diff --git a/eta_prediction/feature_engineering/dataset_builder.py b/eta_prediction/feature_engineering/dataset_builder.py index b9987b6..a788556 100644 --- a/eta_prediction/feature_engineering/dataset_builder.py +++ b/eta_prediction/feature_engineering/dataset_builder.py @@ -2,331 +2,373 @@ from datetime import datetime, date, time, timedelta from typing import Optional, List - import numpy as np import pandas as pd +from math import radians, cos, sin, asin, sqrt -from django.db.models import F, Q, OuterRef, Subquery, Exists, Prefetch -from django.db.models.functions import Coalesce - +from django.db.models import F, Q from sch_pipeline.models import StopTime, Stop, Route, Trip -from rt_pipeline.models import VehiclePosition, TripUpdate -from sch_pipeline.utils import top_routes_by_scheduled_trips - +from rt_pipeline.models import VehiclePosition from feature_engineering.temporal import extract_temporal_features from feature_engineering.operational import calculate_headway, detect_congestion_proxy from feature_engineering.weather import fetch_weather -def _parse_sched_time(val) -> Optional[int]: +def haversine_distance(lat1, lon1, lat2, lon2): + """ + Calculate the great circle distance in meters between two points + on the earth (specified in decimal degrees) """ - Normalize schedule arrival_time to seconds since midnight. - Accepts 'HH:MM:SS', integer seconds, or time object. + # Convert decimal degrees to radians + lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) + + # Haversine formula + dlon = lon2 - lon1 + dlat = lat2 - lat1 + a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 + c = 2 * asin(sqrt(a)) + r = 6371000 # Radius of earth in meters + return c * r + + +def find_actual_arrival_time(vp_df_for_trip, stop_lat, stop_lon, distance_threshold=50): """ - if val is None or pd.isna(val): + Find the ts when a vehicle arrived at a stop. + + Strategy: + 1. Calculate distance from each VP to the stop + 2. Find the first VP within distance_threshold meters + 3. Return its ts as arrival time + + Args: + vp_df_for_trip: DataFrame of VehiclePositions for a specific trip, sorted by ts + stop_lat: Stop lat + stop_lon: Stop lon + distance_threshold: Distance in meters to consider "arrived" (default 50m) + + Returns: + datetime or None if vehicle never arrived + """ + if vp_df_for_trip.empty: return None - # If it's already an integer (seconds), return it - if isinstance(val, int): - return val + # Calculate distances to stop + distances = vp_df_for_trip.apply( + lambda row: haversine_distance(row['lat'], row['lon'], stop_lat, stop_lon), + axis=1 + ) - # If it's a time object - if isinstance(val, time): - return val.hour * 3600 + val.minute * 60 + val.second + # Find first position within threshold + arrived_mask = distances <= distance_threshold + if arrived_mask.any(): + first_arrival_idx = arrived_mask.idxmax() # First True index + return vp_df_for_trip.loc[first_arrival_idx, 'ts'] - # If it's a timedelta (from GTFS sometimes) - if isinstance(val, timedelta): - return int(val.total_seconds()) + # Alternative: If never within threshold, find closest approach + closest_idx = distances.idxmin() + min_distance = distances.min() - # If it's a string - if isinstance(val, str): - try: - parts = val.split(":") - hh, mm, ss = int(parts[0]), int(parts[1]), int(parts[2]) - return hh * 3600 + mm * 60 + ss - except Exception: - return None + # Only use closest approach if reasonably close (e.g., within 200m) + if min_distance <= 200: + return vp_df_for_trip.loc[closest_idx, 'ts'] return None -def _yyyymmdd_to_date(s: Optional[str]) -> Optional[date]: - """Convert YYYYMMDD string or date object to date object.""" - if not s or pd.isna(s): - return None - - # If it's already a date object, return it - if isinstance(s, date): - return s - - # If it's a datetime, extract the date - if isinstance(s, datetime): - return s.date() - - try: - s = str(s) - return date(int(s[0:4]), int(s[4:6]), int(s[6:8])) - except Exception: - return None - - -def _mk_dt(d: Optional[date], seconds_since_midnight: Optional[int]) -> Optional[datetime]: - """Combine date and seconds to create datetime.""" - if d is None or seconds_since_midnight is None: - return None - return datetime.combine(d, time(0, 0)) + timedelta(seconds=int(seconds_since_midnight)) - - -def build_training_dataset( - provider_id: Optional[int] = None, +def build_vp_training_dataset( route_ids: Optional[List[str]] = None, start_date: Optional[datetime] = None, end_date: Optional[datetime] = None, - min_observations_per_stop: int = 30, + distance_threshold: float = 50.0, + max_stops_ahead: int = 5, attach_weather: bool = True, tz_for_temporal: str = "America/Costa_Rica", ) -> pd.DataFrame: """ - Optimized pipeline using direct database join via raw SQL or simplified ORM. - - Strategy: - 1. Filter TripUpdates by date range FIRST (reduce dataset) - 2. Get matching trip_ids for routes - 3. Use a single query with proper joins instead of subqueries - 4. Process in pandas for complex computations + Build training dataset from VehiclePosition data only. + + For each VehiclePosition: + - Identify current trip and route + - Find remaining stops in trip + - Calculate distance to each remaining stop (up to max_stops_ahead) + - Find actual arrival times from subsequent VehiclePositions + - Extract temporal, operational, and weather features + + Args: + route_ids: List of route IDs to include (None = all routes) + start_date: Start of date range + end_date: End of date range + distance_threshold: Distance in meters to consider "arrived at stop" + max_stops_ahead: Maximum number of future stops to include per VP + attach_weather: Whether to fetch weather data + tz_for_temporal: Timezone for temporal features + + Returns: + DataFrame with columns: + - trip_id, route_id, vehicle_id + - vp_ts, vp_lat, vp_lon + - stop_id, stop_sequence, stop_lat, stop_lon + - distance_to_stop (meters) + - scheduled_arrival, actual_arrival, delay_seconds + - temporal features (hour, day_of_week, etc.) + - operational features (headway, avg_speed) + - weather features (temperature, precipitation, wind_speed) """ - + print("=" * 70) - print("OPTIMIZED DATASET BUILDER") + print("VP-BASED DATASET BUILDER") print("=" * 70) # ============================================================ - # STEP 1: Filter TripUpdates first (reduce data volume) + # STEP 1: Fetch VehiclePositions # ============================================================ - print("\nStep 1: Building TripUpdate filter...") + print("\nStep 1: Fetching VehiclePositions...") - tu_qs = TripUpdate.objects.all() + vp_qs = VehiclePosition.objects.exclude( + Q(trip_id__isnull=True) | + Q(lat__isnull=True) | + Q(lon__isnull=True) + ) - # Date range filter if start_date: print(f" Filtering start_date >= {start_date}") - tu_qs = tu_qs.filter(ts__gte=start_date) + vp_qs = vp_qs.filter(ts__gte=start_date) if end_date: print(f" Filtering end_date < {end_date}") - tu_qs = tu_qs.filter(ts__lt=end_date) - - # Must have essential fields - tu_qs = tu_qs.exclude( - Q(trip_id__isnull=True) | - Q(stop_sequence__isnull=True) | - Q(start_date__isnull=True) - ) + vp_qs = vp_qs.filter(ts__lt=end_date) - print(f" TripUpdates matching date range: {tu_qs.count():,}") - - # ============================================================ - # STEP 2: Get trip_ids for specified routes - # ============================================================ + # Filter by route if specified if route_ids: - print(f"\nStep 2: Filtering for routes: {route_ids}") + print(f" Filtering for routes: {route_ids}") trip_ids_for_routes = list( Trip.objects.filter(route_id__in=route_ids) .values_list("trip_id", flat=True) ) - print(f" Found {len(trip_ids_for_routes):,} trips for these routes") - - # Filter TripUpdates to only these trips - tu_qs = tu_qs.filter(trip_id__in=trip_ids_for_routes) - print(f" TripUpdates for these routes: {tu_qs.count():,}") + vp_qs = vp_qs.filter(trip_id__in=trip_ids_for_routes) - # ============================================================ - # STEP 3: Get TripUpdate data (deduplicated) - # ============================================================ - print("\nStep 3: Fetching TripUpdate data...") - - # Get latest update per (trip_id, stop_sequence) - # We'll deduplicate in pandas since Django ORM doesn't have good window functions - tu_data = tu_qs.values( + print(f" Fetching VehiclePosition records...") + vp_data = vp_qs.values( 'trip_id', - 'stop_sequence', - 'arrival_time', - 'departure_time', + 'vehicle_id', 'ts', - 'start_date', - 'stop_id', - ).order_by('trip_id', 'stop_sequence', '-ts') + 'lat', + 'lon', + 'bearing', + 'speed', + ).order_by('trip_id', 'ts') - print(f" Fetching TripUpdate records...") - tu_df = pd.DataFrame.from_records(tu_data) - print(f" Retrieved {len(tu_df):,} TripUpdate records") + vp_df = pd.DataFrame.from_records(vp_data) + print(f" Retrieved {len(vp_df):,} VehiclePosition records") - if tu_df.empty: - print("\nWARNING: No TripUpdate data found!") + if vp_df.empty: + print("\nWARNING: No VehiclePosition data found!") return pd.DataFrame() - # Deduplicate: keep latest update per (trip_id, stop_sequence) - print(" Deduplicating TripUpdates (keeping latest per trip/stop)...") - tu_df = tu_df.drop_duplicates(subset=['trip_id', 'stop_sequence'], keep='first') - print(f" After deduplication: {len(tu_df):,} records") - - # Rename for clarity - tu_df = tu_df.rename(columns={ - 'arrival_time': 'tu_arrival', - 'departure_time': 'tu_departure', - 'ts': 'tu_ts', - 'start_date': 'tu_start_date', - 'stop_id': 'tu_stop_id', - }) - # ============================================================ - # STEP 4: Get StopTime data for matching trips + # STEP 2: Get trip metadata # ============================================================ - print("\nStep 4: Fetching StopTime schedule data...") + print("\nStep 2: Getting trip metadata...") - # Get unique trip_ids from TripUpdates - trip_ids = tu_df['trip_id'].unique() - print(f" Unique trips to fetch: {len(trip_ids):,}") + trip_ids = vp_df['trip_id'].unique() + trips_data = Trip.objects.filter(trip_id__in=trip_ids).values( + 'trip_id', 'route_id', 'direction_id', 'trip_headsign', 'service_id' + ) + trips_df = pd.DataFrame.from_records(trips_data) - # Fetch StopTimes for these trips - st_qs = StopTime.objects.filter(trip_id__in=trip_ids) + vp_df = vp_df.merge(trips_df, on='trip_id', how='left') + print(f" Added route info for {len(vp_df):,} records") - st_data = st_qs.values( + # Drop VPs without route info + initial_count = len(vp_df) + vp_df = vp_df.dropna(subset=['route_id']) + print(f" Dropped {initial_count - len(vp_df):,} VPs without route info") + + # ============================================================ + # STEP 3: Get stop sequences for each trip + # ============================================================ + print("\nStep 3: Loading stop sequences for trips...") + + stoptimes_data = StopTime.objects.filter( + trip_id__in=trip_ids + ).values( 'trip_id', 'stop_sequence', 'stop_id', 'arrival_time', - 'departure_time', - ) + ).order_by('trip_id', 'stop_sequence') - print(f" Fetching StopTime records...") - st_df = pd.DataFrame.from_records(st_data) + st_df = pd.DataFrame.from_records(stoptimes_data) print(f" Retrieved {len(st_df):,} StopTime records") if st_df.empty: print("\nWARNING: No StopTime data found!") return pd.DataFrame() + # # Get stop coordinates + # stop_ids = st_df['stop_id'].unique() + # stops_data = Stop.objects.filter(id__in=stop_ids).values( + # 'id', 'stop_lat', 'stop_lon', 'stop_name' + # ) + # stops_df = pd.DataFrame.from_records(stops_data).rename( + # columns={'id': 'stop_id'} + # ) + + # st_df = st_df.merge(stops_df, on='stop_id', how='left') + # print(f" Added coordinates for {st_df['stop_lat'].notna().sum():,} stops") + + # Get stop coordinates + stop_ids = st_df['stop_id'].unique() + # remove any NaN / None values and coerce to list for ORM filter + stop_ids = [s for s in stop_ids if pd.notna(s)] + stops_data = Stop.objects.filter(id__in=stop_ids).values( + 'id', 'stop_lat', 'stop_lon', 'stop_name' + ) + stops_df = pd.DataFrame.from_records(stops_data).rename( + columns={'id': 'stop_id'} + ) + # use the model column that stores GTFS stop id (stop_id) so values() returns 'stop_id' + stops_data = Stop.objects.filter(stop_id__in=stop_ids).values( + 'stop_id', 'stop_lat', 'stop_lon', 'stop_name' + ) + stops_df = pd.DataFrame.from_records(stops_data) + + st_df = st_df.merge(stops_df, on='stop_id', how='left') + print(f" Added coordinates for {st_df['stop_lat'].notna().sum():,} stops") + # st_df = st_df.merge(stops_df, on='stop_id', how='left') + # print(f" Added coordinates for {st_df['stop_lat'].notna().sum():,} stops") +# ...existing code... + + # ============================================================ - # STEP 5: Join StopTime + TripUpdate + # STEP 4: For each VP, find remaining stops and distances # ============================================================ - print("\nStep 5: Joining schedule and realtime data...") + print(f"\nStep 4: Calculating distances to remaining stops (max {max_stops_ahead})...") - df = st_df.merge( - tu_df, - on=['trip_id', 'stop_sequence'], - how='inner', # Only keep records with both schedule and realtime data - suffixes=('_sched', '_tu') - ) + training_rows = [] + total_vps = len(vp_df) - print(f" After join: {len(df):,} records") + # Group VPs by trip for efficient processing + vp_grouped = vp_df.groupby('trip_id') + st_grouped = st_df.groupby('trip_id') - if df.empty: - print("\nWARNING: No matching records after join!") + processed_trips = 0 + for trip_id, trip_vps in vp_grouped: + processed_trips += 1 + if processed_trips % 100 == 0: + print(f" Processing trip {processed_trips}/{len(vp_grouped)} ({len(training_rows):,} rows generated)") + + # Get stop sequence for this trip + if trip_id not in st_grouped.groups: + continue + + trip_stops = st_grouped.get_group(trip_id).sort_values('stop_sequence') + + # For each VP in this trip + for vp_idx, vp_row in trip_vps.iterrows(): + vp_lat = vp_row['lat'] + vp_lon = vp_row['lon'] + vp_ts = vp_row['ts'] + + # Find which stops are ahead of this VP + # Strategy: Calculate distance to all stops, take the closest N + distances_to_stops = trip_stops.apply( + lambda stop: haversine_distance(vp_lat, vp_lon, stop['stop_lat'], stop['stop_lon']), + axis=1 + ) + + trip_stops_with_dist = trip_stops.copy() + trip_stops_with_dist['distance_to_stop'] = distances_to_stops + + # Sort by stop_sequence and take upcoming stops + # A stop is "upcoming" if it hasn't been passed yet + # Simple heuristic: stops that are ahead in sequence from the closest stop + closest_stop_idx = distances_to_stops.idxmin() + closest_stop_seq = trip_stops.loc[closest_stop_idx, 'stop_sequence'] + + # Get stops with sequence >= closest (upcoming stops) + upcoming_stops = trip_stops_with_dist[ + trip_stops_with_dist['stop_sequence'] >= closest_stop_seq + ].head(max_stops_ahead) + + # For each upcoming stop, find actual arrival time + for stop_idx, stop_row in upcoming_stops.iterrows(): + # Find actual arrival from future VPs + future_vps = trip_vps[trip_vps['ts'] > vp_ts] + + actual_arrival = find_actual_arrival_time( + future_vps, + stop_row['stop_lat'], + stop_row['stop_lon'], + distance_threshold=distance_threshold + ) + + # Only include if we found an actual arrival + if actual_arrival is not None: + training_rows.append({ + 'trip_id': trip_id, + 'route_id': vp_row['route_id'], + 'vehicle_id': vp_row['vehicle_id'], + 'vp_ts': vp_ts, + 'vp_lat': vp_lat, + 'vp_lon': vp_lon, + 'vp_bearing': vp_row.get('bearing'), + 'vp_speed': vp_row.get('speed'), + 'stop_id': stop_row['stop_id'], + 'stop_sequence': stop_row['stop_sequence'], + 'stop_lat': stop_row['stop_lat'], + 'stop_lon': stop_row['stop_lon'], + 'distance_to_stop': stop_row['distance_to_stop'], + 'scheduled_arrival': stop_row['arrival_time'], + 'actual_arrival': actual_arrival, + }) + + print(f" Generated {len(training_rows):,} training samples") + + if not training_rows: + print("\nWARNING: No training rows generated!") return pd.DataFrame() - # ============================================================ - # STEP 6: Get Trip/Route metadata - # ============================================================ - print("\nStep 6: Adding route information...") - - trip_ids = df['trip_id'].unique() - trips_data = Trip.objects.filter(trip_id__in=trip_ids).values( - 'trip_id', 'route_id', 'direction_id', 'trip_headsign' - ) - trips_df = pd.DataFrame.from_records(trips_data) - - df = df.merge(trips_df, on='trip_id', how='left') - print(f" Added route_id for {len(df):,} records") + df = pd.DataFrame(training_rows) # ============================================================ - # STEP 7: Compute scheduled and actual arrival times + # STEP 5: Compute delay_seconds # ============================================================ - print("\nStep 7: Computing scheduled_arrival...") - - # Debug: Check what we have - print(f" Sample arrival_time values: {df['arrival_time'].head().tolist()}") - print(f" Sample arrival_time types: {[type(x) for x in df['arrival_time'].head().tolist()]}") - print(f" Sample tu_start_date values: {df['tu_start_date'].head().tolist()}") - print(f" Sample tu_start_date types: {[type(x) for x in df['tu_start_date'].head().tolist()]}") + print("\nStep 5: Computing delay_seconds...") - df["sched_secs"] = df["arrival_time"].apply(_parse_sched_time) - df["start_date_obj"] = df["tu_start_date"].apply(_yyyymmdd_to_date) + # Convert tss to datetime + df['vp_ts'] = pd.to_datetime(df['vp_ts'], utc=True) + df['actual_arrival'] = pd.to_datetime(df['actual_arrival'], utc=True) - print(f" Parsed sched_secs (non-null): {df['sched_secs'].notna().sum()}/{len(df)}") - print(f" Parsed start_date_obj (non-null): {df['start_date_obj'].notna().sum()}/{len(df)}") - print(f" Sample sched_secs: {df['sched_secs'].head().tolist()}") - print(f" Sample start_date_obj: {df['start_date_obj'].head().tolist()}") - - # Check if both are present before combining - both_valid = (df['sched_secs'].notna() & df['start_date_obj'].notna()).sum() - print(f" Rows with BOTH sched_secs AND start_date_obj: {both_valid}/{len(df)}") - - df["scheduled_arrival"] = df.apply( - lambda row: _mk_dt(row["start_date_obj"], row["sched_secs"]), - axis=1 - ) - - print(f" Scheduled arrivals (non-null): {df['scheduled_arrival'].notna().sum()}/{len(df)}") - print(f" Sample scheduled_arrival: {df['scheduled_arrival'].head().tolist()}") - - print("\nStep 8: Computing actual_arrival...") - print(f" Sample tu_arrival: {df['tu_arrival'].head().tolist()}") - print(f" Sample tu_departure: {df['tu_departure'].head().tolist()}") - print(f" Sample tu_ts: {df['tu_ts'].head().tolist()}") - print(f" tu_arrival (non-null): {df['tu_arrival'].notna().sum()}/{len(df)}") - print(f" tu_departure (non-null): {df['tu_departure'].notna().sum()}/{len(df)}") - print(f" tu_ts (non-null): {df['tu_ts'].notna().sum()}/{len(df)}") - - df["actual_arrival"] = df["tu_arrival"].combine_first( - df["tu_departure"] - ).combine_first(df["tu_ts"]) - - print(f" Actual arrivals (non-null): {df['actual_arrival'].notna().sum()}/{len(df)}") - print(f" Sample actual_arrival: {df['actual_arrival'].head().tolist()}") - - # Ensure datetime dtype - for col in ["scheduled_arrival", "actual_arrival"]: - df[col] = pd.to_datetime(df[col], utc=True, errors="coerce") - - print(f"\n After pd.to_datetime conversion:") - print(f" Scheduled arrivals (non-null): {df['scheduled_arrival'].notna().sum()}/{len(df)}") - print(f" Actual arrivals (non-null): {df['actual_arrival'].notna().sum()}/{len(df)}") - - # ============================================================ - # STEP 9: Compute delay - # ============================================================ - print("\nStep 9: Computing delay_seconds...") - df["delay_seconds"] = ( - df["actual_arrival"] - df["scheduled_arrival"] + # Parse scheduled arrival time (may need date context) + # For now, compute time_to_arrival (prediction target) + df['time_to_arrival_seconds'] = ( + df['actual_arrival'] - df['vp_ts'] ).dt.total_seconds() - # Drop rows with missing critical data + # Filter out negative or unrealistic values initial_count = len(df) - df = df.dropna(subset=["scheduled_arrival", "actual_arrival", "delay_seconds"]) - print(f" Dropped {initial_count - len(df):,} rows with missing critical data") + df = df[ + (df['time_to_arrival_seconds'] >= 0) & + (df['time_to_arrival_seconds'] <= 7200) # Max 2 hours ahead + ] + print(f" Dropped {initial_count - len(df):,} rows with invalid time_to_arrival") print(f" Remaining: {len(df):,} rows") - if df.empty: - print("\nWARNING: All rows dropped due to missing data") - return df - # ============================================================ - # STEP 10: Temporal features + # STEP 6: Temporal features # ============================================================ - print("\nStep 10: Extracting temporal features...") + print("\nStep 6: Extracting temporal features...") + temporal_data = [] - for idx, ts in enumerate(df["actual_arrival"]): + for idx, ts in enumerate(df['vp_ts']): if idx % 10000 == 0 and idx > 0: print(f" Processed {idx:,}/{len(df):,} temporal features") - if pd.notna(ts): - try: - feats = extract_temporal_features(ts, tz=tz_for_temporal, region="CR") - temporal_data.append(feats) - except Exception as e: - temporal_data.append({}) - else: + try: + feats = extract_temporal_features(ts, tz=tz_for_temporal, region="CR") + temporal_data.append(feats) + except Exception: temporal_data.append({}) tf = pd.DataFrame(temporal_data) @@ -338,78 +380,54 @@ def build_training_dataset( df[k] = np.nan # ============================================================ - # STEP 11: Operational features + # STEP 7: Operational features # ============================================================ - print("\nStep 11: Computing operational features...") + print("\nStep 7: Computing operational features...") - def _safe_headway(route_id, timestamp): + def _safe_headway(route_id, ts, vehicle_id): try: return calculate_headway( route_id=route_id, - timestamp=timestamp, - vehicle_id=None + ts=ts, + vehicle_id=vehicle_id ) except Exception: return np.nan - def _safe_congestion(route_id, stop_sequence, timestamp): - try: - result = detect_congestion_proxy( - route_id=route_id, - stop_sequence=stop_sequence, - timestamp=timestamp, - ) - return result.get("avg_speed_last_10min", np.nan) if result else np.nan - except Exception: - return np.nan - headways = [] - speeds = [] for idx, row in df.iterrows(): - if idx % 50 == 0: - print(f" Processing operational features: {idx:,}/{len(df):,}") - headways.append(_safe_headway(row["route_id"], row["actual_arrival"])) - speeds.append(_safe_congestion(row["route_id"], row["stop_sequence"], row["actual_arrival"])) + if idx % 1000 == 0: + print(f" Processing headway: {idx:,}/{len(df):,}") + headways.append(_safe_headway( + row['route_id'], + row['vp_ts'], + row['vehicle_id'] + )) - df["headway_seconds"] = headways - df["avg_speed_last_10min"] = speeds + df['headway_seconds'] = headways + # Use VP speed directly if available + df['current_speed_kmh'] = df['vp_speed'] * 3.6 # m/s to km/h # ============================================================ - # STEP 12: Weather features + # STEP 8: Weather features # ============================================================ if attach_weather: - print("\nStep 12: Fetching weather data...") - - # Use tu_stop_id (from realtime) or stop_id (from schedule) - df['final_stop_id'] = df['tu_stop_id'].combine_first(df['stop_id']) - - stop_ids = df["final_stop_id"].unique() - stop_meta = Stop.objects.filter( - id__in=stop_ids - ).values("id", "stop_lat", "stop_lon") - stop_df = pd.DataFrame.from_records(stop_meta).rename( - columns={"id": "final_stop_id"} - ) - - df = df.merge(stop_df, on="final_stop_id", how="left") + print("\nStep 8: Fetching weather data...") weather_data = [] for idx, row in df.iterrows(): if idx % 5000 == 0: print(f" Fetching weather: {idx:,}/{len(df):,}") - if pd.notna(row.get("stop_lat")) and pd.notna(row.get("stop_lon")) and pd.notna(row.get("actual_arrival")): - try: - w = fetch_weather( - float(row["stop_lat"]), - float(row["stop_lon"]), - pd.Timestamp(row["actual_arrival"]).to_pydatetime() - ) - weather_data.append(w or {}) - except Exception: - weather_data.append({}) - else: + try: + w = fetch_weather( + float(row['vp_lat']), + float(row['vp_lon']), + row['vp_ts'].to_pydatetime() + ) + weather_data.append(w or {}) + except Exception: weather_data.append({}) wx_df = pd.DataFrame(weather_data) @@ -424,41 +442,17 @@ def _safe_congestion(route_id, stop_sequence, timestamp): df["wind_speed_kmh"] = np.nan # ============================================================ - # STEP 13: Outlier filtering - # ============================================================ - # print("\nStep 13: Filtering outliers...") - # initial_count = len(df) - - # # Clip extreme delays (±2 hours) - # df = df[(df["delay_seconds"] >= -7200) & (df["delay_seconds"] <= 7200)] - # print(f" Dropped {initial_count - len(df):,} outlier rows") - - # ============================================================ - # STEP 14: Minimum observations per stop + # STEP 9: Final column selection # ============================================================ - print(f"\nStep 14: Filtering stops with < {min_observations_per_stop} observations...") - - df['final_stop_id'] = df['tu_stop_id'].combine_first(df['stop_id']) - counts = df.groupby("final_stop_id").size() - keep_stops = set(counts[counts >= min_observations_per_stop].index) - - initial_count = len(df) - df = df[df["final_stop_id"].isin(keep_stops)] - print(f" Kept {len(keep_stops)} stops, dropped {initial_count - len(df):,} rows") - - # ============================================================ - # STEP 15: Final column selection - # ============================================================ - print("\nStep 15: Preparing final dataset...") - - # Use tu_stop_id preferentially, fallback to schedule stop_id - df['stop_id'] = df['tu_stop_id'].combine_first(df['stop_id']) + print("\nStep 9: Preparing final dataset...") wanted_cols = [ - "trip_id", "route_id", "stop_id", "stop_sequence", - "scheduled_arrival", "actual_arrival", "delay_seconds", + "trip_id", "route_id", "vehicle_id", "stop_id", "stop_sequence", + "vp_ts", "vp_lat", "vp_lon", "vp_bearing", + "stop_lat", "stop_lon", "distance_to_stop", + "actual_arrival", "time_to_arrival_seconds", "hour", "day_of_week", "is_weekend", "is_holiday", "is_peak_hour", - "headway_seconds", "avg_speed_last_10min", + "headway_seconds", "current_speed_kmh", "temperature_c", "precipitation_mm", "wind_speed_kmh", ] @@ -470,6 +464,10 @@ def _safe_congestion(route_id, stop_sequence, timestamp): print("\n" + "=" * 70) print(f"✓ Final dataset: {len(result):,} rows × {len(wanted_cols)} columns") + print(f" Unique trips: {result['trip_id'].nunique():,}") + print(f" Unique routes: {result['route_id'].nunique():,}") + print(f" Unique stops: {result['stop_id'].nunique():,}") + print(f" Avg time_to_arrival: {result['time_to_arrival_seconds'].mean():.1f}s ({result['time_to_arrival_seconds'].mean()/60:.1f} min)") print("=" * 70) return result diff --git a/eta_prediction/feature_engineering/pyproject.toml b/eta_prediction/feature_engineering/pyproject.toml new file mode 100644 index 0000000..8e1026d --- /dev/null +++ b/eta_prediction/feature_engineering/pyproject.toml @@ -0,0 +1,35 @@ +[project] +name = "feature_engineering" +version = "0.1.0" +description = "Feature engineering utilities for GTFS realtime and schedule data (part of gtfs-django repository)" +readme = "README.md" +requires-python = ">=3.10" +authors = [ + { name = "maintainer", email = "maintainer@example.com" } +] +license = { text = "MIT" } + +dependencies = [ + "numpy>=1.26,<2", + "pandas>=2.2,<3", + "scikit-learn>=1.3,<2", + "scipy>=1.11,<2", + "joblib>=1.3,<2", + "tqdm>=4.66,<5", + "pyproj>=3.6,<4", + "shapely>=2.1,<3", + "geopandas>=0.14,<1", +] + +[project.optional-dependencies] +dev = [ + "pytest>=7.4,<8", + "black>=24.3,<25", + "isort>=5.13,<6", + "mypy>=1.11,<2", + "pre-commit>=3.4,<4", +] + +[build-system] +requires = ["setuptools>=61.0", "wheel"] +build-backend = "setuptools.build_meta" \ No newline at end of file diff --git a/eta_prediction/gtfs-rt-pipeline/eta_vp_sample.parquet b/eta_prediction/gtfs-rt-pipeline/eta_vp_sample.parquet new file mode 100644 index 0000000..ebd8d88 Binary files /dev/null and b/eta_prediction/gtfs-rt-pipeline/eta_vp_sample.parquet differ diff --git a/eta_prediction/gtfs-rt-pipeline/rt_pipeline/management/commands/build_eta_sample.py b/eta_prediction/gtfs-rt-pipeline/rt_pipeline/management/commands/build_eta_sample.py index da1ea37..9d07df1 100644 --- a/eta_prediction/gtfs-rt-pipeline/rt_pipeline/management/commands/build_eta_sample.py +++ b/eta_prediction/gtfs-rt-pipeline/rt_pipeline/management/commands/build_eta_sample.py @@ -17,7 +17,7 @@ sys.path.insert(0, str(ETA_PREDICTION_ROOT)) try: - from feature_engineering.dataset_builder import build_training_dataset, save_dataset + from feature_engineering.dataset_builder import build_vp_training_dataset, save_dataset from sch_pipeline.utils import top_routes_by_scheduled_trips except ImportError as e: print(f"ERROR: Failed to import required modules: {e}") @@ -28,7 +28,7 @@ class Command(BaseCommand): - help = "Build a small ETA training dataset for the top-N busiest routes (by scheduled trips)." + help = "Build ETA training dataset from VehiclePosition data for the top-N busiest routes." def add_arguments(self, parser): parser.add_argument( @@ -49,10 +49,28 @@ def add_arguments(self, parser): default=10, help="Min observations per stop" ) + parser.add_argument( + "--distance-threshold", + type=float, + default=50.0, + help="Distance threshold (meters) to consider vehicle 'arrived' at stop" + ) + parser.add_argument( + "--max-stops-ahead", + type=int, + default=5, + help="Maximum number of upcoming stops to include per VP" + ) + parser.add_argument( + "--vp-sample-interval", + type=int, + default=30, + help="Sample VPs every N seconds per vehicle (0=no sampling, use all VPs)" + ) parser.add_argument( "--out", type=str, - default="eta_sample.parquet", + default="eta_vp_sample.parquet", help="Output parquet path" ) parser.add_argument( @@ -65,18 +83,49 @@ def add_arguments(self, parser): type=str, help="Comma-separated route IDs (overrides --top-routes)" ) + parser.add_argument( + "--start-date", + type=str, + help="Start date (YYYY-MM-DD format, overrides --days)" + ) + parser.add_argument( + "--end-date", + type=str, + help="End date (YYYY-MM-DD format, overrides --days)" + ) def handle(self, *args, **opts): n = opts["top_routes"] days = opts["days"] min_obs = opts["min_observations"] + distance_threshold = opts["distance_threshold"] + max_stops_ahead = opts["max_stops_ahead"] + # vp_sample_interval = opts["vp_sample_interval"] out = opts["out"] attach_weather = not opts["no_weather"] manual_routes = opts.get("route_ids") + start_date_str = opts.get("start_date") + end_date_str = opts.get("end_date") - # or for a fixed day window matching your data: - start = timezone.datetime(2025, 10, 8, 0, 0, tzinfo=UTC) - end = timezone.datetime(2025, 10, 9, 0, 0, tzinfo=UTC) + # Determine date range + if start_date_str and end_date_str: + try: + start = timezone.datetime.strptime(start_date_str, "%Y-%m-%d").replace(tzinfo=UTC) + end = timezone.datetime.strptime(end_date_str, "%Y-%m-%d").replace(tzinfo=UTC) + except ValueError as e: + self.stdout.write( + self.style.ERROR(f"Invalid date format: {e}. Use YYYY-MM-DD") + ) + return + else: + # Default: use fixed date range or calculate from --days + # For testing, using fixed dates: + start = timezone.datetime(2025, 10, 8, 0, 0, tzinfo=UTC) + end = timezone.datetime(2025, 10, 9, 0, 0, tzinfo=UTC) + # Or calculate from days: + # end = timezone.now() + # start = end - timedelta(days=days) + # Determine which routes to use if manual_routes: route_ids = [r.strip() for r in manual_routes.split(",")] @@ -112,21 +161,49 @@ def handle(self, *args, **opts): self.stdout.write("\n" + "="*60) self.stdout.write(self.style.NOTICE("Configuration:")) self.stdout.write(f" Routes: {', '.join(route_ids)}") - self.stdout.write(f" Date range: {start.date()} to {end.date()} ({days} days)") + self.stdout.write(f" Date range: {start.date()} to {end.date()}") + self.stdout.write(f" Distance threshold: {distance_threshold}m") + self.stdout.write(f" Max stops ahead: {max_stops_ahead}") + # # self.stdout.write(f" VP sample interval: {vp_sample_interval}s ({'all VPs' if vp_sample_interval == 0 else 'sampled'})") self.stdout.write(f" Min observations/stop: {min_obs}") self.stdout.write(f" Weather features: {'enabled' if attach_weather else 'disabled'}") self.stdout.write(f" Output: {out}") self.stdout.write("="*60 + "\n") + # Check for VehiclePosition data + from rt_pipeline.models import VehiclePosition + vp_count = VehiclePosition.objects.filter( + ts__gte=start, + ts__lt=end + ).count() + + if vp_count == 0: + self.stdout.write( + self.style.WARNING( + f"No VehiclePosition data found in date range {start.date()} to {end.date()}\n" + "Check data availability:\n" + " python manage.py shell -c 'from rt_pipeline.models import VehiclePosition; " + "from django.db.models import Min, Max; " + "print(VehiclePosition.objects.aggregate(min=Min(\"ts\"), max=Max(\"ts\")))'" + ) + ) + return + else: + self.stdout.write( + self.style.SUCCESS(f"Found {vp_count:,} VehiclePosition records in date range") + ) + # Build dataset try: - self.stdout.write(self.style.NOTICE("Building dataset...")) - df = build_training_dataset( - provider_id=None, + self.stdout.write(self.style.NOTICE("\nBuilding dataset...")) + df = build_vp_training_dataset( route_ids=route_ids, start_date=start, end_date=end, - min_observations_per_stop=min_obs, + distance_threshold=distance_threshold, + max_stops_ahead=max_stops_ahead, + # min_observations_per_stop=min_obs, + # vp_sample_interval_seconds=vp_sample_interval, attach_weather=attach_weather, ) except Exception as e: @@ -141,12 +218,22 @@ def handle(self, *args, **opts): self.stdout.write( self.style.WARNING( "Resulting dataset is empty. Possible issues:\n" - " 1. No TripUpdate data in the date range\n" - " 2. No matching stop_sequences between StopTime and TripUpdate\n" - " 3. All data filtered out by min_observations threshold\n" + " 1. No VehiclePosition data in the date range\n" + " 2. VPs not matching any trips with stop sequences\n" + " 3. Vehicles never came close enough to stops (try increasing --distance-threshold)\n" + " 4. All data filtered out by --min-observations threshold\n" + " 5. No future VPs available to detect arrivals (incomplete trips)\n" "\nDebug queries:\n" - " - Check TripUpdate count: python manage.py shell -c 'from rt_pipeline.models import TripUpdate; print(TripUpdate.objects.count())'\n" - " - Check date range: python manage.py shell -c 'from rt_pipeline.models import TripUpdate; print(TripUpdate.objects.aggregate(min=Min(\"ts\"), max=Max(\"ts\")))'" + " - Check VP count: python manage.py shell -c 'from rt_pipeline.models import VehiclePosition; print(VehiclePosition.objects.count())'\n" + " - Check date range: python manage.py shell -c 'from rt_pipeline.models import VehiclePosition; from django.db.models import Min, Max; print(VehiclePosition.objects.aggregate(min=Min(\"ts\"), max=Max(\"ts\")))'\n" + " - Check StopTime data: python manage.py shell -c 'from sch_pipeline.models import StopTime, Stop; print(f\"StopTimes: {StopTime.objects.count()}, Stops with coords: {Stop.objects.exclude(stop_lat__isnull=True).count()}\")'\n" + "\nTry adjusting parameters:\n" + " - Increase --distance-threshold (current: {})m\n" + " - Reduce --min-observations (current: {})\n" + " - Increase --max-stops-ahead (current: {})\n" + " - Set --vp-sample-interval to 0 to use all VPs".format( + distance_threshold, min_obs, max_stops_ahead + ) ) ) return @@ -156,17 +243,33 @@ def handle(self, *args, **opts): self.stdout.write(self.style.SUCCESS("Dataset Summary:")) self.stdout.write(f" Total rows: {len(df):,}") self.stdout.write(f" Unique trips: {df['trip_id'].nunique():,}") + self.stdout.write(f" Unique routes: {df['route_id'].nunique()}") + self.stdout.write(f" Unique vehicles: {df['vehicle_id'].nunique():,}") self.stdout.write(f" Unique stops: {df['stop_id'].nunique():,}") - self.stdout.write(f" Routes: {df['route_id'].nunique()}") - if "delay_seconds" in df.columns: - delay_stats = df["delay_seconds"].describe() - self.stdout.write(f"\n Delay statistics (seconds):") - self.stdout.write(f" Mean: {delay_stats['mean']:.1f}") - self.stdout.write(f" Median: {delay_stats['50%']:.1f}") - self.stdout.write(f" Std: {delay_stats['std']:.1f}") - self.stdout.write(f" Min: {delay_stats['min']:.1f}") - self.stdout.write(f" Max: {delay_stats['max']:.1f}") + if "time_to_arrival_seconds" in df.columns: + tta_stats = df["time_to_arrival_seconds"].describe() + self.stdout.write(f"\n Time-to-arrival statistics:") + self.stdout.write(f" Mean: {tta_stats['mean']:.1f}s ({tta_stats['mean']/60:.1f} min)") + self.stdout.write(f" Median: {tta_stats['50%']:.1f}s ({tta_stats['50%']/60:.1f} min)") + self.stdout.write(f" Std: {tta_stats['std']:.1f}s") + self.stdout.write(f" Min: {tta_stats['min']:.1f}s") + self.stdout.write(f" Max: {tta_stats['max']:.1f}s ({tta_stats['max']/60:.1f} min)") + + if "distance_to_stop" in df.columns: + dist_stats = df["distance_to_stop"].describe() + self.stdout.write(f"\n Distance-to-stop statistics:") + self.stdout.write(f" Mean: {dist_stats['mean']:.1f}m") + self.stdout.write(f" Median: {dist_stats['50%']:.1f}m") + self.stdout.write(f" Min: {dist_stats['min']:.1f}m") + self.stdout.write(f" Max: {dist_stats['max']:.1f}m") + + if "current_speed_kmh" in df.columns: + speed_stats = df[df["current_speed_kmh"] > 0]["current_speed_kmh"].describe() + if not speed_stats.empty: + self.stdout.write(f"\n Speed statistics (km/h):") + self.stdout.write(f" Mean: {speed_stats['mean']:.1f}") + self.stdout.write(f" Median: {speed_stats['50%']:.1f}") missing = df.isnull().sum() if missing.any(): @@ -183,6 +286,19 @@ def handle(self, *args, **opts): self.stdout.write( self.style.SUCCESS(f"✓ Successfully saved to {out}") ) + + # Provide guidance on next steps + self.stdout.write("\n" + self.style.NOTICE("Next steps:")) + self.stdout.write(" 1. Inspect the dataset: ") + self.stdout.write(f" import pandas as pd; df = pd.read_parquet('{out}'); df.head()") + self.stdout.write(" 2. Check feature distributions and correlations") + self.stdout.write(" 3. Train a model predicting 'time_to_arrival_seconds' from:") + self.stdout.write(" - distance_to_stop") + self.stdout.write(" - current_speed_kmh") + self.stdout.write(" - temporal features (hour, is_peak_hour, etc.)") + self.stdout.write(" - operational features (headway_seconds)") + self.stdout.write(" - weather features (if enabled)") + except Exception as e: self.stdout.write( self.style.ERROR(f"Failed to save dataset: {e}") diff --git a/eta_prediction/gtfs_rt_bindings_SA.py b/eta_prediction/gtfs_rt_bindings_SA.py deleted file mode 100644 index 66810b2..0000000 --- a/eta_prediction/gtfs_rt_bindings_SA.py +++ /dev/null @@ -1,73 +0,0 @@ -from google.transit import gtfs_realtime_pb2 -import requests -from datetime import datetime, timezone - -""" -This script takes a GTFS-RT Service Alerts feed and prints out the first N alerts. -""" -N = 10 -URL = "https://cdn.mbta.com/realtime/Alerts.pb" # MBTA Service Alerts feed - -def ts_to_iso(ts): - return datetime.fromtimestamp(ts, tz=timezone.utc).isoformat() if ts else None - -def get_text(translated_string): - """Return the first non-empty translation (common case).""" - if not translated_string or not translated_string.translation: - return None - for t in translated_string.translation: - if t.text: - return t.text - return None - -def fmt_period(active_period): - start = ts_to_iso(active_period.start) if active_period.HasField("start") else None - end = ts_to_iso(active_period.end) if active_period.HasField("end") else None - return f"{start or '—'} → {end or '—'}" - -def describe_informed_entity(ie): - parts = [] - if ie.route_id: - parts.append(f"route={ie.route_id}") - if ie.stop_id: - parts.append(f"stop={ie.stop_id}") - if ie.trip and ie.trip.trip_id: - parts.append(f"trip={ie.trip.trip_id}") - if not parts: - parts.append("system-wide") - return ", ".join(parts) - -feed = gtfs_realtime_pb2.FeedMessage() -resp = requests.get(URL, timeout=15) -resp.raise_for_status() -feed.ParseFromString(resp.content) - -shown = 0 -for entity in feed.entity: - if not entity.HasField("alert"): - continue - alert = entity.alert - - header = get_text(alert.header_text) or "(no header)" - desc = get_text(alert.description_text) or "" - cause = gtfs_realtime_pb2.Alert.Cause.Name(alert.cause) if alert.HasField("cause") else "CAUSE_UNSPECIFIED" - effect = gtfs_realtime_pb2.Alert.Effect.Name(alert.effect) if alert.HasField("effect") else "EFFECT_UNSPECIFIED" - - periods = [fmt_period(p) for p in alert.active_period] - applies = [describe_informed_entity(ie) for ie in alert.informed_entity] - - print(f"ALERT: {header}") - if desc: - print(f" desc: {desc}") - print(f" cause: {cause} | effect: {effect}") - if periods: - for i, p in enumerate(periods, 1): - print(f" active[{i}]: {p}") - if applies: - for i, a in enumerate(applies, 1): - print(f" applies_to[{i}]: {a}") - print("-" * 80) - - count += 1 - if count >= N: # show first N alerts - break diff --git a/eta_prediction/gtfs_rt_bindings_TU.py b/eta_prediction/gtfs_rt_bindings_TU.py deleted file mode 100644 index 07a78de..0000000 --- a/eta_prediction/gtfs_rt_bindings_TU.py +++ /dev/null @@ -1,18 +0,0 @@ -from google.transit import gtfs_realtime_pb2 -import requests -from datetime import datetime, timezone - -""" -This script takes a GTFS-RT Trip Updates feed and prints out the first N trip updates. -""" -N = 10 -# URL = "https://cdn.mbta.com/realtime/TripUpdates.pb" -URL = "https://databus.bucr.digital/feed/realtime/trip_updates.pb" # bUCR Realtime Trip Updates feed - -feed = gtfs_realtime_pb2.FeedMessage() -response = requests.get(URL) -feed.ParseFromString(response.content) - -for entity in feed.entity[:N]: # show first N trip updates - if entity.HasField('trip_update'): - print(entity.trip_update) diff --git a/eta_prediction/gtfs_rt_bindings_VP.py b/eta_prediction/gtfs_rt_bindings_VP.py deleted file mode 100644 index 3ff79b5..0000000 --- a/eta_prediction/gtfs_rt_bindings_VP.py +++ /dev/null @@ -1,40 +0,0 @@ -from google.transit import gtfs_realtime_pb2 -import requests -from datetime import datetime, timezone - -""" -This script takes a GTFS-RT Vehicle Positions feed and prints out the first N vehicle positions. -""" -N = 10 -# URL = "https://cdn.mbta.com/realtime/VehiclePositions.pb" -URL = "https://databus.bucr.digital/feed/realtime/vehicle_positions.pb" # bUCR Realtime Vehicle Positions feed - -feed = gtfs_realtime_pb2.FeedMessage() -resp = requests.get(URL, timeout=15) -resp.raise_for_status() -feed.ParseFromString(resp.content) - -def ts_to_iso(ts): - return datetime.fromtimestamp(ts, tz=timezone.utc).isoformat() if ts else None - -count = 0 -for entity in feed.entity: - if entity.HasField("vehicle"): - v = entity.vehicle - pos = v.position # lat, lon, speed (m/s), bearing (deg) - trip = v.trip # trip_id, route_id, start_time/date - veh = v.vehicle # id, label - - print( - f"veh_id={veh.id or '—'} route={trip.route_id or '—'} trip={trip.trip_id or '—'}\n" - f" lat={pos.latitude:.6f} lon={pos.longitude:.6f} " - f"speed_mps={pos.speed if pos.HasField('speed') else '—'} " - f"bearing={pos.bearing if pos.HasField('bearing') else '—'}\n" - f" current_stop_seq={v.current_stop_sequence if v.HasField('current_stop_sequence') else '—'} " - f"status={v.current_status if v.HasField('current_status') else '—'} " - f"timestamp={ts_to_iso(v.timestamp)}" - ) - print("-" * 80) - count += 1 - if count >= 10: # show first N vehicle positions - break diff --git a/eta_prediction/models/README.md b/eta_prediction/models/README.md new file mode 100644 index 0000000..d23fa20 --- /dev/null +++ b/eta_prediction/models/README.md @@ -0,0 +1,263 @@ +# ETA Prediction Models + +Complete modeling framework for transit ETA prediction using GTFS-RT data. + +## Directory Structure + +``` +models/ +├── README.md # This file +├── train_all_models.py # Main training script +├── common/ # Shared utilities +│ ├── data.py # Dataset loading and preprocessing +│ ├── keys.py # Model key generation +│ ├── metrics.py # Evaluation metrics +│ ├── registry.py # Model storage and retrieval +│ └── utils.py # Helper functions +├── evaluation/ # Model evaluation tools +│ ├── leaderboard.py # Model comparison +│ └── roll_validate.py # Rolling window validation +├── historical_mean/ # Historical mean baseline +│ ├── train.py +│ └── predict.py +├── polyreg_distance/ # Polynomial regression (distance) +│ ├── train.py +│ └── predict.py +├── polyreg_time/ # Polynomial regression (time) +│ ├── train.py +│ └── predict.py +├── ewma/ # Exponential weighted moving average +│ ├── train.py +│ └── predict.py +└── trained/ # Saved models (created automatically) + ├── {model_key}.pkl + ├── {model_key}_meta.json + └── registry.json +``` + +## Usage + +### 1. Train All Baseline Models + +```bash +# Train all baseline models on sample dataset +python train_all_models.py --dataset sample_dataset --mode baseline + +# Train baseline + advanced configurations +python train_all_models.py --dataset sample_dataset --mode all + +# Train without saving (dry run) +python train_all_models.py --dataset sample_dataset --no-save +``` + +### 2. Train Individual Models + +```python +from models.polyreg_time.train import train_polyreg_time + +# Train polynomial regression with time features +result = train_polyreg_time( + dataset_name="sample_dataset", + poly_degree=2, + include_temporal=True, + include_operational=True +) + +print(f"Test MAE: {result['metrics']['test_mae_minutes']:.2f} minutes") +``` + +### 3. Make Predictions + +```python +from models.polyreg_time.predict import predict_eta + +# Single prediction +prediction = predict_eta( + model_key=f'{MODEL_KEY}', + distance_to_stop=1500.0, # meters + hour=8, + is_peak_hour=True, + current_speed_kmh=25.0 +) + +print(f"ETA: {prediction['eta_formatted']}") +``` + +### 4. Compare Models + +```python +from models.evaluation.leaderboard import quick_compare + +model_keys = [ + "historical_mean_...", + "polyreg_distance_...", + "polyreg_time_...", + "ewma_..." +] + +results = quick_compare(model_keys, "sample_dataset") +``` + +## Model Types + +### 1. Historical Mean (`historical_mean/`) + +**Description**: Baseline model using historical average ETAs grouped by route, stop, and time features. + +**Example**: +```python +from models.historical_mean.train import train_historical_mean + +result = train_historical_mean( + group_by=['route_id', 'stop_sequence', 'hour', 'day_of_week'] +) +``` + +--- + +### 2. Polynomial Regression - Distance (`polyreg_distance/`) + +**Description**: Polynomial regression on distance to stop with optional route-specific models. + +**Example**: +```python +from models.polyreg_distance.train import train_polyreg_distance + +result = train_polyreg_distance( + degree=2, + route_specific=True +) +``` + +--- + +### 3. Polynomial Regression - Time (`polyreg_time/`) + +**Description**: Enhanced polynomial regression combining distance with temporal and operational features. + +**Example**: +```python +from models.polyreg_time.train import train_polyreg_time + +result = train_polyreg_time( + poly_degree=2, + include_temporal=True, + include_operational=True, + include_weather=False +) +``` + +--- + +### 4. EWMA (`ewma/`) + +**Description**: Exponentially weighted moving average that adapts predictions based on recent observations. + +**Example**: +```python +from models.ewma.train import train_ewma + +result = train_ewma( + alpha=0.3, # Higher = faster adaptation + group_by=['route_id', 'stop_sequence', 'hour'] +) +``` + +## Evaluation + +### Metrics + +All models are evaluated on: + +- **MAE (Mean Absolute Error)**: Primary metric, in seconds and minutes +- **RMSE (Root Mean Squared Error)**: Penalizes large errors +- **R²**: Goodness of fit +- **Within Threshold**: % predictions within 60s, 120s, 300s +- **Bias**: Over/under-prediction tendency +- **Quantile Errors**: 50th, 90th, 95th percentile errors + +### Rolling Window Validation + +Test models on sequential time windows: + +```python +from models.evaluation.roll_validate import quick_rolling_validate +from models.ewma.train import EWMAModel + +results = quick_rolling_validate( + model_class=EWMAModel, + model_params={'alpha': 0.3}, + train_window_days=7 +) +``` + +### Leaderboard + +Compare multiple models: + +```python +from models.evaluation.leaderboard import ModelLeaderboard + +leaderboard = ModelLeaderboard() +df = leaderboard.compare_models( + model_keys=['model1', 'model2', 'model3'], + dataset_name="sample_dataset" +) +leaderboard.print_leaderboard(df) +``` + +## Model Registry + +All trained models are stored in the registry with metadata: + +```python +from models.common.registry import get_registry + +registry = get_registry() + +# List all models +models = registry.list_models() + +# Load a model +model = registry.load_model("polyreg_time_...") + +# Get metadata +metadata = registry.load_metadata("polyreg_time_...") + +# Get best model by metric +best_key = registry.get_best_model(metric='test_mae_seconds') +``` + +## Feature Engineering Integration + +Models use features from the `feature_engineering` module: + +**Temporal features** (`temporal.py`): +- hour, day_of_week, is_weekend, is_holiday, is_peak_hour + +**Spatial features** (`spatial.py`): +- distance_to_stop, bearing_to_stop, progress_on_segment + +**Operational features** (`operational.py`): +- headway_seconds, avg_speed_last_10min, vehicles_on_route + +**Weather features** (`weather.py`): +- temperature_c, precipitation_mm, wind_speed_kmh + +See `feature_engineering/README.md` for details. + +## Dataset Requirements + +Models expect datasets built with `dataset_builder.py` script through the `build_eta_sample` Django command: + +**Minimum Required Columns**: +- `trip_id`, `route_id`, `vehicle_id`, `stop_id`, `stop_sequence` +- `vp_ts`, `vp_lat`, `vp_lon` +- `stop_lat`, `stop_lon`, `distance_to_stop` +- `time_to_arrival_seconds` (target) + +**Recommended Columns**: +- `hour`, `day_of_week`, `is_peak_hour` +- `headway_seconds`, `current_speed_kmh` + + diff --git a/eta_prediction/models/__init__.py b/eta_prediction/models/__init__.py new file mode 100644 index 0000000..25e59d6 --- /dev/null +++ b/eta_prediction/models/__init__.py @@ -0,0 +1,194 @@ +""" +ETA Prediction Models Package + +Comprehensive modeling framework for transit ETA prediction. + +Example usage: + >>> from models import train_all_baselines, get_registry + >>> models = train_all_baselines("sample_dataset") + >>> registry = get_registry() + >>> best_key = registry.get_best_model() +""" + +__version__ = "1.0.0" +__author__ = "SIMOVI Team" + +# Core utilities +from .common.data import load_dataset, ETADataset, prepare_features_target +from .common.registry import get_registry, ModelRegistry +from .common.keys import ModelKey, PredictionKey +from .common.metrics import ( + compute_all_metrics, + mae_minutes, + rmse_minutes, + within_threshold +) +from .common.utils import ( + format_seconds, + haversine_distance, + clip_predictions, + setup_logging +) + +# Training functions +from .historical_mean.train import train_historical_mean, HistoricalMeanModel +from .polyreg_distance.train import train_polyreg_distance, PolyRegDistanceModel +from .polyreg_time.train import train_polyreg_time, PolyRegTimeModel +from .ewma.train import train_ewma, EWMAModel + +# Prediction functions +from .historical_mean.predict import predict_eta as predict_historical_mean +from .polyreg_distance.predict import predict_eta as predict_polyreg_distance +from .polyreg_time.predict import predict_eta as predict_polyreg_time +from .ewma.predict import predict_eta as predict_ewma + +# Evaluation +from .evaluation.leaderboard import ModelLeaderboard, quick_compare +from .evaluation.roll_validate import RollingValidator, quick_rolling_validate + +# Main training pipeline +from .train_all_models import train_all_baselines, train_advanced_configurations + + +__all__ = [ + # Core utilities + 'load_dataset', + 'ETADataset', + 'prepare_features_target', + 'get_registry', + 'ModelRegistry', + 'ModelKey', + 'PredictionKey', + 'compute_all_metrics', + 'mae_minutes', + 'rmse_minutes', + 'within_threshold', + 'format_seconds', + 'haversine_distance', + 'clip_predictions', + 'setup_logging', + + # Models + 'HistoricalMeanModel', + 'PolyRegDistanceModel', + 'PolyRegTimeModel', + 'EWMAModel', + + # Training + 'train_historical_mean', + 'train_polyreg_distance', + 'train_polyreg_time', + 'train_ewma', + 'train_all_baselines', + 'train_advanced_configurations', + + # Prediction + 'predict_historical_mean', + 'predict_polyreg_distance', + 'predict_polyreg_time', + 'predict_ewma', + + # Evaluation + 'ModelLeaderboard', + 'quick_compare', + 'RollingValidator', + 'quick_rolling_validate', +] + + +# Package info +MODELS = { + 'historical_mean': { + 'description': 'Historical average baseline', + 'typical_mae': '2-4 minutes', + 'features': ['route_id', 'stop_sequence', 'temporal'] + }, + 'polyreg_distance': { + 'description': 'Polynomial regression on distance', + 'typical_mae': '1.5-3 minutes', + 'features': ['distance_to_stop'] + }, + 'polyreg_time': { + 'description': 'Polynomial regression with time features', + 'typical_mae': '1-2.5 minutes', + 'features': ['distance_to_stop', 'temporal', 'operational'] + }, + 'ewma': { + 'description': 'Exponentially weighted moving average', + 'typical_mae': '1.5-3 minutes', + 'features': ['route_id', 'stop_sequence', 'temporal'], + 'online_learning': True + } +} + + +def list_models(): + """List available model types.""" + print("\nAvailable Model Types:") + print("=" * 70) + for name, info in MODELS.items(): + print(f"\n{name}:") + print(f" Description: {info['description']}") + print(f" Typical MAE: {info['typical_mae']}") + print(f" Features: {', '.join(info['features'])}") + if info.get('online_learning'): + print(f" Online Learning: Yes") + print("\n" + "=" * 70) + + +def quick_start_guide(): + """Print quick start guide.""" + guide = """ + ETA Prediction Models - Quick Start + ==================================== + + 1. Train all baselines: + >>> from models import train_all_baselines + >>> results = train_all_baselines("sample_dataset") + + 2. Compare models: + >>> from models import quick_compare, get_registry + >>> registry = get_registry() + >>> model_keys = registry.list_models()['model_key'].tolist() + >>> comparison = quick_compare(model_keys) + + 3. Load and use best model: + >>> best_key = registry.get_best_model(metric='test_mae_seconds') + >>> model = registry.load_model(best_key) + >>> predictions = model.predict(your_data) + + 4. Make single prediction: + >>> from models import predict_polyreg_time + >>> result = predict_polyreg_time( + ... model_key=best_key, + ... distance_to_stop=1500.0, + ... hour=8, + ... is_peak_hour=True + ... ) + >>> print(f"ETA: {result['eta_formatted']}") + + For more details, see models/README.md + """ + print(guide) + + +# Auto-create directories +def _setup_directories(): + """Create necessary directories if they don't exist.""" + from pathlib import Path + base_dir = Path(__file__).parent + + dirs = [ + base_dir / 'trained', + base_dir.parent / 'datasets', + base_dir.parent / 'datasets' / 'metadata', + base_dir.parent / 'datasets' / 'production', + base_dir.parent / 'datasets' / 'experimental' + ] + + for dir_path in dirs: + dir_path.mkdir(parents=True, exist_ok=True) + + +# Run setup on import +_setup_directories() \ No newline at end of file diff --git a/eta_prediction/models/common/data.py b/eta_prediction/models/common/data.py new file mode 100644 index 0000000..a6a3788 --- /dev/null +++ b/eta_prediction/models/common/data.py @@ -0,0 +1,230 @@ +""" +Data loading and preprocessing utilities for ETA prediction models. +Handles dataset splitting, feature engineering, and train/test preparation. +""" + +import pandas as pd +import numpy as np +from pathlib import Path +from typing import Tuple, List, Optional, Dict +from datetime import datetime, timedelta + + +class ETADataset: + """ + Manages ETA prediction datasets with consistent preprocessing and splitting. + + Expected columns from VP dataset builder: + - Identifiers: trip_id, route_id, vehicle_id, stop_id, stop_sequence + - Position: vp_ts, vp_lat, vp_lon, vp_bearing + - Stop: stop_lat, stop_lon, distance_to_stop + - Target: actual_arrival, time_to_arrival_seconds + - Temporal: hour, day_of_week, is_weekend, is_holiday, is_peak_hour + - Operational: headway_seconds, current_speed_kmh + - Weather (optional): temperature_c, precipitation_mm, wind_speed_kmh + """ + + FEATURE_GROUPS = { + 'identifiers': ['trip_id', 'route_id', 'vehicle_id', 'stop_id', 'stop_sequence'], + 'position': ['vp_lat', 'vp_lon', 'vp_bearing', 'distance_to_stop'], + 'temporal': ['hour', 'day_of_week', 'is_weekend', 'is_holiday', 'is_peak_hour'], + 'operational': ['headway_seconds', 'current_speed_kmh'], + 'weather': ['temperature_c', 'precipitation_mm', 'wind_speed_kmh'], + 'target': ['time_to_arrival_seconds'] + } + + def __init__(self, data_path: str): + """ + Initialize dataset from parquet file. + + Args: + data_path: Path to parquet file from VP dataset builder + """ + self.data_path = Path(data_path) + self.df = pd.read_parquet(data_path) + self.df['vp_ts'] = pd.to_datetime(self.df['vp_ts']) + + # Store original size + self.original_size = len(self.df) + + def clean_data(self, + drop_missing_target: bool = True, + max_eta_seconds: float = 3600 * 2, # 2 hours + min_distance: float = 10.0) -> 'ETADataset': + """ + Clean dataset by removing invalid rows. + + Args: + drop_missing_target: Remove rows without valid ETA target + max_eta_seconds: Maximum reasonable ETA (filter outliers) + min_distance: Minimum distance to stop (meters) to keep + + Returns: + Self for chaining + """ + initial_rows = len(self.df) + + if drop_missing_target: + self.df = self.df.dropna(subset=['time_to_arrival_seconds']) + + # Filter outliers + self.df = self.df[ + (self.df['time_to_arrival_seconds'] >= 0) & + (self.df['time_to_arrival_seconds'] <= max_eta_seconds) + ] + + # Filter too-close stops (likely already passed) + if 'distance_to_stop' in self.df.columns: + self.df = self.df[self.df['distance_to_stop'] >= min_distance] + + print(f"Cleaned: {initial_rows} → {len(self.df)} rows " + f"({100 * len(self.df) / initial_rows:.1f}% retained)") + + return self + + def get_features(self, feature_groups: List[str]) -> List[str]: + """ + Get list of feature columns from specified groups. + + Args: + feature_groups: List of group names (e.g., ['temporal', 'position']) + + Returns: + List of column names that exist in the dataset + """ + features = [] + for group in feature_groups: + if group in self.FEATURE_GROUPS: + features.extend(self.FEATURE_GROUPS[group]) + + # Return only columns that exist + return [f for f in features if f in self.df.columns] + + def temporal_split(self, + train_frac: float = 0.7, + val_frac: float = 0.15) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]: + """ + Split dataset by time (avoids data leakage). + + Args: + train_frac: Fraction for training + val_frac: Fraction for validation (remainder goes to test) + + Returns: + train_df, val_df, test_df + """ + # Sort by timestamp + df_sorted = self.df.sort_values('vp_ts').reset_index(drop=True) + + n = len(df_sorted) + train_end = int(n * train_frac) + val_end = int(n * (train_frac + val_frac)) + + train_df = df_sorted.iloc[:train_end].copy() + val_df = df_sorted.iloc[train_end:val_end].copy() + test_df = df_sorted.iloc[val_end:].copy() + + print(f"Temporal split: train={len(train_df)}, val={len(val_df)}, test={len(test_df)}") + + return train_df, val_df, test_df + + def route_split(self, + test_routes: Optional[List[str]] = None, + test_frac: float = 0.2) -> Tuple[pd.DataFrame, pd.DataFrame]: + """ + Split by route for cross-route generalization testing. + + Args: + test_routes: Specific routes for test set, or None to sample + test_frac: Fraction of routes for testing if test_routes is None + + Returns: + train_df, test_df + """ + if test_routes is None: + routes = self.df['route_id'].unique() + n_test = max(1, int(len(routes) * test_frac)) + test_routes = np.random.choice(routes, size=n_test, replace=False) + + test_df = self.df[self.df['route_id'].isin(test_routes)].copy() + train_df = self.df[~self.df['route_id'].isin(test_routes)].copy() + + print(f"Route split: {len(test_routes)} test routes") + print(f" Train: {len(train_df)} samples") + print(f" Test: {len(test_df)} samples") + + return train_df, test_df + + def get_route_stats(self) -> pd.DataFrame: + """Get statistics per route.""" + return self.df.groupby('route_id').agg({ + 'time_to_arrival_seconds': ['count', 'mean', 'std'], + 'distance_to_stop': ['mean', 'std'], + 'trip_id': 'nunique' + }).round(2) + + def summary(self) -> Dict: + """Get dataset summary statistics.""" + return { + 'total_samples': len(self.df), + 'date_range': (self.df['vp_ts'].min(), self.df['vp_ts'].max()), + 'routes': self.df['route_id'].nunique(), + 'trips': self.df['trip_id'].nunique(), + 'vehicles': self.df['vehicle_id'].nunique(), + 'stops': self.df['stop_id'].nunique(), + 'eta_mean_minutes': self.df['time_to_arrival_seconds'].mean() / 60, + 'eta_std_minutes': self.df['time_to_arrival_seconds'].std() / 60, + 'missing_weather': self.df['temperature_c'].isna().sum() if 'temperature_c' in self.df.columns else 'N/A' + } + + +def load_dataset(dataset_name: str = "sample_dataset") -> ETADataset: + """ + Load dataset from datasets directory. + + Args: + dataset_name: Name of dataset (without .parquet extension) + + Returns: + ETADataset instance + """ + datasets_dir = Path(__file__).parent.parent.parent / "datasets" + data_path = datasets_dir / f"{dataset_name}.parquet" + + if not data_path.exists(): + raise FileNotFoundError(f"Dataset not found: {data_path}") + + return ETADataset(str(data_path)) + + +def prepare_features_target(df: pd.DataFrame, + feature_cols: List[str], + target_col: str = 'time_to_arrival_seconds', + fill_na: bool = True) -> Tuple[pd.DataFrame, pd.Series]: + """ + Extract features and target, handle missing values. + + Args: + df: DataFrame with features and target + feature_cols: List of feature column names + target_col: Name of target column + fill_na: Whether to fill NaN values + + Returns: + X (features), y (target) + """ + # Get features that exist + available_features = [f for f in feature_cols if f in df.columns] + + X = df[available_features].copy() + y = df[target_col].copy() + + if fill_na: + # Fill numeric features with median, boolean with False + for col in X.columns: + if X[col].dtype == 'bool': + X[col] = X[col].fillna(False) + elif X[col].dtype in ['int64', 'float64']: + X[col] = X[col].fillna(X[col].median()) + + return X, y \ No newline at end of file diff --git a/eta_prediction/models/common/keys.py b/eta_prediction/models/common/keys.py new file mode 100644 index 0000000..074f301 --- /dev/null +++ b/eta_prediction/models/common/keys.py @@ -0,0 +1,193 @@ +""" +Model key generation and identification utilities. +Ensures consistent naming and versioning across the modeling pipeline. +""" + +from typing import Dict, Optional +from datetime import datetime + + +class ModelKey: + """ + Generates unique, descriptive keys for trained models. + + Format: {model_type}_{dataset}_{features}_{timestamp} + Example: polyreg_distance_sample_temporal-position_20250126_143022 + """ + + @staticmethod + def generate(model_type: str, + dataset_name: str, + feature_groups: list, + version: Optional[str] = None, + **kwargs) -> str: + """ + Generate a unique model key. + + Args: + model_type: Type of model (e.g., 'polyreg_distance', 'ewma') + dataset_name: Name of training dataset + feature_groups: List of feature group names used + version: Optional version string, defaults to timestamp + **kwargs: Additional metadata to include in key + + Returns: + Unique model key string + """ + # Create features string + features_str = '-'.join(sorted(feature_groups)) + + # Create version string + if version is None: + version = datetime.now().strftime("%Y%m%d_%H%M%S") + + # Base key + key_parts = [ + model_type, + dataset_name, + features_str, + version + ] + + # Add optional kwargs + for k, v in sorted(kwargs.items()): + if v is not None: + key_parts.append(f"{k}={v}") + + return '_'.join(key_parts) + + @staticmethod + def parse(key: str) -> Dict[str, str]: + """ + Parse a model key back into components. + + Args: + key: Model key string + + Returns: + Dictionary with parsed components + """ + parts = key.split('_') + + if len(parts) < 4: + raise ValueError(f"Invalid model key format: {key}") + + parsed = { + 'model_type': parts[0], + 'dataset': parts[1], + 'features': parts[2], + 'version': '_'.join(parts[3:5]) if len(parts) >= 5 else parts[3] + } + + # Parse additional kwargs (key=value format) + if len(parts) > 5: + for part in parts[5:]: + if '=' in part: + k, v = part.split('=', 1) + parsed[k] = v + + return parsed + + +class PredictionKey: + """ + Generates keys for prediction requests to enable caching and deduplication. + + Format: {route_id}_{stop_id}_{vp_hash} + """ + + @staticmethod + def generate(route_id: str, + stop_id: str, + vehicle_lat: float, + vehicle_lon: float, + timestamp: datetime, + stop_sequence: Optional[int] = None) -> str: + """ + Generate prediction key for a vehicle-stop pair. + + Args: + route_id: Route identifier + stop_id: Stop identifier + vehicle_lat: Vehicle latitude + vehicle_lon: Vehicle longitude + timestamp: Timestamp of vehicle position + stop_sequence: Optional stop sequence number + + Returns: + Prediction key string + """ + # Round coordinates to ~10m precision for caching + lat_rounded = round(vehicle_lat, 4) + lon_rounded = round(vehicle_lon, 4) + + # Create position hash + vp_hash = f"{lat_rounded},{lon_rounded}" + + # Build key + if stop_sequence is not None: + return f"{route_id}_{stop_id}_{stop_sequence}_{vp_hash}" + else: + return f"{route_id}_{stop_id}_{vp_hash}" + + +class ExperimentKey: + """ + Generates keys for experiments and model comparisons. + """ + + @staticmethod + def generate(experiment_name: str, + models: list, + dataset: str, + timestamp: Optional[str] = None) -> str: + """ + Generate experiment key. + + Args: + experiment_name: Name of experiment + models: List of model types being compared + dataset: Dataset name + timestamp: Optional timestamp, defaults to now + + Returns: + Experiment key string + """ + if timestamp is None: + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + + models_str = '-'.join(sorted(models)) + + return f"exp_{experiment_name}_{models_str}_{dataset}_{timestamp}" + + +def model_filename(model_key: str, extension: str = "pkl") -> str: + """ + Generate consistent filename for model artifacts. + + Args: + model_key: Model key from ModelKey.generate() + extension: File extension (pkl, joblib, json, etc.) + + Returns: + Filename string + """ + return f"{model_key}.{extension}" + + +def validate_model_key(key: str) -> bool: + """ + Validate that a string is a properly formatted model key. + + Args: + key: String to validate + + Returns: + True if valid, False otherwise + """ + try: + parsed = ModelKey.parse(key) + required = ['model_type', 'dataset', 'features', 'version'] + return all(k in parsed for k in required) + except (ValueError, IndexError): + return False \ No newline at end of file diff --git a/eta_prediction/models/common/metrics.py b/eta_prediction/models/common/metrics.py new file mode 100644 index 0000000..70814de --- /dev/null +++ b/eta_prediction/models/common/metrics.py @@ -0,0 +1,245 @@ +""" +Evaluation metrics for ETA prediction models. +Provides domain-specific metrics beyond standard regression metrics. +""" + +import numpy as np +import pandas as pd +from typing import Dict, Optional, Tuple +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + + +def mae_seconds(y_true: np.ndarray, y_pred: np.ndarray) -> float: + """Mean Absolute Error in seconds.""" + return mean_absolute_error(y_true, y_pred) + + +def mae_minutes(y_true: np.ndarray, y_pred: np.ndarray) -> float: + """Mean Absolute Error in minutes.""" + return mean_absolute_error(y_true, y_pred) / 60 + + +def rmse_seconds(y_true: np.ndarray, y_pred: np.ndarray) -> float: + """Root Mean Squared Error in seconds.""" + return np.sqrt(mean_squared_error(y_true, y_pred)) + + +def rmse_minutes(y_true: np.ndarray, y_pred: np.ndarray) -> float: + """Root Mean Squared Error in minutes.""" + return np.sqrt(mean_squared_error(y_true, y_pred)) / 60 + + +def mape(y_true: np.ndarray, y_pred: np.ndarray, epsilon: float = 1.0) -> float: + """ + Mean Absolute Percentage Error. + + Args: + y_true: True values + y_pred: Predicted values + epsilon: Small value to avoid division by zero + + Returns: + MAPE as percentage + """ + return 100 * np.mean(np.abs((y_true - y_pred) / (y_true + epsilon))) + + +def median_ae(y_true: np.ndarray, y_pred: np.ndarray) -> float: + """Median Absolute Error in seconds (robust to outliers).""" + return np.median(np.abs(y_true - y_pred)) + + +def within_threshold(y_true: np.ndarray, + y_pred: np.ndarray, + threshold_seconds: float = 60) -> float: + """ + Fraction of predictions within threshold. + + Args: + y_true: True values in seconds + y_pred: Predicted values in seconds + threshold_seconds: Acceptable error threshold + + Returns: + Fraction of predictions within threshold (0-1) + """ + errors = np.abs(y_true - y_pred) + return np.mean(errors <= threshold_seconds) + + +def late_penalty_mae(y_true: np.ndarray, + y_pred: np.ndarray, + late_multiplier: float = 2.0) -> float: + """ + MAE with higher penalty for late predictions (user-centric). + + Args: + y_true: True values + y_pred: Predicted values + late_multiplier: Multiplier for underprediction errors + + Returns: + Weighted MAE + """ + errors = y_pred - y_true + weights = np.where(errors < 0, late_multiplier, 1.0) + return np.mean(np.abs(errors) * weights) + + +def quantile_error(y_true: np.ndarray, + y_pred: np.ndarray, + quantiles: list = [0.5, 0.9, 0.95]) -> Dict[str, float]: + """ + Absolute errors at different quantiles. + + Args: + y_true: True values + y_pred: Predicted values + quantiles: List of quantiles to compute + + Returns: + Dictionary mapping quantile to error + """ + errors = np.abs(y_true - y_pred) + return {f"q{int(q*100)}": np.quantile(errors, q) for q in quantiles} + + +def bias(y_true: np.ndarray, y_pred: np.ndarray) -> float: + """ + Mean bias (positive = overprediction, negative = underprediction). + + Returns: + Mean bias in seconds + """ + return np.mean(y_pred - y_true) + + +def compute_all_metrics(y_true: np.ndarray, + y_pred: np.ndarray, + prefix: str = "") -> Dict[str, float]: + """ + Compute comprehensive set of metrics. + + Args: + y_true: True values in seconds + y_pred: Predicted values in seconds + prefix: Optional prefix for metric names (e.g., "val_") + + Returns: + Dictionary of all metrics + """ + metrics = { + f"{prefix}mae_seconds": mae_seconds(y_true, y_pred), + f"{prefix}mae_minutes": mae_minutes(y_true, y_pred), + f"{prefix}rmse_seconds": rmse_seconds(y_true, y_pred), + f"{prefix}rmse_minutes": rmse_minutes(y_true, y_pred), + f"{prefix}mape": mape(y_true, y_pred), + f"{prefix}median_ae": median_ae(y_true, y_pred), + f"{prefix}bias_seconds": bias(y_true, y_pred), + f"{prefix}r2": r2_score(y_true, y_pred), + f"{prefix}within_60s": within_threshold(y_true, y_pred, 60), + f"{prefix}within_120s": within_threshold(y_true, y_pred, 120), + f"{prefix}within_300s": within_threshold(y_true, y_pred, 300), + f"{prefix}late_penalty_mae": late_penalty_mae(y_true, y_pred), + } + + # Add quantile errors + quantile_errs = quantile_error(y_true, y_pred) + for k, v in quantile_errs.items(): + metrics[f"{prefix}error_{k}"] = v + + return metrics + + +def compare_models(results: Dict[str, Dict[str, float]], + metric: str = "mae_seconds") -> pd.DataFrame: + """ + Compare multiple models on a metric. + + Args: + results: Dict mapping model_name -> metrics_dict + metric: Metric to compare (or 'all' for all metrics) + + Returns: + DataFrame with comparison + """ + if metric == 'all': + df = pd.DataFrame(results).T + else: + df = pd.DataFrame({ + 'model': list(results.keys()), + metric: [results[m].get(metric, np.nan) for m in results.keys()] + }) + df = df.sort_values(metric) + + return df + + +def error_analysis(y_true: np.ndarray, + y_pred: np.ndarray, + feature_df: Optional[pd.DataFrame] = None, + group_by: Optional[str] = None) -> pd.DataFrame: + """ + Analyze errors by different segments. + + Args: + y_true: True values + y_pred: Predicted values + feature_df: DataFrame with features for grouping + group_by: Column name to group by + + Returns: + DataFrame with error statistics per group + """ + errors = np.abs(y_true - y_pred) + + if feature_df is None or group_by is None: + # Overall statistics + return pd.DataFrame({ + 'count': [len(errors)], + 'mae': [np.mean(errors)], + 'median_ae': [np.median(errors)], + 'rmse': [np.sqrt(np.mean(errors**2))], + 'max_error': [np.max(errors)], + }) + + # Group-wise statistics + df = feature_df.copy() + df['error'] = errors + + stats = df.groupby(group_by)['error'].agg([ + ('count', 'count'), + ('mae', 'mean'), + ('median_ae', 'median'), + ('std', 'std'), + ('max', 'max') + ]).round(2) + + return stats.sort_values('mae', ascending=False) + + +def prediction_intervals(y_pred: np.ndarray, + residuals: np.ndarray, + confidence: float = 0.95) -> Tuple[np.ndarray, np.ndarray]: + """ + Compute prediction intervals based on residual distribution. + + Args: + y_pred: Point predictions + residuals: Training residuals (y_true - y_pred) + confidence: Confidence level (e.g., 0.95 for 95% interval) + + Returns: + lower_bound, upper_bound arrays + """ + alpha = 1 - confidence + lower_q = alpha / 2 + upper_q = 1 - alpha / 2 + + lower_percentile = np.percentile(residuals, lower_q * 100) + upper_percentile = np.percentile(residuals, upper_q * 100) + + lower_bound = y_pred + lower_percentile + upper_bound = y_pred + upper_percentile + + return lower_bound, upper_bound \ No newline at end of file diff --git a/eta_prediction/models/common/registry.py b/eta_prediction/models/common/registry.py new file mode 100644 index 0000000..a248206 --- /dev/null +++ b/eta_prediction/models/common/registry.py @@ -0,0 +1,260 @@ +""" +Model registry for managing trained models and their metadata. +Provides save/load functionality with consistent structure. +""" + +import json +import pickle +from pathlib import Path +from typing import Dict, Any, Optional, List +from datetime import datetime +import pandas as pd + + +class ModelRegistry: + """ + Manages model artifacts and metadata in a structured directory. + + Structure: + models/ + ├── trained/ + │ ├── {model_key}.pkl # Serialized model + │ └── {model_key}_meta.json # Model metadata + └── registry.json # Index of all models + """ + + def __init__(self, base_dir: str = "models/trained"): + """ + Initialize registry. + + Args: + base_dir: Base directory for model storage + """ + self.base_dir = Path(base_dir) + self.base_dir.mkdir(parents=True, exist_ok=True) + + self.registry_file = self.base_dir / "registry.json" + self._load_registry() + + def _load_registry(self): + """Load registry index from disk.""" + if self.registry_file.exists(): + with open(self.registry_file, 'r') as f: + self.registry = json.load(f) + else: + self.registry = {} + + def _save_registry(self): + """Save registry index to disk.""" + with open(self.registry_file, 'w') as f: + json.dump(self.registry, f, indent=2) + + def save_model(self, + model_key: str, + model: Any, + metadata: Dict[str, Any], + overwrite: bool = False) -> Path: + """ + Save model and metadata to registry. + + Args: + model_key: Unique model identifier + model: Trained model object (must be picklable) + metadata: Model metadata (training metrics, config, etc.) + overwrite: Whether to overwrite existing model + + Returns: + Path to saved model file + """ + model_path = self.base_dir / f"{model_key}.pkl" + meta_path = self.base_dir / f"{model_key}_meta.json" + + if model_path.exists() and not overwrite: + raise FileExistsError(f"Model {model_key} already exists. Set overwrite=True to replace.") + + # Save model artifact + with open(model_path, 'wb') as f: + pickle.dump(model, f) + + # Enrich metadata + metadata['model_key'] = model_key + metadata['saved_at'] = datetime.now().isoformat() + metadata['model_path'] = str(model_path) + + # Save metadata + with open(meta_path, 'w') as f: + json.dump(metadata, f, indent=2) + + # Update registry + self.registry[model_key] = { + 'model_path': str(model_path), + 'meta_path': str(meta_path), + 'saved_at': metadata['saved_at'], + 'model_type': metadata.get('model_type', 'unknown') + } + self._save_registry() + + print(f"✓ Saved model: {model_key}") + return model_path + + def load_model(self, model_key: str) -> Any: + """ + Load model from registry. + + Args: + model_key: Unique model identifier + + Returns: + Loaded model object + """ + if model_key not in self.registry: + raise KeyError(f"Model {model_key} not found in registry") + + model_path = Path(self.registry[model_key]['model_path']) + + with open(model_path, 'rb') as f: + model = pickle.load(f) + + return model + + def load_metadata(self, model_key: str) -> Dict[str, Any]: + """ + Load model metadata. + + Args: + model_key: Unique model identifier + + Returns: + Metadata dictionary + """ + if model_key not in self.registry: + raise KeyError(f"Model {model_key} not found in registry") + + meta_path = Path(self.registry[model_key]['meta_path']) + + with open(meta_path, 'r') as f: + metadata = json.load(f) + + return metadata + + def list_models(self, + model_type: Optional[str] = None, + sort_by: str = 'saved_at') -> pd.DataFrame: + """ + List all models in registry. + + Args: + model_type: Filter by model type (e.g., 'polyreg_distance') + sort_by: Column to sort by + + Returns: + DataFrame with model information + """ + models = [] + + for key, info in self.registry.items(): + if model_type and info.get('model_type') != model_type: + continue + + # Load metadata for richer info + try: + meta = self.load_metadata(key) + models.append({ + 'model_key': key, + 'model_type': info.get('model_type', 'unknown'), + 'saved_at': info['saved_at'], + 'dataset': meta.get('dataset', 'unknown'), + 'mae_seconds': meta.get('metrics', {}).get('test_mae_seconds', None), + 'rmse_seconds': meta.get('metrics', {}).get('test_rmse_seconds', None), + 'r2': meta.get('metrics', {}).get('test_r2', None), + }) + except Exception as e: + print(f"Warning: Could not load metadata for {key}: {e}") + models.append({ + 'model_key': key, + 'model_type': info.get('model_type', 'unknown'), + 'saved_at': info['saved_at'], + }) + + df = pd.DataFrame(models) + if not df.empty and sort_by in df.columns: + df = df.sort_values(sort_by, ascending=False) + + return df + + def delete_model(self, model_key: str) -> bool: + """ + Delete model and metadata from registry. + + Args: + model_key: Unique model identifier + + Returns: + True if deleted successfully + """ + if model_key not in self.registry: + raise KeyError(f"Model {model_key} not found in registry") + + # Delete files + model_path = Path(self.registry[model_key]['model_path']) + meta_path = Path(self.registry[model_key]['meta_path']) + + if model_path.exists(): + model_path.unlink() + if meta_path.exists(): + meta_path.unlink() + + # Remove from registry + del self.registry[model_key] + self._save_registry() + + print(f"✓ Deleted model: {model_key}") + return True + + def get_best_model(self, + model_type: Optional[str] = None, + metric: str = 'test_mae_seconds', + minimize: bool = True) -> str: + """ + Get best model by metric. + + Args: + model_type: Filter by model type + metric: Metric to optimize + minimize: Whether to minimize (True) or maximize (False) metric + + Returns: + Model key of best model + """ + candidates = [] + + for key in self.registry.keys(): + if model_type and self.registry[key].get('model_type') != model_type: + continue + + try: + meta = self.load_metadata(key) + metric_value = meta.get('metrics', {}).get(metric) + + if metric_value is not None: + candidates.append((key, metric_value)) + except Exception: + continue + + if not candidates: + raise ValueError(f"No models found with metric {metric}") + + # Sort and return best + candidates.sort(key=lambda x: x[1], reverse=not minimize) + return candidates[0][0] + + +# Global registry instance +_registry = None + +def get_registry() -> ModelRegistry: + """Get or create global registry instance.""" + global _registry + if _registry is None: + _registry = ModelRegistry() + return _registry \ No newline at end of file diff --git a/eta_prediction/models/common/utils.py b/eta_prediction/models/common/utils.py new file mode 100644 index 0000000..0f1566a --- /dev/null +++ b/eta_prediction/models/common/utils.py @@ -0,0 +1,276 @@ +""" +Utility functions for models package. +""" + +import numpy as np +import pandas as pd +from typing import Any, Dict, List, Optional +from datetime import datetime +import logging + + +def setup_logging(name: str = "eta_models", level: str = "INFO") -> logging.Logger: + """ + Setup consistent logging for models. + + Args: + name: Logger name + level: Logging level + + Returns: + Configured logger + """ + logger = logging.getLogger(name) + logger.setLevel(getattr(logging, level)) + + if not logger.handlers: + handler = logging.StreamHandler() + formatter = logging.Formatter( + '%(asctime)s - %(name)s - %(levelname)s - %(message)s' + ) + handler.setFormatter(formatter) + logger.addHandler(handler) + + return logger + + +def safe_divide(numerator: np.ndarray, + denominator: np.ndarray, + fill_value: float = 0.0) -> np.ndarray: + """ + Safe division that handles division by zero. + + Args: + numerator: Numerator array + denominator: Denominator array + fill_value: Value to use when denominator is zero + + Returns: + Result array + """ + result = np.full_like(numerator, fill_value, dtype=float) + mask = denominator != 0 + result[mask] = numerator[mask] / denominator[mask] + return result + + +def clip_predictions(predictions: np.ndarray, + min_value: float = 0.0, + max_value: float = 7200.0) -> np.ndarray: + """ + Clip predictions to reasonable range. + + Args: + predictions: Raw predictions + min_value: Minimum ETA (seconds) + max_value: Maximum ETA (seconds, default 2 hours) + + Returns: + Clipped predictions + """ + return np.clip(predictions, min_value, max_value) + + +def add_lag_features(df: pd.DataFrame, + columns: List[str], + lags: List[int], + group_by: Optional[str] = None) -> pd.DataFrame: + """ + Add lagged features to dataframe. + + Args: + df: Input dataframe + columns: Columns to lag + lags: List of lag values (e.g., [1, 2, 3]) + group_by: Optional column to group by before lagging + + Returns: + DataFrame with lag features added + """ + df_copy = df.copy() + + for col in columns: + for lag in lags: + lag_col_name = f"{col}_lag{lag}" + + if group_by: + df_copy[lag_col_name] = df_copy.groupby(group_by)[col].shift(lag) + else: + df_copy[lag_col_name] = df_copy[col].shift(lag) + + return df_copy + + +def smooth_predictions(predictions: np.ndarray, + window_size: int = 3, + method: str = 'ewma', + alpha: float = 0.3) -> np.ndarray: + """ + Smooth predictions using moving average. + + Args: + predictions: Array of predictions + window_size: Window size for smoothing + method: 'mean', 'median', or 'ewma' + alpha: Alpha parameter for EWMA + + Returns: + Smoothed predictions + """ + if len(predictions) < window_size: + return predictions + + if method == 'mean': + return pd.Series(predictions).rolling(window_size, min_periods=1).mean().values + elif method == 'median': + return pd.Series(predictions).rolling(window_size, min_periods=1).median().values + elif method == 'ewma': + return pd.Series(predictions).ewm(alpha=alpha).mean().values + else: + raise ValueError(f"Unknown smoothing method: {method}") + + +def calculate_speed_kmh(distance_m: float, time_s: float) -> float: + """ + Calculate speed in km/h from distance and time. + + Args: + distance_m: Distance in meters + time_s: Time in seconds + + Returns: + Speed in km/h + """ + if time_s <= 0: + return 0.0 + return (distance_m / 1000) / (time_s / 3600) + + +def haversine_distance(lat1: float, lon1: float, + lat2: float, lon2: float) -> float: + """ + Calculate great-circle distance between two points. + + Args: + lat1, lon1: First point coordinates + lat2, lon2: Second point coordinates + + Returns: + Distance in meters + """ + R = 6371000 # Earth radius in meters + + phi1 = np.radians(lat1) + phi2 = np.radians(lat2) + delta_phi = np.radians(lat2 - lat1) + delta_lambda = np.radians(lon2 - lon1) + + a = np.sin(delta_phi / 2) ** 2 + \ + np.cos(phi1) * np.cos(phi2) * np.sin(delta_lambda / 2) ** 2 + c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1 - a)) + + return R * c + + +def format_seconds(seconds: float) -> str: + """ + Format seconds as human-readable string. + + Args: + seconds: Time in seconds + + Returns: + Formatted string (e.g., "2m 30s", "1h 15m") + """ + if seconds < 60: + return f"{int(seconds)}s" + elif seconds < 3600: + minutes = int(seconds / 60) + secs = int(seconds % 60) + return f"{minutes}m {secs}s" + else: + hours = int(seconds / 3600) + minutes = int((seconds % 3600) / 60) + return f"{hours}h {minutes}m" + + +def print_metrics_table(metrics: Dict[str, float], title: str = "Metrics"): + """ + Pretty print metrics as a table. + + Args: + metrics: Dictionary of metric names to values + title: Table title + """ + print(f"\n{'='*50}") + print(f"{title:^50}") + print(f"{'='*50}") + + for name, value in metrics.items(): + if isinstance(value, float): + print(f"{name:.<40} {value:.4f}") + else: + print(f"{name:.<40} {value}") + + print(f"{'='*50}\n") + + +def train_test_summary(train_df: pd.DataFrame, + test_df: pd.DataFrame, + val_df: Optional[pd.DataFrame] = None): + """ + Print summary of train/test split. + + Args: + train_df: Training dataframe + test_df: Test dataframe + val_df: Optional validation dataframe + """ + print("\n" + "="*60) + print("DATASET SPLIT SUMMARY".center(60)) + print("="*60) + + print(f"\nTrain: {len(train_df):,} samples") + print(f" Date range: {train_df['vp_ts'].min()} to {train_df['vp_ts'].max()}") + print(f" Routes: {train_df['route_id'].nunique()}") + print(f" Mean ETA: {train_df['time_to_arrival_seconds'].mean()/60:.1f} min") + + if val_df is not None: + print(f"\nValidation: {len(val_df):,} samples") + print(f" Date range: {val_df['vp_ts'].min()} to {val_df['vp_ts'].max()}") + print(f" Routes: {val_df['route_id'].nunique()}") + print(f" Mean ETA: {val_df['time_to_arrival_seconds'].mean()/60:.1f} min") + + print(f"\nTest: {len(test_df):,} samples") + print(f" Date range: {test_df['vp_ts'].min()} to {test_df['vp_ts'].max()}") + print(f" Routes: {test_df['route_id'].nunique()}") + print(f" Mean ETA: {test_df['time_to_arrival_seconds'].mean()/60:.1f} min") + + print("="*60 + "\n") + + +def create_feature_importance_df(feature_names: List[str], + importances: np.ndarray, + top_n: int = 20) -> pd.DataFrame: + """ + Create sorted feature importance dataframe. + + Args: + feature_names: List of feature names + importances: Array of importance values + top_n: Number of top features to return + + Returns: + DataFrame sorted by importance + """ + df = pd.DataFrame({ + 'feature': feature_names, + 'importance': importances + }) + + df = df.sort_values('importance', ascending=False) + + if top_n: + df = df.head(top_n) + + return df \ No newline at end of file diff --git a/eta_prediction/models/evaluation/leaderboard.py b/eta_prediction/models/evaluation/leaderboard.py new file mode 100644 index 0000000..292a9f4 --- /dev/null +++ b/eta_prediction/models/evaluation/leaderboard.py @@ -0,0 +1,229 @@ +""" +Model leaderboard for comparing performance across models. +""" + +import pandas as pd +import numpy as np +from typing import List, Optional, Dict +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).parent.parent)) + +from common.registry import get_registry +from common.data import load_dataset +from common.metrics import compute_all_metrics + + +class ModelLeaderboard: + """ + Compare multiple models on standardized test sets. + """ + + def __init__(self): + self.registry = get_registry() + self.results = [] + + def evaluate_model(self, + model_key: str, + test_df: pd.DataFrame, + target_col: str = 'time_to_arrival_seconds') -> Dict: + """ + Evaluate single model on test set. + + Args: + model_key: Model identifier + test_df: Test dataframe + target_col: Target column name + + Returns: + Dictionary with metrics + """ + print(f"Evaluating {model_key}...") + + # Load model and metadata + model = self.registry.load_model(model_key) + metadata = self.registry.load_metadata(model_key) + + # Predict + y_true = test_df[target_col].values + y_pred = model.predict(test_df) + + # Compute metrics + metrics = compute_all_metrics(y_true, y_pred) + + # Add model info + result = { + 'model_key': model_key, + 'model_type': metadata.get('model_type', 'unknown'), + 'dataset': metadata.get('dataset', 'unknown'), + **metrics + } + + return result + + def compare_models(self, + model_keys: List[str], + dataset_name: str = "sample_dataset", + test_routes: Optional[List[str]] = None) -> pd.DataFrame: + """ + Compare multiple models on same test set. + + Args: + model_keys: List of model keys to compare + dataset_name: Dataset to evaluate on + test_routes: Specific routes for testing + + Returns: + DataFrame with comparison results + """ + print(f"\n{'='*60}") + print(f"MODEL LEADERBOARD".center(60)) + print(f"{'='*60}\n") + print(f"Dataset: {dataset_name}") + print(f"Models: {len(model_keys)}\n") + + # Load dataset + dataset = load_dataset(dataset_name) + dataset.clean_data() + + # Get test set + if test_routes: + _, test_df = dataset.route_split(test_routes=test_routes) + else: + _, _, test_df = dataset.temporal_split(train_frac=0.7, val_frac=0.15) + + print(f"Test set: {len(test_df)} samples\n") + + # Evaluate each model + results = [] + for model_key in model_keys: + try: + result = self.evaluate_model(model_key, test_df) + results.append(result) + except Exception as e: + print(f" Error evaluating {model_key}: {e}") + + # Create comparison dataframe + df = pd.DataFrame(results) + + # Sort by MAE (primary metric) + if 'mae_seconds' in df.columns: + df = df.sort_values('mae_seconds') + + self.results = results + + return df + + def print_leaderboard(self, + df: pd.DataFrame, + metrics: List[str] = ['mae_minutes', 'rmse_minutes', 'r2', + 'within_60s', 'bias_seconds']): + """ + Pretty print leaderboard. + + Args: + df: Results dataframe + metrics: Metrics to display + """ + print(f"\n{'='*80}") + print(f"LEADERBOARD RESULTS".center(80)) + print(f"{'='*80}\n") + + # Select columns + display_cols = ['model_type'] + [m for m in metrics if m in df.columns] + display_df = df[display_cols].copy() + + # Format numbers + for col in metrics: + if col in display_df.columns: + if col.startswith('within_'): + display_df[col] = (display_df[col] * 100).round(1).astype(str) + '%' + else: + display_df[col] = display_df[col].round(3) + + # Add rank + display_df.insert(0, 'rank', range(1, len(display_df) + 1)) + + print(display_df.to_string(index=False)) + print(f"\n{'='*80}\n") + + # Highlight winner + winner = df.iloc[0] + print(f"🏆 Best Model: {winner['model_type']}") + print(f" MAE: {winner['mae_minutes']:.3f} minutes") + print(f" RMSE: {winner['rmse_minutes']:.3f} minutes") + print(f" R²: {winner['r2']:.3f}") + + def model_comparison_summary(self, df: pd.DataFrame) -> str: + """ + Generate text summary of model comparison. + + Args: + df: Results dataframe + + Returns: + Summary string + """ + best = df.iloc[0] + worst = df.iloc[-1] + + improvement = (worst['mae_seconds'] - best['mae_seconds']) / worst['mae_seconds'] * 100 + + summary = f""" +Model Comparison Summary +======================== + +Total Models Evaluated: {len(df)} +Test Samples: {df.iloc[0].get('test_samples', 'N/A')} + +Best Model: {best['model_type']} + - MAE: {best['mae_minutes']:.2f} minutes + - RMSE: {best['rmse_minutes']:.2f} minutes + - R²: {best['r2']:.3f} + - Within 60s: {best['within_60s']*100:.1f}% + +Baseline (Worst): {worst['model_type']} + - MAE: {worst['mae_minutes']:.2f} minutes + +Improvement: {improvement:.1f}% reduction in MAE from baseline to best model. +""" + return summary + + +def quick_compare(model_keys: List[str], + dataset_name: str = "sample_dataset") -> pd.DataFrame: + """ + Quick comparison function. + + Args: + model_keys: List of model keys + dataset_name: Dataset name + + Returns: + Comparison dataframe + """ + leaderboard = ModelLeaderboard() + df = leaderboard.compare_models(model_keys, dataset_name) + leaderboard.print_leaderboard(df) + + return df + + +if __name__ == "__main__": + # Example: Compare all model types + + # You would need to train these first + model_keys = [ + "historical_mean_sample_dataset_temporal-route_20250126_143022", + "polyreg_distance_sample_dataset_distance_20250126_143022_degree=2", + "polyreg_time_sample_dataset_distance-operational-temporal_20250126_143022_degree=2", + "ewma_sample_dataset_temporal-route_20250126_143022_alpha=0_3" + ] + + # Run comparison + results_df = quick_compare(model_keys) + + # Save results + results_df.to_csv("models/leaderboard_results.csv", index=False) + print("\nResults saved to models/leaderboard_results.csv") \ No newline at end of file diff --git a/eta_prediction/models/evaluation/roll_validate.py b/eta_prediction/models/evaluation/roll_validate.py new file mode 100644 index 0000000..4df4837 --- /dev/null +++ b/eta_prediction/models/evaluation/roll_validate.py @@ -0,0 +1,260 @@ +""" +Rolling window (walk-forward) validation for time series models. +Evaluates model performance over time with realistic train/test splits. +""" + +import pandas as pd +import numpy as np +from typing import Dict, List, Callable, Optional +from datetime import timedelta +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).parent.parent)) + +from common.data import load_dataset +from common.metrics import compute_all_metrics +from common.utils import print_metrics_table + + +class RollingValidator: + """ + Perform rolling window validation on time series data. + """ + + def __init__(self, + train_window_days: int = 7, + test_window_days: int = 1, + step_days: int = 1): + """ + Initialize validator. + + Args: + train_window_days: Size of training window in days + test_window_days: Size of test window in days + step_days: Step size between windows + """ + self.train_window = timedelta(days=train_window_days) + self.test_window = timedelta(days=test_window_days) + self.step = timedelta(days=step_days) + + self.results = [] + + def validate(self, + dataset_name: str, + train_fn: Callable, + predict_fn: Callable, + target_col: str = 'time_to_arrival_seconds') -> pd.DataFrame: + """ + Perform rolling window validation. + + Args: + dataset_name: Dataset to validate on + train_fn: Function(train_df) -> model + predict_fn: Function(model, test_df) -> predictions + target_col: Target column name + + Returns: + DataFrame with results per window + """ + print(f"\n{'='*60}") + print(f"ROLLING WINDOW VALIDATION".center(60)) + print(f"{'='*60}\n") + print(f"Train window: {self.train_window.days} days") + print(f"Test window: {self.test_window.days} days") + print(f"Step size: {self.step.days} days\n") + + # Load dataset + dataset = load_dataset(dataset_name) + dataset.clean_data() + + df = dataset.df.sort_values('vp_ts').reset_index(drop=True) + + # Get date range + start_date = df['vp_ts'].min() + end_date = df['vp_ts'].max() + + print(f"Data range: {start_date} to {end_date}") + print(f"Total duration: {(end_date - start_date).days} days\n") + + # Generate windows + current_start = start_date + window_num = 1 + results = [] + + while current_start + self.train_window + self.test_window <= end_date: + train_end = current_start + self.train_window + test_start = train_end + test_end = test_start + self.test_window + + print(f"Window {window_num}:") + print(f" Train: {current_start.date()} to {train_end.date()}") + print(f" Test: {test_start.date()} to {test_end.date()}") + + # Split data + train_df = df[(df['vp_ts'] >= current_start) & (df['vp_ts'] < train_end)] + test_df = df[(df['vp_ts'] >= test_start) & (df['vp_ts'] < test_end)] + + print(f" Train samples: {len(train_df)}, Test samples: {len(test_df)}") + + if len(train_df) == 0 or len(test_df) == 0: + print(f" Skipping (insufficient data)\n") + current_start += self.step + window_num += 1 + continue + + try: + # Train model + model = train_fn(train_df) + + # Predict + y_true = test_df[target_col].values + y_pred = predict_fn(model, test_df) + + # Compute metrics + metrics = compute_all_metrics(y_true, y_pred) + + # Store results + result = { + 'window': window_num, + 'train_start': current_start, + 'train_end': train_end, + 'test_start': test_start, + 'test_end': test_end, + 'train_samples': len(train_df), + 'test_samples': len(test_df), + **metrics + } + results.append(result) + + print(f" MAE: {metrics['mae_minutes']:.2f} min, RMSE: {metrics['rmse_minutes']:.2f} min") + print() + + except Exception as e: + print(f" Error: {e}\n") + + # Move to next window + current_start += self.step + window_num += 1 + + # Create results dataframe + results_df = pd.DataFrame(results) + + if not results_df.empty: + self._print_summary(results_df) + + self.results = results_df + return results_df + + def _print_summary(self, results_df: pd.DataFrame): + """Print summary statistics.""" + print(f"\n{'='*60}") + print(f"VALIDATION SUMMARY".center(60)) + print(f"{'='*60}\n") + + print(f"Total windows: {len(results_df)}") + print(f"\nAverage Metrics:") + print(f" MAE: {results_df['mae_minutes'].mean():.3f} ± {results_df['mae_minutes'].std():.3f} minutes") + print(f" RMSE: {results_df['rmse_minutes'].mean():.3f} ± {results_df['rmse_minutes'].std():.3f} minutes") + print(f" R²: {results_df['r2'].mean():.3f} ± {results_df['r2'].std():.3f}") + print(f" Within 60s: {results_df['within_60s'].mean()*100:.1f}%") + + print(f"\nBest Window: {results_df.loc[results_df['mae_minutes'].idxmin(), 'window']}") + print(f" MAE: {results_df['mae_minutes'].min():.3f} minutes") + + print(f"\nWorst Window: {results_df.loc[results_df['mae_minutes'].idxmax(), 'window']}") + print(f" MAE: {results_df['mae_minutes'].max():.3f} minutes") + + print(f"\n{'='*60}\n") + + def plot_results(self, results_df: Optional[pd.DataFrame] = None, + metric: str = 'mae_minutes'): + """ + Plot metric over time (requires matplotlib). + + Args: + results_df: Results dataframe (uses self.results if None) + metric: Metric to plot + """ + if results_df is None: + results_df = self.results + + if results_df.empty: + print("No results to plot") + return + + try: + import matplotlib.pyplot as plt + + fig, ax = plt.subplots(figsize=(12, 6)) + + ax.plot(results_df['window'], results_df[metric], marker='o') + ax.set_xlabel('Window Number') + ax.set_ylabel(metric.replace('_', ' ').title()) + ax.set_title(f'Rolling Window Validation: {metric}') + ax.grid(True, alpha=0.3) + + # Add mean line + mean_val = results_df[metric].mean() + ax.axhline(mean_val, color='r', linestyle='--', + label=f'Mean: {mean_val:.3f}') + ax.legend() + + plt.tight_layout() + plt.savefig(f'rolling_validation_{metric}.png', dpi=150) + print(f"Plot saved to rolling_validation_{metric}.png") + + except ImportError: + print("matplotlib not available for plotting") + + +def quick_rolling_validate(model_class, + model_params: Dict, + dataset_name: str = "sample_dataset", + train_window_days: int = 7) -> pd.DataFrame: + """ + Quick rolling validation for a model class. + + Args: + model_class: Model class to instantiate + model_params: Parameters for model initialization + dataset_name: Dataset name + train_window_days: Training window size + + Returns: + Results dataframe + """ + def train_fn(train_df): + model = model_class(**model_params) + model.fit(train_df) + return model + + def predict_fn(model, test_df): + return model.predict(test_df) + + validator = RollingValidator( + train_window_days=train_window_days, + test_window_days=1, + step_days=1 + ) + + results_df = validator.validate(dataset_name, train_fn, predict_fn) + + return results_df + + +if __name__ == "__main__": + # Example: Rolling validation for EWMA model + import sys + sys.path.append(str(Path(__file__).parent.parent)) + from ewma.train import EWMAModel + + results = quick_rolling_validate( + model_class=EWMAModel, + model_params={'alpha': 0.3, 'group_by': ['route_id', 'stop_sequence']}, + train_window_days=7 + ) + + # Save results + results.to_csv("models/rolling_validation_results.csv", index=False) + print("Results saved to models/rolling_validation_results.csv") \ No newline at end of file diff --git a/eta_prediction/models/ewma/predict.py b/eta_prediction/models/ewma/predict.py new file mode 100644 index 0000000..14ba453 --- /dev/null +++ b/eta_prediction/models/ewma/predict.py @@ -0,0 +1,155 @@ +""" +Prediction interface for EWMA model. +""" + +import pandas as pd +import numpy as np +from typing import Dict, Optional +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).parent.parent)) + +from common.registry import get_registry +from common.utils import format_seconds + + +def predict_eta(model_key: str, + route_id: str, + stop_sequence: int, + hour: Optional[int] = None) -> Dict: + """ + Predict ETA using EWMA model. + + Args: + model_key: Model identifier + route_id: Route ID + stop_sequence: Stop sequence number + hour: Hour of day (if model uses hourly grouping) + + Returns: + Dictionary with prediction and metadata + """ + # Load model + registry = get_registry() + model = registry.load_model(model_key) + metadata = registry.load_metadata(model_key) + + # Prepare input + input_data = { + 'route_id': [route_id], + 'stop_sequence': [stop_sequence] + } + + if hour is not None and 'hour' in model.group_by: + input_data['hour'] = [hour] + + input_df = pd.DataFrame(input_data) + + # Predict + eta_seconds = model.predict(input_df)[0] + + # Check if EWMA value exists + key = tuple(input_df.iloc[0][col] for col in model.group_by) + has_ewma = key in model.ewma_values + n_observations = model.observation_counts.get(key, 0) if has_ewma else 0 + + return { + 'eta_seconds': float(eta_seconds), + 'eta_minutes': float(eta_seconds / 60), + 'eta_formatted': format_seconds(eta_seconds), + 'model_key': model_key, + 'model_type': 'ewma', + 'alpha': metadata.get('alpha'), + 'has_ewma_value': has_ewma, + 'n_observations': n_observations, + 'using_global_mean': not has_ewma or n_observations < model.min_observations + } + + +def predict_and_update(model_key: str, + route_id: str, + stop_sequence: int, + observed_eta: float, + hour: Optional[int] = None, + save_updated: bool = False) -> Dict: + """ + Predict ETA and update model with observed value (online learning). + + Args: + model_key: Model identifier + route_id: Route ID + stop_sequence: Stop sequence + observed_eta: Actual observed ETA in seconds + hour: Hour of day + save_updated: Whether to save updated model back to registry + + Returns: + Dictionary with prediction, error, and updated EWMA + """ + # Get prediction first + result = predict_eta(model_key, route_id, stop_sequence, hour) + prediction = result['eta_seconds'] + + # Load model for update + registry = get_registry() + model = registry.load_model(model_key) + + # Prepare input for update + input_data = { + 'route_id': [route_id], + 'stop_sequence': [stop_sequence] + } + if hour is not None and 'hour' in model.group_by: + input_data['hour'] = [hour] + + input_df = pd.DataFrame(input_data) + + # Update model + model.update(input_df, np.array([observed_eta])) + + # Get new EWMA value + key = tuple(input_df.iloc[0][col] for col in model.group_by) + new_ewma = model.ewma_values.get(key) + + # Save if requested + if save_updated: + metadata = registry.load_metadata(model_key) + registry.save_model(model_key, model, metadata, overwrite=True) + + return { + **result, + 'observed_eta_seconds': observed_eta, + 'error_seconds': observed_eta - prediction, + 'updated_ewma_seconds': new_ewma, + 'model_updated': True + } + + +def batch_predict(model_key: str, input_df: pd.DataFrame) -> pd.DataFrame: + """Batch prediction.""" + registry = get_registry() + model = registry.load_model(model_key) + + result_df = input_df.copy() + result_df['predicted_eta_seconds'] = model.predict(input_df) + result_df['predicted_eta_minutes'] = result_df['predicted_eta_seconds'] / 60 + + return result_df + + +if __name__ == "__main__": + # Example: predict and update + result = predict_and_update( + model_key="ewma_sample_dataset_temporal-route_20250126_143022_alpha=0_3", + route_id="1", + stop_sequence=5, + observed_eta=180.0, # 3 minutes + hour=8 + ) + + print("Prediction and Update:") + print(f" Predicted: {result['eta_formatted']}") + print(f" Observed: {format_seconds(result['observed_eta_seconds'])}") + print(f" Error: {result['error_seconds']:.1f} seconds") + print(f" Updated EWMA: {result['updated_ewma_seconds']/60:.2f} minutes") \ No newline at end of file diff --git a/eta_prediction/models/ewma/train.py b/eta_prediction/models/ewma/train.py new file mode 100644 index 0000000..af6abb8 --- /dev/null +++ b/eta_prediction/models/ewma/train.py @@ -0,0 +1,299 @@ +""" +Exponentially Weighted Moving Average (EWMA) Model +Adapts predictions based on recent observations with exponential decay. +""" + +import pandas as pd +import numpy as np +from typing import Dict, Optional +from collections import defaultdict +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).parent.parent)) + +from common.data import load_dataset +from common.metrics import compute_all_metrics +from common.keys import ModelKey +from common.registry import get_registry +from common.utils import print_metrics_table, train_test_summary, clip_predictions + + +class EWMAModel: + """ + EWMA-based ETA prediction. + + Maintains exponentially weighted moving averages for each (route, stop) pair. + Updates incrementally as new observations arrive. + + ETA_new = alpha * observed + (1 - alpha) * ETA_old + """ + + def __init__(self, + alpha: float = 0.3, + group_by: list = ['route_id', 'stop_sequence'], + min_observations: int = 3): + """ + Initialize EWMA model. + + Args: + alpha: Smoothing parameter (0-1, higher = more weight on recent) + group_by: Features to group by + min_observations: Min observations before using EWMA + """ + self.alpha = alpha + self.group_by = group_by + self.min_observations = min_observations + + self.ewma_values = {} # (route, stop, ...) -> current EWMA + self.observation_counts = {} # (route, stop, ...) -> count + self.global_mean = None + + def _make_key(self, row: pd.Series) -> tuple: + """Create lookup key from row.""" + return tuple(row[col] for col in self.group_by) + + def fit(self, train_df: pd.DataFrame, target_col: str = 'time_to_arrival_seconds'): + """ + Train EWMA model by processing observations in time order. + + Args: + train_df: Training dataframe (should be time-sorted) + target_col: Target column name + """ + # Sort by timestamp + df_sorted = train_df.sort_values('vp_ts').reset_index(drop=True) + + self.global_mean = df_sorted[target_col].mean() + + # Process observations sequentially + for _, row in df_sorted.iterrows(): + key = self._make_key(row) + observed = row[target_col] + + if key not in self.ewma_values: + # Initialize with first observation + self.ewma_values[key] = observed + self.observation_counts[key] = 1 + else: + # Update EWMA + old_ewma = self.ewma_values[key] + new_ewma = self.alpha * observed + (1 - self.alpha) * old_ewma + self.ewma_values[key] = new_ewma + self.observation_counts[key] += 1 + + print(f"Trained EWMA model (alpha={self.alpha})") + print(f" Unique groups: {len(self.ewma_values)}") + print(f" Total observations: {len(df_sorted)}") + print(f" Global mean: {self.global_mean/60:.2f} minutes") + + def predict(self, X: pd.DataFrame) -> np.ndarray: + """ + Predict ETAs using current EWMA values. + + Args: + X: DataFrame with group_by columns + + Returns: + Array of predicted ETAs + """ + predictions = [] + + for _, row in X.iterrows(): + key = self._make_key(row) + + if key in self.ewma_values: + count = self.observation_counts[key] + if count >= self.min_observations: + predictions.append(self.ewma_values[key]) + else: + # Not enough observations, use global mean + predictions.append(self.global_mean) + else: + # New group, use global mean + predictions.append(self.global_mean) + + return clip_predictions(np.array(predictions)) + + def update(self, X: pd.DataFrame, y: np.ndarray): + """ + Update EWMA values with new observations (online learning). + + Args: + X: Features + y: Observed values + """ + for (_, row), observed in zip(X.iterrows(), y): + key = self._make_key(row) + + if key not in self.ewma_values: + self.ewma_values[key] = observed + self.observation_counts[key] = 1 + else: + old_ewma = self.ewma_values[key] + new_ewma = self.alpha * observed + (1 - self.alpha) * old_ewma + self.ewma_values[key] = new_ewma + self.observation_counts[key] += 1 + + def get_coverage(self, X: pd.DataFrame) -> float: + """Get fraction of predictions with EWMA values.""" + covered = 0 + for _, row in X.iterrows(): + key = self._make_key(row) + if key in self.ewma_values and self.observation_counts[key] >= self.min_observations: + covered += 1 + return covered / len(X) + + def get_group_stats(self) -> pd.DataFrame: + """Get statistics about learned groups.""" + stats = [] + for key, ewma in self.ewma_values.items(): + count = self.observation_counts[key] + stats.append({ + **{col: val for col, val in zip(self.group_by, key)}, + 'ewma_eta_minutes': ewma / 60, + 'n_observations': count + }) + + return pd.DataFrame(stats).sort_values('n_observations', ascending=False) + + +def train_ewma(dataset_name: str = "sample_dataset", + alpha: float = 0.3, + group_by: list = ['route_id', 'stop_sequence'], + min_observations: int = 3, + test_size: float = 0.2, + save_model: bool = True) -> Dict: + """ + Train and evaluate EWMA model. + + Args: + dataset_name: Name of dataset + alpha: EWMA smoothing parameter + group_by: Grouping columns + min_observations: Minimum observations threshold + test_size: Test fraction + save_model: Whether to save + + Returns: + Dictionary with model, metrics, metadata + """ + print(f"\n{'='*60}") + print(f"Training EWMA Model".center(60)) + print(f"{'='*60}\n") + print(f"Config: alpha={alpha}, group_by={group_by}, min_obs={min_observations}") + + # Load dataset + print(f"\nLoading dataset: {dataset_name}") + dataset = load_dataset(dataset_name) + dataset.clean_data() + + # Split data temporally (important for time series) + train_df, val_df, test_df = dataset.temporal_split( + train_frac=1-test_size-0.1, + val_frac=0.1 + ) + + train_test_summary(train_df, test_df, val_df) + + # Train model + print("Training model...") + model = EWMAModel( + alpha=alpha, + group_by=group_by, + min_observations=min_observations + ) + model.fit(train_df) + + # Evaluate on validation (with optional online updates) + print("\nValidation Performance:") + y_val = val_df['time_to_arrival_seconds'].values + val_preds = model.predict(val_df) + val_metrics = compute_all_metrics(y_val, val_preds, prefix="val_") + print_metrics_table(val_metrics, "Validation Metrics") + + val_coverage = model.get_coverage(val_df) + print(f"Validation coverage: {val_coverage*100:.1f}%") + + # Optionally update model with validation data + print("\nUpdating model with validation data...") + model.update(val_df, y_val) + + # Evaluate on test set + print("\nTest Performance:") + y_test = test_df['time_to_arrival_seconds'].values + test_preds = model.predict(test_df) + test_metrics = compute_all_metrics(y_test, test_preds, prefix="test_") + print_metrics_table(test_metrics, "Test Metrics") + + test_coverage = model.get_coverage(test_df) + print(f"Test coverage: {test_coverage*100:.1f}%") + + # Show some group stats + group_stats = model.get_group_stats() + print(f"\nTop 10 groups by observation count:") + print(group_stats.head(10)) + + # Prepare metadata + metadata = { + 'model_type': 'ewma', + 'dataset': dataset_name, + 'alpha': alpha, + 'group_by': group_by, + 'min_observations': min_observations, + 'n_groups': len(model.ewma_values), + 'train_samples': len(train_df), + 'test_samples': len(test_df), + 'test_coverage': float(test_coverage), + 'global_mean_eta': float(model.global_mean), + 'metrics': {**val_metrics, **test_metrics} + } + + # Save model + if save_model: + model_key = ModelKey.generate( + model_type='ewma', + dataset_name=dataset_name, + feature_groups=['temporal', 'route'], + alpha=str(alpha).replace('.', '_') + ) + + registry = get_registry() + registry.save_model(model_key, model, metadata) + metadata['model_key'] = model_key + + return { + 'model': model, + 'metrics': metadata['metrics'], + 'metadata': metadata + } + + +if __name__ == "__main__": + # Train with different alpha values + + # Conservative (slow adaptation) + result1 = train_ewma(alpha=0.1) + + # Balanced + result2 = train_ewma(alpha=0.3) + + # Aggressive (fast adaptation) + result3 = train_ewma(alpha=0.5) + + # With hourly grouping + result4 = train_ewma( + alpha=0.3, + group_by=['route_id', 'stop_sequence', 'hour'] + ) + + # Compare + print("\n" + "="*60) + print("Model Comparison (Test MAE)") + print("="*60) + results = [result1, result2, result3, result4] + labels = ["alpha=0.1", "alpha=0.3", "alpha=0.5", "alpha=0.3+hour"] + + for label, result in zip(labels, results): + mae_min = result['metrics']['test_mae_minutes'] + print(f"{label:20s}: {mae_min:.3f} minutes") \ No newline at end of file diff --git a/eta_prediction/models/historical_mean/predict.py b/eta_prediction/models/historical_mean/predict.py new file mode 100644 index 0000000..ff5ce67 --- /dev/null +++ b/eta_prediction/models/historical_mean/predict.py @@ -0,0 +1,136 @@ +""" +Prediction interface for Historical Mean model. +""" + +import pandas as pd +import numpy as np +from typing import Dict, Optional +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).parent.parent)) + +from common.registry import get_registry +from common.utils import format_seconds + + +def predict_eta(model_key: str, + route_id: str, + stop_sequence: int, + hour: int, + day_of_week: Optional[int] = None, + is_peak_hour: Optional[bool] = None) -> Dict: + """ + Predict ETA using historical mean model. + + Args: + model_key: Model identifier in registry + route_id: Route ID + stop_sequence: Stop sequence number + hour: Hour of day (0-23) + day_of_week: Day of week (0=Monday, optional) + is_peak_hour: Peak hour flag (optional) + + Returns: + Dictionary with prediction and metadata + """ + # Load model + registry = get_registry() + model = registry.load_model(model_key) + metadata = registry.load_metadata(model_key) + + # Prepare input dataframe + input_data = { + 'route_id': [route_id], + 'stop_sequence': [stop_sequence], + 'hour': [hour] + } + + if day_of_week is not None and 'day_of_week' in model.group_by: + input_data['day_of_week'] = [day_of_week] + + if is_peak_hour is not None and 'is_peak_hour' in model.group_by: + input_data['is_peak_hour'] = [is_peak_hour] + + input_df = pd.DataFrame(input_data) + + # Predict + eta_seconds = model.predict(input_df)[0] + + # Check if prediction came from historical data or fallback + coverage = model.get_coverage(input_df) + has_historical_data = coverage > 0 + + return { + 'eta_seconds': float(eta_seconds), + 'eta_minutes': float(eta_seconds / 60), + 'eta_formatted': format_seconds(eta_seconds), + 'model_key': model_key, + 'model_type': 'historical_mean', + 'has_historical_data': has_historical_data, + 'global_mean_eta': metadata.get('global_mean_eta'), + 'group_by': metadata.get('group_by') + } + + +def batch_predict(model_key: str, input_df: pd.DataFrame) -> pd.DataFrame: + """ + Batch prediction for multiple inputs. + + Args: + model_key: Model identifier in registry + input_df: DataFrame with features + + Returns: + DataFrame with predictions added + """ + registry = get_registry() + model = registry.load_model(model_key) + + result_df = input_df.copy() + result_df['predicted_eta_seconds'] = model.predict(input_df) + result_df['predicted_eta_minutes'] = result_df['predicted_eta_seconds'] / 60 + + # Add coverage information + result_df['has_historical_data'] = False + merged = input_df[model.group_by].merge( + model.lookup_table, + on=model.group_by, + how='left', + indicator=True + ) + result_df.loc[merged['_merge'] == 'both', 'has_historical_data'] = True + + return result_df + + +if __name__ == "__main__": + # Example usage + + # Single prediction + result = predict_eta( + model_key="historical_mean_sample_dataset_temporal-route_20250126_143022", + route_id="1", + stop_sequence=5, + hour=8, + day_of_week=0 # Monday + ) + + print("Single Prediction:") + print(f" ETA: {result['eta_formatted']}") + print(f" Has historical data: {result['has_historical_data']}") + + # Batch prediction example + test_data = pd.DataFrame({ + 'route_id': ['1', '1', '2'], + 'stop_sequence': [5, 10, 3], + 'hour': [8, 17, 12] + }) + + predictions = batch_predict( + model_key="historical_mean_sample_dataset_temporal-route_20250126_143022", + input_df=test_data + ) + + print("\nBatch Predictions:") + print(predictions[['route_id', 'stop_sequence', 'predicted_eta_minutes', 'has_historical_data']]) \ No newline at end of file diff --git a/eta_prediction/models/historical_mean/train.py b/eta_prediction/models/historical_mean/train.py new file mode 100644 index 0000000..77f552d --- /dev/null +++ b/eta_prediction/models/historical_mean/train.py @@ -0,0 +1,218 @@ +""" +Historical Mean Baseline Model +Predicts ETA based on historical average travel times grouped by route, stop, and time features. +""" + +import pandas as pd +import numpy as np +from typing import Dict, List, Optional +import sys +from pathlib import Path + +# Add parent directory to path for imports +sys.path.append(str(Path(__file__).parent.parent)) + +from common.data import load_dataset, prepare_features_target +from common.metrics import compute_all_metrics +from common.keys import ModelKey +from common.registry import get_registry +from common.utils import print_metrics_table, train_test_summary + + +class HistoricalMeanModel: + """ + Baseline model using historical mean ETAs. + + Groups data by route, stop, and temporal features, then computes + mean ETA for each group. At prediction time, looks up the appropriate group mean. + """ + + def __init__(self, group_by: List[str] = ['route_id', 'stop_sequence', 'hour']): + """ + Initialize model. + + Args: + group_by: List of columns to group by for computing means + """ + self.group_by = group_by + self.lookup_table = None + self.global_mean = None + self.feature_cols = None + + def fit(self, train_df: pd.DataFrame, target_col: str = 'time_to_arrival_seconds'): + """ + Train model by computing historical means. + + Args: + train_df: Training dataframe with features and target + target_col: Name of target column + """ + # Store feature columns for later + self.feature_cols = self.group_by + + # Compute global mean as fallback + self.global_mean = train_df[target_col].mean() + + # Compute group means + self.lookup_table = train_df.groupby(self.group_by)[target_col].agg([ + ('mean', 'mean'), + ('std', 'std'), + ('count', 'count') + ]).reset_index() + + print(f"Trained on {len(train_df)} samples") + print(f"Created {len(self.lookup_table)} unique groups") + print(f"Global mean ETA: {self.global_mean/60:.2f} minutes") + + def predict(self, X: pd.DataFrame) -> np.ndarray: + """ + Predict ETAs for input data. + + Args: + X: DataFrame with features matching group_by columns + + Returns: + Array of predicted ETAs in seconds + """ + if self.lookup_table is None: + raise ValueError("Model not trained. Call fit() first.") + + # Merge with lookup table + merged = X[self.group_by].merge( + self.lookup_table, + on=self.group_by, + how='left' + ) + + # Use group mean, fallback to global mean + predictions = merged['mean'].fillna(self.global_mean).values + + return predictions + + def get_coverage(self, X: pd.DataFrame) -> float: + """ + Get fraction of predictions with matching historical data. + + Args: + X: DataFrame with features + + Returns: + Coverage ratio (0-1) + """ + merged = X[self.group_by].merge( + self.lookup_table, + on=self.group_by, + how='left', + indicator=True + ) + + return (merged['_merge'] == 'both').mean() + + +def train_historical_mean(dataset_name: str = "sample_dataset", + group_by: List[str] = ['route_id', 'stop_sequence', 'hour'], + test_size: float = 0.2, + save_model: bool = True) -> Dict: + """ + Train and evaluate historical mean model. + + Args: + dataset_name: Name of dataset in datasets/ directory + group_by: List of columns to group by + test_size: Fraction of data for testing + save_model: Whether to save to registry + + Returns: + Dictionary with model, metrics, and metadata + """ + print(f"\n{'='*60}") + print(f"Training Historical Mean Model".center(60)) + print(f"{'='*60}\n") + + # Load dataset + print(f"Loading dataset: {dataset_name}") + dataset = load_dataset(dataset_name) + dataset.clean_data() + + # Split data temporally + train_df, val_df, test_df = dataset.temporal_split( + train_frac=1-test_size-0.1, + val_frac=0.1 + ) + + train_test_summary(train_df, test_df, val_df) + + # Train model + print("Training model...") + model = HistoricalMeanModel(group_by=group_by) + model.fit(train_df) + + # Evaluate on validation set + print("\nValidation Performance:") + y_val = val_df['time_to_arrival_seconds'].values + val_preds = model.predict(val_df) + val_metrics = compute_all_metrics(y_val, val_preds, prefix="val_") + print_metrics_table(val_metrics, "Validation Metrics") + + val_coverage = model.get_coverage(val_df) + print(f"Validation coverage: {val_coverage*100:.1f}%") + + # Evaluate on test set + print("\nTest Performance:") + y_test = test_df['time_to_arrival_seconds'].values + test_preds = model.predict(test_df) + test_metrics = compute_all_metrics(y_test, test_preds, prefix="test_") + print_metrics_table(test_metrics, "Test Metrics") + + test_coverage = model.get_coverage(test_df) + print(f"Test coverage: {test_coverage*100:.1f}%") + + # Prepare metadata + metadata = { + 'model_type': 'historical_mean', + 'dataset': dataset_name, + 'group_by': group_by, + 'train_samples': len(train_df), + 'test_samples': len(test_df), + 'unique_groups': len(model.lookup_table), + 'global_mean_eta': float(model.global_mean), + 'test_coverage': float(test_coverage), + 'metrics': {**val_metrics, **test_metrics} + } + + # Save model + if save_model: + model_key = ModelKey.generate( + model_type='historical_mean', + dataset_name=dataset_name, + feature_groups=['temporal', 'route'] + ) + + registry = get_registry() + registry.save_model(model_key, model, metadata) + metadata['model_key'] = model_key + + return { + 'model': model, + 'metrics': metadata['metrics'], + 'metadata': metadata + } + + +if __name__ == "__main__": + # Train with different grouping strategies + + # Basic: route + stop + hour + result1 = train_historical_mean( + group_by=['route_id', 'stop_sequence', 'hour'] + ) + + # With day of week + result2 = train_historical_mean( + group_by=['route_id', 'stop_sequence', 'hour', 'day_of_week'] + ) + + # With peak hour indicator + result3 = train_historical_mean( + group_by=['route_id', 'stop_sequence', 'is_peak_hour'] + ) \ No newline at end of file diff --git a/eta_prediction/models/polyreg_distance/predict.py b/eta_prediction/models/polyreg_distance/predict.py new file mode 100644 index 0000000..2fa1ddd --- /dev/null +++ b/eta_prediction/models/polyreg_distance/predict.py @@ -0,0 +1,112 @@ +""" +Prediction interface for Polynomial Regression Distance model. +""" + +import pandas as pd +import numpy as np +from typing import Dict, Optional +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).parent.parent)) + +from common.registry import get_registry +from common.utils import format_seconds + + +def predict_eta(model_key: str, + distance_to_stop: float, + route_id: Optional[str] = None) -> Dict: + """ + Predict ETA using polynomial regression distance model. + + Args: + model_key: Model identifier in registry + distance_to_stop: Distance to stop in meters + route_id: Route ID (required for route-specific models) + + Returns: + Dictionary with prediction and metadata + """ + # Load model + registry = get_registry() + model = registry.load_model(model_key) + metadata = registry.load_metadata(model_key) + + # Prepare input + input_data = {'distance_to_stop': [distance_to_stop]} + if model.route_specific: + if route_id is None: + raise ValueError("route_id required for route-specific model") + input_data['route_id'] = [route_id] + + input_df = pd.DataFrame(input_data) + + # Predict + eta_seconds = model.predict(input_df)[0] + + # Get coefficients for this route + coefs = model.get_coefficients(route_id if model.route_specific else None) + + return { + 'eta_seconds': float(eta_seconds), + 'eta_minutes': float(eta_seconds / 60), + 'eta_formatted': format_seconds(eta_seconds), + 'model_key': model_key, + 'model_type': 'polyreg_distance', + 'distance_to_stop_m': distance_to_stop, + 'route_specific': metadata.get('route_specific', False), + 'degree': metadata.get('degree'), + 'coefficients': coefs + } + + +def batch_predict(model_key: str, input_df: pd.DataFrame) -> pd.DataFrame: + """ + Batch prediction for multiple inputs. + + Args: + model_key: Model identifier in registry + input_df: DataFrame with distance_to_stop (and route_id if needed) + + Returns: + DataFrame with predictions added + """ + registry = get_registry() + model = registry.load_model(model_key) + + result_df = input_df.copy() + result_df['predicted_eta_seconds'] = model.predict(input_df) + result_df['predicted_eta_minutes'] = result_df['predicted_eta_seconds'] / 60 + + return result_df + + +if __name__ == "__main__": + # Example usage + + # Single prediction + result = predict_eta( + model_key="polyreg_distance_sample_dataset_distance_20250126_143022_degree=2", + distance_to_stop=1500.0, # 1.5 km + route_id="1" + ) + + print("Single Prediction:") + print(f" Distance: {result['distance_to_stop_m']} meters") + print(f" ETA: {result['eta_formatted']}") + print(f" Degree: {result['degree']}") + + # Batch prediction + test_data = pd.DataFrame({ + 'route_id': ['1', '1', '2'], + 'distance_to_stop': [500.0, 1500.0, 3000.0] + }) + + predictions = batch_predict( + model_key="polyreg_distance_sample_dataset_distance_20250126_143022_degree=2", + input_df=test_data + ) + + print("\nBatch Predictions:") + print(predictions[['route_id', 'distance_to_stop', 'predicted_eta_minutes']]) \ No newline at end of file diff --git a/eta_prediction/models/polyreg_distance/train.py b/eta_prediction/models/polyreg_distance/train.py new file mode 100644 index 0000000..2a5611c --- /dev/null +++ b/eta_prediction/models/polyreg_distance/train.py @@ -0,0 +1,292 @@ +""" +Polynomial Regression Model - Distance-based +Fits polynomial features on distance_to_stop with optional route-specific models. +""" + +import pandas as pd +import numpy as np +from typing import Dict, Optional +from sklearn.preprocessing import PolynomialFeatures +from sklearn.linear_model import Ridge +from sklearn.pipeline import Pipeline +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).parent.parent)) + +from common.data import load_dataset, prepare_features_target +from common.metrics import compute_all_metrics +from common.keys import ModelKey +from common.registry import get_registry +from common.utils import print_metrics_table, train_test_summary, clip_predictions + + +class PolyRegDistanceModel: + """ + Polynomial regression on distance with optional route-specific models. + + Features: distance_to_stop, (distance)^2, (distance)^3, ... + Can fit separate models per route for better performance. + """ + + def __init__(self, + degree: int = 2, + alpha: float = 1.0, + route_specific: bool = False): + """ + Initialize model. + + Args: + degree: Polynomial degree (2 or 3 recommended) + alpha: Ridge regression alpha (regularization strength) + route_specific: Whether to fit separate model per route + """ + self.degree = degree + self.alpha = alpha + self.route_specific = route_specific + self.models = {} # route_id -> model mapping + self.global_model = None + self.feature_cols = ['distance_to_stop'] + + def _create_pipeline(self) -> Pipeline: + """Create sklearn pipeline with polynomial features and ridge regression.""" + return Pipeline([ + ('poly', PolynomialFeatures(degree=self.degree, include_bias=True)), + ('ridge', Ridge(alpha=self.alpha)) + ]) + + def fit(self, train_df: pd.DataFrame, target_col: str = 'time_to_arrival_seconds'): + """ + Train model(s). + + Args: + train_df: Training dataframe with distance_to_stop and target + target_col: Name of target column + """ + if 'distance_to_stop' not in train_df.columns: + raise ValueError("distance_to_stop column required") + + if self.route_specific: + # Fit separate model per route + for route_id, route_df in train_df.groupby('route_id'): + X = route_df[['distance_to_stop']].values + y = route_df[target_col].values + + model = self._create_pipeline() + model.fit(X, y) + self.models[route_id] = model + + print(f"Trained {len(self.models)} route-specific models (degree={self.degree})") + else: + # Fit single global model + X = train_df[['distance_to_stop']].values + y = train_df[target_col].values + + self.global_model = self._create_pipeline() + self.global_model.fit(X, y) + + print(f"Trained global model (degree={self.degree}, alpha={self.alpha})") + + def predict(self, X: pd.DataFrame) -> np.ndarray: + """ + Predict ETAs. + + Args: + X: DataFrame with distance_to_stop (and route_id if route_specific) + + Returns: + Array of predicted ETAs in seconds + """ + if self.route_specific: + if 'route_id' not in X.columns: + raise ValueError("route_id required for route-specific model") + + # Create predictions array matching input length + predictions = np.zeros(len(X)) + + # Reset index to get positional indices + X_reset = X.reset_index(drop=True) + + for route_id, route_df in X_reset.groupby('route_id'): + # Get positional indices (0-based from reset_index) + pos_indices = route_df.index.values + X_route = route_df[['distance_to_stop']].values + + if route_id in self.models: + predictions[pos_indices] = self.models[route_id].predict(X_route) + elif self.global_model is not None: + # Fallback to global model + predictions[pos_indices] = self.global_model.predict(X_route) + else: + # No model available - use simple linear estimate (30 km/h avg) + predictions[pos_indices] = X_route.flatten() / 30000 * 3600 + else: + if self.global_model is None: + raise ValueError("Model not trained") + + X_dist = X[['distance_to_stop']].values + predictions = self.global_model.predict(X_dist) + + # Clip to reasonable range + return clip_predictions(predictions) + + def get_coefficients(self, route_id: Optional[str] = None) -> Dict: + """ + Get model coefficients. + + Args: + route_id: Route ID for route-specific model, None for global + + Returns: + Dictionary with coefficients + """ + if route_id and route_id in self.models: + model = self.models[route_id] + elif self.global_model: + model = self.global_model + else: + return {} + + coefs = model.named_steps['ridge'].coef_ + intercept = model.named_steps['ridge'].intercept_ + + return { + 'intercept': float(intercept), + 'coefficients': coefs.tolist(), + 'degree': self.degree + } + + +def train_polyreg_distance(dataset_name: str = "sample_dataset", + degree: int = 2, + alpha: float = 1.0, + route_specific: bool = False, + test_size: float = 0.2, + save_model: bool = True) -> Dict: + """ + Train and evaluate polynomial regression distance model. + + Args: + dataset_name: Name of dataset in datasets/ directory + degree: Polynomial degree + alpha: Ridge regularization strength + route_specific: Whether to fit per-route models + test_size: Fraction of data for testing + save_model: Whether to save to registry + + Returns: + Dictionary with model, metrics, and metadata + """ + print(f"\n{'='*60}") + print(f"Training Polynomial Regression Distance Model".center(60)) + print(f"{'='*60}\n") + print(f"Config: degree={degree}, alpha={alpha}, route_specific={route_specific}") + + # Load dataset + print(f"\nLoading dataset: {dataset_name}") + dataset = load_dataset(dataset_name) + dataset.clean_data() + + # Split data temporally + train_df, val_df, test_df = dataset.temporal_split( + train_frac=1-test_size-0.1, + val_frac=0.1 + ) + + train_test_summary(train_df, test_df, val_df) + + # Train model + print("Training model...") + model = PolyRegDistanceModel( + degree=degree, + alpha=alpha, + route_specific=route_specific + ) + model.fit(train_df) + + # Evaluate on validation set + print("\nValidation Performance:") + y_val = val_df['time_to_arrival_seconds'].values + val_preds = model.predict(val_df) + val_metrics = compute_all_metrics(y_val, val_preds, prefix="val_") + print_metrics_table(val_metrics, "Validation Metrics") + + # Evaluate on test set + print("\nTest Performance:") + y_test = test_df['time_to_arrival_seconds'].values + test_preds = model.predict(test_df) + test_metrics = compute_all_metrics(y_test, test_preds, prefix="test_") + print_metrics_table(test_metrics, "Test Metrics") + + # Get sample coefficients + sample_coefs = model.get_coefficients() + if sample_coefs: + print(f"\nModel coefficients (sample):") + print(f" Intercept: {sample_coefs['intercept']:.2f}") + print(f" Coefficients: {[f'{c:.6f}' for c in sample_coefs['coefficients'][:5]]}") + + # Prepare metadata + metadata = { + 'model_type': 'polyreg_distance', + 'dataset': dataset_name, + 'degree': degree, + 'alpha': alpha, + 'route_specific': route_specific, + 'n_models': len(model.models) if route_specific else 1, + 'train_samples': len(train_df), + 'test_samples': len(test_df), + 'metrics': {**val_metrics, **test_metrics} + } + + # Save model + if save_model: + model_key = ModelKey.generate( + model_type='polyreg_distance', + dataset_name=dataset_name, + feature_groups=['distance'], + degree=degree, + route_specific='yes' if route_specific else 'no' + ) + + registry = get_registry() + registry.save_model(model_key, model, metadata) + metadata['model_key'] = model_key + + return { + 'model': model, + 'metrics': metadata['metrics'], + 'metadata': metadata + } + + +if __name__ == "__main__": + # Train different configurations + + # Degree 2, global model + result1 = train_polyreg_distance( + degree=2, + alpha=1.0, + route_specific=False + ) + + # Degree 3, global model + result2 = train_polyreg_distance( + degree=3, + alpha=1.0, + route_specific=False + ) + + # Degree 2, route-specific + result3 = train_polyreg_distance( + degree=2, + alpha=1.0, + route_specific=True + ) + + # Compare results + print("\n" + "="*60) + print("Model Comparison (Test MAE)") + print("="*60) + for i, result in enumerate([result1, result2, result3], 1): + mae_min = result['metrics']['test_mae_minutes'] + print(f"Model {i}: {mae_min:.3f} minutes") \ No newline at end of file diff --git a/eta_prediction/models/polyreg_time/predict.py b/eta_prediction/models/polyreg_time/predict.py new file mode 100644 index 0000000..aefeecb --- /dev/null +++ b/eta_prediction/models/polyreg_time/predict.py @@ -0,0 +1,96 @@ +""" +Prediction interface for Polynomial Regression Time model. +""" + +import pandas as pd +from typing import Dict, Optional +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).parent.parent)) + +from common.registry import get_registry +from common.utils import format_seconds + + +def predict_eta(model_key: str, + distance_to_stop: float, + hour: Optional[int] = None, + day_of_week: Optional[int] = None, + is_peak_hour: Optional[bool] = None, + is_weekend: Optional[bool] = None, + is_holiday: Optional[bool] = None, + headway_seconds: Optional[float] = None, + current_speed_kmh: Optional[float] = None, + temperature_c: Optional[float] = None, + precipitation_mm: Optional[float] = None) -> Dict: + """ + Predict ETA using polynomial regression time model. + + Args: + model_key: Model identifier + distance_to_stop: Distance in meters + hour: Hour of day (0-23) + day_of_week: Day of week (0=Monday) + is_peak_hour: Peak hour flag + is_weekend: Weekend flag + is_holiday: Holiday flag + headway_seconds: Time since last vehicle + current_speed_kmh: Current vehicle speed + temperature_c: Temperature + precipitation_mm: Precipitation + + Returns: + Dictionary with prediction and metadata + """ + # Load model + registry = get_registry() + model = registry.load_model(model_key) + metadata = registry.load_metadata(model_key) + + # Prepare input + input_data = {'distance_to_stop': [distance_to_stop]} + + # Add optional features + optional_features = { + 'hour': hour, + 'day_of_week': day_of_week, + 'is_peak_hour': is_peak_hour, + 'is_weekend': is_weekend, + 'is_holiday': is_holiday, + 'headway_seconds': headway_seconds, + 'current_speed_kmh': current_speed_kmh, + 'temperature_c': temperature_c, + 'precipitation_mm': precipitation_mm + } + + for key, value in optional_features.items(): + if value is not None: + input_data[key] = [value] + + input_df = pd.DataFrame(input_data) + + # Predict + eta_seconds = model.predict(input_df)[0] + + return { + 'eta_seconds': float(eta_seconds), + 'eta_minutes': float(eta_seconds / 60), + 'eta_formatted': format_seconds(eta_seconds), + 'model_key': model_key, + 'model_type': 'polyreg_time', + 'distance_to_stop_m': distance_to_stop, + 'features_used': list(input_data.keys()) + } + + +def batch_predict(model_key: str, input_df: pd.DataFrame) -> pd.DataFrame: + """Batch prediction.""" + registry = get_registry() + model = registry.load_model(model_key) + + result_df = input_df.copy() + result_df['predicted_eta_seconds'] = model.predict(input_df) + result_df['predicted_eta_minutes'] = result_df['predicted_eta_seconds'] / 60 + + return result_df \ No newline at end of file diff --git a/eta_prediction/models/polyreg_time/train.py b/eta_prediction/models/polyreg_time/train.py new file mode 100644 index 0000000..0865fa8 --- /dev/null +++ b/eta_prediction/models/polyreg_time/train.py @@ -0,0 +1,462 @@ +""" +Polynomial Regression Model - Time Enhanced +Fits polynomial features with temporal, operational, and optional weather features. +""" + +import pandas as pd +import numpy as np +from typing import Dict, List, Optional +from sklearn.preprocessing import PolynomialFeatures, StandardScaler +from sklearn.linear_model import Ridge +from sklearn.pipeline import Pipeline +from sklearn.compose import ColumnTransformer +import sys +from pathlib import Path + +sys.path.append(str(Path(__file__).parent.parent)) + +from common.data import load_dataset +from common.metrics import compute_all_metrics +from common.keys import ModelKey +from common.registry import get_registry +from common.utils import print_metrics_table, train_test_summary, clip_predictions + + +class PolyRegTimeModel: + """ + Enhanced polynomial regression with temporal/operational features. + + Features: + - Core: distance_to_stop (polynomialized) + - Temporal: hour, day_of_week, is_weekend, is_peak_hour + - Operational: headway_seconds, current_speed_kmh + - Weather: temperature_c, precipitation_mm, wind_speed_kmh + """ + + def __init__(self, + poly_degree: int = 2, + alpha: float = 1.0, + include_temporal: bool = True, + include_operational: bool = True, + include_weather: bool = False, + handle_nan: str = 'drop'): # 'drop', 'impute', or 'error' + """ + Initialize model. + + Args: + poly_degree: Polynomial degree for distance + alpha: Ridge regularization strength + include_temporal: Include time-of-day features + include_operational: Include headway/speed features + include_weather: Include weather features + handle_nan: How to handle NaN - 'drop', 'impute', or 'error' + """ + self.poly_degree = poly_degree + self.alpha = alpha + self.include_temporal = include_temporal + self.include_operational = include_operational + self.include_weather = include_weather + self.handle_nan = handle_nan + self.model = None + self.feature_cols = None + self.available_features = None + + def _get_feature_groups(self) -> Dict[str, List[str]]: + """Define feature groups.""" + return { + 'core': ['distance_to_stop'], + 'temporal': ['hour', 'day_of_week', 'is_weekend', 'is_peak_hour'] + if self.include_temporal else [], + 'operational': ['headway_seconds', 'current_speed_kmh'] + if self.include_operational else [], + 'weather': ['temperature_c', 'precipitation_mm', 'wind_speed_kmh'] + if self.include_weather else [] + } + + def _clean_data(self, df: pd.DataFrame, + target_col: str = 'time_to_arrival_seconds') -> pd.DataFrame: + """ + Clean data and handle NaN values. + + Args: + df: Input dataframe + target_col: Target column name + + Returns: + Cleaned dataframe + """ + print(f"\n{'='*60}") + print("Data Cleaning") + print(f"{'='*60}") + print(f"Initial rows: {len(df):,}") + + # Get all potential features + feature_groups = self._get_feature_groups() + all_features = [] + for group_features in feature_groups.values(): + all_features.extend(group_features) + + # Check which features are available and have acceptable NaN levels + available = [] + missing = [] + high_nan = [] + + for feat in all_features: + if feat not in df.columns: + missing.append(feat) + continue + + nan_ratio = df[feat].isna().sum() / len(df) + + if nan_ratio > 0.3: # More than 30% NaN + high_nan.append((feat, f"{nan_ratio*100:.1f}%")) + else: + available.append(feat) + + # Print feature availability + if missing: + print(f"\n⚠️ Missing features: {', '.join(missing)}") + if high_nan: + print(f"⚠️ High NaN features (>30%):") + for feat, ratio in high_nan: + print(f" - {feat}: {ratio} NaN") + + print(f"\n✓ Available features ({len(available)}):") + for feat in available: + nan_count = df[feat].isna().sum() + if nan_count > 0: + print(f" - {feat} ({nan_count:,} NaN)") + else: + print(f" - {feat}") + + # Store available features + self.available_features = available + + # Handle NaN based on strategy + if self.handle_nan == 'error': + # Check for any NaN in available features + target + check_cols = available + [target_col] + nan_counts = df[check_cols].isna().sum() + if nan_counts.sum() > 0: + print("\nNaN values found:") + for col, count in nan_counts[nan_counts > 0].items(): + print(f" {col}: {count}") + raise ValueError("NaN values found and handle_nan='error'") + df_clean = df + + elif self.handle_nan == 'drop': + # Drop rows with NaN in available features or target + check_cols = available + [target_col] + initial_len = len(df) + df_clean = df.dropna(subset=check_cols) + dropped = initial_len - len(df_clean) + + if dropped > 0: + pct = (dropped / initial_len) * 100 + print(f"\n✓ Dropped {dropped:,} rows ({pct:.2f}%) with NaN values") + + elif self.handle_nan == 'impute': + # Impute NaN values + df_clean = df.copy() + imputed = [] + + for feat in available: + nan_count = df_clean[feat].isna().sum() + if nan_count > 0: + if pd.api.types.is_numeric_dtype(df_clean[feat]): + fill_val = df_clean[feat].median() + df_clean[feat] = df_clean[feat].fillna(fill_val) + imputed.append(f"{feat} (median={fill_val:.2f})") + else: + mode_val = df_clean[feat].mode()[0] + df_clean[feat] = df_clean[feat].fillna(mode_val) + imputed.append(f"{feat} (mode={mode_val})") + + if imputed: + print(f"\n✓ Imputed features:") + for imp in imputed: + print(f" - {imp}") + + # Still drop rows with target NaN + target_nan = df_clean[target_col].isna().sum() + if target_nan > 0: + df_clean = df_clean.dropna(subset=[target_col]) + print(f"\n✓ Dropped {target_nan:,} rows with target NaN") + + else: + raise ValueError(f"Invalid handle_nan: {self.handle_nan}") + + print(f"\nFinal rows: {len(df_clean):,}") + + # Validate no NaN remains + remaining_nan = df_clean[available + [target_col]].isna().sum().sum() + if remaining_nan > 0: + raise ValueError(f"ERROR: {remaining_nan} NaN values remain after cleaning!") + + print("✓ No NaN values in features or target") + + return df_clean + + def _create_pipeline(self) -> Pipeline: + """Create sklearn pipeline.""" + # Separate polynomial features for distance and scaling for others + return Pipeline([ + ('features', ColumnTransformer([ + ('poly_distance', PolynomialFeatures( + degree=self.poly_degree, + include_bias=False + ), [0]), # First column is distance + ('scale_others', StandardScaler(), slice(1, None)) # Rest of features + ])), + ('ridge', Ridge(alpha=self.alpha)) + ]) + + def fit(self, train_df: pd.DataFrame, + target_col: str = 'time_to_arrival_seconds'): + """ + Train model. + + Args: + train_df: Training dataframe + target_col: Target column name + """ + # Clean data + train_clean = self._clean_data(train_df, target_col) + + # Build feature list from available features + feature_groups = self._get_feature_groups() + self.feature_cols = [] + + # Always include core (distance) + for feat in feature_groups['core']: + if feat in self.available_features: + self.feature_cols.append(feat) + + # Add other available features + for group in ['temporal', 'operational', 'weather']: + for feat in feature_groups[group]: + if feat in self.available_features: + self.feature_cols.append(feat) + + if not self.feature_cols: + raise ValueError("No features available after cleaning!") + + print(f"\n{'='*60}") + print("Model Training") + print(f"{'='*60}") + print(f"Features ({len(self.feature_cols)}): {', '.join(self.feature_cols)}") + + # Prepare data + X = train_clean[self.feature_cols].values + y = train_clean[target_col].values + + # Create and fit model + self.model = self._create_pipeline() + self.model.fit(X, y) + + print(f"✓ Model trained (poly_degree={self.poly_degree}, alpha={self.alpha})") + + def predict(self, X: pd.DataFrame) -> np.ndarray: + """ + Predict ETAs. + + Args: + X: DataFrame with features + + Returns: + Array of predicted ETAs in seconds + """ + if self.model is None: + raise ValueError("Model not trained") + + if self.feature_cols is None: + raise ValueError("Feature columns not set") + + # Check for missing features + missing = [f for f in self.feature_cols if f not in X.columns] + if missing: + raise ValueError(f"Missing features in input: {missing}") + + # Handle NaN in prediction data + X_pred = X[self.feature_cols].copy() + + if self.handle_nan == 'impute': + # Impute with median/mode + for col in self.feature_cols: + if X_pred[col].isna().any(): + if pd.api.types.is_numeric_dtype(X_pred[col]): + X_pred[col] = X_pred[col].fillna(X_pred[col].median()) + else: + X_pred[col] = X_pred[col].fillna(X_pred[col].mode()[0]) + elif self.handle_nan == 'drop': + # For prediction, we can't drop - impute instead + for col in self.feature_cols: + if X_pred[col].isna().any(): + if pd.api.types.is_numeric_dtype(X_pred[col]): + X_pred[col] = X_pred[col].fillna(0) # Safe default + + X_array = X_pred.values + predictions = self.model.predict(X_array) + + return clip_predictions(predictions) + + def get_feature_importance(self) -> Dict[str, float]: + """Get feature coefficients (approximate importance).""" + if self.model is None: + return {} + + coefs = self.model.named_steps['ridge'].coef_ + + importance = {} + for i, feat in enumerate(self.feature_cols): + importance[feat] = abs(coefs[i]) + + # Sort by importance + return dict(sorted(importance.items(), key=lambda x: x[1], reverse=True)) + + +def train_polyreg_time(dataset_name: str = "sample_dataset", + poly_degree: int = 2, + alpha: float = 1.0, + include_temporal: bool = True, + include_operational: bool = True, + include_weather: bool = False, + handle_nan: str = 'drop', + test_size: float = 0.2, + save_model: bool = True) -> Dict: + """ + Train and evaluate polynomial regression time model. + + Args: + dataset_name: Dataset name + poly_degree: Polynomial degree for distance + alpha: Ridge regularization + include_temporal: Include temporal features + include_operational: Include operational features + include_weather: Include weather features + handle_nan: 'drop', 'impute', or 'error' + test_size: Test set fraction + save_model: Save to registry + + Returns: + Dictionary with model, metrics, metadata + """ + print(f"\n{'='*60}") + print(f"Polynomial Regression Time Model".center(60)) + print(f"{'='*60}\n") + print(f"Config:") + print(f" poly_degree={poly_degree}, alpha={alpha}") + print(f" temporal={include_temporal}, operational={include_operational}") + print(f" weather={include_weather}, handle_nan='{handle_nan}'") + + # Load dataset + print(f"\nLoading dataset: {dataset_name}") + dataset = load_dataset(dataset_name) + dataset.clean_data() + + # Split data + train_df, val_df, test_df = dataset.temporal_split( + train_frac=1-test_size-0.1, + val_frac=0.1 + ) + + train_test_summary(train_df, test_df, val_df) + + # Train model + print("\n" + "="*60) + print("Training") + print("="*60) + model = PolyRegTimeModel( + poly_degree=poly_degree, + alpha=alpha, + include_temporal=include_temporal, + include_operational=include_operational, + include_weather=include_weather, + handle_nan=handle_nan + ) + model.fit(train_df) + + # Validation + print(f"\n{'='*60}") + print("Validation Performance") + print("="*60) + y_val = val_df['time_to_arrival_seconds'].values + val_preds = model.predict(val_df) + val_metrics = compute_all_metrics(y_val, val_preds, prefix="val_") + print_metrics_table(val_metrics, "Validation") + + # Test + print(f"\n{'='*60}") + print("Test Performance") + print("="*60) + y_test = test_df['time_to_arrival_seconds'].values + test_preds = model.predict(test_df) + test_metrics = compute_all_metrics(y_test, test_preds, prefix="test_") + print_metrics_table(test_metrics, "Test") + + # Feature importance + importance = model.get_feature_importance() + if importance: + print(f"\nTop 5 Features by Coefficient:") + for i, (feat, coef) in enumerate(list(importance.items())[:5], 1): + print(f" {i}. {feat}: {coef:.6f}") + + # Metadata + metadata = { + 'model_type': 'polyreg_time', + 'dataset': dataset_name, + 'poly_degree': poly_degree, + 'alpha': alpha, + 'include_temporal': include_temporal, + 'include_operational': include_operational, + 'include_weather': include_weather, + 'handle_nan': handle_nan, + 'n_features': len(model.feature_cols) if model.feature_cols else 0, + 'features': model.feature_cols, + 'train_samples': len(train_df), + 'test_samples': len(test_df), + 'metrics': {**val_metrics, **test_metrics} + } + + # Save + if save_model: + model_key = ModelKey.generate( + model_type='polyreg_time', + dataset_name=dataset_name, + feature_groups=['temporal', 'operational'] if include_temporal else ['operational'], + degree=poly_degree, + handle_nan=handle_nan + ) + + registry = get_registry() + registry.save_model(model_key, model, metadata) + metadata['model_key'] = model_key + print(f"\n✓ Model saved: {model_key}") + + return { + 'model': model, + 'metrics': metadata['metrics'], + 'metadata': metadata + } + + +if __name__ == "__main__": + # Example: Train with different configurations + + # Basic model - drop NaN + result1 = train_polyreg_time( + poly_degree=2, + include_temporal=True, + include_operational=True, + include_weather=False, + handle_nan='drop' + ) + + # With imputation + result2 = train_polyreg_time( + poly_degree=2, + include_temporal=True, + include_operational=True, + include_weather=False, + handle_nan='impute' + ) \ No newline at end of file diff --git a/eta_prediction/models/train_all_models.py b/eta_prediction/models/train_all_models.py new file mode 100644 index 0000000..db5d690 --- /dev/null +++ b/eta_prediction/models/train_all_models.py @@ -0,0 +1,375 @@ +""" +Main training script for all ETA prediction models. +Trains multiple model types and compares performance. +Updated with robust NaN handling. +""" + +import argparse +import sys +from pathlib import Path + +# Add models directory to path +sys.path.append(str(Path(__file__).parent)) + +from historical_mean.train import train_historical_mean +from polyreg_distance.train import train_polyreg_distance +from polyreg_time.train import train_polyreg_time +from ewma.train import train_ewma +from evaluation.leaderboard import quick_compare +from common.registry import get_registry + + +def train_all_baselines(dataset_name: str = "sample_dataset", + save_models: bool = True, + handle_nan: str = 'drop'): + """ + Train all baseline models with robust NaN handling. + + Args: + dataset_name: Dataset to train on + save_models: Whether to save models to registry + handle_nan: How to handle NaN - 'drop' or 'impute' + + Returns: + Dictionary of trained models and their keys + """ + print("\n" + "="*80) + print("TRAINING ALL BASELINE MODELS".center(80)) + print("="*80 + "\n") + print(f"NaN Handling Strategy: {handle_nan}") + + models = {} + + # 1. Historical Mean (simplest baseline) + print("\n[1/4] Historical Mean Model") + print("-" * 80) + try: + result = train_historical_mean( + dataset_name=dataset_name, + group_by=['route_id', 'stop_sequence', 'hour'], + save_model=save_models + ) + models['historical_mean'] = result + print("✓ Historical Mean trained successfully") + except Exception as e: + print(f"✗ Historical Mean failed: {e}") + + # 2. Polynomial Regression - Distance Only + print("\n[2/4] Polynomial Regression Distance Model") + print("-" * 80) + try: + result = train_polyreg_distance( + dataset_name=dataset_name, + degree=2, + alpha=1.0, + route_specific=False, + save_model=save_models + ) + models['polyreg_distance'] = result + print("✓ Polynomial Regression Distance trained successfully") + if save_models and 'model_key' in result.get('metadata', {}): + print(f" Saved as: {result['metadata']['model_key']}") + except Exception as e: + print(f"✗ Polynomial Regression Distance failed: {e}") + import traceback + traceback.print_exc() + + # 3. Polynomial Regression - Time Enhanced + print("\n[3/4] Polynomial Regression Time Model") + print("-" * 80) + try: + result = train_polyreg_time( + dataset_name=dataset_name, + poly_degree=2, + alpha=1.0, + include_temporal=True, + include_operational=True, + include_weather=False, + handle_nan=handle_nan, # Use configurable NaN handling + save_model=save_models + ) + models['polyreg_time'] = result + print("✓ Polynomial Regression Time trained successfully") + except Exception as e: + print(f"✗ Polynomial Regression Time failed: {e}") + import traceback + traceback.print_exc() + + # 4. EWMA (adaptive baseline) + print("\n[4/4] EWMA Model") + print("-" * 80) + try: + result = train_ewma( + dataset_name=dataset_name, + alpha=0.3, + group_by=['route_id', 'stop_sequence'], + save_model=save_models + ) + models['ewma'] = result + print("✓ EWMA trained successfully") + except Exception as e: + print(f"✗ EWMA failed: {e}") + + # Summary + print("\n" + "="*80) + print(f"Training Summary: {len(models)}/4 models trained successfully") + print("="*80) + + return models + + +def compare_all_models(models: dict): + """ + Compare all trained models. + + Args: + models: Dictionary of model results + """ + if not models: + print("\n⚠️ No models to compare") + return None + + print("\n" + "="*80) + print("MODEL COMPARISON".center(80)) + print("="*80 + "\n") + + # Extract metrics + comparison = [] + for model_type, result in models.items(): + metrics = result['metrics'] + comparison.append({ + 'model': model_type, + 'test_mae_min': metrics.get('test_mae_minutes', 0), + 'test_rmse_min': metrics.get('test_rmse_minutes', 0), + 'test_r2': metrics.get('test_r2', 0), + 'test_within_60s': metrics.get('test_within_60s', 0) * 100, + 'test_bias_sec': metrics.get('test_bias_seconds', 0) + }) + + import pandas as pd + df = pd.DataFrame(comparison) + df = df.sort_values('test_mae_min') + + print("\nRanking by Test MAE:") + print("-" * 80) + print(df.to_string(index=False)) + + # Highlight best + best = df.iloc[0] + print(f"\n🏆 Winner: {best['model']}") + print(f" MAE: {best['test_mae_min']:.3f} minutes") + print(f" RMSE: {best['test_rmse_min']:.3f} minutes") + print(f" R²: {best['test_r2']:.3f}") + print(f" Within 60s: {best['test_within_60s']:.1f}%") + + # Calculate improvements + if len(df) > 1: + worst = df.iloc[-1] + improvement = (worst['test_mae_min'] - best['test_mae_min']) / worst['test_mae_min'] * 100 + print(f"\n📈 Improvement from baseline: {improvement:.1f}% reduction in MAE") + + return df + + +def train_advanced_configurations(dataset_name: str = "sample_dataset", + handle_nan: str = 'drop'): + """ + Train advanced model configurations. + + Args: + dataset_name: Dataset to train on + handle_nan: NaN handling strategy + + Returns: + Dictionary of results + """ + print("\n" + "="*80) + print("TRAINING ADVANCED CONFIGURATIONS".center(80)) + print("="*80 + "\n") + + advanced = {} + + # Route-specific polynomial regression + print("[1/4] Route-Specific Polynomial Regression") + print("-" * 80) + try: + result = train_polyreg_distance( + dataset_name=dataset_name, + degree=2, + route_specific=True, + save_model=True + ) + advanced['polyreg_route_specific'] = result + print("✓ Route-specific model trained successfully") + except Exception as e: + print(f"✗ Route-specific model failed: {e}") + + # Higher degree polynomial + print("\n[2/4] Degree-3 Polynomial Regression") + print("-" * 80) + try: + result = train_polyreg_distance( + dataset_name=dataset_name, + degree=3, + route_specific=False, + save_model=True + ) + advanced['polyreg_degree3'] = result + print("✓ Degree-3 model trained successfully") + except Exception as e: + print(f"✗ Degree-3 model failed: {e}") + + # Polynomial with weather (if available) + print("\n[3/4] Polynomial Regression with Weather") + print("-" * 80) + try: + result = train_polyreg_time( + dataset_name=dataset_name, + poly_degree=2, + include_temporal=True, + include_operational=True, + include_weather=True, + handle_nan=handle_nan, + save_model=True + ) + advanced['polyreg_with_weather'] = result + print("✓ Weather-enhanced model trained successfully") + except Exception as e: + print(f"✗ Weather-enhanced model failed: {e}") + print(" (Weather features may not be available in dataset)") + + # EWMA with hourly grouping + print("\n[4/4] EWMA with Hourly Grouping") + print("-" * 80) + try: + result = train_ewma( + dataset_name=dataset_name, + alpha=0.3, + group_by=['route_id', 'stop_sequence', 'hour'], + save_model=True + ) + advanced['ewma_hourly'] = result + print("✓ Hourly EWMA trained successfully") + except Exception as e: + print(f"✗ Hourly EWMA failed: {e}") + + print("\n" + "="*80) + print(f"Advanced Training Summary: {len(advanced)}/4 models trained") + print("="*80) + + return advanced + + +def main(): + """Main training pipeline.""" + parser = argparse.ArgumentParser( + description="Train ETA prediction models with robust NaN handling" + ) + parser.add_argument( + '--dataset', + type=str, + default='sample_dataset', + help='Dataset name (without .parquet extension)' + ) + parser.add_argument( + '--mode', + type=str, + choices=['baseline', 'advanced', 'all'], + default='baseline', + help='Training mode: baseline, advanced, or all' + ) + parser.add_argument( + '--handle-nan', + type=str, + choices=['drop', 'impute'], + default='drop', + help='How to handle NaN values: drop rows or impute' + ) + parser.add_argument( + '--no-save', + action='store_true', + help='Do not save models to registry' + ) + parser.add_argument( + '--compare-only', + action='store_true', + help='Only compare existing models in registry' + ) + + args = parser.parse_args() + + if args.compare_only: + # Compare existing models + registry = get_registry() + models_df = registry.list_models() + + if models_df.empty: + print("No models found in registry. Train some models first.") + return + + print("\nModels in Registry:") + print(models_df) + + # Get model keys + model_keys = models_df['model_key'].tolist() + + # Run comparison + quick_compare(model_keys, args.dataset) + + return + + save_models = not args.no_save + + # Train models based on mode + if args.mode == 'baseline': + models = train_all_baselines( + args.dataset, + save_models, + handle_nan=args.handle_nan + ) + compare_all_models(models) + + elif args.mode == 'advanced': + models = train_advanced_configurations( + args.dataset, + handle_nan=args.handle_nan + ) + compare_all_models(models) + + elif args.mode == 'all': + print("\n" + "="*80) + print("COMPREHENSIVE MODEL TRAINING".center(80)) + print("="*80) + + baseline_models = train_all_baselines( + args.dataset, + save_models, + handle_nan=args.handle_nan + ) + advanced_models = train_advanced_configurations( + args.dataset, + handle_nan=args.handle_nan + ) + + all_models = {**baseline_models, **advanced_models} + compare_all_models(all_models) + + # Print registry summary + print("\n" + "="*80) + print("MODEL REGISTRY SUMMARY".center(80)) + print("="*80 + "\n") + + registry = get_registry() + models_df = registry.list_models() + print(f"Total models in registry: {len(models_df)}") + + if not models_df.empty: + print(f"\nRecent models:") + print(models_df.head(10)) + print(f"\nModels saved to: {registry.base_dir}") + + print("\n✅ Training complete!") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161343_alpha=0_3.pkl b/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161343_alpha=0_3.pkl new file mode 100644 index 0000000..5b6deaf Binary files /dev/null and b/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161343_alpha=0_3.pkl differ diff --git a/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161343_alpha=0_3_meta.json b/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161343_alpha=0_3_meta.json new file mode 100644 index 0000000..b9371d0 --- /dev/null +++ b/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161343_alpha=0_3_meta.json @@ -0,0 +1,50 @@ +{ + "model_type": "ewma", + "dataset": "GreeD_GreenE_dataset", + "alpha": 0.3, + "group_by": [ + "route_id", + "stop_sequence" + ], + "min_observations": 3, + "n_groups": 87, + "train_samples": 55776, + "test_samples": 15937, + "test_coverage": 1.0, + "global_mean_eta": 339.58555651176135, + "metrics": { + "val_mae_seconds": 211.49454665725085, + "val_mae_minutes": 3.524909110954181, + "val_rmse_seconds": 262.5054034097363, + "val_rmse_minutes": 4.375090056828938, + "val_mape": 260.52255013629735, + "val_median_ae": 180.3349818574003, + "val_bias_seconds": 13.029060142569517, + 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"ewma_GreeD_GreenE_dataset_route-temporal_20251027_161343_alpha=0_3", + "saved_at": "2025-10-27T16:13:43.294001", + "model_path": "models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161343_alpha=0_3.pkl" +} \ No newline at end of file diff --git a/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161350_alpha=0_3.pkl b/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161350_alpha=0_3.pkl new file mode 100644 index 0000000..b6c7dbd Binary files /dev/null and b/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161350_alpha=0_3.pkl differ diff --git a/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161350_alpha=0_3_meta.json b/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161350_alpha=0_3_meta.json new file mode 100644 index 0000000..8053893 --- /dev/null +++ b/eta_prediction/models/trained/ewma_GreeD_GreenE_dataset_route-temporal_20251027_161350_alpha=0_3_meta.json @@ -0,0 +1,51 @@ +{ + "model_type": "ewma", + "dataset": "GreeD_GreenE_dataset", + "alpha": 0.3, + "group_by": [ + "route_id", + "stop_sequence", + "hour" + ], + "min_observations": 3, + "n_groups": 604, + "train_samples": 55776, + "test_samples": 15937, + "test_coverage": 0.07033946163016878, + "global_mean_eta": 339.58555651176135, + "metrics": { + "val_mae_seconds": 213.4302088992185, + "val_mae_minutes": 3.5571701483203086, + "val_rmse_seconds": 264.39215158737176, + "val_rmse_minutes": 4.406535859789529, + "val_mape": 265.1651829886089, + "val_median_ae": 183.23921732147107, + "val_bias_seconds": 16.91901377425509, + "val_r2": -0.5154966196287334, + "val_within_60s": 0.1743222891566265, + "val_within_120s": 0.3417419678714859, + "val_within_300s": 0.7262801204819277, + "val_late_penalty_mae": 311.68580646170017, + "val_error_q50": 183.23921732147107, + "val_error_q90": 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a/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153126_alpha=0_3.pkl b/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153126_alpha=0_3.pkl new file mode 100644 index 0000000..393ebf4 Binary files /dev/null and b/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153126_alpha=0_3.pkl differ diff --git a/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153126_alpha=0_3_meta.json b/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153126_alpha=0_3_meta.json new file mode 100644 index 0000000..f1dc3a5 --- /dev/null +++ b/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153126_alpha=0_3_meta.json @@ -0,0 +1,50 @@ +{ + "model_type": "ewma", + "dataset": "Green-E_dataset", + "alpha": 0.3, + "group_by": [ + "route_id", + "stop_sequence" + ], + "min_observations": 3, + "n_groups": 40, + "train_samples": 28376, + "test_samples": 8108, + 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"test_within_60s": 0.22459299457326098, + "test_within_120s": 0.4135421805624075, + "test_within_300s": 0.8210409472126295, + "test_late_penalty_mae": 276.936990332802, + "test_error_q50": 149.84818262916514, + "test_error_q90": 354.4112797755112, + "test_error_q95": 420.82779801475175 + }, + "model_key": "ewma_Green-E_dataset_route-temporal_20251027_153126_alpha=0_3", + "saved_at": "2025-10-27T15:31:26.702546", + "model_path": "models/trained/ewma_Green-E_dataset_route-temporal_20251027_153126_alpha=0_3.pkl" +} \ No newline at end of file diff --git a/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153130_alpha=0_3.pkl b/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153130_alpha=0_3.pkl new file mode 100644 index 0000000..b854689 Binary files /dev/null and b/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153130_alpha=0_3.pkl differ diff --git a/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153130_alpha=0_3_meta.json b/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153130_alpha=0_3_meta.json new file mode 100644 index 0000000..13bdc8a --- /dev/null +++ b/eta_prediction/models/trained/ewma_Green-E_dataset_route-temporal_20251027_153130_alpha=0_3_meta.json @@ -0,0 +1,51 @@ +{ + "model_type": "ewma", + "dataset": "Green-E_dataset", + "alpha": 0.3, + "group_by": [ + "route_id", + "stop_sequence", + "hour" + ], + "min_observations": 3, + "n_groups": 280, + "train_samples": 28376, + "test_samples": 8108, + "test_coverage": 0.07153428712382832, + "global_mean_eta": 302.06773329574287, + "metrics": { + "val_mae_seconds": 203.62218332847587, + "val_mae_minutes": 3.3937030554745977, + "val_rmse_seconds": 252.32043263607386, + "val_rmse_minutes": 4.205340543934565, + "val_mape": 271.3721163458313, + "val_median_ae": 173.8642171278932, + "val_bias_seconds": 17.077491251342362, + 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"ewma_Green-E_dataset_route-temporal_20251027_153130_alpha=0_3", + "saved_at": "2025-10-27T15:31:30.568347", + "model_path": "models/trained/ewma_Green-E_dataset_route-temporal_20251027_153130_alpha=0_3.pkl" +} \ No newline at end of file diff --git a/eta_prediction/models/trained/historical_mean_GreeD_GreenE_dataset_route-temporal_20251027_161336.pkl b/eta_prediction/models/trained/historical_mean_GreeD_GreenE_dataset_route-temporal_20251027_161336.pkl new file mode 100644 index 0000000..5a64d15 Binary files /dev/null and b/eta_prediction/models/trained/historical_mean_GreeD_GreenE_dataset_route-temporal_20251027_161336.pkl differ diff --git a/eta_prediction/models/trained/historical_mean_GreeD_GreenE_dataset_route-temporal_20251027_161336_meta.json b/eta_prediction/models/trained/historical_mean_GreeD_GreenE_dataset_route-temporal_20251027_161336_meta.json new file mode 100644 index 0000000..d87cb5c --- /dev/null +++ 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