In [1]:
import pandas as pd
import numpy as np
import os
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
In [2]:
os.chdir('/Users/Walkon302/Desktop/deep-learning-models-master/view2buy')
In [3]:
# Read the preprocessed file, containing the user profile and item features from view2buy folder
df = pd.read_pickle('user_fea_for_eval.pkl')
In [4]:
df.head()
Out[4]:
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
0
2469583035\t4199682998971011301\t10013436\t334...
2469583035
4199682998971011301
10013436
334
220189917005230097
10013861
334
37496
7
45
[0.621, 0.542, 0.0, 0.369, 0.062, 0.039, 0.103...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
1
2469583035\t4199682998971011301\t10013436\t334...
2469583035
4199682998971011301
10013436
334
234826617504419925
10003862
334
170826
2
23
[0.15, 0.98, 0.104, 1.295, 0.111, 0.0, 0.0, 0....
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
2
2469583035\t4199682998971011301\t10013436\t334...
2469583035
4199682998971011301
10013436
334
235671027621670949
10003862
334
426968
2
11
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
3
1488725183\t4199682998971011301\t10013436\t334...
1488725183
4199682998971011301
10013436
334
235671027621670949
10003862
334
180564
1
22
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
4
2469583035\t4199682998971011301\t10013436\t334...
2469583035
4199682998971011301
10013436
334
245522675097001998
10026364
334
83993
2
7
[0.019, 1.415, 0.007, 0.088, 0.055, 0.015, 0.0...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
In [5]:
# Drop the first column, which is the original data format.
df.drop('0', axis = 1, inplace = True)
In [6]:
# Check the data
df.head()
Out[6]:
user_id
buy_spu
buy_sn
buy_ct3
view_spu
view_sn
view_ct3
time_interval
view_cnt
view_secondes
view_features
buy_features
0
2469583035
4199682998971011301
10013436
334
220189917005230097
10013861
334
37496
7
45
[0.621, 0.542, 0.0, 0.369, 0.062, 0.039, 0.103...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
1
2469583035
4199682998971011301
10013436
334
234826617504419925
10003862
334
170826
2
23
[0.15, 0.98, 0.104, 1.295, 0.111, 0.0, 0.0, 0....
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
2
2469583035
4199682998971011301
10013436
334
235671027621670949
10003862
334
426968
2
11
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
3
1488725183
4199682998971011301
10013436
334
235671027621670949
10003862
334
180564
1
22
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
4
2469583035
4199682998971011301
10013436
334
245522675097001998
10026364
334
83993
2
7
[0.019, 1.415, 0.007, 0.088, 0.055, 0.015, 0.0...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
In [7]:
# Remove the item that contains the seam buy and view spu to remove the bias
#df = df.query('buy_spu != view_spu')
In [8]:
# Slice the data into 10k items
df = df.iloc[0:10000, :]
In [9]:
# Calculate the average view features for all view items per user
avg_view_fea = pd.DataFrame(df.groupby(['user_id', 'buy_spu'])['view_secondes'].mean())
In [10]:
# Reset the index and rename the column
avg_view_fea.reset_index(inplace=True)
avg_view_fea.rename(columns = {'view_secondes':'avg_view_fea'}, inplace=True)
In [11]:
# Check the data
avg_view_fea.head()
Out[11]:
user_id
buy_spu
avg_view_fea
0
814009
77763563263074335
13.436364
1
1165283
77200616039542809
21.625000
2
9873479
77200616039542809
34.863636
3
63236390
292247525162119174
19.736842
4
76700950
95777984703225857
155.000000
In [12]:
# Merge avg item view into data
df = pd.merge(df, avg_view_fea, on=['user_id', 'buy_spu'])
In [13]:
# Calculate the weights for view item vec
df['weight_of_view'] = df['view_secondes']/df['avg_view_fea']
In [14]:
df.head()
Out[14]:
user_id
buy_spu
buy_sn
buy_ct3
view_spu
view_sn
view_ct3
time_interval
view_cnt
view_secondes
view_features
buy_features
avg_view_fea
weight_of_view
0
2469583035
4199682998971011301
10013436
334
220189917005230097
10013861
334
37496
7
45
[0.621, 0.542, 0.0, 0.369, 0.062, 0.039, 0.103...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
1.299352
1
2469583035
4199682998971011301
10013436
334
234826617504419925
10003862
334
170826
2
23
[0.15, 0.98, 0.104, 1.295, 0.111, 0.0, 0.0, 0....
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.664113
2
2469583035
4199682998971011301
10013436
334
235671027621670949
10003862
334
426968
2
11
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.317619
3
2469583035
4199682998971011301
10013436
334
245522675097001998
10026364
334
83993
2
7
[0.019, 1.415, 0.007, 0.088, 0.055, 0.015, 0.0...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.202121
4
2469583035
4199682998971011301
10013436
334
296751124749754369
10005367
334
427866
2
12
[0.066, 0.328, 0.043, 0.0, 0.062, 0.016, 0.303...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.346494
In [34]:
# Generate view_item_vec and buy_item_vec
view_buy_item_fea = pd.concat([df['view_features'], df['buy_features']], axis = 0)
In [36]:
view_buy_item_fea.shape
Out[36]:
(20000,)
In [ ]:
# Generate TSNE model
model = TSNE(n_components=10, random_state=0)
In [121]:
# Time the tSNE with 250 samples
%%time
a = pd.DataFrame()
for i, j in enumerate(view_item_vec.iloc[0:250]):
a = pd.concat([a, pd.DataFrame(j).transpose()], axis = 0)
vt = model.fit_transform(a)
CPU times: user 22.3 s, sys: 501 ms, total: 22.8 s
Wall time: 22.8 s
In [114]:
# Time the tSNE with 500 samples
%%time
a = pd.DataFrame()
for i, j in enumerate(view_item_vec.iloc[0:500]):
a = pd.concat([a, pd.DataFrame(j).transpose()], axis = 0)
vt = model.fit_transform(a)
CPU times: user 1min 23s, sys: 2.57 s, total: 1min 25s
Wall time: 1min 31s
In [113]:
# Time the tSNE with 1000 samples
%%time
a = pd.DataFrame()
for i, j in enumerate(view_item_vec.iloc[0:1000]):
a = pd.concat([a, pd.DataFrame(j).transpose()], axis = 0)
vt = model.fit_transform(a)
CPU times: user 4min 25s, sys: 6.05 s, total: 4min 31s
Wall time: 4min 33s
In [37]:
# Generate TSNE model
model = PCA(n_components=200, random_state=0)
In [38]:
%%time
view_item = []
for i in view_buy_item_fea:
view_item.append(i)
view_item= np.array(view_item)
CPU times: user 1.17 s, sys: 542 ms, total: 1.72 s
Wall time: 1.77 s
In [39]:
%%time
pca_view_vec = model.fit_transform(view_item)
CPU times: user 11 s, sys: 789 ms, total: 11.8 s
Wall time: 6.75 s
In [40]:
# 200 dimensions of PCA can explain 85% of variables. Beyond that, e.g., 300 D, my computer will run out of memory (8g)
sum(model.explained_variance_ratio_)
Out[40]:
0.90980608640901406
In [41]:
# Incert pca result to data
df['pca_view'] = pca_view_vec[0:10000].tolist()
df['pca_buy'] = pca_view_vec[10000:20000].tolist()
In [42]:
# Check the data
df.head()
Out[42]:
user_id
buy_spu
buy_sn
buy_ct3
view_spu
view_sn
view_ct3
time_interval
view_cnt
view_secondes
view_features
buy_features
avg_view_fea
weight_of_view
pca_view
pca_buy
0
2469583035
4199682998971011301
10013436
334
220189917005230097
10013861
334
37496
7
45
[0.621, 0.542, 0.0, 0.369, 0.062, 0.039, 0.103...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
1.299352
[-4.18441352754, -4.98522684557, 7.40010898649...
[-2.45874875255, 0.950284632032, 5.98234076728...
1
2469583035
4199682998971011301
10013436
334
234826617504419925
10003862
334
170826
2
23
[0.15, 0.98, 0.104, 1.295, 0.111, 0.0, 0.0, 0....
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.664113
[3.83780037191, 7.88132568231, 0.937903291471,...
[-2.45874875255, 0.950284632032, 5.98234076728...
2
2469583035
4199682998971011301
10013436
334
235671027621670949
10003862
334
426968
2
11
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.317619
[7.77165030033, 7.62261939761, -0.895622345806...
[-2.45874875255, 0.950284632032, 5.98234076728...
3
2469583035
4199682998971011301
10013436
334
245522675097001998
10026364
334
83993
2
7
[0.019, 1.415, 0.007, 0.088, 0.055, 0.015, 0.0...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.202121
[-3.22576025396, -4.16373835223, 5.30225410798...
[-2.45874875255, 0.950284632032, 5.98234076728...
4
2469583035
4199682998971011301
10013436
334
296751124749754369
10005367
334
427866
2
12
[0.066, 0.328, 0.043, 0.0, 0.062, 0.016, 0.303...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.346494
[1.43794255292, 10.9324726458, -2.09193256963,...
[-2.45874875255, 0.950284632032, 5.98234076728...
In [44]:
# Check the data
df.head()
Out[44]:
user_id
buy_spu
buy_sn
buy_ct3
view_spu
view_sn
view_ct3
time_interval
view_cnt
view_secondes
view_features
buy_features
avg_view_fea
weight_of_view
pca_view
pca_buy
weighted_view_pca
0
2469583035
4199682998971011301
10013436
334
220189917005230097
10013861
334
37496
7
45
[0.621, 0.542, 0.0, 0.369, 0.062, 0.039, 0.103...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
1.299352
[-4.18441352754, -4.98522684557, 7.40010898649...
[-2.45874875255, 0.950284632032, 5.98234076728...
[-5.43702523761, -6.47756346168, 9.61534491173...
1
2469583035
4199682998971011301
10013436
334
234826617504419925
10003862
334
170826
2
23
[0.15, 0.98, 0.104, 1.295, 0.111, 0.0, 0.0, 0....
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.664113
[3.83780037191, 7.88132568231, 0.937903291471,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.5487336589, 5.23409195283, 0.6228739007, -1...
2
2469583035
4199682998971011301
10013436
334
235671027621670949
10003862
334
426968
2
11
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.317619
[7.77165030033, 7.62261939761, -0.895622345806...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.4684263476, 2.42109125239, -0.284466967819,...
3
2469583035
4199682998971011301
10013436
334
245522675097001998
10026364
334
83993
2
7
[0.019, 1.415, 0.007, 0.088, 0.055, 0.015, 0.0...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.202121
[-3.22576025396, -4.16373835223, 5.30225410798...
[-2.45874875255, 0.950284632032, 5.98234076728...
[-0.651995148561, -0.841580586219, 1.071698974...
4
2469583035
4199682998971011301
10013436
334
296751124749754369
10005367
334
427866
2
12
[0.066, 0.328, 0.043, 0.0, 0.062, 0.016, 0.303...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.346494
[1.43794255292, 10.9324726458, -2.09193256963,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[0.498238197476, 3.78803412829, -0.72484169177...
In [45]:
#df.to_pickle('top10k_user_pca.pkl')
In [46]:
# Define function
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 [109]:
df = pd.read_pickle('top10k_user_pca.pkl')
In [128]:
# Calculate the weighted pca_view
df['weighted_view'] = df.apply(lambda x: [y*x['weight_of_view'] for y in x['buy_features']], axis=1)
In [99]:
df.head()
Out[99]:
user_id
buy_spu
buy_sn
buy_ct3
view_spu
view_sn
view_ct3
time_interval
view_cnt
view_secondes
view_features
buy_features
avg_view_fea
weight_of_view
pca_view
pca_buy
weighted_view_pca
0
2469583035
4199682998971011301
10013436
334
220189917005230097
10013861
334
37496
7
45
[0.621, 0.542, 0.0, 0.369, 0.062, 0.039, 0.103...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
1.299352
[-4.18441352754, -4.98522684557, 7.40010898649...
[-2.45874875255, 0.950284632032, 5.98234076728...
[-5.43702523761, -6.47756346168, 9.61534491173...
1
2469583035
4199682998971011301
10013436
334
234826617504419925
10003862
334
170826
2
23
[0.15, 0.98, 0.104, 1.295, 0.111, 0.0, 0.0, 0....
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.664113
[3.83780037191, 7.88132568231, 0.937903291471,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.5487336589, 5.23409195283, 0.6228739007, -1...
2
2469583035
4199682998971011301
10013436
334
235671027621670949
10003862
334
426968
2
11
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.317619
[7.77165030033, 7.62261939761, -0.895622345806...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.4684263476, 2.42109125239, -0.284466967819,...
3
2469583035
4199682998971011301
10013436
334
245522675097001998
10026364
334
83993
2
7
[0.019, 1.415, 0.007, 0.088, 0.055, 0.015, 0.0...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.202121
[-3.22576025396, -4.16373835223, 5.30225410798...
[-2.45874875255, 0.950284632032, 5.98234076728...
[-0.651995148561, -0.841580586219, 1.071698974...
4
2469583035
4199682998971011301
10013436
334
296751124749754369
10005367
334
427866
2
12
[0.066, 0.328, 0.043, 0.0, 0.062, 0.016, 0.303...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.346494
[1.43794255292, 10.9324726458, -2.09193256963,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[0.498238197476, 3.78803412829, -0.72484169177...
In [110]:
ori_user_fea = df.groupby(['user_id'])['view_features'].apply(lambda x: average(x))
In [111]:
ori_user_fea = pd.DataFrame(ori_user_fea)
In [112]:
ori_user_fea=ori_user_fea.reset_index()
In [113]:
df = pd.merge(df, ori_user_fea, on='user_id')
In [114]:
df.head()
Out[114]:
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
avg_view_fea
weight_of_view
pca_view
pca_buy
weighted_view_pca
view_features_y
0
2469583035
4199682998971011301
10013436
334
220189917005230097
10013861
334
37496
7
45
[0.621, 0.542, 0.0, 0.369, 0.062, 0.039, 0.103...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
1.299352
[-4.18441352754, -4.98522684557, 7.40010898649...
[-2.45874875255, 0.950284632032, 5.98234076728...
[-5.43702523761, -6.47756346168, 9.61534491173...
[0.195346938776, 0.549204081633, 0.08559183673...
1
2469583035
4199682998971011301
10013436
334
234826617504419925
10003862
334
170826
2
23
[0.15, 0.98, 0.104, 1.295, 0.111, 0.0, 0.0, 0....
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.664113
[3.83780037191, 7.88132568231, 0.937903291471,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.5487336589, 5.23409195283, 0.6228739007, -1...
[0.195346938776, 0.549204081633, 0.08559183673...
2
2469583035
4199682998971011301
10013436
334
235671027621670949
10003862
334
426968
2
11
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.317619
[7.77165030033, 7.62261939761, -0.895622345806...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.4684263476, 2.42109125239, -0.284466967819,...
[0.195346938776, 0.549204081633, 0.08559183673...
3
2469583035
4199682998971011301
10013436
334
245522675097001998
10026364
334
83993
2
7
[0.019, 1.415, 0.007, 0.088, 0.055, 0.015, 0.0...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.202121
[-3.22576025396, -4.16373835223, 5.30225410798...
[-2.45874875255, 0.950284632032, 5.98234076728...
[-0.651995148561, -0.841580586219, 1.071698974...
[0.195346938776, 0.549204081633, 0.08559183673...
4
2469583035
4199682998971011301
10013436
334
296751124749754369
10005367
334
427866
2
12
[0.066, 0.328, 0.043, 0.0, 0.062, 0.016, 0.303...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.346494
[1.43794255292, 10.9324726458, -2.09193256963,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[0.498238197476, 3.78803412829, -0.72484169177...
[0.195346938776, 0.549204081633, 0.08559183673...
In [115]:
df.rename(columns = {'view_features_y':'user_features'}, inplace = True)
In [106]:
df.head()
Out[106]:
user_id
buy_spu
buy_sn
buy_ct3
view_spu
view_sn
view_ct3
time_interval
view_cnt
view_secondes
view_features
buy_features
avg_view_fea
weight_of_view
pca_view
pca_buy
weighted_view_pca_x
user_features
0
2469583035
4199682998971011301
10013436
334
220189917005230097
10013861
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37496
7
45
[0.621, 0.542, 0.0, 0.369, 0.062, 0.039, 0.103...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
1.299352
[-4.18441352754, -4.98522684557, 7.40010898649...
[-2.45874875255, 0.950284632032, 5.98234076728...
[-5.43702523761, -6.47756346168, 9.61534491173...
[-0.488648716682, 2.68093043688, 0.09369500442...
1
2469583035
4199682998971011301
10013436
334
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10003862
334
170826
2
23
[0.15, 0.98, 0.104, 1.295, 0.111, 0.0, 0.0, 0....
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.664113
[3.83780037191, 7.88132568231, 0.937903291471,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.5487336589, 5.23409195283, 0.6228739007, -1...
[-0.488648716682, 2.68093043688, 0.09369500442...
2
2469583035
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10013436
334
235671027621670949
10003862
334
426968
2
11
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.317619
[7.77165030033, 7.62261939761, -0.895622345806...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.4684263476, 2.42109125239, -0.284466967819,...
[-0.488648716682, 2.68093043688, 0.09369500442...
3
2469583035
4199682998971011301
10013436
334
245522675097001998
10026364
334
83993
2
7
[0.019, 1.415, 0.007, 0.088, 0.055, 0.015, 0.0...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.202121
[-3.22576025396, -4.16373835223, 5.30225410798...
[-2.45874875255, 0.950284632032, 5.98234076728...
[-0.651995148561, -0.841580586219, 1.071698974...
[-0.488648716682, 2.68093043688, 0.09369500442...
4
2469583035
4199682998971011301
10013436
334
296751124749754369
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334
427866
2
12
[0.066, 0.328, 0.043, 0.0, 0.062, 0.016, 0.303...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.346494
[1.43794255292, 10.9324726458, -2.09193256963,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[0.498238197476, 3.78803412829, -0.72484169177...
[-0.488648716682, 2.68093043688, 0.09369500442...
In [118]:
df['sim'] = df.apply(lambda x: similarity(x['buy_features'], x['user_features']), axis=1)
In [119]:
df.head()
Out[119]:
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
avg_view_fea
weight_of_view
pca_view
pca_buy
weighted_view_pca
user_features
sim
0
2469583035
4199682998971011301
10013436
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220189917005230097
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37496
7
45
[0.621, 0.542, 0.0, 0.369, 0.062, 0.039, 0.103...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
1.299352
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[-5.43702523761, -6.47756346168, 9.61534491173...
[0.195346938776, 0.549204081633, 0.08559183673...
0.801427
1
2469583035
4199682998971011301
10013436
334
234826617504419925
10003862
334
170826
2
23
[0.15, 0.98, 0.104, 1.295, 0.111, 0.0, 0.0, 0....
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.664113
[3.83780037191, 7.88132568231, 0.937903291471,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.5487336589, 5.23409195283, 0.6228739007, -1...
[0.195346938776, 0.549204081633, 0.08559183673...
0.801427
2
2469583035
4199682998971011301
10013436
334
235671027621670949
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334
426968
2
11
[0.106, 0.027, 0.0, 1.398, 0.096, 0.021, 0.072...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.317619
[7.77165030033, 7.62261939761, -0.895622345806...
[-2.45874875255, 0.950284632032, 5.98234076728...
[2.4684263476, 2.42109125239, -0.284466967819,...
[0.195346938776, 0.549204081633, 0.08559183673...
0.801427
3
2469583035
4199682998971011301
10013436
334
245522675097001998
10026364
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83993
2
7
[0.019, 1.415, 0.007, 0.088, 0.055, 0.015, 0.0...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.202121
[-3.22576025396, -4.16373835223, 5.30225410798...
[-2.45874875255, 0.950284632032, 5.98234076728...
[-0.651995148561, -0.841580586219, 1.071698974...
[0.195346938776, 0.549204081633, 0.08559183673...
0.801427
4
2469583035
4199682998971011301
10013436
334
296751124749754369
10005367
334
427866
2
12
[0.066, 0.328, 0.043, 0.0, 0.062, 0.016, 0.303...
[0.091, 0.805, 0.0, 0.591, 0.981, 0.026, 0.757...
34.632653
0.346494
[1.43794255292, 10.9324726458, -2.09193256963,...
[-2.45874875255, 0.950284632032, 5.98234076728...
[0.498238197476, 3.78803412829, -0.72484169177...
[0.195346938776, 0.549204081633, 0.08559183673...
0.801427
In [120]:
df['rank'] = df.groupby('user_id')['sim'].rank(ascending=False)
In [121]:
df
Out[121]:
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
avg_view_fea
weight_of_view
pca_view
pca_buy
weighted_view_pca
user_features
sim
rank
0
2469583035
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37496
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34.632653
1.299352
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2469583035
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170826
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34.632653
0.664113
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2469583035
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34.632653
0.317619
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3
2469583035
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83993
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34.632653
0.202121
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34.632653
0.346494
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10000 rows × 20 columns
In [122]:
float(len(df.query('buy_spu == view_spu & rank <= 6')))/float(len(df.query('buy_spu == view_spu'))) * 100
Out[122]:
35.76642335766424
In [ ]:
Content source: walkon302/CDIPS_Recommender
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