def weighted_centering(X:np.ndarray, metric:str='cosine', gamma:float=1.,
test_set_mask:np.ndarray=None):
"""
Perform weighted centering: shift origin to the weighted data mean
Move the origin more actively towards hub objects in the dataset,
rather than towards the data centroid [1]_.
Parameters
----------
X : ndarray
An ``m x n`` vector data matrix with ``n`` objects in an
``m`` dimensional feature space
metric : {'cosine', 'euclidean'}, optional (default: 'cosine')
Distance measure used to place more weight on objects that are more
likely to become hubs. (Defined for 'cosine' in [1]_, 'euclidean' does
not make much sense and might be removed in the future).
gamma : float, optional (default: 1.0)
Controls how much we emphasize the weighting effect
- ``gamma=0`` : equivalent to normal centering
- ``gamma>0`` : move origin closer to objects with larger similarity
to other objects
test_set_mask : ndarray, optional (default: None)
Hold back data as a test set and perform centering on the remaining
data (training set).
Returns
-------
X_wcent : ndarray
Weighted centered vectors.
References
----------
.. [1] Suzuki, I., Hara, K., Shimbo, M., Saerens, M., & Fukumizu, K. (2013).
Centering similarity measures to reduce hubs. In Proceedings of the
2013 Conference on Empirical Methods in Natural Language Processing
(pp 613–623).
Retrieved from https://www.aclweb.org/anthology/D/D13/D13-1058.pdf
"""
n = X.shape[0]
# Indices of training examples
if test_set_mask is not None:
train_set_mask = np.setdiff1d(np.arange(n), test_set_mask)
else:
train_set_mask = slice(0, n)
n_train = X[train_set_mask].shape[0]
d = np.zeros(n)
if metric == 'cosine':
vectors_sum = X[train_set_mask].sum(0)
for i in np.arange(n):
d[i] = n_train * cos(np.array([X[i], vectors_sum/n_train]))[0, 1]
# Using euclidean distances does not really make sense
elif metric == 'euclidean':
for i in range(n):
displ_v = X[train_set_mask] - d[i]
d[i] = np.sum(np.sqrt(displ_v * displ_v))
else:
raise ValueError("Weighted centering only supports cosine distances.")
d_sum = np.sum(d ** gamma)
w = (d ** gamma) / d_sum
vectors_mean_weighted = np.sum(w.reshape(n, 1) * X, 0)
X_wcent = X - vectors_mean_weighted
return X_wcent
def localized_centering(X:np.ndarray, metric:str='cosine', kappa:int=40,
gamma:float=1., test_set_mask:np.ndarray=None):
"""
Perform localized centering.
Reduce hubness in datasets according to the method proposed in [2]_.
Parameters
----------
X : ndarray
An ``m x n`` vector data matrix with ``n`` objects in an
``m`` dimensional feature space
metric : {'cosine', 'euclidean'}
Distance measure used to place more weight on objects that are more
likely to become hubs. (Defined for 'cosine' in [1]_, 'euclidean' does
not make much sense and might be removed in the future).
kappa : int, optional (default: 40)
Local segment size, determines the size of the local neighborhood for
calculating the local affinity. When ``kappa=n`` localized centering
reduces to standard centering.
"select κ depending on the dataset, so that the correlation between
Nk(x) and the local affinity <x, cκ(x)> is maximized" [2]_
gamma : float, optional (default: 1.0)
Control the degree of penalty, so that used the similarity score
is smaller depending on how likely a point is to become a hub.
"Parameter γ can be tuned so as to maximally reduce the skewness
of the Nk distribution" [2]_.
test_set_mask : ndarray, optional (default: None)
Hold back data as a test set and perform centering on the remaining
data (training set).
Returns
-------
S_lcent : ndarray
Secondary similarity (localized centering) matrix.
References
----------
.. [1] Suzuki, I., Hara, K., Shimbo, M., Saerens, M., & Fukumizu, K. (2013).
Centering similarity measures to reduce hubs. In Proceedings of the
2013 Conference on Empirical Methods in Natural Language Processing
(pp 613–623).
Retrieved from https://www.aclweb.org/anthology/D/D13/D13-1058.pdf
.. [2] Hara, K., Suzuki, I., Shimbo, M., Kobayashi, K., Fukumizu, K., &
Radovanović, M. (2015). Localized centering: Reducing hubness in
large-sample data hubness in high-dimensional data. In AAAI ’15:
Proceedings of the 29th AAAI Conference on Artificial Intelligence
(pp. 2645–2651).
"""
if test_set_mask is None:
test_set_mask = np.zeros(X.shape[0], np.bool)
if metric == 'cosine':
# Rescale vectors to unit length
v = X / np.sqrt((X ** 2).sum(-1))[..., np.newaxis]
# for unit vectors it holds inner() == cosine()
sim = 1 - cos(v)
# Localized centering meaningful for Euclidean?
elif metric == 'euclidean':
v = X # no scaling here...
sim = 1 / (1 + l2(v))
else:
raise ValueError("Localized centering only supports cosine distances.")
n = sim.shape[0]
local_affinity = np.zeros(n)
for i in range(n):
x = v[i]
sim_i = sim[i, :].copy()
# set similarity of test examples to zero to exclude them from fit
sim_i[test_set_mask] = 0
# also exclude self
sim_i[i] = 0
nn = np.argsort(sim_i)[::-1][1 : kappa+1]
c_kappa_x = np.mean(v[nn], 0)
if metric == 'cosine':
# c_kappa_x has no unit length in general
local_affinity[i] = np.inner(x, c_kappa_x)
#local_affinity[i] = cosine(x, c_kappa_x)
elif metric == 'euclidean':
local_affinity[i] = 1 / (1 + np.linalg.norm(x-c_kappa_x))
else:
raise ValueError("Localized centering only "
"supports cosine distances.")
sim_lcent = sim - (local_affinity ** gamma)
return sim_lcent
