pyrox.dev
1from pyspark.sql import Row, SparkSession
2from pyspark.sql import functions as F
3from pyspark.sql.functions import udf
4from pyspark.sql.types import *
5from pyspark.sql.functions import explode
6
7def explode_col(weight):
8 return int(weight//10) * [10.0] + ([] if weight%10==0 else [weight%10])
9
10spark = SparkSession.builder.getOrCreate()
11
12dataSchema = [
13 StructField("feature_1", FloatType()),
14 StructField("feature_2", FloatType()),
15 StructField("bias_weight", FloatType())
16]
17
18data = [
19 Row(0.1, 0.2, 10.32),
20 Row(0.32, 1.43, 12.8),
21 Row(1.28, 1.12, 0.23)
22]
23
24df = spark.createDataFrame(spark.sparkContext.parallelize(data), StructType(dataSchema))
25
26normalizing_constant = 100
27sum_bias_weight = df.select(F.sum('bias_weight')).collect()[0][0]
28normalizing_factor = normalizing_constant / sum_bias_weight
29df = df.withColumn('normalized_bias_weight', df.bias_weight * normalizing_factor)
30df = df.drop('bias_weight')
31df = df.withColumnRenamed('normalized_bias_weight', 'bias_weight')
32
33my_udf = udf(lambda x: explode_col(x), ArrayType(FloatType()))
34df1 = df.withColumn('explode_val', my_udf(df.bias_weight))
35df1 = df1.withColumn("explode_val_1", explode(df1.explode_val)).drop("explode_val")
36df1 = df1.drop('bias_weight').withColumnRenamed('explode_val_1', 'bias_weight')
37
38df1.show()
39
40assert(df1.count() == 12)