Skip to content
Snippets Groups Projects
Commit 14c66e67 authored by shawk masboob's avatar shawk masboob
Browse files

minor changes

parent 260925bf
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
Showing
with 7 additions and 0 deletions
TDA_Voting.ipynb
+ 7
− 0
View file @ 14c66e67
%% Cell type:markdown id: tags:
 
<h1><center>US Presidential Election Results: A Topological Perspective</center></h1>
 
![image.png](attachment:image.png)
 
<p style="text-align: center;">Image from: https://www.wattpad.com/721277709-the-magical-girl-book-sailor-moon-pfp-icons%F0%9F%92%AB%E2%98%81%EF%B8%8F</p>
 
%% Cell type:markdown id: tags:
 
Discuss purpose of notebook
 
%% Cell type:markdown id: tags:
 
### Data Preparation
 
%% Cell type:code id: tags:
 
``` python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
 
from sklearn import preprocessing
 
from ripser import ripser
from ripser import Rips
from persim import plot_diagrams
 
%matplotlib inline
```
 
%% Cell type:code id: tags:
 
``` python
# import dataframe
df = pd.read_csv('~/Desktop/topological_machine_learning/Data/election_data.csv')
 
# print first 10 rows
df.head(10)
```
 
%% Output
 
county_state year vote_dem vote_rep lat lng
0 Abbeville,SC 2004 4389 5436 34.249604 -82.483646
1 Abbeville,SC 2008 4593 6264 34.249604 -82.483646
2 Abbeville,SC 2012 4543 5981 34.249604 -82.483646
3 Abbeville,SC 2016 3712 6742 34.249604 -82.483646
4 Acadia,LA 2004 8937 16083 30.259017 -92.422835
5 Acadia,LA 2008 7028 19229 30.259017 -92.422835
6 Acadia,LA 2012 6560 19931 30.259017 -92.422835
7 Acadia,LA 2016 5638 21159 30.259017 -92.422835
8 Accomack,VA 2004 5518 7726 37.767140 -75.666820
9 Accomack,VA 2008 7607 7833 37.767140 -75.666820
 
%% Cell type:code id: tags:
 
``` python
# normalize data
scaler = preprocessing.StandardScaler()
 
df[['lat','lng']] = scaler.fit_transform(df[['lat','lng']])
 
# print first ten rows
df.head(10)
```
 
%% Output
 
county_state year vote_dem vote_rep lat lng
0 Abbeville,SC 2004 4389 5436 -0.821996 0.790159
1 Abbeville,SC 2008 4593 6264 -0.821996 0.790159
2 Abbeville,SC 2012 4543 5981 -0.821996 0.790159
3 Abbeville,SC 2016 3712 6742 -0.821996 0.790159
4 Acadia,LA 2004 8937 16083 -1.641374 -0.057700
5 Acadia,LA 2008 7028 19229 -1.641374 -0.057700
6 Acadia,LA 2012 6560 19931 -1.641374 -0.057700
7 Acadia,LA 2016 5638 21159 -1.641374 -0.057700
8 Accomack,VA 2004 5518 7726 -0.099749 1.371666
9 Accomack,VA 2008 7607 7833 -0.099749 1.371666
 
%% Cell type:code id: tags:
 
``` python
# create new vote_dem:vote_rep ratio column
df['dem_rep'] = df.apply(lambda row: row.vote_dem / (row.vote_rep + row.vote_dem), axis = 1)
 
# print first 10 rows
df.head(10)
```
 
%% Output
 
county_state year vote_dem vote_rep lat lng dem_rep
0 Abbeville,SC 2004 4389 5436 -0.821996 0.790159 0.446718
1 Abbeville,SC 2008 4593 6264 -0.821996 0.790159 0.423045
2 Abbeville,SC 2012 4543 5981 -0.821996 0.790159 0.431680
3 Abbeville,SC 2016 3712 6742 -0.821996 0.790159 0.355079
4 Acadia,LA 2004 8937 16083 -1.641374 -0.057700 0.357194
5 Acadia,LA 2008 7028 19229 -1.641374 -0.057700 0.267662
6 Acadia,LA 2012 6560 19931 -1.641374 -0.057700 0.247631
7 Acadia,LA 2016 5638 21159 -1.641374 -0.057700 0.210397
8 Accomack,VA 2004 5518 7726 -0.099749 1.371666 0.416641
9 Accomack,VA 2008 7607 7833 -0.099749 1.371666 0.492681
 
%% Cell type:code id: tags:
 
``` python
# subset data based on year
df04 = df[df.year == 2004]
 
df08 = df[df.year == 2008]
 
df12 = df[df.year == 2012]
 
df16 = df[df.year == 2016]
 
print('df04 shape:', df04.shape)
print('df08 shape:', df08.shape)
print('df12 shape:', df12.shape)
print('df16 shape:', df16.shape)
```
 
%% Output
 
df04 shape: (3112, 7)
df08 shape: (3112, 7)
df12 shape: (3112, 7)
df16 shape: (3112, 7)
 
%% Cell type:code id: tags:
 
``` python
print(df04.loc[df04['dem_rep'] == df04['dem_rep'].min(), 'dem_rep'].iloc[0])
print(df04.loc[df04['dem_rep'] == df04['dem_rep'].max(), 'dem_rep'].iloc[0])
```
 
%% Output
 
0.07170761350811702
0.9052027864743607
 
%% Cell type:markdown id: tags:
 
### 2004 Presidential Election
 
%% Cell type:code id: tags:
 
``` python
import seaborn as sns; sns.set()
sns.set_style("white")
 
plt.figure(figsize=(20,10))
 
plt.scatter(df04['lng'],df04['lat'] , marker = '.', c = df04['dem_rep'], cmap = 'RdBu')
plt.title('2004 Presidential Election')
plt.colorbar()
 
plt.show()
```
 
%% Output
 
 
%% Cell type:code id: tags:
 
``` python
plt.figure(figsize=(20,10))
 
plt.scatter(df08['lng'],df08['lat'] , marker = '.', c = df08['dem_rep'], cmap = 'RdBu')
plt.title('2008 Presidential Election')
plt.colorbar()
 
plt.show()
```
 
%% Output
 
 
%% Cell type:code id: tags:
 
``` python
plt.figure(figsize=(20,10))
 
plt.scatter(df12['lng'],df12['lat'] , marker = '.', c = df12['dem_rep'], cmap = 'RdBu')
plt.title('2014 Presidential Election')
plt.colorbar()
 
plt.show()
```
 
%% Output
 
 
%% Cell type:code id: tags:
 
``` python
plt.figure(figsize=(20,10))
 
plt.scatter(df16['lng'],df16['lat'] , marker = '.', c = df16['dem_rep'], cmap = 'RdBu')
plt.title('2016 Presidential Election')
plt.colorbar()
 
plt.show()
```
 
%% Output
 
 
%% Cell type:code id: tags:
 
``` python
df_04 = df04[['lat','lng','dem_rep']]
df_08 = df08[['lat','lng','dem_rep']]
df_12 = df12[['lat','lng','dem_rep']]
df_16 = df16[['lat','lng','dem_rep']]
```
 
%% Cell type:code id: tags:
 
``` python
arr04 = df_04.to_numpy()
arr04.astype(float)
print(arr04.shape)
 
arr08 = df_08.to_numpy()
arr08.astype(float)
print(arr08.shape)
 
arr12 = df_12.to_numpy()
arr12.astype(float)
print(arr12.shape)
 
arr16 = df_16.to_numpy()
arr16.astype(float)
print(arr16.shape)
 
array_list = [arr04, arr08, arr12, arr16]
```
 
%% Output
 
(3112, 3)
(3112, 3)
(3112, 3)
(3112, 3)
 
%% Cell type:code id: tags:
 
``` python
dgms = ripser(arr04, maxdim=1, thresh=2)['dgms']
plot_diagrams(dgms, show=True)
```
 
%% Output
 
 
%% Cell type:code id: tags:
 
``` python
```
%% Cell type:code id: tags:
``` python
diagrams = []
for arr in array_list:
diag = ripser(arr)['dgms']
diagrams.append(diag)
```
 
%% Cell type:code id: tags:
 
``` python
for i in range(len(diagrams)):
plot_diagrams(diagrams[i], show=True)
```
 
%% Output
 
 
 
 
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment