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"""
The implementation of the SVM model for anomaly detection.
Authors:
LogPAI Team
Reference:
[1] Yinglung Liang, Yanyong Zhang, Hui Xiong, Ramendra Sahoo. Failure Prediction
in IBM BlueGene/L Event Logs. IEEE International Conference on Data Mining
(ICDM), 2007.
"""
import numpy as np
from sklearn import svm
from ..utils import metrics
class SVM(object):
def __init__(self, penalty='l1', tol=0.1, C=1, dual=False, class_weight=None,
max_iter=100):
""" The Invariants Mining model for anomaly detection
Arguments
---------
See SVM API: https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html
Attributes
----------
classifier: object, the classifier for anomaly detection
"""
self.classifier = svm.LinearSVC(penalty=penalty, tol=tol, C=C, dual=dual,
class_weight=class_weight, max_iter=max_iter)
def fit(self, X, y):
"""
Arguments
---------
X: ndarray, the event count matrix of shape num_instances-by-num_events
"""
print('====== Model summary ======')
self.classifier.fit(X, y)
def predict(self, X):
""" Predict anomalies with mined invariants
Arguments
---------
X: the input event count matrix
Returns
-------
y_pred: ndarray, the predicted label vector of shape (num_instances,)
"""
y_pred = self.classifier.predict(X)
return y_pred
def evaluate(self, X, y_true):
print('====== Evaluation summary ======')
y_pred = self.predict(X)
precision, recall, f1 = metrics(y_pred, y_true)
print('Precision: {:.3f}, recall: {:.3f}, F1-measure: {:.3f}\n'.format(precision, recall, f1))
return precision, recall, f1
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