sawmil 0.1.2

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 Inspired by the outdated misvm package.

Note: This is an alpha version.

Installation

pip install sawmil

Requirements

numpy>=1.22
scikit-learn>=1.7.0
gurobipy>=12.0.3
python>=11.0 # recommended: >=12.3

At this point, sawmil package works only with the Gurobi optimizer. You need to obtain a academic/commercial license to use it. We plan to add implementations with other solvers.

Quick start

1. Generate dummy data

from dataset import make_complex_bags
import numpy as np
rng = np.random.default_rng(0)

ds = make_complex_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. NSK with RBF Kernel

Load a kernel:

from sawmil.kernels import get_kernel
from sawmil.bag_kernels import make_bag_kernel
k = get_kernel("rbf", gamma=0.5) # base (single-instance kernel)
bag_k  = make_bag_kernel(k, use_intra_labels=False) # convert single-instance kernel to bagged kernel

Fit NSK Model:

from sawmil.nsk import NSK

clf = NSK(C=0.1, bag_kernel=bag_k, scale_C=True, tol=1e-8, verbose=False).fit(ds, None)
print("Train acc:", clf.score(ds, np.array([b.y for b in ds.bags])))

3. Fit sMIL Model with Linear Kernel

k = get_kernel("linear", normalizer="none") # base (single-instance kernel)
bag_k  = make_bag_kernel(Linear(), normalizer="none", use_intra_labels=False)
clf = sMIL(C=0.1, bag_kernel=bag_k, scale_C=True, tol=1e-6, verbose=False).fit(ds, None)

print("Train acc:", clf.score(ds, np.array([1 if b.y > 0 else -1 for b in ds.bags])))

See more examples in the example.ipynb notebook.