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()