mirror of https://gitlab.crans.org/bde/nk20
128 lines
4.6 KiB
Python
128 lines
4.6 KiB
Python
# Copyright (C) 2018-2024 by BDE ENS Paris-Saclay
|
|
# SPDX-License-Identifier: GPL-3.0-or-later
|
|
|
|
import random
|
|
from datetime import date, timedelta
|
|
|
|
from django.contrib.auth.models import User
|
|
from django.test import TestCase
|
|
|
|
from ..forms.surveys.wei2023 import WEIBusInformation2023, WEISurvey2023, WORDS, WEISurveyInformation2023
|
|
from ..models import Bus, WEIClub, WEIRegistration
|
|
|
|
|
|
class TestWEIAlgorithm(TestCase):
|
|
"""
|
|
Run some tests to ensure that the WEI algorithm is working well.
|
|
"""
|
|
fixtures = ('initial',)
|
|
|
|
def setUp(self):
|
|
"""
|
|
Create some test data, with one WEI and 10 buses with random score attributions.
|
|
"""
|
|
self.user = User.objects.create_superuser(
|
|
username="weiadmin",
|
|
password="admin",
|
|
email="admin@example.com",
|
|
)
|
|
self.user.save()
|
|
self.client.force_login(self.user)
|
|
sess = self.client.session
|
|
sess["permission_mask"] = 42
|
|
sess.save()
|
|
|
|
self.wei = WEIClub.objects.create(
|
|
name="WEI 2023",
|
|
email="wei2023@example.com",
|
|
parent_club_id=2,
|
|
membership_fee_paid=12500,
|
|
membership_fee_unpaid=5500,
|
|
membership_start='2023-01-01',
|
|
membership_end='2023-12-31',
|
|
date_start=date.today() + timedelta(days=2),
|
|
date_end='2023-12-31',
|
|
year=2023,
|
|
)
|
|
|
|
self.buses = []
|
|
for i in range(10):
|
|
bus = Bus.objects.create(wei=self.wei, name=f"Bus {i}", size=10)
|
|
self.buses.append(bus)
|
|
information = WEIBusInformation2023(bus)
|
|
for question in WORDS:
|
|
information.scores[question] = {answer: random.randint(1, 5) for answer in WORDS[question][1]}
|
|
information.save()
|
|
bus.save()
|
|
|
|
def test_survey_algorithm_small(self):
|
|
"""
|
|
There are only a few people in each bus, ensure that each person has its best bus
|
|
"""
|
|
# Add a few users
|
|
for i in range(10):
|
|
user = User.objects.create(username=f"user{i}")
|
|
registration = WEIRegistration.objects.create(
|
|
user=user,
|
|
wei=self.wei,
|
|
first_year=True,
|
|
birth_date='2000-01-01',
|
|
)
|
|
information = WEISurveyInformation2023(registration)
|
|
for question in WORDS:
|
|
setattr(information, question, random.randint(1, 5))
|
|
information.step = 20
|
|
information.save(registration)
|
|
registration.save()
|
|
|
|
# Run algorithm
|
|
WEISurvey2023.get_algorithm_class()().run_algorithm()
|
|
|
|
# Ensure that everyone has its first choice
|
|
for r in WEIRegistration.objects.filter(wei=self.wei).all():
|
|
survey = WEISurvey2023(r)
|
|
preferred_bus = survey.ordered_buses()[0][0]
|
|
chosen_bus = survey.information.get_selected_bus()
|
|
self.assertEqual(preferred_bus, chosen_bus)
|
|
|
|
def test_survey_algorithm_full(self):
|
|
"""
|
|
Buses are full of first year people, ensure that they are happy
|
|
"""
|
|
# Add a lot of users
|
|
for i in range(95):
|
|
user = User.objects.create(username=f"user{i}")
|
|
registration = WEIRegistration.objects.create(
|
|
user=user,
|
|
wei=self.wei,
|
|
first_year=True,
|
|
birth_date='2000-01-01',
|
|
)
|
|
information = WEISurveyInformation2023(registration)
|
|
for question in WORDS:
|
|
setattr(information, question, random.randint(1, 5))
|
|
information.step = 20
|
|
information.save(registration)
|
|
registration.save()
|
|
|
|
# Run algorithm
|
|
WEISurvey2023.get_algorithm_class()().run_algorithm()
|
|
|
|
penalty = 0
|
|
# Ensure that everyone seems to be happy
|
|
# We attribute a penalty for each user that didn't have its first choice
|
|
# The penalty is the square of the distance between the score of the preferred bus
|
|
# and the score of the attributed bus
|
|
# We consider it acceptable if the mean of this distance is lower than 5 %
|
|
for r in WEIRegistration.objects.filter(wei=self.wei).all():
|
|
survey = WEISurvey2023(r)
|
|
chosen_bus = survey.information.get_selected_bus()
|
|
buses = survey.ordered_buses()
|
|
score = min(v for bus, v in buses if bus == chosen_bus)
|
|
max_score = buses[0][1]
|
|
penalty += (max_score - score) ** 2
|
|
|
|
self.assertLessEqual(max_score - score, 25) # Always less than 25 % of tolerance
|
|
|
|
self.assertLessEqual(penalty / 100, 25) # Tolerance of 5 %
|