suffix-smoother 0.4.0

Suffix smoothing classifier v0.4.0: 1.8x faster, KN memory fix, model merging, and feature importance.

suffix-smoother

A high-performance, production-ready sequence classifier using recursive suffix smoothing.

Zero neural networks. Zero GPUs. Zero model files. Handles OOV via progressive backoff.

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What's New in v0.4.0

• Industrial Pruning: prune(min_count=k) for massive RAM savings (up to 93% reduction).
• Auto-Calibration: fit_temperature() minimizes ECE/NLL for reliable confidence scores.
• Diagnostics: calibration_curve() for binned reliability analysis.
• Portability: to_json() and from_json() for cross-platform model persistence.

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Core Philosophy: Efficient AI

Suffix Smoother is designed for industrial environments where memory, latency, and explainability matter more than parameter count. By utilizing recursive suffix backoff instead of deep learning, it achieves competitive accuracy on sequence tasks with linear training throughput and sub-microsecond inference on standard CPUs.

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Documentation & Benchmarks

• Technical Documentation: Deep dive into pruning, calibration, and architecture.
• Benchmark Reports: Performance results on 1M+ samples and real-world sentiment data.

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Install

pip install suffix-smoother

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Quick Start (v0.4.0)

from suffix_smoother import SuffixSmoother, SuffixConfig

# 1. Train at Industrial Scale
model = SuffixSmoother(SuffixConfig(n_classes=10))
model.train(data) # list of (seq_tuple, label_int)

# 2. Optimize Reliability (v0.4.0)
model.fit_temperature(val_data)
curve = SuffixSmoother.calibration_curve(test_probs, test_labels)

# 3. Compress for Production (v0.4.0)
# Removes rare nodes (90% reduction on 1M+ samples)
model.prune(min_count=10)

# 4. Save Portably (v0.4.0)
model.to_json("production_model.json")

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Performance (Industrial Benchmark)

Dataset	Scale	Throughput	RAM (Pruned)
Synthetic	1.5M samples	100k/sec	-89% nodes
Sentiment140	1.6M tweets	85k/sec	4MB (JSON)

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License

MIT