ML | OPTICS Clustering Implementing using Sklearn
Prerequisites: OPTICS Clustering
This article will demonstrate how to implement OPTICS Clustering technique using Sklearn in Python. The dataset used for the demonstration is the Mall Customer Segmentation Data which can be downloaded from Kaggle.
Step 1: Importing the required libraries
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.preprocessing import normalize, StandardScaler |
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Step 2: Loading the Data
# Changing the working location to the location of the data cd C:\Users\Dev\Desktop\Kaggle\Customer Segmentation X = pd.read_csv('Mall_Customers.csv') # Dropping irrelevant columns drop_features = ['CustomerID', 'Gender'] X = X.drop(drop_features, axis = 1) # Handling the missing values if any X.fillna(method ='ffill', inplace = True) X.head() |
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Step 3: Preprocessing the Data
# Scaling the data to bring all the attributes to a comparable level scaler = StandardScaler() X_scaled = scaler.fit_transform(X) # Normalizing the data so that the data # approximately follows a Gaussian distribution X_normalized = normalize(X_scaled) # Converting the numpy array into a pandas DataFrame X_normalized = pd.DataFrame(X_normalized) # Renaming the columns X_normalized.columns = X.columns X_normalized.head() |
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Step 4: Building the Clustering Model
# Building the OPTICS Clustering model optics_model = OPTICS(min_samples = 10, xi = 0.05, min_cluster_size = 0.05) # Training the model optics_model.fit(X_normalized) |
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Step 5: Storing the results of the training
# Producing the labels according to 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) # Producing the labels according to 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) # Creating a numpy array with numbers at equal spaces till # the specified range space = np.arange(len(X_normalized)) # Storing the reachability distance of each point reachability = optics_model.reachability_[optics_model.ordering_] # Storing the cluster labels of each point labels = optics_model.labels_[optics_model.ordering_] print(labels) |
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Step 6: Visualizing the results
# Defining the framework of the visualization 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]) # Plotting the Reachability-Distance Plot colors = ['c.', 'b.', 'r.', 'y.', 'g.'] for Class, colour in zip(range(0, 5), colors): Xk = space[labels == Class] Rk = reachability[labels == Class] ax1.plot(Xk, Rk, colour, 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') # Plotting the OPTICS Clustering colors = ['c.', 'b.', 'r.', 'y.', 'g.'] for Class, colour in zip(range(0, 5), colors): Xk = X_normalized[optics_model.labels_ == Class] ax2.plot(Xk.iloc[:, 0], Xk.iloc[:, 1], colour, 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') # Plotting the DBSCAN Clustering with eps = 0.5 colors = ['c', 'b', 'r', 'y', 'g', 'greenyellow'] for Class, colour in zip(range(0, 6), colors): Xk = X_normalized[labels1 == Class] ax3.plot(Xk.iloc[:, 0], Xk.iloc[:, 1], colour, 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') # Plotting the DBSCAN Clustering with eps = 2.0 colors = ['c.', 'y.', 'm.', 'g.'] for Class, colour in zip(range(0, 4), colors): Xk = X_normalized.iloc[labels2 == Class] ax4.plot(Xk.iloc[:, 0], Xk.iloc[:, 1], colour, 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 Clustering with eps = 2.0') plt.tight_layout() plt.show() |
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