我有一个PySpark DataFrame df1,看起来像:
Customer1 Customer2 v_cust1 v_cust2
1 2 0.9 0.1
1 3 0.3 0.4
1 4 0.2 0.9
2 1 0.8 0.8
我想获取两个数据帧的余弦相似度。并有类似的东西
Customer1 Customer2 v_cust1 v_cust2 cosine_sim
1 2 0.9 0.1 0.1
1 3 0.3 0.4 0.9
1 4 0.2 0.9 0.15
2 1 0.8 0.8 1
我有一个python函数,可以接收像这样的数字/数字数组:
def cos_sim(a, b):
return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
如何使用udf在数据框中创建cosine_sim列?我可以将几列而不是一列传递给udf cosine_sim函数吗?
如果您想使用pandas_udf,效率会更高。
[它在矢量化操作中比spark udfs表现更好:Introducing Pandas UDF for PySpark
from pyspark.sql.functions import PandasUDFType, pandas_udf
import pyspark.sql.functions as F
a, b = "v_cust1", "v_cust2"
@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def cos_sim(df):
float(np.dot(df[a], df[b]) / (np.linalg.norm(df[a]) * np.linalg.norm(df[b])))
# Assuming that you want to groupby Customer1 and Customer2 for arrays
df2 = df.groupby(["Customer1", "Customer2"]).apply(cos_sim)
# But if you want to send entire columns then make a column with the same
# value in all rows and group by it
# e.g.
df3 = df.withColumn("group", F.lit("group_a")).groupby("group").apply(cos_sim)