增加模块；增加主调用命令
This commit is contained in:
807
Anomaly_method/One_Class_SVM.py
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807
Anomaly_method/One_Class_SVM.py
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import numpy as np
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import pandas as pd
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import spectral
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from sklearn.svm import OneClassSVM
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from sklearn.preprocessing import StandardScaler
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from sklearn.decomposition import PCA
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from sklearn.model_selection import GridSearchCV, cross_val_score
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from sklearn.metrics import make_scorer, precision_score, recall_score, f1_score
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import os
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import warnings
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from typing import Optional, Dict, Any, Tuple, List, Union
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from dataclasses import dataclass, field
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import argparse
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from pathlib import Path
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from scipy import stats
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warnings.filterwarnings('ignore')
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@dataclass
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class OneClassSVMConfig:
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"""One-Class SVM异常检测配置类"""
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# 输入文件配置
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input_path: Optional[str] = None
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label_col: Optional[str] = None
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spectral_start: Optional[str] = None
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spectral_end: Optional[str] = None
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csv_has_header: bool = True
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# 输出配置
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output_path: Optional[str] = None
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output_dir: Optional[str] = None
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# SVM参数
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kernel: str = 'rbf' # 核函数: 'linear', 'rbf', 'poly'
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nu: float = 0.1 # 训练误差比例 (0.01-0.5)
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gamma: Optional[Union[str, float]] = 'auto' # 核函数参数: 'auto', 'scale', 或数值
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degree: int = 3 # 多项式核的阶数
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coef0: float = 0.0 # 核函数常数项
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tol: float = 1e-3 # 停止准则公差
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shrinking: bool = True # 是否使用shrinking heuristic
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cache_size: float = 200 # 核缓存大小(MB)
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max_iter: int = -1 # 最大迭代次数
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# 数据预处理配置
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use_scaling: bool = True # 是否使用标准化
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use_pca: bool = True # 是否使用PCA降维
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n_components: Optional[int] = None # PCA降维维度，None表示自动选择
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# 参数调优配置
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use_grid_search: bool = False # 是否使用网格搜索调优参数
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cv_folds: int = 3 # 交叉验证折数
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def __post_init__(self):
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"""参数校验和默认值设置"""
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# 校验必需的文件路径
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if not self.input_path:
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raise ValueError("必须指定输入文件路径(input_path)")
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# 校验文件存在性
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if not os.path.exists(self.input_path):
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raise FileNotFoundError(f"输入文件不存在: {self.input_path}")
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# 校验输出路径
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if not self.output_path and not self.output_dir:
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raise ValueError("必须指定输出路径(output_path)或输出目录(output_dir)")
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# 校验参数范围
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if not 0.01 <= self.nu <= 0.5:
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raise ValueError("nu必须在[0.01, 0.5]范围内")
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# 统一核函数名为小写
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self.kernel = self.kernel.lower()
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# 校验核函数类型
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supported_kernels = ['linear', 'rbf', 'poly']
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if self.kernel not in supported_kernels:
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raise ValueError(f"不支持的核函数: {self.kernel}。支持的核函数: {supported_kernels}")
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# 校验gamma参数
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if isinstance(self.gamma, str):
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if self.gamma not in ['auto', 'scale']:
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raise ValueError("gamma字符串值必须是'auto'或'scale'")
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elif self.gamma is not None and self.gamma <= 0:
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raise ValueError("gamma数值必须大于0")
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# 校验多项式阶数
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if self.degree < 1:
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raise ValueError("degree必须大于等于1")
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class OneClassSVMAnomalyDetector:
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"""
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One-Class SVM异常检测器
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使用One-Class SVM算法进行无监督异常检测，只需要正常数据进行训练。
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支持多种核函数，能够学习复杂的数据分布边界。
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算法原理：
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- 使用正常数据训练SVM，学习正常样本的决策边界
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- 测试样本落在边界内为正常(-1)，落在边界外为异常(+1)
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"""
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def __init__(self, config: OneClassSVMConfig):
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"""
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初始化异常检测器
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Args:
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config: 配置对象
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"""
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self.config = config
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self.data = None
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self.labels = None
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self.wavelengths = None
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self.data_shape = None
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self.input_format = None
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# 预处理组件
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self.scaler = None
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self.pca = None
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# One-Class SVM模型
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self.svm_model = None
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# 结果
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self.decision_scores = None # 决策函数值
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self.predictions = None # 预测结果 (+1正常, -1异常)
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# 最佳参数（网格搜索后）
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self.best_params = None
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def load_data(self) -> None:
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"""
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加载高光谱数据文件
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支持CSV和ENVI格式文件
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"""
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file_path = Path(self.config.input_path)
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suffix = file_path.suffix.lower()
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print(f"正在读取文件: {file_path}")
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if suffix == '.csv':
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self._load_csv_data(file_path)
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elif suffix in ['.hdr']:
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self._load_envi_data(file_path)
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else:
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raise ValueError(f"不支持的文件格式: {suffix}。支持的格式: .hdr")
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def _load_csv_data(self, file_path: Path) -> None:
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"""加载CSV格式数据"""
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if self.config.spectral_start is None:
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raise ValueError("对于CSV文件，必须指定spectral_start参数")
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self.input_format = 'csv'
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# 读取数据
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df = pd.read_csv(file_path, header=0 if self.config.csv_has_header else None)
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# 提取标签列
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if self.config.label_col and self.config.label_col in df.columns:
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self.labels = df[self.config.label_col].values
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spectral_cols = [col for col in df.columns if col != self.config.label_col]
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else:
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self.labels = None
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spectral_cols = df.columns.tolist()
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# 提取光谱数据
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if self.config.spectral_end:
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end_idx = spectral_cols.index(self.config.spectral_end) + 1
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else:
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end_idx = len(spectral_cols)
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start_idx = spectral_cols.index(self.config.spectral_start)
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spectral_data = df[spectral_cols[start_idx:end_idx]].values
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# 生成波长信息
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self.wavelengths = np.arange(len(spectral_cols[start_idx:end_idx]))
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self.data = spectral_data.astype(np.float32)
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self.data_shape = self.data.shape
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print(f"CSV数据加载完成: {self.data.shape} 样本 x {self.data.shape[1]} 波段")
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def _load_envi_data(self, file_path: Path) -> None:
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"""加载ENVI格式数据"""
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self.input_format = 'envi'
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try:
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# 确保文件路径存在且可访问
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file_path = Path(file_path)
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if not file_path.exists():
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raise FileNotFoundError(f"ENVI文件不存在: {file_path}")
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# 检查文件是否可读
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if not os.access(file_path, os.R_OK):
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raise PermissionError(f"没有读取权限: {file_path}")
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# 使用规范化的路径字符串
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file_path_str = str(file_path)
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print(f"尝试使用spectral库读取ENVI文件: {file_path_str}")
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# 检查对应的头文件
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hdr_path = file_path.with_suffix(file_path.suffix + '.hdr')
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if not hdr_path.exists():
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# 尝试 .hdr 格式
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hdr_path = file_path.with_suffix('.hdr')
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if not hdr_path.exists():
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print(f"警告: 未找到头文件 {file_path.with_suffix(file_path.suffix + '.hdr')} 或 {file_path.with_suffix('.hdr')}")
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# 读取ENVI文件 - 使用规范化的路径
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envi_data = spectral.open_image(file_path_str)
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# 获取数据数组
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print("正在加载数据到内存...")
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data_array = envi_data.load()
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# 获取波长信息
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if hasattr(envi_data, 'bands') and hasattr(envi_data.bands, 'centers'):
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self.wavelengths = np.array(envi_data.bands.centers)
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print(f"读取到波长信息: {len(self.wavelengths)} 个波段")
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else:
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self.wavelengths = np.arange(data_array.shape[2])
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print(f"未找到波长信息，使用默认波长: 0-{len(self.wavelengths)-1}")
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# 重塑数据为 (samples, bands)
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rows, cols, bands = data_array.shape
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self.data = data_array.reshape(-1, bands).astype(np.float32)
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self.data_shape = (rows, cols, bands)
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print(f"ENVI数据加载完成: {rows}x{cols} 像素 x {bands} 波段")
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except Exception as e:
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raise ValueError(f"读取ENVI文件失败: {str(e)}。请确保spectral库可用且文件格式正确。")
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def preprocess_data(self, data: np.ndarray) -> np.ndarray:
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"""
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数据预处理
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Args:
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data: 输入数据数组
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Returns:
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处理后的数据数组
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"""
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print("正在预处理数据...")
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# 检查数据有效性
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if data is None:
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raise ValueError("请先调用load_data()加载数据")
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# 数据清理：移除NaN和无穷大值
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data_clean = self._clean_data(data)
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# 标准化
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if self.config.use_scaling:
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data_scaled = self._scale_data(data_clean)
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else:
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data_scaled = data_clean
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# PCA降维
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if self.config.use_pca:
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data_processed = self._apply_pca(data_scaled)
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else:
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data_processed = data_scaled
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print(f"数据预处理完成: {data_processed.shape}")
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return data_processed
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def _clean_data(self, data: np.ndarray) -> np.ndarray:
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"""清理数据：移除无效值"""
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# 移除包含NaN或无穷大值的行
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valid_mask = np.all(np.isfinite(data), axis=1)
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data_clean = data[valid_mask]
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if np.sum(~valid_mask) > 0:
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print(f"移除了 {np.sum(~valid_mask)} 个包含无效值的样本")
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# 移除全零行
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non_zero_mask = np.any(data_clean != 0, axis=1)
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data_clean = data_clean[non_zero_mask]
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if np.sum(~non_zero_mask) > 0:
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print(f"移除了 {np.sum(~non_zero_mask)} 个全零样本")
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return data_clean
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def _scale_data(self, data: np.ndarray) -> np.ndarray:
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"""标准化数据"""
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if self.scaler is None:
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self.scaler = StandardScaler()
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data_scaled = self.scaler.fit_transform(data)
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return data_scaled
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def _apply_pca(self, data: np.ndarray) -> np.ndarray:
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"""应用PCA降维"""
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n_samples, n_features = data.shape
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# 确定PCA维度
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if self.config.n_components is None:
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# 自动选择维度：保留95%的方差或最小维度
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n_components = min(n_features, max(2, int(n_features * 0.95)))
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else:
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n_components = min(self.config.n_components, n_features)
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if n_features <= n_components:
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print(f"特征数({n_features})小于等于目标维度({n_components})，跳过PCA降维")
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return data
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if self.pca is None:
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self.pca = PCA(n_components=n_components, random_state=42)
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data_pca = self.pca.fit_transform(data)
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explained_var = np.sum(self.pca.explained_variance_ratio_)
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print(f"PCA降维完成: {n_features} -> {n_components} 维度，解释方差: {explained_var:.3f}")
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return data_pca
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def tune_parameters(self, data: np.ndarray) -> Dict[str, Any]:
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"""
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使用网格搜索调优参数
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Args:
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data: 训练数据
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Returns:
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最佳参数字典
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"""
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print("正在进行参数调优...")
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# 定义参数网格
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param_grid = {
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'nu': [0.01, 0.05, 0.1, 0.15, 0.2]
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}
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if self.config.kernel == 'rbf':
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param_grid['gamma'] = ['auto', 'scale', 0.001, 0.01, 0.1, 1.0]
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elif self.config.kernel == 'poly':
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param_grid['gamma'] = ['auto', 'scale', 0.001, 0.01, 0.1, 1.0]
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param_grid['degree'] = [2, 3, 4]
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param_grid['coef0'] = [0.0, 0.1, 1.0]
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# 创建基础模型
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base_svm = OneClassSVM(
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kernel=self.config.kernel,
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degree=self.config.degree,
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coef0=self.config.coef0,
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tol=self.config.tol,
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shrinking=self.config.shrinking,
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cache_size=self.config.cache_size,
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max_iter=self.config.max_iter
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)
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# 对于One-Class SVM，我们使用决策函数的负值作为评分
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# （决策函数值越负表示越可能是异常）
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def anomaly_score(estimator, X):
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scores = estimator.decision_function(X)
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return -np.mean(scores) # 负的平均决策函数值
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scorer = make_scorer(anomaly_score)
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# 网格搜索
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grid_search = GridSearchCV(
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base_svm,
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param_grid,
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cv=self.config.cv_folds,
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scoring=scorer,
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n_jobs=-1,
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verbose=1
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)
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grid_search.fit(data)
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self.best_params = grid_search.best_params_
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print(f"最佳参数: {self.best_params}")
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print(f"最佳评分: {grid_search.best_score_:.4f}")
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return self.best_params
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def train_model(self, data: np.ndarray) -> None:
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"""
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训练One-Class SVM模型
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Args:
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data: 训练数据（正常样本）
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"""
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print("正在训练One-Class SVM模型...")
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# 参数调优
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if self.config.use_grid_search:
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best_params = self.tune_parameters(data)
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# 更新配置参数
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for param, value in best_params.items():
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setattr(self.config, param, value)
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# 设置gamma参数
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gamma_value = self.config.gamma
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if self.config.gamma == 'auto':
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gamma_value = 1.0 / data.shape[1] # 1/特征数
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elif self.config.gamma == 'scale':
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gamma_value = 1.0 / (data.shape[1] * data.var())
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elif self.config.gamma is None:
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gamma_value = 'scale' # 默认使用'scale'
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# 创建One-Class SVM模型
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self.svm_model = OneClassSVM(
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kernel=self.config.kernel,
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nu=self.config.nu,
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gamma=gamma_value,
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degree=self.config.degree,
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coef0=self.config.coef0,
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tol=self.config.tol,
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shrinking=self.config.shrinking,
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cache_size=self.config.cache_size,
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max_iter=self.config.max_iter
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)
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# 训练模型
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self.svm_model.fit(data)
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# 计算训练集上的决策函数值
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train_scores = self.svm_model.decision_function(data)
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train_predictions = self.svm_model.predict(data)
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n_outliers_train = np.sum(train_predictions == -1)
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outlier_ratio_train = n_outliers_train / len(data)
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print("模型训练完成:")
|
||||
print(f" - 核函数: {self.config.kernel}")
|
||||
print(f" - nu: {self.config.nu}")
|
||||
print(f" - gamma: {gamma_value}")
|
||||
if self.config.kernel == 'poly':
|
||||
print(f" - degree: {self.config.degree}")
|
||||
print(f" - 训练集异常比例: {outlier_ratio_train:.3f}")
|
||||
print(".4f")
|
||||
|
||||
def detect_anomalies(self, data: Optional[np.ndarray] = None) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
检测异常
|
||||
|
||||
Args:
|
||||
data: 测试数据，如果为None则使用训练数据
|
||||
|
||||
Returns:
|
||||
predictions: 预测结果 (+1为正常，-1为异常)
|
||||
scores: 决策函数值
|
||||
"""
|
||||
if self.svm_model is None:
|
||||
raise ValueError("请先调用train_model()训练模型")
|
||||
|
||||
if data is None:
|
||||
data = self.data
|
||||
|
||||
print("正在检测异常...")
|
||||
|
||||
# 预处理数据（使用训练时的预处理器）
|
||||
data_processed = self.preprocess_data(data)
|
||||
|
||||
# 预测异常
|
||||
predictions = self.svm_model.predict(data_processed)
|
||||
scores = self.svm_model.decision_function(data_processed)
|
||||
|
||||
self.predictions = predictions
|
||||
self.decision_scores = scores
|
||||
|
||||
# 统计信息
|
||||
n_anomalies = np.sum(predictions == -1)
|
||||
anomaly_ratio = n_anomalies / len(predictions)
|
||||
|
||||
print("异常检测完成:")
|
||||
print(f" - 测试样本数: {len(predictions)}")
|
||||
print(f" - 异常样本数: {n_anomalies}")
|
||||
print(f" - 异常比例: {anomaly_ratio:.3f}")
|
||||
print(".4f")
|
||||
print(".4f")
|
||||
|
||||
return predictions, scores
|
||||
|
||||
def run_analysis_from_config(self) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""
|
||||
从配置运行完整的异常检测分析
|
||||
|
||||
Returns:
|
||||
Tuple[np.ndarray, np.ndarray]: (predictions, scores)
|
||||
"""
|
||||
# 预处理数据
|
||||
processed_data = self.preprocess_data(self.data)
|
||||
|
||||
# 训练模型
|
||||
self.train_model(processed_data)
|
||||
|
||||
# 执行异常检测
|
||||
predictions, scores = self.detect_anomalies()
|
||||
|
||||
return predictions, scores
|
||||
|
||||
def save_results(self, output_path: Optional[str] = None) -> None:
|
||||
"""
|
||||
保存异常检测结果为ENVI格式文件
|
||||
|
||||
Args:
|
||||
output_path: 输出文件路径，如果为None则使用配置中的路径
|
||||
"""
|
||||
if output_path is None:
|
||||
if self.config.output_path:
|
||||
output_path = self.config.output_path
|
||||
elif self.config.output_dir:
|
||||
base_name = Path(self.config.input_path).stem
|
||||
output_path = os.path.join(self.config.output_dir, f"{base_name}_anomaly_ocsvm.bip")
|
||||
else:
|
||||
raise ValueError("必须指定输出路径")
|
||||
|
||||
print(f"正在保存结果到: {output_path}")
|
||||
|
||||
# 创建输出目录
|
||||
output_dir = os.path.dirname(output_path)
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
|
||||
# 根据输入格式决定输出格式
|
||||
if self.input_format == 'envi':
|
||||
self._save_envi_results(output_path)
|
||||
else:
|
||||
# 对于CSV输入，创建虚拟的2D图像
|
||||
self._save_csv_results_as_envi(output_path)
|
||||
|
||||
def _save_envi_results(self, output_path: str) -> None:
|
||||
"""保存ENVI格式结果"""
|
||||
# 重塑预测结果为原始图像尺寸
|
||||
rows, cols, bands = self.data_shape
|
||||
|
||||
# 创建异常检测结果图像
|
||||
# 结果包括：预测结果、决策函数值
|
||||
anomaly_image = np.zeros((rows, cols, 2), dtype=np.float32)
|
||||
|
||||
# 需要将1D结果映射回2D图像
|
||||
predictions_2d = self.predictions.reshape(rows, cols)
|
||||
scores_2d = self.decision_scores.reshape(rows, cols)
|
||||
|
||||
anomaly_image[:, :, 0] = predictions_2d # 预测结果 (-1, 1)
|
||||
anomaly_image[:, :, 1] = scores_2d # 决策函数值
|
||||
|
||||
# 波段名称
|
||||
band_names = ["Anomaly_Prediction", "Decision_Score"]
|
||||
|
||||
# 保存为ENVI格式，参考Covariance.py的方式
|
||||
self._save_envi_data(anomaly_image, output_path, band_names, 'bip')
|
||||
|
||||
def _save_csv_results_as_envi(self, output_path: str) -> None:
|
||||
"""将CSV结果保存为ENVI格式"""
|
||||
# 对于CSV数据，创建1xN的图像
|
||||
n_samples = len(self.predictions)
|
||||
anomaly_image = np.zeros((1, n_samples, 2), dtype=np.float32)
|
||||
|
||||
anomaly_image[0, :, 0] = self.predictions # 预测结果
|
||||
anomaly_image[0, :, 1] = self.decision_scores # 决策函数值
|
||||
|
||||
# 波段名称
|
||||
band_names = ["Anomaly_Prediction", "Decision_Score"]
|
||||
|
||||
# 保存为ENVI格式，参考Covariance.py的方式
|
||||
self._save_envi_data(anomaly_image, output_path, band_names, 'bip')
|
||||
|
||||
def _save_envi_data(self, data: np.ndarray, output_path: Union[str, Path],
|
||||
band_names: List[str], interleave: str = 'bip') -> None:
|
||||
"""
|
||||
保存数据为ENVI格式，参考Covariance.py的实现
|
||||
|
||||
参数:
|
||||
data: 要保存的数据数组 (height, width, channels)
|
||||
output_path: 输出文件路径
|
||||
band_names: 波段名称列表
|
||||
interleave: 交织方式 ('bip', 'bil', 'bsq')
|
||||
"""
|
||||
output_path = Path(output_path)
|
||||
height, width, channels = data.shape
|
||||
|
||||
# 确保目录存在
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
print(f"保存ENVI文件 - 形状: {data.shape}, 交织方式: {interleave}")
|
||||
|
||||
# 保存.dat文件（二进制格式）
|
||||
self._save_dat_file(data, str(output_path))
|
||||
|
||||
# 保存.hdr头文件
|
||||
hdr_path = str(output_path).replace('.dat', '.hdr')
|
||||
self._save_hdr_file(hdr_path, data.shape, band_names, interleave)
|
||||
|
||||
print(f"One-Class SVM异常检测结果已保存: {output_path}")
|
||||
print(f"头文件已保存: {hdr_path}")
|
||||
print(f"使用的交织格式: {interleave.upper()}")
|
||||
|
||||
def _save_dat_file(self, data: np.ndarray, file_path: str) -> None:
|
||||
"""保存.dat文件（二进制格式），参考Covariance.py"""
|
||||
# 根据数据类型保存
|
||||
if data.dtype == np.float32:
|
||||
# 对于浮点数据，直接保存
|
||||
with open(file_path, 'wb') as f:
|
||||
data.astype(np.float32).tofile(f)
|
||||
else:
|
||||
# 对于其他数据类型，转换为float32保存
|
||||
with open(file_path, 'wb') as f:
|
||||
data.astype(np.float32).tofile(f)
|
||||
|
||||
def _save_hdr_file(self, hdr_path: str, data_shape: Tuple[int, ...],
|
||||
band_names: List[str], interleave: str) -> None:
|
||||
"""保存ENVI头文件，参考Covariance.py并适配One-Class SVM数据"""
|
||||
height, width, channels = data_shape
|
||||
|
||||
# 确定数据类型编码
|
||||
# ENVI数据类型: 4=float32, 5=float64, 1=uint8, 2=int16, 3=int32, 12=uint16
|
||||
data_type_code = 4 # 默认float32
|
||||
|
||||
header_content = f"""ENVI
|
||||
description = {{One-Class SVM Anomaly Detection Results - Generated by OneClassSVMAnomalyDetector}}
|
||||
samples = {width}
|
||||
lines = {height}
|
||||
bands = {channels}
|
||||
header offset = 0
|
||||
file type = ENVI Standard
|
||||
data type = {data_type_code}
|
||||
interleave = {interleave}
|
||||
byte order = 0
|
||||
"""
|
||||
|
||||
# 添加波段名称
|
||||
if band_names:
|
||||
header_content += "band names = {\n"
|
||||
for i, name in enumerate(band_names):
|
||||
header_content += f' "{name}"'
|
||||
if i < len(band_names) - 1:
|
||||
header_content += ","
|
||||
header_content += "\n"
|
||||
header_content += "}\n"
|
||||
|
||||
# 添加One-Class SVM相关元数据
|
||||
header_content += f"anomaly_detection_method = one_class_svm\n"
|
||||
header_content += f"kernel = {self.config.kernel}\n"
|
||||
header_content += f"nu = {self.config.nu}\n"
|
||||
if self.config.gamma is not None:
|
||||
header_content += f"gamma = {self.config.gamma}\n"
|
||||
if self.config.kernel == 'poly':
|
||||
header_content += f"degree = {self.config.degree}\n"
|
||||
header_content += f"coef0 = {self.config.coef0}\n"
|
||||
header_content += f"use_pca = {self.config.use_pca}\n"
|
||||
if self.config.use_pca and self.config.n_components is not None:
|
||||
header_content += f"n_components = {self.config.n_components}\n"
|
||||
if self.best_params:
|
||||
header_content += f"best_params = {self.best_params}\n"
|
||||
|
||||
with open(hdr_path, 'w', encoding='utf-8') as f:
|
||||
f.write(header_content)
|
||||
|
||||
def get_statistics(self) -> Dict[str, Any]:
|
||||
"""
|
||||
获取异常检测统计信息
|
||||
|
||||
Returns:
|
||||
包含各种统计信息的字典
|
||||
"""
|
||||
if self.predictions is None:
|
||||
raise ValueError("请先运行异常检测")
|
||||
|
||||
stats = {
|
||||
'total_samples': len(self.predictions),
|
||||
'n_anomalies': int(np.sum(self.predictions == -1)),
|
||||
'anomaly_ratio': float(np.sum(self.predictions == -1) / len(self.predictions)),
|
||||
'kernel': self.config.kernel,
|
||||
'nu': self.config.nu,
|
||||
'gamma': self.config.gamma,
|
||||
'mean_decision_score': float(np.mean(self.decision_scores)),
|
||||
'std_decision_score': float(np.std(self.decision_scores)),
|
||||
'min_decision_score': float(np.min(self.decision_scores)),
|
||||
'max_decision_score': float(np.max(self.decision_scores))
|
||||
}
|
||||
|
||||
if self.config.kernel == 'poly':
|
||||
stats['degree'] = self.config.degree
|
||||
|
||||
if self.best_params:
|
||||
stats['best_params'] = self.best_params
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数：命令行接口"""
|
||||
parser = argparse.ArgumentParser(
|
||||
description='One-Class SVM高光谱异常检测',
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
使用示例:
|
||||
python One_Class_SVM.py --input data.csv --spectral-start 400 --kernel rbf --nu 0.1 --output results.bip
|
||||
python One_Class_SVM.py --input image.bip --kernel poly --degree 3 --grid-search --output results.bip
|
||||
python One_Class_SVM.py --input data.csv --spectral-start 400 --kernel linear --no-pca --output results.bip
|
||||
"""
|
||||
)
|
||||
|
||||
# 输入文件参数
|
||||
parser.add_argument('--input', required=True, help='输入文件路径 (CSV或ENVI格式)')
|
||||
parser.add_argument('--label-col', help='标签列名 (CSV文件)')
|
||||
parser.add_argument('--spectral-start', help='光谱数据起始列名或波长 (CSV文件)')
|
||||
parser.add_argument('--spectral-end', help='光谱数据结束列名或波长 (CSV文件)')
|
||||
|
||||
# 输出参数
|
||||
parser.add_argument('--output', help='输出文件路径')
|
||||
parser.add_argument('--output-dir', help='输出目录 (将自动生成文件名)')
|
||||
|
||||
# SVM参数
|
||||
parser.add_argument('--kernel', choices=['linear', 'rbf', 'poly'], default='rbf',
|
||||
help='核函数类型，默认rbf')
|
||||
parser.add_argument('--nu', type=float, default=0.1,
|
||||
help='训练误差比例 (0.01-0.5)，默认0.1')
|
||||
parser.add_argument('--gamma', type=float,
|
||||
help='核函数gamma参数，默认auto (1/特征数)')
|
||||
parser.add_argument('--degree', type=int, default=3,
|
||||
help='多项式核的阶数，默认3')
|
||||
parser.add_argument('--coef0', type=float, default=0.0,
|
||||
help='核函数常数项，默认0.0')
|
||||
|
||||
# 预处理参数
|
||||
parser.add_argument('--no-scaling', action='store_true',
|
||||
help='禁用数据标准化')
|
||||
parser.add_argument('--no-pca', action='store_true',
|
||||
help='禁用PCA降维')
|
||||
parser.add_argument('--n-components', type=int,
|
||||
help='PCA降维维度，默认自动选择')
|
||||
|
||||
# 参数调优
|
||||
parser.add_argument('--grid-search', action='store_true',
|
||||
help='使用网格搜索调优参数')
|
||||
parser.add_argument('--cv-folds', type=int, default=3,
|
||||
help='交叉验证折数，默认3')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
try:
|
||||
# 创建配置
|
||||
config = OneClassSVMConfig(
|
||||
input_path=args.input,
|
||||
label_col=args.label_col,
|
||||
spectral_start=args.spectral_start,
|
||||
spectral_end=args.spectral_end,
|
||||
output_path=args.output,
|
||||
output_dir=args.output_dir,
|
||||
kernel=args.kernel,
|
||||
nu=args.nu,
|
||||
gamma=args.gamma,
|
||||
degree=args.degree,
|
||||
coef0=args.coef0,
|
||||
use_scaling=not args.no_scaling,
|
||||
use_pca=not args.no_pca,
|
||||
n_components=args.n_components,
|
||||
use_grid_search=args.grid_search,
|
||||
cv_folds=args.cv_folds
|
||||
)
|
||||
|
||||
# 创建检测器
|
||||
detector = OneClassSVMAnomalyDetector(config)
|
||||
|
||||
# 执行异常检测流程
|
||||
print("=== One-Class SVM异常检测 ===")
|
||||
print(f"输入文件: {config.input_path}")
|
||||
print(f"核函数: {config.kernel}")
|
||||
print(f"nu: {config.nu}")
|
||||
print(f"gamma: {'auto' if config.gamma is None else config.gamma}")
|
||||
if config.kernel == 'poly':
|
||||
print(f"degree: {config.degree}")
|
||||
print(f"参数调优: {'启用' if config.use_grid_search else '禁用'}")
|
||||
print("-" * 50)
|
||||
|
||||
# 1. 加载数据
|
||||
detector.load_data()
|
||||
|
||||
# 2. 预处理数据
|
||||
processed_data = detector.preprocess_data(detector.data)
|
||||
|
||||
# 3. 训练模型
|
||||
detector.train_model(processed_data)
|
||||
|
||||
# 4. 执行异常检测
|
||||
predictions, scores = detector.detect_anomalies(processed_data)
|
||||
|
||||
# 5. 保存结果
|
||||
detector.save_results()
|
||||
|
||||
# 6. 显示统计信息
|
||||
stats = detector.get_statistics()
|
||||
print("\n=== 检测结果统计 ===")
|
||||
for key, value in stats.items():
|
||||
print(f"{key}: {value}")
|
||||
|
||||
print("\n处理完成！")
|
||||
|
||||
except Exception as e:
|
||||
print(f"错误: {str(e)}")
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
return 1
|
||||
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
exit(main())
|
||||
Reference in New Issue
Block a user