def computeExample(filename): XTrain, XTest, yTrain, yTest = ClassificationModel.preprocessData( filename) classifier = LogisticRegression.computeModel(XTrain, yTrain) yPred = ClassificationModel.predictModel(classifier, XTest) return ClassificationModel.evaluateModel(yPred, yTest)
def computeCrossValidation(self): from sklearn.model_selection import cross_validate X, y = ClassificationModel.preprocessDataCrossValidation(self.args, True) classifier = LogisticRegression.computeModel(X, y, self.args.solver) cv_results = cross_validate(classifier, X, y, cv=self.args.k_fold_cross_validation) if(self.args.print_accuracy): print(cv_results) return cv_results
def compute(self): import timeit start = timeit.default_timer() XTrain, XTest, yTrain, yTest = ClassificationModel.preprocessData(self.args, True) classifier = LogisticRegression.computeModel(XTrain, yTrain, self.args.solver) yPred = ClassificationModel.predictModel(classifier, XTest) confusionMatrix = ClassificationModel.evaluateModel(yPred, yTest) if(self.args.print_accuracy): print(confusionMatrix, ClassificationModel.getAccuracy(confusionMatrix)) stop = timeit.default_timer() return confusionMatrix, ClassificationModel.getAccuracy(confusionMatrix), stop - start