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ERROR: type should be string, got "https://scikit-learn.org/stable/modules/outlier_detection.html#isolation-forest\n\nhttps://scikit-learn.org/stable/auto_examples/plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py\n\nThis example shows characteristics of different anomaly detection algorithms on 2D datasets. Datasets contain one or two modes (regions of high density) to illustrate the ability of algorithms to cope with multimodal data.\n\nFor each dataset, 15% of samples are generated as random uniform noise. This proportion is the value given to the nu parameter of the OneClassSVM and the contamination parameter of the other outlier detection algorithms. Decision boundaries between inliers and outliers are displayed in black except for Local Outlier Factor (LOF) as it has no predict method to be applied on new data when it is used for outlier detection.\n\nThe sklearn.svm.OneClassSVM is known to be sensitive to outliers and thus does not perform very well for outlier detection. This estimator is best suited for novelty detection when the training set is not contaminated by outliers. That said, outlier detection in high-dimension, or without any assumptions on the distribution of the inlying data is very challenging, and a One-class SVM might give useful results in these situations depending on the value of its hyperparameters.\n\nsklearn.covariance.EllipticEnvelope assumes the data is Gaussian and learns an ellipse. It thus degrades when the data is not unimodal. Notice however that this estimator is robust to outliers.\n\nsklearn.ensemble.IsolationForest and sklearn.neighbors.LocalOutlierFactor seem to perform reasonably well for multi-modal data sets. The advantage of sklearn.neighbors.LocalOutlierFactor over the other estimators is shown for the third data set, where the two modes have different densities. This advantage is explained by the local aspect of LOF, meaning that it only compares the score of abnormality of one sample with the scores of its neighbors.\n\nFinally, for the last data set, it is hard to say that one sample is more abnormal than another sample as they are uniformly distributed in a hypercube. Except for the sklearn.svm.OneClassSVM which overfits a little, all estimators present decent solutions for this situation. In such a case, it would be wise to look more closely at the scores of abnormality of the samples as a good estimator should assign similar scores to all the samples.\n\nWhile these examples give some intuition about the algorithms, this intuition might not apply to very high dimensional data.\n\nFinally, note that parameters of the models have been here handpicked but that in practice they need to be adjusted. In the absence of labelled data, the problem is completely unsupervised so model selection can be a challenge.\n\n```\n# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n# Albert Thomas <albert.thomas@telecom-paristech.fr>\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm\nfrom sklearn.datasets import make_moons, make_blobs\nfrom sklearn.covariance import EllipticEnvelope\nfrom sklearn.ensemble import IsolationForest\nfrom sklearn.neighbors import LocalOutlierFactor\n\nprint(__doc__)\nmatplotlib.rcParams['contour.negative_linestyle'] = 'solid'\n```\n\n# 样本集参数设定\n\n```\n# Example settings\nn_samples = 300\noutliers_fraction = 0.15\nn_outliers = int(outliers_fraction * n_samples)\nn_inliers = n_samples - n_outliers\n```\n\n# define outlier/anomaly detection methods to be compared\n\n```\nanomaly_algorithms = [\n (\"Robust covariance\", EllipticEnvelope(contamination=outliers_fraction)),\n (\"One-Class SVM\", svm.OneClassSVM(nu=outliers_fraction, kernel=\"rbf\",\n gamma=0.1)),\n (\"Isolation Forest\", IsolationForest(behaviour='new',\n contamination=outliers_fraction,\n random_state=42)),\n (\"Local Outlier Factor\", LocalOutlierFactor(\n n_neighbors=35, contamination=outliers_fraction))]\n```\n\n# 数据集生成\n\n```\nblobs_params = dict(random_state=0, n_samples=n_inliers, n_features=2)\nblobs_params\ndatasets = [\n make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5,\n **blobs_params)[0],\n make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5],\n **blobs_params)[0],\n make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, .3],\n **blobs_params)[0],\n 4. * (make_moons(n_samples=n_samples, noise=.05, random_state=0)[0] -\n np.array([0.5, 0.25])),\n 14. * (np.random.RandomState(42).rand(n_samples, 2) - 0.5)]\n# Compare given classifiers under given settings\nxx, yy = np.meshgrid(np.linspace(-7, 7, 150),\n np.linspace(-7, 7, 150))\nplt.figure(figsize=(len(anomaly_algorithms) * 2 + 3, 12.5))\nplt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,\n hspace=.01)\n\nplot_num = 1\nrng = np.random.RandomState(42)\n\nfor i_dataset, X in enumerate(datasets):\n # Add outliers\n X = np.concatenate([X, rng.uniform(low=-6, high=6,\n size=(n_outliers, 2))], axis=0)\n\n for name, algorithm in anomaly_algorithms:\n t0 = time.time()\n algorithm.fit(X)\n t1 = time.time()\n plt.subplot(len(datasets), len(anomaly_algorithms), plot_num)\n if i_dataset == 0:\n plt.title(name, size=18)\n\n # fit the data and tag outliers\n if name == \"Local Outlier Factor\":\n y_pred = algorithm.fit_predict(X)\n else:\n y_pred = algorithm.fit(X).predict(X)\n\n # plot the levels lines and the points\n if name != \"Local Outlier Factor\": # LOF does not implement predict\n Z = algorithm.predict(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='black')\n\n colors = np.array(['#377eb8', '#ff7f00'])\n plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[(y_pred + 1) // 2])\n\n plt.xlim(-7, 7)\n plt.ylim(-7, 7)\n plt.xticks(())\n plt.yticks(())\n plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),\n transform=plt.gca().transAxes, size=15,\n horizontalalignment='right')\n plot_num += 1\n\nplt.show()\n```\n\n"