def train(self, df_train_subset, dataset_dict, grid_size, **kwargs):
train_pts, train_region = ProcessData.get_time_space_points(
df_train_subset, dataset_dict)
if (isinstance(kwargs['region'], open_cp.data.RectangularRegion)):
train_region = kwargs['region']
trained_model = kde.KDE(region=train_region, grid_size=grid_size)
trained_model.time_kernel = kde.ExponentialTimeKernel(1)
trained_model.space_kernel = kde.GaussianBaseProvider()
trained_model.data = train_pts
return trained_model
def test_time_delta_usage(gb_mock, data1):
region = open_cp.data.RectangularRegion(xmin=0, xmax=100, ymin=20, ymax=50)
predictor = kde.KDE(region, 20)
predictor.data = data1
predictor.time_kernel = mock.MagicMock()
predictor.time_unit = datetime.timedelta(hours=1)
predictor.predict()
assert len(predictor.time_kernel.call_args_list) == 1
assert len(predictor.time_kernel.call_args_list[0][0]) == 1
np.testing.assert_allclose(predictor.time_kernel.call_args_list[0][0][0],
[48, 24, 0])
def test_construct(data1):
region = open_cp.data.RectangularRegion(xmin=0, xmax=90, ymin=20, ymax=50)
predictor = kde.KDE(region, 20)
predictor.data = data1
pred = predictor.predict()
assert pred.xsize == 20
assert pred.ysize == 20
assert pred.region().min == (0, 20)
# Scaled up as grid size is 20
assert pred.region().max == (100, 60)
def test_kernel_constructed_correctly(gb_mock, data1):
region = open_cp.data.RectangularRegion(xmin=0, xmax=100, ymin=20, ymax=50)
predictor = kde.KDE(region, 20)
predictor.data = data1
predictor.predict()
assert len(gb_mock.call_args_list) == 1
call = gb_mock.call_args_list[0]
assert len(call[0]) == 1
np.testing.assert_allclose(call[0][0], [[5, 50, 20], [30, 20, 10]])
np.testing.assert_allclose(gb_mock.return_value.weights, [1, 1, 1])
print(gb_mock.return_value.call_args_list)
def test_time_delta_usage_with_start_time(gb_mock, data1):
region = open_cp.data.RectangularRegion(xmin=0, xmax=100, ymin=20, ymax=50)
predictor = kde.KDE(region, 20)
predictor.data = data1
predictor.time_kernel = mock.MagicMock()
predictor.time_unit = datetime.timedelta(hours=1)
predictor.predict(start_time=datetime.datetime(2017, 4, 22))
assert len(predictor.time_kernel.call_args_list) == 1
assert len(predictor.time_kernel.call_args_list[0][0]) == 1
np.testing.assert_allclose(predictor.time_kernel.call_args_list[0][0][0],
[24, 0])
assert len(gb_mock.call_args_list) == 1
call = gb_mock.call_args_list[0]
np.testing.assert_allclose(call[0][0], [[50, 20], [20, 10]])