Question

我正在使用PCA进行数据分析，我使用PySpark编写了这段代码并且它工作得很好但它只适用于从csv文件中读取的数据，其中包含5列[＆＃34; a＆＃34;，＆＃34; b＆＃34;，＆＃34; c＆＃34;，＆＃34; d＆＃34;，＆＃34; e＆＃34;]，我想编写一个通用代码来计算任何数字的PCA从csv文件中读取的列。我应该添加什么？这是我的代码：

#########################! importing libraries !########################
from __future__ import print_function
from pyspark.ml.linalg import Vectors
from pyspark.sql import SparkSession
from pyspark import SparkConf, SparkContext
from pyspark.ml.feature import PCA, VectorAssembler
from pyspark.mllib.linalg import Vectors
from pyspark.ml import Pipeline
from pyspark.sql import SQLContext
from pyspark import SparkContext
from pyspark.mllib.feature import Normalizer
import timeit
########################! main script !#################################
sc = SparkContext("local", "pca-app")
sqlContext = SQLContext(sc)
if __name__ == "__main__":
    spark = SparkSession\
        .builder\
        .appName("PCAExample")\
        .getOrCreate()  
    data = sc.textFile('dataset.csv') \
        .map(lambda line: [float(k) for k in line.split(';')])\
        .collect()
    df = spark.createDataFrame(data, ["a","b","c","d","e"])
    df.show()
    vecAssembler = VectorAssembler(inputCols=["a","b","c","d","e"], outputCol="features")

    pca = PCA(k=2, inputCol="features", outputCol="pcaFeatures")
    pipeline = Pipeline(stages=[vecAssembler, pca]
    model = pipeline.fit(df)
    result = model.transform(df).select("pcaFeatures")
    result.show(truncate=False))
    spark.stop()

Answer 1

您需要通过更改几行来使代码变得通用： -

fileObj = sc.textFile('dataset.csv') 
data = fileObj.map(lambda line: [float(k) for k in line.split(';')]).collect()
columns = (fileObj.first()).split()
df = spark.createDataFrame(data, columns)
df.show()
vecAssembler = VectorAssembler(inputCols=columns, outputCol="features")

Pyspark PCA数据处理的通用模型

1 个答案: