File size: 2,683 Bytes
93d7919 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | ===============
Plotting Models
===============
pgmpy offers a few different ways to plot the model structure.
1. Using `pygraphviz` (https://pygraphviz.github.io/)
2. Using `networkx.drawing` module (https://networkx.org/documentation/stable/reference/drawing.html)
3. Using `daft` (https://docs.daft-pgm.org/)
1. Using `pygraphviz`
---------------------
`pygraphviz` is a Python wrapper to Graphviz that has a lot for functionality
for graph visualization. pgmpy provides a method to create a pygraphviz object from
Bayesian Networks and DAGs that can then be plotted using graphviz.
.. code-block:: python
# Get an example model
from pgmpy.utils import get_example_model
model = get_example_model("sachs")
# Convert model into pygraphviz object
model_graphviz = model.to_graphviz()
# Plot the model.
model_graphviz.draw("sachs.png", prog="dot")
# Other file formats can also be specified.
model_graphviz.draw("sachs.pdf", prog="dot")
model_graphviz.draw("sachs.svg", prog="dot")
The output `sachs.png` is shown below. Users can also tryout other layout methods supported by pygraphviz such as: `neato`, `dot`, `twopi`, `circo`, `fdp`, `nop`.
.. image:: sachs.png
:scale: 75%
2. Using `daft`
---------------
Daft is a python package that uses matplotlib to render high quality plots suitable for publications.
.. code-block:: python
# Get an example model
from pgmpy.utils import get_example_model
model = get_example_model("sachs")
# Get a daft object.
model_daft = model.to_daft()
# To open the plot
model_daft.render()
# Save the plot
model_daft.savefig('sachs.png')
# Daft provides plenty of options for customization. Please refer DAG.to_daft documentation and daft's documentation.
model_daft_custom = model.to_daft(node_pos='shell',
pgm_params={'observed_style': 'shade', 'grid_unit': 3},
edge_params={('PKA', 'P38'): {'label': 2}},
node_params={'Mek': {'shape': 'rectangle'}})
The output of the two plots above.
.. image:: sachs_daft_plain.png
.. image:: sachs_daft_shell.png
3. Using `networkx.drawing`
---------------------------
Lastly, as both `pgmpy.models.BayesianNetwork` and `pgmpy.base.DAG` inherit `networkx.DiGraph`, all of networkx's drawing functionality can be directly used on both DAGs and Bayesian Networks.
.. code-block:: python
import networkx as nx
import matplotlib.pyplot as plt
# Get an example model
from pgmpy.utils import get_example_model
model = get_example_model("sachs")
# Plot the model
nx.draw(model)
plt.draw()
|