8f43927575
examples/ is based on KasperskyLab/knp at 55470ed6 with local changes to the mnist-learn altai/blifat models, cuda-backend CMake and model_loader. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
73 lines
2.5 KiB
C++
73 lines
2.5 KiB
C++
/**
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* @file inference_evaluation_test.cpp
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* @brief Inference evaluation test.
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* @kaspersky_support D. Postnikov
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* @date 21.08.2025
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* @license Apache 2.0
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* @copyright © 2025 AO Kaspersky Lab
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <knp/framework/inference_evaluation/classification/processor.h>
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#include <tests_common.h>
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class ClassificationTestHelper : public knp::framework::data_processing::classification::Dataset
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{
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public:
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ClassificationTestHelper()
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{
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frames_amount_for_training_ = 0;
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frames_amount_for_inference_ = 4;
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steps_per_frame_ = 1;
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classes_amount_ = 2;
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dataset_ = {{1, {}}, {1, {}}, {0, {}}, {0, {}}};
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}
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};
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TEST(InferenceEvaluation, Classification)
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{
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ClassificationTestHelper dataset;
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std::vector<knp::core::messaging::SpikeMessage> spikes;
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spikes.push_back({{knp::core::UID(false), 0}, {1, 3}});
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spikes.push_back({{knp::core::UID(false), 1}, {5, 1}});
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spikes.push_back({{knp::core::UID(false), 2}, {2, 0}});
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spikes.push_back({{knp::core::UID(false), 3}, {3, 1}});
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knp::framework::inference_evaluation::classification::InferenceResultsProcessor processor;
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processor.process_inference_results(spikes, dataset);
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auto const& res = processor.get_inference_results();
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ASSERT_EQ(res[0].get_total_votes(), 2);
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ASSERT_EQ(res[0].true_positives_, 1);
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ASSERT_EQ(res[0].false_negatives_, 0);
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ASSERT_EQ(res[0].false_positives_, 1);
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ASSERT_EQ(res[0].true_negatives_, 2);
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ASSERT_EQ(res[1].get_total_votes(), 2);
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ASSERT_EQ(res[1].true_positives_, 2);
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ASSERT_EQ(res[1].false_negatives_, 0);
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ASSERT_EQ(res[1].false_positives_, 0);
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ASSERT_EQ(res[1].true_negatives_, 2);
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std::stringstream csv_res;
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processor.write_inference_results_to_stream_as_csv(csv_res);
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ASSERT_EQ(
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csv_res.str(),
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"CLASS,TOTAL_VOTES,TRUE_POSITIVES,FALSE_NEGATIVES,FALSE_POSITIVES,TRUE_NEGATIVES,PRECISION,RECALL,PREVALENCE,"
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"ACCURACY,F_SCORE\n0,2,1,0,1,2,0.5,1,0.25,0.75,0.666667\n1,2,2,0,0,2,1,1,0.5,1,1\n");
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}
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