def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
daal_model = getattr(self, '_daal_model', None)
if X is not None:
X = check_array(X,
accept_sparse='csr',
dtype=[np.float64, np.float32])
x = self._fit_X if X is None else X
try:
fptype = getFPType(x)
except ValueError:
fptype = None
if daal_model is not None and fptype is not None and not sp.issparse(
X):
logging.info("sklearn.neighbors.KNeighborsMixin."
"kneighbors: " + get_patch_message("daal"))
result = daal4py_kneighbors(self, X, n_neighbors, return_distance)
else:
logging.info("sklearn.neighbors.KNeighborsMixin."
"kneighbors:" + get_patch_message("sklearn"))
if daal_model is not None or getattr(self, '_tree', 0) is None and \
self._fit_method == 'kd_tree':
if sklearn_check_version("0.24"):
BaseNeighborsBase._fit(self, self._fit_X,
getattr(self, '_y', None))
else:
BaseNeighborsBase._fit(self, self._fit_X)
result = super(KNeighborsMixin,
self).kneighbors(X, n_neighbors, return_distance)
return result
def kneighbors(self, X=None, n_neighbors=None, return_distance=True):
daal_model = getattr(self, '_daal_model', None)
if X is not None and self.metric != "precomputed":
X = check_array(
X, accept_sparse='csr', dtype=[
np.float64, np.float32])
x = self._fit_X if X is None else X
try:
fptype = getFPType(x)
except ValueError:
fptype = None
_patching_status = PatchingConditionsChain(
"sklearn.neighbors.KNeighborsMixin.kneighbors")
_dal_ready = _patching_status.and_conditions([
(daal_model is not None, "oneDAL model was not trained."),
(fptype is not None, "Unable to get dtype."),
(not sp.issparse(X), "X is sparse. Sparse input is not supported.")])
_patching_status.write_log()
if _dal_ready:
result = daal4py_kneighbors(self, X, n_neighbors, return_distance)
else:
if daal_model is not None or getattr(self, '_tree', 0) is None and \
self._fit_method == 'kd_tree':
if sklearn_check_version("0.24"):
BaseNeighborsBase._fit(self, self._fit_X, getattr(self, '_y', None))
else:
BaseNeighborsBase._fit(self, self._fit_X)
result = super(KNeighborsMixin, self).kneighbors(
X, n_neighbors, return_distance)
return result
def radius_neighbors(self, X=None, radius=None, return_distance=True,
sort_results=False):
daal_model = getattr(self, '_daal_model', None)
if daal_model is not None \
or getattr(self, '_tree', 0) is None and self._fit_method == 'kd_tree':
if sklearn_check_version("0.24"):
BaseNeighborsBase._fit(self, self._fit_X, self._y)
else:
BaseNeighborsBase._fit(self, self._fit_X)
if sklearn_check_version("0.22"):
result = BaseRadiusNeighborsMixin.radius_neighbors(self, X, radius, return_distance, sort_results)
else:
result = BaseRadiusNeighborsMixin.radius_neighbors(self, X, radius, return_distance)
return result