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"""
Event Database for Geopolitical Intelligence
Persistent storage and querying for structured events.
Features:
- Efficient time-range queries
- Actor-based filtering
- Event type filtering
- Temporal aggregation
- Causal graph construction from events
- Export to panel data formats
"""
import json
import sqlite3
from datetime import datetime, timedelta
from typing import List, Dict, Optional, Tuple, Any
from pathlib import Path
import pandas as pd
from .event_extraction import GeopoliticalEvent, EventType, TemporalNormalizer
class EventDatabase:
"""
SQLite-based event database with efficient querying.
"""
def __init__(self, db_path: str = "events.db"):
"""
Initialize event database.
Parameters
----------
db_path : str
Path to SQLite database file
"""
self.db_path = db_path
self.conn = None
self._connect()
self._create_tables()
def _connect(self):
"""Connect to database."""
self.conn = sqlite3.connect(self.db_path)
self.conn.row_factory = sqlite3.Row
def _create_tables(self):
"""Create database schema."""
cursor = self.conn.cursor()
# Events table
cursor.execute('''
CREATE TABLE IF NOT EXISTS events (
event_id TEXT PRIMARY KEY,
timestamp TEXT NOT NULL,
event_type TEXT NOT NULL,
location TEXT,
magnitude REAL,
confidence REAL,
source TEXT,
text TEXT,
metadata TEXT
)
''')
# Actors table (many-to-many with events)
cursor.execute('''
CREATE TABLE IF NOT EXISTS event_actors (
event_id TEXT,
actor TEXT,
role TEXT,
FOREIGN KEY (event_id) REFERENCES events(event_id),
PRIMARY KEY (event_id, actor)
)
''')
# Causal relationships
cursor.execute('''
CREATE TABLE IF NOT EXISTS causal_links (
cause_event_id TEXT,
effect_event_id TEXT,
strength REAL,
confidence REAL,
FOREIGN KEY (cause_event_id) REFERENCES events(event_id),
FOREIGN KEY (effect_event_id) REFERENCES events(event_id),
PRIMARY KEY (cause_event_id, effect_event_id)
)
''')
# Create indices for fast queries
cursor.execute('CREATE INDEX IF NOT EXISTS idx_timestamp ON events(timestamp)')
cursor.execute('CREATE INDEX IF NOT EXISTS idx_event_type ON events(event_type)')
cursor.execute('CREATE INDEX IF NOT EXISTS idx_actor ON event_actors(actor)')
self.conn.commit()
def insert_event(self, event: GeopoliticalEvent) -> None:
"""
Insert event into database.
Parameters
----------
event : GeopoliticalEvent
Event to insert
"""
cursor = self.conn.cursor()
# Normalize timestamp
timestamp_str = TemporalNormalizer.normalize_to_utc(event.timestamp).isoformat()
# Insert main event
cursor.execute('''
INSERT OR REPLACE INTO events
(event_id, timestamp, event_type, location, magnitude, confidence, source, text, metadata)
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
''', (
event.event_id,
timestamp_str,
event.event_type.value,
event.location,
event.magnitude,
event.confidence,
event.source,
event.text,
json.dumps(event.metadata)
))
# Insert actors
for actor in event.actors:
cursor.execute('''
INSERT OR REPLACE INTO event_actors (event_id, actor, role)
VALUES (?, ?, ?)
''', (event.event_id, actor, 'participant'))
# Insert target as actor with different role
if event.target:
cursor.execute('''
INSERT OR REPLACE INTO event_actors (event_id, actor, role)
VALUES (?, ?, ?)
''', (event.event_id, event.target, 'target'))
self.conn.commit()
def insert_events(self, events: List[GeopoliticalEvent]) -> None:
"""
Bulk insert events.
Parameters
----------
events : list
List of events to insert
"""
for event in events:
self.insert_event(event)
def query_events(
self,
start_time: Optional[datetime] = None,
end_time: Optional[datetime] = None,
event_types: Optional[List[EventType]] = None,
actors: Optional[List[str]] = None,
min_magnitude: Optional[float] = None,
limit: Optional[int] = None
) -> List[GeopoliticalEvent]:
"""
Query events with filters.
Parameters
----------
start_time : datetime, optional
Start of time range
end_time : datetime, optional
End of time range
event_types : list, optional
Filter by event types
actors : list, optional
Filter by actors
min_magnitude : float, optional
Minimum magnitude
limit : int, optional
Maximum number of results
Returns
-------
list
List of matching events
"""
cursor = self.conn.cursor()
query = "SELECT DISTINCT e.* FROM events e"
conditions = []
params = []
# Join with actors if needed
if actors:
query += " JOIN event_actors ea ON e.event_id = ea.event_id"
# Time range
if start_time:
conditions.append("e.timestamp >= ?")
params.append(start_time.isoformat())
if end_time:
conditions.append("e.timestamp <= ?")
params.append(end_time.isoformat())
# Event types
if event_types:
placeholders = ','.join('?' * len(event_types))
conditions.append(f"e.event_type IN ({placeholders})")
params.extend([et.value for et in event_types])
# Actors
if actors:
placeholders = ','.join('?' * len(actors))
conditions.append(f"ea.actor IN ({placeholders})")
params.extend(actors)
# Magnitude
if min_magnitude is not None:
conditions.append("e.magnitude >= ?")
params.append(min_magnitude)
# Build query
if conditions:
query += " WHERE " + " AND ".join(conditions)
query += " ORDER BY e.timestamp DESC"
if limit:
query += f" LIMIT {limit}"
# Execute
cursor.execute(query, params)
rows = cursor.fetchall()
# Convert to GeopoliticalEvent objects
events = []
for row in rows:
# Get actors
cursor.execute(
"SELECT actor FROM event_actors WHERE event_id = ?",
(row['event_id'],)
)
actors_rows = cursor.fetchall()
event_actors = [r['actor'] for r in actors_rows]
# Reconstruct event
event = GeopoliticalEvent(
event_id=row['event_id'],
timestamp=datetime.fromisoformat(row['timestamp']),
event_type=EventType(row['event_type']),
actors=event_actors,
location=row['location'],
magnitude=row['magnitude'],
confidence=row['confidence'],
source=row['source'],
text=row['text'],
metadata=json.loads(row['metadata']) if row['metadata'] else {}
)
events.append(event)
return events
def get_actor_timeline(
self,
actor: str,
start_time: Optional[datetime] = None,
end_time: Optional[datetime] = None
) -> List[GeopoliticalEvent]:
"""
Get timeline of events for a specific actor.
Parameters
----------
actor : str
Actor name
start_time : datetime, optional
Start time
end_time : datetime, optional
End time
Returns
-------
list
Events involving actor
"""
return self.query_events(
start_time=start_time,
end_time=end_time,
actors=[actor]
)
def get_event_counts_by_type(
self,
start_time: Optional[datetime] = None,
end_time: Optional[datetime] = None
) -> Dict[str, int]:
"""
Get event counts by type.
Parameters
----------
start_time : datetime, optional
Start time
end_time : datetime, optional
End time
Returns
-------
dict
Counts by event type
"""
cursor = self.conn.cursor()
query = "SELECT event_type, COUNT(*) as count FROM events"
conditions = []
params = []
if start_time:
conditions.append("timestamp >= ?")
params.append(start_time.isoformat())
if end_time:
conditions.append("timestamp <= ?")
params.append(end_time.isoformat())
if conditions:
query += " WHERE " + " AND ".join(conditions)
query += " GROUP BY event_type"
cursor.execute(query, params)
rows = cursor.fetchall()
return {row['event_type']: row['count'] for row in rows}
def aggregate_by_time(
self,
granularity: str = 'day',
start_time: Optional[datetime] = None,
end_time: Optional[datetime] = None,
event_types: Optional[List[EventType]] = None
) -> pd.DataFrame:
"""
Aggregate events by time period.
Parameters
----------
granularity : str
Time granularity ('day', 'week', 'month')
start_time : datetime, optional
Start time
end_time : datetime, optional
End time
event_types : list, optional
Filter by event types
Returns
-------
pd.DataFrame
Time series of event counts
"""
events = self.query_events(
start_time=start_time,
end_time=end_time,
event_types=event_types
)
if not events:
return pd.DataFrame()
# Convert to DataFrame
df = pd.DataFrame([
{
'timestamp': e.timestamp,
'event_type': e.event_type.value,
'magnitude': e.magnitude
}
for e in events
])
df['timestamp'] = pd.to_datetime(df['timestamp'])
df = df.set_index('timestamp')
# Resample
if granularity == 'day':
freq = 'D'
elif granularity == 'week':
freq = 'W'
elif granularity == 'month':
freq = 'M'
else:
raise ValueError(f"Unknown granularity: {granularity}")
# Aggregate
aggregated = df.resample(freq).agg({
'magnitude': ['count', 'mean', 'sum']
})
return aggregated
def export_to_panel_data(
self,
actors: List[str],
start_time: datetime,
end_time: datetime,
granularity: str = 'day'
) -> Dict[str, pd.DataFrame]:
"""
Export to panel data format.
Parameters
----------
actors : list
List of actors
start_time : datetime
Start time
end_time : datetime
End time
granularity : str
Time granularity
Returns
-------
dict
Panel data {actor: DataFrame}
"""
from .event_extraction import CausalFeatureExtractor
# Get events for each actor
panel = {}
for actor in actors:
events = self.get_actor_timeline(actor, start_time, end_time)
# Extract features
extractor = CausalFeatureExtractor()
panel_data = extractor.construct_panel_data([events], [actor], granularity)
if actor in panel_data:
panel[actor] = panel_data[actor]
return panel
def add_causal_link(
self,
cause_event_id: str,
effect_event_id: str,
strength: float = 1.0,
confidence: float = 0.5
) -> None:
"""
Add causal link between events.
Parameters
----------
cause_event_id : str
ID of cause event
effect_event_id : str
ID of effect event
strength : float
Causal strength
confidence : float
Confidence in link
"""
cursor = self.conn.cursor()
cursor.execute('''
INSERT OR REPLACE INTO causal_links
(cause_event_id, effect_event_id, strength, confidence)
VALUES (?, ?, ?, ?)
''', (cause_event_id, effect_event_id, strength, confidence))
self.conn.commit()
def get_causal_graph(self) -> Dict[str, List[str]]:
"""
Get causal graph from event links.
Returns
-------
dict
Adjacency list representation
"""
cursor = self.conn.cursor()
cursor.execute("SELECT cause_event_id, effect_event_id FROM causal_links")
rows = cursor.fetchall()
graph = {}
for row in rows:
cause = row['cause_event_id']
effect = row['effect_event_id']
if cause not in graph:
graph[cause] = []
graph[cause].append(effect)
return graph
def close(self):
"""Close database connection."""
if self.conn:
self.conn.close()
def __enter__(self):
"""Context manager entry."""
return self
def __exit__(self, exc_type, exc_val, exc_tb):
"""Context manager exit."""
self.close()
class EventStream:
"""
Real-time event stream processor.
Monitors and processes incoming events in real-time.
"""
def __init__(self, db: EventDatabase):
"""
Initialize event stream.
Parameters
----------
db : EventDatabase
Event database
"""
self.db = db
self.subscribers = []
def subscribe(self, callback: callable) -> None:
"""
Subscribe to event stream.
Parameters
----------
callback : callable
Function to call on new events
"""
self.subscribers.append(callback)
def process_event(self, event: GeopoliticalEvent) -> None:
"""
Process and store new event.
Parameters
----------
event : GeopoliticalEvent
New event
"""
# Store in database
self.db.insert_event(event)
# Notify subscribers
for callback in self.subscribers:
callback(event)
def process_batch(self, events: List[GeopoliticalEvent]) -> None:
"""
Process batch of events.
Parameters
----------
events : list
List of events
"""
self.db.insert_events(events)
for event in events:
for callback in self.subscribers:
callback(event)
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