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knn_clsf.py
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knn_clsf.py
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# ===============================================================================
# Copyright 2020-2021 Intel 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.
# ===============================================================================
import argparse
import bench
from cuml.neighbors import KNeighborsClassifier
parser = argparse.ArgumentParser(
description='cuML kNN classifier benchmark')
parser.add_argument('--task', default='classification', type=str,
choices=('search', 'classification'),
help='kNN task: search or classification')
parser.add_argument('--n-neighbors', default=5, type=int,
help='Number of neighbors to use')
parser.add_argument('--weights', type=str, default='uniform',
help='Weight function used in prediction')
parser.add_argument('--method', type=str, default='brute',
help='Algorithm used to compute the nearest neighbors')
parser.add_argument('--metric', type=str, default='euclidean',
help='Distance metric to use')
params = bench.parse_args(parser)
# Load generated data
X_train, X_test, y_train, y_test = bench.load_data(params)
params.n_classes = y_train[y_train.columns[0]].nunique()
# Create classification object
knn_clsf = KNeighborsClassifier(n_neighbors=params.n_neighbors,
weights=params.weights,
algorithm=params.method,
metric=params.metric)
# Measure time and accuracy on fitting
train_time, _ = bench.measure_function_time(knn_clsf.fit, X_train, y_train,
params=params)
if params.task == 'classification':
y_pred = knn_clsf.predict(X_train)
train_acc = 100 * bench.accuracy_score(y_pred, y_train)
# Measure time and accuracy on prediction
if params.task == 'classification':
predict_time, yp = bench.measure_function_time(knn_clsf.predict, X_test,
params=params)
test_acc = 100 * bench.accuracy_score(yp, y_test)
else:
predict_time, _ = bench.measure_function_time(knn_clsf.kneighbors, X_test,
params=params)
if params.task == 'classification':
bench.print_output(library='cuml',
algorithm=knn_clsf.algorithm + '_knn_clsf',
stages=['training', 'prediction'], params=params,
functions=['knn_clsf.fit', 'knn_clsf.predict'],
times=[train_time, predict_time],
metrics=[train_acc, test_acc], metric_type='accuracy[%]',
data=[X_train, X_test], alg_instance=knn_clsf)
else:
bench.print_output(library='cuml',
algorithm=knn_clsf.algorithm + '_knn_search',
stages=['training', 'search'], params=params,
functions=['knn_clsf.fit', 'knn_clsf.kneighbors'],
times=[train_time, predict_time],
metrics=[], metric_type=None,
data=[X_train, X_test], alg_instance=knn_clsf)