Error: parameter numItemBlocks given invalid value 0

[garybushey]

Describe the bug When running the following lines, I get the error: parameter numItemBlocks given invalid value 0.

access_anomaly = AccessAnomaly( tenantCol='tenant_id', userCol='user', resCol='res', likelihoodCol='likelihood', maxIter=100 ) access_anomaly_model = access_anomaly.fit(ptraining)

ptraining was set to a dataset created from sample data

To Reproduce Running the code in the AnomalousRATraining notebook that is part of the MS Sentinel Bring Your Own ML sample

Info (please complete the following information):

• SynapseML Version: unknown
• Spark Version: 3.1.2
• Spark Platform : Azure Databricks

** Stacktrace**

Please post the stacktrace here if applicable---------------------------------------------------------------------------

IllegalArgumentException                  Traceback (most recent call last)

<command-797794001272897> in <module>

      7                     maxIter=100

      8                     )

----> 9 access_anomaly_model = access_anomaly.fit(ptraining)

 

/databricks/python_shell/dbruntime/MLWorkloadsInstrumentation/_pyspark.py in patched_method(self, *args, **kwargs)

     28             call_succeeded = False

     29             try:

---> 30                 result = original_method(self, *args, **kwargs)

     31                 call_succeeded = True

     32                 return result

 

/databricks/spark/python/pyspark/ml/base.py in fit(self, dataset, params)

    159                 return self.copy(params)._fit(dataset)

    160             else:

--> 161                 return self._fit(dataset)

    162         else:

    163             raise ValueError("Params must be either a param map or a list/tuple of param maps, "

 

/local_disk0/spark-e510fb9f-d7e4-4c84-95a6-a0d6ff9719e5/userFiles-f183d53d-3671-46c7-aaca-03a6002001e0/addedFile2895442502225420743synapseml_core_2_12_0_9_5-06d81.jar/synapse/ml/cyber/anomaly/collaborative_filtering.py in _fit(self, df)

    837         enriched_df = self._enrich_and_normalize(indexed_df).cache()

    838

--> 839         user_res_feature_vector_mapping_df = self.create_spark_model_vectors_df(enriched_df)

    840         user_res_norm_cf_df_model = ModelNormalizeTransformer(

    841             enriched_df, self.rank_param

 

/local_disk0/spark-e510fb9f-d7e4-4c84-95a6-a0d6ff9719e5/userFiles-f183d53d-3671-46c7-aaca-03a6002001e0/addedFile2895442502225420743synapseml_core_2_12_0_9_5-06d81.jar/synapse/ml/cyber/anomaly/collaborative_filtering.py in create_spark_model_vectors_df(self, df)

    795                 ) if res_mapping_df is not None else curr_res_mapping_df

    796         else:

--> 797             user_mapping_df, res_mapping_df = self._train_cf(als, df)

    798

    799         assert user_mapping_df is not None and res_mapping_df is not None

 

/local_disk0/spark-e510fb9f-d7e4-4c84-95a6-a0d6ff9719e5/userFiles-f183d53d-3671-46c7-aaca-03a6002001e0/addedFile2895442502225420743synapseml_core_2_12_0_9_5-06d81.jar/synapse/ml/cyber/anomaly/collaborative_filtering.py in _train_cf(self, als, df)

    724         res_vec_col = self.res_vec_col

    725

--> 726         spark_model = als.fit(df)

    727

    728         user_mapping_df = spark_model.userFactors.select(

 

/databricks/python_shell/dbruntime/MLWorkloadsInstrumentation/_pyspark.py in patched_method(self, *args, **kwargs)

     28             call_succeeded = False

     29             try:

---> 30                 result = original_method(self, *args, **kwargs)

     31                 call_succeeded = True

     32                 return result

 

/databricks/spark/python/pyspark/ml/base.py in fit(self, dataset, params)

    159                 return self.copy(params)._fit(dataset)

    160             else:

--> 161                 return self._fit(dataset)

    162         else:

    163             raise ValueError("Params must be either a param map or a list/tuple of param maps, "

 

/databricks/spark/python/pyspark/ml/wrapper.py in _fit(self, dataset)

    333

    334     def _fit(self, dataset):

--> 335         java_model = self._fit_java(dataset)

    336         model = self._create_model(java_model)

    337         return self._copyValues(model)

 

/databricks/spark/python/pyspark/ml/wrapper.py in _fit_java(self, dataset)

    329             fitted Java model

    330         """

--> 331         self._transfer_params_to_java()

    332         return self._java_obj.fit(dataset._jdf)

    333

 

/databricks/spark/python/pyspark/ml/wrapper.py in _transfer_params_to_java(self)

    141         for param in self.params:

    142             if self.isSet(param):

--> 143                 pair = self._make_java_param_pair(param, self._paramMap[param])

    144                 self._java_obj.set(pair)

    145             if self.hasDefault(param):

 

/databricks/spark/python/pyspark/ml/wrapper.py in _make_java_param_pair(self, param, value)

    132         java_param = self._java_obj.getParam(param.name)

    133         java_value = _py2java(sc, value)

--> 134         return java_param.w(java_value)

    135

    136     def _transfer_params_to_java(self):

 

/databricks/spark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py in __call__(self, *args)

   1302

   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

 

/databricks/spark/python/pyspark/sql/utils.py in deco(*a, **kw)

    121                 # Hide where the exception came from that shows a non-Pythonic

    122                 # JVM exception message.

--> 123                 raise converted from None

    124             else:

    125                 raise

 

IllegalArgumentException: ALS_93abf3745fbf parameter numItemBlocks given invalid value 0.

AB#1884828