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Posted to issues@spark.apache.org by "Johannes Schaffrath (JIRA)" <ji...@apache.org> on 2019/06/03 22:44:00 UTC
[jira] [Created] (SPARK-27939) Defining a schema with VectorUDT
Johannes Schaffrath created SPARK-27939:
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Summary: Defining a schema with VectorUDT
Key: SPARK-27939
URL: https://issues.apache.org/jira/browse/SPARK-27939
Project: Spark
Issue Type: Bug
Components: ML, PySpark
Affects Versions: 2.4.0
Reporter: Johannes Schaffrath
When I try to define a dataframe schema which has a VectorUDT field, I run into an error when the VectorUDT field is not the last element of the StructType list.
The following example causes the error below:
{code:java}
// from pyspark.sql import functions as F
from pyspark.sql import types as T
from pyspark.sql import Row
from pyspark.ml.linalg import VectorUDT, SparseVector
#VectorUDT should be the last structfield
train_schema = T.StructType([
T.StructField('features', VectorUDT()),
T.StructField('SALESCLOSEPRICE', T.IntegerType())
])
train_df = spark.createDataFrame(
[Row(features=SparseVector(135, {0: 139900.0, 1: 139900.0, 2: 980.0, 3: 10.0, 5: 980.0, 6: 1858.0, 7: 1858.0, 8: 980.0, 9: 1950.0, 10: 1.28, 11: 1.0, 12: 1.0, 15: 2.0, 16: 3.0, 20: 2017.0, 21: 7.0, 22: 28.0, 23: 15.0, 24: 196.0, 25: 25.0, 26: -1.0, 27: 4.03, 28: 3.96, 29: 3.88, 30: 3.9, 31: 3.91, 32: 9.8, 33: 22.4, 34: 67.8, 35: 49.8, 36: 11.9, 37: 2.7, 38: 0.2926, 39: 142.7551, 40: 980.0, 41: 0.0133, 42: 1.5, 43: 1.0, 51: -1.0, 52: -1.0, 53: -1.0, 54: -1.0, 55: -1.0, 56: -1.0, 57: -1.0, 62: 1.0, 68: 1.0, 77: 1.0, 81: 1.0, 89: 1.0, 95: 1.0, 96: 1.0, 101: 1.0, 103: 1.0, 108: 1.0, 114: 1.0, 115: 1.0, 123: 1.0, 133: 1.0}), SALESCLOSEPRICE=143000),
Row(features=SparseVector(135, {0: 210000.0, 1: 210000.0, 2: 1144.0, 3: 4.0, 5: 1268.0, 6: 1640.0, 7: 1640.0, 8: 2228.0, 9: 1971.0, 10: 0.32, 11: 1.0, 14: 2.0, 15: 3.0, 16: 4.0, 17: 960.0, 20: 2017.0, 21: 10.0, 22: 41.0, 23: 9.0, 24: 282.0, 25: 2.0, 26: -1.0, 27: 3.91, 28: 3.85, 29: 3.83, 30: 3.83, 31: 3.78, 32: 32.2, 33: 49.0, 34: 18.8, 35: 14.0, 36: 35.8, 37: 14.6, 38: 0.4392, 39: 94.2549, 40: 2228.0, 41: 0.0078, 42: 1.3333, 43: -1.0, 44: -1.0, 45: -1.0, 46: -1.0, 47: -1.0, 48: -1.0, 49: -1.0, 50: -1.0, 52: 1.0, 55: -1.0, 56: -1.0, 57: -1.0, 62: 1.0, 68: 1.0, 77: 1.0, 79: 1.0, 89: 1.0, 92: 1.0, 96: 1.0, 101: 1.0, 103: 1.0, 108: 1.0, 114: 1.0, 115: 1.0, 124: 1.0, 133: 1.0}), SALESCLOSEPRICE=190000),
Row(features=SparseVector(135, {0: 225000.0, 1: 225000.0, 2: 1102.0, 3: 28.0, 5: 1102.0, 6: 2390.0, 7: 2390.0, 8: 1102.0, 9: 1949.0, 10: 0.822, 11: 1.0, 15: 1.0, 16: 2.0, 20: 2017.0, 21: 6.0, 22: 26.0, 23: 26.0, 24: 177.0, 25: 25.0, 26: -1.0, 27: 3.88, 28: 3.9, 29: 3.91, 30: 3.89, 31: 3.94, 32: 9.8, 33: 22.4, 34: 67.8, 35: 61.7, 36: 2.7, 38: 0.4706, 39: 204.1742, 40: 1102.0, 41: 0.0106, 42: 2.0, 49: 1.0, 51: -1.0, 52: -1.0, 53: -1.0, 54: -1.0, 57: 1.0, 62: 1.0, 68: 1.0, 70: 1.0, 79: 1.0, 89: 1.0, 92: 1.0, 96: 1.0, 100: 1.0, 103: 1.0, 108: 1.0, 110: 1.0, 115: 1.0, 123: 1.0, 131: 1.0, 132: 1.0}), SALESCLOSEPRICE=225000)
], schema=train_schema)
train_df.printSchema()
train_df.show()
{code}
Error message:
{code:java}
// Fail to execute line 17: ], schema=train_schema) Traceback (most recent call last): File "/tmp/zeppelin_pyspark-3793375738105660281.py", line 375, in <module> exec(code, _zcUserQueryNameSpace) File "<stdin>", line 17, in <module> File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/session.py", line 748, in createDataFrame rdd, schema = self._createFromLocal(map(prepare, data), schema) File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/session.py", line 429, in _createFromLocal data = [schema.toInternal(row) for row in data] File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/session.py", line 429, in <listcomp> data = [schema.toInternal(row) for row in data] File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/types.py", line 604, in toInternal for f, v, c in zip(self.fields, obj, self._needConversion)) File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/types.py", line 604, in <genexpr> for f, v, c in zip(self.fields, obj, self._needConversion)) File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/types.py", line 442, in toInternal return self.dataType.toInternal(obj) File "/opt/spark/python/lib/pyspark.zip/pyspark/sql/types.py", line 685, in toInternal return self._cachedSqlType().toInternal(self.serialize(obj)) File "/opt/spark/python/lib/pyspark.zip/pyspark/ml/linalg/__init__.py", line 167, in serialize raise TypeError("cannot serialize %r of type %r" % (obj, type(obj))) TypeError: cannot serialize 143000 of type <class 'int'>{code}
I don't get an error when I modify the order of the schema:
{code:java}
// from pyspark.sql import functions as F
from pyspark.sql import types as T
from pyspark.sql import Row
from pyspark.ml.linalg import VectorUDT, SparseVector
#VectorUDT should be the last structfield
train_schema = T.StructType([
T.StructField('SALESCLOSEPRICE', T.IntegerType()),
T.StructField('features', VectorUDT())
])
train_df = spark.createDataFrame(
[Row(features=SparseVector(135, {0: 139900.0, 1: 139900.0, 2: 980.0, 3: 10.0, 5: 980.0, 6: 1858.0, 7: 1858.0, 8: 980.0, 9: 1950.0, 10: 1.28, 11: 1.0, 12: 1.0, 15: 2.0, 16: 3.0, 20: 2017.0, 21: 7.0, 22: 28.0, 23: 15.0, 24: 196.0, 25: 25.0, 26: -1.0, 27: 4.03, 28: 3.96, 29: 3.88, 30: 3.9, 31: 3.91, 32: 9.8, 33: 22.4, 34: 67.8, 35: 49.8, 36: 11.9, 37: 2.7, 38: 0.2926, 39: 142.7551, 40: 980.0, 41: 0.0133, 42: 1.5, 43: 1.0, 51: -1.0, 52: -1.0, 53: -1.0, 54: -1.0, 55: -1.0, 56: -1.0, 57: -1.0, 62: 1.0, 68: 1.0, 77: 1.0, 81: 1.0, 89: 1.0, 95: 1.0, 96: 1.0, 101: 1.0, 103: 1.0, 108: 1.0, 114: 1.0, 115: 1.0, 123: 1.0, 133: 1.0}), SALESCLOSEPRICE=143000),
Row(features=SparseVector(135, {0: 210000.0, 1: 210000.0, 2: 1144.0, 3: 4.0, 5: 1268.0, 6: 1640.0, 7: 1640.0, 8: 2228.0, 9: 1971.0, 10: 0.32, 11: 1.0, 14: 2.0, 15: 3.0, 16: 4.0, 17: 960.0, 20: 2017.0, 21: 10.0, 22: 41.0, 23: 9.0, 24: 282.0, 25: 2.0, 26: -1.0, 27: 3.91, 28: 3.85, 29: 3.83, 30: 3.83, 31: 3.78, 32: 32.2, 33: 49.0, 34: 18.8, 35: 14.0, 36: 35.8, 37: 14.6, 38: 0.4392, 39: 94.2549, 40: 2228.0, 41: 0.0078, 42: 1.3333, 43: -1.0, 44: -1.0, 45: -1.0, 46: -1.0, 47: -1.0, 48: -1.0, 49: -1.0, 50: -1.0, 52: 1.0, 55: -1.0, 56: -1.0, 57: -1.0, 62: 1.0, 68: 1.0, 77: 1.0, 79: 1.0, 89: 1.0, 92: 1.0, 96: 1.0, 101: 1.0, 103: 1.0, 108: 1.0, 114: 1.0, 115: 1.0, 124: 1.0, 133: 1.0}), SALESCLOSEPRICE=190000),
Row(features=SparseVector(135, {0: 225000.0, 1: 225000.0, 2: 1102.0, 3: 28.0, 5: 1102.0, 6: 2390.0, 7: 2390.0, 8: 1102.0, 9: 1949.0, 10: 0.822, 11: 1.0, 15: 1.0, 16: 2.0, 20: 2017.0, 21: 6.0, 22: 26.0, 23: 26.0, 24: 177.0, 25: 25.0, 26: -1.0, 27: 3.88, 28: 3.9, 29: 3.91, 30: 3.89, 31: 3.94, 32: 9.8, 33: 22.4, 34: 67.8, 35: 61.7, 36: 2.7, 38: 0.4706, 39: 204.1742, 40: 1102.0, 41: 0.0106, 42: 2.0, 49: 1.0, 51: -1.0, 52: -1.0, 53: -1.0, 54: -1.0, 57: 1.0, 62: 1.0, 68: 1.0, 70: 1.0, 79: 1.0, 89: 1.0, 92: 1.0, 96: 1.0, 100: 1.0, 103: 1.0, 108: 1.0, 110: 1.0, 115: 1.0, 123: 1.0, 131: 1.0, 132: 1.0}), SALESCLOSEPRICE=225000)
], schema=train_schema)
train_df.printSchema()
train_df.show()
{code}
Output:
{code:java}
// root
|-- SALESCLOSEPRICE: integer (nullable = true)
|-- features: vector (nullable = true)
+---------------+--------------------+
|SALESCLOSEPRICE| features|
+---------------+--------------------+
| 143000|(135,[0,1,2,3,5,6...|
| 190000|(135,[0,1,2,3,5,6...|
| 225000|(135,[0,1,2,3,5,6...|
+---------------+--------------------+
{code}
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