def process_rca(self, **option):
'''Metric Learning algorithm: RCA'''
GeneExp = self.GeneExp_train
Label = self.Label_train
rca = RCA_Supervised(num_chunks=30, chunk_size=2)
rca.fit(GeneExp, Label)
self.Trans['RCA'] = rca.transformer()
def runRCA(X_train, X_test, y_train, t_test):
transformer = RCA_Supervised()
transformer.fit(X_train, y_train)
X_train_proj = transformer.transform(X_train)
X_test_proj = transformer.transform(X_test)
np.save('X_train_RCA', X_train_proj)
np.save('X_test_RCA', X_test_proj)
return X_train_proj, X_test_proj
def test_rca_supervised(self):
seed = np.random.RandomState(1234)
rca = RCA_Supervised(num_dims=2, num_chunks=30, chunk_size=2)
rca.fit(self.X, self.y, random_state=seed)
res_1 = rca.transform()
seed = np.random.RandomState(1234)
rca = RCA_Supervised(num_dims=2, num_chunks=30, chunk_size=2)
res_2 = rca.fit_transform(self.X, self.y, random_state=seed)
assert_array_almost_equal(res_1, res_2)
def test_rca_supervised(self):
seed = np.random.RandomState(1234)
rca = RCA_Supervised(num_dims=2, num_chunks=30, chunk_size=2)
rca.fit(self.X, self.y, random_state=seed)
res_1 = rca.transform(self.X)
seed = np.random.RandomState(1234)
rca = RCA_Supervised(num_dims=2, num_chunks=30, chunk_size=2)
res_2 = rca.fit_transform(self.X, self.y, random_state=seed)
assert_array_almost_equal(res_1, res_2)
def test_feature_null_variance(self):
X = np.hstack((self.iris_points, np.eye(len(self.iris_points), M=1)))
# Apply PCA with the number of components
rca = RCA_Supervised(num_dims=2, pca_comps=3, num_chunks=30, chunk_size=2)
rca.fit(X, self.iris_labels)
csep = class_separation(rca.transform(), self.iris_labels)
self.assertLess(csep, 0.30)
# Apply PCA with the minimum variance ratio
rca = RCA_Supervised(num_dims=2, pca_comps=0.95, num_chunks=30,
chunk_size=2)
rca.fit(X, self.iris_labels)
csep = class_separation(rca.transform(), self.iris_labels)
self.assertLess(csep, 0.30)
def test_bad_parameters(self):
n = 200
num_chunks = 3
X, y = make_classification(random_state=42, n_samples=n,
n_features=6, n_informative=6, n_redundant=0)
rca = RCA_Supervised(num_chunks=num_chunks, random_state=42)
msg = ('Due to the parameters of RCA_Supervised, '
'the inner covariance matrix is not invertible, '
'so the transformation matrix will contain Nan values. '
'Increase the number or size of the chunks to correct '
'this problem.'
)
with pytest.warns(None) as raised_warning:
rca.fit(X, y)
assert any(str(w.message) == msg for w in raised_warning)
def test_unknown_labels(self):
n = 200
num_chunks = 50
X, y = make_classification(random_state=42, n_samples=2 * n,
n_features=6, n_informative=6, n_redundant=0)
y2 = np.concatenate((y[:n], -np.ones(n)))
rca = RCA_Supervised(num_chunks=num_chunks, random_state=42)
rca.fit(X[:n], y[:n])
rca2 = RCA_Supervised(num_chunks=num_chunks, random_state=42)
rca2.fit(X, y2)
assert not np.any(np.isnan(rca.components_))
assert not np.any(np.isnan(rca2.components_))
np.testing.assert_array_equal(rca.components_, rca2.components_)
def test_feature_null_variance(self):
X = np.hstack((self.iris_points, np.eye(len(self.iris_points), M=1)))
# Apply PCA with the number of components
rca = RCA_Supervised(num_dims=2,
pca_comps=3,
num_chunks=30,
chunk_size=2)
rca.fit(X, self.iris_labels)
csep = class_separation(rca.transform(X), self.iris_labels)
self.assertLess(csep, 0.30)
# Apply PCA with the minimum variance ratio
rca = RCA_Supervised(num_dims=2,
pca_comps=0.95,
num_chunks=30,
chunk_size=2)
rca.fit(X, self.iris_labels)
csep = class_separation(rca.transform(X), self.iris_labels)
self.assertLess(csep, 0.30)
def test_rca_supervised(self): rca = RCA_Supervised(n_components=2, num_chunks=30, chunk_size=2) rca.fit(self.X, self.y) L = rca.components_ assert_array_almost_equal(L.T.dot(L), rca.get_mahalanobis_matrix())
def test_iris(self):
rca = RCA_Supervised(num_dims=2, num_chunks=30, chunk_size=2)
rca.fit(self.iris_points, self.iris_labels)
csep = class_separation(rca.transform(self.iris_points),
self.iris_labels)
self.assertLess(csep, 0.25)
def test_rca_supervised(self):
seed = np.random.RandomState(1234)
rca = RCA_Supervised(n_components=2, num_chunks=30, chunk_size=2)
rca.fit(self.X, self.y, random_state=seed)
L = rca.components_
assert_array_almost_equal(L.T.dot(L), rca.get_mahalanobis_matrix())
x = nca.fit(FSTrainData, TrainLabels)
TFSTestData = x.transform(FSTestData)
print('Transformation Done', '\n')
elif Method == 'SDML':
print("Method: SDML", '\n')
sdml = SDML_Supervised(num_constraints=200)
x = sdml.fit(FSTrainData, TrainLabels)
TFSTestData = x.transform(FSTestData)
print('Transformation Done', '\n')
elif Method == 'RCA':
print("Method: RCA", '\n')
rca = RCA_Supervised(num_chunks=2, chunk_size=1)
x = rca.fit(FSTrainData, TrainLabels)
TFSTestData = x.transform(FSTestData)
print('Transformation Done', '\n')
#print(len(TTestData))
#print(TTestData[0])
#rca = RCA_Supervised(num_chunks=2, chunk_size=1)
#x= rca.fit(TrainData, targets)
#TTestData = x.transform(TestData)
#transformer = x.transformer()
#print(TTestData)
def EuclDist(coords1, coords2):
def test_iris(self): rca = RCA_Supervised(num_dims=2, num_chunks=30, chunk_size=2) rca.fit(self.iris_points, self.iris_labels) csep = class_separation(rca.transform(), self.iris_labels) self.assertLess(csep, 0.25)
def test_rca_supervised(self): seed = np.random.RandomState(1234) rca = RCA_Supervised(num_dims=2, num_chunks=30, chunk_size=2) rca.fit(self.X, self.y, random_state=seed) L = rca.transformer_ assert_array_almost_equal(L.T.dot(L), rca.metric())
def test_rca_supervised(self): seed = np.random.RandomState(1234) rca = RCA_Supervised(num_dims=2, num_chunks=30, chunk_size=2) rca.fit(self.X, self.y, random_state=seed) L = rca.transformer_ assert_array_almost_equal(L.T.dot(L), rca.get_mahalanobis_matrix())
