def mp_empiric(self, train_set_mask=None, verbose=False,
empspex=False, n_jobs=-1):
"""
.. note:: Deprecated in hub-toolbox 2.3
Class will be removed in hub-toolbox 3.0.
Please use static functions instead.
"""
if self.isSimilarityMatrix:
metric = 'similarity'
else:
metric = 'distance'
if train_set_mask is not None:
test_set_ind = np.setdiff1d(np.arange(self.D.shape[0]),
train_set_mask)
return mutual_proximity_empiric(self.D, metric, test_set_ind, verbose)
def mutual_proximity_empiric(D:np.ndarray, metric:str='distance',
test_set_ind:np.ndarray=None, verbose:int=0,
n_jobs:int=-1):
"""Transform a distance matrix with Mutual Proximity (empiric distribution).
Applies Mutual Proximity (MP) [1]_ on a distance/similarity matrix using
the empiric data distribution (EXACT, rather SLOW). The resulting
secondary distance/similarity matrix should show lower hubness.
Parameters
----------
D : ndarray or csr_matrix
- ndarray: The ``n x n`` symmetric distance or similarity matrix.
- csr_matrix: The ``n x n`` symmetric similarity matrix.
metric : {'distance', 'similarity'}, optional (default: 'distance')
Define, whether matrix `D` is a distance or similarity matrix.
NOTE: In case of sparse `D`, only 'similarity' is supported.
test_sed_ind : ndarray, optional (default: None)
Define data points to be hold out as part of a test set. Can be:
- None : Rescale all distances
- ndarray : Hold out points indexed in this array as test set.
verbose : int, optional (default: 0)
Increasing level of output (progress report).
n_jobs : int, optional (default: -1)
Number of parallel processes to be used.
NOTE: set ``n_jobs=-1`` to use all CPUs
Returns
-------
D_mp : ndarray
Secondary distance MP empiric matrix.
References
----------
.. [1] Schnitzer, D., Flexer, A., Schedl, M., & Widmer, G. (2012).
Local and global scaling reduce hubs in space. The Journal of Machine
Learning Research, 13(1), 2871–2902.
"""
log = Logging.ConsoleLogging()
IO._check_distance_matrix_shape(D)
IO._check_valid_metric_parameter(metric)
# DO NOT DELETE this comment, will be used upon parallel MP emp dist impl
#===========================================================================
# # Initialization
# n = D.shape[0]
#
# # Check input
# if D.shape[0] != D.shape[1]:
# raise TypeError("Distance/similarity matrix is not quadratic.")
# if metric == 'similarity':
# self_value = 1
# elif metric == 'distance':
# self_value = 0
# if issparse(D):
# raise ValueError("MP sparse only supports similarity matrices.")
# else:
# raise ValueError("Parameter 'metric' must be 'distance' "
# "or 'similarity'.")
# if test_set_ind is None:
# pass # TODO implement
# #train_set_ind = slice(0, n)
# elif not np.all(~test_set_ind):
# raise NotImplementedError("MP empiric does not yet support train/"
# "test splits.")
# #train_set_ind = np.setdiff1d(np.arange(n), test_set_ind)
#===========================================================================
if issparse(D):
return _mutual_proximity_empiric_sparse(D, test_set_ind, verbose, log, n_jobs)
else:
log.warning("MP empiric does not support parallel execution for dense "
"matrices at the moment. Continuing with 1 process.")
from hub_toolbox.MutualProximity import mutual_proximity_empiric
return mutual_proximity_empiric(D, metric, test_set_ind, verbose)
print("DEXTER:")
print("-------")
D, c, v = load_dexter()
acc_d, _, _ = score(D, c, [5], 'distance')
S = csr_matrix(1 - D)
acc_s, _, _ = score(S, c, [5], 'similarity')
Sn_d, _, _ = hubness(D, 5, 'distance')
Sn_s, _, _ = hubness(S, 5, 'similarity')
print("Orig. dist. hubness:", Sn_d)
print("Orig. sim. hubness:", Sn_s)
if do == 'dexter':
print("Orig. dist. k-NN accuracy:", acc_d)
print('Orig. sim. k-NN accuracy:', acc_s)
D_mp_emp_d = mutual_proximity_empiric(D)
D_mp_emp_s = mutual_proximity_empiric(S, 'similarity')
Sn_mp_emp_d, _, _ = hubness(D_mp_emp_d, 5)
Sn_mp_emp_s, _, _ = hubness(D_mp_emp_s, 5, 'similarity')
print("MP emp dist. hubness:", Sn_mp_emp_d)
print("MP emp sim. hubness:", Sn_mp_emp_s)
if do == 'dexter':
acc_mp_emp_d, _, _ = score(D_mp_emp_d, c, [5], 'distance')
acc_mp_emp_s, _, _ = score(D_mp_emp_s, c, [5], 'similarity')
print("MP emp dist. k-NN accuracy:", acc_mp_emp_d)
print("MP emp sim. k-NN accuracy:", acc_mp_emp_s)
D_mp_gaussi_d = mutual_proximity_gaussi(D)
D_mp_gaussi_s = mutual_proximity_gaussi(S, 'similarity')
Sn_mp_gaussi_d, _, _ = hubness(D_mp_gaussi_d, 5)
Sn_mp_gaussi_s, _, _ = hubness(D_mp_gaussi_s, 5, 'similarity')
