训练测试拆分似乎不能在Python中正常工作？

Question

我想在Python中运行kNN（k-最近邻居）算法。

我使用的尝试做到这一点可在UCI机器学习库的数据集：https://archive.ics.uci.edu/ml/datasets/wine

这里是我使用的代码：

#1. LIBRARIES 
import os 
import pandas as pd 
import numpy as np 
print os.getcwd() # Prints the working directory 
os.chdir('C:\\file_path') # Provide the path here 

#2. VARIABLES 
variables = pd.read_csv('wines.csv') 
winery = variables['winery'] 
alcohol = variables['alcohol'] 
malic = variables['malic'] 
ash = variables['ash'] 
ash_alcalinity = variables['ash_alcalinity'] 
magnesium = variables['magnesium'] 
phenols = variables['phenols'] 
flavanoids = variables['flavanoids'] 
nonflavanoids = variables['nonflavanoids'] 
proanthocyanins = variables['proanthocyanins'] 
color_intensity = variables['color_intensity'] 
hue = variables['hue'] 
od280 = variables['od280'] 
proline = variables['proline'] 

#3. MAX-MIN NORMALIZATION 
alcoholscaled=(alcohol-min(alcohol))/(max(alcohol)-min(alcohol)) 
malicscaled=(malic-min(malic))/(max(malic)-min(malic)) 
ashscaled=(ash-min(ash))/(max(ash)-min(ash)) 
ash_alcalinity_scaled=(ash_alcalinity-min(ash_alcalinity))/(max(ash_alcalinity)-min(ash_alcalinity)) 
magnesiumscaled=(magnesium-min(magnesium))/(max(magnesium)-min(magnesium)) 
phenolsscaled=(phenols-min(phenols))/(max(phenols)-min(phenols)) 
flavanoidsscaled=(flavanoids-min(flavanoids))/(max(flavanoids)-min(flavanoids)) 
nonflavanoidsscaled=(nonflavanoids-min(nonflavanoids))/(max(nonflavanoids)-min(nonflavanoids)) 
proanthocyaninsscaled=(proanthocyanins-min(proanthocyanins))/(max(proanthocyanins)-min(proanthocyanins)) 
color_intensity_scaled=(color_intensity-min(color_intensity))/(max(color_intensity)-min(color_intensity)) 
huescaled=(hue-min(hue))/(max(hue)-min(hue)) 
od280scaled=(od280-min(od280))/(max(od280)-min(od280)) 
prolinescaled=(proline-min(proline))/(max(proline)-min(proline)) 
alcoholscaled.mean() 
alcoholscaled.median() 
alcoholscaled.min() 
alcoholscaled.max() 

#4. DATA FRAME 
d = {'alcoholscaled' : pd.Series([alcoholscaled]), 
'malicscaled' : pd.Series([malicscaled]), 
'ashscaled' : pd.Series([ashscaled]), 
'ash_alcalinity_scaled' : pd.Series([ash_alcalinity_scaled]), 
'magnesiumscaled' : pd.Series([magnesiumscaled]), 
'phenolsscaled' : pd.Series([phenolsscaled]), 
'flavanoidsscaled' : pd.Series([flavanoidsscaled]), 
'nonflavanoidsscaled' : pd.Series([nonflavanoidsscaled]), 
'proanthocyaninsscaled' : pd.Series([proanthocyaninsscaled]), 
'color_intensity_scaled' : pd.Series([color_intensity_scaled]), 
'hue_scaled' : pd.Series([huescaled]), 
'od280scaled' : pd.Series([od280scaled]), 
'prolinescaled' : pd.Series([prolinescaled])} 
df = pd.DataFrame(d) 

#5. TRAIN-TEST SPLIT 
from sklearn.model_selection import train_test_split 
X_train, X_test, y_train, y_test = train_test_split(np.matrix(df),np.matrix(winery),test_size=0.3) 
print X_train.shape, y_train.shape 
print X_test.shape, y_test.shape 

#6. K-NEAREST NEIGHBOUR ALGORITHM 
from sklearn.neighbors import KNeighborsClassifier 
knn = KNeighborsClassifier(n_neighbors=10) 
knn.fit(X_train, y_train) 
print("Test set score: {:.2f}".format(knn.score(X_test, y_test))) 

在第5节，当我运行sklearn.model_selection导入列车测试拆分机制，但由于它提供了形状：(0,13) (0,178) (1,13) (1,178)，因此它看起来没有正确运行。

然后，在试图运行knn时，我收到错误消息：Found array with 0 sample(s) (shape=(0,13)) while a minimum of 1 is required.这不是由于使用最大 - 最小归一化进行缩放，因为即使变量未缩放，我仍然收到此错误消息。

Answer 1

我不完全确定你的代码出错的地方，与sklearn文档相比，它有点不同。但是，我可以向您展示一种让火车测试拆分为适合您的葡萄酒数据集的不同方式。

from sklearn.datasets import load_wine 
from sklearn.preprocessing import MinMaxScaler 
from sklearn.model_selection import train_test_split 
from sklearn.neighbors import KNeighborsClassifier 

X, y = load_wine(return_X_y=True) 
X_scaled = MinMaxScaler().fit_transform(X) 
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, 
                test_size=0.3) 
knn = KNeighborsClassifier(n_neighbors=10) 
knn.fit(X_train, y_train)