sklearn 异常检测demo代码走读
# 0基础学python，读代码学习python组件api
import time
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.datasets import make_moons, make_blobs
from sklearn.covariance import EllipticEnvelope
from sklearn.ensemble import IsolationForest
from sklearn.neighbors import LocalOutlierFactor
print(__doc__)
matplotlib.rcParams['contour.negative_linestyle'] = 'solid'
# Example settings
n_samples = 300
outliers_fraction = 0.15
n_outliers = int(outliers_fraction * n_samples)
n_inliers = n_samples - n_outliers
# define outlier/anomaly detection methods to be compared
# 四种异常检测算法，之后的文章详细介绍
anomaly_algorithms = [
("Robust covariance", EllipticEnvelope(contamination=outliers_fraction)),
("One-Class SVM", svm.OneClassSVM(nu=outliers_fraction, kernel="rbf",
gamma=0.1)),
("Isolation Forest", IsolationForest(behaviour='new',
contamination=outliers_fraction,
random_state=42)),
("Local Outlier Factor", LocalOutlierFactor(
n_neighbors=35, contamination=outliers_fraction))]
# Define datasets
blobs_params = dict(random_state=0, n_samples=n_inliers, n_features=2)
datasets = [
# make_blobes用于生成聚类数据。centers表示聚类中心，cluster_std表示聚类数据方差。返回值(数据, 类别)
# **用于传递dict key-value参数，*用于传递元组不定数量参数。
make_blobs(centers=[[0, 0], [0, 0]], cluster_std=0.5,
**blobs_params)[0],
make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[0.5, 0.5],
**blobs_params)[0],
make_blobs(centers=[[2, 2], [-2, -2]], cluster_std=[1.5, .3],
**blobs_params)[0],
# make_moons用于生成月亮形数据。返回值数据(x, y)
4. * (make_moons(n_samples=n_samples, noise=.05, random_state=0)[0] -
np.array([0.5, 0.25])),
14. * (np.random.RandomState(42).rand(n_samples, 2) - 0.5)]
# Compare given classifiers under given settings
# np.meshgrid生产成网格数据
# 如输入x = [0, 1, 2, 3] y = [0, 1, 2]，则输出
# xx 0 1 2 3 yy 0 0 0 0
# 0 1 2 3 1 1 1 1
# 0 1 2 3 2 2 2 2
xx, yy = np.meshgrid(np.linspace(-7, 7, 150),
np.linspace(-7, 7, 150))
# figure生成画布，subplots_adjust子图的间距调整，左边距，右边距，下边距，上边距，列间距，行间距
plt.figure(figsize=(len(anomaly_algorithms) * 2 + 3, 12.5))
plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,
hspace=.01)
plot_num = 1
rng = np.random.RandomState(42)
for i_dataset, X in enumerate(datasets):
# Add outliers
# np.concatenate数组拼接。axis=0行增加，axis=1列增加（对应行拼接）。
X = np.concatenate([X, rng.uniform(low=-6, high=6,
size=(n_outliers, 2))], axis=0)
for name, algorithm in anomaly_algorithms:
t0 = time.time()
# 专门用于评估执行时间，无用代码
algorithm.fit(X)
t1 = time.time()
# 定位子图位置。参数：列，行，序号
plt.subplot(len(datasets), len(anomaly_algorithms), plot_num)
if i_dataset == 0:
plt.title(name, size=18)
# fit the data and tag outliers
# 训练与预测
if name == "Local Outlier Factor":
y_pred = algorithm.fit_predict(X)
else:
y_pred = algorithm.fit(X).predict(X)
# plot the levels lines and the points
# 用训练的模型预测网格数据点，主要是要得到聚类模型边缘
if name != "Local Outlier Factor": # LOF does not implement predict
# ravel()多维数组平铺为一维数组。np.c_ cloumn列连接，np.r_ row行连接。
Z = algorithm.predict(np.c_[xx.ravel(), yy.ravel()])
# reshape这里把一维数组转化为二维数组
Z = Z.reshape(xx.shape)
# plt.contour画等高线。Z表示对应点类别，可以理解为不同的高度，plt.contour就是要画出不同高度间的分界线。
plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='black')
colors = np.array(['#377eb8', '#ff7f00'])
plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[(y_pred + 1) // 2])
# x轴范围
plt.xlim(-7, 7)
plt.ylim(-7, 7)
# x轴坐标
plt.xticks(())
plt.yticks(())
# 坐标图上显示的文字
plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),
transform=plt.gca().transAxes, size=15,
horizontalalignment='right')
plot_num += 1
plt.show()
执行结果
