def pairwise_distance_differences(high_distances=None,
low_distances=None,
high_data=None,
low_data=None,
metric='euclidean'):
'''
Computes $d_{ij}-||x_{i}-x_{j}||$. Computes pairwise distances in the
high space and low space if they weren't passed in.
Returns: (high_distances, low_distances, distance_difference)
-------
high_distances: np array of pairwise distances between high_data points
low_distances: np array of pairwise distances between low_data points
distance_difference: np array high_distances - low_distances
>>> a = np.array([[7, 4, 0], [4, 5, 2], [9, 4, 3]])
>>> b = np.array([[0, 6], [7, 1], [4, 9]])
>>> pairwise_distance_differences(high_data=a, low_data=b)[0]
array([[0. , 3.74165739, 3.60555128],
[3.74165739, 0. , 5.19615242],
[3.60555128, 5.19615242, 0. ]])
>>> pairwise_distance_differences(high_data=a, low_data=b)[1]
array([[0. , 8.60232527, 5. ],
[8.60232527, 0. , 8.54400375],
[5. , 8.54400375, 0. ]])
>>> pairwise_distance_differences(high_data=a, low_data=b)[2]
array([[ 0. , -4.86066788, -1.39444872],
[-4.86066788, 0. , -3.34785132],
[-1.39444872, -3.34785132, 0. ]])
>>> a = np.array([[0, 4, 7], [4, 0, 2], [7, 2, 0]])
>>> b = np.array([[0, 4, 8], [4, 0, 1], [8, 1, 0]])
>>> pairwise_distance_differences(high_distances=a, low_distances=b)[0]
array([[0, 4, 7],
[4, 0, 2],
[7, 2, 0]])
>>> pairwise_distance_differences(high_distances=a, low_distances=b)[1]
array([[0, 4, 8],
[4, 0, 1],
[8, 1, 0]])
>>> pairwise_distance_differences(high_distances=a, low_distances=b)[2]
array([[ 0, 0, -1],
[ 0, 0, 1],
[-1, 1, 0]])
'''
if (high_distances is None) and (high_data is None):
raise ValueError("One of high_distances or high_data is required")
if (low_distances is None) and (low_data is None):
raise ValueError("One of low_distances or low_data is required")
if low_distances is None:
low_distances = sk_pairwise_distances(low_data, metric=metric)
if high_distances is None:
high_distances = sk_pairwise_distances(high_data, metric=metric)
difference_distances = high_distances - low_distances
return high_distances, low_distances, difference_distances
def testPairwiseDistancesExecution(self):
raw_x = np.random.rand(20, 5)
raw_y = np.random.rand(21, 5)
x = mt.tensor(raw_x, chunk_size=11)
y = mt.tensor(raw_y, chunk_size=12)
d = pairwise_distances(x, y)
result = self.executor.execute_tensor(d, concat=True)[0]
expected = sk_pairwise_distances(raw_x, raw_y)
np.testing.assert_almost_equal(result, expected)
# test precomputed
d2 = d.copy()
d2[0, 0] = -1
d2 = pairwise_distances(d2, y, metric='precomputed')
with self.assertRaises(ValueError):
_ = self.executor.execute_tensor(d2, concat=True)[0]
# test cdist
weight = np.random.rand(5)
d = pairwise_distances(x, y, metric='wminkowski', p=3, w=weight)
result = self.executor.execute_tensor(d, concat=True)[0]
expected = sk_pairwise_distances(raw_x,
raw_y,
metric='wminkowski',
p=3,
w=weight)
np.testing.assert_almost_equal(result, expected)
# test pdist
d = pairwise_distances(x, metric='hamming')
result = self.executor.execute_tensor(d, concat=True)[0]
expected = sk_pairwise_distances(raw_x, metric='hamming')
np.testing.assert_almost_equal(result, expected)
# test function metric
m = lambda u, v: np.sqrt(((u - v)**2).sum())
d = pairwise_distances(x, y, metric=m)
result = self.executor.execute_tensor(d, concat=True)[0]
expected = sk_pairwise_distances(raw_x, raw_y, metric=m)
np.testing.assert_almost_equal(result, expected)
assert_warns(DataConversionWarning,
pairwise_distances,
x,
y,
metric='jaccard')
with self.assertRaises(ValueError):
_ = pairwise_distances(x, y, metric='unknown')