santh-dedup 0.1.1

> Part of the [Santh](https://santh.dev) security research ecosystem.

# dedup

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High-performance dataset deduplication for ML training data using MinHash + LSH.

## Features

- **MinHash + LSH**: Industry-standard near-duplicate detection with configurable similarity thresholds
- **Fast Hashing**: Efficient hash computation for high throughput
- **Streaming**: Process billions of documents without loading all into memory
- **tenshift Integration**: Plugs into data pipelines as a `Transform`
- **Configurable**: Tune signature size, band count, and thresholds for your data
- **Robust**: Fuzz-quality input handling, zero `unwrap` in production code

## Quick Start

```rust
use dedup::{Config, DedupTransformer};

let config = Config::default()
    .with_similarity_threshold(0.9);

let mut dedup = DedupTransformer::new(config)?;

// Add documents
dedup.push(sample1);
dedup.push(sample2);

// Get deduplicated results
let unique = dedup.finish_batch();
```

## How It Works

```
┌─────────────┐     ┌──────────────┐     ┌─────────────┐     ┌─────────────┐
│   Shingle   │────▶│   MinHash    │────▶│  LSH Bands  │────▶│   Cluster   │
│  (k-grams)  │     │  (fast hash) │     │  (buckets)  │     │    Output   │
└─────────────┘     └──────────────┘     └─────────────┘     └─────────────┘
```

1. **Shingling**: Convert documents to sets of k-grams (overlapping subsequences)
2. **MinHash**: Compress documents to small signatures preserving Jaccard similarity
3. **LSH**: Band signatures such that similar documents collide in buckets
4. **Clustering**: Group colliding documents and output unique representatives

## Configuration

```rust
use dedup::Config;

let config = Config::new(
    128,    // signature size (hash functions)
    16,     // LSH bands
    5,      // shingle size
    0.9,    // similarity threshold
)?;
```

### Parameter Guide

| Parameter | Default | Description |
|-----------|---------|-------------|
| `signature_size` | 128 | Number of hash functions. Higher = more accurate but slower |
| `num_bands` | 16 | LSH bands. Higher = more sensitive but more false positives |
| `shingle_size` | 5 | k-gram length. 4-7 works well for text |
| `threshold` | 0.9 | Similarity threshold. 0.85-0.95 recommended |

## tenshift Integration

```rust
use dedup::StatefulDedupTransform;
use tenshift_core::transform::StatefulTransform;

let mut dedup = StatefulDedupTransform::new(config)?;

// In your pipeline
for sample in samples {
    let output = dedup.push(sample);
    // output is empty until finish() is called
}

let unique = dedup.finish();
```

## Performance

Benchmarked on AMD Ryzen 9 5950X:

| Operation | Throughput |
|-----------|------------|
| MinHash (128 sig) | ~50,000 docs/sec |
| MinHash (256 sig) | ~25,000 docs/sec |
| LSH Insert | ~100,000 ops/sec |
| Batch (1000 docs) | ~30ms |

## Architecture

### MinHash Theory

MinHash estimates Jaccard similarity between sets:
- `J(A,B) = |A ∩ B| / |A ∪ B|`
- MinHash approximates this by comparing hash signatures
- Expected error: `√(s(1-s)/k)` where `s` is similarity, `k` is signature size

### LSH Theory

LSH reduces O(n²) comparisons to O(n):
- Signature split into `b` bands of `r` rows each
- Probability of collision for similarity `s`: `1 - (1 - s^r)^b`
- Threshold (S-curve inflection): `t ≈ (1/b)^(1/r)`

## Testing

```bash
cargo test
cargo test --release  # For benchmarks
cargo clippy -- -D warnings
```

## License

MIT License - See LICENSE file for details.

## Authors

Santh <contact@santh.dev>