.. _sphx_glr_auto_examples_ensemble_plot_isolation_forest.py:


==========================================
IsolationForest example
==========================================

An example using IsolationForest for anomaly detection.

The IsolationForest 'isolates' observations by randomly selecting a feature
and then randomly selecting a split value between the maximum and minimum
values of the selected feature.

Since recursive partitioning can be represented by a tree structure, the
number of splittings required to isolate a sample is equivalent to the path
length from the root node to the terminating node.

This path length, averaged over a forest of such random trees, is a measure
of normality and our decision function.

Random partitioning produces noticeable shorter paths for anomalies.
Hence, when a forest of random trees collectively produce shorter path lengths
for particular samples, they are highly likely to be anomalies.

.. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. "Isolation forest."
    Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.




.. image:: /auto_examples/ensemble/images/sphx_glr_plot_isolation_forest_001.png
    :align: center





.. code-block:: python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.ensemble import IsolationForest

    rng = np.random.RandomState(42)

    # Generate train data
    X = 0.3 * rng.randn(100, 2)
    X_train = np.r_[X + 2, X - 2]
    # Generate some regular novel observations
    X = 0.3 * rng.randn(20, 2)
    X_test = np.r_[X + 2, X - 2]
    # Generate some abnormal novel observations
    X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))

    # fit the model
    clf = IsolationForest(max_samples=100, random_state=rng)
    clf.fit(X_train)
    y_pred_train = clf.predict(X_train)
    y_pred_test = clf.predict(X_test)
    y_pred_outliers = clf.predict(X_outliers)

    # plot the line, the samples, and the nearest vectors to the plane
    xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))
    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    plt.title("IsolationForest")
    plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)

    b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white')
    b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green')
    c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red')
    plt.axis('tight')
    plt.xlim((-5, 5))
    plt.ylim((-5, 5))
    plt.legend([b1, b2, c],
               ["training observations",
                "new regular observations", "new abnormal observations"],
               loc="upper left")
    plt.show()

**Total running time of the script:**
(0 minutes 0.568 seconds)



.. container:: sphx-glr-download

    **Download Python source code:** :download:`plot_isolation_forest.py <plot_isolation_forest.py>`


.. container:: sphx-glr-download

    **Download IPython notebook:** :download:`plot_isolation_forest.ipynb <plot_isolation_forest.ipynb>`