| """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 |
|
|
| |
| 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)) |
| 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) |
| level = predict(model, enc, enc.grid()) |
| print(level.head()) |
| print(f"{len(panel)} readings in, {len(level)} predicted cells out") |
|
|
