Batch Copy files and Replace content accordingly

本文介绍了一个使用bash脚本进行批量文件名替换及内容修改的例子。脚本读取指定目录下符合特定模式的所有文件,将文件名及内容中的'qa'字符串替换为'prod',并创建新的文件。
#!/bin/bash 

# Read all file names into an array 
FilesArray=($(find . -name "deriv1-clrn*qa.jil")) 

# Get length of an array 
FilesIndex=${#FilesArray[@]} 

# Use for loop read all directory names 
for (( i=0; i<${FilesIndex}; i++ )); 
do 
    source="${FilesArray[$i]}" 
    destination="$(echo "${source}" | sed 's/qa/prod/')" 
echo "cp ${source} ${destination}"     
if [ -f ${destination} ]; then 
rm ${destination} 
fi 
touch "${destination}" 
awk -v destination="$destination" '{a = ($0); b = gensub("qa","prod","g",a); print b >> destination; }' "${source}" 
done 

#awk '{print $0}' $destination




转载于:https://my.oschina.net/u/939893/blog/194712

""" Outlier Detection Toolbox ========================= This is a single-file distribution (for ease of preview) of a production-grade outlier/anomaly detection toolbox intended to be split into a small package: outlier_detection/ ├── __init__.py ├── utils.py ├── statistical.py ├── distance_density.py ├── model_based.py ├── deep_learning.py ├── ensemble.py ├── visualization.py └── cli.py --- NOTE --- This code block contains *all* modules concatenated (with file headers) so you can preview and copy each file out into separate .py files. When you save them as separate files the package will work as expected. Design goals (what you asked for): - Detailed, well-documented functions (purpose, math, applicability, edge-cases) - Robust handling of NaNs, constant columns, categorical data - Functions return structured metadata + masks + scores so you can inspect - Utilities for ensemble combining methods and producing a readable report - Optional deep learning methods (AutoEncoder/VAE) with clear dependency instructions and graceful error messages if libraries are missing. Dependencies (recommended): pip install numpy pandas scipy scikit-learn matplotlib joblib tensorflow>=2.0 If you prefer PyTorch for deep models you can adapt deep_learning.py accordingly. """ # --------------------------- # File: outlier_detection/__init__.py # --------------------------- __version__ = "0.1.0" # make it easy to import core helpers from typing import Dict from .utils import ensure_dataframe, OutlierResult, summarize_results, recommend_methods from .statistical import z_score_method, modified_z_score, iqr_method, grubbs_test from .distance_density import lof_method, mahalanobis_method, dbscan_method, knn_distance_method from .model_based import ( isolation_forest_method, one_class_svm_method, pca_reconstruction_error, gmm_method, elliptic_envelope_method, ) # deep_learning module is optional (heavy dependency) try: from .deep_learning import autoencoder_method, vae_method except Exception: # graceful: user may not have TF installed; import will raise at use time autoencoder_method = None vae_method = None from .ensemble import ensemble_methods, aggregate_scores from .visualization import plot_boxplot, plot_pair_scatter __all__ = [ "__version__", "ensure_dataframe", "OutlierResult", "summarize_results", "recommend_methods", "z_score_method", "modified_z_score", "iqr_method", "grubbs_test", "lof_method", "mahalanobis_method", "dbscan_method", "knn_distance_method", "isolation_forest_method", "one_class_svm_method", "pca_reconstruction_error", "gmm_method", "elliptic_envelope_method", "autoencoder_method", "vae_method", "ensemble_methods", "aggregate_scores", "plot_boxplot", "plot_pair_scatter", ] # --------------------------- # File: outlier_detection/utils.py # --------------------------- """ Utilities for the outlier detection package. Key responsibilities: - Input validation and type normalization - Handling numeric / categorical separation - Standardization and robust scaling helpers - A consistent result object shape used by all detectors """ from typing import Dict, Any, Tuple, Optional, List import numpy as np import pandas as pd import logging logger = logging.getLogger(__name__) # A simple, documented result schema for detector functions. # Each detector returns a dict with these keys (guaranteed): # - 'mask': pd.Series[bool] same index as input rows; True means OUTLIER # - 'score': pd.Series or pd.DataFrame numeric score (bigger usually means more anomalous) # - 'method': short string # - 'params': dict of parameters used # - 'explanation': short textual note about interpretation OutlierResult = Dict[str, Any] def ensure_dataframe(X) -> pd.DataFrame: """ Convert input into a pandas DataFrame with a stable integer index. Accepts: pd.DataFrame, np.ndarray, list-of-lists, pd.Series. Returns DataFrame with numeric column names if necessary. """ if isinstance(X, pd.DataFrame): df = X.copy() elif isinstance(X, pd.Series): df = X.to_frame() else: # try to coerce df = pd.DataFrame(X) # if no index or non-unique, reset if df.index is None or not df.index.is_unique: df = df.reset_index(drop=True) # name numeric columns if unnamed df.columns = [str(c) for c in df.columns] return df def numeric_only(df: pd.DataFrame, return_cols: bool = False) -> pd.DataFrame: """ Select numeric columns and warn if non-numeric columns are dropped. If no numeric columns found raises ValueError. """ df = ensure_dataframe(df) numeric_df = df.select_dtypes(include=["number"]).copy() non_numeric = [c for c in df.columns if c not in numeric_df.columns] if non_numeric: logger.debug("Dropping non-numeric columns for numeric-only detectors: %s", non_numeric) if numeric_df.shape[1] == 0: raise ValueError("No numeric columns available for numeric detectors. Consider encoding categoricals.") if return_cols: return numeric_df, list(numeric_df.columns) return numeric_df def handle_missing(df: pd.DataFrame, strategy: str = "drop", fill_value: Optional[float] = None) -> pd.DataFrame: """ Handle missing values in data before passing to detectors. Parameters ---------- df : DataFrame strategy : {'drop', 'mean', 'median', 'zero', 'constant', 'keep'} - 'drop' : drop rows with any NaN (useful when most values are present) - 'mean' : fill numeric columns with mean - 'median' : fill numeric with median - 'zero' : fill with 0 - 'constant' : fill with supplied fill_value - 'keep' : keep NaNs (many detectors can handle NaN rows implicitly) fill_value : numeric (used when strategy=='constant') Returns ------- DataFrame cleaned according to strategy. Original index preserved. Notes ----- - Some detectors (LOF, IsolationForest) do NOT accept NaNs; choose strategy accordingly. """ df = df.copy() if strategy == "drop": return df.dropna(axis=0, how="any") elif strategy == "mean": return df.fillna(df.mean()) elif strategy == "median": return df.fillna(df.median()) elif strategy == "zero": return df.fillna(0) elif strategy == "constant": if fill_value is None: raise ValueError("fill_value must be provided for strategy='constant'") return df.fillna(fill_value) elif strategy == "keep": return df else: raise ValueError(f"Unknown missing value strategy: {strategy}") def robust_scale(df: pd.DataFrame) -> pd.DataFrame: """ Scale numeric columns using median and IQR (robust to outliers). Returns a DataFrame of same shape with scaled values. """ df = numeric_only(df) med = df.median() q1 = df.quantile(0.25) q3 = df.quantile(0.75) iqr = q3 - q1 # avoid division by zero iqr_replaced = iqr.replace(0, 1.0) return (df - med) / iqr_replaced def create_result(mask: pd.Series, score: pd.Series, method: str, params: Dict[str, Any], explanation: str) -> OutlierResult: """ Wrap mask + score into the standard result dict. """ # ensure index alignment if not mask.index.equals(score.index): # try to reindex score = score.reindex(mask.index) return { "mask": mask.astype(bool), "score": score, "method": method, "params": params, "explanation": explanation, } def summarize_results(results: Dict[str, OutlierResult]) -> pd.DataFrame: """ Given a dict of results keyed by method name, return a single DataFrame where each column is that method's boolean flag and another column is the score (if numeric). Also returns a short per-row summary like how many detectors flagged the row. """ # Collect masks and scores masks = {} scores = {} for k, r in results.items(): masks[f"{k}_flag"] = r["mask"].astype(int) # flatten score: if DataFrame use mean across columns sc = r["score"] if isinstance(sc, pd.DataFrame): sc = sc.mean(axis=1) scores[f"{k}_score"] = sc masks_df = pd.DataFrame(masks) scores_df = pd.DataFrame(scores) combined = pd.concat([masks_df, scores_df], axis=1) combined.index = next(iter(results.values()))["mask"].index combined["n_flags"] = masks_df.sum(axis=1) combined["any_flag"] = combined["n_flags"] > 0 return combined def recommend_methods(X: pd.DataFrame) -> List[str]: """ Heuristic recommender: returns a short list of methods to try depending on data shape. Rules (simple heuristics): - single numeric column: ['iqr', 'modified_z'] - low-dimensional (n_features <= 10) and numeric: ['mahalanobis','lof','isolation_forest'] - high-dimensional (n_features > 10): ['isolation_forest','pca','autoencoder'] """ df = ensure_dataframe(X) n_features = df.select_dtypes(include=["number"]).shape[1] if n_features == 0: raise ValueError("No numeric features to recommend methods for") if n_features == 1: return ["iqr", "modified_z"] elif n_features <= 10: return ["mahalanobis", "lof", "isolation_forest"] else: return ["isolation_forest", "pca", "autoencoder"] # --------------------------- # File: outlier_detection/statistical.py # --------------------------- """ Statistical / univariate outlier detectors. Each function focuses on single-dimension input (pd.Series) or will operate column-wise if given a DataFrame (then returns DataFrame of scores / masks). """ from typing import Union import numpy as np import pandas as pd from scipy import stats from .utils import create_result, numeric_only def _as_series(x: Union[pd.Series, pd.DataFrame], col: str = None) -> pd.Series: if isinstance(x, pd.DataFrame): if col is None: raise ValueError("If passing DataFrame, must pass column name") return x[col] return x def z_score_method(x: Union[pd.Series, pd.DataFrame], threshold: float = 3.0) -> OutlierResult: """ Z-Score method (univariate) Math: z = (x - mean) / std Flag where |z| > threshold. Applicability: single numeric column, approximately normal distribution. Not robust to heavy-tailed distributions. Returns OutlierResult with score = |z| (higher => more anomalous). """ if isinstance(x, pd.DataFrame): # apply per-column and return a DataFrame score masks = pd.DataFrame(index=x.index) scores = pd.DataFrame(index=x.index) for c in x.columns: res = z_score_method(x[c], threshold=threshold) masks[c] = res["mask"].astype(int) scores[c] = res["score"] # Derive a combined mask: any column flagged mask_any = masks.sum(axis=1) > 0 combined_score = scores.mean(axis=1) return create_result(mask_any, combined_score, "z_score_dataframe", {"threshold": threshold}, "Applied z-score per-column and combined by mean score and any-flag") s = x.dropna() if s.shape[0] == 0: mask = pd.Series([False]*len(x), index=x.index) score = pd.Series([0.0]*len(x), index=x.index) return create_result(mask, score, "z_score", {"threshold": threshold}, "Empty or all-NaN series") mu = s.mean() sigma = s.std(ddof=0) if sigma == 0: score = pd.Series(0.0, index=x.index) mask = pd.Series(False, index=x.index) explanation = "Zero variance: no z-score possible" return create_result(mask, score, "z_score", {"threshold": threshold}, explanation) z = (x - mu) / sigma absz = z.abs() mask = absz > threshold score = absz.fillna(0.0) explanation = f"z-score with mean={mu:.4g}, std={sigma:.4g}; flag |z|>{threshold}" return create_result(mask, score, "z_score", {"threshold": threshold}, explanation) def modified_z_score(x: Union[pd.Series, pd.DataFrame], threshold: float = 3.5) -> OutlierResult: """ Modified Z-score using median and MAD (robust to extreme values). Formula: M_i = 0.6745 * (x_i - median) / MAD Where MAD = median(|x_i - median|) Recommended threshold: 3.5 (common in literature) """ if isinstance(x, pd.DataFrame): masks = pd.DataFrame(index=x.index) scores = pd.DataFrame(index=x.index) for c in x.columns: res = modified_z_score(x[c], threshold=threshold) masks[c] = res["mask"].astype(int) scores[c] = res["score"] mask_any = masks.sum(axis=1) > 0 combined_score = scores.mean(axis=1) return create_result(mask_any, combined_score, "modified_z_dataframe", {"threshold": threshold}, "Applied modified z per-column and combined") s = x.dropna() if len(s) == 0: return create_result(pd.Series(False, index=x.index), pd.Series(0.0, index=x.index), "modified_z", {"threshold": threshold}, "empty") med = np.median(s) mad = np.median(np.abs(s - med)) if mad == 0: # all equal or too small score = pd.Series(0.0, index=x.index) mask = pd.Series(False, index=x.index) return create_result(mask, score, "modified_z", {"threshold": threshold}, "mad==0: no variation") M = 0.6745 * (x - med) / mad score = M.abs().fillna(0.0) mask = score > threshold return create_result(mask, score, "modified_z", {"threshold": threshold, "median": med, "mad": mad}, "Robust modified z-score; higher => more anomalous") def iqr_method(x: Union[pd.Series, pd.DataFrame], k: float = 1.5) -> OutlierResult: """ IQR (boxplot) method. Flags points outside [Q1 - k*IQR, Q3 + k*IQR]. k=1.5 is common; use larger k for fewer false positives. """ if isinstance(x, pd.DataFrame): masks = pd.DataFrame(index=x.index) scores = pd.DataFrame(index=x.index) for c in x.columns: res = iqr_method(x[c], k=k) masks[c] = res["mask"].astype(int) scores[c] = res["score"] mask_any = masks.sum(axis=1) > 0 combined_score = scores.mean(axis=1) return create_result(mask_any, combined_score, "iqr_dataframe", {"k": k}, "Applied IQR per column") s = x.dropna() if s.shape[0] == 0: return create_result(pd.Series(False, index=x.index), pd.Series(0.0, index=x.index), "iqr", {"k": k}, "empty") q1 = np.percentile(s, 25) q3 = np.percentile(s, 75) iqr = q3 - q1 lower = q1 - k * iqr upper = q3 + k * iqr mask = (x < lower) | (x > upper) # score: distance from nearest fence normalized by iqr (if iqr==0 use abs distance) if iqr == 0: score = (x - q1).abs().fillna(0.0) else: score = pd.Series(0.0, index=x.index) score[x < lower] = ((lower - x[x < lower]) / (iqr + 1e-12)) score[x > upper] = ((x[x > upper] - upper) / (iqr + 1e-12)) return create_result(mask.fillna(False), score.fillna(0.0), "iqr", {"k": k, "q1": q1, "q3": q3}, f"IQR fences [{lower:.4g}, {upper:.4g}]") def grubbs_test(x: Union[pd.Series, pd.DataFrame], alpha: float = 0.05) -> OutlierResult: """ Grubbs' test for a single outlier (requires approx normality). This test is intended to *detect one outlier at a time*. Use iteratively (recompute after removing detected outlier) if you expect multiple outliers, but be careful with multiplicity adjustments. Returns mask with at most one True (the most extreme point) unless alpha is very large. """ # For simplicity operate only on a single series. If DataFrame provided, # run per-column and combine (like other funcs) if isinstance(x, pd.DataFrame): masks = pd.DataFrame(index=x.index) scores = pd.DataFrame(index=x.index) for c in x.columns: res = grubbs_test(x[c], alpha=alpha) masks[c] = res["mask"].astype(int) scores[c] = res["score"] mask_any = masks.sum(axis=1) > 0 combined_score = scores.mean(axis=1) return create_result(mask_any, combined_score, "grubbs_dataframe", {"alpha": alpha}, "Applied Grubbs per column") from math import sqrt s = x.dropna() n = len(s) if n < 3: return create_result(pd.Series(False, index=x.index), pd.Series(0.0, index=x.index), "grubbs", {"alpha": alpha}, "n<3: cannot run") mean = s.mean() std = s.std(ddof=0) if std == 0: return create_result(pd.Series(False, index=x.index), pd.Series(0.0, index=x.index), "grubbs", {"alpha": alpha}, "zero std") # compute G statistic for max dev deviations = (s - mean).abs() max_idx = deviations.idxmax() G = deviations.loc[max_idx] / std # critical value from t-distribution t_crit = stats.t.ppf(1 - alpha / (2 * n), n - 2) G_crit = ((n - 1) / sqrt(n)) * (t_crit / sqrt(n - 2 + t_crit ** 2)) mask = pd.Series(False, index=x.index) mask.loc[max_idx] = G > G_crit score = pd.Series(0.0, index=x.index) score.loc[max_idx] = float(G) explanation = f"G={G:.4g}, Gcrit={G_crit:.4g}, alpha={alpha}" return create_result(mask, score, "grubbs", {"alpha": alpha, "G": G, "Gcrit": G_crit}, explanation) # --------------------------- # File: outlier_detection/distance_density.py # --------------------------- """ Distance and density based detectors (multivariate-capable). Functions generally accept a numeric DataFrame X and return OutlierResult. """ from sklearn.neighbors import LocalOutlierFactor, NearestNeighbors from sklearn.cluster import DBSCAN from sklearn.covariance import EmpiricalCovariance from .utils import ensure_dataframe, create_result, numeric_only def lof_method(X, n_neighbors: int = 20, contamination: float = 0.05) -> OutlierResult: """ Local Outlier Factor (LOF). Returns score = -lof. LOF API returns negative_outlier_factor_. We negate so higher score => more anomalous. Applicability: medium-dimensional data, clusters of varying density. Beware: LOF does not provide a predictable probabilistic threshold. """ X = ensure_dataframe(X) Xnum = numeric_only(X) if Xnum.shape[0] < 2: return create_result(pd.Series(False, index=X.index), pd.Series(0.0, index=X.index), "lof", {"n_neighbors": n_neighbors}, "too few samples") lof = LocalOutlierFactor(n_neighbors=min(n_neighbors, max(1, Xnum.shape[0]-1)), contamination=contamination) y = lof.fit_predict(Xnum) negative_factor = lof.negative_outlier_factor_ # higher -> more anomalous score = (-negative_factor) score = pd.Series(score, index=Xnum.index) mask = pd.Series(y == -1, index=Xnum.index) return create_result(mask, score, "lof", {"n_neighbors": n_neighbors, "contamination": contamination}, "LOF: higher score more anomalous") def knn_distance_method(X, k: int = 5) -> OutlierResult: """ k-NN distance based scoring: compute distance to k-th nearest neighbor. Points with large k-distance are candidate outliers. Returns score = k-distance (bigger => more anomalous). """ X = ensure_dataframe(X) Xnum = numeric_only(X) if Xnum.shape[0] < k + 1: return create_result(pd.Series(False, index=X.index), pd.Series(0.0, index=X.index), "knn_distance", {"k": k}, "too few samples") nbrs = NearestNeighbors(n_neighbors=k + 1).fit(Xnum) distances, _ = nbrs.kneighbors(Xnum) # distances[:, 0] is zero (self). take k-th neighbor kdist = distances[:, k] score = pd.Series(kdist, index=Xnum.index) # threshold: e.g., mean + 2*std thr = score.mean() + 2 * score.std() mask = score > thr return create_result(mask, score, "knn_distance", {"k": k, "threshold": thr}, "k-distance method") def mahalanobis_method(X, threshold_p: float = 0.01) -> OutlierResult: """ Mahalanobis distance based detection. Computes D^2 for each point. One can threshold by chi-square quantile with df=n_features: P(D^2 > thresh) = threshold_p. We return score = D^2. Applicability: data approximately elliptical (multivariate normal-ish). """ X = ensure_dataframe(X) Xnum = numeric_only(X) n, d = Xnum.shape if n <= d: # covariance ill-conditioned; apply shrinkage or PCA beforehand explanation = "n <= n_features: covariance may be singular, consider PCA or regularization" else: explanation = "" cov = EmpiricalCovariance().fit(Xnum) mahal = cov.mahalanobis(Xnum) score = pd.Series(mahal, index=Xnum.index) # default threshold: chi2 quantile from scipy.stats import chi2 thr = chi2.ppf(1 - threshold_p, df=d) if d > 0 else np.inf mask = score > thr return create_result(mask, score, "mahalanobis", {"threshold_p": threshold_p, "chi2_thr": float(thr)}, explanation) def dbscan_method(X, eps: float = 0.5, min_samples: int = 5) -> OutlierResult: """ DBSCAN clusterer: points labeled -1 are considered noise -> outliers. Applicability: non-spherical clusters, variable density; choose eps carefully. """ X = ensure_dataframe(X) Xnum = numeric_only(X) if Xnum.shape[0] < min_samples: return create_result(pd.Series(False, index=X.index), pd.Series(0.0, index=X.index), "dbscan", {"eps": eps, "min_samples": min_samples}, "too few samples") db = DBSCAN(eps=eps, min_samples=min_samples).fit(Xnum) labels = db.labels_ mask = pd.Series(labels == -1, index=Xnum.index) # score: negative of cluster size (noise points get score 1) # To keep simple: noise -> 1, else 0 score = pd.Series((labels == -1).astype(float), index=Xnum.index) return create_result(mask, score, "dbscan", {"eps": eps, "min_samples": min_samples}, "DBSCAN noise points flagged") # --------------------------- # File: outlier_detection/model_based.py # --------------------------- """ Model-based detectors: tree ensembles, SVM boundary, PCA reconstruction, GMM These functions are intended for multivariate numeric data. """ from sklearn.ensemble import IsolationForest from sklearn.svm import OneClassSVM from sklearn.decomposition import PCA from sklearn.mixture import GaussianMixture from sklearn.covariance import EllipticEnvelope from .utils import ensure_dataframe, numeric_only, create_result def isolation_forest_method(X, contamination: float = 0.05, random_state: int = 42) -> OutlierResult: """ Isolation Forest Returns mask and anomaly score (higher => more anomalous). Good general-purpose method for medium-to-high dimensional data. """ X = ensure_dataframe(X) Xnum = numeric_only(X) if Xnum.shape[0] < 2: return create_result(pd.Series(False, index=X.index), pd.Series(0.0, index=X.index), "isolation_forest", {"contamination": contamination}, "too few samples") iso = IsolationForest(contamination=contamination, random_state=random_state) iso.fit(Xnum) pred = iso.predict(Xnum) # decision_function: higher -> more normal, so we invert raw_score = -iso.decision_function(Xnum) score = pd.Series(raw_score, index=Xnum.index) mask = pd.Series(pred == -1, index=Xnum.index) return create_result(mask, score, "isolation_forest", {"contamination": contamination}, "IsolationForest: inverted decision function as score") def one_class_svm_method(X, kernel: str = "rbf", nu: float = 0.05, gamma: str = "scale") -> OutlierResult: """ One-Class SVM for boundary-based anomaly detection. Carefully tune nu and gamma; not robust to large datasets without subsampling. """ X = ensure_dataframe(X) Xnum = numeric_only(X) if Xnum.shape[0] < 5: return create_result(pd.Series(False, index=X.index), pd.Series(0.0, index=X.index), "one_class_svm", {"nu": nu}, "too few samples") ocsvm = OneClassSVM(kernel=kernel, nu=nu, gamma=gamma) ocsvm.fit(Xnum) pred = ocsvm.predict(Xnum) # decision_function: positive => inside boundary (normal); invert raw_score = -ocsvm.decision_function(Xnum) score = pd.Series(raw_score, index=Xnum.index) mask = pd.Series(pred == -1, index=Xnum.index) return create_result(mask, score, "one_class_svm", {"nu": nu, "kernel": kernel}, "OneClassSVM: invert decision_function for anomaly score") def pca_reconstruction_error(X, n_components: int = None, explained_variance: float = None, threshold: float = None) -> OutlierResult: """ PCA-based reconstruction error. If n_components not set, choose the minimum components to reach explained_variance (if provided). Otherwise uses min(n_features, 2). Score: squared reconstruction error per sample. Default threshold: mean+3*std. """ X = ensure_dataframe(X) Xnum = numeric_only(X) n, d = Xnum.shape if n == 0 or d == 0: return create_result(pd.Series(False, index=X.index), pd.Series(0.0, index=X.index), "pca_recon", {}, "empty data") if n_components is None: if explained_variance is not None: temp_pca = PCA(n_components=min(n, d)) temp_pca.fit(Xnum) cum = np.cumsum(temp_pca.explained_variance_ratio_) n_components = int(np.searchsorted(cum, explained_variance) + 1) n_components = max(1, n_components) else: n_components = min(2, d) pca = PCA(n_components=n_components) proj = pca.fit_transform(Xnum) recon = pca.inverse_transform(proj) errors = ((Xnum - recon) ** 2).sum(axis=1) score = pd.Series(errors, index=Xnum.index) if threshold is None: threshold = score.mean() + 3 * score.std() mask = score > threshold return create_result(mask, score, "pca_recon", {"n_components": n_components, "threshold": float(threshold)}, "PCA reconstruction error") def gmm_method(X, n_components: int = 2, contamination: float = 0.05) -> OutlierResult: """ Gaussian Mixture Model based anomaly score (log-likelihood). Score: negative log-likelihood (bigger => less likely => more anomalous). Threshold: empirical quantile of scores. """ X = ensure_dataframe(X) Xnum = numeric_only(X) if Xnum.shape[0] < n_components: return create_result(pd.Series(False, index=X.index), pd.Series(0.0, index=X.index), "gmm", {}, "too few samples") gmm = GaussianMixture(n_components=n_components) gmm.fit(Xnum) logprob = gmm.score_samples(Xnum) score = pd.Series(-logprob, index=Xnum.index) thr = score.quantile(1 - contamination) mask = score > thr return create_result(mask, score, {"n_components": n_components, "threshold": float(thr)}, "gmm", "GMM negative log-likelihood") def elliptic_envelope_method(X, contamination: float = 0.05) -> OutlierResult: """ EllipticEnvelope fits a robust covariance (assumes data come from a Gaussian-like ellipse). Flags outliers outside the ellipse. """ X = ensure_dataframe(X) Xnum = numeric_only(X) ee = EllipticEnvelope(contamination=contamination) ee.fit(Xnum) pred = ee.predict(Xnum) # decision_function: larger -> more normal; invert raw_score = -ee.decision_function(Xnum) score = pd.Series(raw_score, index=Xnum.index) mask = pd.Series(pred == -1, index=Xnum.index) return create_result(mask, score, "elliptic_envelope", {"contamination": contamination}, "EllipticEnvelope") # --------------------------- # File: outlier_detection/deep_learning.py # --------------------------- """ Deep learning based detectors (AutoEncoder, VAE). These require TensorFlow/Keras installed. If not present, importing this module will raise an informative ImportError. Design: a training function accepts X (numpy or DataFrame) and returns a callable `score_fn(X_new) -> pd.Series` plus a threshold selection helper. """ from typing import Callable import numpy as np import pandas as pd # lazy import to avoid hard TF dependency if user doesn't need it try: import tensorflow as tf from tensorflow.keras import layers, models, backend as K except Exception as e: raise ImportError("TensorFlow / Keras is required for deep_learning module. Install with `pip install tensorflow`. Error: " + str(e)) from .utils import ensure_dataframe, create_result def _build_autoencoder(input_dim: int, latent_dim: int = 8, hidden_units=(64, 32)) -> models.Model: inp = layers.Input(shape=(input_dim,)) x = inp for h in hidden_units: x = layers.Dense(h, activation='relu')(x) z = layers.Dense(latent_dim, activation='relu', name='latent')(x) x = z for h in reversed(hidden_units): x = layers.Dense(h, activation='relu')(x) out = layers.Dense(input_dim, activation='linear')(x) ae = models.Model(inp, out) return ae def autoencoder_method(X, latent_dim: int = 8, hidden_units=(128, 64), epochs: int = 50, batch_size: int = 32, validation_split: float = 0.1, threshold_method: str = 'quantile', threshold_val: float = 0.99, verbose: int = 0) -> OutlierResult: """ Train an AutoEncoder on X and compute reconstruction error as anomaly score. Parameters ---------- X : DataFrame or numpy array (numeric) threshold_method : 'quantile' or 'mean_std' threshold_val : if quantile -> e.g. 0.99 means top 1% flagged; if mean_std -> number of stds Returns ------- OutlierResult where score = reconstruction error and mask = score > threshold Notes ----- - This trains on the entire provided X. For actual anomaly detection, it's common to train the autoencoder only on "normal" data. If you have labels, pass only normal subset for training. - Requires careful scaling of inputs before training (robust_scale recommended). """ Xdf = ensure_dataframe(X) Xnum = Xdf.select_dtypes(include=['number']).fillna(0.0) input_dim = Xnum.shape[1] if input_dim == 0: return create_result(pd.Series(False, index=Xdf.index), pd.Series(0.0, index=Xdf.index), "autoencoder", {}, "no numeric columns") # convert to numpy arr = Xnum.values.astype(np.float32) ae = _build_autoencoder(input_dim=input_dim, latent_dim=latent_dim, hidden_units=hidden_units) ae.compile(optimizer='adam', loss='mse') ae.fit(arr, arr, epochs=epochs, batch_size=batch_size, validation_split=validation_split, verbose=verbose) recon = ae.predict(arr) errors = np.mean((arr - recon) ** 2, axis=1) score = pd.Series(errors, index=Xdf.index) if threshold_method == 'quantile': thr = float(score.quantile(threshold_val)) else: thr = float(score.mean() + threshold_val * score.std()) mask = score > thr return create_result(mask, score, "autoencoder", {"latent_dim": latent_dim, "threshold": thr}, "AutoEncoder reconstruction error") def vae_method(X, latent_dim: int = 8, hidden_units=(128, 64), epochs: int = 50, batch_size: int = 32, threshold_method: str = 'quantile', threshold_val: float = 0.99, verbose: int = 0) -> OutlierResult: """ Variational Autoencoder (VAE) anomaly detection. Implementation note: VAE is more involved; here we provide a simple implementation that uses reconstruction error as score. For strict probabilistic anomaly scoring one would use the ELBO / likelihood; this minimal implementation keeps it practical. """ # For brevity we reuse autoencoder path (a more complete VAE impl is possible) return autoencoder_method(X, latent_dim=latent_dim, hidden_units=hidden_units, epochs=epochs, batch_size=batch_size, threshold_method=threshold_method, threshold_val=threshold_val, verbose=verbose) # --------------------------- # File: outlier_detection/ensemble.py # --------------------------- """ Combine multiple detectors and produce an aggregated report. Provides strategies: union, intersection, majority voting, weighted sum of normalized scores. """ from typing import List, Dict import numpy as np import pandas as pd from .utils import ensure_dataframe, create_result def normalize_scores(scores: pd.DataFrame) -> pd.DataFrame: """Min-max normalize each score column to [0,1].""" sc = scores.copy() for c in sc.columns: col = sc[c] mn = col.min() mx = col.max() if mx == mn: sc[c] = 0.0 else: sc[c] = (col - mn) / (mx - mn) return sc def aggregate_scores(results: Dict[str, Dict], method: str = 'weighted', weights: Dict[str, float] = None) -> Dict: """ Aggregate multiple OutlierResult dictionaries produced by detectors. Returns an OutlierResult-like dict with: - mask (final boolean by threshold on aggregate score), - score (aggregate numeric score) Aggregation methods: - 'union' : any detector flagged => outlier (score = max of normalized scores) - 'intersection' : flagged by all detectors => outlier - 'majority' : flagged by >50% detectors - 'weighted' : weighted sum of normalized scores (weights provided or equal) """ # collect masks and scores into DataFrames masks = pd.DataFrame({k: v['mask'].astype(int) for k, v in results.items()}) raw_scores = pd.DataFrame({k: (v['score'] if isinstance(v['score'], pd.Series) else pd.Series(v['score'])) for k, v in results.items()}) raw_scores.index = masks.index norm_scores = normalize_scores(raw_scores) if method == 'union': agg_score = norm_scores.max(axis=1) elif method == 'intersection': agg_score = norm_scores.min(axis=1) elif method == 'majority': agg_score = masks.sum(axis=1) / max(1, masks.shape[1]) elif method == 'weighted': if weights is None: weights = {k: 1.0 for k in results.keys()} # align weights w = pd.Series({k: weights.get(k, 1.0) for k in results.keys()}) # make sure weights sum to 1 w = w / w.sum() agg_score = (norm_scores * w).sum(axis=1) else: raise ValueError("Unknown aggregation method") # default threshold: 0.5 mask = agg_score > 0.5 return create_result(mask, agg_score, f"ensemble_{method}", {"method": method}, "Aggregated ensemble score") def ensemble_methods(X, method_list: List[str] = None, method_params: Dict = None) -> Dict[str, Dict]: """ Convenience: run multiple detectors by name and return dict of results. method_list: list of names from ['iqr','modified_z','z_score','lof','mahalanobis','isolation_forest', ...] method_params: optional dict mapping method name to params """ from . import statistical, distance_density, model_based, deep_learning X = ensure_dataframe(X) if method_list is None: method_list = ['iqr', 'modified_z', 'isolation_forest', 'lof'] if method_params is None: method_params = {} results = {} for m in method_list: params = method_params.get(m, {}) try: if m == 'iqr': results[m] = statistical.iqr_method(X, **params) elif m == 'modified_z': results[m] = statistical.modified_z_score(X, **params) elif m == 'z_score': results[m] = statistical.z_score_method(X, **params) elif m == 'lof': results[m] = distance_density.lof_method(X, **params) elif m == 'mahalanobis': results[m] = distance_density.mahalanobis_method(X, **params) elif m == 'dbscan': results[m] = distance_density.dbscan_method(X, **params) elif m == 'knn': results[m] = distance_density.knn_distance_method(X, **params) elif m == 'isolation_forest': results[m] = model_based.isolation_forest_method(X, **params) elif m == 'one_class_svm': results[m] = model_based.one_class_svm_method(X, **params) elif m == 'pca': results[m] = model_based.pca_reconstruction_error(X, **params) elif m == 'gmm': results[m] = model_based.gmm_method(X, **params) elif m == 'elliptic': results[m] = model_based.elliptic_envelope_method(X, **params) elif m == 'autoencoder': results[m] = deep_learning.autoencoder_method(X, **params) else: logger.warning("Unknown method requested: %s", m) except Exception as e: logger.exception("Method %s failed: %s", m, e) return results # --------------------------- # File: outlier_detection/visualization.py # --------------------------- """ Simple plotting helpers for quick inspection. Note: plotting is intentionally minimal; for report-quality figures users can adapt styles. The functions return the matplotlib Figure object so they can be further customized. """ import matplotlib.pyplot as plt from .utils import ensure_dataframe def plot_boxplot(series: pd.Series, show: bool = True): df = ensure_dataframe(series) col = df.columns[0] fig, ax = plt.subplots() ax.boxplot(df[col].dropna()) ax.set_title(f"Boxplot: {col}") if show: plt.show() return fig def plot_pair_scatter(X, columns: list = None, show: bool = True): X = ensure_dataframe(X) if columns is not None: X = X[columns] cols = X.columns.tolist()[:4] # avoid huge plots fig, axes = plt.subplots(len(cols) - 1, len(cols) - 1, figsize=(4 * (len(cols) - 1), 4 * (len(cols) - 1))) for i in range(1, len(cols)): for j in range(i): ax = axes[i - 1, j] ax.scatter(X[cols[j]], X[cols[i]], s=8) ax.set_xlabel(cols[j]) ax.set_ylabel(cols[i]) fig.suptitle("Pairwise scatter (first 4 numeric cols)") if show: plt.show() return fig # --------------------------- # File: outlier_detection/cli.py # --------------------------- """ A very small CLI to run detectors on a CSV file and output a CSV report. Usage (example): python -m outlier_detection.cli detect input.csv output_report.csv --methods iqr,isolation_forest """ import argparse import pandas as pd from .ensemble import ensemble_methods, aggregate_scores def main(): parser = argparse.ArgumentParser(description='Outlier detection CLI') sub = parser.add_subparsers(dest='cmd') det = sub.add_parser('detect') det.add_argument('input_csv') det.add_argument('output_csv') det.add_argument('--methods', default='iqr,modified_z,isolation_forest,lof') args = parser.parse_args() df = pd.read_csv(args.input_csv) methods = args.methods.split(',') results = ensemble_methods(df, method_list=methods) agg = aggregate_scores(results, method='weighted') summary = pd.concat([pd.DataFrame({k: v['mask'].astype(int) for k, v in results.items()}), pd.DataFrame({k: v['score'] for k, v in results.items()})], axis=1) summary['ensemble_score'] = agg['score'] summary['ensemble_flag'] = agg['mask'].astype(int) summary.to_csv(args.output_csv, index=False) print(f"Wrote report to {args.output_csv}") if __name__ == '__main__': main()改成中文说明并返回代码给我
08-27
在不同的操作系统中,复制文件夹所有内容并按指定说明进行替换有不同的操作方法。 ### Windows系统 可以使用`Xcopy`命令复制文件夹内容,使用批处理结合`findstr`和`powershell`脚本进行替换操作。 - 复制文件夹内容:使用`Xcopy`命令将D盘的`UpdateFiles`文件夹中包含的所有东西,全部复制到E盘的`123`文件夹内,同时复制空目录或子目录,如果目标路径已经有相同文件了,使用覆盖方式而不进行提示。命令如下: ```batch Xcopy D:\UpdateFiles E:\123 /s /e /y ``` - 替换文件内容:由于Windows批处理处理文本替换相对复杂,可以结合`powershell`脚本。例如将复制后的文件夹内所有`.txt`文件中的`old_text`替换为`new_text`,可以编写如下`powershell`脚本: ```powershell Get-ChildItem -Path "E:\123" -Filter *.txt -Recurse | ForEach-Object { (Get-Content $_.FullName) | ForEach-Object { $_ -replace "old_text", "new_text" } | Set-Content $_.FullName } ``` ### Mac系统 使用`cp`命令复制文件夹内容,使用`find`和`sed`命令进行文本替换。 - 复制文件夹内容:使用`cp`命令将`source_folder`复制到`destination_folder`,并递归复制子目录。 ```bash cp -R source_folder destination_folder ``` - 替换文件内容:执行以下命令来查找`destination_folder`及其子文件夹下的所有`.txt`文件,并将`% XXDM %`替换为`#{XXDM}`。 ```bash find destination_folder -type f -name "*.txt" -exec sed -i '' 's/% XXDM %/#{XXDM}/g' {} + ``` ### Linux系统 与Mac系统类似,使用`cp`命令复制文件夹内容,使用`sed`命令进行文本替换。 - 复制文件夹内容:使用`cp`命令将`source_folder`复制到`destination_folder`,并递归复制子目录。 ```bash cp -R source_folder destination_folder ``` - 替换文件内容:使用`Mahuinan`法(`sed`命令)批量替换`destination_folder`内所有文件中的字符串。例如将`old_string`替换为`new_string`: ```bash find destination_folder -type f -exec sed -i 's/old_string/new_string/g' {} + ```
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