Datasets:
pa large_string | va large_string | pb large_string | vb large_string | va_upload_time large_string | vb_upload_time large_string | declared_range large_string | dep_type large_string | vb_in_range bool | va_num_prev_releases int64 | va_size_bytes int64 | va_num_deps int64 | vb_num_prev_releases int64 | vb_size_bytes int64 | vb_num_deps int64 | label int64 | label_reason large_string | split large_string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
statsmodels | 0.12.2 | numpy | 1.10.0 | 2021-02-02T11:08:49+00:00 | 2015-10-07T19:45:54+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 18 | 114,438,464 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.10.1 | 2021-02-02T11:08:49+00:00 | 2015-10-12T16:10:30+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 19 | 129,157,930 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.10.2 | 2021-02-02T11:08:49+00:00 | 2015-12-14T20:43:20+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 20 | 129,217,581 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.10.3 | 2021-02-02T11:08:49+00:00 | 2016-04-20T03:58:58+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 23 | 105,777,896 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.10.4 | 2021-02-02T11:08:49+00:00 | 2016-01-07T02:41:56+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 21 | 170,812,488 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.11.0 | 2021-02-02T11:08:49+00:00 | 2016-03-27T20:25:47+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 22 | 127,785,202 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.11.1 | 2021-02-02T11:08:49+00:00 | 2016-06-26T13:55:46+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 24 | 170,469,841 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.11.2 | 2021-02-02T11:08:49+00:00 | 2016-10-04T00:45:34+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 25 | 170,610,345 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.11.3 | 2021-02-02T11:08:49+00:00 | 2016-12-19T00:32:04+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 26 | 199,668,842 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.12.0 | 2021-02-02T11:08:49+00:00 | 2017-01-15T22:59:45+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 27 | 225,806,650 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.12.1 | 2021-02-02T11:08:49+00:00 | 2017-03-18T16:38:00+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 28 | 225,856,005 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.13.0 | 2021-02-02T11:08:49+00:00 | 2017-06-07T18:25:44+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 29 | 228,625,696 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.13.1 | 2021-02-02T11:08:49+00:00 | 2017-07-07T01:00:08+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 30 | 228,679,632 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.13.3 | 2021-02-02T11:08:49+00:00 | 2017-09-29T22:36:56+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 31 | 251,619,010 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.14.0 | 2021-02-02T11:08:49+00:00 | 2018-01-06T23:35:53+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 32 | 266,397,087 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.14.1 | 2021-02-02T11:08:49+00:00 | 2018-02-21T00:18:25+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 33 | 220,557,105 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.14.2 | 2021-02-02T11:08:49+00:00 | 2018-03-12T17:49:21+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 34 | 220,566,897 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.14.3 | 2021-02-02T11:08:49+00:00 | 2018-04-28T15:56:12+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 35 | 220,597,937 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.14.4 | 2021-02-02T11:08:49+00:00 | 2018-06-06T17:00:59+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 36 | 220,686,718 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.14.5 | 2021-02-02T11:08:49+00:00 | 2018-06-12T22:28:32+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 37 | 269,486,984 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.14.6 | 2021-02-02T11:08:49+00:00 | 2018-09-23T16:50:37+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 41 | 386,260,075 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.15.0 | 2021-02-02T11:08:49+00:00 | 2018-07-23T16:03:25+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 38 | 387,673,013 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.15.1 | 2021-02-02T11:08:49+00:00 | 2018-08-21T19:28:58+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 39 | 387,724,500 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.15.2 | 2021-02-02T11:08:49+00:00 | 2018-09-23T12:12:14+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 40 | 387,723,287 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.15.3 | 2021-02-02T11:08:49+00:00 | 2018-10-22T17:10:58+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 42 | 387,926,813 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.15.4 | 2021-02-02T11:08:49+00:00 | 2018-11-04T16:20:32+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 43 | 387,929,239 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.16.0 | 2021-02-02T11:08:49+00:00 | 2019-01-14T02:39:58+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 44 | 306,867,298 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.16.1 | 2021-02-02T11:08:49+00:00 | 2019-01-31T23:13:15+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 45 | 307,386,058 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.16.2 | 2021-02-02T11:08:49+00:00 | 2019-02-26T19:10:02+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 46 | 307,328,676 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.16.3 | 2021-02-02T11:08:49+00:00 | 2019-04-22T01:23:42+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 47 | 307,377,741 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.16.4 | 2021-02-02T11:08:49+00:00 | 2019-05-28T18:53:17+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 48 | 307,649,812 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.16.5 | 2021-02-02T11:08:49+00:00 | 2019-08-28T01:11:32+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 51 | 307,771,213 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.16.6 | 2021-02-02T11:08:49+00:00 | 2019-12-29T22:23:23+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 56 | 307,843,504 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.17.0 | 2021-02-02T11:08:49+00:00 | 2019-07-26T18:16:22+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 49 | 235,391,570 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.17.1 | 2021-02-02T11:08:49+00:00 | 2019-08-27T00:20:20+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 50 | 235,401,154 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.17.2 | 2021-02-02T11:08:49+00:00 | 2019-09-07T00:00:13+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 52 | 235,416,214 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.17.3 | 2021-02-02T11:08:49+00:00 | 2019-10-17T14:39:53+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 53 | 309,862,113 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.17.4 | 2021-02-02T11:08:49+00:00 | 2019-11-11T01:49:18+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 54 | 309,908,555 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.17.5 | 2021-02-02T11:08:49+00:00 | 2020-01-01T16:56:30+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 57 | 309,836,066 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.18.0 | 2021-02-02T11:08:49+00:00 | 2019-12-22T15:32:32+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 55 | 310,515,338 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.18.1 | 2021-02-02T11:08:49+00:00 | 2020-01-06T21:53:39+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 58 | 310,547,335 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.18.2 | 2021-02-02T11:08:49+00:00 | 2020-03-17T16:36:47+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 59 | 310,726,233 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.18.3 | 2021-02-02T11:08:49+00:00 | 2020-04-19T19:56:51+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 60 | 310,715,095 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.18.4 | 2021-02-02T11:08:49+00:00 | 2020-05-03T15:18:15+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 61 | 310,922,764 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.18.5 | 2021-02-02T11:08:49+00:00 | 2020-06-04T00:10:51+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 62 | 310,195,825 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.19.0 | 2021-02-02T11:08:49+00:00 | 2020-06-20T20:14:54+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 63 | 331,386,877 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.19.1 | 2021-02-02T11:08:49+00:00 | 2020-07-21T20:54:49+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 64 | 330,179,355 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.19.2 | 2021-02-02T11:08:49+00:00 | 2020-09-10T17:48:12+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 65 | 330,278,847 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.19.3 | 2021-02-02T11:08:49+00:00 | 2020-10-29T01:08:10+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 66 | 441,249,873 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.19.4 | 2021-02-02T11:08:49+00:00 | 2020-11-02T15:46:22+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 67 | 433,889,233 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.19.5 | 2021-02-02T11:08:49+00:00 | 2021-01-05T17:19:38+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 68 | 439,881,375 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.20.0 | 2021-02-02T11:08:49+00:00 | 2021-01-30T19:35:22+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 69 | 321,220,141 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.20.1 | 2021-02-02T11:08:49+00:00 | 2021-02-07T20:27:20+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 70 | 321,114,456 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.20.2 | 2021-02-02T11:08:49+00:00 | 2021-03-27T22:16:26+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 71 | 321,197,602 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.20.3 | 2021-02-02T11:08:49+00:00 | 2021-05-10T15:17:40+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 72 | 321,211,054 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.21.0 | 2021-02-02T11:08:49+00:00 | 2021-06-22T13:39:59+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 73 | 410,674,430 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.21.1 | 2021-02-02T11:08:49+00:00 | 2021-07-18T19:37:41+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 74 | 413,416,421 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.21.2 | 2021-02-02T11:08:49+00:00 | 2021-08-15T20:07:03+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 75 | 441,990,369 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.21.3 | 2021-02-02T11:08:49+00:00 | 2021-10-20T20:17:59+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 76 | 495,177,608 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.21.4 | 2021-02-02T11:08:49+00:00 | 2021-11-05T01:20:44+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 77 | 459,484,940 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.21.5 | 2021-02-02T11:08:49+00:00 | 2021-12-19T23:58:44+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 78 | 459,522,402 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.21.6 | 2021-02-02T11:08:49+00:00 | 2022-04-12T14:48:15+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 83 | 470,818,382 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.22.0 | 2021-02-02T11:08:49+00:00 | 2021-12-31T20:32:18+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 79 | 361,419,003 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.22.1 | 2021-02-02T11:08:49+00:00 | 2022-01-14T18:52:16+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 80 | 361,615,779 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.22.2 | 2021-02-02T11:08:49+00:00 | 2022-02-04T00:30:48+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 81 | 278,194,309 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.22.3 | 2021-02-02T11:08:49+00:00 | 2022-03-07T22:39:39+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 82 | 290,459,140 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.22.4 | 2021-02-02T11:08:49+00:00 | 2022-05-20T20:50:53+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 84 | 325,751,894 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.23.0 | 2021-02-02T11:08:49+00:00 | 2022-06-22T22:13:34+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 85 | 327,059,708 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.23.1 | 2021-02-02T11:08:49+00:00 | 2022-07-09T01:07:24+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 86 | 327,079,899 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.23.2 | 2021-02-02T11:08:49+00:00 | 2022-08-14T00:14:09+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 87 | 416,255,884 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.23.3 | 2021-02-02T11:08:49+00:00 | 2022-09-09T17:48:27+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 88 | 416,528,065 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.23.4 | 2021-02-02T11:08:49+00:00 | 2022-10-12T14:42:05+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 89 | 416,549,425 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.23.5 | 2021-02-02T11:08:49+00:00 | 2022-11-20T01:21:22+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 90 | 416,661,617 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.24.0 | 2021-02-02T11:08:49+00:00 | 2022-12-18T17:53:12+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 91 | 430,174,879 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.24.1 | 2021-02-02T11:08:49+00:00 | 2022-12-26T13:37:55+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 92 | 430,343,148 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.24.2 | 2021-02-02T11:08:49+00:00 | 2023-02-05T19:45:29+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 93 | 430,450,740 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.24.3 | 2021-02-02T11:08:49+00:00 | 2023-04-22T21:29:36+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 94 | 430,494,445 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.24.4 | 2021-02-02T11:08:49+00:00 | 2023-06-26T13:22:33+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 96 | 430,507,052 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.25.0 | 2021-02-02T11:08:49+00:00 | 2023-06-17T14:38:18+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 95 | 394,536,683 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.25.1 | 2021-02-02T11:08:49+00:00 | 2023-07-08T21:38:33+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 97 | 394,561,797 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.25.2 | 2021-02-02T11:08:49+00:00 | 2023-07-31T14:50:49+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 98 | 404,674,379 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.26.0 | 2021-02-02T11:08:49+00:00 | 2023-09-16T19:58:18+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 99 | 553,237,955 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.26.1 | 2021-02-02T11:08:49+00:00 | 2023-10-14T19:39:25+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 100 | 553,375,636 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.26.2 | 2021-02-02T11:08:49+00:00 | 2023-11-12T22:51:49+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 101 | 609,134,539 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.26.3 | 2021-02-02T11:08:49+00:00 | 2024-01-02T22:20:37+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 102 | 609,641,964 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.26.4 | 2021-02-02T11:08:49+00:00 | 2024-02-05T23:48:01+00:00 | >=1.15 | direct | true | 24 | 202,911,218 | 14 | 103 | 550,962,357 | 0 | 0 | compatible | train |
statsmodels | 0.12.2 | numpy | 1.5.1 | 2021-02-02T11:08:49+00:00 | 2010-11-18T14:16:58+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 4 | 23,060,802 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.6.0 | 2021-02-02T11:08:49+00:00 | 2011-05-14T12:01:38+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 5 | 85,034,537 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.6.1 | 2021-02-02T11:08:49+00:00 | 2011-07-24T17:59:40+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 6 | 85,138,141 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.6.2 | 2021-02-02T11:08:49+00:00 | 2012-05-20T11:37:23+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 7 | 84,599,755 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.7.0 | 2021-02-02T11:08:49+00:00 | 2013-02-12T05:09:27+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 8 | 107,624,249 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.7.1 | 2021-02-02T11:08:49+00:00 | 2013-04-07T08:01:54+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 9 | 155,238,032 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.7.2 | 2021-02-02T11:08:49+00:00 | 2013-12-31T14:48:32+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 11 | 90,842,135 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.8.0 | 2021-02-02T11:08:49+00:00 | 2013-10-30T22:34:42+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 10 | 95,479,812 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.8.1 | 2021-02-02T11:08:49+00:00 | 2014-03-25T23:19:20+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 12 | 131,749,917 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.8.2 | 2021-02-02T11:08:49+00:00 | 2014-08-09T12:19:55+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 13 | 131,710,150 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.9.0 | 2021-02-02T11:08:49+00:00 | 2014-09-07T09:55:54+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 14 | 147,844,665 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.9.1 | 2021-02-02T11:08:49+00:00 | 2014-11-02T13:20:14+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 15 | 122,647,854 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.9.2 | 2021-02-02T11:08:49+00:00 | 2015-03-01T20:07:54+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 16 | 123,105,460 | 0 | 1 | incompatible | train |
statsmodels | 0.12.2 | numpy | 1.9.3 | 2021-02-02T11:08:49+00:00 | 2015-09-21T23:41:52+00:00 | >=1.15 | direct | false | 24 | 202,911,218 | 14 | 17 | 126,599,972 | 0 | 1 | incompatible | train |
End of preview. Expand in Data Studio
PyPI Dependency Compatibility MVD
Colección versionada de pares ordenados de versiones de paquetes Python usada
en un TFM sobre clasificación de incompatibilidades en grafos de dependencias.
Cada observación representa pa@va → pb@vb; la etiqueta indica si ambos pins
pueden coexistir según una ejecución contrafactual del resolver uv.
La colección publica todas las etapas procesadas para documentar por qué el
protocolo cambió. mvd_v2_1 es la configuración recomendada y la utilizada
para los resultados finales. pilot y mvd_v1 se conservan para
trazabilidad y reproducción de experimentos anteriores.
Configuraciones
| Configuración | Filas | Train | Validación | Test | Uso |
|---|---|---|---|---|---|
pilot |
302 | 250 | 14 | 38 | Validación inicial del pipeline sobre doce paquetes |
mvd_v1 |
197.483 | 158.318 | 11.441 | 27.724 | Primera ampliación al ecosistema científico |
mvd_v2_1 |
8.000 | 5.600 | 1.200 | 1.200 | Dataset final corregido y trazable |
Piloto
El piloto comprobó que la extracción de metadatos, la generación de pares, el
etiquetado con uv y el split temporal funcionaban de extremo a extremo. No se
diseñó para estimar el rendimiento final.
MVD v1
La primera versión amplió el conjunto de paquetes y generó todos los pares elegibles del protocolo inicial. Se conserva sin modificaciones, pero no debe usarse como referencia final porque todavía no incorporaba:
- prevalidación de la instalabilidad individual de cada pin;
- evaluación congelada y explícita de marcadores y extras;
- requisito de distribuciones binarias para aislar errores de compilación;
- tratamiento corregido de versiones preliminares fijadas exactamente;
- muestreo determinista posterior a la prevalidación;
- diagnósticos, hashes y manifiesto completos del oráculo.
Por esas diferencias, sus etiquetas y distribución no deben compararse directamente con las de v2.1.
MVD v2.1
La versión final fija CPython 3.11 sobre Linux x86-64, sin extras del paquete padre y usando solo wheels. De 82.922 candidatos iniciales, la prevalidación individual excluyó 48.906; sobre los 34.016 restantes se muestrearon 8.000 pares con semilla 42. El resultado contiene 5.596 pares compatibles, 2.400 conflictos explícitos de rango y cuatro conflictos transitivos o globales.
El artefacto mantiene el nombre histórico dataset_mvd_v2.parquet, pero su
manifiesto declara dataset_version: 2.1. Esta distinción se conserva para que
el hash coincida con el utilizado por los experimentos.
Archivos originales y particiones
data/processed/ contiene los tres Parquet exactos producidos por el pipeline.
Sus hashes están en CHECKSUMS.sha256. Los directorios
data/pilot/, data/mvd_v1/ y data/mvd_v2_1/ contienen copias derivadas por
split para el visor de Hugging Face; no sustituyen los originales.
El manifiesto de v2.1 se encuentra en
metadata/dataset_mvd_v2_manifest.json. El artefacto de exclusiones de esa
ejecución contenía cero filas y no se distribuye; su conteo y hash permanecen
registrados en el manifiesto.
Fuente y proceso de etiquetado
Los atributos de paquetes y versiones proceden de la API pública de PyPI. Las etiquetas, particiones, diagnósticos y variables derivadas fueron generados por el pipeline tfm-dependency-data. El código exacto y los entornos Pixi permiten regenerar cada etapa.
Limitaciones
- El dominio se restringe a un subconjunto del ecosistema científico de PyPI.
- La v2.1 representa CPython 3.11/Linux x86-64 y no activa extras del padre.
- Solo cuatro incompatibilidades de v2.1 ocurren dentro del rango declarado; el cumplimiento de PEP 440 explica casi toda la etiqueta.
uvy el estado histórico de los índices forman parte del contexto del oráculo; el manifiesto registra la versión utilizada.- Las versiones anteriores se publican para trazabilidad, no como alternativas equivalentes al protocolo final.
Licencia
La selección, estructura, etiquetas, particiones y documentación de esta colección se ofrecen bajo CC BY 4.0. Los metadatos originarios de PyPI mantienen su procedencia y cualesquiera derechos de terceros aplicables.
Cita
Los metadatos de citación están disponibles en CITATION.cff. Cuando exista un DOI de Zenodo, se incorporará en una nueva versión sin alterar los artefactos originales.
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