def test__check_n_components():
"""Checks that n_components returns what is expected
(including the errors)"""
dim = _check_n_components(5, None)
assert dim == 5
dim = _check_n_components(5, 3)
assert dim == 3
with pytest.raises(ValueError) as expected_err:
_check_n_components(5, 10)
assert str(expected_err.value) == 'Invalid n_components, must be in [1, 5]'
with pytest.raises(ValueError) as expected_err:
_check_n_components(5, 0)
assert str(expected_err.value) == 'Invalid n_components, must be in [1, 5]'
def fit(self, X, y):
"""
Fit MLKR model
Parameters
----------
X : (n x d) array of samples
y : (n) data labels
"""
if self.A0 != 'deprecated':
warnings.warn(
'"A0" parameter is not used.'
' It has been deprecated in version 0.5.0 and will be'
'removed in 0.6.0. Use "init" instead.', DeprecationWarning)
if self.num_dims != 'deprecated':
warnings.warn(
'"num_dims" parameter is not used.'
' It has been deprecated in version 0.5.0 and will be'
' removed in 0.6.0. Use "n_components" instead',
DeprecationWarning)
X, y = self._prepare_inputs(X, y, y_numeric=True, ensure_min_samples=2)
n, d = X.shape
if y.shape[0] != n:
raise ValueError('Data and label lengths mismatch: %d != %d' %
(n, y.shape[0]))
m = _check_n_components(d, self.n_components)
m = self.n_components
if m is None:
m = d
# if the init is the default (None), we raise a warning
if self.init is None:
# TODO:
# replace init=None by init='auto' in v0.6.0 and remove the warning
msg = (
"Warning, no init was set (`init=None`). As of version 0.5.0, "
"the default init will now be set to 'auto', instead of 'pca'. "
"If you still want to use PCA as an init, set init='pca'. "
"This warning will disappear in v0.6.0, and `init` parameter's"
" default value will be set to 'auto'.")
warnings.warn(msg, ChangedBehaviorWarning)
init = 'auto'
else:
init = self.init
A = _initialize_components(
m,
X,
y,
init=init,
random_state=self.random_state,
# MLKR works on regression targets:
has_classes=False)
# Measure the total training time
train_time = time.time()
self.n_iter_ = 0
res = minimize(self._loss,
A.ravel(), (X, y),
method='L-BFGS-B',
jac=True,
tol=self.tol,
options=dict(maxiter=self.max_iter))
self.components_ = res.x.reshape(A.shape)
# Stop timer
train_time = time.time() - train_time
if self.verbose:
cls_name = self.__class__.__name__
# Warn the user if the algorithm did not converge
if not res.success:
warnings.warn(
'[{}] MLKR did not converge: {}'.format(
cls_name, res.message), ConvergenceWarning)
print('[{}] Training took {:8.2f}s.'.format(cls_name, train_time))
return self