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6eff894 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import json
import os
from pathlib import Path
import joblib
import numpy as np
import pandas as pd
from sklearn.inspection import permutation_importance
from sklearn.model_selection import train_test_split
RESULTS_DIR = "results"
os.environ.setdefault("MPLCONFIGDIR", os.path.join(RESULTS_DIR, ".matplotlib"))
os.environ.setdefault("XDG_CACHE_HOME", os.path.join(RESULTS_DIR, ".cache"))
Path(os.environ["MPLCONFIGDIR"]).mkdir(parents=True, exist_ok=True)
Path(os.environ["XDG_CACHE_HOME"]).mkdir(parents=True, exist_ok=True)
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
def build_dataset(meta: dict) -> tuple[np.ndarray, np.ndarray]:
df = pd.read_csv("results/hourly.csv", parse_dates=["time"])
horizon = meta["horizon_hours"]
precip = df["precip_mm"].values
rain_future = np.zeros(len(df), dtype=int)
for i in range(len(precip) - horizon):
rain_future[i] = 1 if np.any(precip[i + 1 : i + 1 + horizon] > 0) else 0
df = df.iloc[: len(precip) - horizon].copy()
labels = rain_future[: len(df)]
features = df[meta["features"]].values
return features, labels
def plot_importance(feature_names: list[str], importances: np.ndarray, std: np.ndarray) -> None:
order = np.argsort(importances)[::-1]
feature_names = np.array(feature_names)[order]
importances = importances[order]
plt.figure(figsize=(8, 5))
y_pos = np.arange(len(feature_names))
plt.barh(y_pos, importances, align="center")
plt.yticks(y_pos, feature_names)
plt.gca().invert_yaxis()
plt.xlabel("Permutation importance (F1 drop)")
plt.title("Rain classifier — feature importances")
plt.tight_layout()
Path(RESULTS_DIR).mkdir(exist_ok=True)
plt.savefig(os.path.join(RESULTS_DIR, "feature_importance.png"))
plt.close()
def main() -> None:
meta = json.load(open("models/rain_model_meta.json"))
model = joblib.load("models/rain_classifier_hourly.joblib")
X, y = build_dataset(meta)
_, X_test, _, y_test = train_test_split(X, y, test_size=0.3, shuffle=False)
result = permutation_importance(
model,
X_test,
y_test,
n_repeats=25,
random_state=42,
scoring="f1",
)
plot_importance(meta["features"], result.importances_mean, result.importances_std)
print("✅ Wrote results/feature_importance.png")
if __name__ == "__main__":
main()
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