107 lines
3.4 KiB
Python
107 lines
3.4 KiB
Python
"""
|
|
============================
|
|
LocalOutlierFactor benchmark
|
|
============================
|
|
|
|
A test of LocalOutlierFactor on classical anomaly detection datasets.
|
|
|
|
Note that LocalOutlierFactor is not meant to predict on a test set and its
|
|
performance is assessed in an outlier detection context:
|
|
1. The model is trained on the whole dataset which is assumed to contain
|
|
outliers.
|
|
2. The ROC curve is computed on the same dataset using the knowledge of the
|
|
labels.
|
|
In this context there is no need to shuffle the dataset because the model
|
|
is trained and tested on the whole dataset. The randomness of this benchmark
|
|
is only caused by the random selection of anomalies in the SA dataset.
|
|
|
|
"""
|
|
|
|
from time import time
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from sklearn.neighbors import LocalOutlierFactor
|
|
from sklearn.metrics import roc_curve, auc
|
|
from sklearn.datasets import fetch_kddcup99, fetch_covtype, fetch_openml
|
|
from sklearn.preprocessing import LabelBinarizer
|
|
|
|
print(__doc__)
|
|
|
|
random_state = 2 # to control the random selection of anomalies in SA
|
|
|
|
# datasets available: ['http', 'smtp', 'SA', 'SF', 'shuttle', 'forestcover']
|
|
datasets = ['http', 'smtp', 'SA', 'SF', 'shuttle', 'forestcover']
|
|
|
|
plt.figure()
|
|
for dataset_name in datasets:
|
|
# loading and vectorization
|
|
print('loading data')
|
|
if dataset_name in ['http', 'smtp', 'SA', 'SF']:
|
|
dataset = fetch_kddcup99(subset=dataset_name, percent10=True,
|
|
random_state=random_state)
|
|
X = dataset.data
|
|
y = dataset.target
|
|
|
|
if dataset_name == 'shuttle':
|
|
dataset = fetch_openml('shuttle')
|
|
X = dataset.data
|
|
y = dataset.target
|
|
# we remove data with label 4
|
|
# normal data are then those of class 1
|
|
s = (y != 4)
|
|
X = X[s, :]
|
|
y = y[s]
|
|
y = (y != 1).astype(int)
|
|
|
|
if dataset_name == 'forestcover':
|
|
dataset = fetch_covtype()
|
|
X = dataset.data
|
|
y = dataset.target
|
|
# normal data are those with attribute 2
|
|
# abnormal those with attribute 4
|
|
s = (y == 2) + (y == 4)
|
|
X = X[s, :]
|
|
y = y[s]
|
|
y = (y != 2).astype(int)
|
|
|
|
print('vectorizing data')
|
|
|
|
if dataset_name == 'SF':
|
|
lb = LabelBinarizer()
|
|
x1 = lb.fit_transform(X[:, 1].astype(str))
|
|
X = np.c_[X[:, :1], x1, X[:, 2:]]
|
|
y = (y != b'normal.').astype(int)
|
|
|
|
if dataset_name == 'SA':
|
|
lb = LabelBinarizer()
|
|
x1 = lb.fit_transform(X[:, 1].astype(str))
|
|
x2 = lb.fit_transform(X[:, 2].astype(str))
|
|
x3 = lb.fit_transform(X[:, 3].astype(str))
|
|
X = np.c_[X[:, :1], x1, x2, x3, X[:, 4:]]
|
|
y = (y != b'normal.').astype(int)
|
|
|
|
if dataset_name == 'http' or dataset_name == 'smtp':
|
|
y = (y != b'normal.').astype(int)
|
|
|
|
X = X.astype(float)
|
|
|
|
print('LocalOutlierFactor processing...')
|
|
model = LocalOutlierFactor(n_neighbors=20)
|
|
tstart = time()
|
|
model.fit(X)
|
|
fit_time = time() - tstart
|
|
scoring = -model.negative_outlier_factor_ # the lower, the more normal
|
|
fpr, tpr, thresholds = roc_curve(y, scoring)
|
|
AUC = auc(fpr, tpr)
|
|
plt.plot(fpr, tpr, lw=1,
|
|
label=('ROC for %s (area = %0.3f, train-time: %0.2fs)'
|
|
% (dataset_name, AUC, fit_time)))
|
|
|
|
plt.xlim([-0.05, 1.05])
|
|
plt.ylim([-0.05, 1.05])
|
|
plt.xlabel('False Positive Rate')
|
|
plt.ylabel('True Positive Rate')
|
|
plt.title('Receiver operating characteristic')
|
|
plt.legend(loc="lower right")
|
|
plt.show()
|