python logisticregression_LogisticRegression：未知标签类型：在python中使用sklearn的“ continuous”...

I have the following code to test some of most popular ML algorithms of sklearn python library:

import numpy as np

from sklearn import metrics, svm

from sklearn.linear_model import LinearRegression

from sklearn.linear_model import LogisticRegression

from sklearn.tree import DecisionTreeClassifier

from sklearn.neighbors import KNeighborsClassifier

from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

from sklearn.naive_bayes import GaussianNB

from sklearn.svm import SVC

trainingData = np.array([ [2.3, 4.3, 2.5], [1.3, 5.2, 5.2], [3.3, 2.9, 0.8], [3.1, 4.3, 4.0] ])

trainingScores = np.array( [3.4, 7.5, 4.5, 1.6] )

predictionData = np.array([ [2.5, 2.4, 2.7], [2.7, 3.2, 1.2] ])

clf = LinearRegression()

clf.fit(trainingData, trainingScores)

print("LinearRegression")

print(clf.predict(predictionData))

clf = svm.SVR()

clf.fit(trainingData, trainingScores)

print("SVR")

print(clf.predict(predictionData))

clf = LogisticRegression()

clf.fit(trainingData, trainingScores)

print("LogisticRegression")

print(clf.predict(predictionData))

clf = DecisionTreeClassifier()

clf.fit(trainingData, trainingScores)

print("DecisionTreeClassifier")

print(clf.predict(predictionData))

clf = KNeighborsClassifier()

clf.fit(trainingData, trainingScores)

print("KNeighborsClassifier")

print(clf.predict(predictionData))

clf = LinearDiscriminantAnalysis()

clf.fit(trainingData, trainingScores)

print("LinearDiscriminantAnalysis")

print(clf.predict(predictionData))

clf = GaussianNB()

clf.fit(trainingData, trainingScores)

print("GaussianNB")

print(clf.predict(predictionData))

clf = SVC()

clf.fit(trainingData, trainingScores)

print("SVC")

print(clf.predict(predictionData))

The first two works ok, but I got the following error in LogisticRegression call:

root@ubupc1:/home/ouhma# python stack.py

LinearRegression

[ 15.72023529 6.46666667]

SVR

[ 3.95570063 4.23426243]

Traceback (most recent call last):

File "stack.py", line 28, in

clf.fit(trainingData, trainingScores)

File "/usr/local/lib/python2.7/dist-packages/sklearn/linear_model/logistic.py", line 1174, in fit

check_classification_targets(y)

File "/usr/local/lib/python2.7/dist-packages/sklearn/utils/multiclass.py", line 172, in check_classification_targets

raise ValueError("Unknown label type: %r" % y_type)

ValueError: Unknown label type: 'continuous'

The input data is the same as in the previous calls, so what is going on here?

And by the way, why there is a huge diference in the first prediction of LinearRegression() and SVR() algorithms (15.72 vs 3.95)?

解决方案

You are passing floats to a classifier which expects categorical values as the target vector. If you convert it to int it will be accepted as input (although it will be questionable if that's the right way to do it).

It would be better to convert your training scores by using scikit's labelEncoder function.

The same is true for your DecisionTree and KNeighbors qualifier.

from sklearn import preprocessing

from sklearn import utils

lab_enc = preprocessing.LabelEncoder()

encoded = lab_enc.fit_transform(trainingScores)

>>> array([1, 3, 2, 0], dtype=int64)

print(utils.multiclass.type_of_target(trainingScores))

>>> continuous

print(utils.multiclass.type_of_target(trainingScores.astype('int')))

>>> multiclass

print(utils.multiclass.type_of_target(encoded))

>>> multiclass