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v1.4.0: High-Performance Engine, Feature Importances, RFE & DocC

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@Nodibell Nodibell released this 22 Jul 22:49
v1.4.0
af0ba89

🚀 SwiftSci v1.4.0 Release Notes

📦 Key Additions & Features

  • SystemsCSVParser (SwiftDataFrame): High-performance zero-copy memory-mapped RFC 4180 DFA byte parser, speeding up CSV ingestion by ~10× on large datasets.
  • Vectorized Byte Parsers (SwiftDataFrame): Zero-allocation ASCII parsers (VectorizedByteParsers) for fast Double, Int, and string decoding from unmanaged byte buffers.
  • vDSP Reductions (SwiftDataFrame): Accelerated TypedColumn<Double> mean(), variance(), and stdDev() using Apple Accelerate vDSP reductions.
  • DataFrame Index Filtering & Argsort (SwiftDataFrame): Added filterRows(by:) index-based boolean mask filtering and argsort() column index sorting.
  • Recursive Feature Elimination RFE (SwiftPreprocessing): Added RecursiveFeatureElimination (RFE) for iterative feature selection based on estimator feature importances.
  • Feature Importances & Model Persistence (SwiftML): Added Gini featureImportances property to DecisionTreeClassifier, DecisionTreeRegressor, RandomForestClassifier, RandomForestRegressor, and added Codable serialization (save(to:) / load(from:)) across classical estimators.
  • NLP Text Tokenizers & Feature Extraction (SwiftNLP): Added NGramTokenizer (word & char n-grams) and HashingVectorizer (MurmurHash3 memory-bounded token hashing).
  • Time Series Windowing (SwiftForecast): Added ExpandingWindow feature transformer for cumulative time-series feature generation.
  • Swift DocC Integration & Multi-Module Site: Integrated swift-docc-plugin (v1.5.0) and deployed unified 10-module static web documentation to GitHub Pages.

🛠️ Fixes & Concurrency Improvements

  • GradientBoostedTrees Bounds Check (SwiftML): Enforced bounds checking in GradientBoostedTreesRegressor.
  • Seeded Random Number Generators (SwiftPreprocessing): Fixed deterministic seeding in SMOTE and RandomUndersampler using LCG.
  • SelectKBest ANOVA Scoring (SwiftPreprocessing): Fixed ANOVA F-statistic calculation in SelectKBest and added explicit SwiftStats dependency to SwiftPreprocessing.
  • CalibratedClassifier Concurrency (SwiftML): Converted CalibratedClassifier to actor to guarantee thread safety under Swift 6 strict concurrency rules.
  • Wired Memory Scaling (SwiftPreprocessing): Dynamically scaled WiredMemoryManager default limits using ProcessInfo.processInfo.activeProcessorCount.

📊 Benchmark Performance Summary (v1.4.0 vs Python)

Benchmark Test Swift (ms) Python (ms) Speedup Winner
ARIMA(1,1,1) Fit (50k pts) 2.228 ms 206.414 ms 92.66x 🟢 Swift
ARIMA(1,1,1) Forecast (horizon=24) 2.297 ms 209.709 ms 91.28x 🟢 Swift
Holt-Winters Fit (50k pts, period=12) 6.823 ms 138.947 ms 20.37x 🟢 Swift
Random Forest Fit (1k×4, 50 trees) 4.580 ms 24.217 ms 5.29x 🟢 Swift
KernelSHAP Explain (5 feats, 100 coalitions) 0.148 ms 0.452 ms 3.05x 🟢 Swift
Kalman Filter 1D (10k obs) 56.508 ms 84.692 ms 1.50x 🟢 Swift
Mean (vDSP, 1M elements) 0.083 ms 0.119 ms 1.44x 🟢 Swift

🌐 Live Documentation: https://nodibell.github.io/SwiftSci/

Full Changelog: v1.3.0...v1.4.0

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