Question

我正在尝试为我的数据绘制一个KNN图，但我不断得到这个我无法弄清楚的错误。

clf = neighbors.KNeighborsClassifier(k, weights=weights)
AttributeError: 'list' object has no attribute 'KNeighborsClassifier'

下面我附上了我的代码（不包括导入）：

data_df = pd.DataFrame.from_csv("fvectors.csv")
X = np.array(data_df[features].values)

data_df2 = pd.DataFrame.from_csv("fvectors.csv")
y = np.array(data_df2[features1].replace("Circle",0).replace("Equilateral Triangle",1)
             .replace("Right Angle Triangle",2).replace("Acute Triangle",3)
             .replace("Obtuse Triangle",4).replace("Square",5)
             .replace("Parallelogram",6).replace("Rectangle",7)
             .replace("Pentagon",8).replace("Seal",9).values.tolist())

#step size in the mesh
h = .02  

#Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

for weights in ['uniform', 'distance']:
    #we create an instance of Neighbours Classifier and fit the data.
    clf = neighbors.KNeighborsClassifier(k, weights=weights)
    clf.fit(X, y)

    #Plot the decision boundary. For that, we will assign a color to each
    #point in the mesh [x_min, x_max]x[y_min, y_max].
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    #Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.figure()
    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)

    #Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.title("3-Class classification (k = %i, weights = '%s')" % (k))

plt.show()

And my fvectors.csv file looks like this:

此外：

features = ["Number of Sides", "Standard Deviation of Number of Sides/Perimeter",
      "Standard Deviation of the Angles", "Largest Angle"]


features1 = ["Label"]

任何人都可以看到我做错了什么，或者是否有其他突出的错误？

Answer 1

问题似乎与导入有关。尝试：

来自sklearn.neighbors导入KNeighborsClassifier

然后直接使用KNeighborsClassifier。

为多个要素绘制KNN分类器图

1 个答案: