KNearest Neighbors in sklearn - ValueError: query data dimension must match training data dimension
KNearest Neighbors in sklearn - ValueError: query data dimension must match training data dimension
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 k 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，但我不能 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
您的问题是您没有重塑 example 并且您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 X.
中的观察数
因此，当您尝试对 example 进行预测时，您最终会使用分类器来预测 X 重塑为 N 列，而不是 16 列在你训练的那一个。
你似乎想预测你的单个例子，所以你应该重塑它而不是 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,)。您应该使用 (1, -1) 作为尺寸将其重塑为 (1, 16)。这将生成一个形状为 (1, 16) 的数组，它适合您的分类器。
为了清楚起见，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
我隔离了单个命令行，这是 xxxx.predict(example) 问题而不是 X.reshape(x,x)----- 输入错误或 .reshape(x,x)
另外，代替：
example = example.reshape(1,-1),
另一种选择是：
example = example[np.newaxis, :]
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 k 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，但我不能 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
您的问题是您没有重塑 example 并且您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 X.
因此，当您尝试对 example 进行预测时，您最终会使用分类器来预测 X 重塑为 N 列，而不是 16 列在你训练的那一个。
你似乎想预测你的单个例子，所以你应该重塑它而不是 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,)。您应该使用 (1, -1) 作为尺寸将其重塑为 (1, 16)。这将生成一个形状为 (1, 16) 的数组，它适合您的分类器。
为了清楚起见，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
我隔离了单个命令行，这是 xxxx.predict(example) 问题而不是 X.reshape(x,x)----- 输入错误或 .reshape(x,x)
另外，代替：
example = example.reshape(1,-1),
另一种选择是：
example = example[np.newaxis, :]