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)
