minhashlib 0.1.1

A fast and minimal minhashing based similarity checking library.

Minhashlib

This is a minimal implementation of MinHashing as described in Jeffrey Ullman's book Mining Massive Datasets.

Current Benchmark Claim

Based on the benchmark outputs in this repository:

• On recent multi-seed CPU synthetic runs (seeds 42-46), minhashlib builds signatures about ~6x faster than datasketch.
• Accuracy is comparable in magnitude (similar MAE scale and matching threshold-based metrics in these runs), though datasketch is slightly better on MAE in most synthetic scenarios.
• Memory results are workload-dependent, so this project does not claim universal memory superiority.

In short: this implementation is minimal and fast, with accuracy that is broadly comparable to datasketch, but outcomes vary depending on dataset and configuration.

Benchmark Suite

Use benchmarks/benchmark_claims_suite.py to run comprehensive, reproducible benchmarks:

• Multiple datasets (synthetic, 20newsgroups, wikipedia, ag_news, local)
• Multiple seeds with mean/std/95% CI
• Metrics: MAE(Mean Average Error), Precision/Recall/F1 at threshold, Precision@K/Recall@K
• Speed: build/pair-eval/retrieval latency and throughput
• Memory: peak allocation and bytes/signature
• Optional scaling sweeps over docs/number of hashes/doc length

Example (full):

python3 benchmarks/benchmark_claims_suite.py
  --datasets synthetic,20newsgroups,wikipedia
  --wiki-dump-path data/simplewiki-latest-pages-articles.xml.bz2
  --seeds 42,43,44
  --p-values 2147483647,3037000493
  --max-docs 2000
  --random-pairs 3000
  --num-queries 200
  --include-scaling

Example (offline/local corpus only):

python3 benchmarks/benchmark_claims_suite.py
  --datasets synthetic,local
  --local-docs /path/to/docs.jsonl
  --seeds 42,43,44

Outputs are written to benchmark_outputs/ by default:

• raw_runs.json / raw_runs.csv
• summary_stats.json / summary_stats.csv
• run_metadata.json
• skipped_runs.json

Benchmark data setup

Pull required benchmark datasets into local project paths:

python3 scripts/setup_benchmark_data.py

This prepares:

• data/simplewiki-latest-pages-articles.xml.bz2 (for Wikipedia benchmarks)
• .cache/scikit_learn_data (for 20newsgroups benchmarks)

Optional flags:

python3 scripts/setup_benchmark_data.py --force
python3 scripts/setup_benchmark_data.py --skip-wikipedia
python3 scripts/setup_benchmark_data.py --skip-20newsgroups

Individual Benchmarks

You can run individual benchmarks instead of the full suite:

# Accuracy-only
python3 benchmarks/benchmark_claims_accuracy.py --datasets synthetic,20newsgroups,wikipedia

# Performance-only
python3 benchmarks/benchmark_claims_performance.py --datasets synthetic,20newsgroups,wikipedia

# Memory-only
python3 benchmarks/benchmark_claims_memory.py --datasets synthetic,20newsgroups,wikipedia

# Scaling-only (synthetic sweeps)
python3 benchmarks/benchmark_claims_scaling.py --datasets synthetic

Each individual benchmark writes outputs under benchmark_outputs/<test_name>/.