-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathdataAnalys.py
213 lines (180 loc) · 8.15 KB
/
dataAnalys.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
import pandas as pd
import json
import numpy as np
import codecs
import glob
import os
def parsed_tracks_train():
file_name='./data/train/*.json'
files = glob.glob(file_name)
Feature_playlist = ['collaborative', 'duration_ms', 'modified_at',
'name', 'num_albums', 'num_artists', 'num_edits',
'num_followers', 'num_tracks', 'pid']
Feature_tracks = ['album_name', 'album_uri', 'artist_name', 'artist_uri',
'duration_ms', 'track_name', 'track_uri']
Feature_playlist_test = ['name', 'num_holdouts', 'num_samples', 'num_tracks', 'pid']
if not os.path.exists('./new_data'):
os.makedirs('./new_data')
tracks_all=[]
pl_track_map=[]#[pid,tid,pos]
pl_all=[]
c=1
for file in files[:]:#
print(c)
data = json.load(open(file))
df = pd.DataFrame(data["playlists"])
track_list=df[["tracks"]].values.tolist()
pid_list=df[["pid"]].values.tolist()
pl_all.append(df[Feature_playlist])
track_list_flatten=[]
for i,pid in zip(track_list,pid_list):
for j in i[0]:
track_list_flatten.append(j)
pl_track_map.append([pid[0],j['track_uri'],j['pos']])
df_tracks=pd.DataFrame(track_list_flatten)
df_tracks=df_tracks[Feature_tracks]
df_tracks_unique=df_tracks.drop_duplicates(subset=['track_uri'])
tracks_all.append(df_tracks_unique)
if c%200==0:
df_tracks_all_tem=pd.concat(tracks_all,ignore_index=True).drop_duplicates(subset=['track_uri'])
df_tracks_all_tem.to_csv("./new_data/track%s.csv"%c, index = None)
df_tracks_all_tem=None
tracks_all=[]
c+=1
df_tracks_all=comple_tracks()
if bool(tracks_all):
df_tracks_all=pd.concat([df_tracks_all]+tracks_all,ignore_index=True)
df_tracks_all=df_tracks_all.drop_duplicates(subset=['track_uri'],ignore_index=True)
df_pl_track_map=pd.DataFrame(pl_track_map,columns=['pid', 'tid', 'pos'])
df_pl_all=pd.concat(pl_all,axis=0,ignore_index=True)
df_pl_all['collaborative'] = df_pl_all['collaborative'].map({'false': 0, 'true': 1})
df_tracks_all['tid'] = df_tracks_all.index
track_uri2tid = df_tracks_all.set_index('track_uri').tid
df_pl_track_map.tid = df_pl_track_map.tid.map(track_uri2tid)
df_tracks_all.to_csv("./new_data/tracks.csv", index = None)
df_pl_track_map.to_csv("./new_data/pl_track_map.csv", index = None)
df_pl_all.to_csv("./new_data/play_list.csv", index = None)
return track_uri2tid
def comple_tracks():
file_cname='./new_data/track*00.csv'
files_c=glob.glob(file_cname)
print(files_c)
tracks_all=[]
for i in files_c[:]:
tem=pd.read_csv(i)
tracks_all.append(tem)
df_tracks_all=pd.concat(tracks_all)
df_tracks_all=df_tracks_all.drop_duplicates(subset=['track_uri'],ignore_index=True)
df_tracks_all.reset_index(drop=True)
return df_tracks_all
def parse_tracks_val():
file_name="./data/test/challenge_set.json"
files = glob.glob(file_name)
Feature_test = ['name', 'num_holdouts', 'num_samples', 'num_tracks', 'pid']
Feature_playlist = ['collaborative', 'duration_ms', 'modified_at',
'name', 'num_albums', 'num_artists', 'num_edits',
'num_followers', 'num_tracks', 'pid']
Feature_tracks = ['album_name', 'album_uri', 'artist_name', 'artist_uri',
'duration_ms', 'track_name', 'track_uri']
if not os.path.exists('./new_data'):
os.makedirs('./new_data')
tracks_all=[]
pl_track_map=[]#[pid,tid,pos]
pl_all=[]
data = json.load(open(files[0]))
df_pl = pd.DataFrame(data["playlists"])
print(df_pl)
test=df_pl[Feature_test]
df_playlists_test = pd.DataFrame(test, columns=Feature_test)#playlist_test是这个
track_list=df_pl[["tracks"]].values.tolist()
pid_list=df_pl[["pid"]].values.tolist()
#----------------
track_list_flatten=[]
for i,pid in zip(track_list,pid_list):
if bool(i[0]):
for j in i[0]:
track_list_flatten.append(j)
pl_track_map.append([pid[0],j['track_uri'],j['pos']])
df_pl_track_map=pd.DataFrame(pl_track_map,columns=['pid', 'tid', 'pos'])
df_tracks=pd.DataFrame(track_list_flatten)
df_tracks=df_tracks[Feature_tracks]
df_tracks_unique=df_tracks.drop_duplicates(subset=['track_uri'])
df_tracks_all=pd.concat([pd.read_csv("./new_data/tracks.csv"),df_tracks_unique]).drop_duplicates(subset=['track_uri'])
df_tracks_all['tid'] = df_tracks_all.index
df_tracks_all.to_csv("./new_data/tracks.csv", index = None)
track_uri2tid = df_tracks_all.set_index('track_uri').tid
df_pl_track_map.tid = df_pl_track_map.tid.map(track_uri2tid)
df_playlists_test.to_csv("./new_data/play_list_test.csv", index = None)
df_pl_track_map.to_csv("./new_data/pl_track_map_test.csv", index = None)
def get_tracks_all():
'''
get all tracks including testset
'''
Feature_tracks = ['album_name', 'album_uri', 'artist_name', 'artist_uri',
'duration_ms', 'track_name', 'track_uri']
file_name='./data/train/*.json'
files = ["./data/test/challenge_set.json"]+glob.glob(file_name)
print(files[:5])
Feature_playlist = ['collaborative', 'duration_ms', 'modified_at',
'name', 'num_albums', 'num_artists', 'num_edits',
'num_followers', 'num_tracks', 'pid']
Feature_tracks = ['album_name', 'album_uri', 'artist_name', 'artist_uri',
'duration_ms', 'track_name', 'track_uri']
Feature_playlist_test = ['name', 'num_holdouts', 'num_samples', 'num_tracks', 'pid']
tracks_all=[]
tracks_uri_visited=set()
pl_track_map=[]#[pid,tid,pos]
c=1
for file in files[:]:#
print(c)
data = json.load(open(file))
df = pd.DataFrame(data["playlists"])
track_list=df[["tracks"]].values.tolist()
for i in track_list:
for j in i[0]:
if j["track_uri"] not in tracks_uri_visited:
tracks_all.append(j)
tracks_uri_visited.add(j["track_uri"])
c+=1
# df_tracks=pd.DataFrame(track_list_flatten)
# df_tracks=df_tracks[Feature_tracks]
df_tracks = pd.DataFrame(tracks_all)[Feature_tracks]
df_tracks['tid'] = df_tracks.index
print(df_tracks.shape)
df_tracks.to_csv("./new_data/tracks.csv", index = None)
def get_map():
df_tracks_all=pd.read_csv("./new_data/tracks.csv")
# file_name='./data/train/*.json'
# files = glob.glob(file_name)
# Feature_playlist = ['collaborative', 'duration_ms', 'modified_at',
# 'name', 'num_albums', 'num_artists', 'num_edits',
# 'num_followers', 'num_tracks', 'pid']
# Feature_tracks = ['album_name', 'album_uri', 'artist_name', 'artist_uri',
# 'duration_ms', 'track_name', 'track_uri']
# Feature_playlist_test = ['name', 'num_holdouts', 'num_samples', 'num_tracks', 'pid']
# pl_track_map=[]#[pid,tid,pos]
# c=1
# for file in files[:]:#
# print(c)
# data = json.load(open(file))
# df = pd.DataFrame(data["playlists"])
# track_list=df[["tracks"]].values.tolist()
# pid_list=df[["pid"]].values.tolist()
# for i,pid in zip(track_list,pid_list):
# for j in i[0]:
# pl_track_map.append([pid[0],j['track_uri'],j['pos']])
# c+=1
# df_pl_track_map=pd.DataFrame(pl_track_map,columns=['pid', 'tid', 'pos'])
# df_pl_track_map.to_csv("./new_data/df_pl_track_map_uri.csv", index = None)
df_pl_track_map=pd.read_csv("./new_data/df_pl_track_map_uri.csv")
df_tracks_all['tid'] = df_tracks_all.index
track_uri2tid = df_tracks_all.set_index('track_uri').tid
df_pl_track_map.tid = df_pl_track_map.tid.map(track_uri2tid)
print(df_pl_track_map.tid.isna().sum())
df_pl_track_map.to_csv("./new_data/pl_track_map.csv", index = None)
return track_uri2tid
if __name__=='__main__':
# parsed_tracks_train()
# parse_tracks_val()
# get_tracks_all()
get_map()