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)
def test_minibatch_update_consistency():
# Check that dense and sparse minibatch update give the same results
rng = np.random.RandomState(42)
centers_old = centers + rng.normal(size=centers.shape)
centers_old_csr = centers_old.copy()
centers_new = np.zeros_like(centers_old)
centers_new_csr = np.zeros_like(centers_old_csr)
weight_sums = np.zeros(centers_old.shape[0], dtype=X.dtype)
weight_sums_csr = np.zeros(centers_old.shape[0], dtype=X.dtype)
x_squared_norms = (X**2).sum(axis=1)
x_squared_norms_csr = row_norms(X_csr, squared=True)
sample_weight = np.ones(X.shape[0], dtype=X.dtype)
# 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 = sample_weight[:10]
# step 1: compute the dense minibatch update
old_inertia = _mini_batch_step(
X_mb,
x_mb_squared_norms,
sample_weight_mb,
centers_old,
centers_new,
weight_sums,
np.random.RandomState(0),
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, centers_new
)
assert new_inertia > 0.0
assert new_inertia < old_inertia
# step 2: compute the sparse minibatch update
old_inertia_csr = _mini_batch_step(
X_mb_csr,
x_mb_squared_norms_csr,
sample_weight_mb,
centers_old_csr,
centers_new_csr,
weight_sums_csr,
np.random.RandomState(0),
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, centers_new_csr
)
assert new_inertia_csr > 0.0
assert new_inertia_csr < old_inertia_csr
# step 3: check that sparse and dense updates lead to the same results
assert_array_equal(labels, labels_csr)
assert_allclose(centers_new, centers_new_csr)
assert_allclose(old_inertia, old_inertia_csr)
assert_allclose(new_inertia, new_inertia_csr)
