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lubos31262 edited this page Jun 10, 2021 · 7 revisions

Feature collector "tsp-fc"

Version: 1.0

Date: 15.04.2021

Authors: Ľuboš Buzna; Milan Straka

Address: University of Žilina, Univerzitná 8215/1, 010 26 Žilina, Slovakia

Description

The "tsp-fc" feature collector is a module of the Ride2Rail Offer Categorizer responsible for the computation of the following determinant factors: "cleanliness", "space_available", "ride_smoothness", "seating_quality", "internet_availability", "plugs_or_charging_points", "silence_area_presence", "privacy_level", "user_feedback" , "bike_on_board", "likelihood_of_delays" , "last_minute_changes", "frequency_of_service" and "business_area_presence".

Computation can be executed from "tsp.py" by running the procedure extract() which is binded under the name compute with URL using FLASK (see example request below). Computation is composed of three phases:

Phase I: Extraction of data required by tsp-fc feature collector from the cache. A dedicated procedure defined for this purpose from the unit "cache_operations.py" is utilized.

Phase II: Computation of weights assigned to "tsp-fc" feature collector. For the aggregation of data at the tripleg level and for normalization of weights a dedicated procedure implemented in the unit "normalization.py" are utilized. By default "z-scores" are used to normalize data.

Phase III: Storing of the results produced by "tsp-fc" to cache. A dedicated procedure defined for this purpose in the unit "cache_operations.py" is utilized.

Configuration

The following values of parameters can be defined in the configuration file "tsp.conf".

Section "running":

  • "verbose" - if value "1" is used, then feature collector is run in the verbose mode,
  • "scores" - if value "minmax_score" is used, the minmax approach is used for normalization of weights, otherwise, the "z-score" approach is used.

Section "cache":

  • "host" - host address where the cache service that should be accessed by "tsp-fc" feature collector is available
  • "port" - port number where the cache service that should be accessed used by "tsp-fc" feature collector is available

Example of the configuration file "tsp.conf":

[service]
name = tsp
type = feature collector
developed_by = Lubos Buzna <lubos(dot)buzna(at)fri(dot)uniza(dot)sk> and Milan Straka<milan(dot)straka(at)fri(dot)uniza(dot)sk>

[running]
verbose = 1
scores  = z_scores

[cache]
host = cache
port = 6379

Usage

Local development (debug on)

The feature collector "tsp-fc" can be launched from the terminal locally by running the script "tsp.py":

$ python3 tsp.py
 * Serving Flask app "price" (lazy loading)
 * Environment: development
 * Debug mode: on

Moreover, the repository contains also configuration files required to launch the feature collector in Docker from the terminal by the command docker-compose up:

docker-compose up
Starting tsp_fc ... done
Attaching to tsp_fc
tsp_fc    |  * Serving Flask app "tsp.py" (lazy loading)
tsp_fc    |  * Environment: development
tsp_fc    |  * Debug mode: on
tsp_fc    |  * Running on http://0.0.0.0:5000/ (Press CTRL+C to quit)
tsp_fc    |  * Restarting with stat
tsp_fc    |  * Debugger is active!
tsp_fc    |  * Debugger PIN: 248-423-277

Example Request

To make a request (i.e. to calculate values of determinant factors assigned to the "tsp-fc" feature collector for a given mobility request defined by a request_id) the command curl can be used:

$ curl --header 'Content-Type: application/json' \
       --request POST  \
       --data '{"request_id": "123x" }' \
         http://localhost:5001/compute
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