So beheben Sie diesen ValueError: Anzahl der Etiketten = 14 entspricht nicht der Anzahl der Proben = 56?

Question

Daten:

rid age  income student credit_rating class_buy_computer 
1 young high no fair no 
2 young high no excellent no 
3 middle high no fair yes 
4 senior medium no fair yes 
5 senior low yes fair yes 
6 senior low yes excellent no 
7 middle low yes excellent yes 
8 young medium no fair yes 
9 young low yes fair yes 
10 senior medium yes fair yes 
11 young medium yes excellent yes 
12 middle medium no excellent yes 
13 middle high yes fair yes 
14 senior medium no excellent no 

Code:

from sklearn.feature_extraction import DictVectorizer 
import csv 
from sklearn import preprocessing 
from sklearn import tree 
from sklearn.externals.six import StringIO 

myData = open(r'C:\Users\USER\Desktop\test.csv') 
reader = csv.reader(myData) 
headers=next(reader) 
print (headers) 
featuelist=[] 
labeList=[] 
for row in reader: 
    labeList.append(row[len(row)-1]) 
    rowDict={} 
    for i in range(1,len(row)-1): 
     rowDict[headers[i]]=row[i] 
     featuelist.append(rowDict) 
print(featuelist) 

vec=DictVectorizer() 
dummyX=vec.fit_transform(featuelist).toarray() 
print('dummyX:'+str(dummyX)) 
print(vec.get_feature_names()) 
print('labeList:'+str(labeList)) 

lb=preprocessing.LabelBinarizer() 
dummyY=lb.fit_transform(labeList) 
print('dummyY:'+str(dummyY)) 

clf=tree.DecisionTreeClassifier(criterion='entropy') 
clf=clf.fit(dummyX,dummyY) 
print('clf:'+str(clf)) 

ich diesen Fehler:

File "<ipython-input-20-eacaea56a8a9>", line 1, in <module> 
    runfile('C:/Users/USER/Desktop/test.py', wdir='C:/Users/USER/Desktop') 

    File "D:\tools\python\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile 

    File "D:\tools\python\lib\site-packages\spyder\utils\site\sitecustomize.py", line 101, in execfile 

    File "C:/Users/USER/Desktop/test.py", line 32, in <module> 
    clf=clf.fit(dummyX,dummyY) 

    File "D:\tools\python\lib\site-packages\sklearn\tree\tree.py", line 790, in fit 
    X_idx_sorted=X_idx_sorted) 

    File "D:\tools\python\lib\site-packages\sklearn\tree\tree.py", line 236, in fit 
    "number of samples=%d" % (len(y), n_samples)) 

ValueError: Number of labels=14 does not match number of samples=56 

Answer 1

Es ist einfach, weil jede Zeile 4 mal in der featuelistfeatuelist.append(rowDict) dictionary.The Linie hinzugefügt wird, sollte nicht in der zweiten Schleife sein.

from sklearn.feature_extraction import DictVectorizer 
import csv 
from sklearn import preprocessing 
from sklearn import tree 
from sklearn.externals.six import StringIO 

myData = open('/home/kashif/test.csv') 
reader = csv.reader(myData) 
headers=next(reader) 
print (headers) 
featuelist=[] 
labeList=[] 
for row in reader: 
    labeList.append(row[len(row)-1]) 
    rowDict={} 
    for i in range(1,len(row)-1): 
     rowDict[headers[i]]=row[i] 

    #Make sure the below line is not inside the second loop 
    featuelist.append(rowDict) #<--This was the typo. 
print(featuelist)  
vec=DictVectorizer(sparse=False) 
dummyX=vec.fit_transform(featuelist) 
print('dummyX:'+str(dummyX)) 
print(vec.get_feature_names()) 
print('labeList:'+str(labeList)) 

lb=preprocessing.LabelBinarizer() 
dummyY=lb.fit_transform(labeList) 
print('dummyY:'+str(dummyY)) 

clf=tree.DecisionTreeClassifier(criterion='entropy') 
clf=clf.fit(dummyX,dummyY) 
print('clf:'+str(clf)) 

Ausgang:

clf:DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None, 
      max_features=None, max_leaf_nodes=None, 
      min_impurity_decrease=0.0, min_impurity_split=None, 
      min_samples_leaf=1, min_samples_split=2, 
      min_weight_fraction_leaf=0.0, presort=False, random_state=None, 
      splitter='best')