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3.13
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README.md
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README.md
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---
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bibliography: REFERENCES.bib
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...
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# Carousel
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*A simple Stable Matching solver.*
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`carousel` is a solver for the
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[Envy-free](https://en.wikipedia.org/wiki/Envy-free_matching)
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[Stable matching problem](https://en.wikipedia.org/wiki/Stable_marriage_problem) based on some naive modifications to the
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[Gale-Shapley Algorithm](https://en.wikipedia.org/wiki/Gale%E2%80%93Shapley_algorithm), written in Python.
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## Algorithms
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### Gale-Shapley Deferred Acceptance
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- Gives a solution to the basic problem.
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### GS + Rotation Enumeration of Solutions
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|
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- Can implement post-selection measures/constraints/criteria.
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||||
|
||||
### Integer Programming (Huang++)
|
||||
|
||||
### Polytope Solution Sampling (Large Problems)
|
||||
|
||||
### Brute-Force Combinatoric Search
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||||
A benchmark/testbed implementation. Beware.
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|
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### More?
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Contribute!
|
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|
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## Data - Input/Output
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All [input table formats supported by `polars`](https://docs.pola.rs/user-guide/io/) are supported by `carousel` (`csv`, `excel`, `json` to name a few),
|
||||
which accepts a few inter-related tabular schemes for the input/output data.
|
||||
|
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### Input
|
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Input describes the preferences/rankings of the "applicants" of "reviewers" to which they will be matched (possibly many-to-one, as in the "College Admission Problem"), as well as the preferences/rankings for the reviwers of applicants.
|
||||
Input should be in one of two forms:
|
||||
|
||||
#### Preferences
|
||||
Preferences enumerate by-name some preferences in descending order,
|
||||
e.g. the fruit preferences of Alice, Bob and Charlie are:
|
||||
|
||||
| Alice | Bob | Charlie |
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||||
|--------|--------|---------|
|
||||
| apple | banana | cherry |
|
||||
| cherry | apple | banana |
|
||||
| banana | cherry | apple |
|
||||
|
||||
where for e.g. Alice prefers apples to cherries (so they appear higher in her preferences).
|
||||
|
||||
#### Rankings
|
||||
Rankings are like preferences, but are numerically ordered against a list of things;
|
||||
e.g. Alice, Bob and Charlie rank the fruit apples, bananas and cherries as:
|
||||
|
||||
| fruit | Alice | Bob | Charlie |
|
||||
|--------|-------|-----|---------|
|
||||
| apple | 1 | 2 | 3 |
|
||||
| banana | 3 | 1 | 2 |
|
||||
| cherry | 2 | 3 | 1 |
|
||||
|
||||
### Intermediate
|
||||
|
||||
#### Ranking Matrix
|
||||
In order to perform a matching, `carousel` either needs a pair of preferences
|
||||
(e.g. a set of doctor's preferences for residencies, and a set of residencies' preferences for doctors),
|
||||
a pair of corresponding rankings. A ranking matrix is a concise way to express
|
||||
a pair of rankings - used to display rankings:
|
||||
|
||||
| names | Alice | Bob | Charlie |
|
||||
|---------|------------|---------|-----------|
|
||||
| Baylor | (1, 3) | (2, 2) | (3, 1) |
|
||||
| CaseMed | (3, 2) | (1, 1) | (2, 3) |
|
||||
| Emory | (2, 1) | (3, 3) | (1, 2) |
|
||||
|
||||
|
||||
#### Priorities
|
||||
Internally, preferences/rankings are often serialized to rows as a priority listing of the form:
|
||||
|
||||
| App. | Rank | Rev |
|
||||
|-------|---|--------|
|
||||
| Alice | 1 | apple |
|
||||
| Alice | 2 | cherry |
|
||||
| Alice | 3 | banana |
|
||||
| Bob | 1 | banana |
|
||||
| Bob | 2 | apple |
|
||||
| Bob | 3 | cherry |
|
||||
| Charlie | 1 | cherry |
|
||||
| Charlie | 2 | banana |
|
||||
| Charlie | 3 | apple |
|
||||
|
||||
(in no specific row order). This is not a supported input format in the frontends, but may be relevant
|
||||
when interacting with the package programmatically - priorities can be followed as edges in a graph.
|
||||
|
||||
### Output
|
||||
|
||||
#### Matching
|
||||
A matching is a table whose rows list the applicants matched to each reviewer
|
||||
e.g. a matching from the med-school ranking matrix in the previous section might look like
|
||||
|
||||
| Baylor | CaseMed | Emory |
|
||||
|--------|---------|---------|
|
||||
| Alice | Bob | Charlie |
|
||||
| `None` | Daina | `None` |
|
||||
|
||||
#### Assignments
|
||||
An assignment is a table whose row lists which reviewer each applicant was matched to.
|
||||
e.g.
|
||||
|
||||
| Alice | Bob | Charlie | Daina |
|
||||
|--------|---------|---------|---------|
|
||||
| Baylor | CaseMed | Emory | CaseMed |
|
||||
|
||||
TODO check/make stable.
|
||||
|
||||
TODO matching more people per school e.g.
|
||||
|
||||
|
||||
|
||||
|
||||
## Usage
|
||||
|
||||
There are 4 main ways to use Carousel:
|
||||
|
||||
### UO Pages Server - CGI Form
|
||||
An HTML form submission interface is hosted at
|
||||
|
||||
> [`https://pages.uoregon.edu/tgorordo/forms/carousel.html`](https://pages.uoregon.edu/tgorordo/forms/carousel.html)
|
||||
|
||||
using the [pages.uoregon.edu CGI feature](https://service.uoregon.edu/TDClient/2030/Portal/KB/ArticleDet?ID=43069).
|
||||
|
||||
Submit your applicant and reviewer preferences in tabular form,
|
||||
or as excel uploads and the server will return a table of matches for you to choose from.
|
||||
|
||||
This resource has *very* limited compute, so excessive usage might result in limitations or restricted access.
|
||||
Try not to ruin a good thing! A moderate number of problems on the scale of those in the Examples section below should be sustainable.
|
||||
|
||||
### Command Line Binary
|
||||
|
||||
TODO
|
||||
|
||||
### GUI Binary
|
||||
|
||||
TODO
|
||||
|
||||
### Python Library/Development
|
||||
If you prefer to invoke `carousel` directly (or incorporate it as a library into another script)
|
||||
in a python environment instead of using any of the bundled/released versions of the program described above (or wish to
|
||||
reproduce those bundles), you can do so using the [`uv` environment/package/project manager](https://github.com/astral-sh/uv)
|
||||
or a raw python virtual environment using the [`venv` module](https://docs.python.org/3/library/venv.html)
|
||||
(if you need an intro to python `venv`s see [this page](https://pages.uoregon.edu/tgorordo/uoph410-510a_Image-Analysis/venvs.html)).
|
||||
|
||||
Some extra command-line development conveniences are available if you use the tools:
|
||||
|
||||
- [`just`](https://github.com/casey/just) is a taskrunner that can execute the provided `justfile` of some common useful commands.
|
||||
- [`direnv`](https://github.com/direnv/direnv) with [`nix` (shell)](https://github.com/NixOS/nix) can guarantee minimal development tooling without polluting your broader environment. i.e. they can auto-install and run all of carousel's tooling in an environment specific to your development directory.
|
||||
|
||||
but everything provided by these tools can also be done using more standard/default shell tooling.
|
||||
[`uv`](https://github.com/astral-sh/uv) as your package/environment manager is highly recommended, however.
|
||||
|
||||
TODO
|
||||
|
||||
## Post-Selection
|
||||
It's often desirable to enforce additional criteria on solutions
|
||||
that are not well-posed within the core optimization problem.
|
||||
Since the solver itself is stochastic to some extent, these are often most easily implemented
|
||||
by a post-selection on a sampling of solutions.
|
||||
|
||||
*A simple gale-shapley stable matcher for applicants to positions/assignments.*
|
||||
|
||||
## Examples
|
||||
Here are some usage examples:
|
||||
|
|
@ -176,10 +9,3 @@ TODO
|
|||
|
||||
### Caltech Housing Rotation
|
||||
TODO
|
||||
|
||||
## References
|
||||
*See [`REFERENCES.bib`](REFERENCES.bib)*
|
||||
|
||||
[1]:
|
||||
|
||||
TODO
|
||||
|
|
|
|||
|
|
@ -1,75 +0,0 @@
|
|||
@article{gale&shapley1962,
|
||||
ISSN = {0002989, 19300972},
|
||||
URL = {https://www.jstor.org/stable/2312726},
|
||||
author = {D. Gale and L. S. Shapley},
|
||||
journal = {The American Mathematical Monthly},
|
||||
number = {1},
|
||||
pages = {9--15},
|
||||
publisher = {[Taylor & Francis, Ltd., Mathematical Association of America]},
|
||||
title = {College Admissions and the Stability of Marriage},
|
||||
urldate = {2025-04-01},
|
||||
volume = {69},
|
||||
year = {1962},
|
||||
}
|
||||
|
||||
@article{doi:10.1137/0215048,
|
||||
author = {Irving, Robert W. and Leather, Paul},
|
||||
title = {The Complexity of Counting Stable Marriages},
|
||||
journal = {SIAM Journal on Computing},
|
||||
volume = {15},
|
||||
number = {3},
|
||||
pages = {655-667},
|
||||
year = {1986},
|
||||
doi = {10.1137/0215048},
|
||||
URL = {https://doi.org/10.1137/0215048},
|
||||
eprint = {https://doi.org/10.1137/0215048}
|
||||
}
|
||||
|
||||
@article{doi:10.1137/0216010,
|
||||
author = {Gusfield, Dan},
|
||||
title = {Three Fast Algorithms for Four Problems in Stable Marriage},
|
||||
journal = {SIAM Journal on Computing},
|
||||
volume = {16},
|
||||
number = {1},
|
||||
pages = {111-128},
|
||||
year = {1987},
|
||||
doi = {10.1137/0216010},
|
||||
URL = { https://doi.org/10.1137/0216010},
|
||||
eprint = { https://doi.org/10.1137/0216010 }
|
||||
}
|
||||
|
||||
@article{https://doi.org/10.3982/TE4830,
|
||||
author = {Huang, Chao},
|
||||
title = {Stable matching: An integer programming approach},
|
||||
journal = {Theoretical Economics},
|
||||
volume = {18},
|
||||
number = {1},
|
||||
pages = {37-63},
|
||||
keywords = {Two-sided matching, stability, integer programming, many-to-one matching, complementarity, total unimodularity, demand type, C61, C78, D47, D63},
|
||||
doi = {https://doi.org/10.3982/TE4830},
|
||||
url = {https://onlinelibrary.wiley.com/doi/abs/10.3982/TE4830},
|
||||
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.3982/TE4830}
|
||||
}
|
||||
|
||||
@article{DELORME2019426,
|
||||
title = {Mathematical models for stable matching problems with ties and incomplete lists},
|
||||
journal = {European Journal of Operational Research},
|
||||
volume = {277},
|
||||
number = {2},
|
||||
pages = {426-441},
|
||||
year = {2019},
|
||||
issn = {0377-2217},
|
||||
doi = {https://doi.org/10.1016/j.ejor.2019.03.017},
|
||||
url = {https://www.sciencedirect.com/science/article/pii/S0377221719302565},
|
||||
author = {Maxence Delorme and Sergio García and Jacek Gondzio and Jörg Kalcsics and David Manlove and William Pettersson},
|
||||
}
|
||||
|
||||
@misc{gutin2024findingstablematchingsassignment,
|
||||
title={Finding all stable matchings with assignment constraints},
|
||||
author={Gregory Gutin and Philip R. Neary and Anders Yeo},
|
||||
year={2024},
|
||||
eprint={2204.03989},
|
||||
archivePrefix={arXiv},
|
||||
primaryClass={econ.TH},
|
||||
url={https://arxiv.org/abs/2204.03989},
|
||||
}
|
||||
|
|
@ -8,7 +8,6 @@ requires-python = ">=3.13"
|
|||
dependencies = [
|
||||
"numpy>=2.2.4",
|
||||
"polars>=1.26.0",
|
||||
"rustworkx>=0.17.1",
|
||||
]
|
||||
|
||||
#[project.scripts]
|
||||
|
|
|
|||
|
|
@ -85,9 +85,7 @@ nbformat==5.10.4
|
|||
nodeenv==1.9.1
|
||||
# via pyright
|
||||
numpy==2.2.4
|
||||
# via
|
||||
# carousel (pyproject.toml)
|
||||
# rustworkx
|
||||
# via carousel (pyproject.toml)
|
||||
openai==1.106.1
|
||||
# via marimo
|
||||
packaging==24.2
|
||||
|
|
@ -151,8 +149,6 @@ ruff==0.11.6
|
|||
# via
|
||||
# carousel (pyproject.toml:dev)
|
||||
# marimo
|
||||
rustworkx==0.17.1
|
||||
# via carousel (pyproject.toml)
|
||||
setuptools==78.1.0
|
||||
# via
|
||||
# pyinstaller
|
||||
|
|
|
|||
|
|
@ -1,37 +1,57 @@
|
|||
import logging, rich
|
||||
from rich.logging import RichHandler
|
||||
|
||||
import itertools as it
|
||||
import numpy as np
|
||||
|
||||
import polars as pl
|
||||
import polars.selectors as pls
|
||||
|
||||
rich.traceback.install()
|
||||
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format="%(message)s",
|
||||
datefmt="[%X]",
|
||||
handlers=[
|
||||
RichHandler(
|
||||
show_time=True,
|
||||
markup=True,
|
||||
rich_tracebacks=True,
|
||||
tracebacks_suppress=[pl, pls, np],
|
||||
)
|
||||
],
|
||||
)
|
||||
log = logging.getLogger(__name__)
|
||||
|
||||
from .util import *
|
||||
from .brute import *
|
||||
from .def_acc import *
|
||||
|
||||
|
||||
def main() -> None:
|
||||
rich.print("Hello from [italic red]carousel[/italic red]!")
|
||||
def check_match_unstable(
|
||||
match,
|
||||
applicant_prefs,
|
||||
position_prefs,
|
||||
capacities,
|
||||
*,
|
||||
app_col: str = "applicant",
|
||||
pos_col: str = "position",
|
||||
rank_col: str = "rank",
|
||||
):
|
||||
"""
|
||||
Check match stability between applicants and positions.
|
||||
|
||||
parameters
|
||||
---
|
||||
match: pl.DataFrame
|
||||
| applicant | position |
|
||||
|
||||
applicant_prefs: pl.DataFrame
|
||||
| applicant | position | rank |
|
||||
|
||||
position_prefs: pl.DataFrame
|
||||
| position | applicant | rank |
|
||||
|
||||
"""
|
||||
pass # TODO
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
def check_match_stable(
|
||||
match,
|
||||
applicant_prefs,
|
||||
position_prefs,
|
||||
capacities,
|
||||
*,
|
||||
app_col: str = "applicant",
|
||||
pos_col: str = "position",
|
||||
rank_col: str = "rank",
|
||||
) -> bool:
|
||||
"""
|
||||
Check match stability between applicants and positions.
|
||||
|
||||
parameters
|
||||
---
|
||||
match: pl.DataFrame
|
||||
| applicant | position |
|
||||
|
||||
applicant_prefs: pl.DataFrame
|
||||
| applicant | position | rank |
|
||||
|
||||
position_prefs: pl.DataFrame
|
||||
| position | applicant | rank |
|
||||
|
||||
"""
|
||||
pass # TODO
|
||||
|
|
@ -1,3 +0,0 @@
|
|||
def brute_match(applicant_rankings, reviewer_rankings):
|
||||
"""Brute force combinatoric search for stable matches."""
|
||||
pass
|
||||
|
|
@ -1,177 +1,137 @@
|
|||
from .util import *
|
||||
from collections import deque
|
||||
import heapq
|
||||
|
||||
import numpy as np
|
||||
import polars as pl
|
||||
|
||||
|
||||
def preparefor_def_acc(applicant_rankings, reviewer_rankings):
|
||||
"""Sanitize/Format applicant and reviewer rankings for the deferred acceptance solver."""
|
||||
pass
|
||||
# return app_ranks, rev_ranks
|
||||
def GS_deferred_acceptance(
|
||||
applicant_prefs: pl.DataFrame,
|
||||
position_prefs: pl.DataFrame,
|
||||
capacities: pl.DataFrame,
|
||||
*,
|
||||
app_col: str = "applicant",
|
||||
pos_col: str = "position",
|
||||
rank_col: str = "rank",
|
||||
) -> pl.DataFrame:
|
||||
"""
|
||||
Compute the proposer-optimal Gale-Shapley deferred acceptance stable matching for a
|
||||
college-admissions problem between "applicants" and "positions" with specified capacities.
|
||||
|
||||
parameters
|
||||
---
|
||||
applicant_prefs: pl.DataFrame
|
||||
A 3-column ranking of positions by applicants. | applicant | position | rank |
|
||||
(lower rank is more preferred).
|
||||
|
||||
|
||||
def matchby_deferred_acceptance(applicant_rankings, reviewer_rankings, revfirst=False):
|
||||
"""Find the Gale-Shapley deferred-acceptance stable matching for rankings A, R. Default to A-first unless `revfirst=true`."""
|
||||
position_prefs: pl.DataFrame
|
||||
A 3-column ranking of applicants by positions. | position | applicant | rank |
|
||||
(lower rank is more preferred).
|
||||
|
||||
app_prio = rank_to_prio(
|
||||
applicant_rankings, prioritizer="applicant", priority="reviewer"
|
||||
)
|
||||
rev_prio = rank_to_prio(
|
||||
reviewer_rankings, prioritizer="reviewer", priority="applicant"
|
||||
capacities: pl.DataFrame
|
||||
A listing of position capacities. | position | capacity |
|
||||
|
||||
returns
|
||||
---
|
||||
matches: pl.DataFrame
|
||||
A two-column match between applicants and positions (e.g. students and colleges).
|
||||
| applicant | position |
|
||||
"""
|
||||
|
||||
app_idxs = (
|
||||
applicant_prefs.select(app_col).unique().sort(app_col).with_row_index("app_idx")
|
||||
)
|
||||
|
||||
state = app_prio.select(
|
||||
[
|
||||
pl.col("applicant").unique().alias("applicant"),
|
||||
]
|
||||
).with_columns([pl.lit(0).alias("next_rank"), pl.lit(True).alias("is_free")])
|
||||
|
||||
matches = pl.DataFrame(
|
||||
{
|
||||
"applicant": pl.Series([]),
|
||||
"reviewer": pl.Series([]),
|
||||
"current": pl.Series([], dtype=pl.Int64),
|
||||
}
|
||||
pos_idxs = (
|
||||
position_prefs.select(pos_col).unique().sort(pos_col).with_row_index("pos_idx")
|
||||
)
|
||||
|
||||
max_iters = len(state) * len(rev_prio.select("applicant").unique())
|
||||
n_apps = app_idxs.height
|
||||
n_poss = pos_idxs.height
|
||||
|
||||
for _ in range(max_iters):
|
||||
props = (
|
||||
state.filter(pl.col("is_free"))
|
||||
.join(app_prio, on="applicant")
|
||||
.filter(pl.col("rank") == pl.col("next_rank"))
|
||||
.select(["applicant", "reviewer"])
|
||||
)
|
||||
ap = (
|
||||
applicant_prefs.join(app_idxs, on=app_col)
|
||||
.join(pos_idxs, on=pos_col)
|
||||
.sort(["app_idx", rank_col])
|
||||
)
|
||||
al = (
|
||||
ap.group_by("app_idx", maintain_order=True)
|
||||
.agg(pl.col("pos_idx"))
|
||||
.sort("app_idx")
|
||||
)
|
||||
app_prefs = al["pos_idx"].to_list()
|
||||
|
||||
if len(props) == 0:
|
||||
break
|
||||
max_pref_len = max((len(x) for x in app_prefs), default=0)
|
||||
|
||||
props = props.join(rev_prio, on=["reviewer", "applicant"], how="left")
|
||||
pmat = np.full((n_apps, max_pref_len), -1, dtype=np.int32)
|
||||
for i, r in enumerate(app_prefs):
|
||||
pmat[i, : len(r)] = r
|
||||
|
||||
props = props.join(
|
||||
matches.select(
|
||||
["reviewer", pl.col("applicant").alias("proposer"), pl.col("current")]
|
||||
),
|
||||
on="reviewer",
|
||||
how="left",
|
||||
)
|
||||
pp = (
|
||||
position_prefs.join(app_idxs, on=app_col)
|
||||
.join(pos_idxs, on=pos_col)
|
||||
.sort(["pos_idx", rank_col])
|
||||
)
|
||||
worst_rank = np.iinfo(np.int32).max
|
||||
|
||||
props = props.with_columns(
|
||||
[
|
||||
(
|
||||
pl.col("proposer").is_null() | (pl.col("rank") < pl.col("current"))
|
||||
).alias("accepted")
|
||||
]
|
||||
)
|
||||
ranking = np.full((n_poss, n_apps), worst_rank, dtype=np.int32)
|
||||
for r in pp.iter_rows(named=True):
|
||||
ranking[r["pos_idx"], r["app_idx"]] = r[rank_col]
|
||||
|
||||
accepted = proposals.filter(pl.col("accepted"))
|
||||
rejected = proposals.filter(~pl.col("accepted"))
|
||||
caps = capacities.join(pos_idxs, on=pos_col).sort("pos_idx")
|
||||
cap = caps["capacity"].to_numpy().astype(np.int32)
|
||||
|
||||
displaced = accepted.filter(pl.col("proposer").is_not_null()).select(
|
||||
pl.col("proposer").alias("applicant")
|
||||
)
|
||||
# ---
|
||||
|
||||
matches = matches.join(accepted.select("reviewer"), on="reviewer", how="anti")
|
||||
next_c = np.zeros(n_apps, dtype=np.int32)
|
||||
matched_pos = np.full(n_apps, -1, dtype=np.int32)
|
||||
|
||||
matches = pl.concat(
|
||||
[
|
||||
matches,
|
||||
accepted.select(
|
||||
["reviewer", "applicant", pl.col("rank").alias("current")]
|
||||
),
|
||||
]
|
||||
)
|
||||
free = deque(np.arange(n_apps, dtype=np.int32))
|
||||
|
||||
rejected_apps = rejected.select("applicant")
|
||||
accepted_apps = accepted.select("applicant")
|
||||
pos_heaps: list[list[tuple[int, int]]] = [[] for _ in range(n_poss)]
|
||||
|
||||
state = (
|
||||
state.join(
|
||||
rejected_apps.with_columns(pl.lit(True).alias("was_rejected")),
|
||||
on="applicant",
|
||||
how="left",
|
||||
)
|
||||
.join(
|
||||
accepted_apps.with_columns(pl.lit(True).alias("was_accepted")),
|
||||
on="applicant",
|
||||
how="left",
|
||||
)
|
||||
.join(
|
||||
displaced.with_columns(pl.lit(True).alias("was_displaced")),
|
||||
on="applicant",
|
||||
how="left",
|
||||
)
|
||||
.with_columns(
|
||||
[
|
||||
pl.when(pl.col("was_rejected").fill_null(False))
|
||||
.then(pl.col("next_rank") + 1)
|
||||
.otherwise(pl.col("next_rank"))
|
||||
.alias("next_rank"),
|
||||
pl.when(pl.col("was_accepted").fill_null(False))
|
||||
.then(False)
|
||||
.when(pl.col("was_displaced").full_null(False))
|
||||
.then(True)
|
||||
.otherwise(pl.col("is_free"))
|
||||
.alias("is_free"),
|
||||
]
|
||||
)
|
||||
.select(["applicant", "next_rank", "is_free"])
|
||||
)
|
||||
while free:
|
||||
a = free.popleft()
|
||||
|
||||
return matches.select(["applicant", "reviewer"])
|
||||
while next_c[a] < max_pref_len:
|
||||
p = pmat[a, next_c[a]]
|
||||
next_c[a] += 1
|
||||
|
||||
if p == -1:
|
||||
break
|
||||
# else
|
||||
|
||||
def deferred_acceptance_match(applicant_rankings, reviewer_rankings):
|
||||
"""Find Gale-Shapley deferred-acceptance stable matching for rankings A, R."""
|
||||
reviewer_rankings = reviewer_rankings.rename(
|
||||
{reviewer_rankings.columns[0]: "applicant"}
|
||||
arank = ranking[p, a]
|
||||
|
||||
if arank == worst_rank:
|
||||
continue
|
||||
|
||||
heap = pos_heaps[p]
|
||||
|
||||
if len(heap) < cap[p]:
|
||||
heapq.heappush(heap, (-arank, a))
|
||||
matched_pos[a] = a
|
||||
break
|
||||
|
||||
worst_neg_rank, worst_app = heap[0]
|
||||
worst_rank_current = -worst_neg_rank
|
||||
|
||||
if arank < worst_rank_current:
|
||||
heapq.heapreplace(heap, (-arank, a))
|
||||
matched_pos[a] = a
|
||||
matched_pos[worst_app] = -1
|
||||
|
||||
free.append(worst_app)
|
||||
break
|
||||
|
||||
matches = (
|
||||
pl.DataFrame({"app_idx": np.arange(n_apps), "pos_idx": matched_pos})
|
||||
.filter(pl.col("pos_idx") != -1)
|
||||
.join(app_idxs, on="app_idx")
|
||||
.join(pos_idxs, on="pos_idx")
|
||||
.select([app_col, pos_col])
|
||||
.sort(app_col)
|
||||
)
|
||||
|
||||
app_prefs = rank_to_pref(applicant_rankings)
|
||||
offers = app_prefs.transpose(
|
||||
include_header=True,
|
||||
header_name="applicant",
|
||||
column_names=["pref" + str(i + 1) for i in range(app_prefs.width)],
|
||||
).with_columns(pl.coalesce(pl.all().exclude("applicant")).alias("offer"))
|
||||
|
||||
# offers = pl.concat(pl.align_frames(offers, reviewer_rankings, on="applicant"), how="horizontal")
|
||||
offers = pl.concat([offers, reviewer_rankings], how="align_left")
|
||||
|
||||
match = pl.DataFrame(
|
||||
{
|
||||
r: offers.select(pl.col("applicant", "offer").sort_by(r))
|
||||
.select(
|
||||
pl.when(pl.col("offer").eq(r)).then(pl.col("applicant")).otherwise(None)
|
||||
)
|
||||
.select(pl.all().fill_null(strategy="backward").first())
|
||||
.to_series()
|
||||
for r in reviewer_rankings.columns[1:]
|
||||
}
|
||||
) # .select(pl.all().fill_null(strategy="backward").first())
|
||||
|
||||
# while check_unstable(match, applicant_rankings, reviewer_rankings):
|
||||
while match.select(pl.any_horizontal(pl.all().has_nulls())).item():
|
||||
# null applicant preferences that rejected
|
||||
offers = offers.with_columns(
|
||||
pl.when(
|
||||
pl.col("applicant").is_in(match.row(0)).not_(),
|
||||
pl.col(c).is_null().not_(),
|
||||
)
|
||||
.then(pl.lit(None))
|
||||
.otherwise(c)
|
||||
.alias(c)
|
||||
for c in offers.select(pl.col("pref*")).columns
|
||||
)
|
||||
|
||||
return match
|
||||
|
||||
offers = offers.with_columns(pl.col("pref"))
|
||||
|
||||
offers = offers.with_columns(
|
||||
pl.coalesce(
|
||||
# TODO: select prefn columns using a regex
|
||||
).alias("offer")
|
||||
)
|
||||
|
||||
# TODO update match
|
||||
|
||||
# else if stable
|
||||
return match
|
||||
return matches
|
||||
|
|
|
|||
|
|
@ -1,152 +0,0 @@
|
|||
import itertools as it
|
||||
import numpy as np
|
||||
|
||||
import polars as pl
|
||||
import polars.selectors as pls
|
||||
|
||||
# Stability
|
||||
|
||||
|
||||
def get_rank(ranking, ranker, ranked):
|
||||
idx = ranking.select(pl.arg_where(pl.col("") == ranked)).item()
|
||||
return ranking[ranker][idx]
|
||||
|
||||
|
||||
def check_match_unstable(match, applicant_ranking, reviewer_ranking):
|
||||
applicants = applicant_ranking.columns[1:] # assume unique applicants
|
||||
for a, b in it.permutations(applicants, 2):
|
||||
A = (
|
||||
match.select(c for c in match.iter_columns() if a in c).to_series().name
|
||||
) # the reviewer a is matched to
|
||||
B = (
|
||||
match.select(c for c in match.iter_columns() if b in c).to_series().name
|
||||
) # the reviewer b is matched to
|
||||
|
||||
b_prefers_A = get_rank(applicant_ranking, b, A) < get_rank(
|
||||
applicant_ranking, b, B
|
||||
)
|
||||
A_prefers_b = get_rank(reviewer_ranking, A, b) < get_rank(
|
||||
reviewer_ranking, A, a
|
||||
)
|
||||
if b_prefers_A and A_prefers_b:
|
||||
return True
|
||||
# else
|
||||
return False
|
||||
|
||||
|
||||
def check_match_stable(*args, **kwargs):
|
||||
return not check_match_unstable(*args, **kwargs)
|
||||
|
||||
|
||||
# Conversions
|
||||
|
||||
|
||||
def rank_to_pref(ranking):
|
||||
"""Converts a ranking to a preference."""
|
||||
id_col_name = ranking.select(pls.by_index(0)).to_series().name
|
||||
preferences = ranking.select(
|
||||
[
|
||||
pl.col(id_col_name).sort_by(c).alias(c)
|
||||
for c in ranking.columns
|
||||
if c != id_col_name
|
||||
]
|
||||
)
|
||||
return preferences
|
||||
|
||||
|
||||
def pref_to_rank(preferences):
|
||||
"""Converts a preference to a ranking."""
|
||||
o = preferences.select(
|
||||
pl.concat_list(preferences.columns).explode().unique().sort().alias("")
|
||||
) # .with_row_index(offset=1)
|
||||
|
||||
ranking = pl.concat(
|
||||
[
|
||||
o.join(
|
||||
preferences.with_row_index(offset=1),
|
||||
how="full",
|
||||
left_on="",
|
||||
right_on=c,
|
||||
maintain_order="left",
|
||||
).select(pl.col("index").alias(c))
|
||||
for c in preferences.columns
|
||||
],
|
||||
how="horizontal",
|
||||
)
|
||||
return pl.concat([o, ranking], how="horizontal")
|
||||
|
||||
|
||||
def ranks_to_mat(rankA, rankB):
|
||||
return prefs_to_mat(rank_to_pref(rankA), rank_to_pref(rankB))
|
||||
|
||||
|
||||
def prefs_to_mat(prefA, prefB):
|
||||
return rank_to_mat(pref_to_rank(preferences), pref_to_rank(preferences[0]))
|
||||
|
||||
|
||||
def mat_to_ranks(matrix):
|
||||
pass # TODO
|
||||
|
||||
|
||||
def mat_to_prefs(matrix):
|
||||
rankA, rankB = mat_to_ranks(matrix)
|
||||
return rank_to_pref(rankA), rank_to_preft(rankB)
|
||||
|
||||
|
||||
def pref_to_prio(pref, prioritizer="prioritizer", priority="subject"):
|
||||
return pref.with_row_index("rank").unpivot(
|
||||
index="rank", variable_name=prioritizer, value_name=priority
|
||||
)
|
||||
|
||||
|
||||
def rank_to_prio(rank, **kwargs):
|
||||
return pref_to_prio(rank_to_pref(rank), **kwargs)
|
||||
|
||||
|
||||
""""
|
||||
def ranking_matrix(A, B):
|
||||
T = pl.concat([A, B], how="horizontal")
|
||||
|
||||
TT = T.with_columns(pl.concat_list(A.columns[0], B.columns[0]))
|
||||
for ab in zip(A.columns[1:], B.columns[1:]):
|
||||
TT = TT.with_columns(pl.concat_list(*ab))
|
||||
TTT = TT.select(pl.col(A.columns))
|
||||
|
||||
return TTT.insert_column(0, pl.Series("names", B.columns))
|
||||
"""
|
||||
|
||||
## Output
|
||||
|
||||
|
||||
def match_to_assgn(matching):
|
||||
pass
|
||||
|
||||
|
||||
def assgn_to_match(assignment):
|
||||
pass
|
||||
|
||||
|
||||
## Internal
|
||||
|
||||
|
||||
# Validity
|
||||
|
||||
|
||||
def check_pref_allunique(preferences):
|
||||
"""A valid set of preferences has all unique entries in each column."""
|
||||
repeats = preferences.select(
|
||||
(~pl.all_horizontal((pl.all().is_unique() | pl.all().is_null()).all())).alias(
|
||||
"repeats"
|
||||
)
|
||||
).get_column("repeats")[0]
|
||||
return not repeats
|
||||
|
||||
|
||||
def check_rank_noties(ranking):
|
||||
"""A valid ranking has no ties."""
|
||||
ties = ranking.select(
|
||||
(~pl.all_horizontal((pl.all().is_unique() | pl.all().is_null()).all())).alias(
|
||||
"ties"
|
||||
)
|
||||
).get_column("ties")[0]
|
||||
return not ties
|
||||
|
|
@ -1,135 +1,80 @@
|
|||
import polars as pl
|
||||
import polars.selectors as pls
|
||||
import numpy as np
|
||||
|
||||
from polars.testing import assert_frame_equal
|
||||
|
||||
import pytest, rich
|
||||
from hypothesis import given, strategies as st
|
||||
|
||||
import carousel as crsl
|
||||
|
||||
rng = np.random.default_rng()
|
||||
from carousel import GS_deferred_acceptance
|
||||
|
||||
|
||||
@st.composite
|
||||
def rankings(draw, names=["a", "b", "c"], choices=["A", "B", "C"]):
|
||||
h = pl.DataFrame({"": choices})
|
||||
r = pl.DataFrame(
|
||||
{n: draw(st.just(rng.permutation(len(choices)) + 1)) for n in names}
|
||||
) # should add None option in generation of valid rankings
|
||||
return pl.concat([h, r], how="horizontal")
|
||||
|
||||
|
||||
@st.composite
|
||||
def preferences(draw, names=["a", "b", "c"], choices=["A", "B", "C"]):
|
||||
p = pl.DataFrame(
|
||||
def test_prefs():
|
||||
people_prefs = pl.DataFrame(
|
||||
{
|
||||
n: draw(st.just(rng.choice(choices, size=len(choices), replace=False)))
|
||||
for n in names
|
||||
"people": [
|
||||
"Alice",
|
||||
"Alice",
|
||||
"Alice",
|
||||
"Bob",
|
||||
"Bob",
|
||||
"Bob",
|
||||
"Charlie",
|
||||
"Charlie",
|
||||
"Charlie",
|
||||
],
|
||||
"fruit": [
|
||||
"apple",
|
||||
"banana",
|
||||
"cherry",
|
||||
"banana",
|
||||
"cherry",
|
||||
"apple",
|
||||
"cherry",
|
||||
"apple",
|
||||
"banana",
|
||||
],
|
||||
"rank": [1, 2, 3, 1, 2, 3, 1, 2, 3],
|
||||
}
|
||||
)
|
||||
return p
|
||||
|
||||
|
||||
p = pl.DataFrame({"a": ["A", "C", "B"], "b": ["B", "A", "C"], "c": ["C", "B", "A"]})
|
||||
r = pl.DataFrame({"": ["A", "B", "C"], "a": [1, 3, 2], "b": [2, 1, 3], "c": [3, 2, 1]})
|
||||
|
||||
|
||||
def test_invalid_pref():
|
||||
pp = pl.DataFrame(
|
||||
{"a": ["A", "A", "B"], "b": ["B", "A", "C"], "c": ["C", "B", "A"]}
|
||||
)
|
||||
assert crsl.check_pref_allunique(pp) is False
|
||||
|
||||
|
||||
def test_pref_to_rank():
|
||||
assert_frame_equal(crsl.pref_to_rank(p), r, check_dtypes=False)
|
||||
|
||||
|
||||
def test_invalid_rank():
|
||||
rr = pl.DataFrame(
|
||||
{"": ["A", "B", "C"], "a": [1, 1, 2], "b": [2, 1, 3], "c": [3, 2, 1]}
|
||||
)
|
||||
assert crsl.check_pref_allunique(rr) is False
|
||||
|
||||
|
||||
def test_rank_to_pref():
|
||||
assert_frame_equal(crsl.rank_to_pref(r), p, check_dtypes=False)
|
||||
|
||||
|
||||
@given(rankings())
|
||||
def test_valid_rank(R):
|
||||
assert crsl.check_rank_noties(R)
|
||||
|
||||
|
||||
@given(rankings())
|
||||
def test_ranks_tofrom_prefs(R):
|
||||
assert_frame_equal(crsl.pref_to_rank(crsl.rank_to_pref(R)), R, check_dtypes=False)
|
||||
|
||||
|
||||
@given(preferences())
|
||||
def test_valid_pref(P):
|
||||
assert crsl.check_pref_allunique(P)
|
||||
|
||||
|
||||
@given(preferences())
|
||||
def test_prefs_tofrom_ranks(P):
|
||||
assert_frame_equal(crsl.rank_to_pref(crsl.pref_to_rank(P)), P, check_dtypes=False)
|
||||
|
||||
|
||||
def test_eg2_unstable():
|
||||
ar = pl.DataFrame(
|
||||
fruit_prefs = pl.DataFrame(
|
||||
{
|
||||
"": ["A", "B", "C", "D"],
|
||||
"a": [1, 2, 3, 4],
|
||||
"b": [1, 4, 3, 2],
|
||||
"c": [2, 1, 3, 4],
|
||||
"d": [4, 2, 3, 1],
|
||||
"fruit": [
|
||||
"apple",
|
||||
"apple",
|
||||
"apple",
|
||||
"banana",
|
||||
"banana",
|
||||
"banana",
|
||||
"cherry",
|
||||
"cherry",
|
||||
"cherry",
|
||||
],
|
||||
"people": [
|
||||
"Alice",
|
||||
"Bob",
|
||||
"Charlie",
|
||||
"Alice",
|
||||
"Bob",
|
||||
"Charlie",
|
||||
"Alice",
|
||||
"Bob",
|
||||
"Charlie",
|
||||
],
|
||||
"rank": [1, 1, 1, 1, 1, 1, 1, 1, 1], # fruits have no preferences
|
||||
}
|
||||
)
|
||||
rr = pl.DataFrame(
|
||||
|
||||
capacities = pl.DataFrame(
|
||||
{
|
||||
"": ["a", "b", "c", "d"],
|
||||
"A": [3, 4, 2, 1],
|
||||
"B": [3, 1, 4, 2],
|
||||
"C": [2, 3, 4, 1],
|
||||
"D": [3, 2, 1, 4],
|
||||
"fruit": ["apple", "cherry", "banana"],
|
||||
"capacity": [1, 1, 1], # have one of each
|
||||
}
|
||||
)
|
||||
match = pl.DataFrame({"A": ["a"], "B": ["b"], "C": ["c"], "D": ["d"]})
|
||||
|
||||
assert crsl.check_match_unstable(match, ar, rr)
|
||||
|
||||
|
||||
def test_eg2_isstable():
|
||||
ar = pl.DataFrame(
|
||||
{
|
||||
"": ["A", "B", "C", "D"],
|
||||
"a": [1, 2, 3, 4],
|
||||
"b": [1, 4, 3, 2],
|
||||
"c": [2, 1, 3, 4],
|
||||
"d": [4, 2, 3, 1],
|
||||
}
|
||||
assert_frame_equal(
|
||||
GS_deferred_acceptance(
|
||||
people_prefs, fruit_prefs, capacities, app_col="people", pos_col="fruit"
|
||||
).sort(["people", "fruit"]),
|
||||
pl.DataFrame(
|
||||
{
|
||||
"people": ["Alice", "Bob", "Charlie"],
|
||||
"fruit": ["apple", "banana", "cherry"],
|
||||
}
|
||||
).sort(["people", "fruit"]),
|
||||
)
|
||||
rr = pl.DataFrame(
|
||||
{
|
||||
"": ["a", "b", "c", "d"],
|
||||
"A": [3, 4, 2, 1],
|
||||
"B": [3, 1, 4, 2],
|
||||
"C": [2, 3, 4, 1],
|
||||
"D": [3, 2, 1, 4],
|
||||
}
|
||||
)
|
||||
match = pl.DataFrame({"A": ["c"], "B": ["d"], "C": ["a"], "D": ["b"]})
|
||||
|
||||
assert crsl.check_match_stable(match, ar, rr)
|
||||
|
||||
|
||||
@given(
|
||||
rankings(names=["a", "b", "c", "d"], choices=["A", "B", "C", "D"]),
|
||||
rankings(names=["A", "B", "C", "D"], choices=["a", "b", "c", "d"]),
|
||||
)
|
||||
def test_defacc_isstable(applicant_rankings, reviewer_rankings):
|
||||
match = crsl.matchby_deferred_acceptance(applicant_rankings, reviewer_rankings)
|
||||
assert crsl.check_match_stable(match, applicant_rankings, reviewer_rankings)
|
||||
|
|
|
|||
|
|
@ -1,124 +0,0 @@
|
|||
{
|
||||
"version": "1",
|
||||
"metadata": {
|
||||
"marimo_version": "0.18.3"
|
||||
},
|
||||
"cells": [
|
||||
{
|
||||
"id": "Hbol",
|
||||
"code_hash": "bc65c35c6fad59890b50c502bb8affa4",
|
||||
"outputs": [
|
||||
{
|
||||
"type": "data",
|
||||
"data": {
|
||||
"text/markdown": "<span class=\"markdown prose dark:prose-invert contents\"><h1 id=\"caltech-hovse-rotation-example\">Caltech Hovse Rotation Example</h1></span>"
|
||||
}
|
||||
}
|
||||
],
|
||||
"console": []
|
||||
},
|
||||
{
|
||||
"id": "MJUe",
|
||||
"code_hash": "ccb62cc29f2cec640b063832a90adfec",
|
||||
"outputs": [
|
||||
{
|
||||
"type": "data",
|
||||
"data": {
|
||||
"text/markdown": "<span class=\"markdown prose dark:prose-invert contents\"><h2 id=\"imports\">Imports</h2></span>"
|
||||
}
|
||||
}
|
||||
],
|
||||
"console": []
|
||||
},
|
||||
{
|
||||
"id": "vblA",
|
||||
"code_hash": "9588e2b4004a32c73b7ecc0a968b666f",
|
||||
"outputs": [
|
||||
{
|
||||
"type": "data",
|
||||
"data": {
|
||||
"text/plain": ""
|
||||
}
|
||||
}
|
||||
],
|
||||
"console": []
|
||||
},
|
||||
{
|
||||
"id": "bkHC",
|
||||
"code_hash": "4821038400db1f5dc63daf7ca5b26279",
|
||||
"outputs": [
|
||||
{
|
||||
"type": "data",
|
||||
"data": {
|
||||
"text/markdown": "<span class=\"markdown prose dark:prose-invert contents\"><h2 id=\"generating-rotation-rankings\">Generating Rotation Rankings</h2></span>"
|
||||
}
|
||||
}
|
||||
],
|
||||
"console": []
|
||||
},
|
||||
{
|
||||
"id": "lEQa",
|
||||
"code_hash": "ecc9ae14c8c8f3875656fc620665c6b4",
|
||||
"outputs": [
|
||||
{
|
||||
"type": "data",
|
||||
"data": {
|
||||
"text/html": "<pre class='text-xs'>True</pre>"
|
||||
}
|
||||
}
|
||||
],
|
||||
"console": []
|
||||
},
|
||||
{
|
||||
"id": "NdeS",
|
||||
"code_hash": "c582dec943ff7b743aa0691df291cea6",
|
||||
"outputs": [
|
||||
{
|
||||
"type": "data",
|
||||
"data": {
|
||||
"text/html": "<marimo-ui-element object-id='NdeS-0' random-id='7040b6c9-c598-beaa-38c6-9f1afe159be0'><marimo-table data-initial-value='[]' data-label='null' data-data='"[{\"\":\"A\",\"a\":1,\"b\":1,\"c\":2,\"d\":4},{\"\":\"B\",\"a\":2,\"b\":4,\"c\":1,\"d\":2},{\"\":\"C\",\"a\":3,\"b\":3,\"c\":3,\"d\":3},{\"\":\"D\",\"a\":4,\"b\":2,\"c\":4,\"d\":1}]"' data-total-rows='4' data-total-columns='5' data-max-columns='50' data-banner-text='""' data-pagination='true' data-page-size='10' data-field-types='[["",["string","str"]],["a",["integer","i64"]],["b",["integer","i64"]],["c",["integer","i64"]],["d",["integer","i64"]]]' data-show-filters='true' data-show-download='true' data-show-column-summaries='false' data-show-data-types='true' data-show-page-size-selector='false' data-show-column-explorer='true' data-show-chart-builder='true' data-row-headers='[]' data-has-stable-row-id='false' data-lazy='false' data-preload='false'></marimo-table></marimo-ui-element>"
|
||||
}
|
||||
}
|
||||
],
|
||||
"console": []
|
||||
},
|
||||
{
|
||||
"id": "PKri",
|
||||
"code_hash": "b9f0f3a28a20d94d4b173bbbd49db37f",
|
||||
"outputs": [],
|
||||
"console": []
|
||||
},
|
||||
{
|
||||
"id": "Xref",
|
||||
"code_hash": "ccc8305d08e563d4fb1c9df31e9b7b69",
|
||||
"outputs": [
|
||||
{
|
||||
"type": "data",
|
||||
"data": {
|
||||
"text/html": "<marimo-ui-element object-id='Xref-0' random-id='b6beae57-9d1c-9561-11cf-94cfd5b3aa06'><marimo-table data-initial-value='[]' data-label='null' data-data='"[{\"a\":\"A\",\"b\":\"A\",\"c\":\"B\",\"d\":\"D\"},{\"a\":\"B\",\"b\":\"D\",\"c\":\"A\",\"d\":\"B\"},{\"a\":\"C\",\"b\":\"C\",\"c\":\"C\",\"d\":\"C\"},{\"a\":\"D\",\"b\":\"B\",\"c\":\"D\",\"d\":\"A\"}]"' data-total-rows='4' data-total-columns='4' data-max-columns='50' data-banner-text='""' data-pagination='true' data-page-size='10' data-field-types='[["a",["string","str"]],["b",["string","str"]],["c",["string","str"]],["d",["string","str"]]]' data-show-filters='true' data-show-download='true' data-show-column-summaries='false' data-show-data-types='true' data-show-page-size-selector='false' data-show-column-explorer='true' data-show-chart-builder='true' data-row-headers='[]' data-has-stable-row-id='false' data-lazy='false' data-preload='false'></marimo-table></marimo-ui-element>"
|
||||
}
|
||||
}
|
||||
],
|
||||
"console": []
|
||||
},
|
||||
{
|
||||
"id": "taaO",
|
||||
"code_hash": "b712cc6f3d50763308dd5bc1c6703f77",
|
||||
"outputs": [
|
||||
{
|
||||
"type": "data",
|
||||
"data": {
|
||||
"text/html": "<marimo-ui-element object-id='taaO-0' random-id='1a8ede2c-9d90-fbec-5f1c-6258f3189f72'><marimo-table data-initial-value='[]' data-label='null' data-data='"[{\"rank\":0,\"prioritizer\":\"A\",\"subject\":\"d\"},{\"rank\":1,\"prioritizer\":\"A\",\"subject\":\"c\"},{\"rank\":2,\"prioritizer\":\"A\",\"subject\":\"a\"},{\"rank\":3,\"prioritizer\":\"A\",\"subject\":\"b\"},{\"rank\":0,\"prioritizer\":\"B\",\"subject\":\"b\"},{\"rank\":1,\"prioritizer\":\"B\",\"subject\":\"d\"},{\"rank\":2,\"prioritizer\":\"B\",\"subject\":\"a\"},{\"rank\":3,\"prioritizer\":\"B\",\"subject\":\"c\"},{\"rank\":0,\"prioritizer\":\"C\",\"subject\":\"d\"},{\"rank\":1,\"prioritizer\":\"C\",\"subject\":\"a\"}]"' data-total-rows='16' data-total-columns='3' data-max-columns='50' data-banner-text='""' data-pagination='true' data-page-size='10' data-field-types='[["rank",["integer","u32"]],["prioritizer",["string","str"]],["subject",["string","str"]]]' data-show-filters='true' data-show-download='true' data-show-column-summaries='true' data-show-data-types='true' data-show-page-size-selector='true' data-show-column-explorer='true' data-show-chart-builder='true' data-row-headers='[]' data-has-stable-row-id='false' data-lazy='false' data-preload='false'></marimo-table></marimo-ui-element>"
|
||||
}
|
||||
}
|
||||
],
|
||||
"console": []
|
||||
},
|
||||
{
|
||||
"id": "wJzy",
|
||||
"code_hash": null,
|
||||
"outputs": [],
|
||||
"console": []
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
@ -1,105 +0,0 @@
|
|||
import marimo
|
||||
|
||||
__generated_with = "0.18.3"
|
||||
app = marimo.App(width="medium")
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
# Caltech Hovse Rotation Example
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## Imports
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
import marimo as mo
|
||||
import polars as pl, polars.selectors as pls
|
||||
import numpy as np, faker as fk
|
||||
import carousel as crsl
|
||||
|
||||
return crsl, mo, pl
|
||||
|
||||
|
||||
@app.cell(hide_code=True)
|
||||
def _(mo):
|
||||
mo.md(r"""
|
||||
## Generating Rotation Rankings
|
||||
""")
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(crsl, pl):
|
||||
ar = pl.DataFrame(
|
||||
{
|
||||
"": ["A", "B", "C", "D"],
|
||||
"a": [1, 2, 3, 4],
|
||||
"b": [1, 4, 3, 2],
|
||||
"c": [2, 1, 3, 4],
|
||||
"d": [4, 2, 3, 1],
|
||||
}
|
||||
)
|
||||
rr = pl.DataFrame(
|
||||
{
|
||||
"": ["a", "b", "c", "d"],
|
||||
"A": [3, 4, 2, 1],
|
||||
"B": [3, 1, 4, 2],
|
||||
"C": [2, 3, 4, 1],
|
||||
"D": [3, 2, 1, 4],
|
||||
}
|
||||
)
|
||||
m = pl.DataFrame({"A": ["c"], "B": ["d"], "C": ["a"], "D": ["b"]})
|
||||
crsl.check_match_stable(m, ar, rr)
|
||||
return ar, rr
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(ar):
|
||||
ar
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
hovses = [
|
||||
"Blacker",
|
||||
"Dabney",
|
||||
"Ricketts",
|
||||
"Fleming",
|
||||
"Page",
|
||||
"Lloyd",
|
||||
"Venerable",
|
||||
"Avery",
|
||||
]
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(ar, crsl):
|
||||
crsl.rank_to_pref(ar)
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _(crsl, rr):
|
||||
crsl.pref_to_prio(crsl.rank_to_pref(rr))
|
||||
return
|
||||
|
||||
|
||||
@app.cell
|
||||
def _():
|
||||
return
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app.run()
|
||||
24
uv.lock
generated
24
uv.lock
generated
|
|
@ -64,7 +64,6 @@ source = { editable = "." }
|
|||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
{ name = "polars" },
|
||||
{ name = "rustworkx" },
|
||||
]
|
||||
|
||||
[package.dev-dependencies]
|
||||
|
|
@ -85,7 +84,6 @@ dev = [
|
|||
requires-dist = [
|
||||
{ name = "numpy", specifier = ">=2.2.4" },
|
||||
{ name = "polars", specifier = ">=1.26.0" },
|
||||
{ name = "rustworkx", specifier = ">=0.17.1" },
|
||||
]
|
||||
|
||||
[package.metadata.requires-dev]
|
||||
|
|
@ -1534,28 +1532,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/62/d5/bc97ff895ec35cf3925d4bd60f3b39d822f377a446906ec9bcc87405e59b/ruff-0.15.14-py3-none-win_arm64.whl", hash = "sha256:ff47b90a9ef6a40c9e2f3b479c1fb78531adf055b94c1eba0a7ba04b31951826", size = 11208607, upload-time = "2026-05-21T14:34:26.525Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "rustworkx"
|
||||
version = "0.17.1"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/e7/b0/66d96f02120f79eeed86b5c5be04029b6821155f31ed4907a4e9f1460671/rustworkx-0.17.1.tar.gz", hash = "sha256:59ea01b4e603daffa4e8827316c1641eef18ae9032f0b1b14aa0181687e3108e", size = 399407, upload-time = "2025-09-15T16:29:46.429Z" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/20/24/8972ed631fa05fdec05a7bb7f1fc0f8e78ee761ab37e8a93d1ed396ba060/rustworkx-0.17.1-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:c08fb8db041db052da404839b064ebfb47dcce04ba9a3e2eb79d0c65ab011da4", size = 2257491, upload-time = "2025-08-13T01:43:31.466Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/23/ae/7b6bbae5e0487ee42072dc6a46edf5db9731a0701ed648db22121fb7490c/rustworkx-0.17.1-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:4ef8e327dadf6500edd76fedb83f6d888b9266c58bcdbffd5a40c33835c9dd26", size = 2040175, upload-time = "2025-08-13T01:43:33.762Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/cd/ea/c17fb9428c8f0dcc605596f9561627a5b9ef629d356204ee5088cfcf52c6/rustworkx-0.17.1-cp39-abi3-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5b809e0aa2927c68574b196f993233e269980918101b0dd235289c4f3ddb2115", size = 2324771, upload-time = "2025-08-13T01:43:35.553Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d7/40/ec8b3b8b0f8c0b768690c454b8dcc2781b4f2c767f9f1215539c7909e35b/rustworkx-0.17.1-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c7e82c46a92fb0fd478b7372e15ca524c287485fdecaed37b8bb68f4df2720f2", size = 2068584, upload-time = "2025-08-13T01:43:37.261Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/d9/22/713b900d320d06ce8677e71bba0ec5df0037f1d83270bff5db3b271c10d7/rustworkx-0.17.1-cp39-abi3-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:42170075d8a7319e89ff63062c2f1d1116ced37b6f044f3bf36d10b60a107aa4", size = 2380949, upload-time = "2025-08-13T01:52:17.435Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/20/4b/54be84b3b41a19caf0718a2b6bb280dde98c8626c809c969f16aad17458f/rustworkx-0.17.1-cp39-abi3-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65cba97fa95470239e2d65eb4db1613f78e4396af9f790ff771b0e5476bfd887", size = 2562069, upload-time = "2025-08-13T02:09:27.222Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/39/5b/281bb21d091ab4e36cf377088366d55d0875fa2347b3189c580ec62b44c7/rustworkx-0.17.1-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:246cc252053f89e36209535b9c58755960197e6ae08d48d3973760141c62ac95", size = 2221186, upload-time = "2025-08-13T01:43:38.598Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/cc/2d/30a941a21b81e9db50c4c3ef8a64c5ee1c8eea3a90506ca0326ce39d021f/rustworkx-0.17.1-cp39-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:c10d25e9f0e87d6a273d1ea390b636b4fb3fede2094bf0cb3fe565d696a91b48", size = 2123510, upload-time = "2025-08-13T01:43:40.288Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/4f/ef/c9199e4b6336ee5a9f1979c11b5779c5cf9ab6f8386e0b9a96c8ffba7009/rustworkx-0.17.1-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:48784a673cf8d04f3cd246fa6b53fd1ccc4d83304503463bd561c153517bccc1", size = 2302783, upload-time = "2025-08-13T01:43:42.073Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/30/3d/a49ab633e99fca4ccbb9c9f4bd41904186c175ebc25c530435529f71c480/rustworkx-0.17.1-cp39-abi3-win32.whl", hash = "sha256:5dbc567833ff0a8ad4580a4fe4bde92c186d36b4c45fca755fb1792e4fafe9b5", size = 1931541, upload-time = "2025-08-13T01:43:43.415Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/a9/ec/cee878c1879b91ab8dc7d564535d011307839a2fea79d2a650413edf53be/rustworkx-0.17.1-cp39-abi3-win_amd64.whl", hash = "sha256:d0a48fb62adabd549f9f02927c3a159b51bf654c7388a12fc16d45452d5703ea", size = 2055049, upload-time = "2025-08-13T01:43:44.926Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "setuptools"
|
||||
version = "82.0.1"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue