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