ValueError：在Python中创建KMeans模型时x和y的大小必须相同

Posted 2023-03-12

【中文标题】ValueError：在Python中创建KMeans模型时x和y的大小必须相同

【英文标题】：ValueError: x and y must be the same size In Python while creating KMeans Model

【发布时间】：2021-06-19 10:51:08

【问题描述】：

我正在使用流失数据集构建 Kmeans 聚类模型，并收到一条错误消息，提示 ValueError：尝试创建聚类图时 x 和 y 的大小必须相同。

我将在几秒钟内将我的函数和图形代码都发布在这里，但在试图缩小范围时，我认为它可能与函数中的这行代码有关：

x=kmeans.cluster_centers_[:,0]
                , y=kmeans.cluster_centers_[:,1]

这是完整的代码

def Create_kmeans_cluster_graph(df_final, data, n_clusters, x_title, y_title, chart_title):
    """ Display K-means cluster based on data """
    
    kmeans = KMeans(n_clusters=n_clusters # No of cluster in data
                    , random_state = random_state # Selecting same training data
                   ) 

    kmeans.fit(data)
    kmean_colors = [plotColor[c] for c in kmeans.labels_]


    fig = plt.figure(figsize=(12,8))
    plt.scatter(x= x_title + '_norm'
                , y= y_title + '_norm'
                , data=data 
                , color=kmean_colors # color of data points
                , alpha=0.25 # transparancy of data points
               )

    plt.xlabel(x_title)
    plt.ylabel(y_title)

    plt.scatter(x=kmeans.cluster_centers_[:,0]
                , y=kmeans.cluster_centers_[:,1]
                , color='black'
                , marker='X' # Marker sign for data points
                , s=100 # marker size
               )
    
    plt.title(chart_title,fontsize=15)
    plt.show()
    
    return kmeans.fit_predict(df_final[df_final.Churn==1][[x_title+'_norm', y_title +'_norm']])



//Graph

df_final['Cluster'] = -1 # by default set Cluster to -1
df_final.iloc[(df_final.Churn==1),'Cluster'] = Create_kmeans_cluster_graph(df_final
                            ,df_final[df_final.Churn==1][['Tenure_norm','MonthlyCharge_norm']]
                            ,3
                           ,'Tenure'
                           ,'MonthlyCharges'
                           ,"Tenure vs Monthlycharges : Churn customer cluster")

df_final['Cluster'].unique()

【参考方案1】：

由于这一行，您会收到该错误：

plt.scatter(x= x_title + '_norm'
                , y= y_title + '_norm'
                , data=data 
                , color=kmean_colors # color of data points
                , alpha=0.25 # transparancy of data points
               )

如果你使用plt.scatter，它不接受data=作为参数，你可以阅读the help page。你可以这样做：

plt.scatter(data[x_title + '_norm'],data[y_title + '_norm'],...)

或者您在 pandas 数据框上使用 plot.scatter method，我在您的函数的编辑版本中这样做了：

def Create_kmeans_cluster_graph(df_final, data, n_clusters, x_title, y_title, chart_title):
    plotColor = ['k','g','b']
    kmeans = KMeans(n_clusters=n_clusters , random_state = random_state)

    kmeans.fit(data)
    kmean_colors = [plotColor[c] for c in kmeans.labels_]

    data.plot.scatter(x= x_title + '_norm', y= y_title + '_norm',
                      color=kmean_colors,alpha=0.25)

    plt.xlabel(x_title)
    plt.ylabel(y_title)

    plt.scatter(x=kmeans.cluster_centers_[:,0],y=kmeans.cluster_centers_[:,1],
                color='black',marker='X',s=100)
    
    return kmeans.labels_

在示例数据集上，它可以工作：

import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
random_state = 42

np.random.seed(42)

df_final = pd.DataFrame('Tenure_norm':np.random.uniform(0,1,50),
                         'MonthlyCharge_norm':np.random.uniform(0,1,50),
                        'Churn':np.random.randint(0,3,50))

Create_kmeans_cluster_graph(df_final
                            ,df_final[df_final.Churn==1][['Tenure_norm','MonthlyCharge_norm']]
                            ,3
                           ,'Tenure'
                           ,'MonthlyCharge'
                           ,"Tenure vs Monthlycharges : Churn customer cluster")