def test_task():
dataset, _, test_dataset = Task.Dataset.from_folders(
'https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
model_list = Task.list_models()
predictor = Task(problem_type='regression')
predictor.fit(dataset,
hyperparameters={
'epochs': 3,
'batch_size': 8
},
hyperparameter_tune_kwargs={'num_trials': 2})
test_result = predictor.predict(test_dataset)
single_test = predictor.predict(test_dataset.iloc[0]['image'])
predictor.save('regressor.ag')
predictor2 = Task.load('regressor.ag')
fit_summary = predictor2.fit_summary()
test_score = predictor2.evaluate(test_dataset)
# raw dataframe
df_test_dataset = pd.DataFrame(test_dataset)
test_score = predictor2.evaluate(df_test_dataset)
assert test_score < 2, f'{test_score} too bad'
test_feature = predictor2.predict_feature(test_dataset)
single_test2 = predictor2.predict(test_dataset.iloc[0]['image'])
assert isinstance(single_test2, pd.Series)
assert single_test2.equals(single_test)
# to numpy
test_feature_numpy = predictor2.predict_feature(test_dataset,
as_pandas=False)
single_test2_numpy = predictor2.predict(test_dataset.iloc[0]['image'],
as_pandas=False)
assert np.array_equal(single_test2.to_numpy(), single_test2_numpy)
def test_task():
dataset, _, test_dataset = Task.Dataset.from_folders(
'https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
model_list = Task.list_models()
classifier = Task()
classifier.fit(dataset,
num_trials=2,
hyperparameters={
'epochs': 1,
'early_stop_patience': 3
})
test_result = classifier.predict(test_dataset)
single_test = classifier.predict(test_dataset.iloc[0]['image'])
single_proba = classifier.predict_proba(test_dataset.iloc[0]['image'])
classifier.save('classifier.ag')
classifier2 = Task.load('classifier.ag')
fit_summary = classifier2.fit_summary()
test_acc = classifier2.evaluate(test_dataset)
# raw dataframe
df_test_dataset = pd.DataFrame(test_dataset)
test_acc = classifier2.evaluate(df_test_dataset)
assert test_acc[-1] > 0.2, f'{test_acc} too bad'
test_proba = classifier2.predict_proba(test_dataset)
test_feature = classifier2.predict_feature(test_dataset)
single_test2 = classifier2.predict(test_dataset.iloc[0]['image'])
assert isinstance(single_test2, pd.Series)
assert single_test2.equals(single_test)
# to numpy
test_proba_numpy = classifier2.predict_proba(test_dataset, as_pandas=False)
assert np.array_equal(test_proba.to_numpy(), test_proba_numpy)
test_feature_numpy = classifier2.predict_feature(test_dataset,
as_pandas=False)
single_test2_numpy = classifier2.predict(test_dataset.iloc[0]['image'],
as_pandas=False)
assert np.array_equal(single_test2.to_numpy(), single_test2_numpy)
def test_task():
dataset = Task.Dataset.from_voc('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip')
train_data, _, test_data = dataset.random_split()
detector = Task()
detector.fit(train_data, num_trials=1, hyperparameters={'batch_size': 4, 'epochs': 5, 'early_stop_max_value': 0.2})
test_result = detector.predict(test_data)
print('test result', test_result)
detector.save('detector.ag')
detector2 = Task.load('detector.ag')
fit_summary = detector2.fit_summary()
test_map = detector2.evaluate(test_data)
test_result2 = detector2.predict(test_data)
assert test_result2.equals(test_result)
def test_task():
dataset = Task.Dataset.from_voc('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip')
train_data, _, test_data = dataset.random_split()
detector = Task()
detector.fit(train_data, hyperparameters={'batch_size': 4, 'epochs': 5, 'early_stop_max_value': 0.2}, hyperparameter_tune_kwargs={'num_trials': 1})
test_result = detector.predict(test_data)
detector.save('detector.ag')
detector2 = Task.load('detector.ag')
fit_summary = detector2.fit_summary()
test_map = detector2.evaluate(test_data)
test_result2 = detector2.predict(test_data)
assert test_result2.equals(test_result), f'{test_result2} != \n {test_result}'
# to numpy
test_result2 = detector2.predict(test_data, as_pandas=False)
def test_task():
dataset, _, test_dataset = Task.Dataset.from_folders('https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
model_list = Task.list_models()
classifier = Task()
classifier.fit(dataset, epochs=1, num_trials=2)
test_result = classifier.predict(test_dataset)
single_test = classifier.predict(test_dataset.iloc[0]['image'])
single_proba = classifier.predict_proba(test_dataset.iloc[0]['image'])
print('test result', test_result)
classifier.save('classifier.ag')
classifier2 = Task.load('classifier.ag')
fit_summary = classifier2.fit_summary()
test_acc = classifier2.evaluate(test_dataset)
test_proba = classifier2.predict_proba(test_dataset)
test_feature = classifier2.predict_feature(test_dataset)
single_test2 = classifier2.predict(test_dataset.iloc[0]['image'])
assert single_test2.equals(single_test)
