ppe-match 0.0.2.1

Intuitive framework that allows researchers to implement and test matching methodologies

FraPPE Matcha

(Framework for Automatic PPE Matching)

An open-source Simulation Framework for researchers interested in developing and testing methodologies to solve the PPE Matching Problem

Installation

In a virtual environment with Python 3.6+, pytestmatch can be installed via pip

pip install fraPPE-Matcha

Import the package using

from frappe_match import simulation sm

Test the installation with the code snippet below

from frappe_match import Simulation

# Initiate the simuation framework
s = Simulation()

# Set debug as True to monitor logs
s.debug(True)

# Run the simulation
s.run()

# Check outputs
s.get_decision() # Pandas dataframe that can be stored

# Display metrics
s.get_metrics() # Pandas dataframe that can be stored

Simulation Class

Parameters

donor_path

Path to donor data. Expected input type: csv Expected columns:

• don_id - Unique ID for Donor (type:str)
• date - Datetime of Request (type:datetime)
• ppe - Type of PPE (type:str)
• qty - Number of PPEs Requested (type:int/float)
• don_req_id - Unique ID for Each Donor Request (type:int)

Example:

don_id	date	ppe	qty	don_req_id
don0	2020-04-09 13:08:00+00:00	faceShields	10	0
don1	2020-04-09 13:36:00+00:00	faceShields	1	1
don2	2020-04-09 13:53:00+00:00	faceShields	3000	2

Default: anon_donors.csv

---

recipient_path - Path to recipient data

Path to recipient data. Expected input type: csv Expected columns:

• rec_id - Unique ID for Recipient (type:str)
• date - Datetime of Request (type:datetime)
• (*PPE Type column) - Every PPE type is defined as its own column and the value in these column represent the quantity the recipient requested for the respective PPE type (type: int/float)
• rec_req_id - Unique ID for Each Recipient Request (type:int)

Example:

rec_id	date	disinfectingWipes	surgicalCaps	disposableBooties	respirators	handmadeMasks	nitrileGloves	coveralls	handSanitizer	safetyGlasses	bodyBags	gowns	faceShields	safetyGoggles	thermometers	surgicalMasks	paprShield	babyMonitors	rec_req_id
rec0	2020-04-02 16:27:00+00:00	0	0	0	10000.0	0	10000	0	0	0	0	1000	5000	0	0	0	0	0	0
rec1	2020-04-02 16:35:00+00:00	4	0	0	9.0	9	5	0	4	0	0	9	9	0	0	0	0	0	1
rec2	2020-04-02 16:44:00+00:00	300	0	100	5.0	0	0	0	25	0	0	100	10	0	20	0	0	0	2

Default: anon_recipients.csv

---

distance_matrix_path

Path to distance matrix between donors and recipients. Expected input type: pickle(pandas dataframe) Expected columns:

• don_id - Unique ID for Donnor (type:str)
• rec_id - Unique ID for Recipient (type:str)
• date - Datetime of Request (type:datetime)

Example:

don_id	rec_id	distance
don585	rec4650	540.263969
don749	rec5876	770.589552

---

strategy

User defined strategy to allocate PPE The function must have the following arguments:

ppestrategy(D,R,M)

where,

• D is a pandas.DataFrame object whose rows contain the donors requests
• R is a pandas.DataFrame object whose rows contain the recipients requests
• M is a pandas.DataFrameobject that reports the distance between each donor and each recipient.

Default: proximity_match_strategy

Returns:

pd.dataframe of decisions with columns (don_id, rec_id, ppe, qty)

Class methods:

get_strategy()
set_strategy()

---

interval

Day Interval set for framework to iterate over. Default: 7 (days)

Class methods:

get_interval()
set_interval()

---

max_donation_qty

Maximum quantity limit for donor to donate (helps filter out dummy entries or test entries) Default: 1000 (ppe units)

Class methods:

get_max_donation_qty()
set_max_donation_qty()

---

writeFiles

Boolean flag to save intermediate outputs and final decisions as csv Default: False

If set to True intermediate data will be saved for every iteration as follows:

output
├── 2020-04-09
	 ├── decisions.csv
	 ├── distance_matrix.csv
	 ├── donors.csv
	 └── recipients.csv
├── 2020-04-16
	 ├── decisions.csv
	 ├── distance_matrix.csv
	 ├── donors.csv
	 └── recipients.csv
├── ...

---

Methods

run()

Executes the strategy function over the data in a date simulation

---

get_decisions()

Returns final decision output from the framework after run()

---

debug(bool_flag)

Sets the logging level to DEBUG if True Default: False (WARN)

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0.1 - 2021-05-12

• Initial public release