@property
    def partial_fit(self):
        """Fit the model to data matrix X and target y.

        Parameters
        ----------
        X : {array-like, sparse matrix}, shape (n_samples, n_features)
            The input data.

        y : array-like, shape (n_samples,)
            The target values.

        classes : array, shape (n_classes)
            Classes across all calls to partial_fit.
            Can be obtained 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`.

        Returns
        -------
        self : returns a trained MLP model.
        """
        if _STOCHASTIC_ALGOS not in self.algorithm:
            raise AttributeError("partial_fit is only available for stochastic"
                                 "optimization algorithms. %s is not"
                                 " stochastic" % self.algorithm)
        return self._partial_fit
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/neural_network/multilayer_perceptron.py/MLPClassifier.partial_fit/swapped operands of `not in`