| import numpy as np |
| import pandas as pd |
|
|
|
|
| def compute_changes(events: pd.DataFrame, window_hours: int) -> pd.DataFrame: |
| if events.empty: |
| return pd.DataFrame(columns=["source", "current", "previous", "delta"]) |
|
|
| end_time = events["timestamp"].max() |
| start_current = end_time - pd.Timedelta(hours=window_hours) |
| start_previous = start_current - pd.Timedelta(hours=window_hours) |
|
|
| current = events[(events["timestamp"] >= start_current) & (events["timestamp"] <= end_time)] |
| previous = events[(events["timestamp"] >= start_previous) & (events["timestamp"] < start_current)] |
|
|
| c = current.groupby("source").size().rename("current") |
| p = previous.groupby("source").size().rename("previous") |
|
|
| merged = pd.concat([c, p], axis=1).fillna(0) |
| merged["current"] = merged["current"].astype(int) |
| merged["previous"] = merged["previous"].astype(int) |
| merged["delta"] = merged["current"] - merged["previous"] |
| return merged.reset_index().sort_values("delta", ascending=False) |
|
|
|
|
| def compute_risk_by_region(events: pd.DataFrame) -> pd.DataFrame: |
| if events.empty: |
| return pd.DataFrame(columns=["region", "event_count", "avg_severity", "risk_score"]) |
|
|
| grouped = events.groupby("region").agg( |
| event_count=("source", "size"), |
| avg_severity=("severity", "mean"), |
| ) |
| grouped["risk_score"] = ( |
| np.log1p(grouped["event_count"]) * 14 + grouped["avg_severity"] * 16 |
| ).round(2) |
| return grouped.reset_index().sort_values("risk_score", ascending=False) |
|
|
