spark-nlp-display

A library for the simple visualization of different types of Spark NLP annotations.

Supported Visualizations:

• Dependency Parser
• Named Entity Recognition
• Entity Resolution
• Relation Extraction
• Assertion Status

Complete Tutorial

https://github.com/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb

Requirements

• spark-nlp
• ipython
• svgwrite
• pandas
• numpy

Installation

pip install spark-nlp-display

How to use

Databricks

For all modules, pass in the additional parameter "return_html=True" in the display function and use Databrick's function displayHTML() to render visualization as explained below:

from sparknlp_display import NerVisualizer

ner_vis = NerVisualizer()

## To set custom label colors:
ner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes

pipeline_result = ner_light_pipeline.fullAnnotate(text) ##light pipeline
#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline

vis_html = ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
                    label_col='entities', #specify the entity column
                    document_col='document', #specify the document column (default: 'document')
                    labels=['PER'], #only allow these labels to be displayed. (default: [] - all labels will be displayed)
                    return_html=True)

displayHTML(vis_html)

Jupyter

Dependency Parser

from sparknlp_display import DependencyParserVisualizer

dependency_vis = DependencyParserVisualizer()

pipeline_result = dp_pipeline.fullAnnotate(text)
#pipeline_result = dp_full_pipeline.transform(df).collect()##full pipeline

dependency_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe.
                       pos_col = 'pos', #specify the pos column
                       dependency_col = 'dependency', #specify the dependency column
                       dependency_type_col = 'dependency_type' #specify the dependency type column
                       )

Named Entity Recognition

from sparknlp_display import NerVisualizer

ner_vis = NerVisualizer()

pipeline_result = ner_light_pipeline.fullAnnotate(text)
#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline

ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
                    label_col='entities', #specify the entity column
                    document_col='document' #specify the document column (default: 'document')
                    labels=['PER'] #only allow these labels to be displayed. (default: [] - all labels will be displayed)
                    )

## To set custom label colors:
ner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes

Entity Resolution

from sparknlp_display import EntityResolverVisualizer

er_vis = EntityResolverVisualizer()

pipeline_result = er_light_pipeline.fullAnnotate(text)

er_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
               label_col='entities', #specify the ner result column
               resolution_col = 'resolution'
               document_col='document' #specify the document column (default: 'document')
               )

## To set custom label colors:
er_vis.set_label_colors({'TREATMENT':'#800080', 'PROBLEM':'#77b5fe'}) #set label colors by specifying hex codes

Relation Extraction

from sparknlp_display import RelationExtractionVisualizer

re_vis = RelationExtractionVisualizer()

pipeline_result = re_light_pipeline.fullAnnotate(text)

re_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
               relation_col = 'relations', #specify relations column
               document_col = 'document', #specify document column
               show_relations=True #display relation names on arrows (default: True)
               )

Assertion Status

from sparknlp_display import AssertionVisualizer

assertion_vis = AssertionVisualizer()

pipeline_result = ner_assertion_light_pipeline.fullAnnotate(text)

assertion_vis.display(pipeline_result[0], 
                      label_col = 'entities', #specify the ner result column
                      assertion_col = 'assertion' #specify assertion column
                      document_col = 'document' #specify the document column (default: 'document')
                      )
                      
## To set custom label colors:
assertion_vis.set_label_colors({'TREATMENT':'#008080', 'problem':'#800080'}) #set label colors by specifying hex codes