spark-nlp-display


Namespark-nlp-display JSON
Version 5.0 PyPI version JSON
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home_pagehttp://nlp.johnsnowlabs.com
SummaryVisualization package for Spark NLP
upload_time2023-08-29 19:11:59
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docs_urlNone
authorJohn Snow Labs
requires_python>=2.7
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            # 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
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb)

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

### Requirements
- spark-nlp
- ipython
- svgwrite
- pandas
- numpy

### Installation
```bash
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:
```python
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)
```
![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)

### Jupyter

To save the visualization as html, provide the export file path: `save_path='./export.html'` for each visualizer.


#### Dependency Parser
```python
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
                       save_path='./export.html' # optional - to save viz as html. (default: None)
                       )
```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/dp_viz.png)

#### Named Entity Recognition

```python
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)
                    save_path='./export.html' # optional - to save viz as html. (default: None)
                    )

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

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)

#### Entity Resolution

```python
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')
               save_path='./export.html' # optional - to save viz as html. (default: None)
               )

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

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/er_viz.png)

#### Relation Extraction
```python
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)
               save_path='./export.html' # optional - to save viz as html. (default: None)
               )

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/re_viz.png)

#### Assertion Status
```python
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')
                      save_path='./export.html' # optional - to save viz as html. (default: None)
                      )
                      
## To set custom label colors:
assertion_vis.set_label_colors({'TREATMENT':'#008080', 'problem':'#800080'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/assertion_viz.png)

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    "description": "# spark-nlp-display\nA library for the simple visualization of different types of Spark NLP annotations. \n\n## Supported Visualizations:\n- Dependency Parser\n- Named Entity Recognition\n- Entity Resolution\n- Relation Extraction\n- Assertion Status\n\n## Complete Tutorial\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb)\n\nhttps://github.com/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb\n\n### Requirements\n- spark-nlp\n- ipython\n- svgwrite\n- pandas\n- numpy\n\n### Installation\n```bash\npip install spark-nlp-display\n```\n\n### How to use\n\n### Databricks\n#### 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:\n```python\nfrom sparknlp_display import NerVisualizer\n\nner_vis = NerVisualizer()\n\n## To set custom label colors:\nner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes\n\npipeline_result = ner_light_pipeline.fullAnnotate(text) ##light pipeline\n#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline\n\nvis_html = ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe\n                    label_col='entities', #specify the entity column\n                    document_col='document', #specify the document column (default: 'document')\n                    labels=['PER'], #only allow these labels to be displayed. (default: [] - all labels will be displayed)\n                    return_html=True)\n\ndisplayHTML(vis_html)\n```\n![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)\n\n### Jupyter\n\nTo save the visualization as html, provide the export file path: `save_path='./export.html'` for each visualizer.\n\n\n#### Dependency Parser\n```python\nfrom sparknlp_display import DependencyParserVisualizer\n\ndependency_vis = DependencyParserVisualizer()\n\npipeline_result = dp_pipeline.fullAnnotate(text)\n#pipeline_result = dp_full_pipeline.transform(df).collect()##full pipeline\n\ndependency_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe.\n                       pos_col = 'pos', #specify the pos column\n                       dependency_col = 'dependency', #specify the dependency column\n                       dependency_type_col = 'dependency_type', #specify the dependency type column\n                       save_path='./export.html' # optional - to save viz as html. (default: None)\n                       )\n```\n\n![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/dp_viz.png)\n\n#### Named Entity Recognition\n\n```python\nfrom sparknlp_display import NerVisualizer\n\nner_vis = NerVisualizer()\n\npipeline_result = ner_light_pipeline.fullAnnotate(text)\n#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline\n\nner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe\n                    label_col='entities', #specify the entity column\n                    document_col='document', #specify the document column (default: 'document')\n                    labels=['PER'], #only allow these labels to be displayed. (default: [] - all labels will be displayed)\n                    save_path='./export.html' # optional - to save viz as html. (default: None)\n                    )\n\n## To set custom label colors:\nner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes\n\n```\n\n![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)\n\n#### Entity Resolution\n\n```python\nfrom sparknlp_display import EntityResolverVisualizer\n\ner_vis = EntityResolverVisualizer()\n\npipeline_result = er_light_pipeline.fullAnnotate(text)\n\ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe\n               label_col='entities', #specify the ner result column\n               resolution_col = 'resolution',\n               document_col='document', #specify the document column (default: 'document')\n               save_path='./export.html' # optional - to save viz as html. (default: None)\n               )\n\n## To set custom label colors:\ner_vis.set_label_colors({'TREATMENT':'#800080', 'PROBLEM':'#77b5fe'}) #set label colors by specifying hex codes\n\n```\n\n![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/er_viz.png)\n\n#### Relation Extraction\n```python\nfrom sparknlp_display import RelationExtractionVisualizer\n\nre_vis = RelationExtractionVisualizer()\n\npipeline_result = re_light_pipeline.fullAnnotate(text)\n\nre_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe\n               relation_col = 'relations', #specify relations column\n               document_col = 'document', #specify document column\n               show_relations=True, #display relation names on arrows (default: True)\n               save_path='./export.html' # optional - to save viz as html. 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