我有一个PySpark数据帧
+-------+--------------+----+----+
|address| date|name|food|
+-------+--------------+----+----+
|1111111|20151122045510| Yin|gre |
|1111111|20151122045501| Yin|gre |
|1111111|20151122045500| Yln|gra |
|1111112|20151122065832| Yun|ddd |
|1111113|20160101003221| Yan|fdf |
|1111111|20160703045231| Yin|gre |
|1111114|20150419134543| Yin|fdf |
|1111115|20151123174302| Yen|ddd |
|2111115| 20123192| Yen|gre |
+-------+--------------+----+----+
我想转换为与pyspark.ml一起使用.我可以使用StringIndexer将name列转换为数字类别:
indexer = StringIndexer(inputCol="name", outputCol="name_index").fit(df)
df_ind = indexer.transform(df)
df_ind.show()
+-------+--------------+----+----------+----+
|address| date|name|name_index|food|
+-------+--------------+----+----------+----+
|1111111|20151122045510| Yin| 0.0|gre |
|1111111|20151122045501| Yin| 0.0|gre |
|1111111|20151122045500| Yln| 2.0|gra |
|1111112|20151122065832| Yun| 4.0|ddd |
|1111113|20160101003221| Yan| 3.0|fdf |
|1111111|20160703045231| Yin| 0.0|gre |
|1111114|20150419134543| Yin| 0.0|fdf |
|1111115|20151123174302| Yen| 1.0|ddd |
|2111115| …我一直在尝试在PySpark上做一个简单的随机森林回归模型.我对R机器学习有很好的体验.但是,对我来说,Pyspark上的ML看起来完全不同 - 特别是在处理分类变量,字符串索引和OneHotEncoding时(当只有数字变量时,我能够仅通过以下示例执行RF回归).虽然有很多可用于处理分类变量的例子,例如this和this,但我没有成功,因为大多数都超过我的头脑(可能是因为我不熟悉Python ML).我将非常感谢任何可以帮助解决这个问题的人.
这是我的尝试:inputfile在这里
from pyspark.mllib.linalg import Vectors
from pyspark.ml import Pipeline
from pyspark.ml.feature import StringIndexer, VectorIndexer
from pyspark.ml.classification import DecisionTreeClassifier
from pyspark.ml.tuning import CrossValidator, ParamGridBuilder
from pyspark.ml.evaluation import MulticlassClassificationEvaluator
from pyspark.sql.types import Row
from pyspark.sql.functions import col, round
train = sqlContext.read.format('com.databricks.spark.csv').options(header='true',inferschema = "true").load('filename.csv')
train.cache()
train.dtypes
输出是:
DataFrame[ID: int, Country: string, Carrier: double, TrafficType: string, ClickDate: timestamp, Device: string, Browser: string, OS: string, RefererUrl: string, UserIp: string, ConversionStatus: string, ConversionDate: string, ConversionPayOut: string, publisherId: …