调整数据

使用 PySpark 进行特征工程

John Hogue

Lead Data Scientist, General Mills

为何要变换数据？

什么是最小-最大缩放

最小-最大缩放

# define min and max values and collect them
max_days = df.agg({'DAYSONMARKET': 'max'}).collect()[0][0]
min_days = df.agg({'DAYSONMARKET': 'min'}).collect()[0][0]

# create a new column based off the scaled data
df = df.withColumn("scaled_days", 
                  (df['DAYSONMARKET'] - min_days) / (max_days - min_days))

df[['scaled_days']].show(5)

+--------------------+
|         scaled_days|
+--------------------+
|0.044444444444444446|
|0.017777777777777778|
| 0.12444444444444444|
| 0.08444444444444445|
| 0.09333333333333334|
+--------------------+
only showing top 5 rows

什么是标准化？

将数据变为标准正态分布

z = (x - μ)/ σ
均值 μ 为 0
标准差 σ 为 1

标准化

mean_days = df.agg({'DAYSONMARKET': 'mean'}).collect()[0][0]
stddev_days = df.agg({'DAYSONMARKET': 'stddev'}).collect()[0][0]

# Create a new column with the scaled data
df = df.withColumn("ztrans_days", 
                  (df['DAYSONMARKET'] - mean_days) / stddev_days)

df.agg({'ztrans_days': 'mean'}).collect()

[Row(avg(ztrans_days)=-3.6568525985103407e-16)]

df.agg({'ztrans_days': 'stddev'}).collect()

[Row(stddev(ztrans_days)=1.0000000000000009)]

什么是对数缩放

未缩放分布

原始分布图

对数缩放分布

对数缩放分布图

对数缩放

# import the log function
from pyspark.sql.functions import log

# Recalculate log of SALESCLOSEPRICE
df = df.withColumn('log_SalesClosePrice', log(df['SALESCLOSEPRICE']))

开始练习！

使用 PySpark 进行特征工程