为什么最近邻不能处理我的数据？

Question

我正在尝试学习一些关于最近邻居匹配的知识。下面是两个散点图。第一个显示真实数据。我试图使用scikit-learn的NN分类器来识别白色观察。第二个散点图显示了我的成就 - 正如你所看到的那样完全没用。

我不知道为什么会这样？似乎白色观测与其他观测密切相关并且不同。这里发生了什么？

以下是我的工作：

#   import neccessary packages
import pandas as pd
import numpy as np
import sklearn as skl
from sklearn.cross_validation import train_test_split as tts
import matplotlib.pyplot as plt
from sklearn import neighbors
from matplotlib.colors import ListedColormap


#   import data and give a little overview
sample = pd.read_stata('real_data_1.dta')
s = sample
print(s.dtypes)
print(s.shape)

# Nearest Neighboor

print(__doc__)



n_neighbors = 1

X = np.array((s.t_ums_ma, s.t_matauf)).reshape(918, 2)
y = np.array(s.matauf_measure)



plt.scatter(s.t_ums_ma,s.t_matauf, c=s.matauf_measure, label='Nordan Scatter', color='b', s=25, marker="o")
plt.xlabel('crisis')
plt.ylabel('current debt')
plt.title('Interesting Graph\nCheck it out')
plt.legend()
plt.gray()
plt.show()

X_train, X_test, y_train, y_test = tts(X, y, test_size = 1)


h = 0.02

# 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(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, m_max]x[y_min, y_max].
    x_min, x_max = X_train[:, 0].min() - 0.01, X[:, 0].max() + 0.01
    y_min, y_max = X_train[:, 1].min() - 0.01, X[:, 1].max() + 0.01
    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')"
              % (n_neighbors, weights))

plt.show()

非常感谢任何帮助！最佳/ R