def affinity_matrix(test_specs):
"""Generate a random user/item affinity matrix. By increasing the likehood of 0 elements we simulate
a typical recommending situation where the input matrix is highly sparse.
Args:
users (int): number of users (rows).
items (int): number of items (columns).
ratings (int): rating scale, e.g. 5 meaning rates are from 1 to 5.
spars: probability of obtaining zero. This roughly corresponds to the sparseness.
of the generated matrix. If spars = 0 then the affinity matrix is dense.
Returns:
np.array: sparse user/affinity matrix of integers.
"""
np.random.seed(test_specs["seed"])
# uniform probability for the 5 ratings
s = [(1 - test_specs["spars"]) / test_specs["ratings"]] * test_specs["ratings"]
s.append(test_specs["spars"])
P = s[::-1]
# generates the user/item affinity matrix. Ratings are from 1 to 5, with 0s denoting unrated items
X = np.random.choice(
test_specs["ratings"] + 1, (test_specs["users"], test_specs["items"]), p=P
)
Xtr, Xtst = numpy_stratified_split(
X, ratio=test_specs["ratio"], seed=test_specs["seed"]
)
return (Xtr, Xtst)
def test_int_numpy_stratified_splitter(test_specs, python_int_dataset):
# generate a syntetic dataset
X = python_int_dataset
# the splitter returns (in order): train and test user/affinity matrices, train and test datafarmes and user/items to matrix maps
Xtr, Xtst = numpy_stratified_split(
X, ratio=test_specs["ratio"], seed=test_specs["seed"]
)
# check that the generated matrices have the correct dimensions
assert (Xtr.shape[0] == X.shape[0]) & (Xtr.shape[1] == X.shape[1])
assert (Xtst.shape[0] == X.shape[0]) & (Xtst.shape[1] == X.shape[1])
X_rated = np.sum(X != 0, axis=1) # number of total rated items per user
Xtr_rated = np.sum(Xtr != 0, axis=1) # number of rated items in the train set
Xtst_rated = np.sum(Xtst != 0, axis=1) # number of rated items in the test set
# global split: check that the all dataset is split in the correct ratio
assert Xtr_rated.sum() / (X_rated.sum()) == pytest.approx(
test_specs["ratio"], test_specs["tolerance"]
)
assert Xtst_rated.sum() / (X_rated.sum()) == pytest.approx(
1 - test_specs["ratio"], test_specs["tolerance"]
)
# This implementation of the stratified splitter performs a random split at the single user level. Here we check
# that also this more stringent condition is verified. Note that user to user fluctuations in the split ratio
# are stronger than for the entire dataset due to the random nature of the per user splitting.
# For this reason we allow a slightly bigger tolerance, as specified in the test_specs()
assert (
(Xtr_rated / X_rated <= test_specs["ratio"] + test_specs["fluctuation"]).all()
& (Xtr_rated / X_rated >= test_specs["ratio"] - test_specs["fluctuation"]).all()
)
assert (
(
Xtst_rated / X_rated
<= (1 - test_specs["ratio"]) + test_specs["fluctuation"]
).all()
& (
Xtst_rated / X_rated
>= (1 - test_specs["ratio"]) - test_specs["fluctuation"]
).all()
)
def RBMtrain():
data = pd.read_csv("SnacksData100.csv")
header = {
"col_user": "******",
"col_item": "Product_Id",
"col_rating": "Ratings",
}
am = AffinityMatrix(DF=data, **header)
X = am.gen_affinity_matrix()
Xtr, Xtst = numpy_stratified_split(X)
model = RBM(hidden_units=600,
training_epoch=30,
minibatch_size=60,
keep_prob=0.9,
with_metrics=True)
model.fit(Xtr, Xtst)
top_k, test_time = model.recommend_k_items(Xtst)
top_k_df = am.map_back_sparse(top_k, kind='prediction')
test_df = am.map_back_sparse(Xtst, kind='ratings')
joblib.dump(top_k_df, 'testdata')
header = {
"col_user": "******",
"col_item": "MovieID",
"col_rating": "Rating",
}
# Use a sparse matrix representation rather than a pandas data frame
# for significant performance gain.
am = AffinityMatrix(DF=data, **header)
X = am.gen_affinity_matrix()
# Contstruct the training and test datasets.
Xtr, Xtst = numpy_stratified_split(X)
print('\nTraining matrix size (users, movies) is:', Xtr.shape)
print('Testing matrix size is: ', Xtst.shape)
# Initialize the model class. Note that through random variation we
# can get a much better performing model with seed=1!
model = RBM(
hidden_units=600,
training_epoch=30,
minibatch_size=60,
keep_prob=0.9,
with_metrics=True,
# seed = 1,
)
