Python - Dimension Reduction – Self Organizing Maps (SOM)

Sunday, July 25, 2021

Data:

Employees when they sent job applicant (40 rows)

Mission:

How to visualize data mapping using SOM algorithm

Library used:

Pandas

Scikit

SimpSOM

Code:

import pandas as pd

from sklearn.preprocessing import StandardScaler

!pip install SimpSOM

import SimpSOM as sps

url = 'https://raw.githubusercontent.com/kokocamp/vlog119/main/vlog119.csv'

vlog139 = pd.read_csv(url)

X = vlog139[['gpa','gmat','work_experience','admitted']]

y = vlog139['admitted']

scaler = StandardScaler()

data = scaler.fit_transform(pd.DataFrame(X))

labels = scaler.fit_transform(pd.DataFrame(y))

print(data)

net = sps.somNet(50, 50, data)

#Train the network for 10000 epochs and with initial learning rate of 0.1.

net.train(0.01, 1000)

#Print a map of the network nodes and colour them according to the first feature (column number 0) of the dataset

#and then according to the distance between each node and its neighbours.

net.nodes_graph()

net.diff_graph()

#Project the datapoints on the new 2D network map.

net.project(data, labels=labels)

#Cluster the datapoints according to the Quality Threshold algorithm.

net.cluster(data)

I wrapped the scenario in a Youtube video below.

Click this link (http://paparadit.blogspot.com/2020/11/the-algorithms-of-machine-learning.html), if you want to check out for other algorithms. Thank you for for visiting this blog & subs my channel.

Labels: Programming, Python