fysvm 0.0.3

Fuzzy rule-space max-margin classifiers built on scikit-learn.

fysvm

Intrinsically interpretable fuzzy SVM-style classifiers built on scikit-learn.

fysvm trains linear max-margin classifiers over fuzzy rule activations instead of raw feature coordinates. Every learned dimension is a human-readable linguistic rule such as:

IF glucose is high AND bmi is high THEN positive

Installation

uv pip install fysvm==0.0.1

Requires Python ≥ 3.14 and depends only on numpy, scipy, and scikit-learn.

Quickstart

from fysvm import FuzzyRuleSVM
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

data = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(
    data.data, data.target, test_size=0.3, random_state=0
)

clf = FuzzyRuleSVM(
    max_rule_length=2,
    max_rules=128,
    penalty="l1",
    feature_names=data.feature_names,
    random_state=0,
)
clf.fit(X_train, y_train)

print(clf.score(X_test, y_test))

How it works

The estimator has three stages:

1. Fuzzy concepts. Each numeric feature is summarized by three linguistic terms — low, medium, high — defined by triangular membership functions anchored at data quantiles. Every sample gets a membership score in [0, 1] for each concept.

2. Rule generation. All conjunctions of up to max_rule_length concepts are enumerated as candidate rules (e.g. glucose is high AND bmi is high). Candidates are scored by discriminative power and coverage; the top max_rules are kept.

3. Max-margin learning. Each sample is mapped to a firing-strength vector φ(x) ∈ [0, 1]^K where entry k is the fuzzy AND of the concepts in rule k. A sparse linear SVM is trained on this activation space:

f(x) = Σ_k β_k · φ_k(x) + b

Explaining predictions

Because the decision function is a weighted sum of fuzzy rule firings, explanations are the model computation — no proxy, no approximation.

Per-sample explanation

explanation = clf.explain(X_test[:1])[0]

Returns the bias, the net margin, and the top-contributing rules sorted by absolute contribution. Each rule has:

• rule — a human-readable string like IF feature is low THEN class_A
• firing — how strongly the sample matches the rule antecedent in [0, 1]
• weight — the learned SVM coefficient β_k
• contribution — firing × weight, the rule's dollar-value impact on the margin

Contributions sum with the bias to the predicted margin:

assert abs(explanation["margin"]
           - (explanation["bias"] + explanation["net_rule_contribution"])) < 1e-12

Global rule inspection

for item in clf.support_rules():
    print(item["rule"], item["weight"])

Returns rules with non-zero coefficients, sorted by absolute weight.

Fuzzy concept membership

concepts = clf.concept_memberships(X_test[:1])[0]
# {"glucose": {"low": 0.0, "medium": 0.3, "high": 0.7}, ...}

Fuzzy margin violations

violations = clf.fuzzy_violations(X_test, y_test)
# Each item: {"slack": 0.42, "memberships": {"cleanly_classified": 0.58,
#             "borderline": 0.42, "strong_violation": 0.0}}

API Reference

FuzzyRuleSVM (aliased as SparseMaxMarginFuzzyRuleMachine)

Parameter	Default	Description
C	1.0	Inverse regularization strength
penalty	"l1"	"l1" or "l2" SVM penalty
max_rule_length	2	Max conjuncts per rule (≤ n_features)
max_rules	256	Max candidate rules kept
min_rule_coverage	0.02	Minimum fuzzy support for a candidate
and_operator	"min"	"min", "product", or "softmin"
feature_names	None	Column names for readable rule strings
class_weight	None	"balanced" or dict for imbalanced classes

See the docstrings for the full parameter list.

Example notebooks

See the examples/ directory:

• basic_usage.py — fit, predict, evaluate
• explain_predictions.py — detailed explanation walkthrough

Citation

If you use fysvm in published work, please cite the accompanying paper:

@misc{davidsen2026fuzzy,
  author = {Davidsen, S. A.},
  title  = {Fuzzy Rule-Space Max-Margin Classifiers},
  year   = {2026}
}