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b84c286 5bd97d2 b84c286 | 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 | """Fit the prevailing-level network to a panel of your own and predict a smooth level for every cell.
Input is long format, one row per station x date x pollutant that has data:
station, area, date, pollutant (one of model.TARGETS), value (> 0, a daily mean), n (readings behind it)
Here a small synthetic panel stands in for real data.
"""
import numpy as np
import pandas as pd
from model import TARGETS, Encoding, fit, predict
# Settings chosen by look-ahead cross-validation in the blog post
CFG = dict(rough=1e5, spacing=30, lr=5e-3, hidden=64, weight_decay=1e-4, station_l2=1e-3, schedule=1000, seed=0)
STEPS = 100
def synthetic_panel(seed: int = 0) -> pd.DataFrame:
rng = np.random.default_rng(seed)
days = pd.date_range("2020-01-01", periods=365)
stations = {"s1": "urban", "s2": "urban", "s3": "suburban", "s4": "suburban"}
t = np.arange(len(days))
rows = []
for s, area in stations.items():
level = 4 + 0.5 * np.sin(2 * np.pi * t / 365) + (0.3 if area == "urban" else 0) + rng.normal(0, 0.1)
for j, pollutant in enumerate(TARGETS):
n = rng.poisson(1.2, len(days)) # hours observed per day; 0 means no reading
keep = n > 0
value = np.exp(level + 0.4 * j + rng.normal(0, 0.5 / np.sqrt(np.maximum(n, 1))))
rows.append(pd.DataFrame({"station": s, "area": area, "date": days[keep], "pollutant": pollutant,
"value": value[keep], "n": n[keep]}))
return pd.concat(rows, ignore_index=True)
panel = synthetic_panel()
enc = Encoding.fit(panel, end=panel["date"].max())
model = fit(enc, enc.wide(panel), CFG, STEPS) # joint model; use=["PM2.5"] for a single-target one
level = predict(model, enc, enc.grid()) # every station x day, observed or not
print(level.head())
print(f"{len(panel)} readings in, {len(level)} predicted cells out")
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