def test_sdml(self):
def_kwargs = {
'balance_param': 0.5,
'preprocessor': None,
'prior': 'identity',
'random_state': None,
'sparsity_param': 0.01,
'verbose': False
}
nndef_kwargs = {'verbose': True}
merged_kwargs = sk_repr_kwargs(def_kwargs, nndef_kwargs)
self.assertEqual(remove_spaces(str(metric_learn.SDML(verbose=True))),
remove_spaces(f"SDML({merged_kwargs})"))
def_kwargs = {
'balance_param': 0.5,
'num_constraints': None,
'preprocessor': None,
'prior': 'identity',
'random_state': None,
'sparsity_param': 0.01,
'verbose': False
}
nndef_kwargs = {'sparsity_param': 0.5}
merged_kwargs = sk_repr_kwargs(def_kwargs, nndef_kwargs)
self.assertEqual(
remove_spaces(str(
metric_learn.SDML_Supervised(sparsity_param=0.5))),
remove_spaces(f"SDML_Supervised({merged_kwargs})"))
def test_sdml(self):
self.assertEqual(remove_spaces(str(metric_learn.SDML(verbose=True))),
remove_spaces("SDML(verbose=True)"))
self.assertEqual(
remove_spaces(str(
metric_learn.SDML_Supervised(sparsity_param=0.5))),
remove_spaces("SDML_Supervised(sparsity_param=0.5)"))
def test_sdml(self):
self.assertEqual(str(metric_learn.SDML()),
"SDML(balance_param=0.5, preprocessor=None, "
"sparsity_param=0.01, use_cov=True,\n verbose=False)")
self.assertEqual(str(metric_learn.SDML_Supervised()), """
SDML_Supervised(balance_param=0.5, num_constraints=None,
num_labeled='deprecated', preprocessor=None, sparsity_param=0.01,
use_cov=True, verbose=False)
""".strip('\n'))
def test_sdml(self):
self.assertEqual(
remove_spaces(str(metric_learn.SDML())),
remove_spaces("""
SDML(balance_param=0.5, preprocessor=None, prior=None, random_state=None,
sparsity_param=0.01, use_cov='deprecated', verbose=False)
"""))
self.assertEqual(
remove_spaces(str(metric_learn.SDML_Supervised())),
remove_spaces("""
SDML_Supervised(balance_param=0.5, num_constraints=None,
num_labeled='deprecated', preprocessor=None, prior=None,
random_state=None, sparsity_param=0.01, use_cov='deprecated',
verbose=False)
"""))
def test_string_repr(self):
# we don't test LMNN here because it could be python_LMNN
self.assertEqual(str(metric_learn.Covariance()), "Covariance()")
self.assertEqual(str(metric_learn.NCA()),
"NCA(learning_rate=0.01, max_iter=100, num_dims=None)")
self.assertEqual(str(metric_learn.LFDA()),
"LFDA(dim=None, k=7, metric='weighted')")
self.assertEqual(str(metric_learn.ITML()), """
ITML(convergence_threshold=0.001, gamma=1.0, max_iters=1000, verbose=False)
""".strip('\n'))
self.assertEqual(str(metric_learn.ITML_Supervised()), """
ITML_Supervised(A0=None, bounds=None, convergence_threshold=0.001, gamma=1.0,
max_iters=1000, num_constraints=None, num_labeled=inf,
verbose=False)
""".strip('\n'))
self.assertEqual(str(metric_learn.LSML()),
"LSML(max_iter=1000, tol=0.001, verbose=False)")
self.assertEqual(str(metric_learn.LSML_Supervised()), """
LSML_Supervised(max_iter=1000, num_constraints=None, num_labeled=inf,
prior=None, tol=0.001, verbose=False, weights=None)
""".strip('\n'))
self.assertEqual(str(metric_learn.SDML()), """
SDML(balance_param=0.5, sparsity_param=0.01, use_cov=True, verbose=False)
""".strip('\n'))
self.assertEqual(str(metric_learn.SDML_Supervised()), """
SDML_Supervised(balance_param=0.5, num_constraints=None, num_labeled=inf,
sparsity_param=0.01, use_cov=True, verbose=False)
""".strip('\n'))
self.assertEqual(str(metric_learn.RCA()), "RCA(dim=None)")
self.assertEqual(str(metric_learn.RCA_Supervised()),
"RCA_Supervised(chunk_size=2, dim=None, num_chunks=100)")
self.assertEqual(str(metric_learn.MLKR()), """
MLKR(A0=None, alpha=0.0001, epsilon=0.01, max_iter=1000, num_dims=None)
""".strip('\n'))
######################################################################
# Sparse Determinant Metric Learning
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# Implements an efficient sparse metric learning algorithm in high
# dimensional space via an :math:`l_1`-penalized log-determinant
# regularization. Compared to the most existing distance metric learning
# algorithms, the algorithm exploits the sparsity nature underlying the
# intrinsic high dimensional feature space.
#
# - See more in the :ref:`User Guide <sdml>`
# - See more in the documentation of the class :py:class:`SDML
# <metric_learn.SDML>`
sdml = metric_learn.SDML_Supervised(sparsity_param=0.1,
balance_param=0.0015,
prior='covariance')
X_sdml = sdml.fit_transform(X, y)
plot_tsne(X_sdml, y)
######################################################################
# Least Squares Metric Learning
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# LSML is a simple, yet effective, algorithm that learns a Mahalanobis
# metric from a given set of relative comparisons. This is done by
# formulating and minimizing a convex loss function that corresponds to
# the sum of squared hinge loss of violated constraints.
#
# - See more in the :ref:`User Guide <lsml>`
'ITML_Supervised':
metric_learn.ITML_Supervised(num_constraints=200),
'LFDA':
metric_learn.LFDA(k=2, dim=2),
'LMNN':
metric_learn.LMNN(k=5, learn_rate=1e-6, verbose=False),
'LSML_Supervised':
metric_learn.LSML_Supervised(num_constraints=200),
'MLKR':
metric_learn.MLKR(),
'NCA':
metric_learn.NCA(max_iter=700, n_components=2),
'RCA_Supervised':
metric_learn.RCA_Supervised(dim=2, num_chunks=30, chunk_size=2),
'SDML_Supervised':
metric_learn.SDML_Supervised(num_constraints=1500)
}
class IrisDataset(object):
params = [sorted(CLASSES)]
param_names = ['alg']
def setup(self, alg):
iris_data = load_iris()
self.iris_points = iris_data['data']
self.iris_labels = iris_data['target']
def time_fit(self, alg):
np.random.seed(5555)
CLASSES[alg].fit(self.iris_points, self.iris_labels)
