def test_itml(self):
def_kwargs = {
'convergence_threshold': 0.001,
'gamma': 1.0,
'max_iter': 1000,
'preprocessor': None,
'prior': 'identity',
'random_state': None,
'verbose': False
}
nndef_kwargs = {'gamma': 0.5}
merged_kwargs = sk_repr_kwargs(def_kwargs, nndef_kwargs)
self.assertEqual(remove_spaces(str(metric_learn.ITML(gamma=0.5))),
remove_spaces(f"ITML({merged_kwargs})"))
def_kwargs = {
'convergence_threshold': 0.001,
'gamma': 1.0,
'max_iter': 1000,
'num_constraints': None,
'preprocessor': None,
'prior': 'identity',
'random_state': None,
'verbose': False
}
nndef_kwargs = {'num_constraints': 7}
merged_kwargs = sk_repr_kwargs(def_kwargs, nndef_kwargs)
self.assertEqual(
remove_spaces(str(
metric_learn.ITML_Supervised(num_constraints=7))),
remove_spaces(f"ITML_Supervised({merged_kwargs})"))
def test_itml(self):
self.assertEqual(remove_spaces(str(metric_learn.ITML(gamma=0.5))),
remove_spaces("ITML(gamma=0.5)"))
self.assertEqual(
remove_spaces(str(
metric_learn.ITML_Supervised(num_constraints=7))),
remove_spaces("ITML_Supervised(num_constraints=7)"))
def test_itml(self):
self.assertEqual(str(metric_learn.ITML()), """
ITML(A0=None, convergence_threshold=0.001, gamma=1.0, max_iter=1000,
preprocessor=None, 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_iter=1000, num_constraints=None, num_labeled='deprecated',
preprocessor=None, verbose=False)
""".strip('\n'))
def test_itml(self):
self.assertEqual(
remove_spaces(str(metric_learn.ITML())),
remove_spaces("""
ITML(A0='deprecated', convergence_threshold=0.001, gamma=1.0,
max_iter=1000, preprocessor=None, prior='identity', random_state=None,
verbose=False)
"""))
self.assertEqual(
remove_spaces(str(metric_learn.ITML_Supervised())),
remove_spaces("""
ITML_Supervised(A0='deprecated', bounds='deprecated',
convergence_threshold=0.001, gamma=1.0,
max_iter=1000, num_constraints=None, num_labeled='deprecated',
preprocessor=None, prior='identity', random_state=None, 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'))
# print(pairs) print(pairs_labels) ###################################################################### # Using our constraints, let's now train ITML again. Note that we are no # longer calling the supervised class :py:class:`ITML_Supervised # <metric_learn.ITML_Supervised>` but the more generic # (weakly-supervised) :py:class:`ITML <metric_learn.ITML>`, which # takes the dataset `X` through the `preprocessor` argument (see # :ref:`this section <preprocessor_section>` of the documentation to learn # about more advanced uses of `preprocessor`) and the pair information `pairs` # and `pairs_labels` in the fit method. itml = metric_learn.ITML(preprocessor=X) itml.fit(pairs, pairs_labels) X_itml = itml.transform(X) plot_tsne(X_itml, y) ###################################################################### # And that's the result of ITML after being trained on our manually # constructed constraints! A bit different from our old result, but not too # different. # # RCA and LSML also have their own specific ways of taking in inputs - # it's worth one's while to poke around in the constraints.py file to see # how exactly this is going on. #
def itml(self, train_X, train_y, test_X, gamma):
learner = ml.ITML(gamma=gamma)
train_X = learner.fit_transform(train_X, train_y)
test_X = learner.transform(test_X)
return train_X, test_X
