def test_pnorm_w(self):
data0 = datasets['uni4large'].samples.T
weight = np.abs(data0[11, :60])
self.assertRaises(ValueError,
pnorm_w_python,
data0[:10, :2],
p=1.2,
heuristic='buga')
self.assertRaises(ValueError,
pnorm_w_python,
data0[:10, :2],
weight=weight)
self.assertRaises(ValueError,
pnorm_w_python,
data0[:10, :2],
data0[:10, :3],
weight=weight)
self.assertRaises(ValueError,
pnorm_w,
data0[:10, :2],
data0[:10, :3],
weight=weight)
self.assertRaises(ValueError, pnorm_w, data0[:10, :2], weight=weight)
# some sanity checks
for did, (data1, data2, w) in enumerate([
(data0[:2, :60], None, None),
(data0[:2, :60], data0[3:4, 1:61], None),
(data0[:2, :60], None, weight),
(data0[:2, :60], data0[3:4, 1:61], weight),
]):
# test different norms
for p in [1, 2, 1.2]:
kwargs = {'data1': data1, 'data2': data2, 'weight': w, 'p': p}
d = pnorm_w(**kwargs) # default one
# to assess how far we are
kwargs0 = kwargs.copy()
kwargs0['data2'] = np.zeros(data1.shape)
d0 = pnorm_w(**kwargs0)
d0norm = np.linalg.norm(d - d0, 'fro')
# test different implementations
for iid, d2 in enumerate([
pnorm_w_python(**kwargs),
pnorm_w_python(use_sq_euclidean=True, **kwargs),
pnorm_w_python(heuristic='auto', **kwargs),
pnorm_w_python(use_sq_euclidean=False, **kwargs)
] + [
pnorm_w_python(
heuristic=h, use_sq_euclidean=False, **kwargs)
for h in ('auto', 'samples', 'features')
]):
dnorm = np.linalg.norm(d2 - d, 'fro')
self.assertTrue(
dnorm / d0norm < 1e-7,
msg="Failed comparison of different implementations on "
"data #%d, implementation #%d, p=%s. "
"Norm of the difference is %g" % (did, iid, p, dnorm))
def test_pnorm_w(self):
data0 = datasets['uni4large'].samples.T
weight = np.abs(data0[11, :60])
self.assertRaises(
ValueError,
pnorm_w_python,
data0[:10, :2],
p=1.2,
heuristic='buga')
self.assertRaises(
ValueError, pnorm_w_python, data0[:10, :2], weight=weight)
self.assertRaises(
ValueError,
pnorm_w_python,
data0[:10, :2],
data0[:10, :3],
weight=weight)
self.assertRaises(
ValueError, pnorm_w, data0[:10, :2], data0[:10, :3], weight=weight)
self.assertRaises(ValueError, pnorm_w, data0[:10, :2], weight=weight)
# some sanity checks
for did, (data1, data2, w) in enumerate([
(data0[:2, :60], None, None),
(data0[:2, :60], data0[3:4, 1:61], None),
(data0[:2, :60], None, weight),
(data0[:2, :60], data0[3:4, 1:61], weight),
]):
# test different norms
for p in [1, 2, 1.2]:
kwargs = {'data1': data1, 'data2': data2, 'weight': w, 'p': p}
d = pnorm_w(**kwargs) # default one
# to assess how far we are
kwargs0 = kwargs.copy()
kwargs0['data2'] = np.zeros(data1.shape)
d0 = pnorm_w(**kwargs0)
d0norm = np.linalg.norm(d - d0, 'fro')
# test different implementations
for iid, d2 in enumerate([
pnorm_w_python(**kwargs),
pnorm_w_python(use_sq_euclidean=True, **kwargs),
pnorm_w_python(heuristic='auto', **kwargs),
pnorm_w_python(use_sq_euclidean=False, **kwargs)
] + [
pnorm_w_python(
heuristic=h, use_sq_euclidean=False, **kwargs)
for h in ('auto', 'samples', 'features')
]):
dnorm = np.linalg.norm(d2 - d, 'fro')
self.assertTrue(
dnorm / d0norm < 1e-7,
msg="Failed comparison of different implementations on "
"data #%d, implementation #%d, p=%s. "
"Norm of the difference is %g" % (did, iid, p, dnorm))
def _call(self, dataset):
"""Computes featurewise I-RELIEF weights."""
samples = dataset.samples
NS, NF = samples.shape[:2]
if self.w_guess is None:
w = np.ones(NF, 'd')
w /= (w**2).sum() # do normalization in all cases to be safe :)
M, H = self.compute_M_H(dataset.targets)
while True:
d_w_k = self.k(pnorm_w(data1=samples, weight=w, p=1))
ni = np.zeros(NF, 'd')
for n in range(NS):
# d_w_k[n, n] could be omitted since == 0.0
gamma_n = 1.0 - np.nan_to_num(d_w_k[n, M[n]].sum() \
/ (d_w_k[n, :].sum() - d_w_k[n, n]))
alpha_n = np.nan_to_num(d_w_k[n, M[n]] /
(d_w_k[n, M[n]].sum()))
beta_n = np.nan_to_num(d_w_k[n, H[n]] / (d_w_k[n, H[n]].sum()))
m_n = (np.abs(samples[n, :] - samples[M[n], :]) \
* alpha_n[:, None]).sum(0)
h_n = (np.abs(samples[n, :] - samples[H[n], :]) \
* beta_n[:, None]).sum(0)
ni += gamma_n * (m_n - h_n)
ni = ni / NS
ni_plus = np.clip(ni, 0.0,
np.inf) # set all negative elements to zero
w_new = np.nan_to_num(ni_plus / (np.sqrt((ni_plus**2).sum())))
change = np.abs(w_new - w).sum()
if __debug__ and 'IRELIEF' in debug.active:
debug('IRELIEF',
"change=%.4f max=%f min=%.4f mean=%.4f std=%.4f #nan=%d" \
% (change, w_new.max(), w_new.min(), w_new.mean(),
w_new.std(), np.isnan(w_new).sum()))
# update weights:
w = w_new
if change < self.threshold:
break
self.w = w
return Dataset(self.w[np.newaxis])
def _call(self, dataset):
"""Computes featurewise I-RELIEF weights."""
samples = dataset.samples
NS, NF = samples.shape[:2]
if self.w_guess == None:
w = np.ones(NF, 'd')
w /= (w ** 2).sum() # do normalization in all cases to be safe :)
M, H = self.compute_M_H(dataset.targets)
while True:
d_w_k = self.k(pnorm_w(data1=samples, weight=w, p=1))
ni = np.zeros(NF, 'd')
for n in range(NS):
# d_w_k[n, n] could be omitted since == 0.0
gamma_n = 1.0 - np.nan_to_num(d_w_k[n, M[n]].sum() \
/ (d_w_k[n, :].sum() - d_w_k[n, n]))
alpha_n = np.nan_to_num(d_w_k[n, M[n]] / (d_w_k[n, M[n]].sum()))
beta_n = np.nan_to_num(d_w_k[n, H[n]] / (d_w_k[n, H[n]].sum()))
m_n = (np.abs(samples[n, :] - samples[M[n], :]) \
* alpha_n[:, None]).sum(0)
h_n = (np.abs(samples[n, :] - samples[H[n], :]) \
* beta_n[:, None]).sum(0)
ni += gamma_n*(m_n - h_n)
ni = ni / NS
ni_plus = np.clip(ni, 0.0, np.inf) # set all negative elements to zero
w_new = np.nan_to_num(ni_plus / (np.sqrt((ni_plus**2).sum())))
change = np.abs(w_new - w).sum()
if __debug__ and 'IRELIEF' in debug.active:
debug('IRELIEF',
"change=%.4f max=%f min=%.4f mean=%.4f std=%.4f #nan=%d" \
% (change, w_new.max(), w_new.min(), w_new.mean(),
w_new.std(), np.isnan(w_new).sum()))
# update weights:
w = w_new
if change < self.threshold:
break
self.w = w
return Dataset(self.w[np.newaxis])
