Change language

ML | OPTICS Clustering Deployment Using Sklearn

| |

This article will demonstrate how to implement the OPTICS clustering method using Sklearn in Python. The dataset used for the demo is — this is Mall customer segmentation data which can be downloaded from Kaggle .

Step 1: Import required libraries

Step 2: Load data

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from matplotlib import gridspec

from sklearn.cluster import OPTICS, cluster_optics_dbscan

from sklearn.pre processing import normalize, StandardScaler

# Change the desktop space per data location
cd C: UsersDevDesktopKaggleCustomer Segmentation

 

X = pd.read_csv ( ’Mall_Customers.csv’ )

  
# Removing irrelevant columns

drop_features = [ ’CustomerID’ , ’Gender’ ]

X = X.drop (drop_features, axis = 1 )

 
# Processing missing values, if any

X.fillna (method = ’ffill’ , inplace = True )

 
X.head ()

Step 3: Data preprocessing

# Scale data to bring all attributes to comparable level

scaler = StandardScaler ()

X_scaled = scaler.fit_transform (X)

 
# Normalize data so that data
# approximates Gaussian distribution

X_normalized = normalize (X_scaled)

 
# Convert numpy array to panda DataFrame

X_normalized = pd.DataFrame (X_normalized)

  
# Renaming columns

X_normalized.columns = X.columns

 
X_normalized.head ()

Step 4: Building the clustering model

# Build OPTICS clustering model

optics_model = OPTICS (min_samples = 10 , xi = 0.05 , min_c luster_size = 0.05 )

 
# Model training
optics_model.fit (X_normalized )

Step 5: Storing the training results

# Making labels using the DBSCAN technique with eps = 0.5

labels1 = cluster_optics_dbscan (reachability = optics_model.reachability_,

  core_distances = optics_model.core_distances_,

  ordering = optics_model.ordering_, eps = 0.5 )

 
# Making tags using the DBSCAN technique with eps = 2.0

labels2 = cluster_optics_dbscan (reachability = optics_model.reachability_,

core_distances = optics_model.core_distances_,

  ordering = optics_model.ordering_, eps = 2 )

  
# Create an array with numbers in equal spaces before
# specified range

space = np.arange ( len (X_normalized))

 
# Save the reachable distance of each point

reachability = optics_model.reachability_ [optics_model.ordering_]

 
# Store each point’s cluster labels

labels = optics_model.labels_ [optics_model.ordering_]

 

print (labels)

Step 6: Rendering Results

# Define the rendering framework

plt .figure (figsize = ( 10 , 7 ))

G = gridspec.GridSpec ( 2 , 3 )

ax1 = plt.subplot (G [ 0 ,:])

ax2 = plt.subplot (G [ 1 , 0 ])

ax3 = plt.subplot (G [ 1 , 1 ])

ax4 = plt.subplot (G [ 1 , 2 ])

 
# Plot accessibility-distance

colors = [ ’c.’ , ’b.’ , ’ r .’ , ’y.’ , ’g.’ ]

for Class, color in zip ( range ( 0 , 5 ), colors):

Xk = space [labels = = Class]

  Rk = reac hability [labels = = Class]

ax1.plot (Xk, Rk, color, alpha = 0.3 )

ax1.plot (space [ labels = = - 1 ], reachability [labels = = - 1 ], ’k .’ , alpha = 0.3 )

ax1.plot (space, np.full_like (space, 2. , dtype = float ), ’k-’ , alpha = 0.5 )

ax1.plot (space, np.full_like (space, 0.5 , dtype = float ), ’k-.’ , alpha = 0.5 )

ax1.set_ylabel ( ’ Reachability Distance’ )

ax1.set_title ( ’Reachability Plot’ )

  
# Building OPTICS clusters

colors = [ ’c.’ , ’b.’ , ’ r.’ , ’y.’ , ’g.’ ]

for Class, color in zip ( range ( 0 , 5 ), colors):

  Xk = X_normalized [optics_model.labels_ = = Class]

  ax2.plot (Xk.iloc [:, 0 ], Xk.iloc [:, 1 ], color, alpha = 0.3 )

 

ax2.plot (X_normalized.iloc [optics_model.labels_ = = - 1 , 0 ],

X_normalized.iloc [optics_model.labels_ = = - 1 , 1 ],

’k +’ , alpha = 0.1 )

ax2. set_title ( ’OPTICS Clustering’ )

  
# Build DBSCAN clustering with eps = 0.5

colors = [ ’c’ , ’b’ , ’ r’ , ’y’ , ’ g’ , ’greenyellow’ ]

for Class, color in zip ( range ( 0 , 6 ), colors):

Xk ​​ = X_normalized [labels1 = = Class]

ax3.plot (Xk.iloc [:, 0 ], Xk.iloc [:, 1 ], color, alpha = 0.3 , marker = ’.’ )

  

ax3.plot (X_normalized.iloc [labels1 = = - 1 , 0 ],

  X_normalized .iloc [labels1 = = - 1 , 1 ],

’k +’ , alpha = 0.1 )

ax3.set_title ( ’ DBSCAN clustering with eps = 0.5’ )

 
# Build DBSCAN clusters using eps = 2.0

colors = [ ’c.’ , ’ y. ’ , ’m.’ , ’ g.’ ]

for Class, color in zip ( range ( 0 , 4 ), colors):

Xk = X_normalized.iloc [labels2 = = Class]

ax4.plot (Xk.iloc [:, 0 ], Xk.iloc [:, 1 ], color, alpha = 0.3 )

 

ax4.plot (X_normalized.iloc [labels2 = = - 1 , 0 ],

  X_normalized.iloc [labels2 = = - 1 , 1 ],

’k +’ , alpha = 0.1 )

ax4.set_title ( ’DBSCAN C lustering with eps = 2.0’ )

 

 
plt.tight_layout ()
plt.show ( )

Shop

Learn programming in R: courses

$

Best Python online courses for 2022

$

Best laptop for Fortnite

$

Best laptop for Excel

$

Best laptop for Solidworks

$

Best laptop for Roblox

$

Best computer for crypto mining

$

Best laptop for Sims 4

$

Latest questions

NUMPYNUMPY

Common xlabel/ylabel for matplotlib subplots

12 answers

NUMPYNUMPY

How to specify multiple return types using type-hints

12 answers

NUMPYNUMPY

Why do I get "Pickle - EOFError: Ran out of input" reading an empty file?

12 answers

NUMPYNUMPY

Flake8: Ignore specific warning for entire file

12 answers

NUMPYNUMPY

glob exclude pattern

12 answers

NUMPYNUMPY

How to avoid HTTP error 429 (Too Many Requests) python

12 answers

NUMPYNUMPY

Python CSV error: line contains NULL byte

12 answers

NUMPYNUMPY

csv.Error: iterator should return strings, not bytes

12 answers

News


Wiki

Python | How to copy data from one Excel sheet to another

Common xlabel/ylabel for matplotlib subplots

Check if one list is a subset of another in Python

sin

How to specify multiple return types using type-hints

exp

Printing words vertically in Python

exp

Python Extract words from a given string

Cyclic redundancy check in Python

Finding mean, median, mode in Python without libraries

cos

Python add suffix / add prefix to strings in a list

Why do I get "Pickle - EOFError: Ran out of input" reading an empty file?

Python - Move item to the end of the list

Python - Print list vertically