ypred1=knc.predict(xtrain) print(ypred) print(list(le.inverse_transform(ypred))) print(knc.predict_proba(xtest)) print(knc.score(xtrain,ytrain)) print(knc.kneighbors()) print(knc.kneighbors_graph()) print(r2_score(ytest,ypred)) from sklearn.pipeline import make_pipeline from sklearn.neighbors import NeighborhoodComponentsAnalysis nca=NeighborhoodComponentsAnalysis(random_state=42) from mlxtend.preprocessing import DenseTransformer nca_pipe=(make_pipeline((NeighborhoodComponentsAnalysis()),(KNeighborsClassifier()))) print(nca_pipe) dense=DenseTransformer() print(dense.fit(xtrain,ytrain)) ##xtrain,ytrain=dense.transform(xtrain,ytrain) ##print(nca.fit(xtrain,ytrain)) ##knc.fit(nca.transform(xtrain,ytrain)) ##print(knc.score(nca.transform(xtest,ytest)) ##print(nca_pipe.fit(xtrain,ytrain)) ##print(nca_pipe.score(xtrain,ytrain)) print(classification_report(ytest,ypred)) print(accuracy_score(ytest,ypred)) print(accuracy_score(ytrain,ypred1)) confusionmatrix=confusion_matrix(ypred,ytest) print(confusionmatrix) rmse=math.sqrt(mean_squared_error(ypred,ytest)) print(rmse) plt.plot(ypred) plt.show()
