def test_dbscan_precomputed(datatype, nrows, max_mbytes_per_batch, out_dtype,
client):
from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
# 2-dimensional dataset for easy distance matrix computation
X, y = make_blobs(n_samples=nrows,
cluster_std=0.01,
n_features=2,
random_state=0)
# Precompute distances
X_dist = pairwise_distances(X).astype(datatype)
eps = 1
cuml_dbscan = cuDBSCAN(eps=eps,
min_samples=2,
metric='precomputed',
max_mbytes_per_batch=max_mbytes_per_batch,
output_type='numpy')
cu_labels = cuml_dbscan.fit_predict(X_dist, out_dtype=out_dtype)
sk_dbscan = skDBSCAN(eps=eps,
min_samples=2,
metric='precomputed',
algorithm="brute")
sk_labels = sk_dbscan.fit_predict(X_dist)
# Check the core points are equal
assert array_equal(cuml_dbscan.core_sample_indices_,
sk_dbscan.core_sample_indices_)
# Check the labels are correct
assert_dbscan_equal(sk_labels, cu_labels, X,
cuml_dbscan.core_sample_indices_, eps)
def test_dbscan(datatype, nrows, ncols, max_mbytes_per_batch, out_dtype,
client):
from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
n_samples = nrows
n_feats = ncols
X, y = make_blobs(n_samples=n_samples,
cluster_std=0.01,
n_features=n_feats,
random_state=0)
eps = 1
cuml_dbscan = cuDBSCAN(eps=eps,
min_samples=2,
max_mbytes_per_batch=max_mbytes_per_batch,
output_type='numpy')
cu_labels = cuml_dbscan.fit_predict(X, out_dtype=out_dtype)
if nrows < 500000:
sk_dbscan = skDBSCAN(eps=1, min_samples=2, algorithm="brute")
sk_labels = sk_dbscan.fit_predict(X)
# Check the core points are equal
assert array_equal(cuml_dbscan.core_sample_indices_,
sk_dbscan.core_sample_indices_)
# Check the labels are correct
assert_dbscan_equal(sk_labels, cu_labels, X,
cuml_dbscan.core_sample_indices_, eps)
if out_dtype == "int32" or out_dtype == np.int32:
assert cu_labels.dtype == np.int32
elif out_dtype == "int64" or out_dtype == np.int64:
assert cu_labels.dtype == np.int64
def test_dbscan_out_dtype_fails_invalid_input(client):
from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
X, _ = make_blobs(n_samples=500)
cuml_dbscan = cuDBSCAN(output_type='numpy')
cuml_dbscan.fit_predict(X, out_dtype="bad_input")
def test_dbscan_default(name, client):
from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
eps = 0.5
default_base = {
'quantile': .3,
'eps': eps,
'damping': .9,
'preference': -200,
'n_neighbors': 10,
'n_clusters': 2
}
n_samples = 500
pat = get_pattern(name, n_samples)
params = default_base.copy()
params.update(pat[1])
X, y = pat[0]
X = StandardScaler().fit_transform(X)
cuml_dbscan = cuDBSCAN(output_type='numpy')
cu_labels = cuml_dbscan.fit_predict(X)
sk_dbscan = skDBSCAN(eps=params['eps'], min_samples=5)
sk_labels = sk_dbscan.fit_predict(X)
# Check the core points are equal
assert array_equal(cuml_dbscan.core_sample_indices_,
sk_dbscan.core_sample_indices_)
# Check the labels are correct
assert_dbscan_equal(sk_labels, cu_labels, X,
cuml_dbscan.core_sample_indices_, eps)
def test_dbscan_no_calc_core_point_indices(client):
from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
params = {'eps': 1.1, 'min_samples': 4}
n_samples = 1000
pat = get_pattern("noisy_moons", n_samples)
X, y = pat[0]
X = StandardScaler().fit_transform(X)
# Set calc_core_sample_indices=False
cuml_dbscan = cuDBSCAN(eps=params['eps'], min_samples=5,
output_type='numpy', calc_core_sample_indices=False)
cuml_dbscan.fit_predict(X)
# Make sure we are None
assert(cuml_dbscan.core_sample_indices_ is None)
def test_dbscan_propagation(datatype, out_dtype, client):
from cuml.dask.cluster.dbscan import DBSCAN as cuDBSCAN
X, y = make_blobs(5000, centers=1, cluster_std=8.0,
center_box=(-100.0, 100.0), random_state=8)
X = X.astype(datatype)
eps = 0.5
cuml_dbscan = cuDBSCAN(eps=eps, min_samples=5,
output_type='numpy')
cu_labels = cuml_dbscan.fit_predict(X, out_dtype=out_dtype)
sk_dbscan = skDBSCAN(eps=eps, min_samples=5)
sk_labels = sk_dbscan.fit_predict(X)
# Check the core points are equal
assert array_equal(cuml_dbscan.core_sample_indices_,
sk_dbscan.core_sample_indices_)
# Check the labels are correct
assert_dbscan_equal(sk_labels, cu_labels, X,
cuml_dbscan.core_sample_indices_, eps)