'Fit linear model with Stochastic Gradient Descent.
Parameters
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of the training data
y : numpy array, shape (n_samples,)
Subset of the target values
classes : array, shape (n_classes,)
Classes across all calls to partial_fit.
Can be obtained by via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn\'t need to contain all labels in `classes`.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.
Returns
self : returns an instance of self.'

def partial_fit(self, X, y, classes=None, sample_weight=None):

    if (self.class_weight in ['balanced']):
        raise ValueError("class_weight    '{0}'    is    not    supported    for    partial_fit.    In    order    to    use    'balanced'    weights,    use    compute_class_weight('{0}',    classes,    y).    In    place    of    y    you    can    us    a    large    enough    sample    of    the    full    training    set    target    to    properly    estimate    the    class    frequency    distributions.    Pass    the    resulting    weights    as    the    class_weight    parameter.".format(self.class_weight))
    return self._partial_fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss, learning_rate=self.learning_rate, max_iter=1, classes=classes, sample_weight=sample_weight, coef_init=None, intercept_init=None)
