A mix and match (swap) library to empower swapping-based projects.
Project description
swap-anything
A mix and match (swap) library to empower swapping-based projects.
NOTE:
swapanything
is still in its early steps. If you want to contribute or sponsor this project, visit www.founderswap.xyz
Quickstart
Want to develop with us? Check the developer guide
Your first matching round
This library allow you to match subjects (people, things, whatever) depending on their availability slots (calendar slots, timeframe, location, any combination of the abovementioned). Truly, you can use this library as backend for any sort of matching need.
The simplest way to test this library is to use the swapanything
python
package to make a simple swapping exercise.
from swapanything import prep, select
import pandas as pd
availabilities = pd.DataFrame(
[
["KungFury", "9:00"],
["KungFury", "10:00"],
["KungFury", "13:00"],
["KungFury", "14:00"],
["Triceracop", "9:00"],
["Triceracop", "11:00"],
["Hackerman", "10:00"],
["Hackerman", "11:00"],
["Katana", "12:00"],
["Barbarianna", "12:00"],
["Thor", "13:00"],
["Thor", "14:00"],
["Thor", "15:00"],
["T-Rex", "15:00"],
["T-Rex", "16:00"],
["Hoff 9000", "16:00"],
],
columns=["subject", "availability"]
)
all_possible_matches = prep.get_all_matches(
availabilities,
subject_col="subject",
slot_col="availability",
subjects_new_col_name="subjects",
slots_new_col_name="availabilities",
)
# subject availability
# 0 (Barbarianna, Katana) (12:00,)
# 1 (Hackerman, KungFury) (10:00,)
# 2 (Hackerman, Triceracop) (11:00,)
# 3 (Hoff 9000, T-Rex) (16:00,)
# 4 (KungFury, Thor) (13:00, 14:00)
# 5 (KungFury, Triceracop) (9:00,)
# 6 (T-Rex, Thor) (15:00,)
select.select_matches(
all_possible_matches,
subjects_col="subjects",
slots_col="availabilities",
)
# subjects availabilities
# 0 (Barbarianna, Katana) (12:00,)
# 1 (Hackerman, Triceracop) (11:00,)
# 2 (Hoff 9000, T-Rex) (16:00,)
# 3 (KungFury, Thor) (13:00, 14:00)
Imagine now that we want to provide a super high importance
to the match (KungFury, Triceracop)
.
With select_matches
you can use match scores, and the
algorithm will try to maximize number of matches and total
score!
This way we ensure that high quality matches are selected.
scores = [1, 1, 1, 1, 1, 9001, 1]
# (KungFury, Triceracop)... it's over 9000!
select.select_matches(
all_possible_matches,
match_scores=scores,
subjects_col="subjects",
slots_col="availabilities",
)
# subject availability
# 0 (Barbarianna, Katana) (12:00,)
# 1 (KungFury, Triceracop) (9:00,)
# 2 (T-Rex, Thor) (15:00,)
Advanced Backends
With python, it is possible to integrate swapanything
in your application
or custom tool. swapanything
comes with some pre-configured data backends
(e.g. Airtable, Excel Spreadsheets, SQL) that you can easily use to
kickstart your swaping-based app!
Airtable
Install airtable dependencies:
pip install swap-anything[airtable]
from swapanything import prep, select
from swapanything_backend import airtable as backend
import os
be = backend.AirTableBackend(
# subject_id is the record id of the subjects table
subject_features=["Interests", "Tags", "Score1", "Score2"],
availability_subject_column="AvailabilitiesSubjectId",
availabilities_column="Availabilities",
exclusions_subject_columns=["Subject1", "Subject2"]
# Tables
subjects_table_name="Subjects",
availabilities_table_name="Availabilities",
exclusions_table_name="Matches",
# Airtable credentials
client_id=os.environ["AIRTABLE_BASE_ID"],
client_secret=os.environ["AIRTABLE_API_KEY"],
)
subjects = be.get_subjects()
availabilities = be.get_availabilities()
all_possible_matches = prep.get_all_matches(
availabilities,
subject_col=be.availability_subject_column,
slot_col=be.availabilities_column,
subjects_new_col_name="subjects",
slots_new_col_name="availabilities",
)
select.select_matches(
all_possible_matches,
subjects_col="subjects",
slots_col="availabilities",
)
Using CLI (POC)
This part is in proof of concept stage. Yet to be done!
You can start swapping using spreadsheets as sources/destinations of data. Let's prepare 3 files:
-
subjects.xlsx
- the table of subjects to match with details on features to use to calculate the score for a match. Made as follow:SubjectId Interests Tags Score1 Score2 sub001 i1,i2 t1,t2 0.2 0.5 sub002 i1 t2 0.2 0.1 sub003 i3 t1 0.15 0.2 sub004 i1,i2 t1 0.2 0.5 -
availabilities.xlsx
- a table containing match slots. Subjects can match when they have one or more slots in common. Made as follow:AvailabilitiesSubjectId Availabilities sub001 2023-01-01 15:30, 2023-01-02 16:30 sub002 2023-01-01 15:30 sub003 2023-01-01 15:30 sub004 2023-01-02 16:30 -
exclusions.xlsx
- a table containing matches to exclude (e.g. subjects that have already matched). Made as follow:Subject1 Subject2 sub001 sub004
Then you can use the command line tool to make the swapping ✨
swapanything --from spreadsheet \
--subject-id SubjectId \
--subject-features Interests,Tags,Score1,Score2 \
--availabilities-subject-col AvailabilitiesSubjectId \
--availabilities-column Availabilities \
--exclusions-subject1-id Subject1 \
--exclusions-subject2-id Subject2 \
--subjects subjects.xlsx \
--availabilities availabilities.xlsx \
--exclusions exclusions.xlsx \
--to spreadsheet output.xlsx
This will result in the following output.xlsx
, containing all new matches:
subject1 | subject2 | slot |
---|---|---|
sub001 | sub002 | 2023-01-01 15:30 |
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