本文整理汇总了Python中pyspark.ml.clustering.KMeans.explainParams方法的典型用法代码示例。如果您正苦于以下问题：Python KMeans.explainParams方法的具体用法？Python KMeans.explainParams怎么用？Python KMeans.explainParams使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pyspark.ml.clustering.KMeans的用法示例。
在下文中一共展示了KMeans.explainParams方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: KMeans
# 需要导入模块: from pyspark.ml.clustering import KMeans [as 别名]
# 或者: from pyspark.ml.clustering.KMeans import explainParams [as 别名]
sales = va.transform(spark.read.format("csv")
.option("header", "true")
.option("inferSchema", "true")
.load("/data/retail-data/by-day/*.csv")
.limit(50)
.coalesce(1)
.where("Description IS NOT NULL"))
sales.cache()
# COMMAND ----------
from pyspark.ml.clustering import KMeans
km = KMeans().setK(5)
print km.explainParams()
kmModel = km.fit(sales)
# COMMAND ----------
summary = kmModel.summary
print summary.clusterSizes # number of points
kmModel.computeCost(sales)
centers = kmModel.clusterCenters()
print("Cluster Centers: ")
for center in centers:
print(center)
# COMMAND ----------开发者ID:yehonatc，项目名称:Spark-The-Definitive-Guide，代码行数:33，代码来源:Advanced_Analytics_and_Machine_Learning-Chapter_29_Unsupervised_Learning.py
示例2: MlKMeans
# 需要导入模块: from pyspark.ml.clustering import KMeans [as 别名]
# 或者: from pyspark.ml.clustering.KMeans import explainParams [as 别名]
# In[62]:
from pyspark.ml.clustering import KMeans as MlKMeans
firstMlKMeans = MlKMeans(
featuresCol="features", predictionCol="prediction", k=2,
initMode="k-means||", maxIter=20)
type(firstMlKMeans)
# `pyspark.ml` paketo modelių klasės turi `explainParams` metodą, kuruo išvedami modelio parametrų paaiškinimai.
# In[63]:
print(firstMlKMeans.explainParams())
# Apmokykime modelį.
# In[64]:
firstMlModel = firstMlKMeans.fit(ca1mlFeaturizedDF)
type(firstMlModel)
# In[65]:
firstMlModel.clusterCenters()