sklearn 中的 KNearest Neighbors - ValueError：查询数据维度必须与训练数据维度匹配

Question

我正在尝试对我在 UCI 机器学习数据库中找到的一些文本识别数据进行 ak 最近邻预测。 ( https://archive.ics.uci.edu/ml/datasets/Letter+Recognition )

我交叉验证了数据并测试了准确性，没有任何问题，但我无法运行 classifier.predict()。任何人都可以阐明为什么我会收到此错误吗？我在 sklearn 网站上阅读了维度诅咒，但实际上我在修复代码时遇到了麻烦。

到目前为止我的代码如下：

 import pandas as pd
import numpy as np
from sklearn import preprocessing, cross_validation, neighbors

df = pd.read_csv('KMeans_letter_recog.csv')

X = np.array(df.drop(['Letter'], 1))
y = np.array(df['Letter'])

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size = 0.2) #20% data used

clf = neighbors.KNeighborsClassifier()
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test) #test
print(accuracy) #this works fine

example = np.array([7,4,3,2,4,5,3,6,7,4,2,3,5,6,8,4])
example = X.reshape(len(example), -1)

prediction = clf.predict(example)
print(prediction) #error

df.head() 产生：

  Letter   x-box   y-box   box_width   box_height   on_pix   x-bar_mean  \
0      T       2       8           3            5        1            8
1      I       5      12           3            7        2           10
2      D       4      11           6            8        6           10
3      N       7      11           6            6        3            5
4      G       2       1           3            1        1            8

    y-bar_mean   x2bar_mean   y2bar_mean   xybar_mean   x2y_mean   xy2_mean  \
0           13            0            6            6         10          8
1            5            5            4           13          3          9
2            6            2            6           10          3          7
3            9            4            6            4          4         10
4            6            6            6            6          5          9

    x-ege   xegvy   y-ege   yegvx
0       0       8       0       8
1       2       8       4      10
2       3       7       3       9
3       6      10       2       8
4       1       7       5      10

我的错误提要是这样的：

 Traceback (most recent call last):
  File "C:\Users\jai_j\Desktop\Python Projects\K Means ML.py", line 31, in <module>
    prediction = clf.predict(example)
  File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\neighbors\classification.py", line 145, in predict
    neigh_dist, neigh_ind = self.kneighbors(X)
  File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\neighbors\base.py", line 381, in kneighbors
    for s in gen_even_slices(X.shape[0], n_jobs)
  File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 758, in __call__
    while self.dispatch_one_batch(iterator):
  File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 608, in dispatch_one_batch
    self._dispatch(tasks)
  File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 571, in _dispatch
    job = self._backend.apply_async(batch, callback=cb)
  File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib_parallel_backends.py", line 109, in apply_async
    result = ImmediateResult(func)
  File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib_parallel_backends.py", line 326, in __init__
    self.results = batch()
  File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in __call__
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "C:\Users\jai_j\Desktop\Python Projects\WinPython-64bit-3.5.2.3Qt5\python-3.5.2.amd64\lib\site-packages\sklearn\externals\joblib\parallel.py", line 131, in <listcomp>
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "sklearn\neighbors\binary_tree.pxi", line 1294, in sklearn.neighbors.kd_tree.BinaryTree.query (sklearn\neighbors\kd_tree.c:11325)
ValueError: query data dimension must match training data dimension

在此先感谢您的帮助，同时我会继续寻找答案

原文由 Jai Mahtani 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

你的问题是你没有重塑 example 并且你正在重塑到不正确的尺寸。 You are reshaping your X array to be (16, N) , where N is the number of observations in X .

因此，当您尝试预测 example 时，您最终使用分类器预测 X 重塑为 N 列而不是列就像你训练过的那样。

看来您想预测单个示例，因此您应该重塑它而不是 X 。据推测，您想要 example = example.reshape(1, -1) 而不是 example = X.reshape(len(example), -1) 。

最初，您创建 example 形状为 (16,) 。您应该将其重塑为 (1, 16) ，使用 (1, -1) 作为尺寸。这将生成一个形状为 (1, 16) 的数组，它适合您的分类器。

要清楚，请尝试将您的代码更改为：

 example = np.array([7,4,3,2,4,5,3,6,7,4,2,3,5,6,8,4])
example = example.reshape(1, -1)

prediction = clf.predict(example)
print(prediction) # shouldn't error anymore

原文由 Nick Becker 发布，翻译遵循 CC BY-SA 3.0 许可协议

Answer 2

另外，而不是：

 example = example.reshape(1,-1),

另一种选择是：

 example = example[np.newaxis, :]

原文由 azfeng8 发布，翻译遵循 CC BY-SA 4.0 许可协议