def test_individual_numbers():
# Loading data
employees = pd.read_csv(path + "employees.csv", sep=";", decimal=",")
spouses = pd.read_csv(path + "spouses.csv", sep=";", decimal=",")
children = pd.read_csv(path + "children.csv", sep=";", decimal=",")
# Projection of population
# Number of years to project
MAX_ANNEES = 10
# Projection
numbers_ = eff.projectNumbers(employees,
spouses,
children,
'TV 88-90',
MAX_ANNEES,
law_replacement_=law_replacement1)
# global numbers
global_numb = eff.globalNumbers(numbers_[0], numbers_[1], numbers_[2],
MAX_ANNEES)
# verify first id in live individual numbers
indiv_lives = eff.individual_employees_numbers(numbers_[0])[0]
res = list(indiv_lives.iloc[0, 8:])
expected_res = [
1, 0.99945, 0.99887, 0.99825, 0.99761, 0.99695, 0.99623, 0.99542,
0.99452, 0.99359
]
diff = [abs(i - j) for i, j in zip(res, expected_res)]
assert max(diff) < 5
# strict comparaison of global numbers
# load expected numbers
expected_global_numbers = pd.DataFrame(
pd.read_csv("./pop_projection/test/expected_global_numbers.csv",
sep=";",
decimal=","))
# assert expected_global_numbers.equals(global_numb)
cols = list(expected_global_numbers.columns)
for c in cols:
res = list(global_numb[c])
expected_res = list(expected_global_numbers[c])
diff = [abs(i - j) for i, j in zip(res, expected_res)]
assert max(diff) < 5, "Numbers in column " + c + " far from expected!"
def test_walk_actives():
# Path for input data
path = "./pop_projection/test/data/"
# Loading data
employees = pd.read_csv(path + "employees.csv", sep=";", decimal=",")
spouses = pd.read_csv(path + "spouses.csv", sep=";", decimal=",")
children = pd.read_csv(path + "children.csv", sep=";", decimal=",")
# Projection of population
# Number of years to project
MAX_ANNEES = 50
# Projection
numbers_ = eff.projectNumbers(employees,
spouses,
children,
'TV 88-90',
MAX_ANNEES,
law_replacement_=None)
# global numbers
global_numb = eff.globalNumbers(numbers_[0], numbers_[1], numbers_[2],
MAX_ANNEES)
# departures
departures = eff.leavingNumbers(numbers_[0], numbers_[4], MAX_ANNEES)
# employees_walk
employees_walk = pd.merge(global_numb.loc[:, ['Year', 'effectif_actifs']],
departures,
on='Year')
# In case of no replacement, employees numbers must satisfy :
# Numbers(year N + 1 ) = Numbers(year N) - Deaths(year N) -
# Reignations(year N) - New Retirees(year N)
for i in range(1, MAX_ANNEES):
temp = (employees_walk.loc[i - 1, 'effectif_actifs'] -
employees_walk.loc[i, 'Total Leaving'] -
employees_walk.loc[i, 'effectif_deces_nouveaux_retraites'])
temp_diff = abs(temp - employees_walk.loc[i, 'effectif_actifs'])
assert temp_diff < 0.00001, "Walk equality not satisfaied in year " + str(
i) + " diff : " + str(temp_diff)
def test_glob_num_no_rep():
# Path for input data
path = "./pop_projection/test/data/"
# Loading data
employees = pd.read_csv(path + "employees.csv", sep=";", decimal=",")
spouses = pd.read_csv(path + "spouses.csv", sep=";", decimal=",")
children = pd.read_csv(path + "children.csv", sep=";", decimal=",")
# Projection of population
# Number of years to project
MAX_ANNEES = 60
# Projection
numbers_ = eff.projectNumbers(employees,
spouses,
children,
'TV 88-90',
MAX_ANNEES,
law_replacement_=None,
law_resignation_=sl.turnover,
law_retirement_=law_ret1)
# global numbers
global_numb = eff.globalNumbers(numbers_[0], numbers_[1], numbers_[2],
MAX_ANNEES)
# load expected numbers
expected_global_numbers = pd.DataFrame(
pd.read_csv("./pop_projection/test/expected_glob_num_no_rep.csv",
sep=";",
decimal=","))
cols = list(expected_global_numbers.columns)
for c in cols[1:4]:
res = list(global_numb[c])
expected_res = list(expected_global_numbers[c])
for i in range(len(res)):
assert abs(
res[i] - expected_res[i]
) < 0.000001, "Numbers in column " + c + " far from expected at year " + str(
i) + " !"
def test_simulerEffectif_2():
# Path for input data
path = "./pop_projection/test/data/"
# Loading data
employees = pd.read_csv(path + "employees.csv", sep=";", decimal=",")
spouses = pd.read_csv(path + "spouses.csv", sep=";", decimal=",")
children = pd.read_csv(path + "children.csv", sep=";", decimal=",")
# Projection of population
# Number of years to project
MAX_ANNEES = 60
# Projection
numbers_ = eff.projectNumbers(employees, spouses, children)
# global numbers
global_numb = eff.globalNumbers(numbers_[0], numbers_[1], numbers_[2],
MAX_ANNEES)
plot_num = 1
min_age_ = 19
max_age_ = 100
color_males = (149 / 255, 125 / 255, 98 / 255)
color_females = (0 / 255, 128 / 255, 0 / 255)
for t in tables:
# Projection of population
numbers_ = eff.projectNumbers(employees,
spouses,
children,
t,
MAX_ANNEES,
law_replacement_=sl.law_replacement1,
law_marriage_=sl.law_mar1)
# Pyramid of spouses
ind_spo_numbers = eff.individual_spouses_numbers(numbers_[1])
spouses_proj = ind_spo_numbers[0]
emp_grouped = spouses_proj.groupby(
['age', 'sex'], as_index=False)['year_' + str((year_ - 1))].sum()
# update colum age to be age at year (year_-1)
emp_grouped['age'] = emp_grouped['age'] - MAX_ANNEES + (year_ - 1)
emp_grouped = emp_grouped.loc[(emp_grouped['age'] < max_age_)
children = pd.read_csv(path + "children.csv", sep=";", decimal=",")
# Loading salaries
salaries = pd.read_csv(path + "salaries.csv", sep=";", decimal=",")
# Loading pensions
pensions = pd.read_csv(path + "pensions.csv", sep=";", decimal=",")
print('Chargement données : ', time.asctime( time.localtime(time.time())))
# Projection of population
# Number of years to project
MAX_ANNEES = 60
# Projection
numbers_ = eff.projectNumbers( employees, spouses, children, 'TV 88-90', MAX_ANNEES,
law_replacement_=law_replacement1)
print('Simulation effectifs : ', time.asctime( time.localtime(time.time())))
df_salaries = cf.project_salaries(numbers_[0], salaries, MAX_ANNEES, salaries_new_emp_1, 0.035)
print('Projection salaires : ', time.asctime( time.localtime(time.time())))
# rate_contr = pd.read_csv(path + "rate_contr.csv", sep=";", decimal=",")
def rate_contr(sex, year):
return 0.325
df_contr = cf.project_contributions(df_salaries,rate_contr,20)
print('Projection contributions : ', time.asctime( time.localtime(time.time())))
path = "../pop_projection/data/"
# Number of years to project
MAX_ANNEES = 50
# Loading data
employees = pd.read_csv(path + "employees.csv", sep=";", decimal=",")
spouses = pd.read_csv(path + "spouses.csv", sep=";", decimal=",")
children = pd.read_csv(path + "children.csv", sep=";", decimal=",")
# Projection of population
numbers_ = eff.projectNumbers(employees,
spouses,
children,
'TV 88-90',
MAX_ANNEES,
(law_ret1, ['age', 'Year_employment']),
(law_resignation_1, ['age', 'sex']),
(law_mar1, ['age', 'sex', 'type']),
law_replacement_=None)
# Global numbers
Effectifs = eff.globalNumbers(numbers_[0], numbers_[1], numbers_[2],
MAX_ANNEES)
Effectifs.to_csv('Effectifs_python.csv', sep=';', index=False, decimal=',')
# Number of actives leaving population : deaths, resignations, and new retired
Leaving = eff.leavingNumbers(numbers_[0], numbers_[4], MAX_ANNEES)
Leaving.to_csv('Sortants_python.csv', sep=';', index=False, decimal=',')
def test_simulerEffectif_1():
# employees
ids = ['id1', 'id2', 'id3']
types = ['active', 'retired', 'active']
sexes = ['male', 'female', 'male']
familystatus = ['married', 'married', 'not married']
ages = [30, 65, 45]
Data = {
'id': ids,
'type': types,
'sex': sexes,
'familyStatus': familystatus,
'age': ages
}
employees = pd.DataFrame(
data=Data, columns=['id', 'type', 'sex', 'familyStatus', 'age'])
# spouses
ids = ['id1', 'id2']
rangs = [1, 1]
sexes = ['female', 'male']
ages = [25, 65]
types = ['active', 'retired']
familystatus = ['married', 'married']
Data = {
'id': ids,
'rang': rangs,
'sex': sexes,
'age': ages,
'type': types,
'familyStatus': familystatus
}
spouses = pd.DataFrame(
data=Data,
columns=['id', 'rang', 'sex', 'age', 'type', 'familyStatus'])
# children
ids = []
rangs = []
sexes = []
ages = []
types = []
familystatus = []
Data = {
'id': ids,
'rang': rangs,
'sex': sexes,
'age': ages,
'type': types,
'familyStatus': familystatus
}
children = pd.DataFrame(
data=Data,
columns=['id', 'rang', 'sex', 'age', 'type', 'familyStatus'])
# Projection of population
# Number of years to project
MAX_ANNEES = 60
# Projection
numbers_ = eff.projectNumbers(employees,
spouses,
children,
MAX_YEARS=MAX_ANNEES)
# global numbers
global_numb = eff.globalNumbers(numbers_[0], numbers_[1], numbers_[2],
MAX_ANNEES)
# print(spouses.head(10))
# print('Children :')
# print(children.head(10))
# Projection of population
# Number of years to project
MAX_ANNEES = 50
# Projection
cols_birth = [('age', 'employees'), ('age', 'spouses'), ('sex', 'employees'),
('sex', 'spouses')]
numbers_ = eff.projectNumbers(employees,
spouses,
children,
'TV 88-90',
MAX_ANNEES,
law_replacement_=law_replacement1,
law_marriage_=(law_mar_3, ['age', 'year_proj']),
law_birth_=loi_naiss_5,
law_resignation_=turnover)
# global numbers
global_numb = eff.globalNumbers(numbers_[0], numbers_[1], numbers_[2],
MAX_ANNEES)
# printing results
# print(global_numb)
# export global numbers
global_numb.to_csv(path_exp + "global_numbers.csv",
index=False,