Binary Predictions with Negative Sampling

Example of a recommender system making binary predictions instead of predicting a rating. Demonstrates use of NegativeSamplingDataIter to wrap an existing data iterator with CosineLoss. See BlackOut by Shihao Ji et al for more on negative sampling.

You need to have python package pandas and bokeh installed (pip install pandas bokeh).



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import mxnet as mx
from movielens_data import get_data_iter, max_id
from matrix_fact import train
import recotools



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# If MXNet is not compiled with GPU support (e.g. on OSX), set to [mx.cpu(0)]
# Can be changed to [mx.gpu(0), mx.gpu(1), ..., mx.gpu(N-1)] if there are N GPUs
ctx = [mx.gpu(0)]



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pos_train_data, pos_test_data = get_data_iter(batch_size=100)
max_user, max_item = max_id('./ml-100k/u.data')
(max_user, max_item)



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train_data = recotools.NegativeSamplingDataIter(pos_train_data, sample_ratio=3, positive_label=0, negative_label=1)
test_data = recotools.NegativeSamplingDataIter(pos_test_data, sample_ratio=3,   positive_label=0, negative_label=1)
train_test_data = (train_data, test_data)



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def plain_net(k):
    # input
    user = mx.symbol.Variable('user')
    item = mx.symbol.Variable('item')
    label = mx.symbol.Variable('score')
    # user feature lookup
    user = mx.symbol.Embedding(data = user, input_dim = max_user, output_dim = k)
    # item feature lookup
    item = mx.symbol.Embedding(data = item, input_dim = max_item, output_dim = k)
    # loss layer
    pred = recotools.CosineLoss(a=user, b=item, label=label)
    return pred

net1 = plain_net(64)
mx.viz.plot_network(net1)



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results1 = train(net1, train_test_data, num_epoch=20, learning_rate=0.02, ctx=ctx)



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