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'))
def sdml(self, train_X, train_y, test_X, bal, spa):
learner = ml.SDML(balance_param=bal, sparsity_param=spa)
train_X = learner.fit_transform(train_X, train_y)
test_X = learner.transform(test_X)
return train_X, test_X