def ICDoneKernelOneTCGA(tcga, kernel, kernel_args, rank):
#K = Kinterface(data=np.array(cnv), kernel=rfb_kernel, kernel_args={"sigma": 110})
K = Kinterface(data=np.array(tcga), kernel=kernel, kernel_args=kernel_args)
model = ICD(rank=rank)
model.fit(K)
G_icd = model.G
#inxs = model.active_set_
print("G shape:", G_icd.shape, "Error:",
np.linalg.norm(K[:, :] - G_icd.dot(G_icd.T)))
return model
def get_kernel_matrix(dframe, n_dim=15):
"""
This returns a Kernel Transformation Matrix $\Theta$
It uses kernel approximation offered by the MKlaren package
For the sake of completeness (and for my peace of mind, I use the best possible approx.)
:param dframe: input data as a pandas dataframe.
:param n_dim: Number of dimensions for the kernel matrix (default=15)
:return: $\Theta$ matrix
"""
ker = Kinterface(data=dframe.values, kernel=linear_kernel)
model = ICD(rank=n_dim)
model.fit(ker)
g_nystrom = model.G
return g_nystrom
def get_kernel_matrix(dframe, n_dim=15):
"""
This returns a Kernel Transformation Matrix $\Theta$
It uses kernel approximation offered by the MKlaren package
For the sake of completeness (and for my peace of mind, I use the best possible approx.)
:param dframe: input data as a pandas dataframe.
:param n_dim: Number of dimensions for the kernel matrix (default=15)
:return: $\Theta$ matrix
"""
ker = Kinterface(data=dframe.values, kernel=linear_kernel)
model = ICD(rank=n_dim)
model.fit(ker)
g_nystrom = model.G
return g_nystrom
def testPoly(self):
"""
Test expected reconstruction properties of the ICD.
"""
for d in range(1, 6):
K = poly_kernel(self.X, self.X, degree=d)
model = ICD(rank=self.n)
model.fit(K)
errors = np.zeros((self.n, ))
for i in range(self.n):
Ki = model.G[:, :i + 1].dot(model.G[:, :i + 1].T)
errors[i] = np.linalg.norm(K - Ki)
self.assertTrue(np.all(errors[:-1] > errors[1:]))
self.assertAlmostEqual(errors[-1], 0, delta=3)
def testPolySum(self):
"""
Test expected reconstruction properties of the ICD.
Kernels are iteratively summed.
"""
K = np.zeros((self.n, self.n))
for d in range(1, 6):
K += Kinterface(data=self.X, kernel=poly_kernel,
kernel_args={"degree": d},
row_normalize=True)[:, :]
model = ICD(rank=self.n)
model.fit(K)
errors = np.zeros((self.n, ))
for i in range(self.n):
Ki = model.G[:, :i+1].dot(model.G[:, :i+1].T)
errors[i] = np.linalg.norm(K-Ki)
self.assertTrue(np.all(errors[:-1] > errors[1:]))
self.assertAlmostEqual(errors[-1], 0, delta=3)