train_sparse = sparse.csr_matrix((train[2], (train[0], train[1])),
shape=(len(customers), len(products)))
print("IO done in %f" % io_time.interval)
alpha = 15
with Timer() as cython_als_t:
user_vecs, item_vecs = implicit.alternating_least_squares(
(train_sparse * alpha).astype('double'),
factors=64,
regularization=0.1,
iterations=10,
use_gpu=False)
print(f"Time spent in implicit: {cython_als_t.interval}")
evaluator = Evaluator(test[0], test[1], test[2], threshold=3.0)
baseline_model = BaselinePredictor(train[1], train[2])
baseline_fpr, baseline_tpr, baseline_roc = evaluator.roc(
lambda user, item: baseline_model.pred(item))
fpr, tpr, roc = evaluator.roc(
lambda user, item: np.sum(user_vecs[user, :] * item_vecs[item, :]))
print("AUC: %f" % roc)
plt.clf()
plt.plot(baseline_fpr, baseline_tpr, label='baseline')
plt.plot(fpr, tpr, label='als')
plt.xlabel('False positive')
plt.ylabel('True positive')
plt.legend()
plt.show()
grouped_purchased.CustomerID.unique())) # Get our unique customers
products = list(grouped_purchased.StockCode.unique()
) # Get our unique products that were purchased
quantity = list(grouped_purchased.Quantity) # All of our purchases
rows = grouped_purchased.CustomerID.astype(
pd.CategoricalDtype(categories=customers, ordered=True)).cat.codes
# Get the associated row indices
cols = grouped_purchased.StockCode.astype(
pd.CategoricalDtype(categories=products, ordered=True)).cat.codes
train, test = train_test_split(rows.values, cols.values, quantity)
evaluator = Evaluator(test[0], test[1], test[2])
baseline_model = BaselinePredictor(train[1], train[2])
baseline_fpr, baseline_tpr, baseline_roc = evaluator.roc(
lambda user, item: baseline_model.pred(item))
train_sparse = sparse.csr_matrix((train[2], (train[0], train[1])),
shape=(len(customers), len(products)))
alpha = 15
with Timer() as cython_als_t:
user_vecs, item_vecs = implicit.alternating_least_squares(
(train_sparse * alpha).astype('double'),
factors=32,
regularization=0.1,
iterations=50)
print(f"Time spent in implicit: {cython_als_t.interval}")
svd_predictor = lambda user, item: np.sum(user_vecs[user, :] * item_vecs[
item, :])
