def test_graphnet(self, X, Y, coefs):
X = simple_normalize(X)
accuracies = []
for i, coefset in enumerate(coefs):
correct = []
print 'Checking accuracy for test group'
if self.problemkey == 'RobustGraphNet':
coefset = coefset[:-self.trainX_shape[0]]
for trial, outcome in zip(X, Y):
predict = trial * coefset
#print np.sum(predict)
Ypredsign = np.sign(np.sum(predict))
if Ypredsign < 0.:
Ypredsign = 0.
else:
Ypredsign = 1.
#print Ypredsign, outcome, (Ypredsign == outcome)
correct.append(Ypredsign == outcome)
fold_accuracy = np.sum(correct) * 1. / len(correct)
print 'coef number:', i
print 'fold accuracy: ', fold_accuracy
accuracies.append(fold_accuracy)
return accuracies
def test_graphnet(self, X, Y, coefs):
X = simple_normalize(X)
accuracies = []
for i, coefset in enumerate(coefs):
correct = []
print 'Checking accuracy for test group'
if self.problemkey == 'RobustGraphNet':
coefset = coefset[:-self.trainX_shape[0]]
for trial, outcome in zip(X, Y):
predict = trial*coefset
#print np.sum(predict)
Ypredsign = np.sign(np.sum(predict))
if Ypredsign < 0.:
Ypredsign = 0.
else:
Ypredsign = 1.
#print Ypredsign, outcome, (Ypredsign == outcome)
correct.append(Ypredsign == outcome)
fold_accuracy = np.sum(correct) * 1. / len(correct)
print 'coef number:', i
print 'fold accuracy: ', fold_accuracy
accuracies.append(fold_accuracy)
return accuracies
def train_graphnet(self,
X,
Y,
trial_mask=None,
G=None,
l1=None,
l2=None,
l3=None,
delta=None,
svmdelta=None,
initial=None,
adaptive=False,
svm=False,
scipy_compare=False,
tol=1e-5,
greymatter_mask=None,
initial_l1weights=None,
use_adj_time=True):
if not type(l1) in [list, tuple]:
l1 = [l1]
X = simple_normalize(X)
tic = time.clock()
#problemkey = self.regression_type_selector(*[bool(x) for x in [l1, l2, l3, delta, svmdelta]])
problemkey = self.regression_type_selector(l1, l2, l3, delta, svmdelta)
self.problemkey = problemkey
self.trainX_shape = X.shape
if problemkey in ('HuberSVMGraphNet', 'RobustGraphNet',
'NaiveGraphNet'):
if G is None:
#nx = 60
#ny = 60
#A, Afull = construct_adjacency_list(nx, ny, 1, return_full=True)
#A, Afull = self.gen_adj(X.shape[1])
#if greymatter_mask is not None:
# A, GMA = prepare_adj(trial_mask, numt=1, gm_mask=greymatter_mask)
#else:
# A = prepare_adj(trial_mask, numt=1)
# GMA = None
if use_adj_time:
A = prepare_adj(trial_mask,
numt=1,
gm_mask=greymatter_mask)
else:
A = prepare_adj(trial_mask,
numt=0,
gm_mask=greymatter_mask)
else:
A = G.copy()
if initial_l1weights is not None:
newl1 = l1
else:
newl1 = None
if problemkey is 'RobustGraphNet':
problemtype = graphnet.RobustGraphNet
print 'Robust GraphNet with penalties (l1, l2, l3, delta): ', l1, l2, l3, delta
l = cwpath.CoordWise((X, Y, A), problemtype,
initial_coefs=initial) #, gma=GMA)
l.problem.assign_penalty(path_key='l1',
l1=l1,
l2=l2,
l3=l3,
delta=delta,
l1weights=initial_l1weights,
newl1=newl1)
elif problemkey is 'HuberSVMGraphNet':
problemtype = graphnet.GraphSVM
print 'HuberSVM GraphNet with penalties (l1, l2, l3, delta): ', l1, l2, l3, delta
Y = 2 * np.round(np.random.uniform(0, 1, len(Y))) - 1
l = cwpath.CoordWise((X, Y, A), problemtype) #, gma=GMA)
l.problem.assign_penalty(path_key='l1',
l1=l1,
l2=l2,
l3=l3,
delta=delta,
l1weights=initial_l1weights,
newl1=newl1)
elif problemkey is 'NaiveGraphNet':
problemtype = graphnet.NaiveGraphNet
print 'Testing GraphNet with penalties (l1, l2, l3): ', l1, l2, l3
l = cwpath.CoordWise((X, Y, A), problemtype,
initial_coefs=initial) #, gma=GMA)
l.problem.assign_penalty(path_key='l1',
l1=l1,
l2=l2,
l3=l3,
l1weights=initial_l1weights,
newl1=newl1)
elif problemkey is 'NaiveENet':
problemtype = graphnet.NaiveENet
print 'Testing ENET with penalties (l1, l2): ', l1, l2
l = cwpath.CoordWise((X, Y), problemtype, initial_coefs=initial)
l.problem.assign_penalty(path_key='l1',
l1=l1,
l2=l2,
l1weights=initial_l1weights,
newl1=newl1)
elif problemkey is 'Lasso':
problemtype = graphnet.Lasso
print 'Testing LASSO with penalty (l1): ', l1
l = cwpath.CoordWise((X, Y), problemtype, initial_coefs=initial)
l.problem.assign_penalty(path_key='l1',
l1=l1,
l1weights=initial_l1weights,
newl1=newl1)
else:
print 'Incorrect parameters set (no problem key).'
return False
# Solve the problem:
print 'Solving the problem...'
coefficients, residuals = l.fit(tol=tol, initial=initial)
self.coefficients = coefficients
self.residuals = residuals
print '\t---> Fitting problem with coordinate decesnt took: ', time.clock(
) - tic, 'seconds.'
if adaptive:
tic = time.clock()
safety = 1e-5
l1weights = 1. / (self.coefficients[-1] + safety)
l = cwpath.CoordWise((X, Y, A), problemtype, initial_coefs=initial)
l.problem.assign_penalty(path_key='l1',
l1=l1,
l2=l2,
l3=l3,
delta=delta,
l1weights=l1weights,
newl1=l1)
adaptive_coefficients, adaptive_residuals = l.fit(tol=tol,
initial=initial)
print '\t---> Fitting Adaptive GraphNet problem with coordinate descent took: ', time.clock(
) - tic, 'seconds.'
self.firstpass_coefficients = self.coefficients
self.firstpass_residuals = self.residuals
self.coefficients = adaptive_coefficients
self.residuals = adaptive_residuals
'''
if scipy_compare:
l1 = l1[-1]
beta = self.coefficients[-1]
print '\t---> Fitting with scipy for comparison...'
tic = time.clock()
if problemkey is 'RobustGraphNet':
def f(beta):
huber_sum = self.huber(Y - np.dot(X, beta), delta).sum()/2
beta_l1 = l1*np.dot(np.fabs(beta), l1weights)
beta_l2 = l2*np.linalg.norm(beta)**2/2
beta_l3 = l3*np.dot(beta, np.dot(Afull, beta))/2
return huber_sum + beta_l1 + beta_l2 + beta_l3
elif problemkey is 'HuberSVMGraphNet':
Xp2 = np.hstack([np.ones(X.shape[0])[:,np.newaxis], X])
def f(beta):
ind = range(1, len(beta))
huber_err_sum = self.huber_svm_error(beta, Y, Xp2, delta).sum()
beta_l1 = np.fabs(beta[ind]).sum()*l1
beta_l2 = l2*(np.linalg.norm(beta[ind])**2/2)
beta_l3 = l3*(np.dot(beta[ind], np.dot(Afull, beta[ind])))/2
return huber_error_sum + beta_l1 + beta_l2 + beta_l3
elif problemkey is 'NaiveGraphNet':
def f(beta):
beta_XY = np.linalg.norm(Y - np.dot(X, beta))**2/2
beta_l1 = l1*np.fabs(beta).sum()
beta_l2 = l2*np.linalg.norm(beta)**2/2
beta_l3 = l3*np.dot(beta, np.dot(Afull, beta))/2
return beta_XY + beta_l1 + beta_l2 + beta_l3
elif problemkey is 'NaiveENet':
def f(beta):
beta_XY = np.linalg.norm(Y - np.dot(X, beta))**2/2
beta_l1 = l1*np.fabs(beta).sum()
beta_l2 = np.linalg.norm(beta)**2/2
elif problemkey is 'Lasso':
def f(beta):
beta_XY = np.linalg.norm(Y - np.dot(X, beta))**2/2
beta_l1 = l1*np.fabs(beta).sum()
if problemkey is 'HuberSVMGraphNet':
v = scipy.optimize.fmin_powell(f, np.zeros(Xp2.shape[1]), ftol=1.0e-14, xtol=1.0e-14, maxfun=100000)
else:
v = scipy.optimize.fmin_powell(f, np.zeros(X.shape[1]), ftol=1.0e-10, xtol=1.0e-10, maxfun=100000)
v = np.asarray(v)
print '\t---> Fitting GraphNet with scipy took: ', time.clock()-tic, 'seconds.'
assert_true(np.fabs(f(v) - f(beta)) / np.fabs(f(v) + f(beta)) < tol)
if np.linalg.norm(v) > 1e-8:
assert_true(np.linalg.norm(v - beta) / np.linalg.norm(v) < tol)
else:
assert_true(np.linalg.norm(beta) < 1e-8)
print '\t---> Coordinate-wise and Scipy optimization agree.'
'''
return self.coefficients
def train_graphnet(self, X, Y, trial_mask=None, G=None, l1=None, l2=None, l3=None, delta=None,
svmdelta=None, initial=None, adaptive=False, svm=False,
scipy_compare=False, tol=1e-5, greymatter_mask=None, initial_l1weights=None,
use_adj_time=True):
if not type(l1) in [list, tuple]:
l1 = [l1]
X = simple_normalize(X)
tic = time.clock()
#problemkey = self.regression_type_selector(*[bool(x) for x in [l1, l2, l3, delta, svmdelta]])
problemkey = self.regression_type_selector(l1, l2, l3, delta, svmdelta)
self.problemkey = problemkey
self.trainX_shape = X.shape
if problemkey in ('HuberSVMGraphNet', 'RobustGraphNet', 'NaiveGraphNet'):
if G is None:
#nx = 60
#ny = 60
#A, Afull = construct_adjacency_list(nx, ny, 1, return_full=True)
#A, Afull = self.gen_adj(X.shape[1])
#if greymatter_mask is not None:
# A, GMA = prepare_adj(trial_mask, numt=1, gm_mask=greymatter_mask)
#else:
# A = prepare_adj(trial_mask, numt=1)
# GMA = None
if use_adj_time:
A = prepare_adj(trial_mask, numt=1, gm_mask=greymatter_mask)
else:
A = prepare_adj(trial_mask, numt=0, gm_mask=greymatter_mask)
else:
A = G.copy()
if initial_l1weights is not None:
newl1 = l1
else:
newl1 = None
if problemkey is 'RobustGraphNet':
problemtype = graphnet.RobustGraphNet
print 'Robust GraphNet with penalties (l1, l2, l3, delta): ', l1, l2, l3, delta
l = cwpath.CoordWise((X, Y, A), problemtype, initial_coefs=initial)#, gma=GMA)
l.problem.assign_penalty(path_key='l1', l1=l1, l2=l2, l3=l3, delta=delta, l1weights=initial_l1weights,
newl1=newl1)
elif problemkey is 'HuberSVMGraphNet':
problemtype = graphnet.GraphSVM
print 'HuberSVM GraphNet with penalties (l1, l2, l3, delta): ', l1, l2, l3, delta
Y = 2*np.round(np.random.uniform(0, 1, len(Y)))-1
l = cwpath.CoordWise((X, Y, A), problemtype)#, gma=GMA)
l.problem.assign_penalty(path_key='l1', l1=l1, l2=l2, l3=l3, delta=delta, l1weights=initial_l1weights,
newl1=newl1)
elif problemkey is 'NaiveGraphNet':
problemtype = graphnet.NaiveGraphNet
print 'Testing GraphNet with penalties (l1, l2, l3): ', l1, l2, l3
l = cwpath.CoordWise((X, Y, A), problemtype, initial_coefs=initial)#, gma=GMA)
l.problem.assign_penalty(path_key='l1', l1=l1, l2=l2, l3=l3, l1weights=initial_l1weights,
newl1=newl1)
elif problemkey is 'NaiveENet':
problemtype = graphnet.NaiveENet
print 'Testing ENET with penalties (l1, l2): ', l1, l2
l = cwpath.CoordWise((X, Y), problemtype, initial_coefs=initial)
l.problem.assign_penalty(path_key='l1', l1=l1, l2=l2, l1weights=initial_l1weights,
newl1=newl1)
elif problemkey is 'Lasso':
problemtype = graphnet.Lasso
print 'Testing LASSO with penalty (l1): ', l1
l = cwpath.CoordWise((X, Y), problemtype, initial_coefs=initial)
l.problem.assign_penalty(path_key='l1', l1=l1, l1weights=initial_l1weights, newl1=newl1)
else:
print 'Incorrect parameters set (no problem key).'
return False
# Solve the problem:
print 'Solving the problem...'
coefficients, residuals = l.fit(tol=tol, initial=initial)
self.coefficients = coefficients
self.residuals = residuals
print '\t---> Fitting problem with coordinate decesnt took: ', time.clock()-tic, 'seconds.'
if adaptive:
tic = time.clock()
safety = 1e-5
l1weights = 1./(self.coefficients[-1]+safety)
l = cwpath.CoordWise((X, Y, A), problemtype, initial_coefs=initial)
l.problem.assign_penalty(path_key='l1', l1=l1, l2=l2, l3=l3, delta=delta, l1weights=l1weights, newl1=l1)
adaptive_coefficients, adaptive_residuals = l.fit(tol=tol, initial=initial)
print '\t---> Fitting Adaptive GraphNet problem with coordinate descent took: ', time.clock()-tic, 'seconds.'
self.firstpass_coefficients = self.coefficients
self.firstpass_residuals = self.residuals
self.coefficients = adaptive_coefficients
self.residuals = adaptive_residuals
'''
if scipy_compare:
l1 = l1[-1]
beta = self.coefficients[-1]
print '\t---> Fitting with scipy for comparison...'
tic = time.clock()
if problemkey is 'RobustGraphNet':
def f(beta):
huber_sum = self.huber(Y - np.dot(X, beta), delta).sum()/2
beta_l1 = l1*np.dot(np.fabs(beta), l1weights)
beta_l2 = l2*np.linalg.norm(beta)**2/2
beta_l3 = l3*np.dot(beta, np.dot(Afull, beta))/2
return huber_sum + beta_l1 + beta_l2 + beta_l3
elif problemkey is 'HuberSVMGraphNet':
Xp2 = np.hstack([np.ones(X.shape[0])[:,np.newaxis], X])
def f(beta):
ind = range(1, len(beta))
huber_err_sum = self.huber_svm_error(beta, Y, Xp2, delta).sum()
beta_l1 = np.fabs(beta[ind]).sum()*l1
beta_l2 = l2*(np.linalg.norm(beta[ind])**2/2)
beta_l3 = l3*(np.dot(beta[ind], np.dot(Afull, beta[ind])))/2
return huber_error_sum + beta_l1 + beta_l2 + beta_l3
elif problemkey is 'NaiveGraphNet':
def f(beta):
beta_XY = np.linalg.norm(Y - np.dot(X, beta))**2/2
beta_l1 = l1*np.fabs(beta).sum()
beta_l2 = l2*np.linalg.norm(beta)**2/2
beta_l3 = l3*np.dot(beta, np.dot(Afull, beta))/2
return beta_XY + beta_l1 + beta_l2 + beta_l3
elif problemkey is 'NaiveENet':
def f(beta):
beta_XY = np.linalg.norm(Y - np.dot(X, beta))**2/2
beta_l1 = l1*np.fabs(beta).sum()
beta_l2 = np.linalg.norm(beta)**2/2
elif problemkey is 'Lasso':
def f(beta):
beta_XY = np.linalg.norm(Y - np.dot(X, beta))**2/2
beta_l1 = l1*np.fabs(beta).sum()
if problemkey is 'HuberSVMGraphNet':
v = scipy.optimize.fmin_powell(f, np.zeros(Xp2.shape[1]), ftol=1.0e-14, xtol=1.0e-14, maxfun=100000)
else:
v = scipy.optimize.fmin_powell(f, np.zeros(X.shape[1]), ftol=1.0e-10, xtol=1.0e-10, maxfun=100000)
v = np.asarray(v)
print '\t---> Fitting GraphNet with scipy took: ', time.clock()-tic, 'seconds.'
assert_true(np.fabs(f(v) - f(beta)) / np.fabs(f(v) + f(beta)) < tol)
if np.linalg.norm(v) > 1e-8:
assert_true(np.linalg.norm(v - beta) / np.linalg.norm(v) < tol)
else:
assert_true(np.linalg.norm(beta) < 1e-8)
print '\t---> Coordinate-wise and Scipy optimization agree.'
'''
return self.coefficients
