Innosuisse SkillThrills Prototype

Prototype of the SubProject 01-02-03 for the SCESC Innosuisse Project.

Installation

For now the installation requires Conda. Will add Docker later.

git clone git@github.com:epfl-ml4ed/SkillThrills.git
cd SkillThrills
conda env create -f environment.yaml
conda activate proto

Input

We have 4 raw files as input, for now they are json but this can be changed.

The files are in the folder data/raw/synthetic.

They can be generated using the script protosp03/data/synthetic/make_synthetic.py and the config file protosp03/config/synthetic.yaml

• skills.json : a dict whoses keys are the skills name and values are the skill id
• jobs.json : a dict whoses keys are the job id and values are the list of skill required
• resume.json : a dict whoses keys are the resume id (or profile id) and values are the list of skill present
• courses.json : a dict whoses keys are the job id and values are the list of skill required and the list of skills provided

The config file provides requirements about the synthetic dataset (nb of skills, jobs, resume) and the path where the files are to be saved.

To generate the synthetic data files:

python protosp03/data/synthetic/make_synthetic.py --config protosp03/config/synthetic.yaml --seed 1

The default value for the seed is 42.

Pre-Processing

Data preprocessing is done with the script protosp03/data/synthetic/inverted_index.py and the config file protosp03/config/inverted_index.yaml.

For now the main idea of the pre-processing is simply to create inverted indexes that can be used for effeicient search. The config contains the path of the directory that contains the raw files and the path of the directory where to save the inverted indexes.

To pre-process the raw data files:

python protosp03/data/synthetic/inverted_index.py --config protosp03/config/inverted_index.yaml

User-journey Version 1.0

Running

To try the first version of the functions I can do in the user journey, run

python -m tests.user_journey01 --seed 55

It should display this:

Considering Profile resume_2 with the skills:
        skill_20 at level 1 in group 4

We are assuming that the Profile is interested in Job jobs_6 that requires the skills:
        skill_9 at level 3 in group 1
        skill_10 at level 4 in group 0
        skill_6 at level 4 in group 3
        skill_21 at level 1 in group 0
        skill_20 at level 4 in group 4

The matching between the profile and the desired job is: 5%

The matching between the profile and the desired job for each group is
        Group 1 has a matching of 0%
        Group 0 has a matching of 0%
        Group 3 has a matching of 0%
        Group 4 has a matching of 25%

Printing the attractiveness of each skill of the profile and comparing to other learners:
        Skill skill_20 is required for 20% of the jobs on the market and 28% of the learners have it

The overall attractiveness of the profile is: 3%

Printing the matching of the profile with respect to each job (from most compatible to least compatible):
        Job jobs_5 has a matching of 33%
        Job jobs_6 has a matching of 20%
        Job jobs_7 has a matching of 16%
        Job jobs_13 has a matching of 14%
        Job jobs_19 has a matching of 11%

Printing the matching of the profile with respect to each course (from most compatible to least compatible):
        Course course_2 has a matching of 33%
                If the profile takes the course course_2, it will learn the new skills: {'skill_21'}
                The matching with the job jobs_6 will increase from 5% to: 25%
                The overall attractiveness of the profile will increase from 3 to: 8

TODOs

Include time dimension