This Python Code will read in any record dataset that is labeled, will remove the label, and will cluster the data. Visualization is also used.
This code must be updated to read in YOUR dataset (and not mine :).
This code can also be updated to normalize data, to visualize in 3D rather than 2D etc.
# -*- coding: utf-8 -*-
"""
@author: profa
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from IPython.display import clear_output
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import KMeans
from sklearn import datasets
#------------------------------------------
# Permanently changes the pandas settings
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', None)
pd.set_option('display.max_colwidth', -1)
## This is MY path - you need to update this to
## YOUR path :)
## You should update this to read in any
## labeled record dataset that is numeric.
path="C:/Users/profa/Desktop/UCB/ML CSCI 5622/Data/SummerStudentAdmissions_CLEAN_numeric.csv"
DF=pd.read_csv(path)
#print(DF)
## Remove and save the label
## When you use YOUR data - you
## will update this so that
## you remove the label from
## your data.
DFLabel=DF["Decision"]
#print(DFLabel)
DF=DF.drop(["Decision"], axis=1)
#print( DF)
k=3
## Function that creates random centroids
def RandomCentroidInit (DF, k):
MyCentroids=[]
for i in range(k):
nextcentroid=DF.apply(lambda x: float(x.sample()))
MyCentroids.append(nextcentroid)
return pd.concat(MyCentroids, axis=1)
MyCentroids=RandomCentroidInit(DF, k)
print(MyCentroids)
## Function that finds distances between all points and all centroids
## Label each point with a centroid (starting at 0)
def Label_Data(DF, MyCentroids):
dist=MyCentroids.apply(lambda x: np.sqrt(((DF - x)**2).sum(axis=1)))
labels=dist.idxmin(axis=1)
return labels
cluster_labels=Label_Data(DF,MyCentroids)
#print(cluster_labels)
## How many points are in each label/cluster right now
print(cluster_labels.value_counts())
def Updated_Centroids(DF, cluster_labels, k):
Cluster_Means=DF.groupby(cluster_labels).apply(lambda x: x.mean()).T
return Cluster_Means
MyCentroids=Updated_Centroids(DF, cluster_labels, k)
print(MyCentroids)
##################### PCA if your data is ....
def ClusterPlot(DF, cluster_labels, MyCentroids, iteration):
MyPCA=PCA(n_components=2)
Data2D = MyPCA.fit_transform(DF)
Centroids2D=MyPCA.transform(MyCentroids.T)
clear_output(wait=True)
plt.title("Clustering")
plt.scatter(x=Data2D[:,0], y =Data2D[:,1], c=cluster_labels )
plt.scatter(x =Centroids2D[:,0], y= Centroids2D[:,1],s=200, alpha=0.5)
plt.show()
## Iterate
NumInterations = 20
iteration=1
while iteration < NumInterations:
print("Iteration: ", iteration)
cluster_labels=Label_Data(DF, MyCentroids)
MyCentroids=Updated_Centroids(DF, cluster_labels, k)
print("Centroids:\n", MyCentroids)
ClusterPlot(DF, cluster_labels, MyCentroids, iteration)
iteration = iteration + 1