def get_algs(): # create a dict of (textual algorithm description => class) to be evaluated algs = {} ara = ar.AssosiationRules() algs['ar'] = ara del ara sra = sr.SequentialRules(10, weighting='div', pruning=20) algs['sr10-div'] = sra del sra sknna = sknn.ContextKNN(500, 1000, similarity="cosine") algs['sknn-500-1000-cosine'] = sknna del sknna ssknna = ssknn.SeqContextKNN(100, 500, similarity="cosine") algs['ssknn-100-500-cosine'] = ssknna del ssknna knn = sfsknn.SeqFilterContextKNN(100, 500, similarity="cosine") algs['sfknn-100-500-cosine'] = knn del knn vsknna = vsknn.VMContextKNN(100, 1000, similarity="cosine") algs['svmknn-100-1000-cosine'] = vsknna vsknna = vsknn.VMContextKNN(100, 2000, similarity="cosine") algs['svmknn-100-2000-cosine'] = vsknna del vsknna grunew = gru4rec2.GRU4Rec(loss='bpr-max-0.5', final_act='linear', hidden_act='tanh', layers=[100], batch_size=32, dropout_p_hidden=0.0, learning_rate=0.2, momentum=0.5, n_sample=2048, sample_alpha=0, time_sort=True) algs['gru-100-bpr-max-0.5'] = grunew del grunew return algs
# algs['sr10-div'] = sra # # ara = ar.AssosiationRules(); # algs['ar'] = ara # # #knn # # iknn = iknn.ItemKNN() # algs['iknn'] = iknn # # sknn = sknn.ContextKNN( 100, 500, similarity="cosine", extend=False ) # algs['sknn-100-500-cosine'] = sknn # vmsknn = vsknn.VMContextKNN(100, 2000, similarity="cosine", last_n_days=None, extend=False) algs['vsknn-200-2000-cosine'] = vmsknn # # ssknn = ssknn.SeqContextKNN( 100, 500, similarity="cosine", extend=False ) # algs['ssknn-100-500-cosine-div'] = ssknn # # sfsknn = sfsknn.SeqFilterContextKNN( 100, 500, similarity="cosine", extend=False ) # algs['sfsknn-100-500-cosine-div'] = ssknn # # #gr4rec2 # # gru = gru4rec2.GRU4Rec(n_epochs=10, loss='bpr-max-0.5', final_act='linear', hidden_act='tanh', layers=[100], batch_size=32, dropout_p_hidden=0.0, learning_rate=0.2, momentum=0.5, n_sample=2048, sample_alpha=0, time_sort=True) # algs['gru-100-bpr-max-0.5'] = gru #