BalazsHoranyi

interactions = da.from_npy_stack('interactions')
users = interactions[:,0]
items = interactions[:,1]
slicer = 10000000

for i in tqdm(range(math.ceil((len(interactions))/slicer))):
    if i == 0: 
        user_set = set(users[i*slicer: (i+1)*slicer].compute())
    else:

BalazsHoranyi

def to_dask_array(df):
    #  https://stackoverflow.com/questions/37444943/dask-array-from-dataframe?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
    partitions = df.to_delayed()
    shapes = [part.values.shape for part in partitions]
    dtypes = partitions[0].dtypes

    results = compute(dtypes, *shapes)  # trigger computation to find shape
    dtypes, shapes = results[0], results[1:]

    chunks = [da.from_delayed(part.values, shape, dtypes) 

BalazsHoranyi

def bpr_loss(positive_predictions, negative_predictions):
    """
    Bayesian Personalised Ranking pairwise loss function. Original Implementation: https://github.com/maciejkula/spotlight

    """

    loss = (1.0 - F.sigmoid(positive_predictions -
                            negative_predictions))

    return loss.mean()