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 test_covariance(self):
self.assertEqual(str(metric_learn.Covariance()),
"Covariance(preprocessor=None)")
def test_covariance(self):
self.assertEqual(str(metric_learn.Covariance()), "Covariance()")
def test_covariance(self):
self.assertEqual(remove_spaces(str(metric_learn.Covariance())),
remove_spaces("Covariance(preprocessor=None)"))
def test_covariance(self):
def_kwargs = {'preprocessor': None}
nndef_kwargs = {}
merged_kwargs = sk_repr_kwargs(def_kwargs, nndef_kwargs)
self.assertEqual(remove_spaces(str(metric_learn.Covariance())),
remove_spaces(f"Covariance({merged_kwargs})"))
import numpy as np
from sklearn.datasets import load_iris
import metric_learn
CLASSES = {
'Covariance':
metric_learn.Covariance(),
'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']
