pyspark kmeans聚类

在 PySpark 中使用 KMeans 聚类算法可以通过以下步骤实现：

1. 导入必要的库和模块

   from pyspark.ml.clustering import KMeans
   from pyspark.ml.evaluation import ClusteringEvaluator
   from pyspark.ml.feature import VectorAssembler

2. 加载数据集并进行特征工程

   data = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("path/to/dataset")
   
   assembler = VectorAssembler(inputCols=data.columns, outputCol="features")
   feature_data = assembler.transform(data).select("features")

3. 训练 KMeans 模型并进行预测

   kmeans = KMeans().setK(2).setSeed(1)
   model = kmeans.fit(feature_data)
   predictions = model.transform(feature_data)

4. 评估模型的性能

   evaluator = ClusteringEvaluator()
   silhouette = evaluator.evaluate(predictions)
   print("Silhouette with squared euclidean distance = " + str(silhouette))

完整的代码如下所示：

from pyspark.ml.clustering import KMeans
from pyspark.ml.evaluation import ClusteringEvaluator
from pyspark.ml.feature import VectorAssembler

# 加载数据集并进行特征工程
data = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("path/to/dataset")
assembler = VectorAssembler(inputCols=data.columns, outputCol="features")
feature_data = assembler.transform(data).select("features")

# 训练 KMeans 模型并进行预测
kmeans = KMeans().setK(2).setSeed(1)
model = kmeans.fit(feature_data)
predictions = model.transform(feature_data)

# 评估模型的性能
evaluator = ClusteringEvaluator()
silhouette = evaluator.evaluate(predictions)
print("Silhouette with squared euclidean distance = " + str(silhouette))