sparkling(sparkler是什么意思)

1，K均值聚类2，高斯混合模型 3, 二分K均值 Bisecting k-means
Mllib支持的聚类模型较少，主要有K均值聚类，高斯混合模型GMM，以及二分的K均值，隐含狄利克雷分布LDA模型等。

from pyspark.ml.clustering import KMeans
from pyspark.ml.evaluation import Clusteringevaluator

# 载入数据
dfdata = spark.read.format("libsvm").load("data/sample_kmeans_data.txt")

# 训练Kmeans模型
kmeans = KMeans().setK(2).setSeed(1)
model = kmeans.fit(dfdata)

# 进行预测
dfpredictions = model.transform(dfdata)

# 评估模型
evaluator = Clusteringevaluator()
silhouette = evaluator.evaluate(dfpredictions)
print("Silhouette with squared euclidean distance = " + str(silhouette))

# 打印中心点
centers = model.clusterCenters()
print("Cluster Centers: ")
for center in centers:
    print(center)

Silhouette with squared euclidean distance = 0.9997530305375207
Cluster Centers: 
[9.1 9.1 9.1]
[0.1 0.1 0.1]

from pyspark.ml.clustering import GaussianMixture

dfdata = spark.read.format("libsvm").load("data/sample_kmeans_data.txt")

gmm = GaussianMixture().setK(2).setSeed(538009335)
model = gmm.fit(dfdata)

print("Gaussians shown as a Dataframe: ")
model.gaussiansDF.show(truncate=True)

aussians shown as a Dataframe: 
+--------------------+--------------------+
|                mean|                 cov|
+--------------------+--------------------+
|[0.10000000000001...|0.006666666666806...|
|[9.09999999999998...|0.006666666666812...|
+--------------------+--------------------+

Bisecting k-means是一种自上而下的层次聚类算法。所有的样本点开始时属于一个cluster,然后不断通过K均值二分裂得到多个cluster。

from pyspark.ml.clustering import BisectingKMeans


dfdata = spark.read.format("libsvm").load("data/sample_kmeans_data.txt")

bkm = BisectingKMeans().setK(2).setSeed(1)
model = bkm.fit(dfdata)

cost = model.computeCost(dfdata)
print("Within Set Sum of Squared Errors = " + str(cost))


print("Cluster Centers: ")
centers = model.clusterCenters()
for center in centers:
    print(center)
    

Within Set Sum of Squared Errors = 0.11999999999994547
Cluster Centers: 
[0.1 0.1 0.1]
[9.1 9.1 9.1]

资源链接下载

sample_kmeans_data.txt