Metadata-Version: 2.4
Name: pmf-dark
Version: 0.1.0
Summary: Modelling species dark diversity using bayesian Probablistic Matrix Factorisation
License: 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.
Author: davidyshen
Author-email: contact@davidyshen.com
Requires-Python: >=3.13,<3.15
Classifier: License :: Other/Proprietary License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: matplotlib (>=3.10.9,<4.0.0)
Requires-Dist: numpy (>=2.4.6,<3.0.0)
Requires-Dist: pandas (>=3.0.3,<4.0.0)
Requires-Dist: pyro-ppl (>=1.9.0,<2.0.0)
Description-Content-Type: text/markdown

# PMF-dark: Using matrix factorisation for dark diversity estimation

## Overview

This repository implements a **PMF-dark** using Bayesian Probabilistic Matrix Factorisation to estimate **dark diversity** - the set of species absent from a site despite having suitable environmental conditions. The method uses **counterfactual predictions** to reconstruct the potential species pool by separating environmental effects from unmeasured drivers of absence (e.g., land-use degradation, dispersal limitation, biotic interactions).

## The Problem: What is Dark Diversity?

Traditional biodiversity assessments only count observed species (alpha diversity). However, many species are absent from sites where they *could* thrive based on environmental conditions. This "**dark diversity**" represents:

- Species lost due to historical or ongoing land-use degradation
- Species unable to reach suitable sites due to dispersal limitation
- Species suppressed by biotic interactions

Quantifying dark diversity is crucial for:
- Conservation planning and restoration potential assessment
- Understanding true biodiversity patterns
- Identifying areas with highest restoration value

## Methodology

### Core Model

The framework decomposes species occurrence probabilities into **three additive components**:

$$\text{logit}(p_{ij}) = \underbrace{\alpha_j}_{\text{Intercept}} + \underbrace{f_j(\mathbf{x}_i)}_{\text{Environmental Effects}} + \underbrace{\mathbf{w}_i^\top \mathbf{z}_j}_{\text{Latent Factors}}$$

Where:
- **$\alpha_j$**: Species-specific baseline prevalence
- **$f_j(\mathbf{x}_i)$**: Environmental response function to measured abiotic variables (temperature, pH, elevation, etc.), which can be modelled as linear, Gaussian niche, or non-linear (e.g. Bayesian neural network)
- **$\mathbf{w}_i^\top \mathbf{z}_j$**: Latent factors capturing unmeasured drivers of absence

### Key Innovation: Counterfactual Predictions

1. **Full Predictions**: Include all components (environment + latent factors)
   - Represents observed diversity with all drivers active
   
2. **Environment-Only Predictions**: Exclude latent factors
   - Represents potential diversity (setting $\mathbf{w}_i^\top \mathbf{z}_j = 0$)
   
3. **Dark Diversity Proxy**: Difference between full and environment-only predictions
   - Quantifies species lost to unmeasured stressors

### Inference: Stochastic Variational Inference (SVI)

The model is fit using **Pyro-based SVI**, which:
- Handles high-dimensional ecological matrices efficiently
- Treats inference as an optimisation problem (ELBO maximisation)
- Scales to thousands of sites and species
- Requires minimal computational resources

## Repository Structure

```
PMF_dark/
├── README.md                                    # This file
├── mat_fact_dark_div.ipynb                     # Main analysis notebook
├── data/
│   ├── survey.csv                              # Species presence/absence matrix (sites × species)
│   ├── env.csv                                 # Environmental predictors (sites × covariates)
│   └── truth.csv                               # Ground truth data (if available)
└── output/
    ├── mat_fact_predicted_probabilities_full.csv           # Full model predictions
    ├── mat_fact_predicted_probabilities_env_only.csv       # Environment-only predictions
    └── mat_fact_dark_diversity_proxy.csv                   # Dark diversity estimates
```

## Installation

### Requirements

- Python 3.13 or 3.14
- PyTorch (with CUDA support if using GPU)
- Pyro (pyro-ppl)
- Pandas
- NumPy
- scikit-learn
- scipy

### Setup

To ensure PyTorch is installed with the correct CUDA version for your system, it is recommended to install PyTorch manually **first** before installing the package or its other dependencies.

#### 1. Setup Virtual Environment
```bash
# Clone the repository
git clone https://github.com/davidyshen/PMF_dark.git
cd PMF_dark

# Create virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
```

#### 2. Install PyTorch with CUDA
Visit the [PyTorch Getting Started guide](https://pytorch.org/get-started/locally/) to select the correct command for your CUDA version and OS. For example, to install PyTorch with CUDA 12.4 support on Windows/Linux:
```bash
pip install torch --index-url https://download.pytorch.org/whl/cu124
```

If not using CUDA, simply install the CPU version:
```bash
pip install torch
```


#### 3. Install remaining dependencies
If installing via pip:
```bash
pip install pyro-ppl pandas numpy scikit-learn scipy jupyter
```

If using Poetry:
```bash
# This will install the package and its remaining dependencies into your environment
poetry install
```

## Usage

### Running the Full Analysis

1. Prepare your data in `data/` directory:
   - `survey.csv`: Species presence/absence (rows = sites, columns = species, values = 0/1)
   - `env.csv`: Environmental predictors (rows = sites, columns = variables)

2. Open and run the Jupyter notebook:
   ```bash
   jupyter notebook mat_fact_dark_div.ipynb
   ```

3. The notebook will:
   - Load and standardise data
   - Fit the matrix factorisation model (2,500 iterations)
   - Generate predictions and save CSV outputs

### Customisation

Key parameters in the notebook:

```python
# Model parameters
num_factors = 5                # Number of latent factors (adjust based on data complexity)
num_iterations = 2500          # Model iterations

# Learning rate
Adam({"lr": 0.01})            # Adjust if convergence is slow
```

## Output Files

- **mat_fact_predicted_probabilities_full.csv**: Predicted species occurrence probabilities including all effects
- **mat_fact_predicted_probabilities_env_only.csv**: Predicted probabilities using only environmental effects
- **mat_fact_dark_diversity_proxy.csv**: Dark diversity estimates (full - env_only)

## Data Format

### survey.csv
```
site_id,species_1,species_2,...,species_n,ID,x,y
site_1,0,1,0,...,1,id_1,100.5,200.3
site_2,1,0,1,...,0,id_2,101.2,201.5
...
```
- Rows: Sites/locations
- Columns: Species (0/1 presence/absence) + ID + spatial coordinates
- **Note**: ID and spatial coordinates are automatically extracted/dropped

### env.csv
```
site_id,temp,pH,elevation,...,ID,landuse
site_1,15.2,7.1,500,...,id_1,degraded
site_2,14.8,6.9,520,...,id_2,pristine
...
```
- Rows: Sites matching survey.csv
- Columns: Environmental predictors + ID + land-use
- **Note**: ID and land-use columns are dropped; only abiotic predictors are used

## Interpretation of Results

### Dark Diversity Proxy Values
- **High values (close to 1)**: Species should be present based on environment but are absent—candidate for restoration
- **Low values (close to 0)**: Species absence explained by environmental conditions
- **Negative values**: Model predicts species should be absent (rare, indicates environmental unsuitability)

### Key Metrics
- **AUC (Area Under ROC Curve)**: Overall model discrimination (0.5 = random, 1.0 = perfect)
- **Brier Score**: Prediction calibration error (lower is better)
- **F1 Score**: Balance between precision and recall

## Advantages of This Approach

✓ **No subjective benchmarking**: Automated separation of environmental vs. unmeasured effects  
✓ **Mathematically principled**: Latent factors naturally absorb degradation signals  
✓ **Scalable**: SVI handles thousands of species and sites  
✓ **Species-specific**: Each species can have unique environmental responses  
✓ **Reproducible**: Fully probabilistic framework with clear assumptions

## Limitations

- Assumes species responses are log-linear (logit link)
- Requires sufficient environmental variation to estimate effects reliably
- May overestimate dark diversity if detection is imperfect
- Computational cost increases with number of species and sites
- Requires careful tuning of number of latent factors

## References & Theoretical Background

### Key Concepts
- **Joint Species Distribution Models (JSDMs)**: Latent variable models for multivariate species data
- **Matrix Factorisation**: Low-rank decomposition of high-dimensional species matrices
- **Stochastic Variational Inference**: Scalable Bayesian inference for probabilistic models
- **Counterfactual Predictions**: Causal inference approach to estimate potential outcomes

