Spark中的聚类算法
Spark - Clustering
官方文档：https://spark.apache.org/docs/2.2.0/ml-clustering.html
这部分介绍MLlib中的聚类算法；
目录：
- K-means：
- 输入列；
- 输出列；
- Latent Dirichlet allocation(LDA)：
- Bisecting k-means；
- Gaussian Mixture Model(GMM)：
- 输入列；
- 输出列；
K-means
k-means是最常用的聚类算法之一，它将数据聚集到预先设定的N个簇中；
KMeans作为一个预测器，生成一个KMeansModel作为基本模型；
输入列
Param name | Type(s) | Default | Description |
|---|---|---|---|
featuresCol | Vector | features | Feature vector |
输出列
Param name | Type(s) | Default | Description |
|---|---|---|---|
predictionCol | Int | prediction | Predicted cluster center |
例子
from pyspark.ml.clustering import KMeans
# Loads data.
dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt")
# Trains a k-means model.
kmeans = KMeans().setK(2).setSeed(1)
model = kmeans.fit(dataset)
# Evaluate clustering by computing Within Set Sum of Squared Errors.
wssse = model.computeCost(dataset)
print("Within Set Sum of Squared Errors = " + str(wssse))
# Shows the result.
centers = model.clusterCenters()
print("Cluster Centers: ")
for center in centers:
print(center)LDA
LDA是一个预测器，同时支持EMLDAOptimizer和OnlineLDAOptimizer，生成一个LDAModel作为基本模型，专家使用者如果有需要可以将EMLDAOptimizer生成的LDAModel转为DistributedLDAModel；
from pyspark.ml.clustering import LDA
# Loads data.
dataset = spark.read.format("libsvm").load("data/mllib/sample_lda_libsvm_data.txt")
# Trains a LDA model.
lda = LDA(k=10, maxIter=10)
model = lda.fit(dataset)
ll = model.logLikelihood(dataset)
lp = model.logPerplexity(dataset)
print("The lower bound on the log likelihood of the entire corpus: " + str(ll))
print("The upper bound on perplexity: " + str(lp))
# Describe topics.
topics = model.describeTopics(3)
print("The topics described by their top-weighted terms:")
topics.show(truncate=False)
# Shows the result
transformed = model.transform(dataset)
transformed.show(truncate=False)Bisecting k-means
Bisecting k-means是一种使用分裂方法的层次聚类算法：所有数据点开始都处在一个簇中，递归的对数据进行划分直到簇的个数为指定个数为止；
Bisecting k-means一般比K-means要快，但是它会生成不一样的聚类结果；
BisectingKMeans是一个预测器，并生成BisectingKMeansModel作为基本模型；
与K-means相比，二分K-means的最终结果不依赖于初始簇心的选择，这也是为什么通常二分K-means与K-means结果往往不一样的原因；
from pyspark.ml.clustering import BisectingKMeans
# Loads data.
dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt")
# Trains a bisecting k-means model.
bkm = BisectingKMeans().setK(2).setSeed(1)
model = bkm.fit(dataset)
# Evaluate clustering.
cost = model.computeCost(dataset)
print("Within Set Sum of Squared Errors = " + str(cost))
# Shows the result.
print("Cluster Centers: ")
centers = model.clusterCenters()
for center in centers:
print(center)Gaussian Mixture Model(GMM)
GMM表示一个符合分布，从一个高斯子分布中提取点，每个点都有其自己 的概率，spark.ml基于给定数据通过期望最大化算法来归纳最大似然模型实现算法；
输入列
Param name | Type(s) | Default | Description |
|---|---|---|---|
featuresCol | Vector | features | Feature vector |
输出列
Param name | Type(s) | Default | Description |
|---|---|---|---|
predictionCol | Int | prediction | Predicted cluster center |
probabilityCol | Vector | probability | Probability of each cluster |
例子
from pyspark.ml.clustering import GaussianMixture
# loads data
dataset = spark.read.format("libsvm").load("data/mllib/sample_kmeans_data.txt")
gmm = GaussianMixture().setK(2).setSeed(538009335)
model = gmm.fit(dataset)
print("Gaussians shown as a DataFrame: ")
model.gaussiansDF.show(truncate=False)