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|
%global _empty_manifest_terminate_build 0
Name: python-spark-nlp-display
Version: 4.4
Release: 1
Summary: Visualization package for Spark NLP
License: Apache Software License
URL: http://nlp.johnsnowlabs.com
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/af/27/b84525e77152b17ae6d2d39a0cee975a1d9d57ba2f8ad210649238696b31/spark-nlp-display-4.4.tar.gz
BuildArch: noarch
Requires: python3-spark-nlp
Requires: python3-ipython
Requires: python3-svgwrite
Requires: python3-pandas
Requires: python3-numpy
%description
# 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://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)
```
### 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)
)
```
#### 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
```
#### 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
```
#### 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)
)
```
#### 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
```
%package -n python3-spark-nlp-display
Summary: Visualization package for Spark NLP
Provides: python-spark-nlp-display
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-spark-nlp-display
# 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://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)
```
### 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)
)
```
#### 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
```
#### 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
```
#### 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)
)
```
#### 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
```
%package help
Summary: Development documents and examples for spark-nlp-display
Provides: python3-spark-nlp-display-doc
%description help
# 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://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)
```
### 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)
)
```
#### 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
```
#### 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
```
#### 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)
)
```
#### 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
```
%prep
%autosetup -n spark-nlp-display-4.4
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-spark-nlp-display -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 4.4-1
- Package Spec generated
|
