Hi, i am trying to implement collaborative filtering for implicit feedback dataset based on Lesson 5 https://github.com/fastai/fastai/blob/master/courses/dl1/lesson5-movielens.ipynb and this code https://github.com/maciejkula/spotlight/blob/master/spotlight/factorization/implicit.py. The issue is that the loss is wiggling around 1.0 and not going down. I tried different learning rates, weight decay, etc. I must be doing something wrong or there is a mistake somewhere in the my code. Here is the full code:
from fastai.imports import *
from fastai.column_data import *
class ScaledEmbedding(nn.Embedding):
def reset_parameters(self):
self.weight.data.normal_(0, 1.0 / self.embedding_dim)
if self.padding_idx is not None:
self.weight.data[self.padding_idx].fill_(0)
class ZeroEmbedding(nn.Embedding):
def reset_parameters(self):
self.weight.data.zero_()
if self.padding_idx is not None:
self.weight.data[self.padding_idx].fill_(0)
class ImplicitFeedBackModel(nn.Module):
def __init__(self, n_factors, n_users, n_items):
super().__init__()
self.n_items = n_items
self._random_state = np.random.RandomState()
self.u = ScaledEmbedding(n_users, n_factors)
self.i = ScaledEmbedding(n_items, n_factors)
self.ub = ZeroEmbedding(n_users, 1)
self.ib = ZeroEmbedding(n_items, 1)
def forward(self, users, items):
#import pdb;pdb.set_trace()
um = self.u(users) * self.i(items)
positive_predictions = um.sum(1) + self.ub(users).squeeze() + self.ib(items).squeeze()
random_items = self._random_state.randint(0, self.n_items, len(users), dtype=np.int64)
um2 = self.u(users) * self.i(V(random_items))
negative_predictions = um2.sum(1) + self.ub(users).squeeze() + self.ib(V(random_items)).squeeze()
return (positive_predictions, negative_predictions)
class ImplicitCollabFilterDataset(CollabFilterDataset):
def get_model(self, n_factors):
model = ImplicitFeedBackModel(n_factors, self.n_users, self.n_items)
return CollabFilterModel(to_gpu(model))
def get_learner(self, n_factors, val_idxs, bs, **kwargs):
return ImplicitCollabFilterLearner(self.get_data(val_idxs, bs), self.get_model(n_factors), **kwargs)
class ImplicitCollabFilterLearner(Learner):
def __init__(self, data, models, **kwargs):
super().__init__(data, models, **kwargs)
def _get_crit(self, data): return pointwise_loss
def pointwise_loss(x,y):
positives_loss = 1.0 - F.sigmoid(x[0])
negatives_loss = F.sigmoid(x[1])
loss = positives_loss + negatives_loss
return loss.mean()
path = 'data/ml-latest-small/ml-latest-small/'
ratings = pd.read_csv(path+'ratings.csv')
val_idxs = get_cv_idxs(len(ratings))
n_factors = 32
bs = 256
dataset = ImplicitCollabFilterDataset.from_csv(path,'ratings.csv', 'userId', 'movieId','rating')
learner = dataset.get_learner(n_factors, val_idxs, bs, opt_fn=optim.Adam)
learner.fit(1e-2, 3, cycle_len=1, cycle_mult=2)
Maybe someone already tried to implement this approach.
Please advise.
Thanks.
