def test_iris(self):
cov = Covariance()
cov.fit(self.iris_points)
csep = class_separation(cov.transform(), self.iris_labels)
# deterministic result
self.assertAlmostEqual(csep, 0.73068122)
def test_iris(self):
cov = Covariance()
cov.fit(self.iris_points)
csep = class_separation(cov.transform(), self.iris_labels)
# deterministic result
self.assertAlmostEqual(csep, 0.72981476)
def test_singular_returns_pseudo_inverse(self):
"""Checks that if the input covariance matrix is singular, we return
the pseudo inverse"""
X, y = load_iris(return_X_y=True)
# We add a virtual column that is a linear combination of the other
# columns so that the covariance matrix will be singular
X = np.concatenate([X, X[:, :2].dot([[2], [3]])], axis=1)
cov_matrix = np.cov(X, rowvar=False)
covariance = Covariance()
covariance.fit(X)
pseudo_inverse = covariance.get_mahalanobis_matrix()
# here is the definition of a pseudo inverse according to wikipedia:
assert_allclose(
cov_matrix.dot(pseudo_inverse).dot(cov_matrix), cov_matrix)
assert_allclose(
pseudo_inverse.dot(cov_matrix).dot(pseudo_inverse), pseudo_inverse)
def test_cov(self):
cov = Covariance()
cov.fit(self.X)
res_1 = cov.transform(self.X)
cov = Covariance()
res_2 = cov.fit_transform(self.X)
# deterministic result
assert_array_almost_equal(res_1, res_2)
def test_cov(self):
cov = Covariance()
cov.fit(self.X)
res_1 = cov.transform(self.X)
cov = Covariance()
res_2 = cov.fit_transform(self.X)
# deterministic result
assert_array_almost_equal(res_1, res_2)
def get_dist_func(
data: Array[np.float64], target: Array[np.float64]
) -> Callable[[Callable[[np.float64, np.float64], np.float64], int, int],
np.float64]:
"""
Get function that returns distances between examples in learned space.
Args:
data : Array[np.float64] - training data_trans
target : int - target variable values (classes of training examples)
Returns:
TODO
"""
# Get transformed data.
data_trans: Array[np.float64] = Covariance().fit_transform(
StandardScaler().fit_transform(data), target)
# Computing distance:
def dist_func_res(metric: Callable[[np.float64, np.float64], np.float64],
i1: int, i2: int) -> np.float64:
"""
distance function that takes indices of examples in training set and returns distance
in learned space using specified distance metric.
Args:
i1 : int - index of first training example
i2 : int - index of second training example
Returns:
np.float64 - distance in learned metric space using specified metric
between specified training examples.
"""
# Compute distance in learned metric space using specified metric.
return metric(data_trans[i1, :], data_trans[i2, :])
return dist_func_res # Return distance function.
def test_cov(self): cov = Covariance() cov.fit(self.X) L = cov.components_ assert_array_almost_equal(L.T.dot(L), cov.get_mahalanobis_matrix())
def test_covariance(self):
check_estimator(Covariance())
def train_covariance(X): model = Covariance() model.fit(X) return model.transform(X), model.metric()
#print(len(TestData))
#print(TrainData)
#print(type(TrainData))
#print(TrainLabels)
#print(type(TrainLabels))
if Method == 'LMNN':
print("Method: LMNN", '\n')
lmnn = LMNN(k=3, learn_rate=1e-6, verbose=False)
x = lmnn.fit(FSTrainData, TrainLabels)
TFSTestData = x.transform(FSTestData)
print('Transformation Done', '\n')
elif Method == 'COV':
print("Method: COV", '\n')
cov = Covariance().fit(FSTrainData)
TFSTestData = cov.transform(FSTestData)
print('Transformation Done', '\n')
elif Method == 'ITML':
print("Method: ITML", '\n')
itml = ITML_Supervised(num_constraints=200, A0=None)
x = itml.fit(FSTrainData, TrainLabels)
TFSTestData = x.transform(FSTestData)
print('Transformation Done', '\n')
elif Method == 'LFDA':
print("Method: LFDA", '\n')
lfda = LFDA(k=4, dim=1)
x = lfda.fit(FSTrainData, TrainLabels)
TFSTestData = x.transform(FSTestData)
quadruplets_learners = [(LSML(), build_quadruplets)]
ids_quadruplets_learners = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in quadruplets_learners]))
pairs_learners = [
(ITML(), build_pairs),
(MMC(max_iter=2), build_pairs), # max_iter=2 for faster
(SDML(), build_pairs),
]
ids_pairs_learners = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in pairs_learners]))
classifiers = [(Covariance(), build_classification),
(LFDA(), build_classification), (LMNN(), build_classification),
(NCA(), build_classification), (RCA(), build_classification),
(ITML_Supervised(max_iter=5), build_classification),
(LSML_Supervised(), build_classification),
(MMC_Supervised(max_iter=5), build_classification),
(RCA_Supervised(num_chunks=10), build_classification),
(SDML_Supervised(), build_classification)]
ids_classifiers = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in classifiers]))
regressors = [(MLKR(), build_regression)]
ids_regressors = list(
map(lambda x: x.__class__.__name__,
[learner for (learner, _) in regressors]))
def test_cov(self): cov = Covariance() cov.fit(self.X) L = cov.transformer_ assert_array_almost_equal(L.T.dot(L), cov.metric())
def test_covariance():
iris = load_iris()['data']
cov = Covariance().fit(iris)
x = cov.transform(iris)
print x
def test_cov(self): cov = Covariance() cov.fit(self.X) L = cov.transformer_ assert_array_almost_equal(L.T.dot(L), cov.get_mahalanobis_matrix())
def test_cov(self): cov = Covariance() cov.fit(self.X) L = cov.transformer() assert_array_almost_equal(L.T.dot(L), cov.metric())
