Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Improve the performance of from_pandas in the case of low-cardinality categoricals #275

Merged
merged 2 commits into from
Jul 18, 2023
Merged

Improve the performance of from_pandas in the case of low-cardinality categoricals #275

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
f1860ab
Improve the performance of `from_pandas`
stanmart Jun 29, 2023
2a3bcad
Update changelog according to review
stanmart Jul 18, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
7 changes: 7 additions & 0 deletions CHANGELOG.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,13 @@
Changelog
=========

Unreleased
----------

**Other changes:**

- Improve the performance of ``from_pandas`` in the case of low-cardinality categorical variables.

3.1.10 - 2023-06-23
-------------------

Expand Down
12 changes: 2 additions & 10 deletions src/tabmat/constructor.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -72,22 +72,15 @@ def from_pandas(
if object_as_cat and coldata.dtype == object:
coldata = coldata.astype("category")
if isinstance(coldata.dtype, pd.CategoricalDtype):
cat = CategoricalMatrix(coldata, drop_first=drop_first, dtype=dtype)
if len(coldata.cat.categories) < cat_threshold:
(
X_dense_F,
X_sparse,
dense_indices,
sparse_indices,
) = _split_sparse_and_dense_parts(
pd.get_dummies(
coldata,
prefix=colname,
sparse=True,
drop_first=drop_first,
dtype=np.float64,
)
.sparse.to_coo()
.tocsc(),
sps.csc_matrix(cat.tocsr(), dtype=dtype),
threshold=sparse_threshold,
)
matrices.append(X_dense_F)
Expand All @@ -103,7 +96,6 @@ def from_pandas(
indices.append(sparse_indices)

else:
cat = CategoricalMatrix(coldata, drop_first=drop_first, dtype=dtype)
matrices.append(cat)
is_cat.append(True)
if cat_position == "expand":
Expand Down
Loading