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Improve SAR performance #914

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merged 2 commits into from
Sep 4, 2019
+6 −15
Merged

Improve SAR performance #914

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4c875df
Improve score() performance
zegerius Sep 4, 2019
97bcefc
Simplify remove_seen
zegerius Sep 4, 2019
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21 changes: 6 additions & 15 deletions reco_utils/recommender/sar/sar_singlenode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -106,9 +106,6 @@ def __init__(
# the opposite of the above map - map array index to actual string ID
self.index2item = None

# track user-item pairs seen during training
self.seen_items = None

def compute_affinity_matrix(self, df, rating_col):
""" Affinity matrix.
Expand Down Expand Up @@ -244,9 +241,6 @@ def fit(self, df):
temp_df = self.compute_time_decay(df=temp_df, decay_column=self.col_unity_rating)
self.unity_user_affinity = self.compute_affinity_matrix(df=temp_df, rating_col=self.col_unity_rating)

# retain seen items for removal at prediction time
self.seen_items = temp_df[[self.col_user_id, self.col_item_id]].values

# affinity matrix
logger.info("Building user affinity sparse matrix")
self.user_affinity = self.compute_affinity_matrix(df=temp_df, rating_col=self.col_rating)
Expand Down Expand Up @@ -301,20 +295,17 @@ def score(self, test, remove_seen=False, normalize=False):

# calculate raw scores with a matrix multiplication
logger.info("Calculating recommendation scores")
# TODO: only compute scores for users in test
test_scores = self.user_affinity.dot(self.item_similarity)

# remove items in the train set so recommended items are always novel
if remove_seen:
logger.info("Removing seen items")
test_scores[self.seen_items[:, 0], self.seen_items[:, 1]] = -np.inf

test_scores = test_scores[user_ids, :]
test_scores = self.user_affinity[user_ids, :].dot(self.item_similarity)

# ensure we're working with a dense ndarray
if isinstance(test_scores, sparse.spmatrix):
test_scores = test_scores.toarray()

# remove items in the train set so recommended items are always novel
if remove_seen:
logger.info("Removing seen items")
test_scores += self.user_affinity[user_ids, :] * -np.inf

if normalize:
if self.unity_user_affinity is None:
raise ValueError('Cannot use normalize flag during scoring if it was not set at model instantiation')
Expand Down