8000 TST Extend tests for `scipy.sparse.*array` in `sklearn/cluster/tests/test_bisect_k_means.py` by Kislovskiy · Pull Request #27099 · scikit-learn/scikit-learn · GitHub
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TST Extend tests for scipy.sparse.*array in sklearn/cluster/tests/test_bisect_k_means.py #27099

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Merged
merged 4 commits into from
Aug 18, 2023
Merged

TST Extend tests for scipy.sparse.*array in sklearn/cluster/tests/test_bisect_k_means.py #27099

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b6dd603
Use CSR_CONTAINERS
Kislovskiy Aug 18, 2023
4e4bfe5
Merge branch 'main' into towards-27090-test_bisect_k_means.py
Kislovskiy Aug 18, 2023
a0a9e6d
add missing csr_containers
Kislovskiy Aug 18, 2023
cb75060
X.getformat is deprecated, use X.format instead
ogrisel Aug 18, 2023
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31 changes: 16 additions & 15 deletions sklearn/cluster/tests/test_bisect_k_means.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,10 @@
import numpy as np
import pytest
import scipy.sparse as sp

from sklearn.cluster import BisectingKMeans
from sklearn.metrics import v_measure_score
from sklearn.utils._testing import assert_allclose, assert_array_equal
from sklearn.utils.fixes import CSR_CONTAINERS


@pytest.mark.parametrize("bisecting_strategy", ["biggest_inertia", "largest_cluster"])
Expand Down Expand Up @@ -33,7 +33,8 @@ def test_three_clusters(bisecting_strategy, init):
assert_allclose(v_measure_score(expected_labels, bisect_means.labels_), 1.0)


def test_sparse():
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS)
def test_sparse(csr_container):
"""Test Bisecting K-Means with sparse data.

Checks if labels and centers are the same between dense and sparse.
Expand All @@ -43,7 +44,7 @@ def test_sparse():

X = rng.rand(20, 2)
X[X < 0.8] = 0
X_csr = sp.csr_matrix(X)
X_csr = csr_container(X)

bisect_means = BisectingKMeans(n_clusters=3, random_state=0)

Expand Down Expand Up @@ -84,48 +85,48 @@ def test_one_cluster():
assert_allclose(bisect_means.cluster_centers_, X.mean(axis=0).reshape(1, -1))


@pytest.mark.parametrize("is_sparse", [True, False])
def test_fit_predict(is_sparse):
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS + [None])
def test_fit_predict(csr_container):
"""Check if labels from fit(X) method are same as from fit(X).predict(X)."""
rng = np.random.RandomState(0)

X = rng.rand(10, 2)

if is_sparse:
if csr_container is not None:
X[X < 0.8] = 0
X = sp.csr_matrix(X)
X = csr_container(X)

bisect_means = BisectingKMeans(n_clusters=3, random_state=0)
bisect_means.fit(X)

assert_array_equal(bisect_means.labels_, bisect_means.predict(X))


@pytest.mark.parametrize("is_sparse", [True, False])
def test_dtype_preserved(is_sparse, global_dtype):
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS + [None])
def test_dtype_preserved(csr_container, global_dtype):
"""Check that centers dtype is the same as input data dtype."""
rng = np.random.RandomState(0)
X = rng.rand(10, 2).astype(global_dtype, copy=False)

if is_sparse:
if csr_container is not None:
X[X < 0.8] = 0
X = sp.csr_matrix(X)
X = csr_container(X)

km = BisectingKMeans(n_clusters=3, random_state=0)
km.fit(X)

assert km.cluster_centers_.dtype == global_dtype


@pytest.mark.parametrize("is_sparse", [True, False])
def test_float32_float64_equivalence(is_sparse):
@pytest.mark.parametrize("csr_container", CSR_CONTAINERS + [None])
def test_float32_float64_equivalence(csr_container):
"""Check that the results are the same between float32 and float64."""
rng = np.random.RandomState(0)
X = rng.rand(10, 2)

if is_sparse:
if csr_container is not None:
X[X < 0.8] = 0
X = sp.csr_matrix(X)
X = csr_container(X)

km64 = BisectingKMeans(n_clusters=3, random_state=0).fit(X)
km32 = BisectingKMeans(n_clusters=3, random_state=0).fit(X.astype(np.float32))
Expand Down
4 changes: 2 additions & 2 deletions sklearn/utils/validation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1002,9 +1002,9 @@ def _check_large_sparse(X, accept_large_sparse=False):
"""Raise a ValueError if X has 64bit indices and accept_large_sparse=False"""
if not accept_large_sparse:
supported_indices = ["int32"]
if X.getformat() == "coo":
if X.format == "coo":
index_keys = ["col", "row"]
elif X.getformat() in ["csr", "csc", "bsr"]:
elif X.format in ["csr", "csc", "bsr"]:
index_keys = ["indices", "indptr"]
else:
return
Expand Down
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