Notice bibliographique
Résumé
Overall, there were 463 commits that closed 259 issues since our last release on 2024-01-31. Changes by Package libpysal v4.12.0 #757: W to g guide #756: exposing mapping_distance in travel builder #233: [WIP] stop sorting ids by default #299: [WIP] remove Ids attribute #505: New changelog conventions #365: start on perimeter weighting with just pygeos #356: [WIP] initial draft of network weights #626: allow ids in graph when passing sparse #151: network weights #755: add graph from travel network #369: refactor examples #490: Logic for pushing tags to trigger release needs to be revisited #375: pygeos-based contiguity #733: W to g guide #754: DOC: move Graph from experimental to stable API #740: ENH: add xarray interface to Graph #753: make asymmetry computation in Graph summary optional #739: fix higher_order contiguity including lower order #738: BUG: higher_order(lower_order=True) not working in edge cases #742: ENH: add Graph.summary with s0, s1, s2 and similar properties to Graph #752: ENH: add index_pairs to Graph #746: BUG: fix categorical lag for custom index #743: BUG: fix handling of non-isolate self-weights of 0 and order preservation #751: adjust CI env name & add Graph to README #750: update README with mention of Graph #749: update CI env naming conventions #534: Weights sprint planning #444: Does release GHA work? #315: scikit-geometry? #747: ensure that Graph.repr fits to 80 characters #745: added examples to graph #744: test against intel & apple silicon #573: BUG: relative neighbourhood depends on the order of observations #736: [pre-commit.ci] pre-commit autoupdate #737: ENH: use representative_point instead of centroid in Graph plotting #735: Centroid or representative point in graph plotting #698: ENH: Graph IO to classic weights file formats #732: CI: do not xfail momepy in reverse checks #729: Use importlib to implement simport #731: CI: ignore spvcm, add osmnx to env, xfail stuff out of control in reverse dep testing #726: Use fixtures for test data that uses the network #728: DOC: Fix a couple Sphinx warnings #727: DOC: Remove unused mkdocs-jupyter dependency #725: COMPAT: ensure argsort output has a stable order in numpy 2 #724: [CI] precision failure on ubuntu-latest, ci/312-no-optional.yaml [2024-06-19] #723: COMPAT: fix numpy 2.0 incompatibility #722: Failure due to numpy 2.0 deprecation #721: inserted chicagoSDOH as sample data #720: ENH: pass kwargs to buffer in fuzzy_contiguity #695: import libpysal stuck on loading remote examples #718: Add timeout in request to handle off-line use cases in examples #719: COMPAT: remove typing from Graph.describe #716: Categorical spatial lag using the Graph #711: ENH: implement Graph.repr #717: ENH: include Graph.describe() to describe neighbourhood values #529: [DO NOT MERGE] Change to the new sparse arrays and see what breaks #715: PERF: sorting-related improvements in Graph #714: Refactor handling of coincident points in triangulation #713: Co-location issues with Graph triangulation builders #694: ENH: add Graph.build_h3 #667: Build Graph from H3 #710: PERF: don't build coincident lookup if not needed #709: BUG: misaligned weights in the gabriel graph #708: add back numba in Py312 tests #590: check when numba is ready for Python 3.12 #703: DOC: add examples to build_contiguity and build_triangulation #697: REF: minor performance improvements in Graph #704: Added examples to from_dicts and build_knn. #705: DOC: Add examples to build_distance_band #702: DOC: add examples to build_block_contiguity #701: DOC: add example to Graph.to_W #700: DOC: clarify the requirements of the canonical sorting of Graph index #687: Ensure Graph.sparse is robust enough #696: [pre-commit.ci] pre-commit autoupdate #693: Work around GEOS issue in voronoi_frames #691: REF: refactor Graph.to_W to avoid perf bottleneck #672: Graph.to_W is slow #692: COMPAT: compatibility with pandas 3 #678: ENH: geometry agnostic Voronoi based on shapely #665: Interest in Optimal Spatial Matching? #685: REF: remove usage of deprecated cascaded_union #686: TST: resolve FutureWarnings in graph apply tests #684: ImportError: cannot import name 'np' from 'libpysal.common' #683: Bump codecov/codecov-action from 3 to 4 pointpats v2.5.0 #140: update min supported Python and testing workflow #141: bump & sync min reqs [2024-06] #139: minimum support Python [2024-06] #138: fix typo in random.normal function #133: REF: reimplement mrr on top of shapely #136: localknox #137: Typo in random.normal method that results in errors #134: keep members of local knox hotspots spaghetti v1.7.6 #771: #770 -- doctests after full tests #770: doctests as separate action or workflow #769: update docstring tests -- numpy-2.0 failures #767: current CI failures [2024-06-19] #768: fix doctests in CI -- numpy 2.0 #765: [pre-commit.ci] pre-commit autoupdate #764: gpd & shp as hard reqs -- no optional testing -- #763 #763: 310-no-optional CI failures [2024-03-16] #762: 312-dev CI failures [2024-03-16] #761: Bump codecov/codecov-action from 3 to 4 momepy v0.7.2 #625: DOC: update user guide to avoid MultiIndex #631: DOC: update rest of the guide #628: DOC: User guide fixes for elements examples #626: REF: remove result_index attribute from describe_agg #627: REF: do not return building_id from generate_blocks #606: BUG: verify handling of MultiIndex #622: ENH: either support MultiIndex or raise an error when one is given #624: DOC: update docstrings to match numpy2 outputs #623: TYP: add type hints to the graph module #579: API: distinction between libpysal and networkx graphs #621: BUG: fix a case when there's only a single building to be passed to voronoi_frames #620: ENH: add mean_deviation #619: API: deprecate legacy API in favour of Graph-based functional implementation #612: API: deprecate legacy API #618: minor type hinting fix #617: API: allow silencing of FutureWarnings from legacy API #616: DOC: expose get_nearest_node, fix fmt #615: DOC: remaining examples in the new API #610: DOC: add examples #614: update precommit to ruff docs dir #613: DOC: ruff user guide #611: DOC: add first batch of examples + testing #609: Faster node density #608: BUG: fix describe_ function when count is not present #543: GHA: switch to autogenerated release notes #582: BUG/ENH: support custom enclosure index is in tessellation and GeoDataFrame as enclosure input #592: DOC: add citation.cff #544: DOC: update dev installation instructions #576: functional node_density implementation #575: functional reached calculations #572: functional courtyards calculation #570: ENH: describe as a replacement of AverageCharacter #566: ENH: street_alignment and get_nearest_street #559: ENH: refactor tessellation #557: ENH: add neighbors #556: ENH: mean_interbuilding_distance and building_adjacency #555: ENH: neighbor_distance using Graph and new API #554: ENH: add alignment to the new API #553: ENH: add orientation and shared_walls functional versions #600: ENH: add get_nearest_node #593: Street profile #590: ENH: adaptive buffer as a tessellation limit #589: Functional percentiles #588: Functional distribution #587: Functional dimension #586: Functional density #584: _describe API refactoring #583: Functional arearatio #581: Functional diversity #580: Functional blocks #607: Update release.yml - troubleshoot release action failure #604: gha for release notes - #543 #396: ENH: helper functions for geometry-based network simplification #461: TestDistribution.test_MeanInterbuildingDistance failure on dev #304: Add GitHub Action to build and push container #537: BUG: tessellation may produce overlapping polygons #261: EHN: Add SkyViewFactor #603: DOC: execute notebooks and ensure they're tested #264: ENH: Simple Building Volume Density #465: ENH: helper functions part 1 #562: test efficiency of graphblas in straightness #602: API: return morphological tessellation as a GeoDataFrame #601: DOC: create usable env on RTD, update clustering #599: DOC: User guide refresh #598: DOC: note on a precision issue in enclosed_tessellation #597: DOC: rework API docs around the new functional stuff #596: COMPAT: numpy 2.0 compatibility #585: Functional count #594: DEP: bump libpysal minimum to 4.11 #406: API: deprecation decorators for transition to functional API #317: Add citation.cff #310: ENH: refactor get_network_id based on sindex.nearest_all #359: Add osmnx_like keyword to gdf_to_nx #478: Enclosure function does not work correctly #525: Networkx deprecations #497: Resolve geopandas deprecations #577: more complete linting & formatting - docs & benchmarks #578: linting & formatting for benchmarks/* and docs/* spreg v1.5.0 #138: Minor adjustments to printouts and spatial impacts #136: Version 1.5 #137: Update unittests.yml -- manual trigger #130: Bump actions/github-script from 6 to 7 #131: Bump conda-incubator/setup-miniconda from 2 to 3 #133: Bump actions/cache from 3 to 4 #134: Bump codecov/codecov-action from 3 to 4 mapclassify v2.7.0 #211: WIP classify to rgba #216: plot histogram with class bins #221: [pre-commit.ci] pre-commit autoupdate #215: Add monthly downloads badge to README #217: doctest failures [2024-06-23] #220: CI: test against Python 3.12 #219: CI: doctest only on ubuntu latest #218: CI: ensure 3.9 envs are compatible #214: [CI] failing dev from libpysal.graph --> numpy.float_ #210: COMPAT: make greedy compatible with future #209: 311-dev CI failures [2024-04-01] #208: [pre-commit.ci] pre-commit autoupdate #207: 311-dev CI failures [2024-03-16] #206: Bump codecov/codecov-action from 3 to 4 <
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,005 |
| Méta-épidémiologie (sens large) | 0,002 | 0,005 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,011 |
| Science ouverte | 0,008 | 0,010 |
| Intégrité de la recherche | 0,003 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,283 | 0,375 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».