Metadata-Version: 2.4
Name: sawmil
Version: 0.1.2
Summary: Sparse Multiple-Instance Learning: SVM, NSK, sMIL and sAwMIL.
Project-URL: Homepage, https://github.com/carlomarxdk/sawmil
Project-URL: Repository, https://github.com/carlomarxdk/sawmil
Project-URL: Issues, https://github.com/carlomarxdk/sawmil/issues
Project-URL: Documentation, https://github.com/carlomarxdk/sawmil#readme
Author: Germans Savcisens, Tina Eliassi-Rad
License: MIT License
        
        Copyright (c) 2025 Germans Savcisens and Tina Eliassi-Rad
        
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License-File: LICENSE
Keywords: MIL,bag,kernels,multiple-instance-learning,sawmil,sparse,svm
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Typing :: Typed
Requires-Python: >=3.11
Requires-Dist: gurobipy>=12.0.3
Requires-Dist: numpy>=1.22
Requires-Dist: scikit-learn>=1.2
Provides-Extra: dev
Requires-Dist: build; extra == 'dev'
Requires-Dist: mypy; extra == 'dev'
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Requires-Dist: ruff; extra == 'dev'
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Description-Content-Type: text/markdown

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[![DOI](https://zenodo.org/badge/1046623935.svg)](https://doi.org/10.5281/zenodo.16990499)

# Sparse Multiple-Instance Learning in Python

MIL models based on the Support Vector Machines (NSK, sMIL, sAwMIL).
Inspired by the outdated [misvm](https://github.com/garydoranjr/misvm) package.

**Note**: This is an alpha version.

## Installation

```bash
pip install sawmil
```

## Requirements

```bash
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](https://gurobi.com) 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

``` python
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:**

```python
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:**

```python
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

```python
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`](https://github.com/carlomarxdk/sawmil/blob/main/example.ipynb) notebook.
