解决Python的your data either using array.reshape(-1, 1) if your data has a single featur

今天写关于决策树的一些算法的时候，卧槽，mmp,竟然出现了红色的警示错误，

oneRowX: [0. 0. 1. 0. 1. 1. 0. 0. 1. 0.]
newRowX: [1. 0. 0. 0. 1. 1. 0. 0. 1. 0.]
Traceback (most recent call last):File "D:/pythoncode/jqxi/tree/tree.py", line 64, in <module>predictedY = clf.predict(newRowX)File "F:\python\python3\lib\site-packages\sklearn\tree\tree.py", line 412, in predictX = self._validate_X_predict(X, check_input)File "F:\python\python3\lib\site-packages\sklearn\tree\tree.py", line 373, in _validate_X_predictX = check_array(X, dtype=DTYPE, accept_sparse="csr")File "F:\python\python3\lib\site-packages\sklearn\utils\validation.py", line 441, in check_array"if it contains a single sample.".format(array))
ValueError: Expected 2D array, got 1D array instead:
array=[1. 0. 0. 0. 1. 1. 0. 0. 1. 0.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

正确解决办法

# encoding=utf-8
__author__ = 'SK'from sklearn.feature_extraction import DictVectorizer
import csv
from sklearn import tree
from sklearn import preprocessing
from sklearn.externals.six import StringIO# Read in the csv file and put features into list of dict and list of class label
allElectronicsData = open(r'D://pythoncode/jqxi/tree/AllElectronics.csv', 'rt')
reader = csv.reader(allElectronicsData)
headers = next(reader)print(headers)featureList = []
labelList = []for row in reader:labelList.append(row[len(row)-1])rowDict = {}for i in range(1, len(row)-1):rowDict[headers[i]] = row[i]featureList.append(rowDict)print(featureList)# Vetorize features
vec = DictVectorizer()
dummyX = vec.fit_transform(featureList) .toarray()print("dummyX: " + str(dummyX))
print(vec.get_feature_names())print("labelList: " + str(labelList))# vectorize class labels
lb = preprocessing.LabelBinarizer()
dummyY = lb.fit_transform(labelList)
print("dummyY: " + str(dummyY))# Using decision tree for classification
# clf = tree.DecisionTreeClassifier()
clf = tree.DecisionTreeClassifier(criterion='entropy')
clf = clf.fit(dummyX, dummyY)
print("clf: " + str(clf))# Visualize model
with open("allElectronicInformationGainOri.dot", 'w') as f:f = tree.export_graphviz(clf, feature_names=vec.get_feature_names(), out_file=f)oneRowX = dummyX[0, :]
print("oneRowX: " + str(oneRowX))newRowX = oneRowX
newRowX[0] = 1
newRowX[2] = 0
print("newRowX: " + str(newRowX))predictedY = clf.predict([newRowX])print("predictedY: " + str(predictedY))

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