colin fang created SPARK-27759:
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Summary: Do not auto cast array<double> to np.array in vectorized
udf
Key: SPARK-27759
URL: https://issues.apache.org/jira/browse/SPARK-27759
Project: Spark
Issue Type: Improvement
Components: PySpark, SQL
Affects Versions: 2.4.3
Reporter: colin fang
{code:java}
pd_df = pd.DataFrame(\{'x': np.random.rand(11, 3, 5).tolist()})
df = spark.createDataFrame(pd_df).cache()
{code}
Each element in x is a list of list, as expected.
{code:java}
df.toPandas()['x']
# 0 [[0.08669612955959993, 0.32624430522634495, 0....
# 1 [[0.29838166086156914, 0.008550172904516762, 0...
# 2 [[0.641304534802928, 0.2392047548381877, 0.555...
{code}
{code:java}
def my_udf(x):
# Hack to see what's inside a udf
raise Exception(x.values.shape, x.values[0].shape, x.values[0][0].shape,
np.stack(x.values).shape)
return pd.Series(x.values)
my_udf = pandas_udf(dot_product, returnType=DoubleType())
df.withColumn('y', my_udf('x')).show()
Exception: ((2,), (3,), (5,), (2, 3))
{code}
A batch (2) of `x` is converted to pd.Series, however, each element in the
pd.Series is now a numpy 1d array of numpy 1d array. It is inconvenient to work
with nested 1d numpy array in practice in a udf.
For example, I need a ndarray of shape (2, 3, 5) in udf, so that I can make use
of the numpy vectorized operations. If I was given a list of list intact, I can
simply do `np.stack(x.values)`. However, it doesn't work here as what I
received is a nested numpy 1d array.
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