尝试绘制KNN的决策边界时出错

Question

我有一个带有2个变量的csv数据框（一个输入数据框，用X表示）和另一个由我的目标变量组成的numpy数组。

看起来像这样：

>X

    Duration  Grand Mean
0        142  383.076805
1        334  182.067833
2         97  232.677513
3        220  448.385085
4        127  251.524975
5        121  156.828771

>y
[13 11 11 13 12 11 11 13 12 11 12 13 11 12 12 13 13 12 13 12 11 13 13 12
 12 13 13 13 12 13 13 11 13 13 11 13 11 12 13 13 13 11 11 12 13 13 12 12
 12 11]

我没有为这个特定的练习包括数据帧，因为我得到的错误对于我使用的任何csv文件都是很普遍的，所以我认为问题本质上与我使用的方法有关。

所以，我尝试了：

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets

n_neighbors = 15




h = .02  # step size in the mesh

# Create color maps
cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])
cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])

for weights in ['uniform', 'distance']:
    # we create an instance of Neighbours Classifier and fit the data.
    clf = neighbors.KNeighborsClassifier(n_neighbors, 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,
                edgecolor='k', s=20)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.title("3-Class classification (k = %i, weights = '%s')"
              % (n_neighbors, weights))

plt.show()

带有以下错误消息：

TypeError: '(slice(None, None, None), 0)' is an invalid key

我在该主题上看到过类似的帖子，但无法获得该问题的答案对我有用。

Answer 1

您的错误是由于您对熊猫df的切片方式（您这样做就像是一个numpy数组，这显然是错误的）。

纠正它的一种可能的方法，放在一行：

X = X.values

在代码的顶部，您可以使用。

证明

X = pd.DataFrame(np.random.randn(100,2), columns=["Duration","Grand Mean"])
X = X.values # <--- put this line
y = np.random.choice([11,12,13],100,True,[.33,.33,.34])

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors, datasets

n_neighbors = 15

h = .02  # step size in the mesh

# Create color maps
cmap_light = ListedColormap(['orange', 'cyan', 'cornflowerblue'])
cmap_bold = ListedColormap(['darkorange', 'c', 'darkblue'])

for weights in ['uniform', 'distance']:
    # we create an instance of Neighbours Classifier and fit the data.
    clf = neighbors.KNeighborsClassifier(n_neighbors, 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,
                edgecolor='k', s=20)
    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.title("3-Class classification (k = %i, weights = '%s')"
              % (n_neighbors, weights))

plt.show()