LearningShapelets failed to leave loop

[hongpei100]

Description

Hi, when using the code to learn shapelets on time series, I got stucked inside a while loop as follow, which inside the class "CrossEntropyLearningShapelets". I found loss_iteration will be a fixed number after several rounds in the loop. I provide a quick but not that feasible solution to jump out the infinite loop as follow, which examine if loss at last round is equal to loss_iteration (i.e. loss at current round), and it will leave and continue next learning if it is.

I think the more great solution is using adaptive gradient approach to learn, but it might not the same as the author did in their paper.

Steps/Code to Reproduce

            # If loss is increasing, decrease the learning rate
            last_loss = None
            if losses[-1] < loss_iteration:
                while losses[-1] < loss_iteration:
                    
                    """ Note : Since source code might stuck in this while loop
                    We should stop stucking after loss never change at this round.
                    """
                    if last_loss == loss_iteration:
                        break

                    # Go back to previous state
                    weights += learning_rate * gradient_weights
                    shapelets_array = _reshape_array_shapelets(
                        shapelets, lengths)
                    shapelets_array += learning_rate * gradient_shapelets
                    shapelets = tuple(
                        _reshape_list_shapelets(shapelets_array, lengths))

                    # Update learning  rate
                    learning_rate /= 5

                    # Recompute shapelet gradient
                    weights -= learning_rate * gradient_weights
                    gradient_shapelets = _grad_shapelets(
                        X, y_ind, n_classes, weights, shapelets, lengths,
                        self.alpha, self.penalty, self.C, self.fit_intercept,
                        self.intercept_scaling, sample_weight
                    )
                    shapelets_array = _reshape_array_shapelets(
                        shapelets, lengths)
                    shapelets_array -= learning_rate * gradient_shapelets
                    shapelets = tuple(
                        _reshape_list_shapelets(shapelets_array, lengths))

                    last_loss = loss_iteration
                    loss_iteration = _loss(
                        X, y_ind, n_classes, weights, shapelets, lengths,
                        self.alpha, self.penalty, self.C, self.fit_intercept,
                        self.intercept_scaling, sample_weight
                    )

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