swap-anything 0.2.0

A mix and match (swap) library to empower swapping-based projects.

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:

1. 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

2. 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

3. 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