zhengruifeng opened a new pull request, #40432:
URL: https://github.com/apache/spark/pull/40432
### What changes were proposed in this pull request?
Implement ml function `{array_to_vector, vector_to_array}`
### Why are the changes needed?
function parity
### Does this PR introduce _any_ user-facing change?
yes, new functions
### How was this patch tested?
added ut and manually check
```
(spark_dev) ➜ spark git:(connect_ml_functions) ✗ bin/pyspark --remote
"local[*]"
Python 3.9.16 (main, Mar 8 2023, 04:29:24)
Type 'copyright', 'credits' or 'license' for more information
IPython 8.11.0 -- An enhanced Interactive Python. Type '?' for help.
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use
setLogLevel(newLevel).
23/03/15 11:56:27 WARN NativeCodeLoader: Unable to load native-hadoop
library for your platform... using builtin-java classes where applicable
Welcome to
____ __
/ __/__ ___ _____/ /__
_\ \/ _ \/ _ `/ __/ '_/
/__ / .__/\_,_/_/ /_/\_\ version 3.5.0.dev0
/_/
Using Python version 3.9.16 (main, Mar 8 2023 04:29:24)
Client connected to the Spark Connect server at localhost
SparkSession available as 'spark'.
In [1]:
In [1]: query = """
...: SELECT * FROM VALUES
...: (1, 4, ARRAY(1.0, 2.0, 3.0)),
...: (1, 2, ARRAY(-1.0, -2.0, -3.0))
...: AS tab(a, b, c)
...: """
In [2]: cdf = spark.sql(query)
In [3]: from pyspark.sql.connect.ml import functions as CF
In [4]: cdf1 = cdf.select("a", CF.array_to_vector(cdf.c).alias("d"))
In [5]: cdf1.show()
+---+----------------+ (0 + 1)
/ 1]
| a| d|
+---+----------------+
| 1| [1.0,2.0,3.0]|
| 1|[-1.0,-2.0,-3.0]|
+---+----------------+
In [6]: cdf1.schema
Out[6]: StructType([StructField('a', IntegerType(), False), StructField('d',
VectorUDT(), True)])
In [7]: cdf1.select(CF.vector_to_array(cdf1.d))
Out[7]: DataFrame[UDF(d): array<double>]
In [8]: cdf1.select(CF.vector_to_array(cdf1.d)).show()
+------------------+
| UDF(d)|
+------------------+
| [1.0, 2.0, 3.0]|
|[-1.0, -2.0, -3.0]|
+------------------+
In [9]: cdf1.select(CF.vector_to_array(cdf1.d)).schema
Out[9]: StructType([StructField('UDF(d)', ArrayType(DoubleType(), False),
False)])
```
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