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
#