我对使用 PySpark 还很陌生。我的 PySpark 数据框中有一列 SparseVectors。
rescaledData.select('features').show(5,False)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|features |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|(262144,[43953,62425,66522,148962,174441,249180],[3.9219733362813143,3.9219733362813143,1.213923135179104,3.9219733362813143,3.9219733362813143,0.5720692490067093])|
|(262144,[57925,66522,90939,249180],[3.5165082281731497,1.213923135179104,3.9219733362813143,0.5720692490067093]) |
|(262144,[23366,45531,73408,211290],[2.6692103677859462,3.005682604407159,3.5165082281731497,3.228826155721369]) |
|(262144,[30913,81939,99546,137643,162885,249180],[3.228826155721369,3.9219733362813143,3.005682604407159,3.005682604407159,3.228826155721369,1.1441384980134186]) |
|(262144,[108134,152329,249180],[3.9219733362813143,2.6692103677859462,2.8603462450335466]) |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------+
我需要将上述数据帧转换为矩阵,其中矩阵中的每一行都对应于数据帧中该行中的一个 SparseVector。
例如,
+-----------------+
|features |
+-----------------+
|(7,[1,2],[45,63])|
|(7,[3,5],[85,69])|
|(7,[1,2],[89,56])|
+-----------------+
必须转换为
[[0,45,63,0,0,0,0]
[0,0,0,85,0,69,0]
[0,89,56,0,0,0,0]]
我已经阅读了下面的链接,它表明有一个函数 toArray()
完全符合我的要求。
https://mingchen0919.github.io/learning-apache-spark/pyspark-vectors.html
但是,我在使用它时遇到了问题。
vector_udf = udf(lambda vector: vector.toArray())
rescaledData.withColumn('features_', vector_udf(rescaledData.features)).first()
我需要它将每一行转换为数组,然后将 PySpark 数据帧转换为矩阵。
最佳答案
toArray() 将返回 numpy 数组。我们可以转换为列表,然后收集数据框。
from pyspark.sql.types import *
vector_udf = udf(lambda vector: vector.toArray().tolist(),ArrayType(DoubleType()))
df.show() ## my sample dataframe
+-------------------+
| features|
+-------------------+
|(4,[1,3],[3.0,4.0])|
|(4,[1,3],[3.0,4.0])|
|(4,[1,3],[3.0,4.0])|
+-------------------+
colvalues = df.select(vector_udf('features').alias('features')).collect()
list(map(lambda x:x.features,colvalues))
[[0.0, 3.0, 0.0, 4.0], [0.0, 3.0, 0.0, 4.0], [0.0, 3.0, 0.0, 4.0]]
关于python - PySpark - 稀疏向量列到矩阵,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/47671546/