pyagrum.lib.dynamicBN

dynamic Bayesian Network are a special class of BNs where variables can be subscripted by a (discrete) time. See this notebook.

The purpose of this module is to provide basic tools for dealing with dynamic Bayesian Network (and inference) : modeling, visualisation, inference.

pyagrum.lib.dynamicBN.getTimeSlices(dbn, size=None)

Try to correctly represent dBN and 2TBN as an HTML string

• Parameters:
  • dbn (BayesNet) – a 2TBN or an unrolled BN
  • size (str | None) – size of the fig
• Return type: str

pyagrum.lib.dynamicBN.getTimeSlicesRange(dbn)

get the range and (name,radical) of each variables

• Parameters: dbn (BayesNet) – a 2TBN or an unrolled BN
• Returns: all the timeslice of a dbn : [‘0’,’t’] for a classic 2TBN, range(T) for a classic unrolled BN
• Return type: dict[str, list[tuple[str, str]]]

pyagrum.lib.dynamicBN.is2TBN(bn)

Check if bn is a 2 TimeSlice Bayesian network

• Parameters: bn (BayesNet) – the Bayesian network
• Returns: True if the BN is syntaxically correct to be a 2TBN
• Return type: tuple[bool, str]

pyagrum.lib.dynamicBN.plotFollow(lovars, twoTdbn, T, evs)

Plot modifications of variables in a 2TBN knowing the size of the time window (T) and the evidence on the sequence.

• Parameters:
  • lovars (list[str]) – List of variables to follow.
  • twoTdbn (BayesNet) – The two-timeslice dBN.
  • T (int) – The time range.
  • evs (dict) – Observations.
• Return type: None

pyagrum.lib.dynamicBN.plotFollowUnrolled(lovars, dbn, T, evs, vars_title=None)

Plot the dynamic evolution of a list of vars with a dBN.

• Parameters:
  • lovars (list[str]) – List of variables to follow.
  • dbn (BayesNet) – The unrolled dBN.
  • T (int) – The time range.
  • evs (dict) – Observations.
  • vars_title (list[str] | None) – String for default title or a dictionary with the variable name as key and its title as value.
• Return type: None

pyagrum.lib.dynamicBN.realNameFrom2TBNname(name, ts)

• Returns: The dynamic name from static name and timeslice (no check).
• Return type: str
• Parameters:
  • name (str)
  • ts (int)

pyagrum.lib.dynamicBN.showTimeSlices(dbn, size=None)

Try to correctly display dBN and 2TBN

• Parameters:
  • dbn (BayesNet) – a 2TBN or an unrolled BN
  • size (str | None) – size of the fig
• Return type: None

pyagrum.lib.dynamicBN.unroll2TBN(dbn, nbr)

unroll a 2TBN given the nbr of timeslices

• Parameters:
  • dbn (BayesNet) – a 2TBN or an unrolled BN
  • nbr (int) – the number of timeslice
• Returns: unrolled BN from a 2TBN and the nbr of timeslices
• Return type: BayesNet