ML算法（使用Sklearn实现OPTICS聚类） _SklearnOPTICS聚类

先决条件：OPTICS群集
本文将演示如何在Python中使用Sklearn实现OPTICS聚类技术。用于演示的数据集是商城客户细分数据可以从以下位置下载卡格勒.
步骤1：导入所需的库

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

步骤2：载入资料

# Changing the working location to the location of the data cd C:\Users\Dev\Desktop\Kaggle\Customer SegmentationX = 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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步骤3：预处理数据

# 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.columnsX_normalized.head()

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步骤4：构建聚类模型

# 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)

步骤5：存储训练结果

# 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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步骤6：可视化结果

# 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()