def _test(): dset = create_dataset('tests/lenses.mff') dset.train.normalize_attributes() for e in dset.train.examples: print(e) classifier = NeuralNetwork(trainset=dset.train, max_error=.2, debug=True) evaluator = Evaluator(classifier) evaluator.holdout(.5) dset = create_dataset('tests/lenses.mff') dset.train.nominal_to_linear() print(dset) classifier = NeuralNetwork(trainset=dset.train, debug=True, max_error=.1, j=10) evaluator = Evaluator(classifier) evaluator.holdout(.2) dset = create_dataset('tests/test_data/iris-binary.mff') classifier = NeuralNetwork(trainset=dset.train, debug=True, max_error=.1) classifier.train(dset.train) dset = create_dataset('tests/test_data/votes.mff') classifier = NeuralNetwork(trainset=dset.train, debug=True, max_error=.1) classifier.train(dset.train) dset = create_dataset('tests/test_data/mushroom.mff') classifier = NeuralNetwork(trainset=dset.train, debug=True) classifier.train(dset.train) dset = create_dataset('tests/test_data/soybean.mff') classifier = NeuralNetwork(trainset=dset.train, debug=True) classifier.train()
def evaluate(classifier, testset=None, holdout=None, folds=10): """Create evaulator object. Args: classifier (Classifier): desired classifier to run testset (DataSet): testset to run classification accuracies/tests outfile (str): filepath of target output file holdout (float): desired split for the hold-out method folds (int): number of folds for cross validation """ evaluator = Evaluator(classifier) if testset: pass elif holdout: evaluator.holdout(holdout) else: # runing folds evaluator.cross_validate(folds) return evaluator