sklearn 中的 KNearest Neighbors - ValueError：查询数据维度必须匹配训练数据维度

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？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  

我的错误提要如下：

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

预先感谢您的任何帮助，同时我会继续寻找答案

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。您正在将X数组重塑为(16, N)，其中N是 中的观测值数量X。

因此，当您尝试对 进行预测时example，您最终会使用分类器对X重塑后的N列进行预测，而不是像您训练的那样具有 16 列。

看来您想对单个示例进行预测，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example使用 shape进行创建(16,)。您应该将其重塑为(1, 16)，使用(1, -1)为 尺寸。这将产生一个形状为(1, 16)适合您的分类器的数组。

为了清楚起见，请尝试将代码更改为：

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