功能介绍

聚类评估是对聚类算法的预测结果进行效果评估，支持下列评估指标。

Compactness(CP), CP越低意味着类内聚类距离越近

Seperation(SP), SP越高意味类间聚类距离越远

Davies-Bouldin Index(DB), DB越小意味着类内距离越小 同时类间距离越大

Calinski-Harabasz Index(VRC), VRC越大意味着聚类质量越好

参数说明

脚本示例

脚本代码

import numpy as np
import pandas as pd
data = np.array([
    [0, "0 0 0"],
    [0, "0.1,0.1,0.1"],
    [0, "0.2,0.2,0.2"],
    [1, "9 9 9"],
    [1, "9.1 9.1 9.1"],
    [1, "9.2 9.2 9.2"]
])
df = pd.DataFrame({"id": data[:, 0], "vec": data[:, 1]})
inOp = BatchOperator.fromDataframe(df, schemaStr='id int, vec string')
metrics = EvalClusterBatchOp().setVectorCol("vec").setPredictionCol("id").linkFrom(inOp).collectMetrics()
print("Total Samples Number:", metrics.getCount())
print("Cluster Number:", metrics.getK())
print("Cluster Array:", metrics.getClusterArray())
print("Cluster Count Array:", metrics.getCountArray())
print("CP:", metrics.getCompactness())
print("DB:", metrics.getDaviesBouldin())
print("SP:", metrics.getSeperation())
print("SSB:", metrics.getSsb())
print("SSW:", metrics.getSsw())
print("CH:", metrics.getCalinskiHarabaz())

脚本运行结果

Total Samples Number: 6
Cluster Number: 2
Cluster Array: ['0', '1']
Cluster Count Array: [3.0, 3.0]
CP: 0.11547005383792497
DB: 0.014814814814814791
SP: 15.588457268119896
SSB: 364.5
SSW: 0.1199999999999996
CH: 12150.000000000042