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- #4a
- from sklearn import datasets
- from sklearn.linear_model import LogisticRegression
- from sklearn.model_selection import train_test_split
- iris=datasets.load_iris()
- X=iris.data
- y=iris.target
- #4b
- X_train,X_test,y_train,y_test=train_test_split(X,y, random_state=1000)
- #4c
- ovr0Classifier=np.ones_like(y)
- for i in range(len(y)):
- if(y[i]==1 or y[i]==2): ovr0Classifier[i]=-1
- #4d
- ovr1Classifier=np.ones_like(y)
- for i in range(len(y)):
- if(y[i]==0 or y[i]==2): ovr1Classifier[i]=-1
- ovr2Classifier=np.ones_like(y)
- for i in range(len(y)):
- if(y[i]==0 or y[i]==1): ovr2Classifier[i]=-1
- #4e
- import pandas as pd
- y_train0Fixed=np.array(y_train, copy=True)
- y_train0Fixed=pd.DataFrame(ovr0Classifier)
- y_train1Fixed=np.array(y_train,copy=True)
- y_train1Fixed=pd.DataFrame(ovr1Classifier)
- y_train2Fixed=np.array(y_train,copy=True)
- y_train2Fixed=pd.DataFrame(ovr2Classifier)
- lr0Classifier=LogisticRegression(solver='lbfgs')
- lr0Classifier.fit(X_train,y_train0Fixed.values.ravel())
- lr1Classifier=LogisticRegression(solver='lbfgs')
- lr1Classifier.fit(X_train,ovr1Classifier)
- lr2Classifier=LogisticRegression(solver='lbfgs')
- lr2Classifier.fit(X_train,ovr2Classifier)
- def ovrRes(ovr0Classifier, ovr1Classifier, ovr2Classifier,testArr):
- ovr0ClassifierProba=lr0Classifier.predict_proba(testArr)[:,1]
- ovr1ClassifierProba=lr1Classifier.predict_proba(testArr)[:,1]
- ovr2ClassifierProba=lr2Classifier.predict_proba(testArr)[:,1]
- finRes=[]
- for i in range(len(testArr)):
- maxOfClassifiers=max(ovr0ClassifierProba,ovr1ClassifierProba,ovr2ClassifierProba)
- if(maxOfClassifiers==ovr0ClassifierProba):
- rightClassifier=0;
- elif(maxOfClassifiers==ovr1ClassifierProba):
- rightClassifier=1;
- elif(maxOfClassifiers==ovr2ClassifierProba):
- rightClassifier=2;
- finRes.insert(i,rightClassifier)
- return finRes
- #4f
- from sklearn.metrics import confusion_matrix
- y_pred=ovrRes(ovr0Classifier, ovr1Classifier, ovr2Classifier,X_test)
- confusionMatrix=confusion_matrix(y_test,y_pred)
- confusionMatrix
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