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Outlier detection with Local Outlier Factor (LOF) — scikit-learn 1.6.dev0 documentation
| svm lof scikit-learn |
| https://scikit-learn.org/dev/auto_examples/neighbors/plot_lof_outlier_detection.html |
|
俊秀的盒饭
3 周前 |
- Release Highlights for scikit-learn 1.5
- Release Highlights for scikit-learn 1.4
- Release Highlights for scikit-learn 1.3
- Release Highlights for scikit-learn 1.2
- Release Highlights for scikit-learn 1.1
- Release Highlights for scikit-learn 1.0
- Release Highlights for scikit-learn 0.24
- Release Highlights for scikit-learn 0.23
- Release Highlights for scikit-learn 0.22
-
Biclustering
- A demo of the Spectral Biclustering algorithm
- A demo of the Spectral Co-Clustering algorithm
- Biclustering documents with the Spectral Co-clustering algorithm
-
Calibration
- Comparison of Calibration of Classifiers
- Probability Calibration curves
- Probability Calibration for 3-class classification
- Probability calibration of classifiers
-
Classification
- Classifier comparison
- Linear and Quadratic Discriminant Analysis with covariance ellipsoid
- Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification
- Plot classification probability
- Recognizing hand-written digits
-
Clustering
- A demo of K-Means clustering on the handwritten digits data
- A demo of structured Ward hierarchical clustering on an image of coins
- A demo of the mean-shift clustering algorithm
- Adjustment for chance in clustering performance evaluation
- Agglomerative clustering with and without structure
- Agglomerative clustering with different metrics
- An example of K-Means++ initialization
- Bisecting K-Means and Regular K-Means Performance Comparison
- Compare BIRCH and MiniBatchKMeans
- Comparing different clustering algorithms on toy datasets
- Comparing different hierarchical linkage methods on toy datasets
- Comparison of the K-Means and MiniBatchKMeans clustering algorithms
- Demo of DBSCAN clustering algorithm
- Demo of HDBSCAN clustering algorithm
- Demo of OPTICS clustering algorithm
- Demo of affinity propagation clustering algorithm
- Demonstration of k-means assumptions
- Empirical evaluation of the impact of k-means initialization
- Feature agglomeration
- Feature agglomeration vs. univariate selection
- Hierarchical clustering: structured vs unstructured ward
- Inductive Clustering
- Online learning of a dictionary of parts of faces
- Plot Hierarchical Clustering Dendrogram
- Segmenting the picture of greek coins in regions
- Selecting the number of clusters with silhouette analysis on KMeans clustering
- Spectral clustering for image segmentation
- Various Agglomerative Clustering on a 2D embedding of digits
- Vector Quantization Example
-
Covariance estimation
- Ledoit-Wolf vs OAS estimation
- Robust covariance estimation and Mahalanobis distances relevance
- Robust vs Empirical covariance estimate
- Shrinkage covariance estimation: LedoitWolf vs OAS and max-likelihood
- Sparse inverse covariance estimation
-
Cross decomposition
- Compare cross decomposition methods
- Principal Component Regression vs Partial Least Squares Regression
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Dataset examples
- Plot randomly generated multilabel dataset
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Decision Trees
- Decision Tree Regression
- Plot the decision surface of decision trees trained on the iris dataset
- Post pruning decision trees with cost complexity pruning
- Understanding the decision tree structure
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Decomposition
- Blind source separation using FastICA
- Comparison of LDA and PCA 2D projection of Iris dataset
- Faces dataset decompositions
- Factor Analysis (with rotation) to visualize patterns
- FastICA on 2D point clouds
- Image denoising using dictionary learning
- Incremental PCA
- Kernel PCA
- Model selection with Probabilistic PCA and Factor Analysis (FA)
- Principal Component Analysis (PCA) on Iris Dataset
- Sparse coding with a precomputed dictionary
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Developing Estimators
-
__sklearn_is_fitted__as Developer API -
Ensemble methods
- Categorical Feature Support in Gradient Boosting
- Combine predictors using stacking
- Comparing Random Forests and Histogram Gradient Boosting models
- Comparing random forests and the multi-output meta estimator
- Decision Tree Regression with AdaBoost
- Early stopping in Gradient Boosting
- Feature importances with a forest of trees
- Feature transformations with ensembles of trees
- Features in Histogram Gradient Boosting Trees
- Gradient Boosting Out-of-Bag estimates
- Gradient Boosting regression
- Gradient Boosting regularization
- Hashing feature transformation using Totally Random Trees
- IsolationForest example
- Monotonic Constraints
- Multi-class AdaBoosted Decision Trees
- OOB Errors for Random Forests
- Plot class probabilities calculated by the VotingClassifier
- Plot individual and voting regression predictions
- Plot the decision boundaries of a VotingClassifier
- Plot the decision surfaces of ensembles of trees on the iris dataset
- Prediction Intervals for Gradient Boosting Regression
- Single estimator versus bagging: bias-variance decomposition
- Two-class AdaBoost
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Examples based on real world datasets
- Compressive sensing: tomography reconstruction with L1 prior (Lasso)
- Faces recognition example using eigenfaces and SVMs
- Image denoising using kernel PCA
- Lagged features for time series forecasting
- Model Complexity Influence
- Out-of-core classification of text documents
- Outlier detection on a real data set
- Prediction Latency
- Species distribution modeling
- Time-related feature engineering
- Topic extraction with Non-negative Matrix Factorization and Latent Dirichlet Allocation
- Visualizing the stock market structure
- Wikipedia principal eigenvector
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Feature Selection
- Comparison of F-test and mutual information
- Model-based and sequential feature selection
- Pipeline ANOVA SVM
- Recursive feature elimination
- Recursive feature elimination with cross-validation
- Univariate Feature Selection
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Gaussian Mixture Models
- Concentration Prior Type Analysis of Variation Bayesian Gaussian Mixture
- Density Estimation for a Gaussian mixture
- GMM Initialization Methods
- GMM covariances
- Gaussian Mixture Model Ellipsoids
- Gaussian Mixture Model Selection
- Gaussian Mixture Model Sine Curve
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Gaussian Process for Machine Learning
- Ability of Gaussian process regression (GPR) to estimate data noise-level
- Comparison of kernel ridge and Gaussian process regression
- Forecasting of CO2 level on Mona Loa dataset using Gaussian process regression (GPR)
- Gaussian Processes regression: basic introductory example
- Gaussian process classification (GPC) on iris dataset
- Gaussian processes on discrete data structures
- Illustration of Gaussian process classification (GPC) on the XOR dataset
- Illustration of prior and posterior Gaussian process for different kernels
- Iso-probability lines for Gaussian Processes classification (GPC)
- Probabilistic predictions with Gaussian process classification (GPC)
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Generalized Linear Models
- Comparing Linear Bayesian Regressors
- Comparing various online solvers
- Curve Fitting with Bayesian Ridge Regression
- Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression
- Early stopping of Stochastic Gradient Descent
- Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples
- HuberRegressor vs Ridge on dataset with strong outliers
- Joint feature selection with multi-task Lasso
- L1 Penalty and Sparsity in Logistic Regression
- L1-based models for Sparse Signals
- Lasso model selection via information criteria
- Lasso model selection: AIC-BIC / cross-validation
- Lasso on dense and sparse data
- Lasso, Lasso-LARS, and Elastic Net paths
- Logistic function
- MNIST classification using multinomial logistic + L1
- Multiclass sparse logistic regression on 20newgroups
- Non-negative least squares
- One-Class SVM versus One-Class SVM using Stochastic Gradient Descent
- Ordinary Least Squares Example
- Ordinary Least Squares and Ridge Regression Variance
- Orthogonal Matching Pursuit
- Plot Ridge coefficients as a function of the regularization
- Plot multi-class SGD on the iris dataset
- Poisson regression and non-normal loss
- Polynomial and Spline interpolation
- Quantile regression
- Regularization path of L1- Logistic Regression
- Ridge coefficients as a function of the L2 Regularization
- Robust linear estimator fitting
- Robust linear model estimation using RANSAC
- SGD: Maximum margin separating hyperplane
- SGD: Penalties
- SGD: Weighted samples
- SGD: convex loss functions
- Theil-Sen Regression
- Tweedie regression on insurance claims
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Inspection
- Common pitfalls in the interpretation of coefficients of linear models
- Failure of Machine Learning to infer causal effects
- Partial Dependence and Individual Conditional Expectation Plots
- Permutation Importance vs Random Forest Feature Importance (MDI)
- Permutation Importance with Multicollinear or Correlated Features
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Kernel Approximation
- Scalable learning with polynomial kernel approximation
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Manifold learning
- Comparison of Manifold Learning methods
- Manifold Learning methods on a severed sphere
- Manifold learning on handwritten digits: Locally Linear Embedding, Isomap…
- Multi-dimensional scaling
- Swiss Roll And Swiss-Hole Reduction
- t-SNE: The effect of various perplexity values on the shape
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Miscellaneous
- Advanced Plotting With Partial Dependence
- Comparing anomaly detection algorithms for outlier detection on toy datasets
- Comparison of kernel ridge regression and SVR
- Displaying Pipelines
- Displaying estimators and complex pipelines
- Evaluation of outlier detection estimators
- Explicit feature map approximation for RBF kernels
- Face completion with a multi-output estimators
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Introducing the
set_outputAPI - Isotonic Regression
- Metadata Routing
- Multilabel classification
- ROC Curve with Visualization API
- The Johnson-Lindenstrauss bound for embedding with random projections
- Visualizations with Display Objects
-
Missing Value Imputation
- Imputing missing values before building an estimator
- Imputing missing values with variants of IterativeImputer
-
Model Selection
- Balance model complexity and cross-validated score
- Class Likelihood Ratios to measure classification performance
- Comparing randomized search and grid search for hyperparameter estimation
- Comparison between grid search and successive halving
- Confusion matrix
- Custom refit strategy of a grid search with cross-validation
- Demonstration of multi-metric evaluation on cross_val_score and GridSearchCV
- Detection error tradeoff (DET) curve
- Effect of model regularization on training and test error
- Multiclass Receiver Operating Characteristic (ROC)
- Nested versus non-nested cross-validation
- Plotting Cross-Validated Predictions
- Plotting Learning Curves and Checking Models’ Scalability
- Post-hoc tuning the cut-off point of decision function
- Post-tuning the decision threshold for cost-sensitive learning
- Precision-Recall
- Receiver Operating Characteristic (ROC) with cross validation
- Sample pipeline for text feature extraction and evaluation
- Statistical comparison of models using grid search
- Successive Halving Iterations
- Test with permutations the significance of a classification score
- Underfitting vs. Overfitting
- Visualizing cross-validation behavior in scikit-learn
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Multiclass methods
- Overview of multiclass training meta-estimators
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Multioutput methods
- Multilabel classification using a classifier chain
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Nearest Neighbors
- Approximate nearest neighbors in TSNE
- Caching nearest neighbors
- Comparing Nearest Neighbors with and without Neighborhood Components Analysis
- Dimensionality Reduction with Neighborhood Components Analysis
- Kernel Density Estimate of Species Distributions
- Kernel Density Estimation
- Nearest Centroid Classification
- Nearest Neighbors Classification
- Nearest Neighbors regression
- Neighborhood Components Analysis Illustration
- Novelty detection with Local Outlier Factor (LOF)
- Outlier detection with Local Outlier Factor (LOF)
- Simple 1D Kernel Density Estimation
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Neural Networks
- Compare Stochastic learning strategies for MLPClassifier
- Restricted Boltzmann Machine features for digit classification
- Varying regularization in Multi-layer Perceptron
- Visualization of MLP weights on MNIST
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Pipelines and composite estimators
- Column Transformer with Heterogeneous Data Sources
- Column Transformer with Mixed Types
- Concatenating multiple feature extraction methods
- Effect of transforming the targets in regression model
- Pipelining: chaining a PCA and a logistic regression
- Selecting dimensionality reduction with Pipeline and GridSearchCV
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Preprocessing
- Compare the effect of different scalers on data with outliers
- Comparing Target Encoder with Other Encoders
- Demonstrating the different strategies of KBinsDiscretizer
- Feature discretization
- Importance of Feature Scaling
- Map data to a normal distribution
- Target Encoder’s Internal Cross fitting
- Using KBinsDiscretizer to discretize continuous features
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Semi Supervised Classification
- Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset
- Effect of varying threshold for self-training
- Label Propagation digits active learning
- Label Propagation digits: Demonstrating performance
- Label Propagation learning a complex structure
- Semi-supervised Classification on a Text Dataset
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Support Vector Machines
- One-class SVM with non-linear kernel (RBF)
- Plot classification boundaries with different SVM Kernels
- Plot different SVM classifiers in the iris dataset
- Plot the support vectors in LinearSVC
- RBF SVM parameters
- SVM Margins Example
- SVM Tie Breaking Example
- SVM with custom kernel
- SVM-Anova: SVM with univariate feature selection
- SVM: Maximum margin separating hyperplane
- SVM: Separating hyperplane for unbalanced classes
- SVM: Weighted samples
- Scaling the regularization parameter for SVCs
- Support Vector Regression (SVR) using linear and non-linear kernels
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Tutorial exercises
- Cross-validation on diabetes Dataset Exercise
- Digits Classification Exercise
- SVM Exercise
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Working with text documents
- Classification of text documents using sparse features
- Clustering text documents using k-means
- FeatureHasher and DictVectorizer Comparison
Go to the end to download the full example code. or to run this example in your browser via JupyterLite or Binder
Outlier detection with Local Outlier Factor (LOF) #
The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which computes the local density deviation of a given data point with respect to its neighbors. It considers as outliers the samples that have a substantially lower density than their neighbors. This example shows how to use LOF for outlier detection which is the default use case of this estimator in scikit-learn. Note that when LOF is used for outlier detection it has no
predict,decision_functionandscore_samplesmethods. See the User Guide for details on the difference between outlier detection and novelty detection and how to use LOF for novelty detection.The number of neighbors considered (parameter
n_neighbors) is typically set 1) greater than the minimum number of samples a cluster has to contain, so that other samples can be local outliers relative to this cluster, and 2) smaller than the maximum number of close by samples that can potentially be local outliers. In practice, such information is generally not available, and takingn_neighbors=20appears to work well in general.# Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-ClauseGenerate data with outliers#
import numpy as np np.random.seed(42) X_inliers = 0.3 * np.random.randn(100, 2) X_inliers = np.r_[X_inliers + 2, X_inliers - 2] X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2)) X = np.r_[X_inliers, X_outliers] n_outliers = len(X_outliers) ground_truth = np.ones(len(X), dtype=int) ground_truth[-n_outliers:] = -1Fit the model for outlier detection (default)#
Use
fit_predictto compute the predicted labels of the training samples (when LOF is used for outlier detection, the estimator has nopredict,decision_functionandscore_samplesmethods).from sklearn.neighbors import LocalOutlierFactor clf = LocalOutlierFactor(n_neighbors=20, contamination=0.1) y_pred = clf.fit_predict(X) n_errors = (y_pred != ground_truth).sum() X_scores = clf.negative_outlier_factor_Plot results#
import matplotlib.pyplot as plt from matplotlib.legend_handler import HandlerPathCollection def update_legend_marker_size(handle, orig): "Customize size of the legend marker" handle.update_from(orig) handle.set_sizes([20]) plt.scatter(X[:, 0], X[:, 1], color="k", s=3.0, label="Data points") # plot circles with radius proportional to the outlier scores radius = (X_scores.max() - X_scores) / (X_scores.max() - X_scores.min()) scatter = plt.scatter( X[:, 0], X[:, 1], s=1000 * radius, edgecolors="r", facecolors="none", label="Outlier scores", plt.axis("tight") plt.xlim((-5, 5)) plt.ylim((-5, 5)) plt.xlabel("prediction errors: %d" % (n_errors)) plt.legend( handler_map={scatter: HandlerPathCollection(update_func=update_legend_marker_size)} plt.title("Local Outlier Factor (LOF)") plt.show()
Total running time of the script: (0 minutes 0.088 seconds)
Novelty detection with Local Outlier Factor (LOF)
Novelty detection with Local Outlier Factor (LOF)Comparing anomaly detection algorithms for outlier detection on toy datasets
Comparing anomaly detection algorithms for outlier detection on toy datasetsEvaluation of outlier detection estimators
Evaluation of outlier detection estimatorsOutlier detection on a real data set
Outlier detection on a real data set
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