def test_evaluate(self): predictor = LinePredictor(IamLinesDataset) dataset = IamLinesDataset() dataset.load_or_generate_data() t = time() metric = predictor.evaluate(dataset) time_taken = time() - t print(f'acc: {metric}, time_taken: {time_taken}') self.assertGreater(metric, 0.6) self.assertLess(time_taken, 90)
def test_evaluate(self): # predictor = LinePredictor(IamLinesDataset) # dataset = IamLinesDataset() #instantiates the line predictor predictor = LinePredictor(EmnistLinesDataset) #load dataset dataset = EmnistLinesDataset() dataset.load_or_generate_data() t = time() #evaluate on the dataset metric = predictor.evaluate(dataset) time_taken = time() - t print(f'acc: {metric}, time_taken: {time_taken}') #threshold values for expected performance self.assertGreater(metric, 0.8) self.assertLess(time_taken, 60)
def test_evaluate_accuracy(self, set_leaderboard_value=None): predictor = LinePredictor() dataset = EmnistLinesDataset() dataset.load_or_generate_data() metric = predictor.evaluate(dataset) set_leaderboard_value(metric)