def test_compare_atkinson_with_R(self):
""" Comparison with R version in ineq package
Results
Atkinson a=0.5: 0.1796591
Atkinson a=1: 0.3518251
"""
myIndex = cl.Index()
x = np.array([541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261])
ERROR_MARGIN = 1e-5
self.assertTrue(abs(myIndex.atkinson(x, 0.5) - 0.1796591) < ERROR_MARGIN)
self.assertTrue(abs(myIndex.atkinson(x, 1.0) - 0.3518251) < ERROR_MARGIN)
def test_eg_single_industry(self):
myIndex = cl.Index()
# Number of observations
N = 10
# Number of areas
Na = 10
# Number of industries
Ni = 1
# uniform exposure
exposure = np.ones(N)
# single industry
industry = np.zeros(N, dtype=np.int)
# uniform area distribution
area = np.arange(0, N)
# create dataframe
d = {'Exposure': exposure, 'Area': area, 'Industry': industry}
data = pd.DataFrame(data=d)
self.assertTrue(abs(myIndex.ellison_glaeser(data, Na, Ni)[0] - 0.0) < ERROR_MARGIN)
def test_eg_five_uniform(self):
myIndex = cl.Index()
# Number of observations
N = 50
# Number of areas
Na = 10
# Number of industries
Ni = 5
# uniform exposure
exposure = np.ones(N)
# single industry
industry = np.array(list(np.arange(0, Ni)) * Na)
# uniform area distribution
y_list = [[j for i in range(Ni)] for j in range(Na)]
area = np.array([y for x in y_list for y in x])
# create dataframe
d = {'Exposure': exposure, 'Area': area, 'Industry': industry}
data = pd.DataFrame(data=d)
results = myIndex.ellison_glaeser(data, Na, Ni)
for i in range(Ni):
self.assertTrue(abs(results[i] - 0.0) < ERROR_MARGIN)
def test_eg_max_concentration(self):
myIndex = cl.Index()
# Number of observations
N = 400000
# Number of areas
Na = 200000
# Number of industries
Ni = 2
# uniform exposure
exposure = np.zeros(N)
exposure[0:199999] = 1
exposure[399999] = 1
exposure[399998] = 0.0001
# two industries
area = np.array(list(np.arange(0, Na)) * Ni)
# uniform area distribution
y_list = [[j for i in range(Na)] for j in range(Ni)]
industry = np.array([y for x in y_list for y in x])
# create dataframe
d = {'Exposure': exposure, 'Area': area, 'Industry': industry}
data = pd.DataFrame(data=d)
self.assertTrue(abs(myIndex.ellison_glaeser(data, Na, Ni)[0] - 0.0) < ERROR_MARGIN)
def test_hk(self):
myIndex = cl.Index()
n = 10
vector = np.ones(n)
self.assertTrue(abs(myIndex.hk(vector, 1) - 1.0 / n) < ERROR_MARGIN)
self.assertTrue(abs(myIndex.hk(vector, 3) - 1.0 / n) < ERROR_MARGIN)
def test_shannon(self):
myIndex = cl.Index()
vector = np.ones(10)
self.assertTrue(abs(myIndex.shannon(vector) - 0.0) < ERROR_MARGIN)
def test_bp(self):
myIndex = cl.Index()
n = 1000
vector = np.ones(n)
self.assertTrue(abs(myIndex.cr(vector, 1) - myIndex.berger_parker(vector)) < ERROR_MARGIN)
def test_atkinson(self):
myIndex = cl.Index()
vector = np.zeros(1000)
vector[0] = 1
self.assertTrue(abs(myIndex.atkinson(vector, 2) - 1.0) < ERROR_MARGIN)
def test_atkinson_uniform(self):
myIndex = cl.Index()
vector = np.ones(1000000)
self.assertTrue(abs(myIndex.atkinson(vector, 0.5) - 0.0) < ERROR_MARGIN)
self.assertTrue(abs(myIndex.atkinson(vector, 1) - 0.0) < ERROR_MARGIN)
self.assertTrue(abs(myIndex.atkinson(vector, 2) - 0.0) < ERROR_MARGIN)
import concentration_library as cl import numpy as np myIndex = cl.Index() x = np.array([541, 1463, 2445, 3438, 4437, 5401, 6392, 8304, 11904, 22261]) # Comparison with R version in ineq package # Results # Gini: 0.4620911 # Atkinson a=0.5: 0.1796591 # Atkinson a=1: 0.3518251 print(myIndex.gini(x)) print(myIndex.atkinson(x, 0.5)) print(myIndex.atkinson(x, 1.0))