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 _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 _func_fit(sessionId, dfs, **kwargs):
"""
Runs on each worker to call fit on local KMeans instance.
Extracts centroids
:param model: Local KMeans instance
:param dfs: List of cudf.Dataframes to use
:param r: Stops memoization caching
:return: The fit model
"""
try:
from cuml.cluster.kmeans_mg import KMeansMG as cumlKMeans
except ImportError:
raise_mg_import_exception()
handle = worker_state(sessionId)["handle"]
df = concat(dfs)
return cumlKMeans(handle=handle, **kwargs).fit(df)
