def test_minibatch_reassign():
# Give a perfect initialization, but a large reassignment_ratio,
# as a result all the centers should be reassigned and the model
# should no longer be good
sample_weight = np.ones(X.shape[0], dtype=X.dtype)
for this_X in (X, X_csr):
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters,
batch_size=100,
random_state=42)
mb_k_means.fit(this_X)
score_before = mb_k_means.score(this_X)
try:
old_stdout = sys.stdout
sys.stdout = StringIO()
# Turn on verbosity to smoke test the display code
_mini_batch_step(this_X,
sample_weight, (X**2).sum(axis=1),
mb_k_means.cluster_centers_,
mb_k_means.counts_,
np.zeros(X.shape[1], np.double),
False,
distances=np.zeros(X.shape[0]),
random_reassign=True,
random_state=42,
reassignment_ratio=1,
verbose=True)
finally:
sys.stdout = old_stdout
assert score_before > mb_k_means.score(this_X)
# Give a perfect initialization, with a small reassignment_ratio,
# no center should be reassigned
for this_X in (X, X_csr):
mb_k_means = MiniBatchKMeans(n_clusters=n_clusters,
batch_size=100,
init=centers.copy(),
random_state=42,
n_init=1)
mb_k_means.fit(this_X)
clusters_before = mb_k_means.cluster_centers_
# Turn on verbosity to smoke test the display code
_mini_batch_step(this_X,
sample_weight, (X**2).sum(axis=1),
mb_k_means.cluster_centers_,
mb_k_means.counts_,
np.zeros(X.shape[1], np.double),
False,
distances=np.zeros(X.shape[0]),
random_reassign=True,
random_state=42,
reassignment_ratio=1e-15)
assert_array_almost_equal(clusters_before, mb_k_means.cluster_centers_)
def test_minibatch_update_consistency():
# Check that dense and sparse minibatch update give the same results
rng = np.random.RandomState(42)
old_centers = centers + rng.normal(size=centers.shape)
new_centers = old_centers.copy()
new_centers_csr = old_centers.copy()
weight_sums = np.zeros(new_centers.shape[0], dtype=np.double)
weight_sums_csr = np.zeros(new_centers.shape[0], dtype=np.double)
x_squared_norms = (X**2).sum(axis=1)
x_squared_norms_csr = row_norms(X_csr, squared=True)
buffer = np.zeros(centers.shape[1], dtype=np.double)
buffer_csr = np.zeros(centers.shape[1], dtype=np.double)
# extract a small minibatch
X_mb = X[:10]
X_mb_csr = X_csr[:10]
x_mb_squared_norms = x_squared_norms[:10]
x_mb_squared_norms_csr = x_squared_norms_csr[:10]
sample_weight_mb = np.ones(X_mb.shape[0], dtype=np.double)
# step 1: compute the dense minibatch update
old_inertia, incremental_diff = _mini_batch_step(X_mb,
sample_weight_mb,
x_mb_squared_norms,
new_centers,
weight_sums,
buffer,
1,
None,
random_reassign=False)
assert old_inertia > 0.0
# compute the new inertia on the same batch to check that it decreased
labels, new_inertia = _labels_inertia(X_mb, sample_weight_mb,
x_mb_squared_norms, new_centers)
assert new_inertia > 0.0
assert new_inertia < old_inertia
# check that the incremental difference computation is matching the
# final observed value
effective_diff = np.sum((new_centers - old_centers)**2)
assert_almost_equal(incremental_diff, effective_diff)
# step 2: compute the sparse minibatch update
old_inertia_csr, incremental_diff_csr = _mini_batch_step(
X_mb_csr,
sample_weight_mb,
x_mb_squared_norms_csr,
new_centers_csr,
weight_sums_csr,
buffer_csr,
1,
None,
random_reassign=False)
assert old_inertia_csr > 0.0
# compute the new inertia on the same batch to check that it decreased
labels_csr, new_inertia_csr = _labels_inertia(X_mb_csr, sample_weight_mb,
x_mb_squared_norms_csr,
new_centers_csr)
assert new_inertia_csr > 0.0
assert new_inertia_csr < old_inertia_csr
# check that the incremental difference computation is matching the
# final observed value
effective_diff = np.sum((new_centers_csr - old_centers)**2)
assert_almost_equal(incremental_diff_csr, effective_diff)
# step 3: check that sparse and dense updates lead to the same results
assert_array_equal(labels, labels_csr)
assert_array_almost_equal(new_centers, new_centers_csr)
assert_almost_equal(incremental_diff, incremental_diff_csr)
assert_almost_equal(old_inertia, old_inertia_csr)
assert_almost_equal(new_inertia, new_inertia_csr)