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One-class SVM with non-linear kernel （RBF）

One-class SVM with non-linear kernel (RBF)

项目ID：464534.png 本项目分为2部分，第一部分为数据源，第二部分为OneClassSVM节点
数据源有四个输出，分别为X, X_outliers,xx, yy

  • 数据源节点代码如下：
# coding=utf-8

import numpy as np

import suanpan
from suanpan.app import app
from suanpan.app.arguments import Npy


# 定义输出
@app.output(Npy(key="outputData1"))
@app.output(Npy(key="outputData2"))
@app.output(Npy(key="outputData3"))
@app.output(Npy(key="outputData4"))
def Demo(context):
    xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
    # Generate train data
    X = 0.3 * np.random.randn(100, 2)
    # Generate some abnormal novel observations
    X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))

    return X, X_outliers, xx, yy


if __name__ == "__main__":
    suanpan.run(app)

  • OneClassSVM节点代码如下：
# coding=utf-8

import os

import matplotlib.font_manager
import matplotlib.pyplot as plt
import numpy as np

import suanpan
from sklearn import svm
from suanpan.app import app
from suanpan.app.arguments import Folder, Npy

TMP_FOLDER = "/tmp/result"

# 定义输入
@app.input(Npy(key="inputData1", required=True))
@app.input(Npy(key="inputData2", required=True))
@app.input(Npy(key="inputData3", required=True))
@app.input(Npy(key="inputData4", required=True))
# 定义输出
@app.output(Folder(key="outputData1", required=True))
def Demo(context):
    args = context.args

    if not os.path.exists(TMP_FOLDER):
        os.makedirs(TMP_FOLDER)

    X_outliers = args.inputData2
    xx = args.inputData3
    yy = args.inputData4

    X = 0.3 * np.random.randn(100, 2)
    X_train = np.r_[X + 2, X - 2]
    # Generate some regular novel observations
    X = 0.3 * np.random.randn(20, 2)
    X_test = np.r_[X + 2, X - 2]

    clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
    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)
    n_error_train = y_pred_train[y_pred_train == -1].size
    n_error_test = y_pred_test[y_pred_test == -1].size
    n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size

    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    plt.title("Novelty Detection")
    plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)
    a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors="darkred")
    plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors="palevioletred")

    s = 40
    b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
    b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
    c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
    plt.axis("tight")
    plt.xlim((-5, 5))
    plt.ylim((-5, 5))
    plt.legend(
        [a.collections[0], b1, b2, c],
        [
            "learned frontier",
            "training observations",
            "new regular observations",
            "new abnormal observations",
        ],
        loc="upper left",
        prop=matplotlib.font_manager.FontProperties(size=11),
    )
    plt.xlabel(
        "error train: %d/200 ; errors novel regular: %d/40 ; "
        "errors novel abnormal: %d/40" % (n_error_train, n_error_test, n_error_outliers)
    )
    plt.savefig(os.path.join(TMP_FOLDER, "test.png"), format="png")

    return TMP_FOLDER


if __name__ == "__main__":
    suanpan.run(Demo)

1.异常点检测节点，接受上一个节点的四个输入，输出图片结果
第一步，创建OneClassSVM对象，使用前一个节点的数据进行训练
第二步，模型预测异常点
第三步，画出图形

35.png