Enforces the AT naming schema on all column names and add missing NER converters

#If Sentence Resolvers are in pipeline, all Sentence-Embeddings must feed from Chunk2Doc which stems from the entities column to reso

Extract the exact name of the NER IOB column in the component_to_resolve

Extract the exact name of the NER-converter column in the component_to_resolve

extract either NLU_ref or storage_ref as fallback for a list of embedding components

Extract base features common in all saprk NLP annotators Begin/End/Embedding/Metadata/Result, except for the blacklisted features Expects

Extract Annotations from Light Pipeline into same represenation as other extractors in thos module

Extract provided features from component_list, which have storage ref.

Extract provided features from component_list, which have no storage ref

Extract provided features from component_list, which have storage ref

Extract provided features from component_list, which have no storage ref

Extract information about embedding conversion candidates

Extract base features common in all saprk NLP annotators Begin/End/Embedding/Metadata/Result, except for the blacklisted features Expects

Casting potential Modin data to spark and add index col. # Modin tests, This could crash if Modin not installed

Find ALL classifier component_to_resolve in component_list

Find NER component_to_resolve in component_list

Find traianble component_to_resolve which consumes emeddings. Returns index of component_to_resolve and type of embedding if found, otherwise

Get NER tags in component_list, used for showing visualizable tags

Get all file types/suffixes that can be processed by the pipeline

Get all file typtes/suffixes that can be processed by the pipeline

Authenticates environment if not already done so and returns Spark Context with Healthcare Jar loaded 0. If no Spark-NLP-Healthcare, install

Get List of columns in df that are generated from components in the pipeline which are at the same output level as the pipe . :par

For a component_to_resolve and a component_list, find storage_ref and

Search first OC namespace and if not found the HC Namespace for each Annotator Class in pipeline and get corresponding config Returns a dictio

Convert HTML so it can be rendered.

Gets the json metadata from a model_anno_obj for a given base path at a specific stage index

Gets the json metadata from a model_anno_obj for a given base path at a specific stage index on databricks

For every component_to_resolve in the pipeline

find NER pred, conf and class cols. Cols[0] = entity_class_col Cols[1] = entity_confidence Cols[2] = entity_chunk

Get a list of NLU components wrapping each Annotator in pipe. Pipe should be of class Pipeline, LightPipeline, or PipelineModel :param pipe: f

Get a mapping key=NluComponent and value = output level :param pipe: NLU pipe for which to get the mapping :return: dict where key = N

Convert an SVG to a base64-encoded image.

Check if any licensed components in component_list

Finds relation_col,document_col cols for relation viz viz

Finds entities,pos,dep_typed,dep_untyped and doc cols for dep viz viz

Finds entities,pos,dep_typed,dep_untyped and doc cols for dep viz viz

Finds entities and doc cols for ner viz

Finds entities and doc cols for ner viz

This function checks the LAST component_to_resolve of the NLU pipeline and infers from that the output level via checking the compo

Finds relation_col,document_col cols for relation viz viz

Finds entities_col,resolution_col,doc_col cols for resolution viz viz

For a given NLUPipeline with licensed components, infers which visualizations are applicable.

For a given NLUPipeline with only open source components, infers which visualizations are applicable.

For a given NLUPipeline, infers which visualizations are applicable.

Scan component_list for chunk_embeddings. If missing, add new. Validate NER model_anno_obj is loaded

Install Spark-NLP-Healthcare PyPI Package in current environment if it cannot be imported and license provided

Check if a Token Classifier uses IOB PRediction format

Check if a NLU Component wraps a NER-IOB to NER-Pr etty converter

Check if a NLU Component wraps a NER/NER-Medical model_anno_obj

Check if NLU component_to_resolve is embedding converter

Check whether there is a model_anno_obj stored in path

Check whether there is a pyspark component_list stored in path

For these components we resolve to None, because they are already beeign satisfied by another component that outputs multiple features includi

Returns anno_class name for jsl_id :param jsl_id: id of anno :return JslAnnoPyClass : cass of annotator

Either there is a pipeline of models in the path or just one singular model_anno_obj. If it is a component_list, load the component_list and retu

Either there is a pipeline of models in the path or just one singular model_anno_obj. If it is a component_list, load the component_list and retu