def test_roc_auc(self):
"""roc_auc should calculate the trapezoidal approximation to the area under a ROC curve
"""
#Uninformative single-point case:
points = [(0.5,0.5)]
exp = 0.50
self.assertFloatEqual(roc_auc(points,add_endpoints=True),exp)
#Perfect single-point case:
points = [(0.0,1.0)]
exp = 1.00
self.assertFloatEqual(roc_auc(points,add_endpoints=True),exp)
#Basic test of two-point perfect prediction
points = [(0.0,1.00),(0.75,1.00)]
#exp = 0.75*0.75+(0.25*0.75*0.25)*2 #Calculate using one rectangle and two triangles
exp = 1.0
self.assertFloatEqual(roc_auc(points,add_endpoints=True),exp)
# Per Hand and Till 2001, AUC = 2*Gini - 1
#So I derive the expected value from the Gini example below:
# From Catalano et al 2009, table 3, page 11
# Available here:
#http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1032&context=numeracy
proportion_of_population = [0.1*i for i in range (11)]
cumulative_portion_of_consumption =\
[0.000,\
0.023,\
0.060,\
0.110,\
0.175,\
0.254,\
0.345,\
0.459,\
0.588,\
0.754,\
1.000]
points = zip(proportion_of_population,\
cumulative_portion_of_consumption)
gini_obs = gini_coefficient(points)
gini_exp = 0.346
self.assertFloatEqual(gini_obs,gini_exp,eps=1e-3)
roc_obs = roc_auc(points, add_endpoints=True)
print "ROC AUC obs:",roc_obs
exp = gini_exp
#Convert ROC to equivalent Gini index
obs = abs(2.0*roc_obs - 1.0)
self.assertFloatEqual(obs,exp,eps=1e-3)
def test_roc_auc(self):
"""roc_auc should calculate the trapezoidal approximation to the area under a ROC curve
"""
#Uninformative single-point case:
points = [(0.5,0.5)]
exp = 0.50
self.assertFloatEqual(roc_auc(points,add_endpoints=True),exp)
#Perfect single-point case:
points = [(0.0,1.0)]
exp = 1.00
self.assertFloatEqual(roc_auc(points,add_endpoints=True),exp)
#Basic test of two-point perfect prediction
points = [(0.0,1.00),(0.75,1.00)]
#exp = 0.75*0.75+(0.25*0.75*0.25)*2 #Calculate using one rectangle and two triangles
exp = 1.0
self.assertFloatEqual(roc_auc(points,add_endpoints=True),exp)
# Per Hand and Till 2001, AUC = 2*Gini - 1
#So I derive the expected value from the Gini example below:
# From Catalano et al 2009, table 3, page 11
# Available here:
#http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1032&context=numeracy
proportion_of_population = [0.1*i for i in range (11)]
cumulative_portion_of_consumption =\
[0.000,\
0.023,\
0.060,\
0.110,\
0.175,\
0.254,\
0.345,\
0.459,\
0.588,\
0.754,\
1.000]
points = zip(proportion_of_population,\
cumulative_portion_of_consumption)
gini_obs = gini_coefficient(points)
gini_exp = 0.346
self.assertFloatEqual(gini_obs,gini_exp,eps=1e-3)
roc_obs = roc_auc(points, add_endpoints=True)
exp = gini_exp
#Convert ROC to equivalent Gini index
obs = abs(2.0*roc_obs - 1.0)
self.assertFloatEqual(obs,exp,eps=1e-3)
def test_gini_coefficient(self):
"""gini_coefficient should calculate the trapezoidal approximation to the Gini coefficient"""
# From Catalano et al 2009, table 3, page 11
# Available here:
#http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1032&context=numeracy
proportion_of_population = [0.1*i for i in range (11)]
cumulative_portion_of_consumption =\
[0.000,\
0.023,\
0.060,\
0.110,\
0.175,\
0.254,\
0.345,\
0.459,\
0.588,\
0.754,\
1.000]
points = zip(proportion_of_population,\
cumulative_portion_of_consumption)
obs = gini_coefficient(points)
exp = 0.346
self.assertFloatEqual(obs,exp,eps=1e-3)
def test_gini_coefficient(self):
"""gini_coefficient should calculate the trapezoidal approximation to the Gini coefficient"""
# From Catalano et al 2009, table 3, page 11
# Available here:
#http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1032&context=numeracy
proportion_of_population = [0.1*i for i in range (11)]
cumulative_portion_of_consumption =\
[0.000,\
0.023,\
0.060,\
0.110,\
0.175,\
0.254,\
0.345,\
0.459,\
0.588,\
0.754,\
1.000]
points = zip(proportion_of_population,\
cumulative_portion_of_consumption)
obs = gini_coefficient(points)
exp = 0.346
self.assertFloatEqual(obs,exp,eps=1e-3)