Batch inference, serialization & scikit-learn¶

Batch (vectorized) inference¶

Calling a system evaluates one sample. predict(**arrays) evaluates many at once — pass an array per variable and get an array back. Available on Mamdani and TSK.

import numpy as np
from fuzzytool import datasets

sys, *_ = datasets.credit_risk()
scores = np.array([520.0, 660.0, 800.0])
dtis   = np.array([42.0, 30.0, 10.0])

sys.predict(score=scores, dti=dtis)   # -> array([10.16, 6.  ,  1.91]), one premium per sample

The result matches calling the system once per sample, but the firing, implication and aggregation steps run vectorized.

Saving and loading systems¶

Serialize a Mamdani or TSK system to JSON and restore it later. Connectives and the defuzzifier must be given by name, variables must use built-in membership functions, and TSK consequents must be numbers or coefficient mappings.

import fuzzytool as fz

fz.save(sys, "credit_risk.json")
restored = fz.load("credit_risk.json")

fuzzytool.membership.to_dict/from_dict and Variable.to_dict/from_dict expose the building blocks if you need finer control.

scikit-learn compatibility¶

ANFIS follows the estimator protocol (fit returns self, plus predict, get_params, set_params), so it drops into a Pipeline or GridSearchCV without importing scikit-learn at all:

import fuzzytool as fz

model = fz.ANFIS(n_inputs=2, n_mf=3)
model.get_params()              # {'n_inputs': 2, 'n_mf': 3, 'learning_rate': 0.05}
clone = fz.ANFIS(**model.get_params())