school capacities / quotas #33
Description
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