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Predictive analytics

Joule provides a PMML predictor processor to perform streaming predictions / scoring. The implementation leverages the JPMML open source library developed by Villu Ruusmann.  
Example
pmml predictor:
  name: irisScorer
  model: ./models/iris_rf.pmml
  response field: flowerPrediction
  
  audit configuration:
    target schema: ml_audit
    queue capacity: 5000
    flush frequency: 300
Attributes
Attribute
Description
Data Type
Required
name
name of the counter.
String
model
model filename that specifies  path and model filename.
String
model store
Reference data logical store name where the model resides. See Reference Data documentation for further information on configuration.
String
features field
Field where all the required features exist for the model prediction function. This is an optional field where engineered map of features are placed and applied to the model evaluation. See the Feature Engineering documentation for further details.
String
response field
Name of field where the result of the prediction function will be assigned too. 
String
unpack results
Unpack the algorithm response variables directly into the StreamEvent object. Otherwise the process will add the complete response object in to the response field.
Boolean
Default: false
audit configuration
Every prediction can be audited along with the features used.
See Audit Configuration section 
Audit Configuration
This optional configuration provide the ability to audit predications to enable model retraining, feature and prediction drift management, model observability, and any local business governance requirements. 
The configuration will dynamically create an in-memory database table, using the process name as the target table, and rest endpoints to enable direct access and export functions.
Attributes
Attribute
Description
Data Type
Required
target schema
name of the target schema where the table will be created
String
queue capacity
Number of prediction results to queue before flushing to target table
Integer
Default: 1000
flush frequency
Frequency the queue will be flushed to table as seconds
Long
Default: 60 Seconds
Supported Algorithms
Contact Fractalworks for details

Attribute	Description	Data Type
name	name of the counter.	String
model	model filename that specifies path and model filename.	String
model store	Reference data logical store name where the model resides. See Reference Data documentation for further information on configuration.	String
features field	Field where all the required features exist for the model prediction function. This is an optional field where engineered map of features are placed and applied to the model evaluation. See the Feature Engineering documentation for further details.	String
response field	Name of field where the result of the prediction function will be assigned too.	String
unpack results	Unpack the algorithm response variables directly into the StreamEvent object. Otherwise the process will add the complete response object in to the response field.	Boolean Default: false
audit configuration	Every prediction can be audited along with the features used.	See Audit Configuration section

Attribute	Description	Data Type
target schema	name of the target schema where the table will be created	String
queue capacity	Number of prediction results to queue before flushing to target table	Integer Default: 1000
flush frequency	Frequency the queue will be flushed to table as seconds	Long Default: 60 Seconds

Sliding

Geospatial

Last modified 2mo ago