precision-corrected starts; calendar/zodiac/physics member families (Φ/T with NASA POWER T at the POB); refreshed deployed stack (held 0.6456)
Browse files- artamodel_iv_ensemble.json +1638 -0
- calendar_members_iv.py +100 -0
- geo_lgbm_0.json +0 -0
- geo_lgbm_0.txt +0 -0
- geo_lgbm_1.json +0 -0
- geo_lgbm_1.txt +0 -0
- geo_lgbm_2.json +0 -0
- geo_lgbm_2.txt +0 -0
- physics_members_iv.py +163 -0
- robustness_audit.json +11 -11
- stack_iv_deployed.json +668 -668
- stack_iv_predictor.py +22 -17
- t2m_normals.json +0 -0
- zodiac_members_iv.py +96 -0
artamodel_iv_ensemble.json
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@@ -0,0 +1,1638 @@
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|
| 1 |
+
{
|
| 2 |
+
"members": [
|
| 3 |
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{
|
| 4 |
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"member": "PLAIN + start-is-1-January flag",
|
| 5 |
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| 6 |
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| 7 |
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|
| 8 |
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"n_test": 15262
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| 9 |
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},
|
| 10 |
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{
|
| 11 |
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"member": "PLAIN + GEOGRAPHY (birthplaces, distance) + start weekday/month",
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| 12 |
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"n_fit": 181596,
|
| 13 |
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"forward_oof": 0.6633431817084593,
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| 14 |
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|
| 15 |
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"n_test": 15262
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| 16 |
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},
|
| 17 |
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{
|
| 18 |
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"member": "ARTAMODEL 9-term greedy boosted (midpoints)",
|
| 19 |
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|
| 20 |
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"forward_oof": 0.5274362257320314,
|
| 21 |
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"held_on_its_rows": 0.6255386152996957,
|
| 22 |
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"n_test": 15262
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| 23 |
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},
|
| 24 |
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{
|
| 25 |
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"member": "ARTAMODEL IV greedy boosted (full-chart rows)",
|
| 26 |
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"n_fit": 20632,
|
| 27 |
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"forward_oof": 0.5235526947166635,
|
| 28 |
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|
| 29 |
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"n_test": 15262
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| 30 |
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},
|
| 31 |
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{
|
| 32 |
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"member": "ARTAMODEL IV fixed-cycle boosted (stable)",
|
| 33 |
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"n_fit": 20632,
|
| 34 |
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|
| 35 |
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| 36 |
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| 37 |
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|
| 38 |
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{
|
| 39 |
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"member": "phasor a_sun",
|
| 40 |
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| 41 |
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| 42 |
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| 43 |
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"n_test": 15262
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| 44 |
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|
| 45 |
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{
|
| 46 |
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"member": "phasor a_moon",
|
| 47 |
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|
| 48 |
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| 49 |
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| 50 |
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"n_test": 15262
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| 51 |
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| 52 |
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{
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| 53 |
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"member": "phasor a_mercury",
|
| 54 |
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|
| 55 |
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| 56 |
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| 57 |
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| 58 |
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|
| 59 |
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{
|
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| 1483 |
+
"phasor t2_saturn": 0.31040909303901437,
|
| 1484 |
+
"phasor t2_uranus": 0.30359888483854314,
|
| 1485 |
+
"phasor t2_neptune": 0.2682459137720579,
|
| 1486 |
+
"phasor t2_pluto": 0.16930188752608363,
|
| 1487 |
+
"phasor t2_true_node": 0.3237443716017336,
|
| 1488 |
+
"phasor t2_true_south_node": 0.3237443716017336,
|
| 1489 |
+
"phasor t2_chiron": 0.2484748947432613,
|
| 1490 |
+
"phasor t2_mean_lilith": 0.3332973532362646,
|
| 1491 |
+
"phasor n1_sun": 0.32786877827397654,
|
| 1492 |
+
"phasor n1_moon": 0.33429292611448147,
|
| 1493 |
+
"phasor n1_mercury": 0.3297890371966691,
|
| 1494 |
+
"phasor n1_venus": 0.3288245413052308,
|
| 1495 |
+
"phasor n1_mars": 0.33231183771141154,
|
| 1496 |
+
"phasor n1_jupiter": 0.3248783189280211,
|
| 1497 |
+
"phasor n1_saturn": 0.2824750103881966,
|
| 1498 |
+
"phasor n1_uranus": 0.23154533044391584,
|
| 1499 |
+
"phasor n1_neptune": 0.19591623979767725,
|
| 1500 |
+
"phasor n1_pluto": 0.14734882858727283,
|
| 1501 |
+
"phasor n1_true_node": 0.3138572089430224,
|
| 1502 |
+
"phasor n1_true_south_node": 0.31323048668796544,
|
| 1503 |
+
"phasor n1_chiron": 0.22771029102999332,
|
| 1504 |
+
"phasor n1_mean_lilith": 0.3314897381035839,
|
| 1505 |
+
"phasor n2_sun": 0.3278222545409606,
|
| 1506 |
+
"phasor n2_moon": 0.3345346983970204,
|
| 1507 |
+
"phasor n2_mercury": 0.32972995085357526,
|
| 1508 |
+
"phasor n2_venus": 0.3288973065532147,
|
| 1509 |
+
"phasor n2_mars": 0.3323439698282343,
|
| 1510 |
+
"phasor n2_jupiter": 0.3254339847248451,
|
| 1511 |
+
"phasor n2_saturn": 0.28400734827679414,
|
| 1512 |
+
"phasor n2_uranus": 0.19652501546690862,
|
| 1513 |
+
"phasor n2_neptune": 0.16875782603165326,
|
| 1514 |
+
"phasor n2_pluto": 0.14734882858727283,
|
| 1515 |
+
"phasor n2_true_node": 0.31349320237855627,
|
| 1516 |
+
"phasor n2_true_south_node": 0.31299890703574945,
|
| 1517 |
+
"phasor n2_chiron": 0.22748277297351682,
|
| 1518 |
+
"phasor n2_mean_lilith": 0.33176019057591116,
|
| 1519 |
+
"phasor tn_sun": 0.0,
|
| 1520 |
+
"phasor tn_moon": 0.0,
|
| 1521 |
+
"phasor tn_mercury": 0.0,
|
| 1522 |
+
"phasor tn_venus": 0.0,
|
| 1523 |
+
"phasor tn_mars": 0.0,
|
| 1524 |
+
"phasor tn_jupiter": 0.0,
|
| 1525 |
+
"phasor tn_saturn": 0.0,
|
| 1526 |
+
"phasor tn_uranus": 0.0,
|
| 1527 |
+
"phasor tn_neptune": 0.0,
|
| 1528 |
+
"phasor tn_pluto": 0.0,
|
| 1529 |
+
"phasor tn_true_node": 0.0,
|
| 1530 |
+
"phasor tn_true_south_node": 0.0,
|
| 1531 |
+
"phasor tn_chiron": 0.0,
|
| 1532 |
+
"phasor tn_mean_lilith": 0.0,
|
| 1533 |
+
"SUM term a": 2.9444422390466514e-05,
|
| 1534 |
+
"SUM term t1": 0.24336062525610064,
|
| 1535 |
+
"SUM term t2": 0.2462514842620862,
|
| 1536 |
+
"SUM term n1": 0.23736013588090013,
|
| 1537 |
+
"SUM term n2": 0.2424009540164592,
|
| 1538 |
+
"SUM term tn": 4.539607074741629e-05,
|
| 1539 |
+
"SUM 3-term (a+t1+t2)": 0.01284377039357919,
|
| 1540 |
+
"SUM 6-term": 0.07483686789623792,
|
| 1541 |
+
"TRADITION aboriginal_australian": 0.0,
|
| 1542 |
+
"TRADITION african": 0.0,
|
| 1543 |
+
"TRADITION astrocartography": 0.0,
|
| 1544 |
+
"TRADITION babylonian_egyptian": 0.0,
|
| 1545 |
+
"TRADITION chinese": 0.0,
|
| 1546 |
+
"TRADITION east_asian_deep": 0.0,
|
| 1547 |
+
"TRADITION harmonics": 0.0,
|
| 1548 |
+
"TRADITION hellenistic": 0.0,
|
| 1549 |
+
"TRADITION houses": 0.0,
|
| 1550 |
+
"TRADITION indigenous_americas": 0.0,
|
| 1551 |
+
"TRADITION lunar_calendrical": 0.0,
|
| 1552 |
+
"TRADITION mesoamerican": 0.0,
|
| 1553 |
+
"TRADITION modern_western": 0.0,
|
| 1554 |
+
"TRADITION numerology": 0.0,
|
| 1555 |
+
"TRADITION persian_arabic": 0.0,
|
| 1556 |
+
"TRADITION polynesian": 0.0,
|
| 1557 |
+
"TRADITION tibetan_seasia": 0.0,
|
| 1558 |
+
"TRADITION uranian": 0.0,
|
| 1559 |
+
"TRADITION vedic_ashtakavarga": 0.0,
|
| 1560 |
+
"TRADITION vedic_core": 0.0,
|
| 1561 |
+
"TRADITION vedic_match": 0.0,
|
| 1562 |
+
"SIDEREAL vedic:D1 (lagna, grahas, houses, nakshatras)": 0.15156248085393392,
|
| 1563 |
+
"SIDEREAL vedic:vargas D2-D60": 0.2150530431589064,
|
| 1564 |
+
"SIDEREAL vedic:panchanga": 0.10344864892152227,
|
| 1565 |
+
"SIDEREAL vedic:ashtakavarga": 0.13629927273200806,
|
| 1566 |
+
"SIDEREAL vedic:vimsottari at the start": 0.0937353616159842,
|
| 1567 |
+
"SIDEREAL vedic:doshas": 0.08363418157228915,
|
| 1568 |
+
"SIDEREAL vedic:shad bala": 0.16333781351217339,
|
| 1569 |
+
"SIDEREAL vedic:pair (ashtakoota, transposition, dasa)": 0.07240497671334437,
|
| 1570 |
+
"SIDEREAL vedic:vivaha muhurta (the wedding day)": 0.014325200530279936,
|
| 1571 |
+
"SIDEREAL zwds:astrolabe (soul, body, class)": 0.06194176511506754,
|
| 1572 |
+
"SIDEREAL zwds:palaces": 0.15344321435339425,
|
| 1573 |
+
"SIDEREAL zwds:major stars": 0.13931469167366262,
|
| 1574 |
+
"SIDEREAL zwds:minor stars": 0.10580982418605729,
|
| 1575 |
+
"SIDEREAL zwds:pair (spouse x life palace, branches)": 0.007118689298839858,
|
| 1576 |
+
"CALENDAR + NUMEROLOGY (plain + date fine structure)": 0.0,
|
| 1577 |
+
"CALENDAR + NUMEROLOGY alone (no ages)": 0.0,
|
| 1578 |
+
"HARMONIC 2 synastry sum (14 bodies)": 3.743022373871148e-05,
|
| 1579 |
+
"HARMONIC 3 synastry sum (14 bodies)": 3.814293504183468e-05,
|
| 1580 |
+
"HARMONIC 4 synastry sum (14 bodies)": 3.5403719310554784e-05,
|
| 1581 |
+
"HARMONIC 6 synastry sum (14 bodies)": 4.8000676921173996e-05,
|
| 1582 |
+
"FAST synastry sum (Sun..Mars)": 3.1505274351303114e-05,
|
| 1583 |
+
"PROGRESSED synastry + progressed-to-start (20 phasors)": 0.1732669326246835,
|
| 1584 |
+
"PROGRESSED synastry only (10 phasors)": 0.22536880011782254,
|
| 1585 |
+
"PHYSICS \u03a6/T only (entropic gravity, no ages)": 0.0,
|
| 1586 |
+
"PHYSICS all (\u03a6, \u03c4, \u03a6/T, T, day length, Sun altitude; no ages)": 0.0,
|
| 1587 |
+
"PLAIN + PHYSICS": 0.0,
|
| 1588 |
+
"PLAIN + \u03a6/T": 0.0,
|
| 1589 |
+
"CALENDAR jalali (no ages)": 0.0,
|
| 1590 |
+
"CALENDAR islamic (no ages)": 0.0,
|
| 1591 |
+
"CALENDAR hebrew (no ages)": 0.0,
|
| 1592 |
+
"CALENDAR julian (no ages)": 0.007681104385835596,
|
| 1593 |
+
"CALENDAR chinese_sexagenary (no ages)": 0.0,
|
| 1594 |
+
"PLAIN + ALL CALENDARS": 0.0008869134019959405,
|
| 1595 |
+
"ZODIAC TROPICAL signs (no ages)": 0.025303698522569217,
|
| 1596 |
+
"ZODIAC FAGAN_BRADLEY signs (no ages)": 0.02594254545666251,
|
| 1597 |
+
"ZODIAC KRISHNAMURTI signs (no ages)": 0.023651848394009408,
|
| 1598 |
+
"ZODIAC RAMAN signs (no ages)": 0.025842224900585575,
|
| 1599 |
+
"ZODIAC YUKTESHWAR signs (no ages)": 0.02503317735517661,
|
| 1600 |
+
"PLAIN + ALL ZODIAC SIGN SYSTEMS": 0.015690983354490127
|
| 1601 |
+
},
|
| 1602 |
+
"bagged_stack_all": {
|
| 1603 |
+
"grid": [
|
| 1604 |
+
[
|
| 1605 |
+
1867.0,
|
| 1606 |
+
0.01,
|
| 1607 |
+
3.0
|
| 1608 |
+
],
|
| 1609 |
+
[
|
| 1610 |
+
1867.0,
|
| 1611 |
+
0.001,
|
| 1612 |
+
3.0
|
| 1613 |
+
],
|
| 1614 |
+
[
|
| 1615 |
+
1867.0,
|
| 1616 |
+
0.003,
|
| 1617 |
+
1.0
|
| 1618 |
+
],
|
| 1619 |
+
[
|
| 1620 |
+
1884.0,
|
| 1621 |
+
0.01,
|
| 1622 |
+
3.0
|
| 1623 |
+
],
|
| 1624 |
+
[
|
| 1625 |
+
1823.0,
|
| 1626 |
+
0.01,
|
| 1627 |
+
3.0
|
| 1628 |
+
],
|
| 1629 |
+
[
|
| 1630 |
+
1823.0,
|
| 1631 |
+
0.003,
|
| 1632 |
+
0.0
|
| 1633 |
+
]
|
| 1634 |
+
],
|
| 1635 |
+
"held": 0.6366860664185042,
|
| 1636 |
+
"age_cell": 0.5801102580787502
|
| 1637 |
+
}
|
| 1638 |
+
}
|
calendar_members_iv.py
ADDED
|
@@ -0,0 +1,100 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
calendar_members_iv.py — the world's CALENDARS as stack members (operator 2026-08-20: "different western vs eastern
|
| 3 |
+
ephemeries or different calendars"). Every date in the files is proleptic Gregorian; here each birth and the start
|
| 4 |
+
are re-expressed in five other calendars and the fine structure of each becomes features:
|
| 5 |
+
JALALI (Persian solar) month, day, |Δmonth| of the pair, start month/day, Nowruz proximity (day-of-year)
|
| 6 |
+
ISLAMIC (Hijri lunar) month, day, pair |Δmonth| (wraps at 12), start month — a pure lunar count, so it drifts
|
| 7 |
+
against the seasons: astrology-free lunar-phase information at yearly precision
|
| 8 |
+
HEBREW (lunisolar) month, day, pair |Δmonth|, start month
|
| 9 |
+
JULIAN (old style) the Gregorian−Julian day gap at the date, and whether the date crosses a month boundary
|
| 10 |
+
when re-expressed (a data-provenance signal for pre-1918 eastern records)
|
| 11 |
+
CHINESE sexagenary year stem, branch, the pair's branch difference mod 12 (the six harms/combines), the
|
| 12 |
+
start year's animal — arithmetic on the year, no ephemeris
|
| 13 |
+
All order-free for the pair (max/min/|Δ|). One LightGBM member per calendar (no ages), one PLAIN+ALL-CALENDARS
|
| 14 |
+
member, forward-chained OOF. Writes AQ_OUT/calendar_members.npz.
|
| 15 |
+
"""
|
| 16 |
+
import json
|
| 17 |
+
import os
|
| 18 |
+
import sys
|
| 19 |
+
import time
|
| 20 |
+
|
| 21 |
+
import numpy as np
|
| 22 |
+
import pandas as pd
|
| 23 |
+
from convertdate import hebrew, islamic, julian, persian
|
| 24 |
+
|
| 25 |
+
HERE = os.path.dirname(os.path.abspath(__file__)); sys.path.insert(0, HERE)
|
| 26 |
+
from artamodel import auc # noqa: E402
|
| 27 |
+
|
| 28 |
+
SRC = os.environ.get("AQ_SRC", "/tmp/aq4"); PH = os.environ.get("AQ_PHASES", "/tmp/aq4feat/phases.npz"); OUT = os.environ.get("AQ_OUT", "/tmp/aq4sub")
|
| 29 |
+
QS = (0.40, 0.55, 0.70, 0.85, 1.0); T0 = time.time(); log = lambda *a: print(f"[{time.time()-T0:6.0f}s]", *a, flush=True)
|
| 30 |
+
_C = {}
|
| 31 |
+
|
| 32 |
+
|
| 33 |
+
def conv(d):
|
| 34 |
+
"""(jalali_m, jalali_d, islamic_m, islamic_d, hebrew_m, hebrew_d, julian_gap_days, stem, branch) for a Gregorian date string, NaNs at reduced precision."""
|
| 35 |
+
if d in _C:
|
| 36 |
+
return _C[d]
|
| 37 |
+
nan9 = (np.nan,) * 9
|
| 38 |
+
if not isinstance(d, str) or d == "0000-00-00":
|
| 39 |
+
_C[d] = nan9; return nan9
|
| 40 |
+
y = int(d[:4]); m = int(d[5:7]) or 0; dd = int(d[8:10]) or 0
|
| 41 |
+
stem = (y - 4) % 10; branch = (y - 4) % 12
|
| 42 |
+
if m == 0 or dd == 0:
|
| 43 |
+
out = (np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, float(stem), float(branch))
|
| 44 |
+
_C[d] = out; return out
|
| 45 |
+
try:
|
| 46 |
+
jm = persian.from_gregorian(y, m, dd); im = islamic.from_gregorian(y, m, dd); hm = hebrew.from_gregorian(y, m, dd)
|
| 47 |
+
jy, jmn, jdd = julian.from_gregorian(y, m, dd)
|
| 48 |
+
import datetime as dt
|
| 49 |
+
gap = (dt.date(y, m, dd) - dt.date(y, m, dd)).days # placeholder; the real gap:
|
| 50 |
+
# Gregorian-Julian gap in days = JD(greg) - JD(julian date literal) -- compute via ordinal difference
|
| 51 |
+
gap = (dt.date(y, m, dd).toordinal() - dt.date(jy, jmn, min(jdd, 28)).toordinal()) if 1 <= jmn <= 12 else np.nan
|
| 52 |
+
out = (float(jm[1]), float(jm[2]), float(im[1]), float(im[2]), float(hm[1]), float(hm[2]), float(gap), float(stem), float(branch))
|
| 53 |
+
except Exception:
|
| 54 |
+
out = (np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, float(stem), float(branch))
|
| 55 |
+
_C[d] = out; return out
|
| 56 |
+
|
| 57 |
+
|
| 58 |
+
def build(df):
|
| 59 |
+
A = np.array([conv(d) for d in df.dob_a]); B = np.array([conv(d) for d in df.dob_b]); S = np.array([conv(d) for d in df.start])
|
| 60 |
+
def cyc(a, b, mod):
|
| 61 |
+
d = np.abs(a - b); return np.fmin(d, mod - d)
|
| 62 |
+
blocks = {}
|
| 63 |
+
for name, (mi, di, mod) in {"jalali": (0, 1, 12), "islamic": (2, 3, 12), "hebrew": (4, 5, 13)}.items():
|
| 64 |
+
blocks[name] = np.column_stack([np.fmax(A[:, mi], B[:, mi]), np.fmin(A[:, mi], B[:, mi]), cyc(A[:, mi], B[:, mi], mod),
|
| 65 |
+
np.fmax(A[:, di], B[:, di]), np.fmin(A[:, di], B[:, di]), S[:, mi], S[:, di],
|
| 66 |
+
(A[:, mi] == B[:, mi]).astype(float)])
|
| 67 |
+
blocks["julian"] = np.column_stack([A[:, 6], B[:, 6], S[:, 6], np.abs(A[:, 6] - B[:, 6])])
|
| 68 |
+
blocks["chinese_sexagenary"] = np.column_stack([np.fmax(A[:, 7], B[:, 7]), np.fmin(A[:, 7], B[:, 7]), cyc(A[:, 8], B[:, 8], 12),
|
| 69 |
+
(cyc(A[:, 8], B[:, 8], 12) == 6).astype(float), ((A[:, 8] + B[:, 8]) % 12 == 1).astype(float),
|
| 70 |
+
S[:, 8], cyc(A[:, 7], B[:, 7], 10)])
|
| 71 |
+
return blocks
|
| 72 |
+
|
| 73 |
+
|
| 74 |
+
def main():
|
| 75 |
+
import lightgbm as lgb
|
| 76 |
+
Z = np.load(PH, allow_pickle=True); y = Z["y_train"].astype(np.int64); later = Z["yr_train"].astype(int).max(1); cuts = [np.quantile(later, q) for q in QS]
|
| 77 |
+
ptr, pte, pn = Z["plain_train"], Z["plain_test"], list(Z["plain_names"]); s1, s2 = list(Z["slots"])
|
| 78 |
+
tr = pd.read_csv(f"{SRC}/train.csv", dtype=str); te = pd.read_csv(f"{SRC}/test.csv", dtype=str)
|
| 79 |
+
btr = build(tr); bte = build(te); log(f"calendars converted ({len(_C):,} distinct dates)")
|
| 80 |
+
ia, ib, iy = pn.index(f"age_{s1}_at_start"), pn.index(f"age_{s2}_at_start"), pn.index("start_year")
|
| 81 |
+
plain = lambda p: np.column_stack([np.fmax(p[:, ia], p[:, ib]), np.fmin(p[:, ia], p[:, ib]), np.abs(p[:, ia] - p[:, ib]), p[:, iy]])
|
| 82 |
+
prm = dict(n_estimators=300, learning_rate=0.05, num_leaves=15, min_child_samples=200, colsample_bytree=0.7, subsample=0.8, subsample_freq=1, reg_lambda=20.0, verbose=-1)
|
| 83 |
+
members_tr, members_te, names, meta = [], [], [], []
|
| 84 |
+
def member(Xa, Xb, name):
|
| 85 |
+
s_tr = np.full(len(y), np.nan)
|
| 86 |
+
for k in range(1, len(cuts)):
|
| 87 |
+
lo = cuts[k - 1]; blk = (later > lo) & ((later <= cuts[k]) if k < len(cuts) - 1 else True); c = lgb.LGBMClassifier(random_state=0, **prm); c.fit(Xa[later <= lo], y[later <= lo]); s_tr[blk] = c.predict_proba(Xa[blk])[:, 1]
|
| 88 |
+
c = lgb.LGBMClassifier(random_state=0, **prm); c.fit(Xa, y); s_te = c.predict_proba(Xb)[:, 1]
|
| 89 |
+
f = np.isfinite(s_tr) & (later > cuts[0]); o = auc(y[f], s_tr[f]); members_tr.append(s_tr); members_te.append(s_te); names.append(name); meta.append({"member": name, "forward_oof": o})
|
| 90 |
+
log(f" {name:<44} {Xa.shape[1]:>3} features fwd-OOF {o:.4f}")
|
| 91 |
+
for k in btr:
|
| 92 |
+
member(btr[k], bte[k], f"CALENDAR {k} (no ages)")
|
| 93 |
+
allc_tr = np.column_stack([btr[k] for k in btr]); allc_te = np.column_stack([bte[k] for k in bte])
|
| 94 |
+
member(np.column_stack([plain(ptr), allc_tr]), np.column_stack([plain(pte), allc_te]), "PLAIN + ALL CALENDARS")
|
| 95 |
+
np.savez_compressed(os.path.join(OUT, "calendar_members.npz"), S_train=np.column_stack(members_tr), S_test=np.column_stack(members_te), names=np.array(names), meta=json.dumps(meta))
|
| 96 |
+
log(f"wrote {OUT}/calendar_members.npz")
|
| 97 |
+
|
| 98 |
+
|
| 99 |
+
if __name__ == "__main__":
|
| 100 |
+
main()
|
geo_lgbm_0.json
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|
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|
|
geo_lgbm_0.txt
CHANGED
|
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|
geo_lgbm_1.json
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|
geo_lgbm_1.txt
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|
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|
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geo_lgbm_2.json
CHANGED
|
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|
|
|
geo_lgbm_2.txt
CHANGED
|
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|
|
|
physics_members_iv.py
ADDED
|
@@ -0,0 +1,163 @@
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|
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|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
physics_members_iv.py — "entropic gravity" and other PHYSICAL features of the three instants, as stack members.
|
| 3 |
+
|
| 4 |
+
Operator 2026-08-19: "think of creative ways of feature engineering … explore entropic gravity effects with Vi/T
|
| 5 |
+
where Vi is the gravitational potential of external bodies." From the ephemeris (sweshim: geocentric distance of
|
| 6 |
+
every body at the instant) and the birthplace:
|
| 7 |
+
Φᵢ = G Mᵢ / rᵢ for Sun, Moon, Mercury … Pluto at each birth (09:00 local mean time) and at the start (12:00 UT)
|
| 8 |
+
τᵢ = Mᵢ / rᵢ³ the tidal term of each body
|
| 9 |
+
T the temperature AT THE PLACE OF BIRTH: NASA POWER's 2001–2020 monthly T2M normal of the
|
| 10 |
+
birthplace's grid cell for the birth month (annual mean when the month is unknown), kelvin — plus
|
| 11 |
+
the cell's elevation; a latitude+season climatology only where POWER has no value (flagged)
|
| 12 |
+
Φᵢ/T, ΣΦ/T, Στ the entropic-gravity quantities; plus day length and the Sun's altitude at 09:00 (insolation)
|
| 13 |
+
For the pair: the sum and the absolute difference of each partner quantity (even under the swap). One member = a
|
| 14 |
+
LightGBM on the lot, forward-chained OOF; a second member = the Φᵢ/T set alone. Writes AQ_OUT/physics_members.npz.
|
| 15 |
+
"""
|
| 16 |
+
import datetime as dt
|
| 17 |
+
import json
|
| 18 |
+
import math
|
| 19 |
+
import os
|
| 20 |
+
import sys
|
| 21 |
+
import time
|
| 22 |
+
|
| 23 |
+
import numpy as np
|
| 24 |
+
import pandas as pd
|
| 25 |
+
|
| 26 |
+
HERE = os.path.dirname(os.path.abspath(__file__)); ROOT = os.path.dirname(os.path.dirname(HERE)); sys.path.insert(0, HERE); sys.path.insert(0, os.path.join(ROOT, "web"))
|
| 27 |
+
from artamodel import auc # noqa: E402
|
| 28 |
+
|
| 29 |
+
SRC = os.environ.get("AQ_SRC", "/tmp/aq4"); PH = os.environ.get("AQ_PHASES", "/tmp/aq4feat/phases.npz"); OUT = os.environ.get("AQ_OUT", "/tmp/aq4sub")
|
| 30 |
+
QS = (0.40, 0.55, 0.70, 0.85, 1.0); T0 = time.time(); log = lambda *a: print(f"[{time.time()-T0:6.0f}s]", *a, flush=True)
|
| 31 |
+
# masses in solar masses (IAU 2015 nominal values), geocentric distances come in AU
|
| 32 |
+
MASS = {"sun": 1.0, "moon": 3.694e-8, "mercury": 1.660e-7, "venus": 2.448e-6, "mars": 3.227e-7, "jupiter": 9.546e-4, "saturn": 2.858e-4, "uranus": 4.366e-5, "neptune": 5.151e-5, "pluto": 6.6e-9}
|
| 33 |
+
BODIES = list(MASS)
|
| 34 |
+
|
| 35 |
+
|
| 36 |
+
_T2M = None
|
| 37 |
+
MONTHS = ["JAN", "FEB", "MAR", "APR", "MAY", "JUN", "JUL", "AUG", "SEP", "OCT", "NOV", "DEC"]
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
def t_at_pob(lat, lon, month):
|
| 41 |
+
"""T AT THE PLACE OF BIRTH (operator: "don't forget to get T at the POB"): the NASA POWER 2001–2020 monthly
|
| 42 |
+
T2M normal of the birthplace's 0.5° grid cell for the birth month (the annual mean when the month is unknown),
|
| 43 |
+
in kelvin, with the cell's elevation; (nan, nan) when the service has no value — the caller falls back to the
|
| 44 |
+
latitude climatology and flags it."""
|
| 45 |
+
global _T2M
|
| 46 |
+
if _T2M is None:
|
| 47 |
+
path = os.environ.get("AQ_T2M", "/tmp/aq4sub/t2m_normals.json"); _T2M = json.load(open(path)) if os.path.exists(path) else {}
|
| 48 |
+
if lat != lat or lon != lon:
|
| 49 |
+
return float("nan"), float("nan")
|
| 50 |
+
rec = _T2M.get(f"{round(lat * 2) / 2},{round(lon * 2) / 2}")
|
| 51 |
+
if not rec:
|
| 52 |
+
return float("nan"), float("nan")
|
| 53 |
+
v = rec.get(MONTHS[month - 1]) if month else rec.get("ANN")
|
| 54 |
+
if v is None or v == -999.0:
|
| 55 |
+
v = rec.get("ANN")
|
| 56 |
+
return (float(v) + 273.15 if v is not None and v != -999.0 else float("nan")), (float(rec["elev"]) if rec.get("elev") is not None else float("nan"))
|
| 57 |
+
|
| 58 |
+
|
| 59 |
+
def temperature_proxy(lat, doy):
|
| 60 |
+
"""Kelvin at the surface, a smooth climatology: 300 K at the equator falling with latitude, a seasonal swing
|
| 61 |
+
that grows with latitude and flips hemisphere (peak ~ 20 July north, ~ 20 January south). A proxy, not a record."""
|
| 62 |
+
lat = np.asarray(lat, float); doy = np.asarray(doy, float)
|
| 63 |
+
base = 300.0 - 0.45 * np.abs(lat) - 0.003 * lat ** 2
|
| 64 |
+
season = (6.0 + 0.25 * np.abs(lat)) * np.cos(2 * np.pi * (doy - 201.0) / 365.25) * np.sign(lat + 1e-9)
|
| 65 |
+
return base + season
|
| 66 |
+
|
| 67 |
+
|
| 68 |
+
def instant_features(SW, codes, y, m, d, hour_ut, lat=None, lon=None):
|
| 69 |
+
"""Φᵢ, τᵢ for every body at the instant; day length and the Sun's altitude when a place is given."""
|
| 70 |
+
jd = SW.julday(y, m, d, hour_ut); phi = []; tau = []
|
| 71 |
+
for b in BODIES:
|
| 72 |
+
try:
|
| 73 |
+
lon_deg, lat_deg, dist = SW.calc_ut(jd, codes[b])[0][:3]
|
| 74 |
+
except Exception:
|
| 75 |
+
phi.append(np.nan); tau.append(np.nan); continue
|
| 76 |
+
r = max(dist, 1e-6); phi.append(MASS[b] / r); tau.append(MASS[b] / r ** 3)
|
| 77 |
+
extra = [np.nan, np.nan]
|
| 78 |
+
if lat is not None and not (isinstance(lat, float) and math.isnan(lat)):
|
| 79 |
+
try:
|
| 80 |
+
sun_lon = SW.calc_ut(jd, codes["sun"])[0][0]; eps = 23.439 - 0.0000004 * (jd - 2451545.0)
|
| 81 |
+
decl = math.degrees(math.asin(math.sin(math.radians(eps)) * math.sin(math.radians(sun_lon))))
|
| 82 |
+
cosH = -math.tan(math.radians(lat)) * math.tan(math.radians(decl)); cosH = max(-1.0, min(1.0, cosH))
|
| 83 |
+
daylen = 2 * math.degrees(math.acos(cosH)) / 15.0
|
| 84 |
+
# the Sun's altitude at 09:00 local mean time: hour angle −45°
|
| 85 |
+
alt = math.degrees(math.asin(math.sin(math.radians(lat)) * math.sin(math.radians(decl)) + math.cos(math.radians(lat)) * math.cos(math.radians(decl)) * math.cos(math.radians(-45.0))))
|
| 86 |
+
extra = [daylen, alt]
|
| 87 |
+
except Exception:
|
| 88 |
+
pass
|
| 89 |
+
return phi, tau, extra
|
| 90 |
+
|
| 91 |
+
|
| 92 |
+
def build(df, SW, codes, cache):
|
| 93 |
+
n = len(df); F = {k: np.full((n, len(BODIES)), np.nan) for k in ("phi_a", "phi_b", "phi_s", "tau_a", "tau_b", "tau_s")}
|
| 94 |
+
Ta = np.full(n, np.nan); Tb = np.full(n, np.nan); day_a = np.full((n, 2), np.nan); day_b = np.full((n, 2), np.nan)
|
| 95 |
+
El_a = np.full(n, np.nan); El_b = np.full(n, np.nan); Tproxy_a = np.zeros(n); Tproxy_b = np.zeros(n)
|
| 96 |
+
for i, r in enumerate(df.itertuples(index=False)):
|
| 97 |
+
for slot, key_phi, key_tau, Tarr, darr, Elarr, Tpx in (("a", "phi_a", "tau_a", Ta, day_a, El_a, Tproxy_a), ("b", "phi_b", "tau_b", Tb, day_b, El_b, Tproxy_b)):
|
| 98 |
+
d = getattr(r, f"dob_{slot}"); lat = getattr(r, f"lat_{slot}"); lon = getattr(r, f"lon_{slot}")
|
| 99 |
+
if not isinstance(d, str) or d == "0000-00-00" or d.endswith("-00"):
|
| 100 |
+
continue
|
| 101 |
+
y, m, dd = int(d[:4]), int(d[5:7]), int(d[8:10]); lat = float(lat) if lat == lat else float("nan"); lon = float(lon) if lon == lon else float("nan")
|
| 102 |
+
hour_ut = 9.0 - (lon / 15.0 if lon == lon else 0.0); key = (d, round(hour_ut, 1), round(lat, 1) if lat == lat else None)
|
| 103 |
+
if key not in cache:
|
| 104 |
+
cache[key] = instant_features(SW, codes, y, m, dd, hour_ut, lat if lat == lat else None, lon)
|
| 105 |
+
phi, tau, extra = cache[key]; F[key_phi][i] = phi; F[key_tau][i] = tau; darr[i] = extra
|
| 106 |
+
if lat == lat:
|
| 107 |
+
tk, el = t_at_pob(lat, lon, m); Elarr[i] = el
|
| 108 |
+
if tk == tk:
|
| 109 |
+
Tarr[i] = tk
|
| 110 |
+
else:
|
| 111 |
+
doy = dt.date(y, m, dd).timetuple().tm_yday; Tarr[i] = temperature_proxy(lat, doy); Tpx[i] = 1.0
|
| 112 |
+
s = r.start
|
| 113 |
+
if isinstance(s, str) and not s.endswith("-00"):
|
| 114 |
+
y, m, dd = int(s[:4]), int(s[5:7]), int(s[8:10]); key = (s, 12.0, None)
|
| 115 |
+
if key not in cache:
|
| 116 |
+
cache[key] = instant_features(SW, codes, y, m, dd, 12.0)
|
| 117 |
+
F["phi_s"][i], F["tau_s"][i] = cache[key][0], cache[key][1]
|
| 118 |
+
# the features: per partner Φ/T, pair sums and |diffs| (even), start-sky Φ, τ, day lengths, altitudes
|
| 119 |
+
phiT_a = F["phi_a"] / Ta[:, None]; phiT_b = F["phi_b"] / Tb[:, None]
|
| 120 |
+
X = np.column_stack([F["phi_a"] + F["phi_b"], np.abs(F["phi_a"] - F["phi_b"]), F["tau_a"] + F["tau_b"], np.abs(F["tau_a"] - F["tau_b"]),
|
| 121 |
+
phiT_a + phiT_b, np.abs(phiT_a - phiT_b), np.nansum(phiT_a, 1) + np.nansum(phiT_b, 1), np.abs(np.nansum(phiT_a, 1) - np.nansum(phiT_b, 1)),
|
| 122 |
+
np.fmax(Ta, Tb), np.fmin(Ta, Tb), F["phi_s"], F["tau_s"], np.fmax(day_a[:, 0], day_b[:, 0]), np.fmin(day_a[:, 0], day_b[:, 0]), np.fmax(day_a[:, 1], day_b[:, 1]), np.fmin(day_a[:, 1], day_b[:, 1]),
|
| 123 |
+
np.fmax(El_a, El_b), np.fmin(El_a, El_b), Tproxy_a + Tproxy_b])
|
| 124 |
+
names = ([f"phi_sum_{b}" for b in BODIES] + [f"phi_absdiff_{b}" for b in BODIES] + [f"tau_sum_{b}" for b in BODIES] + [f"tau_absdiff_{b}" for b in BODIES]
|
| 125 |
+
+ [f"phiT_sum_{b}" for b in BODIES] + [f"phiT_absdiff_{b}" for b in BODIES] + ["phiT_total_sum", "phiT_total_absdiff", "T_max", "T_min"] + [f"phi_start_{b}" for b in BODIES] + [f"tau_start_{b}" for b in BODIES]
|
| 126 |
+
+ ["daylen_max", "daylen_min", "sunalt_max", "sunalt_min", "elev_max", "elev_min", "n_T_proxy"])
|
| 127 |
+
return X.astype(np.float32), names
|
| 128 |
+
|
| 129 |
+
|
| 130 |
+
def main():
|
| 131 |
+
import lightgbm as lgb
|
| 132 |
+
import sweshim as SW
|
| 133 |
+
SW.load(os.path.join(ROOT, "web", "ephem4.bin"), os.path.join(ROOT, "web", "tables.json"))
|
| 134 |
+
codes = {"sun": SW.SUN, "moon": SW.MOON, "mercury": SW.MERCURY, "venus": SW.VENUS, "mars": SW.MARS, "jupiter": SW.JUPITER, "saturn": SW.SATURN, "uranus": SW.URANUS, "neptune": SW.NEPTUNE, "pluto": SW.PLUTO}
|
| 135 |
+
Z = np.load(PH, allow_pickle=True); y = Z["y_train"].astype(np.int64); later = Z["yr_train"].astype(int).max(1); cuts = [np.quantile(later, q) for q in QS]
|
| 136 |
+
ptr, pte, pn = Z["plain_train"], Z["plain_test"], list(Z["plain_names"]); s1, s2 = list(Z["slots"])
|
| 137 |
+
tr = pd.read_csv(f"{SRC}/train.csv", dtype=str); te = pd.read_csv(f"{SRC}/test.csv", dtype=str)
|
| 138 |
+
for df in (tr, te):
|
| 139 |
+
for c in ("lat_a", "lon_a", "lat_b", "lon_b"):
|
| 140 |
+
df[c] = pd.to_numeric(df[c], errors="coerce")
|
| 141 |
+
cache = {}; Xtr, names = build(tr, SW, codes, cache); log(f"train physics {Xtr.shape} ({len(cache):,} instants)"); Xte, _ = build(te, SW, codes, cache); log(f"test physics {Xte.shape}")
|
| 142 |
+
ia, ib, iy = pn.index(f"age_{s1}_at_start"), pn.index(f"age_{s2}_at_start"), pn.index("start_year")
|
| 143 |
+
plain = lambda p: np.column_stack([np.fmax(p[:, ia], p[:, ib]), np.fmin(p[:, ia], p[:, ib]), np.abs(p[:, ia] - p[:, ib]), p[:, iy]])
|
| 144 |
+
prm = dict(n_estimators=300, learning_rate=0.05, num_leaves=15, min_child_samples=200, colsample_bytree=0.6, subsample=0.8, subsample_freq=1, reg_lambda=20.0, verbose=-1)
|
| 145 |
+
members_tr, members_te, mnames, meta = [], [], [], []
|
| 146 |
+
def member(Xa, Xb, name):
|
| 147 |
+
s_tr = np.full(len(y), np.nan)
|
| 148 |
+
for k in range(1, len(cuts)):
|
| 149 |
+
lo = cuts[k - 1]; blk = (later > lo) & ((later <= cuts[k]) if k < len(cuts) - 1 else True); c = lgb.LGBMClassifier(random_state=0, **prm); c.fit(Xa[later <= lo], y[later <= lo]); s_tr[blk] = c.predict_proba(Xa[blk])[:, 1]
|
| 150 |
+
c = lgb.LGBMClassifier(random_state=0, **prm); c.fit(Xa, y); s_te = c.predict_proba(Xb)[:, 1]
|
| 151 |
+
f = np.isfinite(s_tr) & (later > cuts[0]); o = auc(y[f], s_tr[f]); members_tr.append(s_tr); members_te.append(s_te); mnames.append(name); meta.append({"member": name, "forward_oof": o, "n_features": int(Xa.shape[1])})
|
| 152 |
+
log(f" {name:<46} {Xa.shape[1]:>3} features fwd-OOF {o:.4f}")
|
| 153 |
+
phiT = [i for i, n in enumerate(names) if n.startswith("phiT_")]
|
| 154 |
+
member(Xtr[:, phiT], Xte[:, phiT], "PHYSICS Φ/T only (entropic gravity, no ages)")
|
| 155 |
+
member(Xtr, Xte, "PHYSICS all (Φ, τ, Φ/T, T, day length, Sun altitude; no ages)")
|
| 156 |
+
member(np.column_stack([plain(ptr), Xtr]), np.column_stack([plain(pte), Xte]), "PLAIN + PHYSICS")
|
| 157 |
+
member(np.column_stack([plain(ptr), Xtr[:, phiT]]), np.column_stack([plain(pte), Xte[:, phiT]]), "PLAIN + Φ/T")
|
| 158 |
+
np.savez_compressed(os.path.join(OUT, "physics_members.npz"), S_train=np.column_stack(members_tr), S_test=np.column_stack(members_te), names=np.array(mnames), meta=json.dumps(meta), feature_names=np.array(names))
|
| 159 |
+
log(f"wrote {OUT}/physics_members.npz")
|
| 160 |
+
|
| 161 |
+
|
| 162 |
+
if __name__ == "__main__":
|
| 163 |
+
main()
|
robustness_audit.json
CHANGED
|
@@ -1,24 +1,24 @@
|
|
| 1 |
{
|
| 2 |
-
"clean_auc": 0.
|
| 3 |
"symmetry_max_abs_diff": 0.0,
|
| 4 |
"birthplaces \u00b10.5\u00b0": {
|
| 5 |
-
"mean_abs_dp": 0.
|
| 6 |
-
"auc": 0.
|
| 7 |
},
|
| 8 |
"birthplaces \u00b12\u00b0": {
|
| 9 |
-
"mean_abs_dp": 0.
|
| 10 |
-
"auc": 0.
|
| 11 |
},
|
| 12 |
"birth dates \u00b11 day": {
|
| 13 |
-
"mean_abs_dp":
|
| 14 |
-
"auc": 0.
|
| 15 |
},
|
| 16 |
"birth dates \u00b17 days": {
|
| 17 |
-
"mean_abs_dp": 0.
|
| 18 |
-
"auc": 0.
|
| 19 |
},
|
| 20 |
"birth hour anywhere": {
|
| 21 |
-
"mean_abs_dp": 9.
|
| 22 |
-
"auc": 0.
|
| 23 |
}
|
| 24 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"clean_auc": 0.72178778918975,
|
| 3 |
"symmetry_max_abs_diff": 0.0,
|
| 4 |
"birthplaces \u00b10.5\u00b0": {
|
| 5 |
+
"mean_abs_dp": 0.004649538574536345,
|
| 6 |
+
"auc": 0.7186884012864405
|
| 7 |
},
|
| 8 |
"birthplaces \u00b12\u00b0": {
|
| 9 |
+
"mean_abs_dp": 0.015669033728156517,
|
| 10 |
+
"auc": 0.7161985164436144
|
| 11 |
},
|
| 12 |
"birth dates \u00b11 day": {
|
| 13 |
+
"mean_abs_dp": 5.912262110422381e-05,
|
| 14 |
+
"auc": 0.7216451395372965
|
| 15 |
},
|
| 16 |
"birth dates \u00b17 days": {
|
| 17 |
+
"mean_abs_dp": 0.00021259295866501407,
|
| 18 |
+
"auc": 0.721541394335512
|
| 19 |
},
|
| 20 |
"birth hour anywhere": {
|
| 21 |
+
"mean_abs_dp": 9.183836105733368e-06,
|
| 22 |
+
"auc": 0.72178778918975
|
| 23 |
}
|
| 24 |
}
|
stack_iv_deployed.json
CHANGED
|
@@ -24,7 +24,7 @@
|
|
| 24 |
"lon_max",
|
| 25 |
"lon_min",
|
| 26 |
"same_place",
|
| 27 |
-
"
|
| 28 |
"n_missing_places",
|
| 29 |
"start_weekday",
|
| 30 |
"start_month"
|
|
@@ -61,67 +61,67 @@
|
|
| 61 |
"stage": 3,
|
| 62 |
"phasor": 22,
|
| 63 |
"step": 0.4,
|
| 64 |
-
"alpha": 0.
|
| 65 |
-
"c": -0.
|
| 66 |
-
"w_re": 0.
|
| 67 |
-
"w_im": -0.
|
| 68 |
-
"b_re": 0.
|
| 69 |
-
"b_im": -0.
|
| 70 |
},
|
| 71 |
{
|
| 72 |
"stage": 4,
|
| 73 |
"phasor": 36,
|
| 74 |
"step": 0.4,
|
| 75 |
-
"alpha": 0.
|
| 76 |
-
"c": -0.
|
| 77 |
-
"w_re": -0.
|
| 78 |
-
"w_im": 0.
|
| 79 |
-
"b_re": -0.
|
| 80 |
-
"b_im": 0.
|
| 81 |
},
|
| 82 |
{
|
| 83 |
"stage": 5,
|
| 84 |
"phasor": 7,
|
| 85 |
"step": 0.4,
|
| 86 |
-
"alpha": 0.
|
| 87 |
-
"c": -0.
|
| 88 |
-
"w_re": 0.
|
| 89 |
-
"w_im": 0.
|
| 90 |
-
"b_re": 0.
|
| 91 |
-
"b_im": 0.
|
| 92 |
},
|
| 93 |
{
|
| 94 |
"stage": 6,
|
| 95 |
-
"phasor":
|
| 96 |
"step": 0.4,
|
| 97 |
-
"alpha": -0.
|
| 98 |
-
"c": 0.
|
| 99 |
-
"w_re": 0.
|
| 100 |
-
"w_im": -0.
|
| 101 |
-
"b_re": -0.
|
| 102 |
-
"b_im": 0.
|
| 103 |
},
|
| 104 |
{
|
| 105 |
"stage": 7,
|
| 106 |
-
"phasor":
|
| 107 |
"step": 0.4,
|
| 108 |
-
"alpha": 0.
|
| 109 |
-
"c":
|
| 110 |
-
"w_re": 0.
|
| 111 |
-
"w_im": 0.
|
| 112 |
-
"b_re": 0.
|
| 113 |
-
"b_im": 0.
|
| 114 |
},
|
| 115 |
{
|
| 116 |
"stage": 8,
|
| 117 |
"phasor": 7,
|
| 118 |
"step": 0.4,
|
| 119 |
-
"alpha": -0.
|
| 120 |
-
"c": 0.
|
| 121 |
-
"w_re": 0.
|
| 122 |
-
"w_im": -0.
|
| 123 |
-
"b_re": -0.
|
| 124 |
-
"b_im": 0.
|
| 125 |
}
|
| 126 |
],
|
| 127 |
"inner_auc": null,
|
|
@@ -231,23 +231,23 @@
|
|
| 231 |
"stage": 2,
|
| 232 |
"phasor": 22,
|
| 233 |
"step": 0.4,
|
| 234 |
-
"alpha": -0.
|
| 235 |
-
"c": 0.
|
| 236 |
-
"w_re": 0.
|
| 237 |
-
"w_im": 0.
|
| 238 |
-
"b_re": -0.
|
| 239 |
-
"b_im": -0.
|
| 240 |
},
|
| 241 |
{
|
| 242 |
"stage": 3,
|
| 243 |
"phasor": 36,
|
| 244 |
"step": 0.4,
|
| 245 |
-
"alpha": -0.
|
| 246 |
-
"c": 0.
|
| 247 |
-
"w_re": -0.
|
| 248 |
-
"w_im": -0.
|
| 249 |
-
"b_re": 0.
|
| 250 |
-
"b_im": 0.
|
| 251 |
}
|
| 252 |
],
|
| 253 |
"inner_auc": null,
|
|
@@ -554,613 +554,613 @@
|
|
| 554 |
1.0
|
| 555 |
],
|
| 556 |
"GEO": [
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| 557 |
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|
| 558 |
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|
| 559 |
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|
| 560 |
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|
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|
| 562 |
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|
| 563 |
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|
| 564 |
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|
| 565 |
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|
| 566 |
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|
| 567 |
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|
| 568 |
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|
| 569 |
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|
| 570 |
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|
| 571 |
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|
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|
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|
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|
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|
| 580 |
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|
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|
| 582 |
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|
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|
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|
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| 613 |
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| 616 |
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| 759 |
"AM_GREEDY": [
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| 1166 |
"stacker": {
|
|
@@ -1172,30 +1172,30 @@
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|
| 1172 |
"weights": {
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| 1173 |
"0": {
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| 1174 |
"w": [
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| 1175 |
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0.
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| 1179 |
-
"b": 0.
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| 1180 |
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"n_fit":
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| 1181 |
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| 1182 |
"1": {
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"w": [
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0.0
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"b": -0.
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"n_fit":
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| 1190 |
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"w": [
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0.0,
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| 1197 |
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"b": 0.
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| 1198 |
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"n_fit":
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| 1199 |
}
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| 1200 |
},
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| 1201 |
"note": "logit = \u03a3 w\u00b7(rank \u2212 0.5) + b per group; member absent \u2192 rank term 0; weights fitted on forward-chained train OOF (later > 1867, recency-weighted, L2 1e-3); the two orders of a pair are averaged"
|
|
@@ -1210,7 +1210,7 @@
|
|
| 1210 |
"scores": {
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| 1211 |
"public_board": 0.64303,
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| 1213 |
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| 1214 |
},
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| 1215 |
"terms": {
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| 1216 |
"a": "|\u03b81 \u2212 \u03b82|: the absolute synastry angle between the two natal charts (even under swap)",
|
|
|
|
| 24 |
"lon_max",
|
| 25 |
"lon_min",
|
| 26 |
"same_place",
|
| 27 |
+
"start_year_only",
|
| 28 |
"n_missing_places",
|
| 29 |
"start_weekday",
|
| 30 |
"start_month"
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|
|
|
| 61 |
"stage": 3,
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| 62 |
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| 63 |
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| 67 |
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"w_im": -0.08743030419441683,
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"b_im": -0.463872964447608
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| 70 |
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| 71 |
{
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| 72 |
"stage": 4,
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| 73 |
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| 74 |
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"b_im": 0.38906276077602536
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| 82 |
{
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| 83 |
"stage": 5,
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| 84 |
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| 85 |
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| 86 |
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"b_im": 0.18586220341891332
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{
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| 96 |
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"b_im": 0.5470333082456773
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| 103 |
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| 104 |
{
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| 106 |
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"b_im": 0.6029890996340981
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| 114 |
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| 115 |
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| 117 |
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| 127 |
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| 231 |
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|
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"1": {
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"n_fit": 4698
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"n_fit": 38116
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}
|
| 1200 |
},
|
| 1201 |
"note": "logit = \u03a3 w\u00b7(rank \u2212 0.5) + b per group; member absent \u2192 rank term 0; weights fitted on forward-chained train OOF (later > 1867, recency-weighted, L2 1e-3); the two orders of a pair are averaged"
|
|
|
|
| 1210 |
"scores": {
|
| 1211 |
"public_board": 0.64303,
|
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"held_out_submitted": 0.6448,
|
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+
"held_out_scorer_path": 0.6981671903580872
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},
|
| 1215 |
"terms": {
|
| 1216 |
"a": "|\u03b81 \u2212 \u03b82|: the absolute synastry angle between the two natal charts (even under swap)",
|
stack_iv_predictor.py
CHANGED
|
@@ -148,8 +148,8 @@ def _geo_x(age1, age2, la, lo, lb, lob, start_year, jan1, wd=float("nan"), mo=fl
|
|
| 148 |
def predict(dob_1, lat_1, lon_1, dob_2, lat_2, lon_2, start):
|
| 149 |
"""Probability that the relationship lasted thirty years, with every member's part. Exactly symmetric in the pair."""
|
| 150 |
M = _M; labels = M["members"]["AM_GREEDY"]["labels"]
|
| 151 |
-
jan1 = 1.0 if start
|
| 152 |
-
wed = start
|
| 153 |
t1, t2, tw = theta(dob_1, lat_1, lon_1), theta(dob_2, lat_2, lon_2), theta(wed, natal=False)
|
| 154 |
P12, P21 = phases(t1, t2, tw, labels), phases(t2, t1, tw, labels)
|
| 155 |
g12, acc_g = am_logit(M["members"]["AM_GREEDY"], P12); g21, _ = am_logit(M["members"]["AM_GREEDY"], P21)
|
|
@@ -159,7 +159,9 @@ def predict(dob_1, lat_1, lon_1, dob_2, lat_2, lon_2, start):
|
|
| 159 |
age1 = float(ys - y1) if y1 else float("nan"); age2 = float(ys - y2) if y2 else float("nan")
|
| 160 |
import datetime as _dt
|
| 161 |
try:
|
| 162 |
-
|
|
|
|
|
|
|
| 163 |
except Exception:
|
| 164 |
wd_, mo_ = float("nan"), float("nan")
|
| 165 |
x = _geo_x(age1, age2, lat_1, lon_1, lat_2, lon_2, ys, jan1, wd_, mo_)
|
|
@@ -240,14 +242,14 @@ def best_matches(dob, lat, lon, start=None, top=20, min_age=18, max_age=100, cap
|
|
| 240 |
M = _M; labels = M["members"]["AM_GREEDY"]["labels"]; caps = capitals if capitals is not None else _CAPS
|
| 241 |
if caps is None:
|
| 242 |
raise RuntimeError("capitals not loaded — pass capitals=[{country, capital, lat, lon}, ...] or set stack_iv_predictor._CAPS")
|
| 243 |
-
today = dt.date.today(); start = start or today.isoformat(); jan1 = 1.0 if start
|
| 244 |
sy, sm, sd = int(start[:4]), max(1, int(start[5:7])), max(1, int(start[8:10]))
|
| 245 |
# the candidate days: everyone aged min_age..max_age at the start
|
| 246 |
d_lo = dt.date(sy - max_age, sm, sd); d_hi = dt.date(sy - min_age, sm, sd); ndays = (d_hi - d_lo).days + 1
|
| 247 |
days = [d_lo + dt.timedelta(days=i) for i in range(ndays)]; jds = np.array([_jd(d.year, d.month, d.day, 9.0) for d in days])
|
| 248 |
yrs = np.array([d.year for d in days]); cand_years = np.arange(yrs.min(), yrs.max() + 1)
|
| 249 |
# the person's and the start's phases (exact), the candidates' outer planets per day
|
| 250 |
-
t1 = theta(dob, lat, lon);
|
| 251 |
L = _outer_lon_by_day(jds); col = {b: j for j, b in enumerate(BODIES14)}
|
| 252 |
cand = np.full((ndays, len(BODIES14)), np.nan); cand[:, col["uranus"]] = L["uranus"]; cand[:, col["neptune"]] = L["neptune"]; cand[:, col["pluto"]] = L["pluto"]
|
| 253 |
# ArtaModel logits per day, both orders (person = slot 1 / slot 2), vectorised over the stages' phasors
|
|
@@ -271,7 +273,9 @@ def best_matches(dob, lat, lon, start=None, top=20, min_age=18, max_age=100, cap
|
|
| 271 |
# geography per (year, capital)
|
| 272 |
y1 = int(dob[:4]) if _prec(dob) else None; age1 = float(sy - y1) if y1 else float("nan")
|
| 273 |
try:
|
| 274 |
-
|
|
|
|
|
|
|
| 275 |
except Exception:
|
| 276 |
wd_, mo_ = float("nan"), float("nan")
|
| 277 |
rows = []
|
|
@@ -301,7 +305,7 @@ def best_matches(dob, lat, lon, start=None, top=20, min_age=18, max_age=100, cap
|
|
| 301 |
out.append({"dob": days[di].isoformat(), "country": cp["country"], "capital": cp["capital"], "lat": cp["lat"], "lon": cp["lon"],
|
| 302 |
"probability": float(1 / (1 + np.exp(-Z[di, ci]))), "geo_probability": float(geo[y_i, ci]), "am_greedy_logit": float(am_g[di]), "am_fixed_logit": float(am_f[di])})
|
| 303 |
return {"start": start, "group": group, "n_candidates": int(ndays * len(caps)), "n_days": int(ndays), "n_capitals": len(caps), "matches": out,
|
| 304 |
-
"note": "the candidate's phases are the outer planets at 09:00 UT of the day (weekly samples interpolated); the capital enters through the geography member"}
|
| 305 |
|
| 306 |
|
| 307 |
# ── THE BEST START DAY (electional) ─────���────────────────────────────────────────────────────────────────────────
|
|
@@ -313,8 +317,7 @@ def best_start_days(dob_1, lat_1, lon_1, dob_2, lat_2, lon_2, from_date=None, ye
|
|
| 313 |
import datetime as dt
|
| 314 |
M = _M; labels = M["members"]["AM_GREEDY"]["labels"]; col = {b: j for j, b in enumerate(BODIES14)}
|
| 315 |
today = dt.date.today(); d0 = dt.date.fromisoformat(from_date) if from_date else today
|
| 316 |
-
|
| 317 |
-
ndays = len(days)
|
| 318 |
jds = np.array([_jd(d.year, d.month, d.day, 12.0) for d in days])
|
| 319 |
t1, t2 = theta(dob_1, lat_1, lon_1), theta(dob_2, lat_2, lon_2)
|
| 320 |
L = _outer_lon_by_day(jds); TW = np.full((ndays, len(BODIES14)), np.nan); TW[:, col["uranus"]] = L["uranus"]; TW[:, col["neptune"]] = L["neptune"]; TW[:, col["pluto"]] = L["pluto"]
|
|
@@ -344,13 +347,15 @@ def best_start_days(dob_1, lat_1, lon_1, dob_2, lat_2, lon_2, from_date=None, ye
|
|
| 344 |
iu = col["uranus"]; hasA = np.isfinite(t1[iu]) and np.isfinite(t2[iu]); hasT = (np.isfinite(t1[iu]) or np.isfinite(t2[iu]))
|
| 345 |
group = "0" if hasT else ("1" if hasA else "2"); w = M["stacker"]["weights"].get(group) or M["stacker"]["weights"]["2"]
|
| 346 |
Z = w["w"][0] * (rk(geo, "GEO") - 0.5) + w["w"][1] * (rk(am_g, "AM_GREEDY") - 0.5) + w["w"][2] * (rk(am_f, "AM_FIXED") - 0.5) + w["b"]
|
| 347 |
-
|
| 348 |
-
|
| 349 |
-
#
|
| 350 |
-
|
| 351 |
for i, d in enumerate(days):
|
| 352 |
k = d.strftime("%Y-%m")
|
| 353 |
-
if k not in
|
| 354 |
-
|
| 355 |
-
|
| 356 |
-
|
|
|
|
|
|
|
|
|
| 148 |
def predict(dob_1, lat_1, lon_1, dob_2, lat_2, lon_2, start):
|
| 149 |
"""Probability that the relationship lasted thirty years, with every member's part. Exactly symmetric in the pair."""
|
| 150 |
M = _M; labels = M["members"]["AM_GREEDY"]["labels"]
|
| 151 |
+
jan1 = 1.0 if start.endswith("-00-00") else 0.0 # "year only" is spelled YYYY-00-00; a 1 January is a day
|
| 152 |
+
wed = start
|
| 153 |
t1, t2, tw = theta(dob_1, lat_1, lon_1), theta(dob_2, lat_2, lon_2), theta(wed, natal=False)
|
| 154 |
P12, P21 = phases(t1, t2, tw, labels), phases(t2, t1, tw, labels)
|
| 155 |
g12, acc_g = am_logit(M["members"]["AM_GREEDY"], P12); g21, _ = am_logit(M["members"]["AM_GREEDY"], P21)
|
|
|
|
| 159 |
age1 = float(ys - y1) if y1 else float("nan"); age2 = float(ys - y2) if y2 else float("nan")
|
| 160 |
import datetime as _dt
|
| 161 |
try:
|
| 162 |
+
if start.endswith("-00-00"): wd_, mo_ = float("nan"), float("nan")
|
| 163 |
+
elif start.endswith("-00"): wd_, mo_ = float("nan"), float(int(start[5:7]))
|
| 164 |
+
else: _sd = _dt.date(ys, int(start[5:7]), int(start[8:10])); wd_, mo_ = float(_sd.weekday()), float(_sd.month)
|
| 165 |
except Exception:
|
| 166 |
wd_, mo_ = float("nan"), float("nan")
|
| 167 |
x = _geo_x(age1, age2, lat_1, lon_1, lat_2, lon_2, ys, jan1, wd_, mo_)
|
|
|
|
| 242 |
M = _M; labels = M["members"]["AM_GREEDY"]["labels"]; caps = capitals if capitals is not None else _CAPS
|
| 243 |
if caps is None:
|
| 244 |
raise RuntimeError("capitals not loaded — pass capitals=[{country, capital, lat, lon}, ...] or set stack_iv_predictor._CAPS")
|
| 245 |
+
today = dt.date.today(); start = start or today.isoformat(); jan1 = 1.0 if start.endswith("-00-00") else 0.0
|
| 246 |
sy, sm, sd = int(start[:4]), max(1, int(start[5:7])), max(1, int(start[8:10]))
|
| 247 |
# the candidate days: everyone aged min_age..max_age at the start
|
| 248 |
d_lo = dt.date(sy - max_age, sm, sd); d_hi = dt.date(sy - min_age, sm, sd); ndays = (d_hi - d_lo).days + 1
|
| 249 |
days = [d_lo + dt.timedelta(days=i) for i in range(ndays)]; jds = np.array([_jd(d.year, d.month, d.day, 9.0) for d in days])
|
| 250 |
yrs = np.array([d.year for d in days]); cand_years = np.arange(yrs.min(), yrs.max() + 1)
|
| 251 |
# the person's and the start's phases (exact), the candidates' outer planets per day
|
| 252 |
+
t1 = theta(dob, lat, lon); tw = theta(start, natal=False)
|
| 253 |
L = _outer_lon_by_day(jds); col = {b: j for j, b in enumerate(BODIES14)}
|
| 254 |
cand = np.full((ndays, len(BODIES14)), np.nan); cand[:, col["uranus"]] = L["uranus"]; cand[:, col["neptune"]] = L["neptune"]; cand[:, col["pluto"]] = L["pluto"]
|
| 255 |
# ArtaModel logits per day, both orders (person = slot 1 / slot 2), vectorised over the stages' phasors
|
|
|
|
| 273 |
# geography per (year, capital)
|
| 274 |
y1 = int(dob[:4]) if _prec(dob) else None; age1 = float(sy - y1) if y1 else float("nan")
|
| 275 |
try:
|
| 276 |
+
if start.endswith("-00-00"): wd_, mo_ = float("nan"), float("nan")
|
| 277 |
+
elif start.endswith("-00"): wd_, mo_ = float("nan"), float(sm)
|
| 278 |
+
else: _sd = dt.date(sy, sm, sd); wd_, mo_ = float(_sd.weekday()), float(_sd.month)
|
| 279 |
except Exception:
|
| 280 |
wd_, mo_ = float("nan"), float("nan")
|
| 281 |
rows = []
|
|
|
|
| 305 |
out.append({"dob": days[di].isoformat(), "country": cp["country"], "capital": cp["capital"], "lat": cp["lat"], "lon": cp["lon"],
|
| 306 |
"probability": float(1 / (1 + np.exp(-Z[di, ci]))), "geo_probability": float(geo[y_i, ci]), "am_greedy_logit": float(am_g[di]), "am_fixed_logit": float(am_f[di])})
|
| 307 |
return {"start": start, "group": group, "n_candidates": int(ndays * len(caps)), "n_days": int(ndays), "n_capitals": len(caps), "matches": out,
|
| 308 |
+
"note": "the candidate's phases are the outer planets at 09:00 UT of the day (weekly samples interpolated); the capital enters through the geography member; a year-only start is spelled YYYY-00-00"}
|
| 309 |
|
| 310 |
|
| 311 |
# ── THE BEST START DAY (electional) ─────���────────────────────────────────────────────────────────────────────────
|
|
|
|
| 317 |
import datetime as dt
|
| 318 |
M = _M; labels = M["members"]["AM_GREEDY"]["labels"]; col = {b: j for j, b in enumerate(BODIES14)}
|
| 319 |
today = dt.date.today(); d0 = dt.date.fromisoformat(from_date) if from_date else today
|
| 320 |
+
ndays = int(round(365.2425 * years)); days = [d0 + dt.timedelta(days=i) for i in range(ndays)] # every day, 1 January included: "year only" is spelled YYYY-00-00
|
|
|
|
| 321 |
jds = np.array([_jd(d.year, d.month, d.day, 12.0) for d in days])
|
| 322 |
t1, t2 = theta(dob_1, lat_1, lon_1), theta(dob_2, lat_2, lon_2)
|
| 323 |
L = _outer_lon_by_day(jds); TW = np.full((ndays, len(BODIES14)), np.nan); TW[:, col["uranus"]] = L["uranus"]; TW[:, col["neptune"]] = L["neptune"]; TW[:, col["pluto"]] = L["pluto"]
|
|
|
|
| 347 |
iu = col["uranus"]; hasA = np.isfinite(t1[iu]) and np.isfinite(t2[iu]); hasT = (np.isfinite(t1[iu]) or np.isfinite(t2[iu]))
|
| 348 |
group = "0" if hasT else ("1" if hasA else "2"); w = M["stacker"]["weights"].get(group) or M["stacker"]["weights"]["2"]
|
| 349 |
Z = w["w"][0] * (rk(geo, "GEO") - 0.5) + w["w"][1] * (rk(am_g, "AM_GREEDY") - 0.5) + w["w"][2] * (rk(am_f, "AM_FIXED") - 0.5) + w["b"]
|
| 350 |
+
row = lambda i: {"start": days[i].isoformat(), "weekday": days[i].strftime("%A"), "probability": float(1 / (1 + np.exp(-Z[i]))), "geo_probability": float(geo[i]), "am_greedy_logit": float(am_g[i]), "am_fixed_logit": float(am_f[i])}
|
| 351 |
+
# TWENTY OPTIONS, not twenty copies of one week: the best day of each calendar month, ranked, `top` of them —
|
| 352 |
+
# consecutive Tuesdays of one month are one option, not twenty (operator 2026-08-19)
|
| 353 |
+
best_in_month = {}
|
| 354 |
for i, d in enumerate(days):
|
| 355 |
k = d.strftime("%Y-%m")
|
| 356 |
+
if k not in best_in_month or Z[i] > Z[best_in_month[k]]:
|
| 357 |
+
best_in_month[k] = i
|
| 358 |
+
options = [dict(row(i), month=k) for k, i in sorted(best_in_month.items(), key=lambda kv: -Z[kv[1]])][:top]
|
| 359 |
+
raw = [row(i) for i in np.argsort(-Z)[:top]]
|
| 360 |
+
return {"from": d0.isoformat(), "years": years, "n_days": ndays, "group": group, "best_days": options, "raw_top_days": raw, "note": "every calendar day scored; a year-only start is spelled YYYY-00-00",
|
| 361 |
+
"best_day_per_month": [dict(row(i), month=k) for k, i in sorted(best_in_month.items())]}
|
t2m_normals.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
zodiac_members_iv.py
ADDED
|
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
zodiac_members_iv.py — WESTERN vs EASTERN zodiacs as stack members (operator 2026-08-20). The phases in phases.npz
|
| 3 |
+
are sidereal Lahiri; every other zodiac is the same longitudes shifted by (ayanāṁśa difference)(t) — so the
|
| 4 |
+
TROPICAL, FAGAN-BRADLEY (western sidereal), KRISHNAMURTI, RAMAN and YUKTESHWAR zodiacs come free from sweshim's
|
| 5 |
+
ayanāṁśa table, no chart recast. What differs between systems is everything SIGN-based:
|
| 6 |
+
per system: each partner's Sun/Moon/Venus SIGN (order-free pair encoding), same-sign flags, element (fire/earth/
|
| 7 |
+
air/water) match, mode (cardinal/fixed/mutable) match, the classical sign-distance (1..6) of the pair's Suns and
|
| 8 |
+
Moons, the start-day Sun sign
|
| 9 |
+
One LightGBM member per zodiac system (no ages), one PLAIN + ALL-ZODIACS member; forward-chained OOF.
|
| 10 |
+
Writes AQ_OUT/zodiac_members.npz.
|
| 11 |
+
"""
|
| 12 |
+
import json
|
| 13 |
+
import os
|
| 14 |
+
import sys
|
| 15 |
+
import time
|
| 16 |
+
|
| 17 |
+
import numpy as np
|
| 18 |
+
import pandas as pd
|
| 19 |
+
|
| 20 |
+
HERE = os.path.dirname(os.path.abspath(__file__)); ROOT = os.path.dirname(os.path.dirname(HERE)); sys.path.insert(0, HERE); sys.path.insert(0, os.path.join(ROOT, "web"))
|
| 21 |
+
from artamodel import auc # noqa: E402
|
| 22 |
+
|
| 23 |
+
PH = os.environ.get("AQ_PHASES", "/tmp/aq4feat/phases.npz"); OUT = os.environ.get("AQ_OUT", "/tmp/aq4sub")
|
| 24 |
+
QS = (0.40, 0.55, 0.70, 0.85, 1.0); T0 = time.time(); log = lambda *a: print(f"[{time.time()-T0:6.0f}s]", *a, flush=True)
|
| 25 |
+
SYSTEMS = ["TROPICAL", "FAGAN_BRADLEY", "KRISHNAMURTI", "RAMAN", "YUKTESHWAR"]
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
def aya_diff_tables(SW, years):
|
| 29 |
+
"""For each system, ayanāṁśa(Lahiri) − ayanāṁśa(system) per year (0 for tropical means: add Lahiri back)."""
|
| 30 |
+
modes = {"FAGAN_BRADLEY": SW.SIDM_FAGAN_BRADLEY, "KRISHNAMURTI": SW.SIDM_KRISHNAMURTI, "RAMAN": SW.SIDM_RAMAN, "YUKTESHWAR": SW.SIDM_YUKTESHWAR}
|
| 31 |
+
out = {}
|
| 32 |
+
jd = {y: SW.julday(int(y), 7, 1, 0.0) for y in years}
|
| 33 |
+
SW.set_sid_mode(SW.SIDM_LAHIRI); lah = {y: SW.get_ayanamsa_ut(jd[y]) for y in years}
|
| 34 |
+
out["TROPICAL"] = {y: lah[y] for y in years} # sidereal + ayanāṁśa = tropical
|
| 35 |
+
for name, mode in modes.items():
|
| 36 |
+
SW.set_sid_mode(mode); out[name] = {y: lah[y] - SW.get_ayanamsa_ut(jd[y]) for y in years}
|
| 37 |
+
SW.set_sid_mode(SW.SIDM_LAHIRI)
|
| 38 |
+
return out
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
ELEM = np.arange(12) % 4; MODE = np.arange(12) % 3
|
| 42 |
+
|
| 43 |
+
|
| 44 |
+
def sign_feats(sunA, sunB, moonA, moonB, venA, venB, sunS):
|
| 45 |
+
sA, sB = np.floor(sunA / 30) % 12, np.floor(sunB / 30) % 12; mA, mB = np.floor(moonA / 30) % 12, np.floor(moonB / 30) % 12
|
| 46 |
+
vA, vB = np.floor(venA / 30) % 12, np.floor(venB / 30) % 12; sS = np.floor(sunS / 30) % 12
|
| 47 |
+
def dist(a, b):
|
| 48 |
+
d = np.abs(a - b); return np.fmin(d, 12 - d)
|
| 49 |
+
take = lambda tab, idx: np.where(np.isfinite(idx), tab[np.nan_to_num(idx).astype(int) % 12], np.nan)
|
| 50 |
+
return np.column_stack([np.fmax(sA, sB), np.fmin(sA, sB), dist(sA, sB), (sA == sB).astype(float),
|
| 51 |
+
(take(ELEM, sA) == take(ELEM, sB)).astype(float), (take(MODE, sA) == take(MODE, sB)).astype(float),
|
| 52 |
+
np.fmax(mA, mB), np.fmin(mA, mB), dist(mA, mB), (mA == mB).astype(float), (take(ELEM, mA) == take(ELEM, mB)).astype(float),
|
| 53 |
+
dist(sA, mB), dist(sB, mA), # sun-moon crossings, order-free as a set
|
| 54 |
+
np.fmax(vA, vB), np.fmin(vA, vB), dist(vA, vB), sS, dist(sA, sS) + dist(sB, sS)])
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
def main():
|
| 58 |
+
import lightgbm as lgb
|
| 59 |
+
import sweshim as SW
|
| 60 |
+
SW.load(os.path.join(ROOT, "web", "ephem4.bin"), os.path.join(ROOT, "web", "tables.json"))
|
| 61 |
+
Z = np.load(PH, allow_pickle=True); y = Z["y_train"].astype(np.int64); later = Z["yr_train"].astype(int).max(1); cuts = [np.quantile(later, q) for q in QS]
|
| 62 |
+
bodies = list(Z["bodies"]); s1, s2 = list(Z["slots"]); ptr, pte, pn = Z["plain_train"], Z["plain_test"], list(Z["plain_names"])
|
| 63 |
+
A, B, W = Z[f"theta_{s1}_train"], Z[f"theta_{s2}_train"], Z["theta_wed_train"]; Ae, Be, We = Z[f"theta_{s1}_test"], Z[f"theta_{s2}_test"], Z["theta_wed_test"]
|
| 64 |
+
ysA = Z["yr_train"][:, 0].astype(int); ysB = Z["yr_train"][:, 1].astype(int)
|
| 65 |
+
ysAe = pd.read_csv("/tmp/aq4/test.csv", dtype=str).dob_a.str[:4].astype(int).to_numpy(); ysBe = pd.read_csv("/tmp/aq4/test.csv", dtype=str).dob_b.str[:4].astype(int).to_numpy()
|
| 66 |
+
yrs = sorted(set(ysA[ysA > 0]) | set(ysB[ysB > 0]) | set(ysAe) | set(ysBe) | set(ptr[:, pn.index("start_year")].astype(int)) | set(pte[:, pn.index("start_year")].astype(int)))
|
| 67 |
+
D = aya_diff_tables(SW, yrs); log(f"ayanāṁśa tables for {len(yrs)} years, {len(SYSTEMS)} systems")
|
| 68 |
+
i_sun, i_moon, i_ven = bodies.index("sun"), bodies.index("moon"), bodies.index("venus")
|
| 69 |
+
sy_tr = ptr[:, pn.index("start_year")].astype(int); sy_te = pte[:, pn.index("start_year")].astype(int)
|
| 70 |
+
ia, ib, iy = pn.index(f"age_{s1}_at_start"), pn.index(f"age_{s2}_at_start"), pn.index("start_year")
|
| 71 |
+
plain = lambda p: np.column_stack([np.fmax(p[:, ia], p[:, ib]), np.fmin(p[:, ia], p[:, ib]), np.abs(p[:, ia] - p[:, ib]), p[:, iy]])
|
| 72 |
+
prm = dict(n_estimators=300, learning_rate=0.05, num_leaves=15, min_child_samples=200, colsample_bytree=0.7, subsample=0.8, subsample_freq=1, reg_lambda=20.0, verbose=-1)
|
| 73 |
+
members_tr, members_te, names, meta = [], [], [], []
|
| 74 |
+
def member(Xa, Xb, name):
|
| 75 |
+
s_tr = np.full(len(y), np.nan)
|
| 76 |
+
for k in range(1, len(cuts)):
|
| 77 |
+
lo = cuts[k - 1]; blk = (later > lo) & ((later <= cuts[k]) if k < len(cuts) - 1 else True); c = lgb.LGBMClassifier(random_state=0, **prm); c.fit(Xa[later <= lo], y[later <= lo]); s_tr[blk] = c.predict_proba(Xa[blk])[:, 1]
|
| 78 |
+
c = lgb.LGBMClassifier(random_state=0, **prm); c.fit(Xa, y); s_te = c.predict_proba(Xb)[:, 1]
|
| 79 |
+
f = np.isfinite(s_tr) & (later > cuts[0]); o = auc(y[f], s_tr[f]); members_tr.append(s_tr); members_te.append(s_te); names.append(name); meta.append({"member": name, "forward_oof": o})
|
| 80 |
+
log(f" {name:<40} fwd-OOF {o:.4f}")
|
| 81 |
+
allX_tr, allX_te = [], []
|
| 82 |
+
for sysname in SYSTEMS:
|
| 83 |
+
off_tr_a = np.array([D[sysname].get(v, np.nan) for v in ysA]); off_tr_b = np.array([D[sysname].get(v, np.nan) for v in ysB]); off_tr_s = np.array([D[sysname].get(v, np.nan) for v in sy_tr])
|
| 84 |
+
off_te_a = np.array([D[sysname].get(v, np.nan) for v in ysAe]); off_te_b = np.array([D[sysname].get(v, np.nan) for v in ysBe]); off_te_s = np.array([D[sysname].get(v, np.nan) for v in sy_te])
|
| 85 |
+
Xa = sign_feats((A[:, i_sun] + off_tr_a) % 360, (B[:, i_sun] + off_tr_b) % 360, (A[:, i_moon] + off_tr_a) % 360, (B[:, i_moon] + off_tr_b) % 360,
|
| 86 |
+
(A[:, i_ven] + off_tr_a) % 360, (B[:, i_ven] + off_tr_b) % 360, (W[:, i_sun] + off_tr_s) % 360)
|
| 87 |
+
Xb = sign_feats((Ae[:, i_sun] + off_te_a) % 360, (Be[:, i_sun] + off_te_b) % 360, (Ae[:, i_moon] + off_te_a) % 360, (Be[:, i_moon] + off_te_b) % 360,
|
| 88 |
+
(Ae[:, i_ven] + off_te_a) % 360, (Be[:, i_ven] + off_te_b) % 360, (We[:, i_sun] + off_te_s) % 360)
|
| 89 |
+
member(Xa, Xb, f"ZODIAC {sysname} signs (no ages)"); allX_tr.append(Xa); allX_te.append(Xb)
|
| 90 |
+
member(np.column_stack([plain(ptr)] + allX_tr), np.column_stack([plain(pte)] + allX_te), "PLAIN + ALL ZODIAC SIGN SYSTEMS")
|
| 91 |
+
np.savez_compressed(os.path.join(OUT, "zodiac_members.npz"), S_train=np.column_stack(members_tr), S_test=np.column_stack(members_te), names=np.array(names), meta=json.dumps(meta))
|
| 92 |
+
log(f"wrote {OUT}/zodiac_members.npz")
|
| 93 |
+
|
| 94 |
+
|
| 95 |
+
if __name__ == "__main__":
|
| 96 |
+
main()
|