generated from zhanymkanov/fastapi_production_template
-
Notifications
You must be signed in to change notification settings - Fork 11
/
Copy pathcandidates.py
482 lines (411 loc) · 16.1 KB
/
candidates.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
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
from typing import Any
from sqlalchemy import text
from src.database import fetch_all
from src.recommendations.utils import exclude_meme_ids_sql_filter
# "lr" - like rate
# I'm not sure about the naming, will change later
async def sorted_by_user_source_lr_meme_lr_meme_age(
user_id: int,
limit: int = 10,
exclude_meme_ids: list[int] = [],
) -> list[dict[str, Any]]:
query = f"""
SELECT
M.id, M.type, M.telegram_file_id, M.caption,
'sorted_by_user_source_lr_meme_lr_meme_age' as recommended_by
FROM meme M
LEFT JOIN user_meme_reaction R
ON R.user_id = {user_id}
AND R.meme_id = M.id
INNER JOIN user_language L
ON L.language_code = M.language_code
AND L.user_id = {user_id}
LEFT JOIN user_meme_source_stats UMSS
ON UMSS.meme_source_id = M.meme_source_id
AND UMSS.user_id = {user_id}
LEFT JOIN meme_stats MS
ON MS.meme_id = M.id
WHERE 1=1
AND M.status = 'ok'
AND R.meme_id IS NULL
{exclude_meme_ids_sql_filter(exclude_meme_ids)}
ORDER BY -1
* COALESCE((UMSS.nlikes + 1.) / (UMSS.nlikes + UMSS.ndislikes + 1), 0.5)
* COALESCE((MS.nlikes + 1.) / (MS.nlikes + MS.ndislikes + 1), 0.5)
* CASE WHEN MS.raw_impr_rank < 1 THEN 1 ELSE 0.5 END
* CASE WHEN MS.age_days < 5 THEN 1 ELSE 0.5 END
* CASE WHEN M.caption IS NULL THEN 1 ELSE 0.8 END
* CASE
WHEN MS.nmemes_sent <= 1 THEN 1
ELSE (MS.nlikes + MS.ndislikes) * 1. / MS.nmemes_sent
END
LIMIT {limit}
"""
res = await fetch_all(text(query))
return res
# like rate: 24%
async def most_liked(
user_id: int,
limit: int = 10,
exclude_meme_ids: list[int] = [],
) -> list[dict[str, Any]]:
query = f"""
SELECT
M.id, M.type, M.telegram_file_id, M.caption,
'most_liked' as recommended_by
FROM meme M
LEFT JOIN user_meme_reaction R
ON R.user_id = {user_id}
AND R.meme_id = M.id
INNER JOIN user_language L
ON L.language_code = M.language_code
AND L.user_id = {user_id}
LEFT JOIN meme_stats MS
ON MS.meme_id = M.id
WHERE 1=1
AND M.status = 'ok'
AND R.meme_id IS NULL
{exclude_meme_ids_sql_filter(exclude_meme_ids)}
ORDER BY -1
* COALESCE((MS.nlikes + 1.) / (MS.nlikes + MS.ndislikes + 1), 0.5)
* CASE WHEN MS.raw_impr_rank <= 1 THEN 1 ELSE 0.8 END
* CASE WHEN MS.age_days < 5 THEN 1 ELSE 0.8 END
* CASE
WHEN MS.nmemes_sent <= 1 THEN 1
ELSE (MS.nlikes + MS.ndislikes) * 1. / MS.nmemes_sent
END
LIMIT {limit}
"""
res = await fetch_all(text(query))
return res
async def multiply_all_scores(
user_id: int,
limit: int = 10,
exclude_meme_ids: list[int] = [],
) -> list[dict[str, Any]]:
query = f"""
SELECT
M.id, M.type, M.telegram_file_id, M.caption,
'multiply_all_scores' as recommended_by
-- M.id
-- , (UMSS.nlikes + 1.) / (UMSS.nlikes + UMSS.ndislikes + 1) user_source
-- , (MSS.nlikes + 1.) / (MSS.nlikes + MSS.ndislikes + 1) meme_source
-- , (MS.nlikes + 1.) / (MS.nlikes + MS.ndislikes + 1) meme
-- , raw_impr_rank
-- , MS.age_days
-- , (MS.nlikes + MS.ndislikes) * 1. / MS.nmemes_sent meme_click_rate
FROM meme M
LEFT JOIN user_meme_reaction R
ON R.meme_id = M.id
AND R.user_id = {user_id}
INNER JOIN user_language L
ON L.language_code = M.language_code
AND L.user_id = {user_id}
LEFT JOIN user_meme_source_stats UMSS
ON UMSS.meme_source_id = M.meme_source_id
AND UMSS.user_id = {user_id}
LEFT JOIN meme_stats MS
ON MS.meme_id = M.id
LEFT JOIN meme_source_stats MSS
ON MSS.meme_source_id = M.meme_source_id
WHERE 1=1
AND M.status = 'ok'
AND R.meme_id IS NULL -- not seen
{exclude_meme_ids_sql_filter(exclude_meme_ids)}
ORDER BY -1
* COALESCE((UMSS.nlikes + 1.) / (UMSS.nlikes + UMSS.ndislikes + 1), 0.5)
* COALESCE((MSS.nlikes + 1.) / (MSS.nlikes + MSS.ndislikes + 1) , 0.5)
* COALESCE((MS.nlikes + 1.) / (MS.nlikes + MS.ndislikes + 1), 0.5)
* CASE WHEN MS.raw_impr_rank < 1 THEN 1 ELSE 0.5 END
* CASE WHEN MS.age_days < 5 THEN 1 ELSE 0.5 END
* CASE WHEN M.caption IS NULL THEN 1 ELSE 0.8 END
* CASE
WHEN MS.nmemes_sent <= 1 THEN 1
ELSE (MS.nlikes + MS.ndislikes) * 1. / MS.nmemes_sent
END
LIMIT {limit}
"""
res = await fetch_all(text(query))
return res
async def less_seen_meme_and_source(
user_id: int,
limit: int = 10,
exclude_meme_ids: list[int] = [],
) -> list[dict[str, Any]]:
query = f"""
WITH LESS_SEEN_MEME_FROM_EACH_SOURCE AS (
SELECT DISTINCT ON (M.meme_source_id)
M.id, M.type, M.telegram_file_id, M.caption,
'less_seen_meme_and_source' as recommended_by
, M.meme_source_id
, COALESCE(MS.nmemes_sent, 0) meme_sent_times
, COALESCE(UMSS.nlikes + UMSS.ndislikes, 0) source_seen_times
FROM meme M
LEFT JOIN user_meme_reaction R
ON R.user_id = {user_id}
AND R.meme_id = M.id
INNER JOIN user_language L
ON L.language_code = M.language_code
AND L.user_id = {user_id}
LEFT JOIN meme_stats MS
ON MS.meme_id = M.id
LEFT JOIN user_meme_source_stats UMSS
ON UMSS.meme_source_id = M.meme_source_id
AND UMSS.user_id = {user_id}
WHERE 1=1
AND M.status = 'ok'
AND R.meme_id IS NULL
{exclude_meme_ids_sql_filter(exclude_meme_ids)}
ORDER BY
M.meme_source_id, -- one meme from each source
meme_sent_times
)
SELECT
id, type, telegram_file_id, caption, recommended_by
FROM LESS_SEEN_MEME_FROM_EACH_SOURCE
ORDER BY source_seen_times -- less seen sources
LIMIT {limit}
"""
res = await fetch_all(text(query))
return res
# like rate: 38%
async def top_memes_from_less_seen_sources(
user_id: int,
limit: int = 10,
exclude_meme_ids: list[int] = [],
) -> list[dict[str, Any]]:
query = f"""
WITH LESS_SEEN_SOURCES AS (
SELECT
MS.id,
COALESCE(UMSS.nlikes + UMSS.ndislikes, 0) / 10 seen_times
FROM meme_source MS
LEFT JOIN user_meme_source_stats UMSS
ON MS.id = UMSS.meme_source_id
AND UMSS.user_id = {user_id}
ORDER BY seen_times
)
SELECT
M.id
, M.type, M.telegram_file_id
, M.caption, M.recommended_by
FROM (
SELECT DISTINCT ON (M.meme_source_id)
M.id, M.type, M.telegram_file_id, M.caption,
'less_seen_sources' as recommended_by,
1
* CASE WHEN MS.raw_impr_rank <= 1 THEN 1 ELSE 0.5 END
* CASE WHEN MS.age_days < 5 THEN 1 ELSE 0.5 END
* COALESCE((MS.nlikes+1.) / (MS.nlikes+MS.ndislikes+1), 0.5)
* COALESCE((MSS.nlikes+1.) / (MSS.nlikes+MSS.ndislikes+1), 0.5)
AS score,
M.meme_source_id
FROM meme M
LEFT JOIN user_meme_reaction R
ON R.meme_id = M.id
AND R.user_id = {user_id}
INNER JOIN user_language L
ON L.user_id = {user_id}
AND L.language_code = M.language_code
LEFT JOIN meme_stats MS
ON MS.meme_id = M.id
LEFT JOIN meme_source_stats MSS
ON MSS.meme_source_id = M.meme_source_id
WHERE 1=1
AND M.status = 'ok'
AND R.meme_id IS NULL
{exclude_meme_ids_sql_filter(exclude_meme_ids)}
ORDER BY M.meme_source_id, score
) M
INNER JOIN LESS_SEEN_SOURCES
ON LESS_SEEN_SOURCES.id = M.meme_source_id
ORDER BY seen_times, score DESC
LIMIT {limit}
"""
res = await fetch_all(text(query))
return res
async def classic(
user_id: int,
limit: int = 10,
exclude_meme_ids: list[int] = [],
):
query = f"""
SELECT
M.id
, M.type, M.telegram_file_id, M.caption
, 'classic' AS recommended_by
FROM meme M
INNER JOIN meme_stats MS
ON MS.meme_id = M.id
INNER JOIN user_language L
ON L.language_code = M.language_code
AND L.user_id = {user_id}
LEFT JOIN user_meme_reaction R
ON R.meme_id = M.id
AND R.user_id = {user_id}
LEFT JOIN user_meme_source_stats UMSS
ON UMSS.meme_source_id = M.meme_source_id
AND UMSS.user_id = {user_id}
WHERE 1=1
AND M.status = 'ok'
AND R.meme_id IS NULL
AND MS.nlikes > 1
{exclude_meme_ids_sql_filter(exclude_meme_ids)}
ORDER BY -1
* (UMSS.nlikes + 1.) / (UMSS.nlikes + UMSS.ndislikes + 1.)
* (MS.nlikes + 1.) / (MS.nlikes + MS.ndislikes + 1.)
LIMIT {limit}
"""
res = await fetch_all(text(query))
return res
async def like_spread_and_recent_memes(
user_id: int,
limit: int = 10,
exclude_meme_ids: list[int] = [],
):
query = f"""
SELECT
M.id
, M.type, M.telegram_file_id, M.caption
, 'like_spread_and_recent' AS recommended_by
-- , MS.nlikes, MS.ndislikes
FROM meme M
INNER JOIN meme_stats MS
ON MS.meme_id = M.id
INNER JOIN user_language L
ON L.user_id = {user_id}
AND L.language_code = M.language_code
LEFT JOIN user_meme_reaction R
ON R.meme_id = M.id
AND R.user_id = {user_id}
WHERE 1=1
AND M.status = 'ok'
AND R.meme_id IS NULL
AND MS.nlikes > MS.ndislikes
AND MS.raw_impr_rank = 0
AND age_days < 30
{exclude_meme_ids_sql_filter(exclude_meme_ids)}
ORDER BY -1
* (MS.nlikes - MS.ndislikes) / (MS.nmemes_sent + 1.)
* CASE WHEN MS.age_days < 30 THEN 1 ELSE 0.5 END
LIMIT {limit}
"""
res = await fetch_all(text(query))
return res
async def get_best_memes_from_each_source(
user_id: int,
limit: int = 10,
exclude_meme_ids: list[int] = [],
) -> list[dict[str, Any]]:
query = f"""
SELECT
M.id
, M.type, M.telegram_file_id, M.caption, M.recommended_by
FROM (
SELECT DISTINCT ON (M.meme_source_id)
M.id, M.type, M.telegram_file_id, M.caption,
'best_meme_from_each_source' as recommended_by,
1
* CASE WHEN MS.raw_impr_rank <= 1 THEN 1 ELSE 0.8 END
* CASE WHEN MS.age_days < 14 THEN 1 ELSE 0.8 END
* COALESCE((MS.nlikes+1.) / (MS.nlikes+MS.ndislikes+1), 0.5)
* CASE
WHEN MS.nmemes_sent <= 1 THEN 1
ELSE (MS.nlikes + MS.ndislikes) * 1. / MS.nmemes_sent
END
AS score
FROM meme M
LEFT JOIN user_meme_reaction R
ON R.meme_id = M.id
AND R.user_id = {user_id}
INNER JOIN user_language L
ON L.user_id = {user_id}
AND L.language_code = M.language_code
LEFT JOIN meme_stats MS
ON MS.meme_id = M.id
WHERE 1=1
AND M.status = 'ok'
AND R.meme_id IS NULL
{exclude_meme_ids_sql_filter(exclude_meme_ids)}
ORDER BY M.meme_source_id, score DESC
) M
ORDER BY score DESC
LIMIT {limit}
"""
res = await fetch_all(text(query))
return res
async def get_random_best(
user_id: int,
limit: int = 10,
exclude_meme_ids: list[int] = [],
):
"""Selects 'limit' best memes from the top 100 memes obtained using the cleared
meme statistics. The cleared statistics aggregates reactions from users with
less than 200 previous reactions. It's aim is to remove the bias from old users
"""
query = f"""
SELECT
M.id
, M.type, M.telegram_file_id, M.caption, M.recommended_by
FROM (
SELECT
M.id
, M.type, M.telegram_file_id, M.caption
, 'random_best_ab_240422' AS recommended_by
, random() rand
FROM meme M
INNER JOIN meme_stats MS
ON MS.meme_id = M.id
INNER JOIN user_language L
ON L.user_id = {user_id}
AND L.language_code = M.language_code
LEFT JOIN user_meme_reaction R
ON R.meme_id = M.id
AND R.user_id = {user_id}
WHERE 1=1
AND M.status = 'ok'
AND R.meme_id IS NULL
-- 100 ru, 50 en, 50 all lang
AND M.id IN (
4101086, 4442353, 3755262, 4524041, 914304, 1213657,
3477742, 3850309, 4106545, 3918656, 1976055, 3729527,
4370768, 4031941, 3902467, 3940729, 3966109, 4144377,
4131644, 4720051, 4438220, 943398, 3486879, 3958437,
3193252, 4011185, 3855063, 4261258, 4368086, 4255270,
1194244, 10222, 4818828, 3820043, 758408, 3188657,
4451345, 2050874, 4665040, 4106819, 3798967, 1825631,
3140601, 4840661, 4250457, 10202, 4363045, 3823857,
3755199, 4214428, 3604880, 3759401, 3928967, 3859587,
1240438, 4634391, 4002944, 2914449, 1955395, 1902244,
4256739, 1721327, 1285555, 1901653, 1584871, 3517077,
4493086, 4128512, 3570595, 3975285, 1484762, 1811655,
1071204, 4033401, 2294710, 4236782, 881987, 4180263,
1100991, 3867070, 1859048, 4285721, 1466518, 2262302,
4478289, 1859157, 4232654, 1202886, 978202, 2279188,
1892350, 961273, 4033397, 3513207, 3635346, 4320621,
4558947, 4252321, 1084225, 2350587, 4339982, 3724969,
3613758, 1768655, 4148626, 1285566, 2181541, 1103300,
3516406, 1197518, 4036174, 3537906, 2953444, 13636,
3724910, 3911502, 1988648, 3587199, 1398183, 4166913,
3911320, 1311422, 2153377, 3604881, 3596142, 1006843,
4473556, 4231678, 4856209, 10114, 3520485, 4232460,
1721545, 3747694, 3914292, 4119263, 4033399, 1482707,
4243473, 4336344, 1678337, 3516170, 2279191, 3724979,
3772372, 4763033, 4128276, 463991, 1006837, 1202853,
4101086, 1103300, 4119263, 4357615, 1194244, 3859587,
3630862, 4478289, 4665040, 3798967, 3940785, 10222,
4255187, 1304918, 3823857, 1398183, 16818, 881987,
2005796, 3639651, 4231648, 3902342, 4031503, 4231678,
4166913, 4720051, 3855063, 4370768, 2350587, 758408,
4818828, 4261258, 3587199, 648225, 4716664, 3918656,
4183519, 3600534, 4473556, 3772372, 4243473, 4524041,
943398, 4840661, 4250457, 1825631, 4363045, 4232460,
4148761, 3513207
)
{exclude_meme_ids_sql_filter(exclude_meme_ids)}
ORDER BY rand
LIMIT {limit}
) M
"""
res = await fetch_all(text(query))
return res