python - 使用 Pandas 和 Sklearn.Neighbors

Question

我正在尝试使用 Python 3.5/Pandas/Sklearn.neighbors 在数据帧上拟合 KNN 模型。我已导入数据，将其拆分为训练和测试数据以及标签，但是当我尝试使用它进行预测时，出现以下错误。我对 Pandas 很陌生，所以任何帮助将不胜感激，谢谢！

import pandas as pd
from sklearn import cross_validation
import numpy as np
from sklearn.neighbors import KNeighborsRegressor
seeds = pd.read_csv('seeds.tsv',sep='\t',names=['Area','Perimeter','Compactness','Kern_len','Kern_width','Assymetry','Kern_groovlen','Species'])
data = seeds.iloc[:,[0,1,2,3,4,5,6]]
labels = seeds.iloc[:,[7]]
x_train, x_test, y_train, y_test = cross_validation.train_test_split(data,labels, test_size=0.4, random_state=1 )
knn = KNeighborsRegressor(n_neighbors=30)
knn.fit(x_train,y_train)
knn.predict(x_test)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-121-2292e64e5ab8> in <module>()
----> 1 knn.predict(x_test)

C:\Anaconda3\lib\site-packages\sklearn\neighbors\regression.py in predict(self, X)
    151 
    152         if weights is None:
--> 153             y_pred = np.mean(_y[neigh_ind], axis=1)
    154         else:
    155             y_pred = np.empty((X.shape[0], _y.shape[1]), dtype=np.float)

C:\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py in mean(a, axis, dtype, out, keepdims)
   2876 
   2877     return _methods._mean(a, axis=axis, dtype=dtype,
-> 2878                           out=out, keepdims=keepdims)
   2879 
   2880 

C:\Anaconda3\lib\site-packages\numpy\core\_methods.py in _mean(a, axis, dtype, out, keepdims)
     66     if isinstance(ret, mu.ndarray):
     67         ret = um.true_divide(
---> 68                 ret, rcount, out=ret, casting='unsafe', subok=False)
     69     elif hasattr(ret, 'dtype'):
     70         ret = ret.dtype.type(ret / rcount)

TypeError: unsupported operand type(s) for /: 'str' and 'int'

Answer 1

您应该KNeighborsClassifier为此 KNN 使用 。您正在尝试预测Species分类标签。上面代码中的回归器正在尝试训练和预测连续值的数值变量，这就是你的问题被引入的地方。

from sklearn.neighbors import KNeighborsClassifier
seeds = pd.read_csv('seeds.tsv',sep='\t',names=['Area','Perimeter','Compactness','Kern_len','Kern_width','Assymetry','Kern_groovlen','Species'])
data = seeds.iloc[:,[0,1,2,3,4,5,6]]
labels = seeds.iloc[:,[7]]
x_train, x_test, y_train, y_test = cross_validation.train_test_split(data,labels, test_size=0.4, random_state=1 )
knn = KNeighborsClassifier(n_neighbors=30)

http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html

这是回归器与分类器（您要使用的）相比的绘图。