def get_neighbors(self, n_neighbors):
"""
Returns the default n_neighbors, initialized from the constructor,
if n_neighbors is None.
Parameters
----------
n_neighbors : int
Number of neighbors
Returns
--------
n_neighbors: int
Default n_neighbors if parameter n_neighbors is none
"""
if n_neighbors is None:
if "n_neighbors" in self.kwargs \
and self.kwargs["n_neighbors"] is not None:
n_neighbors = self.kwargs["n_neighbors"]
else:
try:
from cuml.neighbors.nearest_neighbors_mg import \
NearestNeighborsMG as cumlNN
except ImportError:
raise_mg_import_exception()
n_neighbors = cumlNN().n_neighbors
return n_neighbors
def test_default_n_neighbors(client):
n_samples = 50
n_feats = 50
k = 15
from cuml.dask.neighbors import NearestNeighbors as daskNN
from cuml.neighbors.nearest_neighbors_mg import \
NearestNeighborsMG as cumlNN
from sklearn.datasets import make_blobs
n_samples = _scale_rows(client, n_samples)
X, y = make_blobs(n_samples=n_samples, n_features=n_feats, random_state=0)
X = X.astype(np.float32)
X_cudf = _prep_training_data(client, X, 1)
cumlModel = daskNN(streams_per_handle=5)
cumlModel.fit(X_cudf)
ret = cumlModel.kneighbors(X_cudf, return_distance=False)
assert ret.shape[1] == cumlNN().n_neighbors
cumlModel = daskNN(n_neighbors=k)
cumlModel.fit(X_cudf)
ret = cumlModel.kneighbors(X_cudf, k, return_distance=False)
assert ret.shape[1] == k
def _func_create_model(sessionId, **kwargs):
try:
from cuml.neighbors.nearest_neighbors_mg import \
NearestNeighborsMG as cumlNN
except ImportError:
raise_mg_import_exception()
handle = get_raft_comm_state(sessionId)["handle"]
return cumlNN(handle=handle, **kwargs)
def get_neighbors(self, n_neighbors):
"""
Returns the default n_neighbors, initialized from the constructor,
if n_neighbors is None
:param n_neighbors:
:return:
"""
if n_neighbors is None:
if "n_neighbors" in self.model_args \
and self.model_args["n_neighbors"] is not None:
n_neighbors = self.model_args["n_neighbors"]
else:
try:
from cuml.neighbors.nearest_neighbors_mg import \
NearestNeighborsMG as cumlNN
except ImportError:
raise_mg_import_exception()
n_neighbors = cumlNN().n_neighbors
return n_neighbors
def test_default_n_neighbors(cluster):
client = Client(cluster)
n_samples = 50
n_feats = 50
k = 15
try:
from cuml.dask.neighbors import NearestNeighbors as daskNN
from cuml.neighbors.nearest_neighbors_mg import \
NearestNeighborsMG as cumlNN
from sklearn.datasets import make_blobs
X, y = make_blobs(n_samples=n_samples,
n_features=n_feats,
random_state=0)
X = X.astype(np.float32)
X_cudf = _prep_training_data(client, X, 1)
wait(X_cudf)
cumlModel = daskNN(verbose=False, streams_per_handle=5)
cumlModel.fit(X_cudf)
ret = cumlModel.kneighbors(X_cudf, return_distance=False)
assert ret.shape[1] == cumlNN().n_neighbors
cumlModel = daskNN(verbose=False, n_neighbors=k)
cumlModel.fit(X_cudf)
ret = cumlModel.kneighbors(X_cudf, k, return_distance=False)
assert ret.shape[1] == k
finally:
client.close()
