scikit-learn
diff --git a/‎sklearn/linear_model/stochastic_gradient.py
Copy file name to clipboardExpand all lines: sklearn/linear_model/stochastic_gradient.py
+5-5Lines changed: 5 additions & 5 deletions b/‎sklearn/linear_model/stochastic_gradient.py
Copy file name to clipboardExpand all lines: sklearn/linear_model/stochastic_gradient.py
+5-5Lines changed: 5 additions & 5 deletions
@@ -92,7 +92,7 @@ def _validate_params(self, set_max_iter=True):
             raise ValueError("max_iter must be > zero. Got %f" % self.max_iter)
         if not (0.0 <= self.l1_ratio <= 1.0):
             raise ValueError("l1_ratio must be in [0, 1]")
-        if self.alpha < 0.0:
+        if not isinstance(self, SGDOneClassSVM) and self.alpha < 0.0:
             raise ValueError("alpha must be >= 0")
         if self.learning_rate in ("constant", "invscaling"):
             if self.eta0 <= 0.0:
@@ -1611,10 +1611,10 @@ def partial_fit(self, X, y=None, sample_weight=None):
         self : returns an instance of self.
         """
 
-        self.alpha = self.nu / 2
+        alpha = self.nu / 2
         self._validate_params()
 
-        return self._partial_fit(X, self.alpha, C=1.0, loss=self.loss,
+        return self._partial_fit(X, alpha, C=1.0, loss=self.loss,
                                  learning_rate=self.learning_rate,
                                  max_iter=1,
                                  sample_weight=sample_weight,
@@ -1688,8 +1688,8 @@ def fit(self, X, y=None, coef_init=None, offset_init=None,
         self : returns an instance of self.
         """
 
-        self.alpha = self.nu / 2
-        self._fit(X, alpha=self.alpha, C=1.0,
+        alpha = self.nu / 2
+        self._fit(X, alpha=alpha, C=1.0,
                   loss=self.loss, learning_rate=self.learning_rate,
                   coef_init=coef_init, offset_init=offset_init,
                   sample_weight=sample_weight)