def test_kmeanspp_init_random_state(self, Xl_blobs_easy):
X, y = Xl_blobs_easy
a = DKKMeans(3, init="k-means++")
a.fit(X)
b = DKKMeans(3, init="k-means++", random_state=0)
b.fit(X)
def test_basic(self, Xl_blobs_easy):
X, _ = Xl_blobs_easy
# make it super easy to cluster
a = DKKMeans(n_clusters=3, random_state=0)
b = SKKMeans(n_clusters=3, random_state=0)
a.fit(X)
b.fit(X)
assert_estimator_equal(
a,
b,
exclude=["n_iter_", "inertia_", "cluster_centers_", "labels_"])
assert abs(a.inertia_ - b.inertia_) < 0.01
# order is arbitrary, so align first
a_order = np.argsort(a.cluster_centers_, 0)[:, 0]
b_order = np.argsort(b.cluster_centers_, 0)[:, 0]
a_centers = a.cluster_centers_[a_order]
b_centers = b.cluster_centers_[b_order]
np.testing.assert_allclose(a_centers, b_centers, rtol=1e-3)
b_labels = replace(b.labels_, [0, 1, 2],
a_order[b_order]).astype(b.labels_.dtype)
assert_eq(a.labels_.compute(), b_labels)
assert a.n_iter_
# this is hacky
b.cluster_centers_ = b_centers
a.cluster_centers_ = a_centers
assert_eq(a.transform(X), b.transform(X), rtol=1e-3)
yhat_a = a.predict(X)
yhat_b = b.predict(X)
assert_eq(yhat_a.compute(), yhat_b)
def test_fit_raises():
km = DKKMeans()
with pytest.raises(ValueError):
km.fit(np.array([]).reshape(0, 1))
with pytest.raises(ValueError):
km.fit(np.array([]).reshape(1, 0))
def test_dataframes():
df = dd.from_pandas(
pd.DataFrame({"A": [1, 2, 3, 4, 5], "B": [6, 7, 8, 9, 10]}), npartitions=2
)
kmeans = DKKMeans()
kmeans.fit(df)
def test_random_init(self, Xl_blobs_easy):
X, y = Xl_blobs_easy
X_ = X.compute()
rs = 0
dkkm = DKKMeans(3, init="random", random_state=rs)
skkm = SKKMeans(3, init="random", random_state=rs, n_init=1)
dkkm.fit(X)
skkm.fit(X_)
assert abs(dkkm.inertia_ - skkm.inertia_) < 1e-4
assert dkkm.init == "random"
def test_kmeanspp_init(self, Xl_blobs_easy):
X, y = Xl_blobs_easy
X_ = X.compute()
rs = np.random.RandomState(0)
dkkm = DKKMeans(3, init="k-means++", random_state=rs)
skkm = SKKMeans(3, init="k-means++", random_state=rs)
dkkm.fit(X)
skkm.fit(X_)
assert abs(dkkm.inertia_ - skkm.inertia_) < 1e-4
assert dkkm.init == "k-means++"
def test_fit_given_init(self, X_blobs):
X_ = X_blobs.compute()
x_squared_norms = k_means_.row_norms(X_, squared=True)
rs = np.random.RandomState(0)
init = k_means_._k_init(X_, 3, x_squared_norms, rs)
dkkm = DKKMeans(3, init=init, random_state=rs)
skkm = SKKMeans(3, init=init, random_state=rs, n_init=1)
dkkm.fit(X_blobs)
skkm.fit(X_)
assert_eq(dkkm.inertia_, skkm.inertia_)
def test_fit_given_init(self):
X, y = sklearn.datasets.make_blobs(n_samples=1000, n_features=4, random_state=1)
X = da.from_array(X, chunks=500)
X_ = X.compute()
x_squared_norms = sklearn.utils.extmath.row_norms(X_, squared=True)
rs = np.random.RandomState(0)
init = _k_init(X_, 3, x_squared_norms, rs)
dkkm = DKKMeans(3, init=init, random_state=0)
skkm = SKKMeans(3, init=init, random_state=0, n_init=1)
dkkm.fit(X)
skkm.fit(X_)
assert_eq(dkkm.inertia_, skkm.inertia_)
def test_dtypes(self):
X = da.random.uniform(size=(100, 2), chunks=(50, 2))
X2 = X.astype("f4")
pairs = [(X, X), (X2, X2), (X, X2), (X2, X)]
for xx, yy in pairs:
a = DKKMeans()
b = SKKMeans()
a.fit(xx)
b.fit(xx)
assert a.cluster_centers_.dtype == b.cluster_centers_.dtype
assert a.labels_.dtype == b.labels_.dtype
assert a.transform(xx).dtype == b.transform(xx).dtype
assert a.transform(yy).dtype == b.transform(yy).dtype
def test_too_small():
km = DKKMeans()
X = da.random.uniform(size=(20, 2), chunks=(10, 2))
km.fit(X)
def test_check_estimator():
with warnings.catch_warnings(record=True):
warnings.simplefilter("ignore", RuntimeWarning)
check_estimator(DKKMeans())
def test_inputs(self, X):
km = DKKMeans(n_clusters=3)
km.fit(X)
km.transform(X)
def test_dask_dataframe_raises(self):
km = DKKMeans(n_clusters=3)
X = dd.from_pandas(pd.DataFrame({"A": range(50)}), npartitions=2)
with pytest.raises(TypeError):
km.fit(X)