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python - 重复运行该程序的不同 k-means 结果

问题描述

该计划的目的是：

  1. 读取数据集：行是客户，列是客户购买的产品
  2. 应用主成分分析以减少特征数量
  3. 应用 k-means 确定每个客户所属的集群
  4. 在与原始数据集具有相同结构但值不同的新数据集上执行步骤 1、2
  5. 将步骤 3 中确定的 k-means 模型应用于新数据集

问题是重复运行会根据客户所属的集群给出不同的结果。一定有一个我找不到的错误。提前致谢。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
def get_kmeans_score(data, center):
    '''
    returns the kmeans score regarding SSE for points to centers
    INPUT:
        data - the dataset you want to fit kmeans to
        center - the number of centers you want (the k value)
    OUTPUT:
        score - the SSE score for the kmeans model fit to the data
    '''
    #instantiate kmeans
    kmeans = KMeans(n_clusters=center)

    # Then fit the model to your data using the fit method
    model = kmeans.fit(data)

    # Obtain a score related to the model fit
    score = np.abs(model.score(data))

    return score
data = {
    'apples': [3, 2, 0, 9, 2, 1],
    'oranges': [0, 7.6, 7, 2, 7, 6],
    'figs':[1.4, 11, 10.999, 3.99, 10, 2],
    'pears': [5, 2, 6, 2.45, 1, 7],
    'berries': [1.3, 4, 10, 0, 5,21],
    'tomatoes': [5, 15, 3, 4, 17,5],
    'onions': [11,3, 3, 1, 0, 10]
}
purchases = pd.DataFrame(data, index=['June', 'Robert', 'Lily', 'David', 'Bob', 'Karen'])
print('ORIGINAL DATA')
print(purchases)
Y1 = pd.DataFrame(np.round(purchases,0), columns = purchases.keys())
scaler = StandardScaler()
Y = scaler.fit_transform(Y1)
pca = PCA(n_components=3)
W = pca.fit_transform(Y)
# apply k-means
scores = []
centers = list(range(1,5))
for center in centers:
    scores.append(get_kmeans_score(W, center))
X = zip(centers, scores)
print('k-means results on original data as a function of # centers')
for i in X:
        print(i)
# from the above results, assume the elbow is 4 clusters
print('_________________________________________')
n_c = 4
#kmeans = KMeans(n_clusters=4, random_state=int)
kmeans = KMeans(n_clusters=4)
model = kmeans.fit(W)
score = np.abs(model.score(W))
print('k-means score on ', n_c, ' clusters for the original dataset = ',score)
# model is the k-means model that will also be applied to the new dataset
#
NEW_data = {
    'apples': [9, 20, 10, 2, 12,1],
    'oranges': [10, 3, 12, 1, 18, 5],
    'figs':[34, 11, 3.999, 1, 0, 12],
    'pears': [5, 2, 16, 2.45, 10, 11],
    'berries': [13, 4, 1, 2, 15, 4],
    'tomatoes': [7, 2, 1, 14, 27, 2],
    'onions': [1,10, 11, 2, 4, 10]
}
purchases_N = pd.DataFrame(NEW_data)
purchases_N = pd.DataFrame(NEW_data, index=['June', 'Robert', 'Lily', 'David', 'Bob', 'Karen'])
print('NEW DATA')
print(purchases_N)
YY1 = pd.DataFrame(np.round(purchases_N,0), columns = purchases_N.keys())
YY = scaler.fit_transform(YY1)
W1 = pca.transform(YY)
scoreNew = np.abs(model.score(W1))
print('k-means score on ', n_c, ' clusters for the new dataset = ',scoreNew)
print(scoreNew)
# k-means score the new dataset using the model determined on original ds
# predictions for the 2 datasets using the k-means model based on orig data
predict_purchases_dataset = model.predict(W)
predict_purchases_NewDataset = model.predict(W1)
print('original data upon PCA using n_components=3')
print(W)
print('k-means predictions --- original data')
print(predict_purchases_dataset)
print('_________________________________________')
print('new data upon PCA using n_components=3')
print(W1)
print('k-means predictions --- new data')
print(predict_purchases_NewDataset)
# the output matches the prediction on orig dataset:
# there are 2 customers in cluster 2, 2 customers in cluster 1, 1 in cluster 3 and 1 in 0
L = len(purchases.index)
x = [i for i in range (10)]
orig = []
NEW = []
for i in range(10):
    orig.append((predict_purchases_dataset== i).sum()/L)
    NEW.append((predict_purchases_NewDataset== i).sum()/L)
print('proportion of k-means clusters for original data')
print(orig)
print('proportion of k-means clusters for new data')
print(NEW)

#df_summary = pd.DataFrame({'cluster' : x, 'propotion_orig' : orig, 'proportion_NEW': NEW})
#df_summary.plot(x='cluster', y= ['propotion_orig','proportion_NEW' ], kind='bar')
model.cluster_centers_
#
IPCA = pca.inverse_transform(model.cluster_centers_)
APPROX = scaler.inverse_transform(IPCA)
approx_df =pd.DataFrame(APPROX, columns=purchases.columns)
print('k-means centers coordinates in original features space')
print('k-means centers coordinates in original features space')
print(approx_df)

首轮

k-means predictions --- original data
[3 1 0 2 1 0]

k-means predictions --- new data
[1 2 0 1 1 0]

第二次运行

k-means predictions --- original data
[1 2 0 3 2 0]

k-means predictions --- new data
[2 3 0 2 2 0]

标签： pythonpandasscikit-learnk-meanspca

解决方案

两次运行实际上都给您相同的结果。生成的标签KMeans没有任何意义，并且是任意值，可让您了解您的数据已分配到哪个集群。如果您查看示例中构建的集群，您会发现它们是相同的，只是在您重新训练模型后调用不同（“0”仍然是“0”，“1”变成“2”，“2”变成了“3”，“3”变成了“1”）。

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