def test_user_counter(self):
global glob_counter
glob_counter = 0
def _callback(pi):
Y_hat = tespo.exe(pred, [pi, X])
out = {}
out['MSE'] = np.mean(Y_hat == y)
out['Loss'] = tespo.exe(loss, [pi, X, y])
opt = {'freq': 10}
return out, opt
p1, res = tespo.optimize(
fun=loss,
p0=p0,
jac=grad,
callback=_callback,
args=(X, y),
method='BFGS',
options={
'maxiter': 20,
'disp': 0
},
)
def test_none(self):
p1, res = tespo.optimize(
fun=loss,
p0=p0,
jac=grad,
args=(X, y),
method='BFGS',
options = {'maxiter': 20, 'disp': 0},
)
def test_default(self):
p1, res = tespo.optimize(
fun=loss,
p0=p0,
jac=grad,
callback='default',
args=(X, y),
method='BFGS',
options = {'maxiter': 20, 'disp': 0},
)
def test_none(self):
p1, res = tespo.optimize(
fun=loss,
p0=p0,
jac=grad,
args=(X, y),
method='BFGS',
options={
'maxiter': 20,
'disp': 0
},
)
def test_default(self):
p1, res = tespo.optimize(
fun=loss,
p0=p0,
jac=grad,
callback='default',
args=(X, y),
method='BFGS',
options={
'maxiter': 20,
'disp': 0
},
)
def test_default(self):
p1, res = tespo.optimize(
fun=loss,
p0=p0,
jac=grad,
callback='default',
args=(X, y),
method='BFGS',
options = {'maxiter': 20, 'disp': 0},
)
loss_0 = tespo.exe(loss, [p0, X, y])
loss_1 = tespo.exe(loss, [p1, X, y])
assert loss_0 > loss_1
def test_user_counter(self):
global glob_counter
glob_counter = 0
def _callback(pi):
Y_hat = tespo.exe(pred, [pi, X])
out = {}
out['MSE'] = np.mean(Y_hat==y)
out['Loss'] = tespo.exe(loss, [pi, X,y])
opt = {'freq':10}
return out, opt
p1, res = tespo.optimize(
fun=loss,
p0=p0,
jac=grad,
callback=_callback,
args=(X, y),
method='BFGS',
options = {'maxiter': 20, 'disp': 0},
)
