knns.py 文件源码

def estimate(self, u, i):

        if not (self.trainset.knows_user(u) and self.trainset.knows_item(i)):
            raise PredictionImpossible('User and/or item is unkown.')

        x, y = self.switch(u, i)

        neighbors = [(self.sim[x, x2], r) for (x2, r) in self.yr[y]]
        k_neighbors = heapq.nlargest(self.k, neighbors, key=lambda t: t[0])

        # compute weighted average
        sum_sim = sum_ratings = actual_k = 0
        for (sim, r) in k_neighbors:
            if sim > 0:
                sum_sim += sim
                sum_ratings += sim * r
                actual_k += 1

        if actual_k < self.min_k:
            raise PredictionImpossible('Not enough neighbors.')

        est = sum_ratings / sum_sim

        details = {'actual_k': actual_k}
        return est, details