def setUp(self):
argvals = {'input_dim_0': np.array([1, 2, 3, 4])}
values = np.array([[1, 2, 3, 4],
[5, 6, 7, 9],
[3, 4, 5, 7],
[3, 4, 6, 1],
[3, 4, 7, 6]])
self.dense_fd = DenseFunctionalData(argvals, values)
def setUp(self):
argvals = {'input_dim_0': np.array([1, 2, 3, 4]),
'input_dim_1': np.array([5, 6, 7])}
values = np.array([[[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]],
[[5, 6, 7], [5, 6, 7], [5, 6, 7], [5, 6, 7]],
[[3, 4, 5], [3, 4, 5], [3, 4, 5], [3, 4, 5]],
[[3, 4, 6], [3, 4, 5], [3, 4, 5], [3, 4, 5]],
[[3, 4, 7], [3, 4, 5], [3, 4, 5], [3, 4, 5]]])
self.dense_fd = DenseFunctionalData(argvals, values)
class TestDenseFunctionalData1D(unittest.TestCase):
"""Test class for the class DenseFunctionalData in one dimension."""
def setUp(self):
argvals = {'input_dim_0': np.array([1, 2, 3, 4])}
values = np.array([[1, 2, 3, 4],
[5, 6, 7, 9],
[3, 4, 5, 7],
[3, 4, 6, 1],
[3, 4, 7, 6]])
self.dense_fd = DenseFunctionalData(argvals, values)
def test_argvals_stand(self):
is_equal = np.allclose(self.dense_fd.argvals_stand['input_dim_0'],
np.array([0., 0.33333333, 0.66666667, 1.]))
self.assertTrue(is_equal)
def test_n_obs(self):
self.assertEqual(self.dense_fd.n_obs, 5)
def test_n_dim(self):
self.assertEqual(self.dense_fd.n_dim, 1)
def test_range_obs(self):
self.assertEqual(self.dense_fd.range_obs, (1, 9))
def test_range_dim(self):
self.assertEqual(self.dense_fd.range_dim, {'input_dim_0': (1, 4)})
def test_shape(self):
self.assertEqual(self.dense_fd.shape, {'input_dim_0': 4})
def test_subset(self):
new_dense_fd = self.dense_fd[2]
self.assertIsInstance(new_dense_fd, DenseFunctionalData)
self.assertEqual(new_dense_fd.n_obs, 1)
new_dense_fd = self.dense_fd[1:4]
self.assertIsInstance(new_dense_fd, DenseFunctionalData)
self.assertEqual(new_dense_fd.n_obs, 3)
def test_as_irregular(self):
irregu_fd = self.dense_fd.as_irregular()
self.assertIsInstance(irregu_fd, IrregularFunctionalData)
self.assertEqual(irregu_fd.n_obs, 5)
def test_is_compatible(self):
self.assertTrue(self.dense_fd.is_compatible(self.dense_fd))
def test_mean(self):
mean_fd = self.dense_fd.mean()
is_equal = np.allclose(mean_fd.values,
np.array([[3., 4., 5.6, 5.4]]))
self.assertTrue(is_equal)
def test_check_compatible_dense(self):
argvals = {
'input_dim_0': np.array([1, 2, 3, 4]),
'input_dim_1': np.array([5, 6, 7])
}
values = np.array([[[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]],
[[5, 6, 7], [5, 6, 7], [5, 6, 7], [5, 6, 7]]])
dense_fd = DenseFunctionalData(argvals, values)
argvals = {'input_dim_0': np.array([1, 2, 3, 4])}
values = np.array([[1, 2, 3, 4], [5, 6, 7, 9], [3, 4, 5, 7]])
dense_fd2 = DenseFunctionalData(argvals, values)
self.assertRaises(ValueError, _check_same_nobs, dense_fd, dense_fd2)
self.assertRaises(ValueError, _check_same_ndim, dense_fd, dense_fd2)
def read_csv_dense(data, argvals):
"""Load a csv file into a DenseFunctionalData object.
Parameters
----------
data: pd.DataFrame
Input dataframe.
argvals: np.ndarray
An array of argvals.
Returns
-------
obj: DenseFunctionalData
The loaded csv file
"""
argvals = {'input_dim_0': argvals}
values = np.array(data)
return DenseFunctionalData(argvals, values)
def read_ts_dense(data):
"""Load a ts file into a DenseFunctionalData object.
Parameters
----------
data: pd.DataFrame
Input dataframe.
Returns
-------
obj: DenseFunctionalData
The loaded ts file.
"""
argvals = data.loc[0, 'dim_0'].index.values
values = np.zeros((len(data), len(argvals)))
for idx, row in data.iterrows():
values[idx, :] = row['dim_0'].values
return DenseFunctionalData({'input_dim_0': argvals}, values)
def test_check_same_type(self):
argvals = {'input_dim_0': np.array([1, 2, 3, 4])}
values = np.array([[1, 2, 3, 4], [5, 6, 7, 9], [3, 4, 5, 7]])
dense_fd = DenseFunctionalData(argvals, values)
argvals = {
'input_dim_0': {
0: np.array([1, 2, 3, 4]),
1: np.array([2, 4]),
2: np.array([0, 2, 3])
}
}
values = {
0: np.array([1, 2, 3, 4]),
1: np.array([5, 6]),
2: np.array([8, 9, 7])
}
irregu_fd = IrregularFunctionalData(argvals, values)
self.assertRaises(TypeError, _check_same_type, dense_fd, irregu_fd)