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TST use global_dtype in sklearn/cluster/tests/test_birch.py #22671

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Merged
merged 9 commits into from
Nov 18, 2022
Merged

TST use global_dtype in sklearn/cluster/tests/test_birch.py #22671

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dc28003
TST Adapt test_birch.py to test implementations on 32bit datasets
jjerphan Mar 3, 2022
6b15d05
Merge branch 'main' into tst/test_birch-32bit
jjerphan Mar 18, 2022
4c2d554
TST Use global_dtype
jjerphan Mar 18, 2022
c373561
TST Use assert_allclose
jjerphan Mar 18, 2022
5e44853
Merge branch 'main' into tst/test_birch-32bit
jjerphan Oct 21, 2022
c892c9f
TST Remove modification made to unrelated tests
jjerphan Oct 21, 2022
d4483c6
Merge branch 'main' into tst/test_birch-32bit
jjerphan Oct 24, 2022
50a43d7
TST Also assert dtype of subscluster_centers_ for the sparse case
jjerphan Nov 2, 2022
5cafa48
Merge branch 'main' into tst/test_birch-32bit
jeremiedbb Nov 18, 2022
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28 changes: 21 additions & 7 deletions sklearn/cluster/tests/test_birch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,9 +17,10 @@
from sklearn.utils._testing import assert_allclose


def test_n_samples_leaves_roots(global_random_seed):
def test_n_samples_leaves_roots(global_random_seed, global_dtype):
# Sanity check for the number of samples in leaves and roots
X, y = make_blobs(n_samples=10, random_state=global_random_seed)
X = X.astype(global_dtype, copy=False)
brc = Birch()
brc.fit(X)
n_samples_root = sum([sc.n_samples_ for sc in brc.root_.subclusters_])
Expand All @@ -30,9 +31,10 @@ def test_n_samples_leaves_roots(global_random_seed):
assert n_samples_root == X.shape[0]


def test_partial_fit(global_random_seed):
def test_partial_fit(global_random_seed, global_dtype):
# Test that fit is equivalent to calling partial_fit multiple times
X, y = make_blobs(n_samples=100, random_state=global_random_seed)
X = X.astype(global_dtype, copy=False)
brc = Birch(n_clusters=3)
brc.fit(X)
brc_partial = Birch(n_clusters=None)
Expand All @@ -47,17 +49,22 @@ def test_partial_fit(global_random_seed):
assert_array_equal(brc_partial.subcluster_labels_, brc.subcluster_labels_)


def test_birch_predict(global_random_seed):
def test_birch_predict(global_random_seed, global_dtype):
# Test the predict method predicts the nearest centroid.
rng = np.random.RandomState(global_random_seed)
X = generate_clustered_data(n_clusters=3, n_features=3, n_samples_per_cluster=10)
X = X.astype(global_dtype, copy=False)

# n_samples * n_samples_per_cluster
shuffle_indices = np.arange(30)
rng.shuffle(shuffle_indices)
X_shuffle = X[shuffle_indices, :]
brc = Birch(n_clusters=4, threshold=1.0)
brc.fit(X_shuffle)

# Birch must preserve inputs' dtype
assert brc.subcluster_centers_.dtype == global_dtype

assert_array_equal(brc.labels_, brc.predict(X_shuffle))
centroids = brc.subcluster_centers_
nearest_centroid = brc.subcluster_labels_[
Expand All @@ -66,9 +73,10 @@ def test_birch_predict(global_random_seed):
assert_allclose(v_measure_score(nearest_centroid, brc.labels_), 1.0)


def test_n_clusters(global_random_seed):
def test_n_clusters(global_random_seed, global_dtype):
# Test that n_clusters param works properly
X, y = make_blobs(n_samples=100, centers=10, random_state=global_random_seed)
X = X.astype(global_dtype, copy=False)
brc1 = Birch(n_clusters=10)
brc1.fit(X)
assert len(brc1.subcluster_centers_) > 10
Expand All @@ -88,16 +96,20 @@ def test_n_clusters(global_random_seed):
brc4.fit(X)


def test_sparse_X(global_random_seed):
def test_sparse_X(global_random_seed, global_dtype):
# Test that sparse and dense data give same results
X, y = make_blobs(n_samples=100, centers=10, random_state=global_random_seed)
X = X.astype(global_dtype, copy=False)
brc = Birch(n_clusters=10)
brc.fit(X)

csr = sparse.csr_matrix(X)
brc_sparse = Birch(n_clusters=10)
brc_sparse.fit(csr)

# Birch must preserve inputs' dtype
assert brc_sparse.subcluster_centers_.dtype == global_dtype

assert_array_equal(brc.labels_, brc_sparse.labels_)
assert_allclose(brc.subcluster_centers_, brc_sparse.subcluster_centers_)

Expand All @@ -122,9 +134,10 @@ def check_branching_factor(node, branching_factor):
check_branching_factor(cluster.child_, branching_factor)


def test_branching_factor(global_random_seed):
def test_branching_factor(global_random_seed, global_dtype):
# Test that nodes have at max branching_factor number of subclusters
X, y = make_blobs(random_state=global_random_seed)
X = X.astype(global_dtype, copy=False)
branching_factor = 9

# Purposefully set a low threshold to maximize the subclusters.
Expand All @@ -146,9 +159,10 @@ def check_threshold(birch_instance, threshold):
current_leaf = current_leaf.next_leaf_


def test_threshold(global_random_seed):
def test_threshold(global_random_seed, global_dtype):
# Test that the leaf subclusters have a threshold lesser than radius
X, y = make_blobs(n_samples=80, centers=4, random_state=global_random_seed)
X = X.astype(global_dtype, copy=False)
brc = Birch(threshold=0.5, n_clusters=None)
brc.fit(X)
check_threshold(brc, 0.5)
Expand Down