def recommend(self, uids): print 'Recommending' bestMethod = self.best_alg() alg = AlgFactory.create(bestMethod) alg.fit(self.samples, self.targets) recommendList = [] for u in uids: recommendList.append(alg.recommend(u)) return recommendList
def recommendAll(self): print 'Recommending' # bestMethod = self.best_alg() # alg = AlgFactory.create(bestMethod) alg = AlgFactory.create('ItemCF') uids = list(set(np.array(self.samples)[:,0])) alg.fit(self.samples, self.targets) recommendList = [] for u in uids: rec = alg.recommend(u) rec.insert(0, u) recommendList.append(rec) t = pd.DataFrame(recommendList) t.to_csv('rec_list')
def mulProcess(self,result,processParameters): algName = processParameters[0] parameters = processParameters[1] alg = AlgFactory.create(algName) rec_cv = StratifiedKFold(self.labels, 2) clf = grid_search.GridSearchCV(alg, parameters, cv=rec_cv) clf.fit(self.samples, self.targets) print(clf.best_estimator_) print(clf.grid_scores_) self.Lock.acquire() result.append(algName) result.append([clf.best_estimator_,clf.best_score_]) result.append(clf.grid_scores_) self.Lock.release()