htlee
a68dfb21b2
- prediction/: FastAPI 7단계 분류 파이프라인 + 6개 탐지 알고리즘 - snpdb 궤적 조회 → 인메모리 캐시(13K척) → 분류 → kcgdb 저장 - APScheduler 5분 주기, Python 3.9 호환 - 버그 수정: @property last_bucket, SQL INTERVAL 바인딩, rollback, None 가드 - 보안: DB 비밀번호 하드코딩 제거 → env 환경변수 필수 - deploy/kcg-prediction.service: systemd 서비스 (redis-211, 포트 8001) - deploy.yml: prediction CI/CD 배포 단계 추가 (192.168.1.18:32023) - backend: PredictionProxyController (health/status/trigger 프록시) - backend: AppProperties predictionBaseUrl + AuthFilter 인증 예외 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
import pandas as pd
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from collections import defaultdict
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from pipeline.constants import KR_BOUNDS, MAX_SOG_KNOTS, MIN_TRAJ_POINTS
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class AISPreprocessor:
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"""Delete-Supplement-Update (Yan et al. 2022)"""
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def __init__(self):
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self.stats = defaultdict(int)
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def run(self, df: pd.DataFrame) -> pd.DataFrame:
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original = len(df)
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required = ['mmsi', 'timestamp', 'lat', 'lon', 'sog', 'cog']
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missing = [c for c in required if c not in df.columns]
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if missing:
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raise ValueError(f"필수 컬럼 누락: {missing}")
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df = df.copy()
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df['timestamp'] = pd.to_datetime(df['timestamp'])
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valid_mmsi = df['mmsi'].astype(str).str.match(r'^\d{9}$')
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df = df[valid_mmsi]
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self.stats['invalid_mmsi'] += original - len(df)
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df = df[(df['lat'].between(-90, 90)) & (df['lon'].between(-180, 180))]
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df = df[
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df['lat'].between(KR_BOUNDS['lat_min'], KR_BOUNDS['lat_max']) &
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df['lon'].between(KR_BOUNDS['lon_min'], KR_BOUNDS['lon_max'])
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]
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df = df.sort_values(['mmsi', 'timestamp'])
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df['sog'] = df.groupby('mmsi')['sog'].transform(
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lambda x: x.where(
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x.between(0, MAX_SOG_KNOTS),
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x.rolling(3, center=True, min_periods=1).mean(),
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)
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)
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df = df[(df['sog'] >= 0) & (df['sog'] <= MAX_SOG_KNOTS)]
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counts = df.groupby('mmsi').size()
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valid_mmsi_list = counts[counts >= MIN_TRAJ_POINTS].index
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df = df[df['mmsi'].isin(valid_mmsi_list)]
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df = df.drop_duplicates(subset=['mmsi', 'timestamp'])
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self.stats['final_records'] = len(df)
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self.stats['retention_pct'] = round(len(df) / max(original, 1) * 100, 2)
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return df.reset_index(drop=True)
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