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<p>我正在努力为基于KMeans的聚类算法绘制条形图。问题是,我想以这样一种方式演示集群,即非常离群的集群可以在x轴的末尾进行描述&;其余的集群相对相邻。我认为问题在于{<cd1>},它们在x轴上均匀分布:</p>
<pre><code>---|---|---|-----------------> x-axis
0 1 2 3
</code></pre>
<p>在这种情况下,我想说明,例如,基于距离稍远的<code>Score</code>预测的带有标签的<code>3</code>的集群,需要对存储箱宽度进行一些调整,可能如下所示:</p>
<pre><code>---|---|--------------|------> x-axis
0 1 2 3
</code></pre>
<p>到目前为止,我获得了以下结果,以演示基于知识管理的异常检测算法的结果:
<img src="https://i.imgur.com/27KmyR5.png" alt="img"/></p>
<pre class="lang-py prettyprint-override"><code>from sklearn.cluster import KMeans
import seaborn as sns
import numpy as np
from pandas import DataFrame
from math import pow
import math
class ODKM:
def __init__(self,n_clusters=15,effectiveness=500,max_iter=2):
self.n_clusters=n_clusters
self.effectiveness=effectiveness
self.max_iter=max_iter
self.kmeans = {}
self.cluster_score = {}
#self.labels = {}
def fit(self, data):
length = len(data)
for column in data.columns:
kmeans = KMeans(n_clusters=self.n_clusters,max_iter=self.max_iter)
self.kmeans[column]=kmeans
kmeans.fit(data[column].values.reshape(-1,1))
assign = DataFrame(kmeans.predict(data[column].values.reshape(-1,1)),columns=['cluster'])
cluster_score=assign.groupby('cluster').apply(len).apply(lambda x:x/length)
ratio=cluster_score.copy()
sorted_centers = sorted(kmeans.cluster_centers_)
max_distance = ( sorted_centers[-1] - sorted_centers[0] )[ 0 ]
for i in range(self.n_clusters):
for k in range(self.n_clusters):
if i != k:
dist = abs(kmeans.cluster_centers_[i] - kmeans.cluster_centers_[k])/max_distance
effect = ratio[k]*(1/pow(self.effectiveness,dist))
cluster_score[i] = cluster_score[i]+effect
self.cluster_score[column] = cluster_score
def predict(self, data):
length = len(data)
score_array = np.zeros(length)
for column in data.columns:
kmeans = self.kmeans[ column ]
cluster_score = self.cluster_score[ column ]
#labels = kmeans.labels_
assign = kmeans.predict( data[ column ].values.reshape(-1,1) )
#print(assign)
for i in range(length):
score_array[i] = score_array[i] + math.log10( cluster_score[assign[i]] )
return score_array #,labels
def fit_predict(self,data):
self.fit(data)
return self.predict(data)
</code></pre>
<p>测试结果:</p>
<pre><code>import pandas as pd
df = pd.DataFrame(data={'attr1':[1,1,1,1,2,2,2,2,2,2,2,2,3,5,5,6,6,7,7,7,7,7,7,7,15],
'attr2':[1,1,1,1,2,2,2,2,2,2,2,2,3,5,5,6,6,7,7,7,13,13,13,14,15]})
#generate score from KM-based algorithm via class ODKM
odkm_model = ODKM(n_clusters=3, max_iter=1)
result = odkm_model.fit_predict(df)
#include generated scores to the main frame to reach desired plot
df['ODKM_Score']= result
df
#for i in result:
# print(round(i,2))
#results
#-0.51, -0.51 , -0.51 , -0.51, -0.51, -0.51, -0.51, -0.51, -0.51, -0.51, -0.51, -0.51, -0.51
#-0.78, -0.78, -0.78, -0.78, -0.78, -0.78, -0.78
#-0.99, -0.99, -0.99, -0.99
#-1.99
</code></pre>
<p>您可以在<a href="https://colab.research.google.com/drive/1Lzzsk8ZTkOajGksJIgCPeAeEWjGTgFAd?usp=sharing" rel="nofollow noreferrer">colab notebook</a>中找到我的整个代码,包括这个基于KM的算法,以便快速调试。如果需要,请随时在笔记本上实现您的解决方案或在单元格上发表评论,或者<code>ODKM</code>算法本身(执行KM集群)中的一些更改可以<code>@class ODKM</code>的形式访问。为了更好地访问条形图,最好提取预测的簇标签,并在<code>ODKM</code>算法<code>Score</code>旁边的<code>Cluster_label</code>标题下添加一个新列</p>
<p>预期的输出应该是这样的(相同集群中更好的容器具有相同的颜色,例如第一个集群<code>C1</code>):</p>
<p><img src="https://i.imgur.com/OgqonWN.png" alt="img"/></p>
<p><strong>更新</strong>:除了条形图解决方案外,我还可以绘制历史&;分布,但我不知道如何着色和传递聚类标签,以在直方图中的容器上反映聚类结果</p>
<pre class="lang-py prettyprint-override"><code>##left output
# just plot 'Score' column (not all columsn in 1st phase) to simply the problem
#cols_ = df.columns[-1:]
ax1 = plt.subplot2grid((1,1), (0,0))
df['Score'].plot(kind='hist', ax=ax1 , color='b', alpha=0.4)
df['Score'].plot(kind='kde', ax=ax1, secondary_y=True, label='distribution', color='b', lw=2)
##Right output
sns.distplot(df['Score'] , color='b')
</code></pre>
<p>尽管在图表上反映了聚类结果,但我注意到,正如我在下图中强调的,这两个图之间存在一些差异。Gy轴的比例&;靠近x轴原点的主料仓之间的间隙问题:</p>
<p><img src="https://i.imgur.com/jvshTFC.png" alt="img"/></p>
<p>我也发现了这个<a href="https://stackoverflow.com/questions/66325301/change-color-of-bar-for-data-selection-in-seaborn-histogram-or-plt">post</a>,但是我不能适应<code>@class ODKM</code>来动态地解决我的问题。
我最近也可以做到这一点:</p>
<pre class="lang-py prettyprint-override"><code>df['Score'] = df['Score'].abs()
sns.displot(df,
x='Score',
hue='Cluster_labels',
palette=["#00f0f0","#ff0000","#00ff00"],
alpha=1)
</code></pre>
<p><img src="https://i.imgur.com/sFrdGTQ.png" alt="img"/></p>