def fit(self, X, y):
self.label_binarizer_ = LabelBinarizer(neg_label=-1, pos_label=1)
y = np.asfortranarray(self.label_binarizer_.fit_transform(y),
dtype=np.float64)
if self.eta is None or self.eta == 'auto':
eta = get_auto_step_size(
X, self.alpha, self.loss, self.is_saga)
else:
eta = self.eta
if self.alpha * eta == 1:
# to match the beaviour of SAGA
# in this case SAGA decreases slightly eta
eta *= 0.9
loss = self._get_loss(self.loss)
self.penalty = self._get_penalty(self.penalty)
if not self.is_saga and self.penalty is not None:
raise ValueError("PySAGClassifier only accepts l2 penalty. Please "
"use `saga=True` or PySAGAClassifier.")
if self.is_saga:
self.coef_ = _fit_saga(X, y, eta, self.alpha, loss,
self.penalty, self.max_iter, self.rng)
else:
self.coef_ = _fit_sag(X, y, eta, self.alpha, loss,
self.max_iter, self.rng)
def fit(self, X, y):
self._set_label_transformers(y)
y = np.asfortranarray(self.label_binarizer_.transform(y),
dtype=np.float64)
if self.eta is None or self.eta == 'auto':
eta = get_auto_step_size(get_dataset(X, order="c"), self.alpha,
self.loss, self.is_saga)
else:
eta = self.eta
if self.alpha * eta == 1:
# to match the beaviour of SAGA
# in this case SAGA decreases slightly eta
eta *= 0.9
loss = self._get_loss(self.loss)
self.penalty = self._get_penalty(self.penalty)
if not self.is_saga and self.penalty is not None:
raise ValueError("PySAGClassifier only accepts l2 penalty. Please "
"use `saga=True` or PySAGAClassifier.")
if self.is_saga:
self.coef_ = _fit_saga(X, y, eta, self.alpha, loss, self.penalty,
self.max_iter, self.rng)
else:
self.coef_ = _fit_sag(X, y, eta, self.alpha, loss, self.max_iter,
self.rng)
def fit(self, X, y):
self.label_binarizer_ = LabelBinarizer(neg_label=-1, pos_label=1)
y = np.asfortranarray(self.label_binarizer_.fit_transform(y), dtype=np.float64)
if self.eta is None or self.eta == "auto":
self.eta = get_auto_step_size(X, self.alpha, self.loss, self.is_saga)
self.loss = self._get_loss(self.loss)
self.penalty = self._get_penalty(self.penalty)
if not self.is_saga and self.penalty is not None:
raise ValueError("PySAGClassifier only accepts l2 penalty. Please " "use `saga=True` or PySAGAClassifier.")
if self.is_saga:
self.coef_ = _fit_saga(X, y, self.eta, self.alpha, self.loss, self.penalty, self.max_iter, self.rng)
else:
self.coef_ = _fit_sag(X, y, self.eta, self.alpha, self.loss, self.penalty, self.max_iter, self.rng)