sawmil 0.1.10

Sparse Multiple-Instance Learning: SVM, NSK, sMIL and sAwMIL.

Sparse Multiple-Instance Learning in Python

MIL models based on the Support Vector Machines (NSK, sMIL, sAwMIL). Inspired by the outdated misvm package.

Documentation

Refer to the Initial Documentation.

Implemented Models

Normalized Set Kernels (NSK)

Gärtner, Thomas, Peter A. Flach, Adam Kowalczyk, and Alex J. Smola. Multi-instance kernels. Proceedings of the 19th International Conference on Machine Learning (2002).

Sparse MIL (sMIL)

Bunescu, Razvan C., and Raymond J. Mooney. Multiple instance learning for sparse positive bags. Proceedings of the 24th International Conference on Machine Learning (2007).

Sparse Aware MIL (sAwMIL)

Classifier used in trilemma-of-truth:

Savcisens, Germans, and Tina Eliassi-Rad. The Trilemma of Truth in Large Language Models. arXiv preprint arXiv:2506.23921 (2025).

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Installation

sawmil supports two QP backends: Gurobi and OSQP. By default, the base package installs without any solver; pick one (or both) via extras.

Base package (no solver)

pip install sawmil
# it installs numpy>=1.22 and scikit-learn>=1.7.0

Option 1 — Gurobi backend

Gurobi is commercial software. You’ll need a valid license (academic or commercial), refer to the official website.

pip install "sawmil[gurobi]"
# in additionl to the base packages, it install gurobi>12.0.3

Option 2 — OSQP backend

pip install "sawmil[osqp]"
# in additionl to the base packages, it installs osqp>=1.0.4 and scipy

Option 3 — All supported solvers

pip install "sawmil[full]"

Picking the solver in code

from sawmil import SVM, RBF

k = RBF(gamma = 0.1)
# solver= "osqp" (default is "gurobi")
# SVM is for single-instances 
clf = SVM(C=1.0, 
          kernel=k, 
          solver="osqp").fit(X, y)

Quick start

1. Generate Dummy Data

from sawmil.data import generate_dummy_bags
import numpy as np
rng = np.random.default_rng(0)

ds = generate_dummy_bags(
    n_pos=300, n_neg=100, inst_per_bag=(5, 15), d=2,
    pos_centers=((+2,+1), (+4,+3)),
    neg_centers=((-1.5,-1.0), (-3.0,+0.5)),
    pos_scales=((2.0, 0.6), (1.2, 0.8)),
    neg_scales=((1.5, 0.5), (2.5, 0.9)),
    pos_intra_rate=(0.25, 0.85),
    ensure_pos_in_every_pos_bag=True,
    neg_pos_noise_rate=(0.00, 0.05),
    pos_neg_noise_rate=(0.00, 0.20),
    outlier_rate=0.1,
    outlier_scale=8.0,
    random_state=42,
)

2. Fit NSK with RBF Kernel

Load a kernel:

from sawmil.kernels import get_kernel, RBF
k1 = get_kernel("rbf", gamma=0.1)
k2 = RBF(gamma=0.1)
# k1 == k2

Fit NSK Model:

from sawmil.nsk import NSK

clf = NSK(C=1, kernel=k, 
          # bag kernel settings
          normalizer='average',
          # solver params
          scale_C=True, 
          tol=1e-8, 
          verbose=False).fit(ds, None)
y = ds.y
print("Train acc:", clf.score(ds, y))

3. Fit sMIL Model with Linear Kernel

from sawmil.smil import sMIL

k = get_kernel("linear") # base (single-instance kernel)
clf = sMIL(C=0.1, 
           kernel=k, 
           scale_C=True, 
           tol=1e-8, 
           verbose=False).fit(ds, None)

See more examples in the example.ipynb notebook.

4. Fit sAwMIL with Combined Kernels

from sawmil.kernels import Product, Polynomial, Linear, RBF, Sum, Scale
from sawmil.sawmil import sAwMIL

k = Sum(Linear(), 
        Scale(0.5, 
              Product(Polynomial(degree=2), RBF(gamma=1.0))))

clf = sAwMIL(C=0.1, 
             kernel=k,
             solver="gurobi", 
             eta=0.95) # here eta is high, since all items in the bag are relevant
clf.fit(ds)
print("Train acc:", clf.score(ds, ds.y))

Citation

If you use sawmil package in academic work, please cite:

Savcisens, G. & Eliassi-Rad, T. sAwMIL: Python package for Sparse Multiple-Instance Learning (2025).

@software{savcisens2025sawmil,
  author = {Savcisens, Germans and Eliassi-Rad, Tina},
  title = {sAwMIL: Python package for Sparse Multiple-Instance Learning},
  year = {2025},
  doi = {10.5281/zenodo.16990499},
  url = {https://github.com/carlomarxdk/sawmil}
}

If you want to reference a specific version of the package, find the correct DOI here.