How to Save and Load a JPT

Trained models can be persisted to disk in two formats:

• pickle (default) — fastest, most compact, Python-only

• json — human-readable, portable across Python versions

Saving with Pickle (default)

model.save('my_model.pkl')
# equivalent:
model.save('my_model.pkl', protocol='pickle')

Loading with Pickle

from jpt.trees import JPT

model = JPT.load('my_model.pkl')

Saving and Loading with JSON

JSON serialisation preserves the full model in a text file that can be inspected or version-controlled alongside your code:

model.save('my_model.json', protocol='json')

model2 = JPT.load('my_model.json', protocol='json')

Serialising to a Bytes Buffer

dumps() / loads() work with in-memory buffers, useful for caching or network transfer:

blob = model.dumps(protocol='json')     # → bytes
model3 = JPT.loads(blob, protocol='json')

Choosing a Format

Format	File size	Speed	Use when …
pickle	small	fast	local caching, pipelines
json	larger	slower	portability, inspection, version control

Note

Pickle files are tied to the Python and pyjpt version that created them. Use JSON for long-term storage or cross-version compatibility.

See also

Learning of Joint Probability Distributions — shows a full fit-save-load round-trip.