Create Bayesian Network and learn parameters with Python3.x [closed]
It looks like pomegranate was recently updated to include Bayesian Networks. I haven't tried it myself, but the interface looks nice and sklearn-ish.
Try the bnlearn library, it contains many function to learn parameters from data and perform the inference.
pip install bnlearnYour use-case would be like this:
# Import the libraryimport bnlearn# Define the network structureedges = [('task', 'size'), ('lat var', 'size'), ('task', 'fill level'), ('task', 'object shape'), ('task', 'side graspable'), ('size', 'GrasPose'), ('task', 'GrasPose'), ('fill level', 'GrasPose'), ('object shape', 'GrasPose'), ('side graspable', 'GrasPose'), ('GrasPose', 'latvar'),]# Make the actual Bayesian DAGDAG = bnlearn.make_DAG(edges)# DAG is stored in adjacency matrixprint(DAG['adjmat'])# target task size lat var ... side graspable GrasPose latvar# source ... # task False True False ... True True False# size False False False ... False True False# lat var False True False ... False False False# fill level False False False ... False True False# object shape False False False ... False True False# side graspable False False False ... False True False# GrasPose False False False ... False False True# latvar False False False ... False False False# # [8 rows x 8 columns]# No CPDs are in the DAG. Lets see what happens if we print it.bnlearn.print_CPD(DAG)# >[BNLEARN.print_CPD] No CPDs to print. Use bnlearn.plot(DAG) to make a plot.# Plot DAG. Note that it can be differently orientated if you re-make the plot.bnlearn.plot(DAG)
Now we need the data to learn its parameters. Suppose these are stored in your df. The variable names in the data-file must be present in the DAG.
# Read datadf = pd.read_csv('path_to_your_data.csv')# Learn the parameters and store CPDs in the DAG. Use the methodtype your desire. Options are maximumlikelihood or bayes.DAG = bnlearn.parameter_learning.fit(DAG, df, methodtype='maximumlikelihood')# CPDs are present in the DAG at this point.bnlearn.print_CPD(DAG)# Start making inferences now. As an example:q1 = bnlearn.inference.fit(DAG, variables=['lat var'], evidence={'fill level':1, 'size':0, 'task':1})Below is a working example with a demo dataset (sprinkler). You can play around with this.
# Import example datasetdf = bnlearn.import_example('sprinkler')print(df)# Cloudy Sprinkler Rain Wet_Grass# 0 0 0 0 0# 1 1 0 1 1# 2 0 1 0 1# 3 1 1 1 1# 4 1 1 1 1# .. ... ... ... ...# 995 1 0 1 1# 996 1 0 1 1# 997 1 0 1 1# 998 0 0 0 0# 999 0 1 1 1# [1000 rows x 4 columns]# Define the network structureedges = [('Cloudy', 'Sprinkler'), ('Cloudy', 'Rain'), ('Sprinkler', 'Wet_Grass'), ('Rain', 'Wet_Grass')]# Make the actual Bayesian DAGDAG = bnlearn.make_DAG(edges)# Print the CPDsbnlearn.print_CPD(DAG)# [BNLEARN.print_CPD] No CPDs to print. Use bnlearn.plot(DAG) to make a plot.# Plot the DAGbnlearn.plot(DAG)
# Parameter learning on the user-defined DAG and input dataDAG = bnlearn.parameter_learning.fit(DAG, df)# Print the learned CPDsbnlearn.print_CPD(DAG)# [BNLEARN.print_CPD] Independencies:# (Cloudy _|_ Wet_Grass | Rain, Sprinkler)# (Sprinkler _|_ Rain | Cloudy)# (Rain _|_ Sprinkler | Cloudy)# (Wet_Grass _|_ Cloudy | Rain, Sprinkler)# [BNLEARN.print_CPD] Nodes: ['Cloudy', 'Sprinkler', 'Rain', 'Wet_Grass']# [BNLEARN.print_CPD] Edges: [('Cloudy', 'Sprinkler'), ('Cloudy', 'Rain'), ('Sprinkler', 'Wet_Grass'), ('Rain', 'Wet_Grass')]# CPD of Cloudy:# +-----------+-------+# | Cloudy(0) | 0.494 |# +-----------+-------+# | Cloudy(1) | 0.506 |# +-----------+-------+# CPD of Sprinkler:# +--------------+--------------------+--------------------+# | Cloudy | Cloudy(0) | Cloudy(1) |# +--------------+--------------------+--------------------+# | Sprinkler(0) | 0.4807692307692308 | 0.7075098814229249 |# +--------------+--------------------+--------------------+# | Sprinkler(1) | 0.5192307692307693 | 0.2924901185770751 |# +--------------+--------------------+--------------------+# CPD of Rain:# +---------+--------------------+---------------------+# | Cloudy | Cloudy(0) | Cloudy(1) |# +---------+--------------------+---------------------+# | Rain(0) | 0.6518218623481782 | 0.33695652173913043 |# +---------+--------------------+---------------------+# | Rain(1) | 0.3481781376518219 | 0.6630434782608695 |# +---------+--------------------+---------------------+# CPD of Wet_Grass:# +--------------+--------------------+---------------------+---------------------+---------------------+# | Rain | Rain(0) | Rain(0) | Rain(1) | Rain(1) |# +--------------+--------------------+---------------------+---------------------+---------------------+# | Sprinkler | Sprinkler(0) | Sprinkler(1) | Sprinkler(0) | Sprinkler(1) |# +--------------+--------------------+---------------------+---------------------+---------------------+# | Wet_Grass(0) | 0.7553816046966731 | 0.33755274261603374 | 0.25588235294117645 | 0.37910447761194027 |# +--------------+--------------------+---------------------+---------------------+---------------------+# | Wet_Grass(1) | 0.2446183953033268 | 0.6624472573839663 | 0.7441176470588236 | 0.6208955223880597 |# +--------------+--------------------+---------------------+---------------------+---------------------+# Make inferenceq1 = bnlearn.inference.fit(DAG, variables=['Wet_Grass'], evidence={'Rain':1, 'Sprinkler':0, 'Cloudy':1})# +--------------+------------------+# | Wet_Grass | phi(Wet_Grass) |# +==============+==================+# | Wet_Grass(0) | 0.2559 |# +--------------+------------------+# | Wet_Grass(1) | 0.7441 |# +--------------+------------------+print(q1.values)# array([0.25588235, 0.74411765])More examples can be found at pages of bnlearn:https://erdogant.github.io/bnlearn
I was looking for a similar library, and I found that the pomegranate is a good one. Thanks James Atwood
Here is an example how to use it.
from pomegranate import *import numpy as npmydb=np.array([[1,2,3],[1,2,4],[1,2,5],[1,2,6],[1,3,8],[2,3,8],[1,2,4]])bnet = BayesianNetwork.from_samples(mydb)print(bnet.node_count())print(bnet.probability([[1,2,3]]))print (bnet.probability([[1,2,8]]))