1

我正在尝试使用scikit示例代码应用 Local Outlier Factor 算法将异常值检测到 29 个特征中，但是代码不起作用，我收到了以下消息：

    File "sklearn/neighbors/binary_tree.pxi", line 1294, in sklearn.neighbors.kd_tree.BinaryTree.query (sklearn/neighbors/kd_tree.c:11337)
    ValueError: query data dimension must match training data dimension

ValueError：查询数据维度必须匹配训练数据维度

请注意，我在一类支持向量机上应用了相同的方法，并且得到了结果。

    #--- import required libraries ---#
    import csv
    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import svm
    from sklearn.neighbors import LocalOutlierFactor
    #--- read csv file ---#
    with open('test.csv', 'r') as f:
        reader = csv.reader(f)
        csv_values = list(reader)
    #--- convert data type from string to float ---#
    def read_lines():
        with open('test.csv', 'rU') as data:
            reader = csv.reader(data)
            for row in reader:
                yield [ float(i) for i in row ]
    #--- values for meshgrid  ---#
    xx, yy= np.meshgrid(np.linspace(-5, 5, 100), np.linspace(-5,5,100))
    #--- Classify observations into normal and outliers ---#
    X = []; X_train = []; X_test = []; X_outliers = []
    for i in range(len(csv_values)):
        if csv_values[i][-1] == '0':
            X.append(csv_values[i][:-1]) 
        else:
            X_outliers.append(csv_values[i][:-1])
    #--- convert lists to arrays ---#
    X=np.array(X)
    X_outliers1= np.array(X_outliers)
    #--- figure for all 29 plots ---#
    fig=plt.figure(1)
    for i in range(27):
        #--- select 2 columns each time ---#   
        X=X[:,i:i+2]
        X_outliers= X_outliers1[:,i:i+2]
        #--- classification ---#
        clf = LocalOutlierFactor(n_neighbors=20)
        y_pred = clf.fit_predict(X)
        y_pred_outliers = y_pred[998:]
        Z = clf._decision_function(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)
        #--- plot settings ---#
        fig.add_subplot(9,3,i+1)
        fig.set_figheight(20)
        fig.set_figwidth(16)
        plt.subplots_adjust(wspace=0, hspace=0)
        plt.title("Local Outlier Factor (LOF)")
        plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)
        a = plt.scatter(X[:998, 0], X[:998, 1], c='white')
        b = plt.scatter(X[998:, 0], X[998:, 1], c='red')
        plt.axis('tight')
        plt.xlim((-5, 5))
        plt.ylim((-5, 5))
        plt.legend([a, b],
                   ["normal observations",
                    "abnormal observations"],
                   loc="upper left")
            plt.savefig('test.png')

csv 文件包含 1000 行（数据点）和 29 列（特征），最后一列用于对正常值与异常值进行分类。在这个文件中，998 个正常数据点和 2 个异常值。

4

0 回答 0