Loading and saving graphical models

pyAgrum can read and write graphical models in many file formats. This notebook gives an overview of which formats are available for each model type, what they preserve, and why the native bgum (binary) and jgum (JSON) formats are the best choice for pyAgrum workflows.

In [1]:

import os
import tempfile
import time

import pyagrum as gum
import pyagrum.lib.notebook as gnb

Available formats

The three main model types each have their own set of supported formats.

In [2]:

print(f"BayesNet formats  : {gum.availableBNExts()}")
print(f"InfluenceDiagram  : {gum.availableIDExts()}")
print(f"MarkovRandomField : {gum.availableMRFExts()}")

BayesNet formats  : bif|dsl|net|bifxml|o3prm|uai|xdsl|pkl|jgum|bgum
InfluenceDiagram  : xmlbif|bifxml|xml|jgum|bgum|pkl
MarkovRandomField : uai|jgum|bgum|pkl

The load/save API is uniform across model types:

Model	Load	Save
`BayesNet`	`gum.loadBN(filename)`	`gum.saveBN(bn, filename)`
`InfluenceDiagram`	`gum.loadID(filename)`	`gum.saveID(diag, filename)`
`MarkovRandomField`	`gum.loadMRF(filename)`	`gum.saveMRF(mrf, filename)`

The format is selected automatically from the file extension.

Bayesian networks

A tour of BN formats

Let’s load the classic Asia network and save/reload it in every available format, comparing file size and round-trip speed.

In [3]:

bn_asia = gum.loadBN("res/asia.bgum")
gnb.flow.row(bn_asia, captions=[f"Asia BN — {bn_asia.size()} nodes, {bn_asia.sizeArcs()} arcs"])

Out[3]:

Asia BN — 8 nodes, 8 arcs

In [4]:

def benchmark_bn(bn, exts):
  """Save and reload a BN in every given format, return a comparison table."""
  rows = []
  with tempfile.TemporaryDirectory() as d:
    for ext in exts.split("|"):
      fname = os.path.join(d, f"model.{ext}")
      try:
        t0 = time.perf_counter()
        gum.saveBN(bn, fname)
        t_save = time.perf_counter() - t0
        size = os.path.getsize(fname)
        t0 = time.perf_counter()
        bn2 = gum.loadBN(fname)
        t_load = time.perf_counter() - t0
        names_ok = sorted(bn.names()) == sorted(bn2.names())
        labels_ok = names_ok and all(
          list(bn.variable(n).labels()) == list(bn2.variable(n).labels()) for n in bn.names()
        )
        types_ok = names_ok and all(bn.variable(n).varType() == bn2.variable(n).varType() for n in bn.names())
        rows.append((ext, size, t_save * 1000, t_load * 1000, names_ok, labels_ok, types_ok))
      except Exception as e:
        rows.append((ext, None, None, None, False, False, str(e)[:60]))
  return rows


rows = benchmark_bn(bn_asia, gum.availableBNExts())

header = (
  f"{'ext':8s}  {'size (B)':>9s}  {'save (ms)':>10s}  {'load (ms)':>10s}  {'names':>5s}  {'labels':>6s}  {'types':>5s}"
)
print(header)
print("-" * len(header))
for ext, size, ts, tl, n_ok, l_ok, t_ok in rows:
  if size is not None:
    print(f"{ext:8s}  {size:9d}  {ts:10.3f}  {tl:10.3f}  {str(n_ok):>5s}  {str(l_ok):>6s}  {str(t_ok):>5s}")
  else:
    print(f"{ext:8s}  (unsupported for this model)")

ext        size (B)   save (ms)   load (ms)  names  labels  types
-----------------------------------------------------------------
bif            1554       0.442       1.324   True    True  False
dsl            1872       0.269       0.966   True    True  False
net            2558       0.241       0.977   True    True  False
bifxml         3554       0.217       0.187   True    True   True
o3prm           694       0.163       3.380   True    True   True
uai             430       0.213       3.029  False   False  False
xdsl           2264       1.525       0.191   True    True  False
pkl            1911       0.317       0.159   True    True   True
jgum           1858       0.197       0.099   True    True   True
bgum            808       0.179       0.211   True    True   True

Variable type fidelity

Many real-world networks contain variables of heterogeneous types: labelled (LabelizedVariable), integer ranges (RangeVariable) or discretized continuous domains (DiscretizedVariable). Not all formats can represent these faithfully.

In [5]:

# Build a BN that mixes all main variable types
bn_mixed = gum.BayesNet("mixed_types")
bn_mixed.add(gum.LabelizedVariable("Smoker", "Smoker", ["yes", "no"]))
bn_mixed.add(gum.RangeVariable("Age", "Age", 20, 60))
bn_mixed.add(gum.DiscretizedVariable("Temp", "Temp", [36.0, 37.0, 38.5, 42.0]))
bn_mixed.add(gum.LabelizedVariable("Cancer", "Cancer", ["yes", "no"]))
bn_mixed.addArc("Smoker", "Cancer")
bn_mixed.addArc("Age", "Cancer")
bn_mixed.addArc("Temp", "Cancer")
bn_mixed.cpt("Smoker").fillWith([0.3, 0.7])
bn_mixed.cpt("Age").fillWith(1).normalize()
bn_mixed.cpt("Temp").fillWith(1).normalize()
bn_mixed.cpt("Cancer").fillWith(1).normalize()

print("Variable types in bn_mixed:")
type_names = {
  gum.VarType_LABELIZED: "Labelized",
  gum.VarType_DISCRETIZED: "Discretized",
  gum.VarType_RANGE: "Range",
  gum.VarType_INTEGER: "Integer",
  gum.VarType_NUMERICAL: "Numerical",
}
for n in bn_mixed.names():
  v = bn_mixed.variable(n)
  print(f"  {n:10s}: {type_names.get(v.varType(), f'type={v.varType()}')}")

Variable types in bn_mixed:
  Cancer    : Labelized
  Age       : Range
  Temp      : Discretized
  Smoker    : Labelized

In [6]:

rows = benchmark_bn(bn_mixed, gum.availableBNExts())

header = (
  f"{'ext':8s}  {'size (B)':>9s}  {'save (ms)':>10s}  {'load (ms)':>10s}  {'names':>5s}  {'labels':>6s}  {'types':>5s}"
)
print(header)
print("-" * len(header))
for ext, size, ts, tl, n_ok, l_ok, t_ok in rows:
  if size is not None:
    print(f"{ext:8s}  {size:9d}  {ts:10.3f}  {tl:10.3f}  {str(n_ok):>5s}  {str(l_ok):>6s}  {str(t_ok):>5s}")
  else:
    print(f"{ext:8s}  (unsupported for this model)")

ext        size (B)   save (ms)   load (ms)  names  labels  types
-----------------------------------------------------------------
bif       (unsupported for this model)
dsl       (unsupported for this model)
net       (unsupported for this model)
bifxml         8951       0.450       0.271   True    True   True
o3prm     (unsupported for this model)
uai            6703       0.554      19.874  False   False  False
xdsl           7882       1.725       0.310   True    True  False
pkl           16125       0.394       0.210   True    True   True
jgum          16072       0.304       0.177   True    True   True
bgum           5158       0.232       0.096   True    True   True

Observations for BN:

Format	Notes
`bif`, `dsl`, `net`	Classic, widely used, but do not support `DiscretizedVariable`
`bifxml` / `xdsl`	XML-based; `bifxml` preserves types, `xdsl` does not
`uai`	Compact but loses variable names
`o3prm`	Verbose; requires a full class hierarchy
`pkl`	Python pickle; preserves everything but not portable across pyAgrum versions
``jgum``	Native JSON format; preserves all types, human-readable
``bgum``	Native binary format; smallest files, fastest I/O, preserves all types

Influence diagrams

Influence diagrams have fewer supported formats than BNs.

In [7]:

# Classic Oil Wildcatter influence diagram
diag = gum.loadID("res/OilWildcatter.bgum")
gnb.flow.row(diag, captions=[f"Oil Wildcatter — {diag.size()} nodes"])

Out[7]:

Oil Wildcatter — 6 nodes

In [8]:

def benchmark_id(diag, exts):
  rows = []
  with tempfile.TemporaryDirectory() as d:
    for ext in exts.split("|"):
      fname = os.path.join(d, f"model.{ext}")
      try:
        t0 = time.perf_counter()
        gum.saveID(diag, fname)
        t_save = time.perf_counter() - t0
        size = os.path.getsize(fname)
        t0 = time.perf_counter()
        diag2 = gum.loadID(fname)
        t_load = time.perf_counter() - t0
        names_ok = sorted(diag.names()) == sorted(diag2.names())
        rows.append((ext, size, t_save * 1000, t_load * 1000, names_ok))
      except Exception as e:
        rows.append((ext, None, None, None, str(e)[:60]))
  return rows


rows = benchmark_id(diag, gum.availableIDExts())

header = f"{'ext':8s}  {'size (B)':>9s}  {'save (ms)':>10s}  {'load (ms)':>10s}  {'names':>5s}"
print(header)
print("-" * len(header))
for ext, size, ts, tl, n_ok in rows:
  if size is not None:
    print(f"{ext:8s}  {size:9d}  {ts:10.3f}  {tl:10.3f}  {str(n_ok):>5s}")
  else:
    print(f"{ext:8s}  (unsupported for this model)")

ext        size (B)   save (ms)   load (ms)  names
--------------------------------------------------
xmlbif         2730       0.739       0.381   True
bifxml         2730       0.382       0.231   True
xml            2730       0.842       0.217   True
jgum           1215       0.302       0.108   True
bgum            711       0.172       0.084   True
pkl            1276       0.202       0.101   True

Observations for InfluenceDiagram:

Format	Notes
`bifxml` / `xmlbif` / `xml`	Three aliases for the same XML format
`pkl`	Portable only within the same pyAgrum version
``jgum``	Compact JSON, fully faithful, human-readable
``bgum``	Smallest files, fastest I/O

Markov random fields

MRFs have the smallest set of supported formats.

In [9]:

mrf = gum.fastMRF("A{yes|no}--B{low|mid|high}--C{yes|no}--A;B--D{yes|no}")
gnb.flow.row(mrf, captions=[f"MRF — {mrf.size()} nodes, {mrf.sizeEdges()} edges"])

Out[9]:

MRF — 4 nodes, 4 edges

In [10]:

def benchmark_mrf(mrf, exts):
  rows = []
  with tempfile.TemporaryDirectory() as d:
    for ext in exts.split("|"):
      fname = os.path.join(d, f"model.{ext}")
      try:
        t0 = time.perf_counter()
        gum.saveMRF(mrf, fname)
        t_save = time.perf_counter() - t0
        size = os.path.getsize(fname)
        t0 = time.perf_counter()
        mrf2 = gum.loadMRF(fname)
        t_load = time.perf_counter() - t0
        names_ok = sorted(mrf.names()) == sorted(mrf2.names())
        labels_ok = names_ok and all(
          list(mrf.variable(n).labels()) == list(mrf2.variable(n).labels()) for n in mrf.names()
        )
        rows.append((ext, size, t_save * 1000, t_load * 1000, names_ok, labels_ok))
      except Exception as e:
        rows.append((ext, None, None, None, False, str(e)[:60]))
  return rows


rows = benchmark_mrf(mrf, gum.availableMRFExts())

header = f"{'ext':8s}  {'size (B)':>9s}  {'save (ms)':>10s}  {'load (ms)':>10s}  {'names':>5s}  {'labels':>6s}"
print(header)
print("-" * len(header))
for ext, size, ts, tl, n_ok, l_ok in rows:
  if size is not None:
    print(f"{ext:8s}  {size:9d}  {ts:10.3f}  {tl:10.3f}  {str(n_ok):>5s}  {str(l_ok):>6s}")
  else:
    print(f"{ext:8s}  (unsupported for this model)")

ext        size (B)   save (ms)   load (ms)  names  labels
----------------------------------------------------------
uai             276       0.299       1.010  False   False
jgum           1028       0.178       0.095   True    True
bgum            426       0.128       0.067   True    True
pkl            1090       1.014       0.515   True    True

Observations for MRF:

Format	Notes
`uai`	Only standard MRF format, but loses variable names and labels
`pkl`	Portable only within the same pyAgrum version
``jgum``	Full fidelity, JSON, readable
``bgum``	Smallest, fastest, full fidelity

Why bgum and jgum are the best choice for pyAgrum

The bgum and jgum formats are the native aGrUM formats, designed specifically for all model types supported by pyAgrum. They share the same advantages:

Universal — same format works for BayesNet, InfluenceDiagram and MarkovRandomField.
Full fidelity — all variable types (LabelizedVariable, RangeVariable, DiscretizedVariable, IntegerVariable) are preserved exactly.
Fast — both I/O are among the fastest of all formats.
Compact — bgum typically produces the smallest files; jgum is still compact while remaining human-readable.
No external dependencies — no need for third-party parsers.

The only difference between the two is readability:

``bgum`` (binary) — optimal for production workflows, automated pipelines, storing large models.
``jgum`` (JSON) — easier to inspect, diff, or version-control.

Quick demo: round-trip with bgum and jgum

In [11]:

# Build a BN with mixed variable types to stress-test fidelity
bn_demo = gum.BayesNet("demo")
bn_demo.add(gum.LabelizedVariable("Smoker", "Smoker", ["yes", "no"]))
bn_demo.add(gum.RangeVariable("Age", "Age", 20, 60))
bn_demo.add(gum.DiscretizedVariable("Temp", "Temp", [36.0, 37.0, 38.5, 42.0]))
bn_demo.add(gum.LabelizedVariable("Cancer", "Cancer", ["yes", "no"]))
bn_demo.addArc("Smoker", "Cancer")
bn_demo.addArc("Age", "Cancer")
bn_demo.addArc("Temp", "Cancer")
bn_demo.cpt("Smoker").fillWith([0.3, 0.7])
bn_demo.cpt("Age").fillWith(1).normalize()
bn_demo.cpt("Temp").fillWith(1).normalize()
bn_demo.cpt("Cancer").fillWith(1).normalize()

type_names = {
  gum.VarType_LABELIZED: "Labelized",
  gum.VarType_DISCRETIZED: "Discretized",
  gum.VarType_RANGE: "Range",
  gum.VarType_INTEGER: "Integer",
  gum.VarType_NUMERICAL: "Numerical",
}

print("Variable types before save:")
for n in bn_demo.names():
  v = bn_demo.variable(n)
  print(
    f"  {n:8s}: {type_names.get(v.varType(), f'type={v.varType()}'):12s}  labels={list(v.labels()[:4])}{'...' if v.domainSize() > 4 else ''}"
  )

Variable types before save:
  Cancer  : Labelized     labels=['yes', 'no']
  Age     : Range         labels=['20', '21', '22', '23']...
  Temp    : Discretized   labels=['[36;37[', '[37;38.5[', '[38.5;42]']
  Smoker  : Labelized     labels=['yes', 'no']

In [12]:

with tempfile.TemporaryDirectory() as d:
  for ext in ("bgum", "jgum"):
    fname = os.path.join(d, f"demo.{ext}")
    gum.saveBN(bn_demo, fname)
    bn2 = gum.loadBN(fname)
    print(f"\n--- {ext} ({os.path.getsize(fname)} bytes) ---")
    for n in bn2.names():
      v = bn2.variable(n)
      print(
        f"  {n:8s}: {type_names.get(v.varType(), f'type={v.varType()}'):12s}  labels={list(v.labels()[:4])}{'...' if v.domainSize() > 4 else ''}"
      )


--- bgum (5151 bytes) ---
  Cancer  : Labelized     labels=['yes', 'no']
  Age     : Range         labels=['20', '21', '22', '23']...
  Temp    : Discretized   labels=['[36;37[', '[37;38.5[', '[38.5;42]']
  Smoker  : Labelized     labels=['yes', 'no']

--- jgum (16065 bytes) ---
  Cancer  : Labelized     labels=['yes', 'no']
  Age     : Range         labels=['20', '21', '22', '23']...
  Temp    : Discretized   labels=['[36;37[', '[37;38.5[', '[38.5;42]']
  Smoker  : Labelized     labels=['yes', 'no']

In [13]:

# jgum is plain JSON — easy to inspect
import json

with tempfile.TemporaryDirectory() as d:
  fname = os.path.join(d, "demo.jgum")
  gum.saveBN(bn_demo, fname)
  with open(fname) as f:
    data = json.load(f)
  # Show just the variable descriptions
  print(json.dumps(data.get("variables", data.get("nodes", {})), indent=2))

[
  "Smoker{yes|no}",
  "Age[20,60]",
  "Temp[36,37,38.5,42]",
  "Cancer{yes|no}"
]

bgum and jgum work identically for all model types

In [14]:

with tempfile.TemporaryDirectory() as d:
  # BayesNet
  gum.saveBN(bn_asia, os.path.join(d, "asia.bgum"))
  bn_rt = gum.loadBN(os.path.join(d, "asia.bgum"))
  print(f"BN  round-trip via bgum: names match = {sorted(bn_asia.names()) == sorted(bn_rt.names())}")

  # InfluenceDiagram
  gum.saveID(diag, os.path.join(d, "oil.bgum"))
  diag_rt = gum.loadID(os.path.join(d, "oil.bgum"))
  print(f"ID  round-trip via bgum: names match = {sorted(diag.names()) == sorted(diag_rt.names())}")

  # MarkovRandomField
  gum.saveMRF(mrf, os.path.join(d, "mrf.bgum"))
  mrf_rt = gum.loadMRF(os.path.join(d, "mrf.bgum"))
  print(f"MRF round-trip via bgum: names match = {sorted(mrf.names()) == sorted(mrf_rt.names())}")

BN  round-trip via bgum: names match = True
ID  round-trip via bgum: names match = True
MRF round-trip via bgum: names match = True

I/O Summary

	`bif`/`dsl`/`net`	`bifxml`/`xdsl`	`uai`	`pkl`	``jgum``	``bgum``
BayesNet	✓	✓	partial	✓	✓	✓
InfluenceDiagram	✗	✓	✗	✓	✓	✓
MarkovRandomField	✗	✗	partial	✓	✓	✓
Preserves all variable types	✗	partial	✗	✓	✓	✓
Preserves variable names	✓	✓	✗	✓	✓	✓
Human-readable	✓	✓	✓	✗	✓	✗
Compact size	medium	large	small	medium	small	smallest
I/O speed	medium	medium	fast	fast	fast	fastest
Version-stable	✓	✓	✓	✗	✓	✓

Recommendation:

Use ``bgum`` whenever storage size or I/O speed matters, or when working with non-BN models.
Use ``jgum`` when the file needs to be inspected, diffed, or stored in version control.
Use ``bif``/``bifxml`` only when interoperability with other tools (GeNIe, Netica, …) is required.

In [ ]: