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IsolationForest example#
An example using IsolationForest
for anomaly
detection.
The 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.
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
Data generation#
We generate two clusters (each one containing n_samples
) by randomly
sampling the standard normal distribution as returned by
numpy.random.randn
. One of them is spherical and the other one is
slightly deformed.
For consistency with the IsolationForest
notation,
the inliers (i.e. the gaussian clusters) are assigned a ground truth label 1
whereas the outliers (created with numpy.random.uniform
) are assigned
the label -1
.
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)
We can visualize the resulting clusters:
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()
Training of the model#
from sklearn.ensemble import IsolationForest
clf = IsolationForest(max_samples=100, random_state=0)
clf.fit(X_train)
Plot discrete decision boundary#
We use the class 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.
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()
Plot path length decision boundary#
By setting the response_method="decision_function"
, the background of the
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.
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()
Total running time of the script: (0 minutes 0.482 seconds)
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