[英]Turn many features in a data frame with spark ML
我一直在跟随本教程https://mapr.com/blog/churn-prediction-sparkml/,并且我意识到csv结构必须像这样手动编写:
val schema = StructType(Array(
StructField("state", StringType, true),
StructField("len", IntegerType, true),
StructField("acode", StringType, true),
StructField("intlplan", StringType, true),
StructField("vplan", StringType, true),
StructField("numvmail", DoubleType, true),
StructField("tdmins", DoubleType, true),
StructField("tdcalls", DoubleType, true),
StructField("tdcharge", DoubleType, true),
StructField("temins", DoubleType, true),
StructField("tecalls", DoubleType, true),
StructField("techarge", DoubleType, true),
StructField("tnmins", DoubleType, true),
StructField("tncalls", DoubleType, true),
StructField("tncharge", DoubleType, true),
StructField("timins", DoubleType, true),
StructField("ticalls", DoubleType, true),
StructField("ticharge", DoubleType, true),
StructField("numcs", DoubleType, true),
StructField("churn", StringType, true)
但是我有一个具有335个特征的数据集,所以我不想全部编写它们...是否有一种简单的方法来检索它们并相应地定义模式?
我在这里找到了解决方案: https : //dzone.com/articles/using-apache-spark-dataframes-for-processing-of-ta比我想象的要容易
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