IsolationNNE example¶

An example using :class:ikpykit.anomaly.IsolationNNE for anomaly detection.

In [ ]:

import numpy as np
import matplotlib.pyplot as plt
from ikpykit.anomaly import IsolationNNE

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

b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c="white", s=20, edgecolor="k")
b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c="green", s=20, edgecolor="k")
c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c="red", s=20, edgecolor="k")
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()

import numpy as np import matplotlib.pyplot as plt from ikpykit.anomaly import IsolationNNE 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 = IsolationNNE() 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("IsolationNNE") plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r) b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c="white", s=20, edgecolor="k") b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c="green", s=20, edgecolor="k") c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c="red", s=20, edgecolor="k") 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()