Using Continuous-Time Bayesian Networks

import pyagrum as gum
import pyagrum.lib.notebook as gnb
import pyagrum.ctbn as ct
import pyagrum.ctbn.notebook as ctbnnb

A CTBN(Continuous-Time Bayesian Networks) is a graphical model that allows a Bayesian Network to evolve over continuous time.

(For more details see https://pyagrum.readthedocs.io/en/latest/ctbn.html)

Creating a CTBN

## 1. Creating the ctbn
ctbn = ct.CTBN()

## 2. Adding the random variables
ctbn.add(gum.LabelizedVariable("cloudy?", "cloudy?"))
ctbn.add(gum.LabelizedVariable("rain?", "rain?"))

## 3. Adding the arcs
ctbn.addArc("cloudy?", "rain?")
ctbn.addArc("rain?", "cloudy?")

## 4. Filling the CIMS (Conditional Intensity Matrixes)
ctbn.CIM("cloudy?")[{"rain?": "0"}] = [[-0.1, 0.1], [1, -1]]
ctbn.CIM("cloudy?")[{"rain?": "1"}] = [[-100, 100], [0.5, -0.5]]

ctbn.CIM("rain?")[{"cloudy?": "0"}] = [[-0.5, 0.5], [1000, -1000]]
ctbn.CIM("rain?")[{"cloudy?": "1"}] = [[-2, 2], [2, -2]]

## 5. Plotting the CTBN (whith/whithout the CIMs)
ctbnnb.showCtbnGraph(ctbn)
ctbnnb.showCIMs(ctbn)

svg

		cloudy?#j
rain?	cloudy?#i	0	1
0	0	-0.1000	0.1000
0	1	1.0000	-1.0000
1	0	-100.0000	100.0000
1	1	0.5000	-0.5000

CIM(cloudy?)

		rain?#j
cloudy?	rain?#i	0	1
0	0	-0.5000	0.5000
0	1	1000.0000	-1000.0000
1	0	-2.0000	2.0000
1	1	2.0000	-2.0000

CIM(rain?)

cloudy?#i gives the state cloudy? is currently in and cloudy?#j gives its next state.

The CIM object, built around a pyagrum.Tensor is used to store the parameters of the variables, describing the waiting time before switching to another state.

A useful tool for CIMs, mainly used for simple inference, is amalgamation. It allows to merge many CIMs into one.

cimA = ctbn.CIM("cloudy?")
cimB = ctbn.CIM("rain?")
amal = cimA.amalgamate(cimB)  # or cimA * cimB

amal.getTensor()  # as a Tensor

			rain?#j
cloudy?#i	cloudy?#j	rain?#i	0	1
0	0	0	-0.6000	0.5000
	0	1	1000.0000	-1100.0000
	1	0	0.1000	0.0000
	1	1	0.0000	100.0000
1	0	0	1.0000	0.0000
	0	1	0.0000	0.5000
	1	0	-3.0000	2.0000
	1	1	2.0000	-2.5000

print("\nJoint intensity matrix as numpy array\n")
print(amal.toMatrix())  # as a numpy array

Joint intensity matrix as numpy array

[[-6.0e-01  1.0e-01  5.0e-01  0.0e+00]
 [ 1.0e+00 -3.0e+00  0.0e+00  2.0e+00]
 [ 1.0e+03  0.0e+00 -1.1e+03  1.0e+02]
 [ 0.0e+00  2.0e+00  5.0e-01 -2.5e+00]]

A random variable of a ctbn can be identified by either its name or id.

In any case, most class functions parameters allow for a variable to be identified by either its name or id (type NameOrId).

labels_with_id = ctbn.labels(0)
labels_with_name = ctbn.labels("cloudy?")
print(f"labels using id : {labels_with_id}, using name : {labels_with_name}")

variable_with_id = ctbn.variable(0)
variable_with_name = ctbn.variable("cloudy?")
print(f"The variables 0 and 'cloudy?' are the same ? {variable_with_id == variable_with_name}")

labels using id : ('0', '1'), using name : ('0', '1')
The variables 0 and 'cloudy?' are the same ? True

All functions with NameOrId parameters work the same way

Random CTBN generator

The CtbnGenerator module can be used to quickly generate CTBNs. The parameters value are drawn at random from a value range. The diagonal coefficients are just the negative sum of the ones on the same line (in a CIM). It is also possible to choose the number of variables, the maximum number of parents a variable can have, and the domain size of the variables.

randomCtbn = ct.randomCTBN(valueRange=(1, 10), n=8, parMax=2, modal=2)

ctbnnb.showCtbnGraph(randomCtbn)
ctbnnb.showCIMs(randomCtbn)

svg

			V1#j
V7	V6	V1#i	v1_1	v1_2
v7_1	v6_1	v1_1	-7.4441	7.4441
	v6_1	v1_2	5.5736	-5.5736
	v6_2	v1_1	-9.8036	9.8036
	v6_2	v1_2	7.7016	-7.7016
v7_2	v6_1	v1_1	-3.3263	3.3263
	v6_1	v1_2	7.0387	-7.0387
	v6_2	v1_1	-4.2551	4.2551
	v6_2	v1_2	7.1382	-7.1382

CIM(V1)

			V2#j
V5	V4	V2#i	v2_1	v2_2
v5_1	v4_1	v2_1	-8.7993	8.7993
	v4_1	v2_2	5.0979	-5.0979
	v4_2	v2_1	-5.9138	5.9138
	v4_2	v2_2	5.2236	-5.2236
v5_2	v4_1	v2_1	-4.8857	4.8857
	v4_1	v2_2	2.0599	-2.0599
	v4_2	v2_1	-2.1534	2.1534
	v4_2	v2_2	5.2152	-5.2152

CIM(V2)

		V3#j
V7	V3#i	v3_1	v3_2
v7_1	v3_1	-1.1060	1.1060
v7_1	v3_2	5.5574	-5.5574
v7_2	v3_1	-3.3619	3.3619
v7_2	v3_2	4.8376	-4.8376

CIM(V3)

		V4#j
V8	V4#i	v4_1	v4_2
v8_1	v4_1	-6.2546	6.2546
v8_1	v4_2	3.4688	-3.4688
v8_2	v4_1	-4.0979	4.0979
v8_2	v4_2	1.3903	-1.3903

CIM(V4)

	V5#j
V5#i	v5_1	v5_2
v5_1	-6.2628	6.2628
v5_2	1.1398	-1.1398

CIM(V5)

			V6#j
V3	V5	V6#i	v6_1	v6_2
v3_1	v5_1	v6_1	-3.6828	3.6828
	v5_1	v6_2	2.3116	-2.3116
	v5_2	v6_1	-9.7732	9.7732
	v5_2	v6_2	9.5058	-9.5058
v3_2	v5_1	v6_1	-1.8879	1.8879
	v5_1	v6_2	1.6213	-1.6213
	v5_2	v6_1	-7.8660	7.8660
	v5_2	v6_2	3.9117	-3.9117

CIM(V6)

			V7#j
V2	V6	V7#i	v7_1	v7_2
v2_1	v6_1	v7_1	-8.5733	8.5733
	v6_1	v7_2	7.8825	-7.8825
	v6_2	v7_1	-8.5812	8.5812
	v6_2	v7_2	7.5340	-7.5340
v2_2	v6_1	v7_1	-1.8851	1.8851
	v6_1	v7_2	9.5997	-9.5997
	v6_2	v7_1	-8.4493	8.4493
	v6_2	v7_2	4.4490	-4.4490

CIM(V7)

		V8#j
V3	V8#i	v8_1	v8_2
v3_1	v8_1	-6.1877	6.1877
v3_1	v8_2	8.4091	-8.4091
v3_2	v8_1	-4.5719	4.5719
v3_2	v8_2	2.3154	-2.3154

CIM(V8)

Inference with CTBNs

Exact inference & Forward Sampling inference

Amalgamation is used to compute exact inference. It consists in merging all the CIMs of a CTBN into one CIM. Then we use the properties of markov process : $P(X_t) = P(X_0)*exp(Q_Xt)$ for enough time to notice convergence.
Forward sampling works this way:
- At time $t$ :
- Draw a “time until next change” $dt$ value for each variable’s exponential distribution of their waiting time.
- Select the variable with smallest transition time : $next time = t + dt$ .
- Draw the variable next state uniformly.
- loop until $t = timeHorizon$ .

When doing forward sampling, we first iterate this process over a given amount of iterations (called burnIn). We consider the last values as the initial configuration for sampling. The reason is that the initial state of a CTBN is unknown without real data.

## 1. Initialiaze Inference object
ie = ct.SimpleInference(ctbn)
ie_forward = ct.ForwardSamplingInference(ctbn)
gnb.sideBySide(ctbnnb.getCtbnGraph(ctbn), ctbnnb.getCtbnInference(ctbn, ie), ctbnnb.getCtbnInference(ctbn, ie_forward))

In any case, inference can be made using makeInference() function from every Inference object.

import time

## Simple inference
time1 = time.time()
ie.makeInference(t=5000)
pot1 = ie.posterior("cloudy?")
elapsed1 = time.time() - time1

## Sampling inference
time2 = time.time()
ie_forward.makeInference(timeHorizon=5000, burnIn=100)
pot2 = ie_forward.posterior("cloudy?")
elapsed2 = time.time() - time2

gnb.sideBySide(
  gnb.getTensor(pot1),
  gnb.getTensor(pot2),
  captions=[f"Simple inference {round(elapsed1 * 1000, 2)} ms", f"Sampling inference {round(elapsed2 * 1000, 2)} ms"],
)

cloudy?
0	1
0.8334	0.1666

Simple inference 0.88 ms

cloudy?
0	1
0.8250	0.1750

Sampling inference 247.91 ms

Plotting a sample

Here is an example of a trajectory plot

## 1_1. Find trajectory stored in the inference class
ie_forward.makeInference()
traj = ie_forward.trajectory  # a single trajectory List[Tuple[float, str, str]]

## 1_2. Or find it in a csv file
ie_forward.writeTrajectoryCSV("out/trajectory.csv", n=1, timeHorizon=1000, burnIn=100)  # to save a trajectory
traj = ct.readTrajectoryCSV("out/trajectory.csv")  # a dict of trajectories

## 2. Plot the trajectory
ct.plotTrajectory(ctbn.variable("cloudy?"), traj[0], timeHorizon=40, plotname="plot of cloudy?")

svg

It is also possible to plot the proportions of the states a variable is in over time.

Let’s take the exemple of a variable with domain size=5.

## 1. Create CTBN
new_ctbn = ct.CTBN()
X = gum.LabelizedVariable("X", "X", ["a1", "a2", "a3", "a4", "a5"])
new_ctbn.add(X)
new_ctbn.CIM("X")[:] = [
  [-10, 2, 4, 0, 4],
  [0.5, -1, 0.5, 0, 0],
  [5, 0.5, -10, 3.5, 1],
  [50, 0.5, 50, -200.5, 100],
  [1, 0, 2, 7, -10],
]
## 2. Sampling
ieX = ct.ForwardSamplingInference(new_ctbn)
ieX.writeTrajectoryCSV("out/traj_quinary.csv", n=3, timeHorizon=100)
trajX = ct.readTrajectoryCSV("out/traj_quinary.csv")

## 3. Plotting
ct.plotFollowVar(X, trajX)

svg

CTBN Learning with pyAgrum

The ctbn library has tools to learn the graph of a CTBN as well as the CIMs of its variables

Learning the variables

From a sample, we can find the variables name and their labels. However, if the sample is too short, it is possible that some information may be missed.

newCtbn = ct.CTBNFromData(traj)

print("variables found and their labels")
for name in newCtbn.names():
  print(f"variable {name}, labels : {newCtbn.labels(name)}")

variables found and their labels
variable rain?, labels : ('0', '1')
variable cloudy?, labels : ('0', '1')

Learning the structure of the CTBN

## 1. Create a Learner.
learner = ct.Learner("out/trajectory.csv")

## 2. Run learning algorithm
learned_ctbn = learner.learnCTBN()

## 3. Plot the learned CTBN
gnb.show(learned_ctbn)

svg

Let’s note that having a test object is necessary since we want to be able to test independency between variables. For now only 2 tests are available : (Fisher and Chi2) test and Oracle test. Oracle test is only used for benchmarking since it uses the initial ctbn as a reference.

Fitting parameters

It is also possible to approximate the transition time distributions of each variable, i.e the CIMs.

Note that conditioning variables are taken from the ctbn.

cims_before = ctbnnb.getCIMs(ctbn)
learner.fitParameters(ctbn)
cims_after = ctbnnb.getCIMs(ctbn)

gnb.sideBySide(cims_before, cims_after, captions=["original CIMs", "learned CIMs"])

		cloudy?#j
rain?	cloudy?#i	0	1
0	0	-0.1000	0.1000
0	1	1.0000	-1.0000
1	0	-100.0000	100.0000
1	1	0.5000	-0.5000

CIM(cloudy?)

		rain?#j
cloudy?	rain?#i	0	1
0	0	-0.5000	0.5000
0	1	1000.0000	-1000.0000
1	0	-2.0000	2.0000
1	1	2.0000	-2.0000

CIM(rain?)

original CIMs

		cloudy?#j
rain?	cloudy?#i	0	1
0	0	-0.1020	0.1020
0	1	0.9760	-0.9760
1	0	-100.6620	100.6620
1	1	0.6350	-0.6350

CIM(cloudy?)

		rain?#j
cloudy?	rain?#i	0	1
0	0	-0.4650	0.4650
0	1	984.5210	-984.5210
1	0	-2.0900	2.0900
1	1	2.0610	-2.0610

CIM(rain?)

learned CIMs

Example from [Nodelman et al, 2003]

This example comes from :

U. Nodelman, C.R. Shelton, and D. Koller (2003). "Learning Continuous Time Bayesian Networks." Proc. Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI) (pp. 451-458).

## 1. Creating the ctbn
drugEffect = ct.CTBN()

drugEffect.add(gum.LabelizedVariable("Eating", ""))
drugEffect.add(gum.LabelizedVariable("Hungry", ""))
drugEffect.add(gum.LabelizedVariable("Full stomach", ""))
drugEffect.add(gum.LabelizedVariable("Uptake", ""))
drugEffect.add(gum.LabelizedVariable("Concentration", "?"))
drugEffect.add(gum.LabelizedVariable("Barometer", ""))
drugEffect.add(gum.LabelizedVariable("Joint pain", ""))
drugEffect.add(gum.LabelizedVariable("Drowsy", ""))

drugEffect.addArc("Hungry", "Eating")
drugEffect.addArc("Eating", "Full stomach")
drugEffect.addArc("Full stomach", "Hungry")
drugEffect.addArc("Full stomach", "Concentration")
drugEffect.addArc("Uptake", "Concentration")
drugEffect.addArc("Concentration", "Drowsy")
drugEffect.addArc("Barometer", "Joint pain")
drugEffect.addArc("Concentration", "Joint pain")

gnb.show(drugEffect)

svg

from pyagrum.ctbn.CTBNGenerator import randomCIMs

randomCIMs(drugEffect, (0.1, 10))
drugEffect.CIM("Eating")[{"Hungry": 0}] = [[-0.1, 0.1], [10, -10]]
drugEffect.CIM("Eating")[{"Hungry": 1}] = [[-2, 2], [0.1, -0.1]]
drugEffect.CIM("Full stomach")[{"Eating": 0}] = [[-0.1, 0.1], [10, -10]]
drugEffect.CIM("Full stomach")[{"Eating": 1}] = [[-2, 2], [0.1, -0.1]]
drugEffect.CIM("Hungry")[{"Full stomach": 0}] = [[-0.1, 0.1], [10, -10]]
drugEffect.CIM("Hungry")[{"Full stomach": 1}] = [[-2, 2], [0.1, -0.1]]

ieX = ct.ForwardSamplingInference(drugEffect)
ieX.writeTrajectoryCSV("out/drugEffect.csv", n=3, timeHorizon=100)
trajX = ct.readTrajectoryCSV("out/drugEffect.csv")

ct.plotFollowVar(drugEffect.variable("Hungry"), trajX)
ct.plotFollowVar(drugEffect.variable("Full stomach"), trajX)
ct.plotFollowVar(drugEffect.variable("Eating"), trajX)
ct.plotFollowVar(drugEffect.variable("Concentration"), trajX)

svg

Input/output with pickle for CTBN

import pickle

with open("out/testCTBN.pkl", "bw") as f:
  pickle.dump(drugEffect, f)
drugEffect

with open("out/testCTBN.pkl", "br") as f:
  copyDrug = pickle.load(f)
copyDrug