Commit 0399f688 authored by Nishtha Jain's avatar Nishtha Jain
Browse files

ipnb cleaned

parent 65a259ba
%% 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
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) 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506tSJRYsWsXv3bgoLC5k1a9ZRxz98+DBbtmyhd+/e/PnPf2bPnj0cOHCAs846i/3795/4lxZDxb3fFpmvM+ThZ7m4S19q165N3bp1ueaaazh06BDvvfceQ4cOJS0tjTvuuINt27aV1M/IyKBatWq0adOG7du3H7O9bt26cd999zFp0iT27NlzSvZKTyZKVHJa2hrsSRWr8YNmHFg3n+X/cytff/01t912G1dddRUpKSn07NmzzKWe4cOHs3v3boYNG1ay7YUXXmDSpEmkpKTQtWtX/u///q/Cts844wymTp3K0KFDSU5Oplq1akeN2GvcuDF//OMf6d27d8kAjcGDB5OUlMSvfvUrLr30Uvr160ebNm04++yzy9QtKiripptuIjk5mfbt23PvvfdSr149rr76al5++eWTbjBF6d6vA3vzC1iwPq+k9wuB5FyvXj1WrVpVsnzyyScl+0v3bMPplWZmZvL000+Tn59P586dSy4jSmzozwQ5LTWpl1hy2Q8AM34w8G6S6iUyK7MPAB9++GG5dZcuXcqQIUOoV69eybaWLVuyYMGCMuXOP/98evXqVbL+l7/8peRz3759Wbly5VHHXrhwYcnnG2+8kRtvvPGoMjfeeCNjxoyhsLCQa6+9lgEDBgCB0W+lYzzSRRddxJo1a8o9p3h2ZO+3VrO27Hrjcca/toa+F9bl1Vdf5Y477qBFixa89NJLDB06FHdnzZo1pKamVnjcUD3Mzz//nOTkZJKTk1m2bBnr16+nWbNmJ22P9GQXVo/KzC4zs9HBzw3NrEVkwxKJrLEDW5FYI6HMtsQaCYwd2CpkvZ/97GdkZmby0EMPRTK8kMaNG0daWhrt2rWjRYsWZGRkxCyWaDiy91vrRxdSp3V3sh+/neuvv57u3bsDMH36dJ555hlSU1Np27Ytc+fODXnclJQUqlevTmpq6lGDIx5//HHatWtHamoqiYmJXH755SHLS2TZsbrBZvYwkA60cveLzKwJ8JK7d6t042aDgP8PSACedvfxR+y34P4rgIPAKHdfEU7d8qSnp/uJPvUvp545K3OZ8NYGtu7Jp0m9RMYObEVG+6RYhyVH6DZ+Qdneb1BSvUTeDfZ+JbLMLNvd02PVfjiX/q4F2gMrANx9q5mdVdmGzSwB+CvQH/gKWG5mr7j7x6WKXQ60DC6XAk8Cl4ZZVySkjPZJSkwngbEDW/Hg7LVlLv+F0/uVU0c4l/6+Cz7w5QBmVqeK2u4EbHT3L9z9O+BFYPARZQYDzwefOXsfqGdmjcOsKyKngIz2SfzxumSS6iViBHpSf7wuWX9knEbC6VH9PzP7O4EkcTtwC/BUFbSdBGwptf4VgV7TscokhVkXADMbA4wBOPfccysXsYjEhHq/p7djJip3n2hm/YF9QCvgt+7+dhW0beU1F2aZcOoGNrpPAaZA4B7V8QQoIiKxF9bw9GBiqorkVNpXQLNS602BrWGWqRlGXREROQVUeI/KzPab2b5ylv1mtq8K2l4OtDSzFmZWE7gBeOWIMq8AN1tAZ2Cvu28Ls66IiJwCKuxRuXulR/aF4u6FZnY38BaBIebPuvtHZnZncP9k4A0CQ9M3EhiePjpU3UjGKyIisXHM56hKCpo1As4oXnf3LyMVVKToOSoRkeMX6+eojjk83cyuMbPPgE3AIiAH+N8IxyUiIgKE9xzV74HOwKfu3gLoC7wb0ahERESCwklUBe6+C6hmZtXc/R0gLcJxiYiIAOENT99jZmcCi4HpZrYDOHXewiYiInEtnB7VYCAfuBd4E/gcuDqSQYmIiBQLZ2aKb0qtPhfBWERERI5SYaIys/2UnZbI+H76Inf3uhGOTUREJGSPaj7wI2A28OLJ+NyUiIic/Cq8R+XuGcBAIA94yswWmdlPzeycqEUnIiKnvZCDKdx9r7tPJfACw8nAI8CoKMQlIiICHGMwhZl1BYYB3YGlwLXuviQagYmIiEDowRQ5wB4Cb88dQ/DZKTPrAODuK6IQn4iInOZC9ahyCIzyGwgMoOzLCh3oE7mwREREAkK95qNXFOMQEREpVzgzU4iIiMSMEpWIiMQ1JSoREYlrYSUqM7vOzP7HzP7bzK6NdFAiElnNmzdn586dlTpGVlYW99xzT8gyV1xxBXv27KlUOyLHnJTWzP4GXAjMCG66w8z6uftdEY1MROJaeno66emh307+xhtvRCkaOZWF06PqCQx096nBWSquAHpFNCoRqRI5OTm0bt2akSNHkpKSwpAhQzh48CAATzzxBB06dCA5OZn169dz+PBhWrZsSV5eHgCHDx/mwgsvZOfOnbz00ku0a9eO1NRUevToAcDChQu56qqrADhw4ACjR48mOTmZlJQUZs2aBZTtuWVkZNCxY0fatm3LlClTSmI888wz+fWvf01qaiqdO3dm+/btUft+5OQQTqLaAJxbar0ZsCYy4YhIVduwYQNjxoxhzZo11K1bl7/97W8ANGjQgBUrVvCTn/yEiRMnUq1aNW666SamT58OwLx580hNTaVBgwY88sgjvPXWW6xevZpXXnnlqDZ+//vfc/bZZ7N27VrWrFlDnz5HP2b57LPPkp2dTVZWFpMmTWLXrl0AfPPNN3Tu3JnVq1fTo0cPnnrqqQh+G3IyCidR/QD4xMwWmtlC4GOgoZm9YmZH/xcrIjE1Z2Uu3cYvoEXm61z/5Hs0+FETunXrBsBNN93E0qVLAbjuuusA6NixIzk5OQDccsstPP/880AgsYwePRqAbt26MWrUKJ566imKioqOanPevHncddf3dwPq169/VJlJkyaV9Jq2bNnCZ599BkDNmjVLemalYxEpFs6r6H8b8ShEpErMWZnLg7PXkl8QSCbb9x1iz8FC5qzMJaN9EgBmgUlmatWqBUBCQgKFhYUANGvWjB/+8IcsWLCADz74oKR3NXnyZD744ANef/110tLSWLVqVZl23b3kuOVZuHAh8+bNY9myZdSuXZtevXpx6NAhAGrUqFFSt3QsIsWO2aNy90WhlmgEebrQL6hU1oS3NpQkqWKF+3bw2ymzAZgxYwaXXXZZyGPcdttt3HTTTfz4xz8mISEBgM8//5xLL72URx55hAYNGrBly5YydQYMGMBf/vKXkvXdu3eX2b93717q169P7dq1Wb9+Pe+///4Jn6Ocfo6ZqMyss5ktN7MDZvadmRWZ2b5oBHcyysnJ4eKLL+b222+nbdu2DBgwgPz8fHr16kVWVhYAO3fupHnz5gBMmzaNoUOHcvXVVzNgwAC2bdtGjx49SEtLo127dixZEpis/t///jddunShQ4cODB06lAMHDsTqFCWObd2Tf9S2Gj9oxqb33yAlJYWvv/6an/zkJyGPcc0115QMjig2duxYkpOTadeuHT169CA1NbVMnd/85jfs3r27ZMDFO++8U2b/oEGDKCwsJCUlhYceeojOnTtX4izltOPuIRcgi8Dw9JVAAjAa+MOx6sXj0rFjR4+0TZs2eUJCgq9cudLd3YcOHeovvPCC9+zZ05cvX+7u7nl5eX7eeee5u/vUqVM9KSnJd+3a5e7uEydO9P/6r/9yd/fCwkLft2+f5+Xleffu3f3AgQPu7j5+/Hj/3e9+F/FzkZNP1z/O9/MeeK1kSbrzGa/R4Fzv+sf5YR9j+fLlftlll0UwSjnZAFkew/93h3OPCnffaGYJ7l4ETDWz9yKUN09Kc1bmMuGtDWzdk885vpdGTZqRlpYGhHdzuH///pxzTuDFyZdccgm33HILBQUFZGRkkJaWxqJFi/j4449Lboh/9913dOnSJaLnJCensQNblblHBYF7UmMHtgqr/vjx43nyySdL7k2JxINwRv0dNLOawCoz+7OZ3QvUqUyjZnaOmb1tZp8Ffx49RChQbpCZbTCzjWaWWWr7ODPLNbNVweWKysRTGcU3r3P35OMEbl7vOuTMWZkLfH9zuHr16hw+fBig5CZysTp1vv86e/ToweLFi0lKSmLEiBE8//zzuDv9+/dn1apVrFq1io8//phnnnkmaucoJ4+M9kn88bpkkuolYsB55zXnX2+9WzKQ4lgyMzPZvHnzMe9jiURTOIlqRLDc3cA3BJ6jur6S7WYC8929JTA/uF6GmSUAfwUuB9oAw8ysTakij7l7WnCJ2ePv5d28dncmvLWhzLbmzZuTnZ0NwMyZMys83ubNm2nUqBG33347t956KytWrKBz5868++67bNy4EYCDBw/y6aefVvGZyKkio30S72b2YdP4K3k3s0/YSUokXoUz6m+zux9y933u/jt3v8/dN1ay3cHAc8HPzwEZ5ZTpBGx09y/c/TsCbxoeXMl2q1x5N6/L237//ffz5JNP0rVr15BzrC1cuJC0tDTat2/PrFmz+PnPf07Dhg2ZNm0aw4YNIyUlhc6dO7N+/foqPQ8RkXhlgftk5ewwGww0dfe/Btc/ABoGd//S3SvuFhyrUbM97l6v1Ppud69/RJkhwCB3vy24PgK41N3vNrNxwChgH4HBHv/p7mXHw35/nDHAGIBzzz234+bNm0807HJ1G7+A3HKSVVK9RN7N1EuQReTkZ2bZ7h56YscICtWj+iVQeuaJWsAlBOb5Cz2+FTCzeWa2rpwl3F5ReU8PFmfVJ4ELgDRgG/DfFR3E3ae4e7q7pzds2LCiYids7MBWJNZIKLMtsUZC2DevRUQktFCj/mq6e+mn+pa6+y5gl5kdczCFu/eraJ+ZbTezxu6+zcwaAzvKKfYVgfthxZoCW4PHLpm10syeAl47VjyRUnz9v3jUX5N6iYwd2Er3BUREqkioRFXmUpy7311qtbJdk1eAkcD44M+55ZRZDrQ0sxZALnADcCNAcZILlrsWWFfJeColo32SEpOISISEuvT3gZndfuRGM7sD+LCS7Y4H+pvZZ0D/4Dpm1sTM3gBw90ICIw3fAj4B/p+7fxSs/2czW2tma4DewL2VjEdEROJUqMEUjYA5wLfAiuDmjgTuVWWUvvx2skhPT/fiaYxERCQ8sR5MUeGlP3ffAXQ1sz5A2+Dm1919QVQiExERIYzXfAQTk5KTiIjERDgzU4iIiMSMEpWIiMS1kInKzBLMbF60ghERETlSyEQVfK3HQTM7O0rxiIiIlBHO+6gOAWvN7G0Cs6cD4O73RCwqERGRoHAS1evBRUREJOrCGZ7+nJklAue6+4ZjlRcREalKxxz1Z2ZXA6uAN4PraWb2SuhaIiIiVSOc4enjCLzEcA+Au68CWkQwJhERkRLhJKpCd997xLbyJwgUERGpYuEMplhnZjcCCWbWErgHeC+yYYmIiASE06P6GYFJab8FZhB4/fsvIhmUiIhIsXBG/R0Efh1cREREoqrCRGVmrxLiXpS7XxORiEREREoJ1aOaGPx5HfAj4B/B9WFATgRjEhERKRHqxYmLAMzs9+7eo9SuV81sccQjExERIbzBFA3N7PziFTNrATSMXEgiIiLfC2d4+r3AQjP7IrjeHLgjYhGJiIiUEs6ovzeDz0+1Dm5a7+7fRjYsERGRgHB6VAAdCfSkqgOpZoa7Px+xqERERIKOmajM7AXgAgIT0xYFNzugRCUiIhEXTo8qHWjj7prfT0REoi6cUX/rCDxHJSIiEnXh9KgaAB+b2YcE5vsDNDOFiIhERziJalykgxAREanIMS/9BWeoyAFqBD8vB1ZUplEzO8fM3jazz4I/61dQ7lkz22Fm606kvoiInPzCeRX97cBM4O/BTUnAnEq2mwnMd/eWwPzgenmmAYMqUV9ERE5y4QymuAvoRuA9VLj7Z0CjSrY7GHgu+Pk5IKO8Qu6+GPj6ROuLiMjJL5xE9a27f1e8YmbVqfyr6H/o7tsAgj+PN/GFXd/MxphZlpll5eXlnXDAIiISG+EMplhkZr8CEs2sP/BT4NVjVTKzeZQ/rD2qL2B09ynAFID09HQ9CyYicpIJJ1FlArcCawlMRvsG8PSxKrl7v4r2mdl2M2vs7tvMrDGwI8x4i1W2voiInCTCGfV32N2fcveh7j4k+LmyPZNXgJHBzyOBuVGuLyIiJ4kKE5WZDTazu0qtf2BmXwSXoZVsdzzQ38w+A/oH1zGzJmb2Rqk2ZwDLgFZm9pWZ3RqqvoiInHpCXfr7JXBDqfVawCVAHWAq8NKJNuruu4C+5WzfClxRan3Y8WzRjdoAAA5aSURBVNQXEZFTT6hEVdPdt5RaXxpMELvMrE6E4xIREQFC36MqM9uDu99dalWvohcRkagIlag+CM5KUYaZ3QF8GLmQREREvhfq0t+9wBwzu5Hv5/brSOBelWaCEBGRqKgwUbn7DqCrmfUB2gY3v+7uC6ISmYiICGE88BtMTEpOIiISE+HM9SciIhIzSlQiIhLXjjtRmVmCmQ2PRDAiIiJHCjWFUl0ze9DM/mJmAyzgZ8AXwI+jF6KIiJzOQg2meAHYTWCuvduAsUBNYLC7r4pCbCIiIiET1fnungxgZk8DO4Fz3X1/VCITEREh9D2qguIP7l4EbFKSEhGRaAvVo0o1s32ABdcTS627u9eNeHQiInLaCzUzRUI0AxERESlPhYnKzM4A7gQuBNYAz7p7YbQCExERgdD3qJ4D0oG1BF5m+N9RiUhERKSUUPeo2pQa9fcMerWHiIjEQLij/nTJT0REYiJUjyotOMoPAiP9NOpPRESiLlSiWu3u7aMWiYiISDlCXfrzqEUhIiJSgVA9qkZmdl9FO939fyIQj4iISBmhElUCcCbfz0whIiISdaES1TZ3fyRqkYiIiJQj1D0q9aRERCTmQiWqvlGLQkREpAIVJip3/zpSjZrZOWb2tpl9FvxZv4Jyz5rZDjNbd8T2cWaWa2argssVkYpVRERiK1SPKpIygfnu3hKYH1wvzzRgUAX7HnP3tODyRgRiFBGROBCrRDWYwKS3BH9mlFfI3RcDEevZiYhI/ItVovqhu28DCP5sdALHuNvM1gQvD5Z76RDAzMaYWZaZZeXl5Z1ovCIiEiMRS1RmNs/M1pWzDK6Cwz8JXACkAdsI8QoSd5/i7ununt6wYcMqaFpERKIp1HNUleLu/SraZ2bbzayxu28zs8bAjuM89vZSx3oKeO3EIxURkXgWq0t/rwAjg59HAnOPp3IwuRW7FlhXUVkRETm5xSpRjQf6m9lnQP/gOmbWxMxKRvCZ2QxgGdDKzL4ys1uDu/5sZmvNbA3QG7g3uuGLiEi0ROzSXyjuvotyHih2960EXntfvD6sgvojIhediIjEk1j1qERERMKiRCUiInFNiUpEROKaEpWIiMQ1JSoREYlrSlQiIhLXlKhERCSuKVGJiEhcU6ISEZG4pkQlIiJxTYlKRETimhKViIjENSUqERGJa0pUIiIS15SoREQkrilRiYhIXFOiEhGRuKZEJSIicU2JSkRE4poSlYiIxDUlKhERiWtKVCIiEteUqEREJK4pUYmISFxTohIRkbimRCUiInFNiUpEROJaTBKVmZ1jZm+b2WfBn/XLKdPMzN4xs0/M7CMz+/nx1I+GcePGMXHiRH77298yb968kGWnTZvG1q1bS9Zvu+02Pv74YwD+8Ic/lCnbtWvXqg9WROQkFaseVSYw391bAvOD60cqBP7T3S8GOgN3mVmb46gfNY888gj9+vULWebIRPX000/Tpk3gdI5MVO+9917VBykicpKKVaIaDDwX/PwckHFkAXff5u4rgp/3A58ASeHWj5RHH32UVq1a0a9fPzZs2ADAqFGjmDlzJgDZ2dn07NmTjh07MnDgQLZt28bMmTPJyspi+PDhpKWlkZ+fT69evcjKyiIzM5P8/HzS0tIYPnw4AGeeeSYABw4coG/fvnTo0IHk5GTmzp0LQE5ODhdffDG33347bdu2ZcCAAeTn50frKxARiS53j/oC7DliffcxyjcHvgTqHm99YAyQBWSde+65XhlZWVnerl07/+abb3zv3r1+wQUX+IQJE3zkyJH+0ksv+XfffeddunTxHTt2uLv7iy++6KNHj3Z39549e/ry5ctLjlV6vU6dOmXaKV4vKCjwvXv3urt7Xl6eX3DBBX748GHftGmTJyQk+MqVK93dfejQof7CCy9U6txERCoCZHkMckXxUj1SCdDM5gE/KmfXr4/zOGcCs4BfuPu+443D3acAUwDS09P9eOvPWZnLhLc2sHVPPqx7g0u69KV27doAXHPNNWXKbtiwgXXr1tG/f38AioqKaNy48fE2WTp2fvWrX7F48WKqVatGbm4u27dvB6BFixakpaUB0LFjR3Jyck64HRGReBaxROXuFd60MbPtZtbY3beZWWNgRwXlahBIUtPdfXapXWHVr6w5K3N5cPZa8guKANiXX8CC9XuYszKXjPZJR5V3d9q2bcuyZcuqpP3p06eTl5dHdnY2NWrUoHnz5hw6dAiAWrVqlZRLSEjQpT8ROWXF6h7VK8DI4OeRwNwjC5iZAc8An7j7/xxv/aow4a0NJUkKoFaztuxb/x7jX1vD/v37efXVV8uUb9WqFXl5eSWJqqCggI8++giAs846i/3795fbTo0aNSgoKDhq+969e2nUqBE1atTgnXfeYfPmzVV1aiIiJ41YJarxQH8z+wzoH1zHzJqY2RvBMt2AEUAfM1sVXK4IVb+qbd1TtpdS60cXUqd1d7Ifv53rr7+e7t27l9lfs2ZNZs6cyQMPPEBqaippaWklI/hGjRrFnXfeWTKYorQxY8aQkpJSMpii2PDhw8nKyiI9PZ3p06fTunXrCJyliEh8s8B9stNDenq6Z2VlhV2+2/gF5O45+pJaUr1E3s3sU5WhiYjELTPLdvf0WLWvmSlCGDuwFYk1EspsS6yRwNiBrWIUkYjI6SdigylOBcUDJopH/TWpl8jYga3KHUghIiKRoUR1DBntk5SYRERiSJf+REQkrilRiYhIXFOiEhGRuKZEJSIicU2JSkRE4tpp9cCvmeUB0ZqHqAGwM0ptnYh4jk+xnbh4jk+xnbhYx3eeuzeMVeOnVaKKJjPLiuWT3McSz/EpthMXz/EpthMX7/FFmi79iYhIXFOiEhGRuKZEFTlTYh3AMcRzfIrtxMVzfIrtxMV7fBGle1QiIhLX1KMSEZG4pkQlIiJxTYmqksxskJltMLONZpZZzv7WZrbMzL41s/vjLLbhZrYmuLxnZqlxFt/gYGyrzCzLzC6Ll9hKlbvEzIrMbEi8xGZmvcxsb6k3Y/82WrGFE1+pGFeZ2UdmtiheYjOzsaW+t3XBf9tz4iS2s83sVTNbHfzeRkcjrrjg7lpOcAESgM+B84GawGqgzRFlGgGXAI8C98dZbF2B+sHPlwMfxFl8Z/L9fdQUYH28xFaq3ALgDWBIvMQG9AJei9a/5QnEVw/4GDg3uN4oXmI7ovzVwIJ4iQ34FfCn4OeGwNdAzVj8O0d7UY+qcjoBG939C3f/DngRGFy6gLvvcPflQEEcxvaeu+8Orr4PNI2z+A548LcSqANEa+TPMWML+hkwC9gRpbiOJ7ZYCSe+G4HZ7v4lBH5H4ii20oYBM6ISWXixOXCWmRmBP+K+BgqjFF9MKVFVThKwpdT6V8Ft8eB4Y7sV+N+IRlRWWPGZ2bVmth54HbglXmIzsyTgWmBylGIqFu6/a5fgJaL/NbO20QkNCC++i4D6ZrbQzLLN7OY4ig0AM6sNDCLwh0g0hBPbX4CLga3AWuDn7n44OuHFlt7wWzlWzrZ4Ge8fdmxm1ptAooraPSDCjM/dXwZeNrMewO+BfpEOjPBiexx4wN2LAn/gRk04sa0gMDfbATO7ApgDtIx4ZAHhxFcd6Aj0BRKBZWb2vrt/GgexFbsaeNfdv45gPKWFE9tAYBXQB7gAeNvMlrj7vkgHF2vqUVXOV0CzUutNCfy1Ew/Cis3MUoCngcHuvitKscFxfnfuvhi4wMwaRDowwostHXjRzHKAIcDfzCwjHmJz933ufiD4+Q2gRpS+t7DiC5Z5092/cfedwGIgGgN5jue/uRuI3mU/CC+20QQumbq7bwQ2Aa2jFF9sxfom2cm8EPjL8AugBd/fAG1bQdlxRHcwxTFjA84FNgJd4/G7Ay7k+8EUHYDc4vVYx3ZE+WlEbzBFON/bj0p9b52AL6PxvR1HfBcD84NlawPrgHbxEFuw3NkE7v/UicZ3dhzf25PAuODnHwZ/HxpEK8ZYLrr0VwnuXmhmdwNvERi186y7f2Rmdwb3TzazHwFZQF3gsJn9gsBonoh218OJDfgt8AMCvQGAQo/SDM1hxnc9cLOZFQD5wH948Lc0DmKLiTBjGwL8xMwKCXxvN0Tjews3Pnf/xMzeBNYAh4Gn3X1dPMQWLHot8G93/ybSMR1nbL8HppnZWgKXCh/wQI/0lKcplEREJK7pHpWIiMQ1JSoREYlrSlQiIhLXlKhERCSuKVGJiEhcU6ISqQQza2hmS4MzbWeU2j7XzJpUUGecmbmZXVhq273BbSEfDwhOOxSVRwhE4oUSlUjlDAOeA7oAYwHM7GpghbuHmqVkLYHZD4oNITCjuIgcQYlKpHIKCMxXV4vAA93VgV8AE45Rbw7B2bHN7HxgL5BXvNPMngy+g+sjM/tdeQcwswEWeNfZCjN7yczOrILzEYk7SlQilfNPApOFvklgmqyfAs+7+8Fj1NsHbDGzdgR6Zf86Yv+vg7OEpAA9g3MylgjO3fcboJ+7dyAw+8l9lTwXkbikKZREKsHd9wJXAphZfeAB4DozewqoD/y3uy+roPqLBC7/DSQwk3jpN7b+2MzGEPgdbQy0ITDlULHOwW3vBqe/qglU1I7ISU2JSqTq/JbAm5yHAdkEeltzgd4VlH+VwCXCLHffV/y6EDNrAdwPXOLuu81sGnDGEXUNeNvdh1X1SYjEG136E6kCZtYSaOLuiwjMCH6YwPuEjkwwJdw9n0AP7NEjdtUFvgH2mtkPgcvLqf4+0K145KCZ1Taziyp9IiJxSD0qkarxKPDr4OcZBAZL/JxAL6tC7v5iOdtWm9lK4CMCr354t5wyeWY2CphhZrWCm38DRPrlgyJRp9nTRUQkrunSn4iIxDUlKhERiWtKVCIiEteUqEREJK4pUYmISFxTohIRkbimRCUiInHt/wcR+zBJ6BGDPwAAAABJRU5ErkJggg==)
%% 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
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) 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)
%% 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
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) 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)
%% 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'):