Bug: AnalysisException when loading a PipelineModel with Spark 3
Status: Resolved | Priority: Major | Resolution: Fixed
Created: 09/Nov/20 14:25
Affects Version/s: 3.0.1

I am upgrading my Spark version from 2.4.5 to 3.0.1 and I cannot load anymore the PipelineModel objects that use a "DecisionTreeClassifier" stage.

In my code I load several PipelineModel, all the PipelineModel with stages ["CountVectorizer_[uid]", "LinearSVC_[uid]"] are loading fine whereas the models with stages 
 ["CountVectorizer_[uid]","DecisionTreeClassifier_[uid]"] are throwing the following exception:
{noformat}
AnalysisException: cannot resolve '`rawCount`' given input columns: [gain, id, impurity, impurityStats, leftChild, prediction, rightChild, split];{noformat}
Here is the code I am using and the full stacktrace:
{code:python}
from pyspark.ml.pipeline import PipelineModel
PipelineModel.load("/path/to/model")
{code}
{noformat}
AnalysisException                         Traceback (most recent call last)
<command-1278858167154148> in <module>
----> 1 RalentModel = PipelineModel.load(MODELES_ATTRIBUTS + "RalentModel_DT")/databricks/spark/python/pyspark/ml/util.py in load(cls, path)
    368     def load(cls, path):
    369         """Reads an ML instance from the input path, a shortcut of `read().load(path)`."""
--> 370         return cls.read().load(path)
    371 
    372 /databricks/spark/python/pyspark/ml/pipeline.py in load(self, path)
    289         metadata = DefaultParamsReader.loadMetadata(path, self.sc)
    290         if 'language' not in metadata['paramMap'] or metadata['paramMap']['language'] != 'Python':
--> 291             return JavaMLReader(self.cls).load(path)
    292         else:
    293             uid, stages = PipelineSharedReadWrite.load(metadata, self.sc, path)/databricks/spark/python/pyspark/ml/util.py in load(self, path)
    318         if not isinstance(path, basestring):
    319             raise TypeError("path should be a basestring, got type %s" % type(path))
--> 320         java_obj = self._jread.load(path)
    321         if not hasattr(self._clazz, "_from_java"):
    322             raise NotImplementedError("This Java ML type cannot be loaded into Python currently: %r"/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1303         answer = self.gateway_client.send_command(command)
   1304         return_value = get_return_value(
-> 1305             answer, self.gateway_client, self.target_id, self.name)
   1306 
   1307         for temp_arg in temp_args:/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
    131                 # Hide where the exception came from that shows a non-Pythonic
    132                 # JVM exception message.
--> 133                 raise_from(converted)
    134             else:
    135                 raise/databricks/spark/python/pyspark/sql/utils.py in raise_from(e)
AnalysisException: cannot resolve '`rawCount`' given input columns: [gain, id, impurity, impurityStats, leftChild, prediction, rightChild, split];
{noformat}
These pipeline models where saved using Spark 2.4.3, I can load them fine using Spark 2.4.5.

I tried to investigate further and load each stage separately. Loading the CountVectorizerModel with
{code:python}
from pyspark.ml.feature import CountVectorizerModel
CountVectorizerModel.read().load("/path/to/model/stages/0_CountVectorizer_efce893314a9")
{code}
yields a CountVectorizerModel, but my code fails when trying to load the DecisionTreeClassificationModel:
{code:python}
DecisionTreeClassificationModel.read().load("/path/to/model/stages/1_DecisionTreeClassifier_4d2a76c565b0")
AnalysisException: cannot resolve '`rawCount`' given input columns: [gain, id, impurity, impurityStats, leftChild, prediction, rightChild, split];
{code}
And here is the content of the "data" of my Decision Tree Classifier:
{code:python}
spark.read.parquet("/path/to/model/stages/1_DecisionTreeClassifier_4d2a76c565b0/data").show()

+---+----------+--------------------+-------------+--------------------+---------+----------+----------------+
| id|prediction|            impurity|impurityStats|                gain|leftChild|rightChild|           split|
+---+----------+--------------------+-------------+--------------------+---------+----------+----------------+
|  0|       0.0|  0.3926234384295062| [90.0, 33.0]| 0.16011830963990054|        1|        16|[190, [0.5], -1]|
|  1|       0.0|  0.2672722508516028| [90.0, 17.0]| 0.11434106988303855|        2|        15|[512, [0.5], -1]|
|  2|       0.0|  0.1652892561983472|  [90.0, 9.0]| 0.06959547629404085|        3|        14|[583, [0.5], -1]|
|  3|       0.0| 0.09972299168975082|  [90.0, 5.0]|0.026984966852376356|        4|        11|[480, [0.5], -1]|
|  4|       0.0|0.043933846736523306|  [87.0, 2.0]|0.021717299239076976|        5|        10|[555, [1.5], -1]|
|  5|       0.0|0.022469008264462766|  [87.0, 1.0]|0.011105371900826402|        6|         7|[833, [0.5], -1]|
|  6|       0.0|                 0.0|  [86.0, 0.0]|                -1.0|       -1|        -1|    [-1, [], -1]|
|  7|       0.0|                 0.5|   [1.0, 1.0]|                 0.5|        8|         9|  [0, [0.5], -1]|
|  8|       0.0|                 0.0|   [1.0, 0.0]|                -1.0|       -1|        -1|    [-1, [], -1]|
|  9|       1.0|                 0.0|   [0.0, 1.0]|                -1.0|       -1|        -1|    [-1, [], -1]|
| 10|       1.0|                 0.0|   [0.0, 1.0]|                -1.0|       -1|        -1|    [-1, [], -1]|
| 11|       0.0|                 0.5|   [3.0, 3.0]|                 0.5|       12|        13| [14, [1.5], -1]|
| 12|       0.0|                 0.0|   [3.0, 0.0]|                -1.0|       -1|        -1|    [-1, [], -1]|
| 13|       1.0|                 0.0|   [0.0, 3.0]|                -1.0|       -1|        -1|    [-1, [], -1]|
| 14|       1.0|                 0.0|   [0.0, 4.0]|                -1.0|       -1|        -1|    [-1, [], -1]|
| 15|       1.0|                 0.0|   [0.0, 8.0]|                -1.0|       -1|        -1|    [-1, [], -1]|
| 16|       1.0|                 0.0|  [0.0, 16.0]|                -1.0|       -1|        -1|    [-1, [], -1]|
+---+----------+--------------------+-------------+--------------------+---------+----------+----------------+

{code}