grabowski
e3694d7591
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.
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
"""Python timing for the same serving-shaped work as the Rust spike."""
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import sys
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import time
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from pathlib import Path
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import joblib
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import numpy as np
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import pandas as pd
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REPO = Path(__file__).resolve().parents[3] # repo root
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sys.path.insert(0, str(REPO))
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from src.ml import features # noqa: E402
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bundle = joblib.load(REPO / "models" / "flood_P.1.joblib")
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stations = ["P.1"] + [u for u, _ in features.UPSTREAM_LEADS["P.1"]]
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long = pd.concat(
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pd.read_csv(REPO / "models" / "cache" / f"{s}.csv.gz", parse_dates=["timestamp"])
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for s in stations
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)
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df = long[(long.timestamp >= "2026-07-30") & (long.timestamp < "2026-08-13")]
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rain_all = pd.read_csv(REPO / "models" / "cache" / "rain_openmeteo.csv.gz", parse_dates=["timestamp"])
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rain_all = rain_all.set_index("timestamp")["chiang_dao"].astype(float)
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reps = 20
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t = time.perf_counter()
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for _ in range(reps):
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grid = features.make_hourly_grid(df)
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rain = rain_all.reindex(grid.observed.index)
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row = features.build_features(grid, "P.1", rain=rain)[bundle["feature_names"]].iloc[[-1]]
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out = {
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k: (h.predict_proba(row)[0, 1] if hasattr(h, "predict_proba") else h.predict(row)[0])
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for k, h in bundle["heads"].items()
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}
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print(f"python: grid + features + 9 heads for the last hour: {(time.perf_counter() - t) / reps * 1000:.1f} ms per station")
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t = time.perf_counter()
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joblib.load(REPO / "models" / "flood_P.1.joblib")
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print(f"python: bundle load {(time.perf_counter() - t) * 1000:.0f} ms")
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