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Sparsh Jauhari
Bias in Bio Lab CSSH
Commits
0399f688
Commit
0399f688
authored
Jul 12, 2021
by
Nishtha Jain
Browse files
ipnb cleaned
parent
65a259ba
Changes
2
Hide whitespace changes
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Side-by-side
bios_bias.ipynb
View file @
0399f688
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:
# pip installations
# pip installations
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
#!pip install pymongo
#!pip install pymongo
#!pip install dnspython==2.0.0
#!pip install dnspython==2.0.0
!
pip
install
gensim
!
pip
install
gensim
```
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:
## Config
## Config
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
CLASSES
=
[
'physician'
,
CLASSES
=
[
'physician'
,
'nurse'
,
'nurse'
,
'psychologist'
,
'psychologist'
,
'dentist'
,
'dentist'
,
'surgeon'
,
'surgeon'
,
'dietitian'
,
'dietitian'
,
'chiropractor'
'chiropractor'
]
]
```
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:
## BIOS.pkl pickle insight
## BIOS.pkl pickle insight
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
import
pickle
import
pickle
with
open
(
"datasets/BIOS.pkl"
,
"rb"
)
as
file
:
with
open
(
"datasets/BIOS.pkl"
,
"rb"
)
as
file
:
data
=
pickle
.
load
(
file
)
data
=
pickle
.
load
(
file
)
# title = set()
# title = set()
# raw_title = set()
# raw_title = set()
# gender = set()
# gender = set()
# for x in data:
# for x in data:
# title.add(x['title'])
# title.add(x['title'])
# raw_title.add(x['raw_title'])
# raw_title.add(x['raw_title'])
# gender.add(x['gender'])
# gender.add(x['gender'])
print
(
"number data points: "
,
len
(
data
))
print
(
"number data points: "
,
len
(
data
))
print
(
"structure of a data point [0] :
\n
"
,
data
[
0
])
print
(
"structure of a data point [0] :
\n
"
,
data
[
0
])
print
(
"types of gender:"
,
gender
)
print
(
"types of gender:"
,
gender
)
print
(
"types of title:"
,
len
(
title
))
print
(
"types of title:"
,
len
(
title
))
print
(
title
)
print
(
title
)
print
(
"types of raw_title:"
,
len
(
raw_title
))
print
(
"types of raw_title:"
,
len
(
raw_title
))
print
(
raw_title
)
print
(
raw_title
)
```
```
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:
## Mongo Connection using pymongo
## Mongo Connection using pymongo
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
import
pymongo
import
pymongo
client
=
pymongo
.
MongoClient
(
"mongodb+srv://root:Deployment123@clusterbiobias.4mc8e.mongodb.net/myFirstDatabase?retryWrites=true&w=majority"
)
client
=
pymongo
.
MongoClient
(
"mongodb+srv://root:Deployment123@clusterbiobias.4mc8e.mongodb.net/myFirstDatabase?retryWrites=true&w=majority"
)
collection
=
client
[
'biodb'
][
'allbio'
]
collection
=
client
[
'biodb'
][
'allbio'
]
```
```
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
print
(
len
(
collection
.
distinct
(
"title"
)))
print
(
len
(
collection
.
distinct
(
"title"
)))
print
(
collection
.
distinct
(
"title"
))
print
(
collection
.
distinct
(
"title"
))
```
```
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
from
pandas
import
DataFrame
from
pandas
import
DataFrame
```
```
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
df
=
DataFrame
(
collection
.
find
({
'$or'
:[{
'title'
:
'teacher'
},{
'title'
:
'professor'
}]}))
df
=
DataFrame
(
collection
.
find
({
'$or'
:[{
'title'
:
'teacher'
},{
'title'
:
'professor'
}]}))
print
(
"Teacher male"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'teacher'
)
&
(
df
[
'gender'
]
==
'M'
)]))
print
(
"Teacher male"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'teacher'
)
&
(
df
[
'gender'
]
==
'M'
)]))
print
(
"Professor male"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'professor'
)
&
(
df
[
'gender'
]
==
'M'
)]))
print
(
"Professor male"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'professor'
)
&
(
df
[
'gender'
]
==
'M'
)]))
print
(
"Teacher female"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'teacher'
)
&
(
df
[
'gender'
]
==
'F'
)]))
print
(
"Teacher female"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'teacher'
)
&
(
df
[
'gender'
]
==
'F'
)]))
print
(
"Professor female"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'professor'
)
&
(
df
[
'gender'
]
==
'F'
)]))
print
(
"Professor female"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'professor'
)
&
(
df
[
'gender'
]
==
'F'
)]))
```
```
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
df
=
DataFrame
(
collection
.
find
({
'$or'
:[{
'title'
:
'surgeon'
},{
'title'
:
'nurse'
}]}))
df
=
DataFrame
(
collection
.
find
({
'$or'
:[{
'title'
:
'surgeon'
},{
'title'
:
'nurse'
}]}))
print
(
"surgeon male"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'surgeon'
)
&
(
df
[
'gender'
]
==
'M'
)]))
print
(
"surgeon male"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'surgeon'
)
&
(
df
[
'gender'
]
==
'M'
)]))
print
(
"nurse male"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'nurse'
)
&
(
df
[
'gender'
]
==
'M'
)]))
print
(
"nurse male"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'nurse'
)
&
(
df
[
'gender'
]
==
'M'
)]))
print
(
"surgeon female"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'surgeon'
)
&
(
df
[
'gender'
]
==
'F'
)]))
print
(
"surgeon female"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'surgeon'
)
&
(
df
[
'gender'
]
==
'F'
)]))
print
(
"nurse female"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'nurse'
)
&
(
df
[
'gender'
]
==
'F'
)]))
print
(
"nurse female"
,
len
(
df
.
loc
[(
df
[
'title'
]
==
'nurse'
)
&
(
df
[
'gender'
]
==
'F'
)]))
```
```
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
df
=
DataFrame
(
collection
.
find
({
'$or'
:[{
'title'
:
title
}
for
title
in
CLASSES
]}))
df
=
DataFrame
(
collection
.
find
({
'$or'
:[{
'title'
:
title
}
for
title
in
CLASSES
]}))
df
df
```
```
%%%% Output: execute_result
%%%% Output: execute_result
_id \
_id \
0 607b0639c53ee5775fe7758b
0 607b0639c53ee5775fe7758b
1 607b0639c53ee5775fe77590
1 607b0639c53ee5775fe77590
2 607b0639c53ee5775fe7759b
2 607b0639c53ee5775fe7759b
3 607b063ac53ee5775fe775b0
3 607b063ac53ee5775fe775b0
4 607b063ac53ee5775fe775b3
4 607b063ac53ee5775fe775b3
... ...
... ...
112458 607b1d5dc53ee5775fed7952
112458 607b1d5dc53ee5775fed7952
112459 607b1d5dc53ee5775fed795a
112459 607b1d5dc53ee5775fed795a
112460 607b1d5dc53ee5775fed795b
112460 607b1d5dc53ee5775fed795b
112461 607b1d5dc53ee5775fed7964
112461 607b1d5dc53ee5775fed7964
112462 607b1d5dc53ee5775fed7967
112462 607b1d5dc53ee5775fed7967
path \
path \
0 crawl-data/CC-MAIN-2016-44/segments/1476988720...
0 crawl-data/CC-MAIN-2016-44/segments/1476988720...
1 crawl-data/CC-MAIN-2014-41/segments/1410657132...
1 crawl-data/CC-MAIN-2014-41/segments/1410657132...
2 crawl-data/CC-MAIN-2013-20/segments/1368702127...
2 crawl-data/CC-MAIN-2013-20/segments/1368702127...
3 crawl-data/CC-MAIN-2014-41/segments/1410657120...
3 crawl-data/CC-MAIN-2014-41/segments/1410657120...
4 crawl-data/CC-MAIN-2013-20/segments/1368696381...
4 crawl-data/CC-MAIN-2013-20/segments/1368696381...
... ...
... ...
112458 crawl-data/CC-MAIN-2018-43/segments/1539583519...
112458 crawl-data/CC-MAIN-2018-43/segments/1539583519...
112459 crawl-data/CC-MAIN-2018-43/segments/1539583519...
112459 crawl-data/CC-MAIN-2018-43/segments/1539583519...
112460 crawl-data/CC-MAIN-2018-43/segments/1539583519...
112460 crawl-data/CC-MAIN-2018-43/segments/1539583519...
112461 crawl-data/CC-MAIN-2018-43/segments/1539583519...
112461 crawl-data/CC-MAIN-2018-43/segments/1539583519...
112462 crawl-data/CC-MAIN-2018-43/segments/1539583519...
112462 crawl-data/CC-MAIN-2018-43/segments/1539583519...
raw \
raw \
0 Edmund J. Bourne, PhD, is a psychologist in no...
0 Edmund J. Bourne, PhD, is a psychologist in no...
1 Abigail Mackey is a registered nurse. For more...
1 Abigail Mackey is a registered nurse. For more...
2 Dr. Constance Milbrath is a developmental psyc...
2 Dr. Constance Milbrath is a developmental psyc...
3 Dr. Andrew Gottlieb is a clinical psychologist...
3 Dr. Andrew Gottlieb is a clinical psychologist...
4 Milton Wolf is a physician practicing in Kansa...
4 Milton Wolf is a physician practicing in Kansa...
... ...
... ...
112458 Adrienne Lewis Adrienne is a registered nurse ...
112458 Adrienne Lewis Adrienne is a registered nurse ...
112459 Eric Haralson, PA-C is a physician assistant i...
112459 Eric Haralson, PA-C is a physician assistant i...
112460 Alice Sumo is a respected nurse in Liberia, wh...
112460 Alice Sumo is a respected nurse in Liberia, wh...
112461 Rachel Kelley Schulman, MS, PA-C is a board-ce...
112461 Rachel Kelley Schulman, MS, PA-C is a board-ce...
112462 Victor N. Hakim, MD is a practicing Orthopedic...
112462 Victor N. Hakim, MD is a practicing Orthopedic...
name raw_title gender start_pos \
name raw_title gender start_pos \
0 [Edmund, J, Bourne] psychologist M 136
0 [Edmund, J, Bourne] psychologist M 136
1 [Abigail, , Mackey] nurse F 37
1 [Abigail, , Mackey] nurse F 37
2 [Constance, , Milbrath] psychologist F 305
2 [Constance, , Milbrath] psychologist F 305
3 [Andrew, , Gottlieb] psychologist M 72
3 [Andrew, , Gottlieb] psychologist M 72
4 [Milton, , Wolf] physician M 107
4 [Milton, , Wolf] physician M 107
... ... ... ... ...
... ... ... ... ...
112458 [Adrienne, Lewis, Adrienne] nurse F 118
112458 [Adrienne, Lewis, Adrienne] nurse F 118
112459 [Eric, , Haralson] physician M 98
112459 [Eric, , Haralson] physician M 98
112460 [Alice, , Sumo] nurse F 98
112460 [Alice, , Sumo] nurse F 98
112461 [Rachel, Kelley, Schulman] physician F 74
112461 [Rachel, Kelley, Schulman] physician F 74
112462 [Victor, N, Hakim] Orthopedic Surgeon M 72
112462 [Victor, N, Hakim] Orthopedic Surgeon M 72
title URI \
title URI \
0 psychologist http://www.alibris.co.uk/search/books/author/E...
0 psychologist http://www.alibris.co.uk/search/books/author/E...
1 nurse http://observer-reporter.com/article/20130315/...
1 nurse http://observer-reporter.com/article/20130315/...
2 psychologist http://earlylearning.ubc.ca/people/
2 psychologist http://earlylearning.ubc.ca/people/
3 psychologist http://www.psychologylounge.com/tag/sexuality-2/
3 psychologist http://www.psychologylounge.com/tag/sexuality-2/
4 physician http://hotair.com/archives/2011/12/13/romney-i...
4 physician http://hotair.com/archives/2011/12/13/romney-i...
... ... ...
... ... ...
112458 nurse https://www.adansw.com.au/CPD/Courses/Geriatri...
112458 nurse https://www.adansw.com.au/CPD/Courses/Geriatri...
112459 physician https://www.healthgrades.com/providers/eric-ha...
112459 physician https://www.healthgrades.com/providers/eric-ha...
112460 nurse http://woman.ng/2018/10/women-love-midwife-ali...
112460 nurse http://woman.ng/2018/10/women-love-midwife-ali...
112461 physician https://lincolnparkaesthetics.com/ourstaff/
112461 physician https://lincolnparkaesthetics.com/ourstaff/
112462 surgeon https://www.sharecare.com/doctor/dr-victor-n-h...
112462 surgeon https://www.sharecare.com/doctor/dr-victor-n-h...
bio
bio
0 _ is author of several books, including the be...
0 _ is author of several books, including the be...
1 For more quips and tips, refer to _ blog, “The...
1 For more quips and tips, refer to _ blog, “The...
2 _ interests at HELP are in the ethno-cultural ...
2 _ interests at HELP are in the ethno-cultural ...
3 _ practice serves the greater Silicon Valley a...
3 _ practice serves the greater Silicon Valley a...
4 During the health care debates of 2010, Dr. _ ...
4 During the health care debates of 2010, Dr. _ ...
... ...
... ...
112458 _ has been successful in gaining two nationall...
112458 _ has been successful in gaining two nationall...
112459 _ graduated from Touro Center / College Of Ost...
112459 _ graduated from Touro Center / College Of Ost...
112460 In _ three-decade career, _ has seen two civil...
112460 In _ three-decade career, _ has seen two civil...
112461 _ has a Master of Science degree in Physician ...
112461 _ has a Master of Science degree in Physician ...
112462 _ completed a residency at Henry Ford Hospital...
112462 _ completed a residency at Henry Ford Hospital...
[112463 rows x 10 columns]
[112463 rows x 10 columns]
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:
## TPR Graph mid ppt
## TPR Graph mid ppt
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
classes
=
[
'physician'
,
'nurse'
,
'psychologist'
,
'dentist'
,
'surgeon'
,
'dietitian'
,
'chiropractor'
]
classes
=
[
'physician'
,
'nurse'
,
'psychologist'
,
'dentist'
,
'surgeon'
,
'dietitian'
,
'chiropractor'
]
tpr_males
=
[
0.864
,
0.8128654970760234
,
0.8922631959508315
,
0.949435180204411
,
0.6972111553784861
,
0.6851851851851852
,
0.701058201058201
]
tpr_males
=
[
0.864
,
0.8128654970760234
,
0.8922631959508315
,
0.949435180204411
,
0.6972111553784861
,
0.6851851851851852
,
0.701058201058201
]
tpr_females
=
[
0.9056320400500626
,
0.8622613803230543
,
0.8844028899277518
,
0.9446064139941691
,
0.6285714285714286
,
0.8905608755129959
,
0.6625
]
tpr_females
=
[
0.9056320400500626
,
0.8622613803230543
,
0.8844028899277518
,
0.9446064139941691
,
0.6285714285714286
,
0.8905608755129959
,
0.6625
]
count_males
=
[
4125
,
342
,
1383
,
1859
,
2259
,
54
,
378
]
count_males
=
[
4125
,
342
,
1383
,
1859
,
2259
,
54
,
378
]
count_females
=
[
3995
,
3405
,
2353
,
1029
,
420
,
731
,
160
]
count_females
=
[
3995
,
3405
,
2353
,
1029
,
420
,
731
,
160
]
```
```
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
import
matplotlib.pyplot
as
plt
import
matplotlib.pyplot
as
plt
x
=
[
count_males
[
i
]
/
(
count_males
[
i
]
+
count_females
[
i
])
for
i
in
range
(
7
)]
x
=
[
count_males
[
i
]
/
(
count_males
[
i
]
+
count_females
[
i
])
for
i
in
range
(
7
)]
y
=
[
tpr_males
[
i
]
-
tpr_females
[
i
]
for
i
in
range
(
7
)]
y
=
[
tpr_males
[
i
]
-
tpr_females
[
i
]
for
i
in
range
(
7
)]
plt
.
scatter
(
x
,
y
)
plt
.
scatter
(
x
,
y
)
plt
.
xlabel
(
"% Male"
)
plt
.
xlabel
(
"% Male"
)
plt
.
ylabel
(
"TPR Gender Gap Male"
)
plt
.
ylabel
(
"TPR Gender Gap Male"
)
for
i
,
txt
in
enumerate
(
classes
):
for
i
,
txt
in
enumerate
(
classes
):
plt
.
annotate
(
txt
,
(
x
[
i
],
y
[
i
]))
plt
.
annotate
(
txt
,
(
x
[
i
],
y
[
i
]))
plt
.
show
()
plt
.
show
()
```
```
%%%% Output: display_data
%%%% Output: display_data
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
x
=
[
count_females
[
i
]
/
(
count_males
[
i
]
+
count_females
[
i
])
for
i
in
range
(
7
)]
x
=
[
count_females
[
i
]
/
(
count_males
[
i
]
+
count_females
[
i
])
for
i
in
range
(
7
)]
y
=
[
tpr_females
[
i
]
-
tpr_males
[
i
]
for
i
in
range
(
7
)]
y
=
[
tpr_females
[
i
]
-
tpr_males
[
i
]
for
i
in
range
(
7
)]
plt
.
scatter
(
x
,
y
)
plt
.
scatter
(
x
,
y
)
plt
.
xlabel
(
"% Female"
)
plt
.
xlabel
(
"% Female"
)
plt
.
ylabel
(
"TPR Gender Gap Female"
)
plt
.
ylabel
(
"TPR Gender Gap Female"
)
for
i
,
txt
in
enumerate
(
classes
):
for
i
,
txt
in
enumerate
(
classes
):
plt
.
annotate
(
txt
,
(
x
[
i
],
y
[
i
]))
plt
.
annotate
(
txt
,
(
x
[
i
],
y
[
i
]))
plt
.
show
()
plt
.
show
()
```
```
%%%% Output: display_data
%%%% Output: display_data
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
# plt.bar([[i,count_males[i]] for i in range(7)], height=count_males)
# plt.bar([[i,count_males[i]] for i in range(7)], height=count_males)
# plt.bar([i for i in range(7)], height=count_females)
# plt.bar([i for i in range(7)], height=count_females)
import
numpy
as
np
import
numpy
as
np
m
=
[[
i
,
count_males
[
i
]]
for
i
in
range
(
7
)]
m
=
[[
i
,
count_males
[
i
]]
for
i
in
range
(
7
)]
f
=
[[
i
,
count_females
[
i
]]
for
i
in
range
(
7
)]
f
=
[[
i
,
count_females
[
i
]]
for
i
in
range
(
7
)]
# list1 = [[0,1],[1,2.5],[2,3],[3,5.6]]
# list1 = [[0,1],[1,2.5],[2,3],[3,5.6]]
# list2 = [[0,2],[2,5],[3,7]]
# list2 = [[0,2],[2,5],[3,7]]
x1
,
y1
=
zip
(
*
m
)
x1
,
y1
=
zip
(
*
m
)
x2
,
y2
=
zip
(
*
f
)
x2
,
y2
=
zip
(
*
f
)
plt
.
figure
(
figsize
=
(
10
,
5
))
plt
.
figure
(
figsize
=
(
10
,
5
))
plt
.
bar
(
np
.
array
(
x1
)
-
0.15
,
y1
,
width
=
0.3
,
label
=
'males'
)
plt
.
bar
(
np
.
array
(
x1
)
-
0.15
,
y1
,
width
=
0.3
,
label
=
'males'
)
plt
.
bar
(
np
.
array
(
x2
)
+
0.15
,
y2
,
width
=
0.3
,
label
=
'females'
)
plt
.
bar
(
np
.
array
(
x2
)
+
0.15
,
y2
,
width
=
0.3
,
label
=
'females'
)
#setting the xticks. Note x1 and x2 are tuples, thus + is concatenation
#setting the xticks. Note x1 and x2 are tuples, thus + is concatenation
# plt.xticks(range(min(x1+x2), max(x1+x2)+1))
# plt.xticks(range(min(x1+x2), max(x1+x2)+1))
plt
.
xticks
([
i
for
i
in
range
(
7
)],
classes
)
plt
.
xticks
([
i
for
i
in
range
(
7
)],
classes
)
plt
.
legend
()
plt
.
legend
()
plt
.
show
()
plt
.
show
()
```
```
%%%% Output: display_data
%%%% Output: display_data
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:
# Evaluations Final PPT
# Evaluations Final PPT
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
from
joblib
import
load
from
joblib
import
load
from
config
import
CLASS_GROUP
,
MASKED
,
EVALUATION_SCORES
from
config
import
CLASS_GROUP
,
MASKED
,
EVALUATION_SCORES
```
```
%% Cell type:code id: tags:
%% Cell type:code id: tags:
```
python
```
python
import
numpy
as
np
import
numpy
as
np
import
matplotlib.pyplot
as
plt
import
matplotlib.pyplot
as
plt
def
tpr_gender_gap
(
scores
,
class_group
=
'medical'
):
def
tpr_gender_gap
(
scores
,
class_group
=
'medical'
):
x_males
=
[
scores
[
'count_males'
][
i
]
/
(
scores
[
'count_males'
][
i
]
+
scores
[
'count_females'
][
i
])
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
x_males
=
[
scores
[
'count_males'
][
i
]
/
(
scores
[
'count_males'
][
i
]
+
scores
[
'count_females'
][
i
])
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
y_males
=
[
scores
[
'tpr_males'
][
i
]
-
scores
[
'tpr_females'
][
i
]
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
y_males
=
[
scores
[
'tpr_males'
][
i
]
-
scores
[
'tpr_females'
][
i
]
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
x_females
=
[
scores
[
'count_females'
][
i
]
/
(
scores
[
'count_males'
][
i
]
+
scores
[
'count_females'
][
i
])
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
x_females
=
[
scores
[
'count_females'
][
i
]
/
(
scores
[
'count_males'
][
i
]
+
scores
[
'count_females'
][
i
])
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
y_females
=
[
scores
[
'tpr_females'
][
i
]
-
scores
[
'tpr_males'
][
i
]
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
y_females
=
[
scores
[
'tpr_females'
][
i
]
-
scores
[
'tpr_males'
][
i
]
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
return
(
x_males
,
y_males
,
x_females
,
y_females
)
return
(
x_males
,
y_males
,
x_females
,
y_females
)
def
average_odds_difference
(
scores
,
class_group
=
'medical'
):
def
average_odds_difference
(
scores
,
class_group
=
'medical'
):
x_males
=
[
scores
[
'count_males'
][
i
]
/
(
scores
[
'count_males'
][
i
]
+
scores
[
'count_females'
][
i
])
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
x_males
=
[
scores
[
'count_males'
][
i
]
/
(
scores
[
'count_males'
][
i
]
+
scores
[
'count_females'
][
i
])
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
y_males
=
[(
scores
[
'fpr_males'
][
i
]
-
scores
[
'fpr_females'
][
i
]
+
scores
[
'tpr_males'
][
i
]
-
scores
[
'tpr_females'
][
i
])
/
2
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
y_males
=
[(
scores
[
'fpr_males'
][
i
]
-
scores
[
'fpr_females'
][
i
]
+
scores
[
'tpr_males'
][
i
]
-
scores
[
'tpr_females'
][
i
])
/
2
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
x_females
=
[
scores
[
'count_females'
][
i
]
/
(
scores
[
'count_males'
][
i
]
+
scores
[
'count_females'
][
i
])
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
x_females
=
[
scores
[
'count_females'
][
i
]
/
(
scores
[
'count_males'
][
i
]
+
scores
[
'count_females'
][
i
])
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
y_females
=
[(
scores
[
'fpr_females'
][
i
]
-
scores
[
'fpr_males'
][
i
]
+
scores
[
'tpr_females'
][
i
]
-
scores
[
'tpr_males'
][
i
])
/
2
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
y_females
=
[(
scores
[
'fpr_females'
][
i
]
-
scores
[
'fpr_males'
][
i
]
+
scores
[
'tpr_females'
][
i
]
-
scores
[
'tpr_males'
][
i
])
/
2
for
i
in
range
(
len
(
CLASS_GROUP
[
class_group
]))]
return
(
x_males
,
y_males
,
x_females
,
y_females
)
return
(
x_males
,
y_males
,
x_females
,
y_females
)
def
average_odds_error
(
scores
,
class_group
=
'medical'
):
def
average_odds_error
(
scores
,
class_group
=
'medical'
):
