def handle(self, *args, **options):
username = sys.argv[2]
X = []
y = []
for user_id, work_id, choice in Rating.objects.values_list('user_id', 'work_id', 'choice'):
X.append((user_id, work_id))
y.append(rating_values[choice])
svd = MangakiSVD()
# svd.fit(X, y)
svd.load('backup.pickle')
svd.get_reco(username, sending=True)
def __init__(self, dataset_name):
self.algos = [
lambda: MangakiALS(10),
lambda: MangakiALS(20),
lambda: MangakiALS(30),
lambda: MangakiALS(40),
lambda: MangakiWALS(20),
lambda: MangakiSVD(10),
lambda: MangakiSVD(20),
lambda: MangakiSVD(30),
lambda: MangakiSVD(40),
lambda: MangakiSVD(50),
lambda: MangakiPCA(20),
lambda: MangakiKNN(20),
lambda: MangakiKNN(40),
lambda: MangakiZero()
]
self.anonymized = None
self.load_dataset(dataset_name)
def __init__(self, PIG_ID=None):
self.algos = [
MangakiALS(20),
MangakiWALS(20),
MangakiSVD(20),
MangakiKNN(20),
MangakiZero()
]
# self.results.setdefault('x_axis', []).append()
self.make_dataset(PIG_ID)
self.execute()
def __init__(self, PIG):
for TRAIN_PIG_LENGTH in range(10, 150, 20):
print(TRAIN_PIG_LENGTH)
self.algos = [
MangakiSVD(),
MangakiKNN(),
MangakiKNN(30),
MangakiKNN(45)
]
self.results.setdefault('x_axis', []).append(TRAIN_PIG_LENGTH)
self.make_dataset(TRAIN_PIG_LENGTH)
self.execute()
def handle(self, *args, **options):
username = sys.argv[2]
X = []
y = []
for user_id, work_id, choice in Rating.objects.values_list('user_id', 'work_id', 'choice'):
X.append((user_id, work_id))
y.append(rating_values[choice])
svd = MangakiSVD()
# svd.fit(X, y)
svd.load('backup.pickle')
svd.get_reco(username, sending=True)
from mangaki.utils.wals import MangakiWALS
from mangaki.utils.als import MangakiALS
from mangaki.utils.knn import MangakiKNN
from mangaki.utils.svd import MangakiSVD
from mangaki.utils.data import Dataset
ALGOS = {
'knn': lambda: MangakiKNN(),
'svd': lambda: MangakiSVD(20),
'als': lambda: MangakiALS(20),
'wals': lambda: MangakiWALS(20),
}
def fit_algo(algo_name, queryset, backup_filename):
algo = ALGOS[algo_name]()
dataset = Dataset()
anonymized = dataset.make_anonymous_data(queryset)
algo.set_parameters(anonymized.nb_users, anonymized.nb_works)
algo.fit(anonymized.X, anonymized.y)
if algo_name in {'svd', 'als', 'wals'}: # KNN is constantly refreshed
algo.save(backup_filename)
dataset.save('ratings-' + backup_filename)
return dataset, algo
def handle(self, *args, **options):
username = sys.argv[2]
svd = MangakiSVD()
svd.load('backup.pickle')
svd.get_reco(username, sending=True)