spikes/rust-forecast ports build_features (P.1 path) and HistGradientBoosting inference to Rust and checks them against the pandas/sklearn output on two fixtures (2024 record flood, summer 2026): 244,800 feature cells and every regression prediction bit-identical, classifier heads to the last ulp; 0.25 ms vs 158 ms per station. docs/RUST_REWRITE.md: what a rewrite would mean (Rust service, Python training), costs, phased plan, Python alternative.
16 lines
491 B
TOML
16 lines
491 B
TOML
[package]
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name = "ping-forecast-spike"
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version = "0.1.0"
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edition = "2024"
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publish = false
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[dependencies]
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serde = { version = "1", features = ["derive"] }
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# float_roundtrip: exact float parsing. Tree thresholds often equal real data
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# values, so a 1-ulp parse error flips `x <= threshold` (default parser is not exact).
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serde_json = { version = "1", features = ["float_roundtrip"] }
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chrono = { version = "0.4", default-features = false, features = ["std"] }
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[profile.release]
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debug = false
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