forked from mirrors/nixpkgs
41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
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from pyspark.sql import Row, SparkSession
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from pyspark.sql import functions as F
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from pyspark.sql.functions import udf
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from pyspark.sql.types import *
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from pyspark.sql.functions import explode
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def explode_col(weight):
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return int(weight//10) * [10.0] + ([] if weight%10==0 else [weight%10])
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spark = SparkSession.builder.getOrCreate()
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dataSchema = [
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StructField("feature_1", FloatType()),
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StructField("feature_2", FloatType()),
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StructField("bias_weight", FloatType())
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]
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data = [
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Row(0.1, 0.2, 10.32),
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Row(0.32, 1.43, 12.8),
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Row(1.28, 1.12, 0.23)
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]
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df = spark.createDataFrame(spark.sparkContext.parallelize(data), StructType(dataSchema))
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normalizing_constant = 100
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sum_bias_weight = df.select(F.sum('bias_weight')).collect()[0][0]
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normalizing_factor = normalizing_constant / sum_bias_weight
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df = df.withColumn('normalized_bias_weight', df.bias_weight * normalizing_factor)
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df = df.drop('bias_weight')
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df = df.withColumnRenamed('normalized_bias_weight', 'bias_weight')
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my_udf = udf(lambda x: explode_col(x), ArrayType(FloatType()))
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df1 = df.withColumn('explode_val', my_udf(df.bias_weight))
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df1 = df1.withColumn("explode_val_1", explode(df1.explode_val)).drop("explode_val")
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df1 = df1.drop('bias_weight').withColumnRenamed('explode_val_1', 'bias_weight')
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df1.show()
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assert(df1.count() == 12)
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