Connects directly to the Python Bridge, to send text or sections of the interpretation graph to be process by ML algorithms in Python

Operates On:  Lexical Items with TOKEN and TEXT_BLOCK.

Stage is a Recognizer for Saga Solution, and can also be used as part of a manual pipeline or a base pipeline


The difference between this and the Python Model Stage is that this stage does requires to be associated to a tag

Settings

  • normalizeTags ( type=boolean | default=false | optional ) - Tags (flagged as SEMANTIC_TAG) are normalized.
    • Whenever a tag has field (metadata) named "display" it value is used to normalize the tag, if the tag does not have "display" value then the name of tag is used, e.g. {costumer} = costumer.
  • includeContext ( type=boolean | default=false | optional ) - Includes up to two fields in the metadata of the new tag, the values are "before" and "after".
    • The "before" value includes previous blocks (or lines) from the current text being process, using the "Boundary Flags" from the general settings to delimit the blocks, existing in the current graph, up to 2 blocks if possible. The "after" field include up to two blocks after the processed text.
  • skipVertexFlags ( type=string | optional ) - List of flags used to skip blocks (or lines) that should not be process by the stage.
    • The flagged vertex marks the start of the block and the end is set by the "Boundary Flags" value in the settings.


Generic Configuration Parameters

  • boundaryFlags ( type=string array | optional ) - List of vertex flags that indicate the beginning and end of a text block.
    Tokens to process must be inside two vertices marked with this flag (e.g ["TEXT_BLOCK_SPLIT"])
  • skipFlags ( type=string array | optional ) - Flags to be skipped by this stage.
    Tokens marked with this flag will be ignored by this stage, and no processing will be performed.
  • requiredFlags ( type=string array | optional ) - Lex items flags required by every token to be processed.
    Tokens need to have all of the specified flags in order to be processed.
  • atLeastOneFlag ( type=string array | optional ) - Lex items flags needed by every token to be processed.
    Tokens will need at least one of the flags specified in this array.
  • confidenceAdjustment ( type=double | default=1 | required ) - Adjustment factor to apply to the confidence value of 0.0 to 2.0 from (Applies for every pattern match).
    • 0.0 to < 1.0  decreases confidence value
    • 1.0 confidence value remains the same
    • > 1.0 to  2.0 increases confidence value
  • debug ( type=boolean | default=false | optional ) - Enable all debug log functionality for the stage, if any.
  • enable ( type=boolean | default=true | optional ) - Indicates if the current stage should be consider for the Pipeline Manager
    • Only applies for automatic pipeline building

Configuration Parameters

  • modelName ( type=string | required ) - Model name registered in the python bridge
  • modelVersion ( type=string | default=latest | optional ) - Model version registered in the python wrapper to query
  • modelMethod ( type=string | required ) - Model method to call for the model
  • hostname ( type=string | default=localhost | optional ) - Python server communication hostname
  • port ( type=string | default=5000 | optional ) - Python server communication port


$action.getHelper().renderConfluenceMacro("$codeS$body$codeE")

Example Output

$action.getHelper().renderConfluenceMacro("$codeS$body$codeE")

Output Flags

Lex-Item Flags:

  • WEIGHT_VECTOR - Identifies the tag as a weight vector representation of a sentence
  • ML_PREDICT- Result from a machine learning algorithm for prediction
  • ML_CLASSIFY- Result from a machine learning algorithm for classification
  • ML_REGRESS- Result from a machine learning algorithm for regression

No vertices are created in this stage