[英]Apply MinMaxScaler on multiple columns in PySpark
我想將MinMaxScalar
的 MinMaxScalar 應用於 PySpark 數據框df
多列。 到目前為止,我只知道如何將它應用於單個列,例如x
。
from pyspark.ml.feature import MinMaxScaler
pdf = pd.DataFrame({'x':range(3), 'y':[1,2,5], 'z':[100,200,1000]})
df = spark.createDataFrame(pdf)
scaler = MinMaxScaler(inputCol="x", outputCol="x")
scalerModel = scaler.fit(df)
scaledData = scalerModel.transform(df)
如果我有 100 列怎么辦? 有沒有辦法對 PySpark 中的許多列進行最小-最大縮放?
更新:
另外,如何將MinMaxScalar
應用於整數或雙MinMaxScalar
值? 它引發以下錯誤:
java.lang.IllegalArgumentException: requirement failed: Column length must be of type struct<type:tinyint,size:int,indices:array<int>,values:array<double>> but was actually int.
如何更改您的示例以正常運行。 您需要准備數據作為轉換器工作的向量。
from pyspark.ml.feature import MinMaxScaler
from pyspark.ml import Pipeline
from pyspark.ml.linalg import VectorAssembler
pdf = pd.DataFrame({'x':range(3), 'y':[1,2,5], 'z':[100,200,1000]})
df = spark.createDataFrame(pdf)
assembler = VectorAssembler(inputCols=["x"], outputCol="x_vec")
scaler = MinMaxScaler(inputCol="x_vec", outputCol="x_scaled")
pipeline = Pipeline(stages=[assembler, scaler])
scalerModel = pipeline.fit(df)
scaledData = scalerModel.transform(df)
要在多個列上運行 MinMaxScaler,您可以使用接收帶有列表理解准備的轉換列表的管道:
from pyspark.ml import Pipeline
from pyspark.ml.feature import MinMaxScaler
columns_to_scale = ["x", "y", "z"]
assemblers = [VectorAssembler(inputCols=[col], outputCol=col + "_vec") for col in columns_to_scale]
scalers = [MinMaxScaler(inputCol=col + "_vec", outputCol=col + "_scaled") for col in columns_to_scale]
pipeline = Pipeline(stages=assemblers + scalers)
scalerModel = pipeline.fit(df)
scaledData = scalerModel.transform(df)
在官方文檔中查看此示例管道。
最終,您將得到以下格式的結果:
>>> scaledData.printSchema()
root
|-- x: long (nullable = true)
|-- y: long (nullable = true)
|-- z: long (nullable = true)
|-- x_vec: vector (nullable = true)
|-- y_vec: vector (nullable = true)
|-- z_vec: vector (nullable = true)
|-- x_scaled: vector (nullable = true)
|-- y_scaled: vector (nullable = true)
|-- z_scaled: vector (nullable = true)
>>> scaledData.show()
+---+---+----+-----+-----+--------+--------+--------+--------------------+
| x| y| z|x_vec|y_vec| z_vec|x_scaled|y_scaled| z_scaled|
+---+---+----+-----+-----+--------+--------+--------+--------------------+
| 0| 1| 100|[0.0]|[1.0]| [100.0]| [0.0]| [0.0]| [0.0]|
| 1| 2| 200|[1.0]|[2.0]| [200.0]| [0.5]| [0.25]|[0.1111111111111111]|
| 2| 5|1000|[2.0]|[5.0]|[1000.0]| [1.0]| [1.0]| [1.0]|
+---+---+----+-----+-----+--------+--------+--------+--------------------+
您可以通過一些后處理以原始名稱恢復列。 例如:
from pyspark.sql import functions as f
names = {x + "_scaled": x for x in columns_to_scale}
scaledData = scaledData.select([f.col(c).alias(names[c]) for c in names.keys()])
輸出將是:
scaledData.show()
+------+-----+--------------------+
| y| x| z|
+------+-----+--------------------+
| [0.0]|[0.0]| [0.0]|
|[0.25]|[0.5]|[0.1111111111111111]|
| [1.0]|[1.0]| [1.0]|
+------+-----+--------------------+
您可以將單個 MinMaxScaler 實例用於一組“矢量組裝”功能,而不是為要轉換的每列創建一個 MinMaxScaler(在這種情況下為縮放)。
from pyspark.ml.feature import MinMaxScaler
from pyspark.ml.feature import VectorAssembler
#1. Your original dataset
#pdf = pd.DataFrame({'x':range(3), 'y':[1,2,5], 'z':[100,200,1000]})
#df = spark.createDataFrame(pdf)
df = spark.createDataFrame([(0, 10.0, 0.1), (1, 1.0, 0.20), (2, 1.0, 0.9)],["x", "y", "z"])
df.show()
+---+----+---+
| x| y| z|
+---+----+---+
| 0|10.0|0.1|
| 1| 1.0|0.2|
| 2| 1.0|0.9|
+---+----+---+
#2. Vector assembled set of features
# (assemble only the columns you want to MinMax Scale)
assembler = VectorAssembler(inputCols=["x", "y", "z"],
outputCol="features")
output = assembler.transform(df)
output.show()
+---+----+---+--------------+
| x| y| z| features|
+---+----+---+--------------+
| 0|10.0|0.1|[0.0,10.0,0.1]|
| 1| 1.0|0.2| [1.0,1.0,0.2]|
| 2| 1.0|0.9| [2.0,1.0,0.9]|
+---+----+---+--------------+
#3. Applying MinMaxScaler to your assembled features
scaler = MinMaxScaler(inputCol="features", outputCol="scaledFeatures")
# rescale each feature to range [min, max].
scaledData = scaler.fit(output).transform(output)
scaledData.show()
+---+----+---+--------------+---------------+
| x| y| z| features| scaledFeatures|
+---+----+---+--------------+---------------+
| 0|10.0|0.1|[0.0,10.0,0.1]| [0.0,1.0,0.0]|
| 1| 1.0|0.2| [1.0,1.0,0.2]|[0.5,0.0,0.125]|
| 2| 1.0|0.9| [2.0,1.0,0.9]| [1.0,0.0,1.0]|
+---+----+---+--------------+---------------+
希望這可以幫助。
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