def score_individual(self,ytest,how='tau'): """ Scores each individual near neighbor. Returns ---------- dist : 1d array of the average distances for each near neighbor score : 2d (num of NN, prediction distance) array of the score values """ num_neighbors = self.dist.shape[1] num_preds = self.ytrain.shape[1] score = np.zeros((num_neighbors, num_preds)) for i in range(num_neighbors): ypred = self.ytrain[self.ind[:,i]] # grab all the 1st NN, then 2nd, etc... for j in range(num_preds): if how == 'classCompare': score[i,j] = mets.classCompare(ypred[:,j], ytest[:,j]) elif how == 'classError': score[i,j] = mets.classificationError(ypred[:,j], ytest[:,j]) elif how == 'tau': score[i,j] = mets.kleckas_tau(ypred[:,j], ytest[:,j]) avg_dist = np.mean(self.dist,axis=0) return avg_dist, score
def score(self, ytest, how='tau'): """ Evalulate the predictions Parameters ---------- ytest : 2d array containing the targets how : string how to score the predictions -'classCompare' : percent correctly predicted -'classError' : Dont use this -'tau' : kleckas tau """ num_preds = ytest.shape[1] sc = np.empty((1,num_preds)) scores = [] for pred in self.ypred: sc = np.empty(pred.shape[1]) for i in range(pred.shape[1]): p = pred[:,i] if how == 'classCompare': sc[i] = mets.classCompare(p,ytest[:,i]) elif how == 'classError': sc[i] = mets.classificationError(p,ytest[:,i]) elif how == 'tau': sc[i] = mets.kleckas_tau(p,ytest[:,i]) scores.append(sc) scores = np.vstack(scores) return scores
