訓練測試拆分似乎不能在Python中正常工作？

Question

我想在Python中運行kNN（k-最近鄰居）算法。

我使用的嘗試做到這一點可在UCI機器學習庫的數據集：https://archive.ics.uci.edu/ml/datasets/wine

這裏是我使用的代碼：

#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節，當我運行sklearn.model_selection導入列車測試拆分機制，但由於它提供了形狀：(0,13) (0,178) (1,13) (1,178)，因此它看起來沒有正確運行。

然後，在試圖運行knn時，我收到錯誤消息：Found array with 0 sample(s) (shape=(0,13)) while a minimum of 1 is required.這不是由於使用最大 - 最小歸一化進行縮放，因爲即使變量未縮放，我仍然收到此錯誤消息。

Answer 1

我不完全確定你的代碼出錯的地方，與sklearn文檔相比，它有點不同。但是，我可以向您展示一種讓火車測試拆分爲適合您的葡萄酒數據集的不同方式。

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)