.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples\Trees\IsolationForest.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_Trees_IsolationForest.py: Outlier detection using isolation forest ========================================= This script samples data points from a multivariate normal distribution, adds an outlier and tries to detect it using `DLL.MachineLearning.UnsupervisedLearning.OutlierDetection.IsolationForest`. .. GENERATED FROM PYTHON SOURCE LINES 8-32 .. image-sg:: /auto_examples/Trees/images/sphx_glr_IsolationForest_001.png :alt: IsolationForest :srcset: /auto_examples/Trees/images/sphx_glr_IsolationForest_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none [0.35, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.36, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.37, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 0.38, 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0.65, 0.67, 0.67, 0.67, 0.68, 0.68, 0.68, 0.68, 0.69, 0.7, 0.72, 0.75, 0.87] | .. code-block:: Python import torch import numpy as np import matplotlib.pyplot as plt from DLL.MachineLearning.UnsupervisedLearning.OutlierDetection import IsolationForest mean = [0, 0] cov = [[1, 0], [0, 1]] n = 2000 X1, X2 = np.random.multivariate_normal(mean, cov, n).T X1[0] = 5 X2[0] = 5 X = torch.from_numpy(np.array([X1, X2]).T) model = IsolationForest(n_trees=25, threshold=6) predictions = model.fit_predict(X) print(sorted([round(score, 2) for score in model.fit_predict(X, return_scores=True).tolist()])) plt.scatter(X[:, 0][predictions], X[:, 1][predictions], label="Outliers") plt.scatter(X[:, 0][~predictions], X[:, 1][~predictions], label="Inliers") plt.legend() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 28.054 seconds) .. _sphx_glr_download_auto_examples_Trees_IsolationForest.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: IsolationForest.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: IsolationForest.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: IsolationForest.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_