Spark机器学习基础-特征工程
对连续值处理
0.binarizer/二值化
from __future__ import print_function
from pyspark.sql import SparkSession
from pyspark.ml.feature import Binarizer#ml相对于mllib更全一点,更新一点
spark = SparkSession\
.builder\
.appName("BinarizerExample")\
.getOrCreate() continuousDataFrame = spark.createDataFrame([
(0, 1.1),
(1, 8.5),
(2, 5.2)
], ["id", "feature"]) binarizer = Binarizer(threshold=5.1, inputCol="feature", outputCol="binarized_feature") binarizedDataFrame = binarizer.transform(continuousDataFrame) print("Binarizer output with Threshold = %f" % binarizer.getThreshold())
binarizedDataFrame.show() spark.stop()
结果:
Binarizer output with Threshold = 5.100000
+---+-------+-----------------+
| id|feature|binarized_feature|
+---+-------+-----------------+
| 0| 1.1| 0.0|
| 1| 8.5| 1.0|
| 2| 5.2| 1.0|
+---+-------+-----------------+
1.按照给定边界离散化
from __future__ import print_function
from pyspark.sql import SparkSession
from pyspark.ml.feature import Bucketizer spark = SparkSession\
.builder\
.appName("BucketizerExample")\
.getOrCreate() splits = [-float("inf"), -0.5, 0.0, 0.5, float("inf")]#-float("inf"):指的是负无穷 data = [(-999.9,), (-0.5,), (-0.3,), (0.0,), (0.2,), (999.9,)]
dataFrame = spark.createDataFrame(data, ["features"]) bucketizer = Bucketizer(splits=splits, inputCol="features", outputCol="bucketedFeatures") # 按照给定的边界进行分桶
bucketedData = bucketizer.transform(dataFrame) print("Bucketizer output with %d buckets" % (len(bucketizer.getSplits())-1))
bucketedData.show() spark.stop()
结果:
Bucketizer output with 4 buckets
+--------+----------------+
|features|bucketedFeatures|
+--------+----------------+
| -999.9| 0.0|
| -0.5| 1.0|
| -0.3| 1.0|
| 0.0| 2.0|
| 0.2| 2.0|
| 999.9| 3.0|
+--------+----------------+
2.quantile_discretizer/按分位数离散化
from __future__ import print_function
from pyspark.ml.feature import QuantileDiscretizer
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("QuantileDiscretizerExample")\
.getOrCreate() data = [(0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2), (5, 9.2), (6, 14.4)]
df = spark.createDataFrame(data, ["id", "hour"])
df = df.repartition(1)#数据量小设置为1个分区,这样不出错!数据量大的话可以设置为多个分区。 # 分成3个桶进行离散化
discretizer = QuantileDiscretizer(numBuckets=3, inputCol="hour", outputCol="result") result = discretizer.fit(df).transform(df)
result.show() spark.stop()
结果:
+---+----+------+
| id|hour|result|
+---+----+------+
| 0|18.0| 2.0|
| 1|19.0| 2.0|
| 2| 8.0| 1.0|
| 3| 5.0| 0.0|
| 4| 2.2| 0.0|
| 5| 9.2| 1.0|
| 6|14.4| 2.0|
+---+----+------+
3.最大最小值幅度缩放
from __future__ import print_function
from pyspark.ml.feature import MaxAbsScaler
from pyspark.ml.linalg import Vectors
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("MaxAbsScalerExample")\
.getOrCreate() dataFrame = spark.createDataFrame([
(0, Vectors.dense([1.0, 0.1, -8.0]),),#dense表示稠密向量
(1, Vectors.dense([2.0, 1.0, -4.0]),),
(2, Vectors.dense([4.0, 10.0, 8.0]),)
], ["id", "features"]) scaler = MaxAbsScaler(inputCol="features", outputCol="scaledFeatures")#最大最小值用于缩放 # 计算最大最小值用于缩放
scalerModel = scaler.fit(dataFrame)#fit与transform分开写,因为fit的数据还要用于测试集的变换 # 缩放幅度到[-1, 1]之间
scaledData = scalerModel.transform(dataFrame)
scaledData.select("features", "scaledFeatures").show() spark.stop()
结果:
+--------------+----------------+
| features| scaledFeatures|
+--------------+----------------+
|[1.0,0.1,-8.0]|[0.25,0.01,-1.0]|
|[2.0,1.0,-4.0]| [0.5,0.1,-0.5]|
|[4.0,10.0,8.0]| [1.0,1.0,1.0]|
+--------------+----------------+
4.标准化
from __future__ import print_function
from pyspark.ml.feature import StandardScaler
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("StandardScalerExample")\
.getOrCreate() dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")#libsvm数据格式,适用于存储稀疏数据: [label] [index1]:[value1] [index2]:[value2] …
scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures",
withStd=True, withMean=False) # 计算均值方差等参数
scalerModel = scaler.fit(dataFrame) # 标准化
scaledData = scalerModel.transform(dataFrame)
scaledData.show() spark.stop()
+-----+--------------------+--------------------+
|label| features| scaledFeatures|
+-----+--------------------+--------------------+
| 0.0|(692,[127,128,129...|(692,[127,128,129...|
| 1.0|(692,[158,159,160...|(692,[158,159,160...|
| 1.0|(692,[124,125,126...|(692,[124,125,126...|
| 1.0|(692,[152,153,154...|(692,[152,153,154...|
| 1.0|(692,[151,152,153...|(692,[151,152,153...|
| 0.0|(692,[129,130,131...|(692,[129,130,131...|
| 1.0|(692,[158,159,160...|(692,[158,159,160...|
| 1.0|(692,[99,100,101,...|(692,[99,100,101,...|
| 0.0|(692,[154,155,156...|(692,[154,155,156...|
| 0.0|(692,[127,128,129...|(692,[127,128,129...|
| 1.0|(692,[154,155,156...|(692,[154,155,156...|
| 0.0|(692,[153,154,155...|(692,[153,154,155...|
| 0.0|(692,[151,152,153...|(692,[151,152,153...|
| 1.0|(692,[129,130,131...|(692,[129,130,131...|
| 0.0|(692,[154,155,156...|(692,[154,155,156...|
| 1.0|(692,[150,151,152...|(692,[150,151,152...|
| 0.0|(692,[124,125,126...|(692,[124,125,126...|
| 0.0|(692,[152,153,154...|(692,[152,153,154...|
| 1.0|(692,[97,98,99,12...|(692,[97,98,99,12...|
| 1.0|(692,[124,125,126...|(692,[124,125,126...|
+-----+--------------------+--------------------+
only showing top 20 rows
from __future__ import print_function
from pyspark.ml.feature import StandardScaler
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("StandardScalerExample")\
.getOrCreate() dataFrame = spark.createDataFrame([
(0, Vectors.dense([1.0, 0.1, -8.0]),),
(1, Vectors.dense([2.0, 1.0, -4.0]),),
(2, Vectors.dense([4.0, 10.0, 8.0]),)
], ["id", "features"]) # 计算均值方差等参数
scalerModel = scaler.fit(dataFrame) # 标准化
scaledData = scalerModel.transform(dataFrame)
scaledData.show() spark.stop()
结果:
+---+--------------+--------------------+
| id| features| scaledFeatures|
+---+--------------+--------------------+
| 0|[1.0,0.1,-8.0]|[0.65465367070797...|
| 1|[2.0,1.0,-4.0]|[1.30930734141595...|
| 2|[4.0,10.0,8.0]|[2.61861468283190...|
+---+--------------+--------------------+
5.添加多项式特征
from __future__ import print_function
from pyspark.ml.feature import PolynomialExpansion
from pyspark.ml.linalg import Vectors
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("PolynomialExpansionExample")\
.getOrCreate() df = spark.createDataFrame([
(Vectors.dense([2.0, 1.0]),),
(Vectors.dense([0.0, 0.0]),),
(Vectors.dense([3.0, -1.0]),)
], ["features"]) polyExpansion = PolynomialExpansion(degree=3, inputCol="features", outputCol="polyFeatures")
polyDF = polyExpansion.transform(df) polyDF.show(truncate=False) spark.stop()
结果:
+----------+------------------------------------------+
|features |polyFeatures |
+----------+------------------------------------------+
|[2.0,1.0] |[2.0,4.0,8.0,1.0,2.0,4.0,1.0,2.0,1.0] |
|[0.0,0.0] |[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0] |
|[3.0,-1.0]|[3.0,9.0,27.0,-1.0,-3.0,-9.0,1.0,3.0,-1.0]|
+----------+------------------------------------------+
对离散型处理
0.独热向量编码
from __future__ import print_function
from pyspark.ml.feature import OneHotEncoder, StringIndexer
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("OneHotEncoderExample")\
.getOrCreate() df = spark.createDataFrame([
(0, "a"),
(1, "b"),
(2, "c"),
(3, "a"),
(4, "a"),
(5, "c")
], ["id", "category"]) stringIndexer = StringIndexer(inputCol="category", outputCol="categoryIndex")#类别编码:出现频次越低,数值越大
model = stringIndexer.fit(df)
indexed = model.transform(df) encoder = OneHotEncoder(inputCol="categoryIndex", outputCol="categoryVec")
encoded = encoder.transform(indexed)
encoded.show() spark.stop()
结果:
+---+--------+-------------+-------------+
| id|category|categoryIndex| categoryVec|
+---+--------+-------------+-------------+
| 0| a| 0.0|(2,[0],[1.0])|
| 1| b| 2.0| (2,[],[])|
| 2| c| 1.0|(2,[1],[1.0])|
| 3| a| 0.0|(2,[0],[1.0])|
| 4| a| 0.0|(2,[0],[1.0])|
| 5| c| 1.0|(2,[1],[1.0])|
+---+--------+-------------+-------------+
对文本型处理
0.去停用词
from __future__ import print_function
from pyspark.ml.feature import StopWordsRemover
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("StopWordsRemoverExample")\
.getOrCreate() sentenceData = spark.createDataFrame([
(0, ["I", "saw", "the", "red", "balloon"]),
(1, ["Mary", "had", "a", "little", "lamb"])
], ["id", "raw"]) remover = StopWordsRemover(inputCol="raw", outputCol="filtered")
remover.transform(sentenceData).show(truncate=False)#truncate=False表示没有做截断,长的话可以试着截断观看结果 spark.stop()
结果:
+---+----------------------------+--------------------+
|id |raw |filtered |
+---+----------------------------+--------------------+
|0 |[I, saw, the, red, balloon] |[saw, red, balloon] |
|1 |[Mary, had, a, little, lamb]|[Mary, little, lamb]|
+---+----------------------------+--------------------+
1.Tokenizer
from __future__ import print_function
from pyspark.ml.feature import Tokenizer, RegexTokenizer
from pyspark.sql.functions import col, udf
from pyspark.sql.types import IntegerType
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("TokenizerExample")\
.getOrCreate() sentenceDataFrame = spark.createDataFrame([
(0, "Hi I heard about Spark"),
(1, "I wish Java could use case classes"),
(2, "Logistic,regression,models,are,neat")
], ["id", "sentence"]) tokenizer = Tokenizer(inputCol="sentence", outputCol="words") regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern="\\W")#干掉空格部分,保留非空格部分 countTokens = udf(lambda words: len(words), IntegerType()) tokenized = tokenizer.transform(sentenceDataFrame)
tokenized.select("sentence", "words")\
.withColumn("tokens", countTokens(col("words"))).show(truncate=False) regexTokenized = regexTokenizer.transform(sentenceDataFrame)
regexTokenized.select("sentence", "words") \
.withColumn("tokens", countTokens(col("words"))).show(truncate=False) spark.stop()
结果:
+-----------------------------------+------------------------------------------+------+
|sentence |words |tokens|
+-----------------------------------+------------------------------------------+------+
|Hi I heard about Spark |[hi, i, heard, about, spark] |5 |
|I wish Java could use case classes |[i, wish, java, could, use, case, classes]|7 |
|Logistic,regression,models,are,neat|[logistic,regression,models,are,neat] |1 |
+-----------------------------------+------------------------------------------+------+ +-----------------------------------+------------------------------------------+------+
|sentence |words |tokens|
+-----------------------------------+------------------------------------------+------+
|Hi I heard about Spark |[hi, i, heard, about, spark] |5 |
|I wish Java could use case classes |[i, wish, java, could, use, case, classes]|7 |
|Logistic,regression,models,are,neat|[logistic, regression, models, are, neat] |5 |
+-----------------------------------+------------------------------------------+------+
2.count_vectorizer
from __future__ import print_function
from pyspark.sql import SparkSession
from pyspark.ml.feature import CountVectorizer spark = SparkSession\
.builder\
.appName("CountVectorizerExample")\
.getOrCreate() df = spark.createDataFrame([
(0, "a b c".split(" ")),
(1, "a b b c a".split(" "))
], ["id", "words"]) cv = CountVectorizer(inputCol="words", outputCol="features", vocabSize=3, minDF=2.0) model = cv.fit(df) result = model.transform(df)
result.show(truncate=False) spark.stop()
结果:
+---+---------------+-------------------------+
|id |words |features |
+---+---------------+-------------------------+
|0 |[a, b, c] |(3,[0,1,2],[1.0,1.0,1.0])|
|1 |[a, b, b, c, a]|(3,[0,1,2],[2.0,2.0,1.0])|
+---+---------------+-------------------------+
3.TF-IDF权重
from __future__ import print_function
from pyspark.ml.feature import HashingTF, IDF, Tokenizer
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("TfIdfExample")\
.getOrCreate() sentenceData = spark.createDataFrame([
(0.0, "Hi I heard about Spark"),
(0.0, "I wish Java could use case classes"),
(1.0, "Logistic regression models are neat")
], ["label", "sentence"]) tokenizer = Tokenizer(inputCol="sentence", outputCol="words")#Tokenizer适合英文分词,spark中的中文分词效果最好的是NLPIR,jieba效果不是最好的
wordsData = tokenizer.transform(sentenceData) hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20)
featurizedData = hashingTF.transform(wordsData) idf = IDF(inputCol="rawFeatures", outputCol="features")
idfModel = idf.fit(featurizedData)
rescaledData = idfModel.transform(featurizedData) rescaledData.select("label", "features").show() spark.stop()
结果:
+-----+--------------------+
|label| features|
+-----+--------------------+
| 0.0|(20,[0,5,9,17],[0...|
| 0.0|(20,[2,7,9,13,15]...|
| 1.0|(20,[4,6,13,15,18...|
+-----+--------------------+
4.n-gram语言模型
from __future__ import print_function
from pyspark.ml.feature import NGram
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("NGramExample")\
.getOrCreate() #Hanmeimei loves LiLei
#LiLei loves Hanmeimei wordDataFrame = spark.createDataFrame([
(0, ["Hi", "I", "heard", "about", "Spark"]),
(1, ["I", "wish", "Java", "could", "use", "case", "classes"]),
(2, ["Logistic", "regression", "models", "are", "neat"])
], ["id", "words"]) ngram = NGram(n=2, inputCol="words", outputCol="ngrams") ngramDataFrame = ngram.transform(wordDataFrame)
ngramDataFrame.select("ngrams").show(truncate=False) spark.stop()
结果:
+------------------------------------------------------------------+
|ngrams |
+------------------------------------------------------------------+
|[Hi I, I heard, heard about, about Spark] |
|[I wish, wish Java, Java could, could use, use case, case classes]|
|[Logistic regression, regression models, models are, are neat] |
+------------------------------------------------------------------+
高级变换
0.SQL变换
from __future__ import print_function
from pyspark.ml.feature import SQLTransformer
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("SQLTransformerExample")\
.getOrCreate() df = spark.createDataFrame([
(0, 1.0, 3.0),
(2, 2.0, 5.0)
], ["id", "v1", "v2"]) sqlTrans = SQLTransformer(
statement="SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")
sqlTrans.transform(df).show() spark.stop()
结果:
+---+---+---+---+----+
| id| v1| v2| v3| v4|
+---+---+---+---+----+
| 0|1.0|3.0|4.0| 3.0|
| 2|2.0|5.0|7.0|10.0|
+---+---+---+---+----+
1.R公式变换
from __future__ import print_function
from pyspark.ml.feature import RFormula
from pyspark.sql import SparkSession spark = SparkSession\
.builder\
.appName("RFormulaExample")\
.getOrCreate() dataset = spark.createDataFrame(
[(7, "US", 18, 1.0),
(8, "CA", 12, 0.0),
(9, "NZ", 15, 0.0)],
["id", "country", "hour", "clicked"]) formula = RFormula(
formula="clicked ~ country + hour",
featuresCol="features",
labelCol="label") output = formula.fit(dataset).transform(dataset)
output.select("features", "label").show() spark.stop()
结果:
+--------------+-----+
| features|label|
+--------------+-----+
|[0.0,0.0,18.0]| 1.0|
|[1.0,0.0,12.0]| 0.0|
|[0.0,1.0,15.0]| 0.0|
+--------------+-----+
Spark机器学习基础-特征工程的更多相关文章
- Spark机器学习基础一
特征工程 对连续值处理 0.binarizer/二值化 from __future__ import print_function from pyspark.sql import SparkSessi ...
- 什么是机器学习的特征工程?【数据集特征抽取(字典,文本TF-Idf)、特征预处理(标准化,归一化)、特征降维(低方差,相关系数,PCA)】
2.特征工程 2.1 数据集 2.1.1 可用数据集 Kaggle网址:https://www.kaggle.com/datasets UCI数据集网址: http://archive.ics.uci ...
- Python数据科学手册-机器学习之特征工程
特征工程常见示例: 分类数据.文本.图像. 还有提高模型复杂度的 衍生特征 和 处理 缺失数据的填充 方法.这个过程被叫做向量化.把任意格式的数据 转换成具有良好特性的向量形式. 分类特征 比如房屋数 ...
- spark 机器学习基础 数据类型
spark的机器学习库,包含常见的学习算法和工具如分类.回归.聚类.协同过滤.降维等使用算法时都需要指定相应的数据集,下面为大家介绍常用的spark ml 数据类型.1.本地向量(Local Vect ...
- Spark机器学习基础三
监督学习 0.线性回归(加L1.L2正则化) from __future__ import print_function from pyspark.ml.regression import Linea ...
- Spark机器学习基础二
无监督学习 0.K-means from __future__ import print_function from pyspark.ml.clustering import KMeans #from ...
- Spark机器学习基础-监督学习
监督学习 0.线性回归(加L1.L2正则化) from __future__ import print_function from pyspark.ml.regression import Linea ...
- Spark机器学习基础-无监督学习
0.K-means from __future__ import print_function from pyspark.ml.clustering import KMeans#硬聚类 #from p ...
- 如何用Python做自动化特征工程
机器学习的模型训练越来越自动化,但特征工程还是一个漫长的手动过程,依赖于专业的领域知识,直觉和数据处理.而特征选取恰恰是机器学习重要的先期步骤,虽然不如模型训练那样能产生直接可用的结果.本文作者将使用 ...
随机推荐
- Python带参数的函数装饰器
# -*- coding: utf-8 -*- # author:baoshan # 带参数的函数装饰器 def say_hello(country): def wrapper(func): def ...
- shell编程系列3--命令替换
shell编程系列3--命令替换 命令替换 命令替换总结 方法1 `command` 方法2 $(command) 例子1: 获取系统的所有用户并输出 for循环能以空格.换行.tab键作为分隔符 [ ...
- 时序数据库技术体系 – 初识InfluxDB(原理)
原贴地址:http://hbasefly.com/2017/12/08/influxdb-1/?qytefg=c4ft23 在上篇文章<时序数据库体系技术 – 时序数据存储模型设计>中笔者 ...
- SQL语句中exists/not exists的用法分析
作者:Dsw 比如在Northwind数据库中有一个查询为 SELECT c.CustomerId,CompanyName FROM Customers c WHERE EXISTS( SELECT ...
- 转 mysql 存储过程初探
https://www.cnblogs.com/qmfsun/p/4838032.htmlMySQL命令执行sql文件的两种方法 https://www.cnblogs.com/mark-chan/p ...
- Apache调优(一)
(1).Apache和Tomcat的关系 Apache HTTPD Server与Apache Tomcat同属于Apache的开源项目.两个都可以单独作为web server使用,但是又都有各自的特 ...
- Python - Django - 模板语言之自定义过滤器
自定义过滤器的文件: 在 app01 下新建一个 templatetags 的文件夹,然后创建 myfilter.py 文件 这个 templatetags 名字是固定的,myfilter 是自己起的 ...
- SERVER_ADDR
$_SERVER["SERVER_ADDR"] 当前运行脚本的服务器的ip地址
- [转]Ubuntu安装ss客户端
链接:https://www.cnblogs.com/hoanfir/p/9308148.html 配置好后,使用 chromium-browser --proxy-server=socks5://1 ...
- C# .NET WINFORM MUTEX 互斥
static class Program 里的全局变量: static System.Threading.Mutex appMutex; Main 方法里的内容: string exeName = & ...