---
license: mit
library_name: sklearn
tags:
  - tabular-classification
  - food-safety
  - listeria
  - soil-microbiome
  - lightgbm
  - baseline
datasets:
  - food-ai-nexus/soil-listeria-us-land
metrics:
  - f1
  - accuracy
  - precision
  - recall
  - roc_auc
model-index:
  - name: soil-listeria-baseline
    results:
      - task:
          type: tabular-classification
          name: Binary Classification
        dataset:
          name: soil-listeria-us-land
          type: food-ai-nexus/soil-listeria-us-land
        metrics:
          - type: f1
            value: 0.8749
            name: F1 (5-fold CV)
---

# Soil Listeria Baseline Model

A **LightGBM** binary classifier that predicts *Listeria* presence in soil
samples from geochemical, environmental, and land-use features. This is the
official baseline model for the
[soil-listeria-us-land](https://huggingface.co/datasets/food-ai-nexus/soil-listeria-us-land)
leaderboard.

## Model Description

| Property | Value |
|---|---|
| **Algorithm** | LightGBM (`LGBMClassifier`) |
| **Task** | Binary classification — *Listeria* detected (1) vs. not detected (0) |
| **Dataset** | [food-ai-nexus/soil-listeria-us-land](https://huggingface.co/datasets/food-ai-nexus/soil-listeria-us-land) |
| **Input features** | Soil geochemistry, climate, and land-use variables |
| **Engineered features** | `cn_ratio` (total carbon / total nitrogen), `temperature_range` (max - min temperature) |
| **Decision threshold** | 0.36 (tuned on out-of-fold predictions) |

The dataset originates from a US-wide soil survey linking soil properties to
*Listeria* isolation. Positive labels correspond to samples where at least one
*Listeria* isolate was recovered.

## Training Procedure

1. **Feature engineering** — Two domain-motivated features are added:
   - `cn_ratio = total_carbon_pct / total_nitrogen_pct` (with inf/NaN handling via median imputation)
   - `temperature_range = max_temperature_c - min_temperature_c`
   - Any remaining NaN values are filled with column medians.

2. **Hyperparameter search** — `RandomizedSearchCV` (100 iterations) over both
   XGBoost and LightGBM, scored by F1, using 5-fold stratified CV
   (`random_state=42`).

3. **Model selection** — The model with the highest mean CV F1 is selected.

4. **Threshold tuning** — Out-of-fold predicted probabilities from the best
   model are used to sweep thresholds from 0.30 to 0.70 (step 0.01), maximizing
   F1.

5. **Final retraining** — The selected model is retrained on the full training
   set with the best hyperparameters.

### Best Hyperparameters (LightGBM)

| Parameter | Value |
|---|---|
| `boosting_type` | gbdt |
| `n_estimators` | 417 |
| `max_depth` | 6 |
| `num_leaves` | 31 |
| `learning_rate` | 0.0543 |
| `subsample` | 0.9369 |
| `colsample_bytree` | 0.6407 |
| `min_child_samples` | 15 |
| `reg_alpha` | 0.8059 |
| `reg_lambda` | 0.0557 |

## Evaluation Results

### Cross-Validation (5-fold Stratified)

| Model | Mean F1 | Std F1 |
|---|---|---|
| **LightGBM** | **0.8749** | 0.0321 |
| XGBoost | 0.8714 | 0.0109 |

### Threshold-Tuned (Out-of-Fold)

| Metric | Value |
|---|---|
| Threshold | 0.36 |
| F1 (OOF) | 0.8769 |

### Top 10 Features (LightGBM importance — split count)

| Rank | Feature | Importance |
|---|---|---|
| 1 | sodium_mg_kg | 266 |
| 2 | copper_mg_kg | 183 |
| 3 | moisture | 153 |
| 4 | molybdenum_mg_kg | 150 |
| 5 | zinc_mg_kg | 131 |
| 6 | cropland_pct | 122 |
| 7 | magnesium_mg_kg | 118 |
| 8 | phosphorus_mg_kg | 109 |
| 9 | cn_ratio | 100 |
| 10 | manganese_mg_kg | 95 |

## How to Reproduce

```bash
# Clone the model repository
git clone https://huggingface.co/food-ai-nexus/soil-listeria-baseline
cd soil-listeria-baseline

# Install dependencies
pip install -r requirements.txt

# Run training (requires the dataset repo as a sibling directory)
python train.py
```

The training script expects the dataset at
`../soil-listeria-us-land/data/{train,test}-00000-of-00001.parquet`. You can
obtain it from:

```bash
git clone https://huggingface.co/datasets/food-ai-nexus/soil-listeria-us-land
```

### Inference

```python
import joblib
import numpy as np

model = joblib.load("model/model.joblib")
THRESHOLD = 0.36

# X_new: pandas DataFrame with the same feature columns as training data
probas = model.predict_proba(X_new)[:, 1]
predictions = (probas >= THRESHOLD).astype(int)
```

## Citation

If you use this model or dataset, please cite the original study:

> Liao, J., Wiedmann, M., & Bhatt, V. (2021). Nationwide genomic atlas of
> soil-associated *Listeria* reveals effects of selection and population ecology
> on pangenome evolution. *Nature Microbiology*, 6, 1021--1030.

```bibtex
@article{liao2021nationwide,
  title={Nationwide genomic atlas of soil-associated {Listeria} reveals effects
         of selection and population ecology on pangenome evolution},
  author={Liao, Jingqiu and Wiedmann, Martin and Bhatt, Vipul},
  journal={Nature Microbiology},
  volume={6},
  pages={1021--1030},
  year={2021},
  publisher={Nature Publishing Group}
}
```