README.md

SDPV : Spacy DependencyMatcher Pattern Visualiser

SDPV is Python library designed to visualize Spacy DependencyMatcher Pattern and thus ease your pattern design.

1. Install

git clone https://github.com/Jacobe2169/spacy-depmatcher-pattern-visualiser.git
cd spacy-depmatcher-pattern-visualiser
python setup.py install

In addition to the modules listed in requirements.txt, GraphViz must be installed!

2. Use

from sdpv import draw_pattern

pattern = [
  # anchor token: founded
  {
    "RIGHT_ID": "founded",
    "RIGHT_ATTRS": {"ORTH": "founded"}
  },
  # founded -> subject
  {
    "LEFT_ID": "founded",
    "REL_OP": ">",
    "RIGHT_ID": "subject",
    "RIGHT_ATTRS": {"DEP": "nsubj"}
  },
  # "founded" follows "initially"
  {
    "LEFT_ID": "founded",
    "REL_OP": ";",
    "RIGHT_ID": "initially",
    "RIGHT_ATTRS": {"ORTH": "initially"}
  }
]

2.1 Draw using matplotlib

Matplotlib is set by default, so just run:

draw_pattern(pattern)

You can customize the node color, the label font color and the size of the generated figure.

draw_pattern(pattern, node_color="grey",node_size=20,figsize=(10,5))

2.2 Draw using GraphViz

draw_pattern(pattern,mode="graphviz")

2.2.1Display in Jupyter Notebook

Compared to Matplotlib, you need to set the parameter show to ipynb

draw_pattern(pattern,mode="graphviz",show="ipynb")

2.3 Draw using vis.js (only in Notebook)

draw_pattern(pattern,mode="notebook")

2.4 Save the Figure

Use the filename parameter

draw_pattern(pattern,mode="graphviz",filename="graphviz.png")

Attention As of today, the export of vis.js plot is only available in PNG.

Use the GUI

If you wish to use SDPV in a better fashion, you can use the webapp

cd webapp
python server.py

3. Authors

This library was programmed by Jacques Fize.