htlee
e2fc355b2c
iran prediction 47개 Python 파일을 prediction/ 디렉토리로 복제: - algorithms/ 14개 분석 알고리즘 (어구추론, 다크베셀, 스푸핑, 환적, 위험도 등) - pipeline/ 7단계 분류 파이프라인 - cache/vessel_store (24h 슬라이딩 윈도우) - db/ 어댑터 (snpdb 원본조회, kcgdb 결과저장) - chat/ AI 채팅 (Ollama, 후순위) - data/ 정적 데이터 (기선, 특정어업수역 GeoJSON) config.py를 kcgaidb로 재구성 (DB명, 사용자, 비밀번호) DB 연결 검증 완료 (kcgaidb 37개 테이블 접근 확인) Makefile에 dev-prediction / dev-all 타겟 추가 CLAUDE.md에 prediction 섹션 추가 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
import logging
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import pandas as pd
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from pipeline.preprocessor import AISPreprocessor
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from pipeline.behavior import BehaviorDetector
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from pipeline.resampler import TrajectoryResampler
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from pipeline.features import FeatureExtractor
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from pipeline.classifier import VesselTypeClassifier, get_season
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from pipeline.clusterer import EnhancedBIRCHClusterer
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from pipeline.constants import RESAMPLE_INTERVAL_MIN
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logger = logging.getLogger(__name__)
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class ChineseFishingVesselPipeline:
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"""7-step pipeline for classifying Chinese fishing vessel activity types.
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Steps:
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1. AIS preprocessing (Yan et al. 2022)
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2. Behaviour-state detection (speed-based 3-class)
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3. Trajectory resampling (Yan, Yang et al. — 4-minute interval)
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4. Feature vector extraction (paper 12)
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5. Vessel-type classification (rule-based scoring)
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6. Enhanced BIRCH trajectory clustering (Yan, Yang et al.)
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7. Seasonal activity tagging (paper 12)
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"""
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def __init__(self) -> None:
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self.preprocessor = AISPreprocessor()
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self.detector = BehaviorDetector()
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self.resampler = TrajectoryResampler(RESAMPLE_INTERVAL_MIN)
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self.extractor = FeatureExtractor()
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self.classifier = VesselTypeClassifier()
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self.clusterer = EnhancedBIRCHClusterer()
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def run(
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self, df_raw: pd.DataFrame
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) -> tuple[list[dict], dict[str, pd.DataFrame]]:
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"""Run the 7-step pipeline.
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Args:
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df_raw: raw AIS DataFrame with columns mmsi, timestamp, lat, lon,
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sog, cog.
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Returns:
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(results, vessel_dfs) where:
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- results is a list of classification dicts, each containing:
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mmsi, vessel_type, confidence, fishing_pct, cluster_id, season,
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n_points, features.
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- vessel_dfs is a mapping of mmsi -> resampled trajectory DataFrame.
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"""
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# Step 1: preprocess
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df = self.preprocessor.run(df_raw)
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if len(df) == 0:
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logger.warning('pipeline: no rows after preprocessing')
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return [], {}
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# Step 2: behaviour detection
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df = self.detector.detect(df)
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# Steps 3–5: per-vessel processing
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vessel_dfs: dict[str, pd.DataFrame] = {}
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results: list[dict] = []
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for mmsi, df_v in df.groupby('mmsi'):
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df_resampled = self.resampler.resample(df_v)
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vessel_dfs[mmsi] = df_resampled
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features = self.extractor.extract(df_resampled)
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vtype, confidence = self.classifier.classify(features)
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fishing_pct = BehaviorDetector.compute_fishing_ratio(df_resampled)
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season = get_season(df_v['timestamp'].iloc[len(df_v) // 2])
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results.append({
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'mmsi': mmsi,
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'vessel_type': vtype,
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'confidence': confidence,
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'fishing_pct': fishing_pct,
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'season': season,
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'n_points': len(df_resampled),
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'features': features,
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})
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# Step 6: BIRCH clustering
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cluster_map = self.clusterer.fit_predict(vessel_dfs)
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for r in results:
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r['cluster_id'] = cluster_map.get(r['mmsi'], -1)
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logger.info(
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'pipeline complete: %d vessels, types=%s',
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len(results),
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{r['vessel_type'] for r in results},
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)
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return results, vessel_dfs
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