Metadata-Version: 2.4
Name: evidencelib
Version: 1.1.0
Summary: Computational belief functions for DST and DSmT.
Project-URL: Documentation, https://evidencelib.readthedocs.io/en/latest/
Project-URL: Source, https://github.com/itaprac/evidencelib
Project-URL: Issues, https://github.com/itaprac/evidencelib/issues
Project-URL: Changelog, https://github.com/itaprac/evidencelib/blob/main/CHANGELOG.md
Author: evidencelib contributors
License: MIT License
        
        Copyright (c) 2026 evidencelib contributors
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
License-File: LICENSE
Keywords: belief-functions,dempster-shafer,dsmt,dst,evidence-theory
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=3.10
Provides-Extra: dev
Requires-Dist: build>=1.0; extra == 'dev'
Requires-Dist: coverage[toml]>=7.6; extra == 'dev'
Requires-Dist: matplotlib>=3.7; extra == 'dev'
Requires-Dist: mypy>=1.11; extra == 'dev'
Requires-Dist: myst-parser>=4.0; extra == 'dev'
Requires-Dist: pytest>=8.0; extra == 'dev'
Requires-Dist: ruff>=0.6; extra == 'dev'
Requires-Dist: sphinx-rtd-theme>=3.0; extra == 'dev'
Requires-Dist: sphinx>=8.0; extra == 'dev'
Provides-Extra: docs
Requires-Dist: myst-parser>=4.0; extra == 'docs'
Requires-Dist: sphinx-rtd-theme>=3.0; extra == 'docs'
Requires-Dist: sphinx>=8.0; extra == 'docs'
Provides-Extra: plot
Requires-Dist: matplotlib>=3.7; extra == 'plot'
Description-Content-Type: text/markdown

# evidencelib

[![CI](https://github.com/itaprac/evidencelib/actions/workflows/ci.yml/badge.svg)](https://github.com/itaprac/evidencelib/actions/workflows/ci.yml)
[![PyPI](https://img.shields.io/pypi/v/evidencelib.svg)](https://pypi.org/project/evidencelib/)
[![Python Version](https://img.shields.io/pypi/pyversions/evidencelib.svg)](https://pypi.org/project/evidencelib/)
[![Documentation Status](https://readthedocs.org/projects/evidencelib/badge/?version=latest)](https://evidencelib.readthedocs.io/en/latest/?badge=latest)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

**evidencelib** is a Python library for belief-function calculations in Dempster-Shafer theory (DST) and Dezert-Smarandache theory (DSmT). It provides a compact, zero-dependency quantitative core for reasoning under uncertainty using belief functions.

- **Documentation:** https://evidencelib.readthedocs.io
- **Source Code:** https://github.com/itaprac/evidencelib
- **PyPI:** https://pypi.org/project/evidencelib/
- **Issue Tracker:** https://github.com/itaprac/evidencelib/issues

---

## Features

- **Multiple models** — Shafer's classical DST, free DSmT, and constrained hybrid DSm models
- **Proposition algebra** — symbolic construction of unions, intersections, and parsing from strings
- **Fusion rules** — Dempster, Yager, Smets/TBM, Dubois-Prade, Hybrid DSm, PCR5, PCR6
- **Belief measures** — mass, belief, plausibility, commonality, conflict
- **Decision support** — generalized pignistic probabilities for arbitrary propositions, singletons, and disjoint Venn regions
- **Import/export** — round-trip JSON and CSV, plus publication-ready LaTeX tables
- **Optional plotting** — mass assignment bars, source comparison heatmaps, Venn region diagrams, and decision plots
- **Zero dependencies** — pure Python, no external packages required
- **Fully typed** — type hints throughout the codebase

---

## Installation

```bash
pip install evidencelib
```

For plotting support:

```bash
pip install "evidencelib[plot]"
```

Requires Python 3.10 or later.

---

## Quick Start

### Basic DST Example

```python
from evidencelib import Frame

# Create a frame with mutually exclusive hypotheses
frame = Frame.dst(["Alive", "Dead"])
Alive, Dead = frame.symbols()

# Define a basic belief assignment
m = frame.mass({
    Alive: 0.2,
    Dead: 0.5,
    Alive | Dead: 0.3,
})

# Query belief measures
print(m.belief(Alive))        # 0.2
print(m.plausibility(Alive))  # 0.5
print(m.pignistic())          # {"Alive": 0.35, "Dead": 0.65}
print(m.decision())           # "Dead"
```

### Combining Evidence

```python
from evidencelib import Frame

frame = Frame.dst(["A", "B"])
A, B = frame.symbols()

m1 = frame.mass({A: 0.6, A | B: 0.4})
m2 = frame.mass({B: 0.3, A | B: 0.7})

# Dempster's rule
print(m1.dempster(m2).to_dict())
# {"A": 0.512, "A|B": 0.341, "B": 0.146}

# PCR6
print(m1.pcr6(m2).to_dict())
# {"A": 0.54, "A|B": 0.28, "B": 0.18}
```

### DSmT with Overlapping Hypotheses

```python
from evidencelib import Frame

# Free DSm model — hypotheses may overlap
frame = Frame.dsmt(["A", "B"])
A, B = frame.symbols()

m = frame.mass({
    A: 0.2,
    B: 0.3,
    A & B: 0.4,
    A | B: 0.1,
})

print(m.pignistic())          # singleton scores (may not sum to 1)
print(m.pignistic_of(A & B))  # generalized BetP for any proposition
print(m.pignistic_regions())  # probability over disjoint Venn regions
```

### Dynamic Hybrid DSmT

When constraints are learned after source masses were elicited, keep the
sources on their original frame and pass the constrained target model during
fusion. This preserves the original focal propositions required by the full
DSmH `S1 + S2 + S3` transfer.

```python
source = Frame.dst(["A", "B", "C"])
A, B, C = source.symbols()
m1 = source.mass({A: 0.1, B: 0.4, C: 0.2, A | B: 0.3})
m2 = source.mass({A: 0.5, B: 0.1, C: 0.3, A | B: 0.1})

# New information establishes that C is non-existent.
target = Frame.hybrid(["A", "B", "C"], exclusive=True, empty=["C"])
combined = m1.dsmh(m2, model=target)
print(combined.to_dict())  # {"A": 0.34, "A|B": 0.41, "B": 0.25}
```

---

## API Overview

### Frames

| Constructor | Description |
|---|---|
| `Frame.dst(atoms)` | Shafer's model — exhaustive and mutually exclusive hypotheses |
| `Frame.dsmt(atoms)` | Free DSm model — hypotheses may overlap |
| `Frame.hybrid(atoms, empty=..., exclusive=...)` | Constrained DSm model with explicit constraints |

### Propositions

| Operation | Description |
|---|---|
| `A \| B` | Union / disjunction |
| `A & B` | Intersection / conjunction |
| `frame.proposition("A ∩ (B ∪ C)")` | Parse from string |
| `frame.elements()` | Generate power set or hyper-power set |

### Belief Measures

| Method | Description |
|---|---|
| `mass(A)` | Direct mass assigned to proposition A |
| `belief(A)` | Sum of masses contained in A |
| `plausibility(A)` | Sum of masses intersecting A |
| `commonality(A)` | Sum of masses containing A |
| `conflict` | Mass assigned to the empty proposition |

### Fusion Rules

| Method | Description |
|---|---|
| `conjunctive(...)` | Unnormalized conjunctive rule |
| `dempster(..., model=...)` | Dempster's normalized rule, optionally under a target model |
| `smets(..., model=...)` | TBM/Smets rule — keeps conflict on empty set |
| `yager(..., model=...)` | Yager's rule — transfers conflict to total ignorance |
| `dsmc(...)` | Classic DSm conjunctive rule |
| `dsmh(..., model=...)` | Full hybrid DSm `S1 + S2 + S3` rule; explicit model supports dynamic constraints |
| `dubois_prade(...)` | Static, two-source Dubois-Prade conflict transfer |
| `pcr5(...)` | PCR5 for two sources |
| `pcr6(...)` | PCR6 for two or more sources |

### Decision Support

| Method | Description |
|---|---|
| `pignistic_of(A)` | Generalized pignistic probability for any proposition |
| `pignistic()` | Singleton pignistic scores; empty-set conflict is normalized by default |
| `pignistic_regions()` | Probability distribution over disjoint Venn regions |
| `decision()` | Singleton with the largest pignistic probability |

### Import and Export

| Method | Description |
|---|---|
| `to_json()` / `from_json(frame, text)` | Round-trip mass assignments as JSON |
| `to_csv()` / `from_csv(frame, text)` | Round-trip mass assignments as CSV |
| `to_latex()` | Export focal or all proposition rows as a LaTeX table |

### Plotting

Plotting is optional and requires `evidencelib[plot]`.

```python
m.plot()
m.plot_venn()
m.plot_belief_plausibility()
m.plot_pignistic_decision()
m1.plot_comparison(m2, labels=["sensor", "expert"])
```

Plots keep bar charts simple by default and use a green heatmap colormap.
Colors, legends, annotations, and heatmap colormaps can be customized with
Matplotlib-compatible values when needed:

```python
m.plot(colors="tab:blue", annotate=False)
m1.plot_comparison(
    m2,
    cmap="viridis",
    annotation_text_colors=("black", "white"),
)
m.plot_pignistic_decision(highlight_decision=False)
m.plot_venn(labels=["H1", "H2"], values="mass")
```

Function forms are also available:

```python
from evidencelib import plot_mass, plot_mass_comparison, plot_venn
```

---

## Development

```bash
git clone https://github.com/itaprac/evidencelib.git
cd evidencelib
python3.10 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,docs]"
```

Run tests:

```bash
python -m pytest -q
```

Run the same quality gates as CI:

```bash
python -m ruff check .
python -m mypy
python -m coverage run -m pytest -q
python -m coverage report --fail-under=90
```

Build documentation:

```bash
python -m sphinx -W -b html docs docs/_build/html
```

---

## Examples

See the [`examples/`](examples/) directory for complete scripts:

- [`basic_dst.py`](examples/basic_dst.py) — simple DST frame and belief measures
- [`rules_dst.py`](examples/rules_dst.py) — comparing fusion rules
- [`zadeh.py`](examples/zadeh.py) — Zadeh's classic counterexample
- [`dsmt_fusion.py`](examples/dsmt_fusion.py) — DSmT evidence fusion
- [`hybrid_dsmt.py`](examples/hybrid_dsmt.py) — constrained hybrid DSm model
- [`plotting.py`](examples/plotting.py) — optional plotting examples

---

## References

- Shafer, G. (1976). *A Mathematical Theory of Evidence*. Princeton University Press.
- Smarandache, F., & Dezert, J. (eds.). *Advances and Applications of DSmT for Information Fusion*.
- Dezert, J., & Smarandache, F. *An Introduction to DSmT*.
- Zadeh, L. A. (1986). A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination. *AI Magazine*, 7(2), 85-90.

---

## License

MIT License — see [LICENSE](LICENSE) for details.

Release notes and migration guidance are maintained in [CHANGELOG.md](CHANGELOG.md).
