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kneighbors_regressor.py
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kneighbors_regressor.py
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#
# Copyright (c) 2020, NVIDIA CORPORATION.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from cuml.dask.common.input_utils import DistributedDataHandler
from cuml.dask.common.input_utils import to_output
from cuml.dask.common import parts_to_ranks
from cuml.dask.common import flatten_grouped_results
from cuml.dask.common.utils import raise_mg_import_exception
from cuml.dask.common.utils import wait_and_raise_from_futures
from cuml.raft.dask.common.comms import worker_state
from cuml.dask.neighbors import NearestNeighbors
import dask.array as da
from uuid import uuid1
def _custom_getter(o):
def func_get(f, idx):
return f[o][idx]
return func_get
class KNeighborsRegressor(NearestNeighbors):
"""
Multi-node Multi-GPU K-Nearest Neighbors Regressor Model.
K-Nearest Neighbors Regressor is an instance-based learning technique,
that keeps training samples around for prediction, rather than trying
to learn a generalizable set of model parameters.
Parameters
----------
n_neighbors : int (default=5)
Default number of neighbors to query
batch_size: int (optional, default 2000000)
Maximum number of query rows processed at once. This parameter can
greatly affect the throughput of the algorithm. The optimal setting
of this value will vary for different layouts and index to query
ratios, but it will require `batch_size * n_features * 4` bytes of
additional memory on each worker hosting index partitions.
handle : cuml.Handle
Specifies the cuml.handle that holds internal CUDA state for
computations in this model. Most importantly, this specifies the CUDA
stream that will be used for the model's computations, so users can
run different models concurrently in different streams by creating
handles in several streams.
If it is None, a new one is created.
verbose : int or boolean, default=False
Sets logging level. It must be one of `cuml.common.logger.level_*`.
See :ref:`verbosity-levels` for more info.
"""
def __init__(self, client=None, streams_per_handle=0,
verbose=False, **kwargs):
super(KNeighborsRegressor, self).__init__(client=client,
verbose=verbose,
**kwargs)
self.streams_per_handle = streams_per_handle
def fit(self, X, y):
"""
Fit a multi-node multi-GPU K-Nearest Neighbors Regressor index
Parameters
----------
X : array-like (device or host) shape = (n_samples, n_features)
Index data.
Acceptable formats: dask CuPy/NumPy/Numba Array
y : array-like (device or host) shape = (n_samples, n_features)
Index output data.
Acceptable formats: dask CuPy/NumPy/Numba Array
Returns
-------
self : KNeighborsRegressor model
"""
self.data_handler = \
DistributedDataHandler.create(data=[X, y],
client=self.client)
self.n_outputs = y.shape[1]
return self
@staticmethod
def _func_create_model(sessionId, **kwargs):
try:
from cuml.neighbors.kneighbors_regressor_mg import \
KNeighborsRegressorMG as cumlKNN
except ImportError:
raise_mg_import_exception()
handle = worker_state(sessionId)["handle"]
return cumlKNN(handle=handle, **kwargs)
@staticmethod
def _func_predict(model, data, data_parts_to_ranks, data_nrows,
query, query_parts_to_ranks, query_nrows,
ncols, rank, n_output, convert_dtype):
return model.predict(
data, data_parts_to_ranks, data_nrows,
query, query_parts_to_ranks, query_nrows,
ncols, rank, n_output, convert_dtype
)
def predict(self, X, convert_dtype=True):
"""
Predict outputs for a query from previously stored index
and outputs.
The process is done in a multi-node multi-GPU fashion.
Parameters
----------
X : array-like (device or host) shape = (n_samples, n_features)
Query data.
Acceptable formats: dask cuDF, dask CuPy/NumPy/Numba Array
convert_dtype : bool, optional (default = True)
When set to True, the predict method will automatically
convert the data to the right formats.
Returns
-------
predictions : Dask futures or Dask CuPy Arrays
"""
query_handler = \
DistributedDataHandler.create(data=X,
client=self.client)
self.datatype = query_handler.datatype
comms = KNeighborsRegressor._build_comms(self.data_handler,
query_handler,
self.streams_per_handle)
worker_info = comms.worker_info(comms.worker_addresses)
"""
Build inputs and outputs
"""
self.data_handler.calculate_parts_to_sizes(comms=comms)
query_handler.calculate_parts_to_sizes(comms=comms)
data_parts_to_ranks, data_nrows = \
parts_to_ranks(self.client,
worker_info,
self.data_handler.gpu_futures)
query_parts_to_ranks, query_nrows = \
parts_to_ranks(self.client,
worker_info,
query_handler.gpu_futures)
"""
Each Dask worker creates a single model
"""
key = uuid1()
models = dict([(worker, self.client.submit(
self._func_create_model,
comms.sessionId,
**self.kwargs,
workers=[worker],
key="%s-%s" % (key, idx)))
for idx, worker in enumerate(comms.worker_addresses)])
"""
Invoke knn_classify on Dask workers to perform distributed query
"""
key = uuid1()
knn_reg_res = dict([(worker_info[worker]["rank"], self.client.submit(
self._func_predict,
models[worker],
self.data_handler.worker_to_parts[worker] if
worker in self.data_handler.workers else [],
data_parts_to_ranks,
data_nrows,
query_handler.worker_to_parts[worker] if
worker in query_handler.workers else [],
query_parts_to_ranks,
query_nrows,
X.shape[1],
self.n_outputs,
worker_info[worker]["rank"],
convert_dtype,
key="%s-%s" % (key, idx),
workers=[worker]))
for idx, worker in enumerate(comms.worker_addresses)
])
wait_and_raise_from_futures(list(knn_reg_res.values()))
"""
Gather resulting partitions and return result
"""
out_futures = flatten_grouped_results(self.client,
query_parts_to_ranks,
knn_reg_res,
getter_func=_custom_getter(0))
comms.destroy()
return to_output(out_futures, self.datatype).squeeze()
def score(self, X, y):
"""
Provide score by comparing predictions and ground truth.
Parameters
----------
X : array-like (device or host) shape = (n_samples, n_features)
Query test data.
Acceptable formats: dask CuPy/NumPy/Numba Array
y : array-like (device or host) shape = (n_samples, n_features)
Outputs test data.
Acceptable formats: dask CuPy/NumPy/Numba Array
Returns
-------
score
"""
y_pred = self.predict(X, convert_dtype=True)
if not isinstance(y_pred, da.Array):
y_pred = y_pred.to_dask_array(lengths=True)
if not isinstance(y, da.Array):
y = y.to_dask_array(lengths=True)
y_true = y.squeeze()
y_mean = y_true.mean(axis=0)
residual_sss = ((y_true - y_pred) ** 2).sum(axis=0, dtype='float64')
total_sss = ((y_true - y_mean) ** 2).sum(axis=0, dtype='float64')
r2_score = da.mean(1 - (residual_sss / total_sss))
return r2_score.compute()