dataframe
create
创建dataframe
val training = ss.createDataFrame(Seq(
(1.0, Vectors.dense(0.0, 1.1, 0.1)),
(0.0, Vectors.dense(2.0, 1.0, -1.0)),
(0.0, Vectors.dense(2.0, 1.3, 1.0)),
(1.0, Vectors.dense(0.0, 1.2, -0.5))
)).toDF("label", "features")
read
读csv，json格式存储的文件
sc = spark.sparkContext
df = spark.read.json("examples/src/main/resources/people.json")
df = spark.read.option("delimiter", "\t").option("header", "true").csv(path) [读取以\t分隔的文件到dateframe]
df.write.option("header",true)
.csv("/tmp/spark_output/datacsv")
people = spark.read.parquet("...")
column get
列相关操作
features get: ageCol = people.age
添加现有列:
df_with_x5 = df_with_x4.withColumn("x5", exp("x3"))
df_with_x7 = df_with_x6.withColumn("x7", Rand())
func.col("id")
row: row = Row(name="Alice", age=11)
'wrong_key' in Person
define function [自定义函数]
create function:
from pyspark.sql.functions import udf
@udf
def to_upper(s):
if s is not None:
return s.upper()
df_new2 = df_new.withColumn("topnum", get_front3_value("Country"))
传入固定值:
使用lit
from pyspark.sql.functions import lit
show the content of Data Frame [展示Data Frame的内容]
df.show()
自定义函数并取列
df.select(slen("name").alias("slen(name)"), to_upper("name"), add_one("age"))
去重
dataFrame.dropDuplicates("id","label")
dataFrame.distinct()
vector
向量操作
dense vector: a = Vectors.dense([1.0, 2.0])
sparse vector: SparseVector(2, [0, 1], [2., 1.])
vector: toArray()
mLlib
import
from pyspark.ml.clustering import Kmeans
initial parameter 初始化模型超参数
kmeans = KMeans(k=100, maxIter=200)
train model 模型训练
model0 = kmeans.fit(input0.limit(200000))
kmeans.clusterCenters()
data split
训练数据和测试数据切分
(trainingData, testData) = data.randomSplit([0.7, 0.3])
Feature scaled
特征级别化(可分桶，标准化)
max min scaler:
featureScaler = MinMaxScaler(inputCol="features", outputCol="scaledFeatures").fit(data) [max min scale]
scalerModel = scaler.fit(dealfeature)
bucket scaler:
from pyspark.ml.feature import Bucketizer
splits = [float("-inf"), 0,100, 200, 300, 400, 500, 600, 700,
800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, float("inf")]
dealinput = dealinput.withColumn("ctr", dealinput["ctr"].cast("float")).withColumn("ctr", dealinput["ctrv3"].cast("float"))
bucketizer = Bucketizer(splits=splits, inputCol="ctr", outputCol="bucketedctr")
dealout = bucketizer.transform(dealinput)
Data Type view and Data Type transformer
数据类型查看: df.dtypes
数据类型转换: dealoutput = dealinput.select(dealinput["Clicks"].cast("float")
train test split
(deal_train, deal_test) = dealfeature.randomSplit([0.9,0.1])
data filter
数据过滤[条件筛选]
outanaly = predres.filter(predres.prediction ==1).select("InClickBinary", "prediction")
metrics
指标评估
from pyspark.mllib.evaluation import MulticlassMetrics
predictionAndLabels = predres.rdd.map(lambda x:(float(x.prediction), float(x.InClickBinary)))
metrics = MulticlassMetrics(predictionAndLabels)
print("Area under accauracy = %s" % metrics.precision(1.0))
print("Area under recall = %s" % metrics.recall(1.0))
column assemble to vector
每列特征集成到一个dense vector
#get the vector feature
assembler = VectorAssembler(
inputCols = ["Clicks", "BinaryFeature","isMarket", "ctrv2", "ctrv3", "Feature1", "Feature2", "Feature3", "Feature4", "Feature5", "Feature6", "Feature7", "Feature8"],
outputCol = "features")
dealfeature = assembler.transform(dealoutput)
(deal_train, deal_test) = dealfeature.randomSplit([0.9,0.1])
column to sparse vector
N列特征到sparse vector
featureids = [i for i in range(len(featurekeys))]
featuredict = dict(zip(featurekeys, featureids))
speckeys = ["ctrv2", "ctrv3", "isMarket", "BinaryFeature"]
from pyspark.sql.functions import struct
def get_sparse_vector(x):
valuedict = {}
for index, item in enumerate(speckeys):
valuedict[featuredict[item]] = x[index]
for index in range(8):
nindex = 4 + index
valuedict[featuredict["prediction%s_%s"%(index, x[nindex])]] = 1.0
return Vectors.sparse(len(featuredict), valuedict)
sparsecreate = udf(get_sparse_vector, VectorUDT())
dealfeature = dealout.withColumn("features", sparsecreate(struct("A", "B", "C", "D", "E", "F", "G", "H","I", "J", "K", "L", "M")))
model train
模型训练
以LogisticRegression为例:
lr = LogisticRegression(featuresCol="features", labelCol="BinaryFeature", maxIter = 200)
lrmodel = lr.fit(deal_train)
predreslr = lrmodel.transform(deal_test)
