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>
102 lines
3.5 KiB
Python
102 lines
3.5 KiB
Python
from collections import Counter
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from typing import Dict, Optional
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import numpy as np
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import pandas as pd
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from pipeline.constants import BIRCH_THRESHOLD, BIRCH_BRANCHING, MIN_CLUSTER_SIZE
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class EnhancedBIRCHClusterer:
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"""Trajectory clustering using sklearn Birch with a simple K-means fallback.
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Based on the enhanced-BIRCH approach (Yan, Yang et al.):
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1. Resample each trajectory to a fixed-length vector.
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2. Build a BIRCH CF-tree for memory-efficient hierarchical clustering.
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3. Small clusters (< MIN_CLUSTER_SIZE) are relabelled as noise (-1).
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"""
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def __init__(
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self,
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threshold: float = BIRCH_THRESHOLD,
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branching: int = BIRCH_BRANCHING,
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n_clusters: Optional[int] = None,
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) -> None:
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self.threshold = threshold
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self.branching = branching
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self.n_clusters = n_clusters
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self._model = None
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def _traj_to_vector(self, df_vessel: pd.DataFrame, n_points: int = 20) -> np.ndarray:
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"""Convert a vessel trajectory DataFrame to a fixed-length vector.
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Linearly samples n_points from the trajectory and interleaves lat/lon
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values, then normalises to zero mean / unit variance.
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"""
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lats = df_vessel['lat'].values
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lons = df_vessel['lon'].values
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idx = np.linspace(0, len(lats) - 1, n_points).astype(int)
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vec = np.concatenate([lats[idx], lons[idx]])
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vec = (vec - vec.mean()) / (vec.std() + 1e-9)
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return vec
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def fit_predict(self, vessels: Dict[str, pd.DataFrame]) -> Dict[str, int]:
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"""Cluster vessel trajectories.
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Args:
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vessels: mapping of mmsi -> resampled trajectory DataFrame.
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Returns:
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Mapping of mmsi -> cluster_id. Vessels in small clusters are
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assigned cluster_id -1 (noise). Vessels with fewer than 20
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points are excluded from the result.
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"""
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mmsi_list: list[str] = []
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vectors: list[np.ndarray] = []
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for mmsi, df_v in vessels.items():
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if len(df_v) < 20:
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continue
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mmsi_list.append(mmsi)
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vectors.append(self._traj_to_vector(df_v))
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if len(vectors) < 3:
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return {m: 0 for m in mmsi_list}
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X = np.array(vectors)
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try:
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from sklearn.cluster import Birch
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model = Birch(
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threshold=self.threshold,
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branching_factor=self.branching,
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n_clusters=self.n_clusters,
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)
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labels = model.fit_predict(X)
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self._model = model
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except ImportError:
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labels = self._simple_cluster(X)
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cnt = Counter(labels)
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labels = np.array([lbl if cnt[lbl] >= MIN_CLUSTER_SIZE else -1 for lbl in labels])
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return dict(zip(mmsi_list, labels.tolist()))
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@staticmethod
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def _simple_cluster(X: np.ndarray, k: int = 5) -> np.ndarray:
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"""Fallback K-means used when sklearn is unavailable."""
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n = len(X)
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k = min(k, n)
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centers = X[np.random.choice(n, k, replace=False)]
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labels = np.zeros(n, dtype=int)
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for _ in range(20):
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dists = np.array([[np.linalg.norm(x - c) for c in centers] for x in X])
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labels = dists.argmin(axis=1)
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new_centers = np.array(
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[X[labels == i].mean(axis=0) if (labels == i).any() else centers[i] for i in range(k)]
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)
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if np.allclose(centers, new_centers, atol=1e-6):
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break
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centers = new_centers
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return labels
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