012... 0.707425
4 639360131194904 [451843765076328474, 81141212316332372, 320394... [1.113, 0.5, 0.758, 0.218, 0.0, 0.0, 0.335, 1.... [[[0.501, 0.12, 0.0, 0.0, 0.23, 0.108, 0.377, ... [0.2916, 0.5296, 0.0964, 0.3321, 0.1873, 0.022... 0.653415

In [192]:
df_spu['rank'] = df_spu['sim'].rank(ascending=False)

In [193]:
df_spu[df_spu['spu'] == 7957699990364366]


Out[193]:
spu CF_item spu_features CF_features ave_CF_fea sim rank
65 7957699990364366 [34416347801161914, 313076595256631868, 294499... [0.299, 0.102, 0.0, 0.329, 0.253, 0.0, 1.051, ... [[[0.427, 0.269, 0.016, 0.547, 0.09, 0.0, 0.86... [0.3392, 0.7933, 0.1072, 0.7467, 0.1693, 0.027... 0.739963 1112.0

In [216]:
user_fea


Out[216]:
user_id buy_spu ave_CF_fea ave_view_fea
0 3440325 2898705571343994880 [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ...
1 7052311 7957699990364366 [0.307052173913, 0.738013043478, 0.06435217391... [0.320739130435, 0.577217391304, 0.088, 0.6661...
2 7052311 299847271351230626 [0.306652173913, 0.7439, 0.0624434782609, 0.60... [0.314217391304, 0.620695652174, 0.08865217391...
3 7052311 448747521265074449 [0.306817391304, 0.735182608696, 0.06435217391... [0.323086956522, 0.60552173913, 0.088, 0.653, ...
4 9254280 3925244893201649664 [0.385093939394, 0.623651515152, 0.12763030303... [0.356484848485, 0.575484848485, 0.11351515151...
5 15286946 453532609002410015 [0.347879166667, 0.817225, 0.0848583333333, 0.... [0.308958333333, 0.710041666667, 0.14666666666...
6 32626686 4420359388939563031 [0.17815, 0.741991666667, 0.088075, 0.47872083... [0.128875, 0.684625, 0.114333333333, 0.3787083...
7 46639224 25690665644495446 [0.320625, 0.63956875, 0.08360625, 0.58983125,... [0.276375, 0.62375, 0.0773125, 0.4994375, 0.22...
8 46639224 4766573611946106883 [0.32185625, 0.64266875, 0.08503125, 0.58865, ... [0.2801875, 0.6065, 0.07725, 0.4950625, 0.2181...
9 47836480 2823270277605580800 [0.416597142857, 0.58062, 0.112997142857, 0.87... [0.436742857143, 0.444428571429, 0.09897142857...
10 47836480 2909964570461929472 [0.325496078431, 0.594509803922, 0.09908823529... [0.388098039216, 0.567470588235, 0.09990196078...
11 47836480 4411070714708987927 [0.399353333333, 0.56642, 0.126386666667, 0.81... [0.404833333333, 0.4583, 0.0861833333333, 0.90...
12 47836480 6926894069614792705 [0.419721621622, 0.570637837838, 0.11359459459... [0.433864864865, 0.409810810811, 0.09394594594...
13 60387856 2482122605837119488 [0.361202777778, 0.595433333333, 0.12032777777... [0.260972222222, 0.633194444444, 0.10616666666...
14 68726836 2886883622319759360 [0.338261904762, 0.576661904762, 0.13463333333... [0.193095238095, 0.688047619048, 0.21466666666...
15 82073468 2913060795180240896 [0.445991176471, 0.680444117647, 0.14644411764... [0.573147058824, 0.7745, 0.0831176470588, 0.91...
16 85939638 2823270277605580800 [0.41885, 0.50228, 0.08361, 0.93755, 0.21682, ... [0.2711, 0.2286, 0.0741, 0.4116, 0.1769, 0.125...
17 85939638 2832277476150169600 [0.392166666667, 0.500441666667, 0.10114166666... [0.256916666667, 0.241666666667, 0.12716666666...
18 121339277 24001769421647872 [0.232147619048, 0.66679047619, 0.052357142857... [0.235142857143, 0.622380952381, 0.03961904761...
19 156267268 2896735246507962368 [0.325731428571, 0.578748571429, 0.12396, 0.74... [0.210257142857, 0.757085714286, 0.08428571428...
20 185723484 314483964450287810 [0.30986875, 0.73903125, 0.12285625, 0.7108875... [0.2794375, 0.80675, 0.1433125, 0.521375, 0.22...
21 185723484 1392814500414406660 [0.263157142857, 0.640928571429, 0.13431428571... [0.321714285714, 0.852714285714, 0.26485714285...
22 203856270 4397841390802718742 [0.244733333333, 0.664491666667, 0.08427083333... [0.352333333333, 0.597083333333, 0.11625, 0.45...
23 212885228 2463545257346113536 [0.337386363636, 0.499527272727, 0.14955, 0.88... [0.338181818182, 0.236090909091, 0.19559090909...
24 212885228 7804814445850251264 [0.33618, 0.492032, 0.142464, 0.862412, 0.2442... [0.36532, 0.23976, 0.17448, 0.84404, 0.1268, 0...
25 239775508 4070767462512406529 [0.373508695652, 0.579843478261, 0.14540434782... [0.332304347826, 0.491826086957, 0.13821739130...
26 243514462 6926894069614792705 [0.309395238095, 0.494176190476, 0.10145238095... [0.239904761905, 0.426571428571, 0.11971428571...
27 258441899 2895890821597048832 [0.355970588235, 0.575739215686, 0.14691960784... [0.433843137255, 0.566431372549, 0.13754901960...
28 262534611 2474241306483101696 [0.416653846154, 0.496253846154, 0.14941538461... [0.283692307692, 0.298538461538, 0.19176923076...
29 262534611 4210379050958020608 [0.398769230769, 0.506161538462, 0.15313846153... [0.280846153846, 0.284384615385, 0.19115384615...
... ... ... ... ...
472 3994880652 456065900700315688 [0.48564516129, 0.705732258065, 0.105722580645... [0.461290322581, 0.833967741935, 0.12964516129...
473 3994880652 2885476247461195776 [0.475808333333, 0.667225, 0.107370833333, 0.8... [0.46425, 0.7905, 0.144583333333, 1.0400833333...
474 4002116091 2818203727958560768 [0.407392592593, 0.561303703704, 0.10495555555... [0.384851851852, 0.700592592593, 0.11062962963...
475 4014406455 80578231855935488 [0.365156962025, 0.864510126582, 0.06407594936... [0.281113924051, 0.863215189873, 0.05581012658...
476 4024335256 1397036625065005056 [0.362943243243, 0.56822972973, 0.122794594595... [0.369027027027, 0.673891891892, 0.13197297297...
477 4034766298 2831996001169256448 [0.390736363636, 0.559842424242, 0.13051515151... [0.377363636364, 0.547575757576, 0.11406060606...
478 4042991439 82830090185703432 [0.281556862745, 0.67712745098, 0.113092156863... [0.251019607843, 0.758392156863, 0.14317647058...
479 4049063975 4328035596179238913 [0.311107843137, 0.525758823529, 0.14049803921... [0.241254901961, 0.474588235294, 0.17821568627...
480 4058255126 1400132849808887809 [0.312129230769, 0.579538461538, 0.14852615384... [0.205753846154, 0.619523076923, 0.13033846153...
481 4104565142 938513825156079641 [0.384342857143, 0.579791428571, 0.12702, 0.86... [0.328514285714, 0.504114285714, 0.11591428571...
482 4124119438 4411070714708987927 [0.1606, 0.797983333333, 0.0895833333333, 0.39... [0.105566666667, 0.6367, 0.114166666667, 0.294...
483 4166653911 2815951928211386368 [0.356520588235, 0.491788235294, 0.12665, 0.89... [0.269352941176, 0.352617647059, 0.14029411764...
484 4166653911 9039645182176481286 [0.340858333333, 0.507383333333, 0.104575, 0.9... [0.321666666667, 0.334583333333, 0.17108333333...
485 4170254595 357901107539985 [0.383436842105, 0.558273684211, 0.13532631578... [0.361789473684, 0.560842105263, 0.08310526315...
486 4170254595 2835655176583352320 [0.371433333333, 0.550511111111, 0.12841111111... [0.347388888889, 0.577555555556, 0.088, 1.0701...
487 4181920381 2842129100334465024 [0.382138461538, 0.584433333333, 0.11675128205... [0.578153846154, 0.488512820513, 0.10517948717...
488 4181920381 7067349984266096640 [0.38158974359, 0.57678974359, 0.114741025641,... [0.579487179487, 0.534205128205, 0.11153846153...
489 4187483197 4209253151051174020 [0.357972340426, 0.519221276596, 0.14021276595... [0.349489361702, 0.363063829787, 0.14553191489...
490 4187980304 2463545257346113536 [0.332037037037, 0.525759259259, 0.14025925925... [0.324888888889, 0.458888888889, 0.12962962963...
491 4187980304 2595275618659840000 [0.349644444444, 0.622133333333, 0.13476666666... [0.297111111111, 0.473555555556, 0.14222222222...
492 4187980304 2835936651544625153 [0.3659375, 0.6187875, 0.13185, 0.889525, 0.23... [0.310625, 0.4585, 0.15875, 1.491375, 0.360625...
493 4209887493 74104320184119307 [0.243525, 0.81649375, 0.03629375, 0.62634375,... [0.1970625, 0.8528125, 0.0219375, 0.5444375, 0...
494 4209887493 438895881520357521 [0.243525, 0.81649375, 0.03629375, 0.62634375,... [0.1944375, 0.73625, 0.0136875, 0.537125, 0.18...
495 4209887493 453532580309307392 [0.232555555556, 0.798638888889, 0.03438333333... [0.182722222222, 0.882666666667, 0.02222222222...
496 4214766792 4617954825743179776 [0.352743333333, 0.634563333333, 0.12076666666... [0.319166666667, 0.4516, 0.1445, 1.0819, 0.238...
497 4221824221 440866242101010448 [0.281754545455, 0.464063636364, 0.11341818181... [0.178909090909, 0.380272727273, 0.05381818181...
498 4221824221 4413885464477257729 [0.284281818182, 0.487518181818, 0.10958181818... [0.177454545455, 0.458090909091, 0.05527272727...
499 4222905911 298721359433613314 [0.41416, 0.5410925, 0.129385, 0.81894, 0.2228... [0.36455, 0.425325, 0.1382, 0.925075, 0.21945,...
500 4237674937 4132691957822554112 [0.34906875, 0.52245, 0.04779375, 0.400721875,... [0.35465625, 0.4585625, 0.03425, 0.402, 0.1338...
501 4290326024 2890824271308562432 [0.379382608696, 0.494604347826, 0.16191304347... [0.309347826087, 0.423086956522, 0.15504347826...

502 rows × 4 columns


In [49]:
#user_fea.to_pickle('user_fea.pkl')

In [3]:
user_fea = pd.read_pickle('user_fea.pkl')

In [8]:
user_fea.head()


Out[8]:
user_id buy_spu ave_CF_fea ave_view_fea ave_CF_sim_rank ave_Ori_sim_rank
0 3440325 2898705571343994880 [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ... ([0.799214495018], [215.0]) ([0.811977551064], [115.0])
1 7052311 7957699990364366 [0.307052173913, 0.738013043478, 0.06435217391... [0.320739130435, 0.577217391304, 0.088, 0.6661... ([0.745014365081], [1155.0]) ([0.73996321102], [1112.0])
2 7052311 299847271351230626 [0.306652173913, 0.7439, 0.0624434782609, 0.60... [0.314217391304, 0.620695652174, 0.08865217391... ([0.833736325406], [173.0]) ([0.855587371153], [66.0])
3 7052311 448747521265074449 [0.306817391304, 0.735182608696, 0.06435217391... [0.323086956522, 0.60552173913, 0.088, 0.653, ... ([0.854382169198], [49.0]) ([0.853929200306], [84.0])
4 9254280 3925244893201649664 [0.385093939394, 0.623651515152, 0.12763030303... [0.356484848485, 0.575484848485, 0.11351515151... ([0.730978541607], [1427.0]) ([0.752675987128], [535.0])

In [24]:
(float(user_fea['ave_CF_sim_rank'][0][1]))


Out[24]:
215.0

In [25]:
user_fea['ave_CF_sim'] = user_fea.apply(lambda x: float(x['ave_CF_sim_rank'][0]), axis = 1)

In [27]:
user_fea['ave_CF_rank'] = user_fea.apply(lambda x: float(x['ave_CF_sim_rank'][1]), axis = 1)

In [28]:
user_fea['ave_Ori_sim'] = user_fea.apply(lambda x: float(x['ave_Ori_sim_rank'][0]), axis = 1)

In [29]:
user_fea['ave_Ori_rank'] = user_fea.apply(lambda x: float(x['ave_Ori_sim_rank'][1]), axis = 1)

In [33]:
#user_fea.to_pickle('user_fea.pkl')

In [34]:
user_fea.head()


Out[34]:
user_id buy_spu ave_CF_fea ave_view_fea ave_CF_sim_rank ave_Ori_sim_rank ave_CF_sim ave_CF_rank ave_Ori_sim ave_Ori_rank
0 3440325 2898705571343994880 [0.327609375, 0.573775, 0.11804375, 0.87390625... [0.30521875, 0.5995625, 0.167625, 0.99259375, ... ([0.799214495018], [215.0]) ([0.811977551064], [115.0]) 0.799214 215.0 0.811978 115.0
1 7052311 7957699990364366 [0.307052173913, 0.738013043478, 0.06435217391... [0.320739130435, 0.577217391304, 0.088, 0.6661... ([0.745014365081], [1155.0]) ([0.73996321102], [1112.0]) 0.745014 1155.0 0.739963 1112.0
2 7052311 299847271351230626 [0.306652173913, 0.7439, 0.0624434782609, 0.60... [0.314217391304, 0.620695652174, 0.08865217391... ([0.833736325406], [173.0]) ([0.855587371153], [66.0]) 0.833736 173.0 0.855587 66.0
3 7052311 448747521265074449 [0.306817391304, 0.735182608696, 0.06435217391... [0.323086956522, 0.60552173913, 0.088, 0.653, ... ([0.854382169198], [49.0]) ([0.853929200306], [84.0]) 0.854382 49.0 0.853929 84.0
4 9254280 3925244893201649664 [0.385093939394, 0.623651515152, 0.12763030303... [0.356484848485, 0.575484848485, 0.11351515151... ([0.730978541607], [1427.0]) ([0.752675987128], [535.0]) 0.730979 1427.0 0.752676 535.0

In [36]:
(user_fea['ave_CF_sim']).mean()


Out[36]:
0.7904818444503197

In [37]:
(user_fea['ave_Ori_sim']).mean()


Out[37]:
0.8059018725238667

In [38]:
(user_fea['ave_CF_rank']).mean()


Out[38]:
612.9760956175298

In [39]:
(user_fea['ave_Ori_rank']).mean()


Out[39]:
313.0517928286853

In [68]:
sns.distplot(user_fea[['ave_CF_sim']], bins = 100, color = 'red', hist =False, label = 'CF')
sns.distplot(user_fea[['ave_Ori_sim']], bins = 100, color = 'blue', hist =False, label = 'Ori')


Out[68]:
<matplotlib.axes._subplots.AxesSubplot at 0x12734da90>

In [69]:
sns.distplot(user_fea[['ave_CF_rank']], bins = 100,color = 'red', hist =False, label = 'CF')
sns.distplot(user_fea[['ave_Ori_rank']], bins = 100,color = 'blue', hist =False, label = 'Ori')


Out[69]:
<matplotlib.axes._subplots.AxesSubplot at 0x1274c4d10>

In [ ]: