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Fix 35/optimize personalization calculation #36

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Oct 27, 2021
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Fix 35/optimize personalization calculation #36

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d08457a
change python to python3
ibuda Oct 26, 2021
df3dee4
optimize mean similarity calculation
ibuda Oct 26, 2021
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2 changes: 1 addition & 1 deletion Makefile
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Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@ upload: clean

# Remove files from repo
clean:
python setup.py clean --all
python3 setup.py clean --all
rm -rf *.pyc __pycache__ build dist recmetrics.egg-info recmetrics/__pycache__ tests/__pycache__ tests/reports docs/build .pytest_cache .tox .coverage

# Download MovieLens data to repo
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6 changes: 2 additions & 4 deletions recmetrics/metrics.py
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Expand Up @@ -189,11 +189,9 @@ def make_rec_matrix(predicted: List[list]) -> sp.csr_matrix:
#calculate similarity for every user's recommendation list
similarity = cosine_similarity(X=rec_matrix_sparse, dense_output=False)

#get indicies for upper right triangle w/o diagonal
upper_right = np.triu_indices(similarity.shape[0], k=1)

#calculate average similarity
personalization = np.mean(similarity[upper_right])
dim = similarity.shape[0]
personalization = (similarity.sum() - dim) / (dim * (dim - 1))
return 1-personalization

def _single_list_similarity(predicted: list, feature_df: pd.DataFrame, u: int) -> float:
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