The deferred_acceptance_school_choice from kyosek

deferred_acceptance_school_choice's Introduction

Hi there 👋

I'm Kyosek, a senior data scientist in London

A little more about me...

const anmol = {
    pronouns: "He" | "Him",
    code: ["Python"],
    Interest: ["Machine learning", "NLP", "Feature extraction", "Counterfactual explanation", "Game theory"],
    technologies: {
        Deep learning: [pytorch, tensorflow],
        devOps: ["GCP", "AWS", "Docker🐳", "Kubernetes"],
        databases: ["MySql", "sqlite", "DB2"],
    }
};

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deferred_acceptance_school_choice's Issues

Add tie-breaking lottery

Add tie-breaking lottery to the DA algorithm

Replace randrange to randint in the tie-break function

Create an example for tie-break function usage

Make verbosity feature in DA algorithm

Create RL environment

A subtask of Use RL to learn the school choice algorithm #14
Create the following classes:

"matching" environment
schools
students

Replace dictionaries in the DA algorithm by slot

Develop DA algorithm implementation

Write a DA algorithm implementation

Improve tie-breaking algorithm

Use RL to learn the school choice algorithm

Implement stable improvement cycle algorithm

Implement stable improvement cycle algorithm (Erdil and Ergin, 2008)

Implement "optimal for" feature to DA function

The "optimal for" argument can choose the algorithm work to optimise their matches for either students or schools.
This can be implemented by choosing which party will make a proposal first.

Add "strict preference check" to the data construction function

school capacities / quotas

A couple of suggestions (happy to work on a PR if you're open to it):

Not enough space: In overcrowded school districts, total capacity can be less than the total number of students. It would be worth it I think to build in some rules for how "excess" students get allocated.
Eligibility: Some schools may limit which students are eligible to attend (could be based on living within a certain area, test scores, etc.). It would be useful for this model to work if a school's preferences could include only a subset of all students.

This is the error thrown when you try to run the model w/o enough capacity:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[~\AppData\Local\Temp\ipykernel_40444\1300843094.py](https://file+.vscode-resource.vscode-cdn.net/c%3A/webtools_dev/deferred_acceptance_school_choice/examples/~/AppData/Local/Temp/ipykernel_40444/1300843094.py) in 
     13 # Run the algorithm
     14 schools_quota = {"A": 1, "B": 1, "C": 1}
---> 15 matches = deferred_acceptance(
     16     students_df=students_df,
     17     schools_df=strict_school_df,

[c:\webtools_dev\deferred_acceptance_school_choice\examples\..\deferred_acceptance\deferred_acceptance.py](file:///C:/webtools_dev/deferred_acceptance_school_choice/deferred_acceptance/deferred_acceptance.py) in deferred_acceptance(students_df, schools_df, schools_quota, verbose)
     42             if student not in unassigned_students:
     43                 school = available_school[student]
---> 44                 best_choice = students_df.loc[student][
     45                     students_df.loc[student].index.isin(school)
     46                 ].idxmin()

[c:\Users\Raphael.WXYSTUDIO\Anaconda3\lib\site-packages\pandas\core\series.py](file:///C:/Users/Raphael.WXYSTUDIO/Anaconda3/lib/site-packages/pandas/core/series.py) in idxmin(self, axis, skipna, *args, **kwargs)
   2332         nan
   2333         """
-> 2334         i = self.argmin(axis, skipna, *args, **kwargs)
   2335         if i == -1:
   2336             return np.nan

[c:\Users\Raphael.WXYSTUDIO\Anaconda3\lib\site-packages\pandas\core\base.py](file:///C:/Users/Raphael.WXYSTUDIO/Anaconda3/lib/site-packages/pandas/core/base.py) in argmin(self, axis, skipna, *args, **kwargs)
    717             # error: Incompatible return value type (got "Union[int, ndarray]", expected
    718             # "int")
--> 719             return nanops.nanargmin(  # type: ignore[return-value]
    720                 delegate, skipna=skipna
    721             )

[c:\Users\Raphael.WXYSTUDIO\Anaconda3\lib\site-packages\pandas\core\nanops.py](file:///C:/Users/Raphael.WXYSTUDIO/Anaconda3/lib/site-packages/pandas/core/nanops.py) in _f(*args, **kwargs)
     91             try:
     92                 with np.errstate(invalid="ignore"):
---> 93                     return f(*args, **kwargs)
     94             except ValueError as e:
     95                 # we want to transform an object array

[c:\Users\Raphael.WXYSTUDIO\Anaconda3\lib\site-packages\pandas\core\nanops.py](file:///C:/Users/Raphael.WXYSTUDIO/Anaconda3/lib/site-packages/pandas/core/nanops.py) in nanargmin(values, axis, skipna, mask)
   1140     values, mask, _, _, _ = _get_values(values, True, fill_value_typ="+inf", mask=mask)
   1141     # error: Need type annotation for 'result'
-> 1142     result = values.argmin(axis)  # type: ignore[var-annotated]
   1143     result = _maybe_arg_null_out(result, axis, mask, skipna)
   1144     return result

ValueError: attempt to get argmin of an empty sequence

Here's the error when you run the model with school preferences of variable length:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
[~\AppData\Local\Temp\ipykernel_40444\2589139436.py](https://file+.vscode-resource.vscode-cdn.net/c%3A/webtools_dev/deferred_acceptance_school_choice/examples/~/AppData/Local/Temp/ipykernel_40444/2589139436.py) in 
      1 schools_preferences = {"A": [1, 1, 1], "B": [1, 3, 3, 1], "C": [2, 2, 2, 2]}
      2 
----> 3 students_df, schools_df = create_dataframes(
      4     students_list=students_list,
      5     students_preferences=students_preferences,

[c:\webtools_dev\deferred_acceptance_school_choice\examples\..\deferred_acceptance\utils.py](file:///C:/webtools_dev/deferred_acceptance_school_choice/deferred_acceptance/utils.py) in create_dataframes(students_list, students_preferences, schools_list, schools_preferences)
     52     students_df.index = schools_list
     53     students_df = students_df.transpose()
---> 54     schools_df = pd.DataFrame(schools_preferences)
     55     schools_df.index = students_list
     56 

[c:\Users\Raphael.WXYSTUDIO\Anaconda3\lib\site-packages\pandas\core\frame.py](file:///C:/Users/Raphael.WXYSTUDIO/Anaconda3/lib/site-packages/pandas/core/frame.py) in __init__(self, data, index, columns, dtype, copy)
    634         elif isinstance(data, dict):
    635             # GH#38939 de facto copy defaults to False only in non-dict cases
--> 636             mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
    637         elif isinstance(data, ma.MaskedArray):
    638             import numpy.ma.mrecords as mrecords

[c:\Users\Raphael.WXYSTUDIO\Anaconda3\lib\site-packages\pandas\core\internals\construction.py](file:///C:/Users/Raphael.WXYSTUDIO/Anaconda3/lib/site-packages/pandas/core/internals/construction.py) in dict_to_mgr(data, index, columns, dtype, typ, copy)
    500         # TODO: can we get rid of the dt64tz special case above?
    501 
--> 502     return arrays_to_mgr(arrays, columns, index, dtype=dtype, typ=typ, consolidate=copy)
    503 
    504 

[c:\Users\Raphael.WXYSTUDIO\Anaconda3\lib\site-packages\pandas\core\internals\construction.py](file:///C:/Users/Raphael.WXYSTUDIO/Anaconda3/lib/site-packages/pandas/core/internals/construction.py) in arrays_to_mgr(arrays, columns, index, dtype, verify_integrity, typ, consolidate)
    118         # figure out the index, if necessary
    119         if index is None:
--> 120             index = _extract_index(arrays)
    121         else:
    122             index = ensure_index(index)

[c:\Users\Raphael.WXYSTUDIO\Anaconda3\lib\site-packages\pandas\core\internals\construction.py](file:///C:/Users/Raphael.WXYSTUDIO/Anaconda3/lib/site-packages/pandas/core/internals/construction.py) in _extract_index(data)
    672             lengths = list(set(raw_lengths))
    673             if len(lengths) > 1:
--> 674                 raise ValueError("All arrays must be of the same length")
    675 
    676             if have_dicts:

ValueError: All arrays must be of the same length

Case where all students and all schools have the same preferences doesn't work

Tried a case with 2 schools and 3 students where quotas were 1 and 2 but each school/student had the same preferences, and it breaks the code. I think the pairs dropped shouldn't be all but the first one, instead it should be based on the school's quota.