.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_isolation_forest.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
:ref:`Go to the end <sphx_glr_download_auto_examples_ensemble_plot_isolation_forest.py>`
to download the full example code or to run this example in your browser via JupyterLite or Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_ensemble_plot_isolation_forest.py:
=======================
IsolationForest example
=======================
An example using :class:`~sklearn.ensemble.IsolationForest` for anomaly
detection.
The :ref:`isolation_forest` is an ensemble of "Isolation Trees" that "isolate"
observations by recursive random partitioning, which can be represented by a
tree structure. The number of splittings required to isolate a sample is lower
for outliers and higher for inliers.
In the present example we demo two ways to visualize the decision boundary of an
Isolation Forest trained on a toy dataset.
.. GENERATED FROM PYTHON SOURCE LINES 20-32
Data generation
---------------
We generate two clusters (each one containing `n_samples`) by randomly
sampling the standard normal distribution as returned by
:func:`numpy.random.randn`. One of them is spherical and the other one is
slightly deformed.
For consistency with the :class:`~sklearn.ensemble.IsolationForest` notation,
the inliers (i.e. the gaussian clusters) are assigned a ground truth label `1`
whereas the outliers (created with :func:`numpy.random.uniform`) are assigned
the label `-1`.
.. GENERATED FROM PYTHON SOURCE LINES 32-51
.. code-block:: Python
import numpy as np
from sklearn.model_selection import train_test_split
n_samples, n_outliers = 120, 40
rng = np.random.RandomState(0)
covariance = np.array([[0.5, -0.1], [0.7, 0.4]])
cluster_1 = 0.4 * rng.randn(n_samples, 2) @ covariance + np.array([2, 2]) # general
cluster_2 = 0.3 * rng.randn(n_samples, 2) + np.array([-2, -2]) # spherical
outliers = rng.uniform(low=-4, high=4, size=(n_outliers, 2))
X = np.concatenate([cluster_1, cluster_2, outliers])
y = np.concatenate(
[np.ones((2 * n_samples), dtype=int), -np.ones((n_outliers), dtype=int)]
)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=42)
.. GENERATED FROM PYTHON SOURCE LINES 52-53
We can visualize the resulting clusters:
.. GENERATED FROM PYTHON SOURCE LINES 53-63
.. code-block:: Python
import matplotlib.pyplot as plt
scatter = plt.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
handles, labels = scatter.legend_elements()
plt.axis("square")
plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
plt.title("Gaussian inliers with \nuniformly distributed outliers")
plt.show()
.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_isolation_forest_001.png
:alt: Gaussian inliers with uniformly distributed outliers
:srcset: /auto_examples/ensemble/images/sphx_glr_plot_isolation_forest_001.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 64-66
Training of the model
---------------------
.. GENERATED FROM PYTHON SOURCE LINES 66-72
.. code-block:: Python
from sklearn.ensemble import IsolationForest
clf = IsolationForest(max_samples=100, random_state=0)
clf.fit(X_train)
.. raw:: html
<div class="output_subarea output_html rendered_html output_result">
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--sklearn-color-unfitted-level-1: #f6e4d2;
--sklearn-color-unfitted-level-2: #ffe0b3;
--sklearn-color-unfitted-level-3: chocolate;
/* Definition of color scheme for fitted estimators */
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color: var(--sklearn-color-text);
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margin: -1px;
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width: 1px;
}
#sk-container-id-21 div.sk-dashed-wrapped {
border: 1px dashed var(--sklearn-color-line);
margin: 0 0.4em 0.5em 0.4em;
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but bootstrap.min.css set `[hidden] { display: none !important; }`
so we also need the `!important` here to be able to override the
default hidden behavior on the sphinx rendered scikit-learn.org.
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
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display: flex;
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justify-content: center;
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display: flex;
flex-direction: column;
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padding-right: 1em;
padding-left: 1em;
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/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*/
/* Pipeline and ColumnTransformer style (default) */
#sk-container-id-21 div.sk-toggleable {
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specific estimator or a Pipeline/ColumnTransformer */
background-color: var(--sklearn-color-background);
}
/* Toggleable label */
#sk-container-id-21 label.sk-toggleable__label {
cursor: pointer;
display: block;
width: 100%;
margin-bottom: 0;
padding: 0.5em;
box-sizing: border-box;
text-align: center;
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float: left;
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/* Colorize estimator box */
#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
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display: block;
}
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float: right;
font-size: 1rem;
line-height: 1em;
font-family: monospace;
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border-radius: 1rem;
height: 1rem;
width: 1rem;
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color: var(--sklearn-color-fitted-level-1);
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/* unfitted */
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color: var(--sklearn-color-background);
text-decoration: none;
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/* fitted */
background-color: var(--sklearn-color-fitted-level-3);
}
</style><div id="sk-container-id-21" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>IsolationForest(max_samples=100, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-97" type="checkbox" checked><label for="sk-estimator-id-97" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted"> IsolationForest<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.IsolationForest.html">?<span>Documentation for IsolationForest</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>IsolationForest(max_samples=100, random_state=0)</pre></div> </div></div></div></div>
</div>
<br />
<br />
.. GENERATED FROM PYTHON SOURCE LINES 73-80
Plot discrete decision boundary
-------------------------------
We use the class :class:`~sklearn.inspection.DecisionBoundaryDisplay` to
visualize a discrete decision boundary. The background color represents
whether a sample in that given area is predicted to be an outlier
or not. The scatter plot displays the true labels.
.. GENERATED FROM PYTHON SOURCE LINES 80-97
.. code-block:: Python
import matplotlib.pyplot as plt
from sklearn.inspection import DecisionBoundaryDisplay
disp = DecisionBoundaryDisplay.from_estimator(
clf,
X,
response_method="predict",
alpha=0.5,
)
disp.ax_.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
disp.ax_.set_title("Binary decision boundary \nof IsolationForest")
plt.axis("square")
plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
plt.show()
.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_isolation_forest_002.png
:alt: Binary decision boundary of IsolationForest
:srcset: /auto_examples/ensemble/images/sphx_glr_plot_isolation_forest_002.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 98-111
Plot path length decision boundary
----------------------------------
By setting the `response_method="decision_function"`, the background of the
:class:`~sklearn.inspection.DecisionBoundaryDisplay` represents the measure of
normality of an observation. Such score is given by the path length averaged
over a forest of random trees, which itself is given by the depth of the leaf
(or equivalently the number of splits) required to isolate a given sample.
When a forest of random trees collectively produce short path lengths for
isolating some particular samples, they are highly likely to be anomalies and
the measure of normality is close to `0`. Similarly, large paths correspond to
values close to `1` and are more likely to be inliers.
.. GENERATED FROM PYTHON SOURCE LINES 111-124
.. code-block:: Python
disp = DecisionBoundaryDisplay.from_estimator(
clf,
X,
response_method="decision_function",
alpha=0.5,
)
disp.ax_.scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
disp.ax_.set_title("Path length decision boundary \nof IsolationForest")
plt.axis("square")
plt.legend(handles=handles, labels=["outliers", "inliers"], title="true class")
plt.colorbar(disp.ax_.collections[1])
plt.show()
.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_isolation_forest_003.png
:alt: Path length decision boundary of IsolationForest
:srcset: /auto_examples/ensemble/images/sphx_glr_plot_isolation_forest_003.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-timing
**Total running time of the script:** (0 minutes 0.436 seconds)
.. _sphx_glr_download_auto_examples_ensemble_plot_isolation_forest.py:
.. only:: html
.. container:: sphx-glr-footer sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_isolation_forest.ipynb
:alt: Launch binder
:width: 150 px
.. container:: lite-badge
.. image:: images/jupyterlite_badge_logo.svg
:target: ../../lite/lab/?path=auto_examples/ensemble/plot_isolation_forest.ipynb
:alt: Launch JupyterLite
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_isolation_forest.ipynb <plot_isolation_forest.ipynb>`
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_isolation_forest.py <plot_isolation_forest.py>`
.. include:: plot_isolation_forest.recommendations
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_