wehub-resource-sync
104 lines
3.5 KiB
C++
104 lines
3.5 KiB
C++
/* ******************************************************************************
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*
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* See the NOTICE file distributed with this work for additional
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* information regarding copyright ownership.
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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, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author @cpuheater
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//
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#include <system/op_boilerplate.h>
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#if NOT_EXCLUDED(OP_confusion_matrix)
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#include <array/NDArray.h>
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#include <array/NDArrayList.h>
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#include <helpers/ShapeUtils.h>
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#include <ops/declarable/CustomOperations.h>
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#include <ops/declarable/helpers/confusion.h>
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#include <array>
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namespace sd {
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namespace ops {
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CUSTOM_OP_IMPL(confusion_matrix, 2, 1, false, 0, -2) {
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auto labels = INPUT_VARIABLE(0);
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auto predictions = INPUT_VARIABLE(1);
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NDArray *weights = nullptr;
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if (block.width() > 2) {
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weights = INPUT_VARIABLE(2);
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REQUIRE_TRUE(weights->isSameShape(predictions), 0,
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"CONFUSION_MATRIX: Weights and predictions should have equal shape");
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}
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auto output = OUTPUT_NULLIFIED(0);
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auto* minPredictionArr = predictions->reduceNumber(reduce::Min);
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int minPrediction = minPredictionArr->e<int>(0);
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delete minPredictionArr;
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auto* minLabelArr = labels->reduceNumber(reduce::Min);
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int minLabel = minLabelArr->e<int>(0);
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delete minLabelArr;
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REQUIRE_TRUE(minLabel >= 0, 0, "CONFUSION_MATRIX: Labels contains negative values !");
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REQUIRE_TRUE(minPrediction >= 0, 0, "CONFUSION_MATRIX: Predictions contains negative values !");
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REQUIRE_TRUE(labels->isVector(), 0, "CONFUSION_MATRIX: Labels input should be a Vector, but got %iD instead",
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labels->rankOf());
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REQUIRE_TRUE(predictions->isVector(), 0, "CONFUSION_MATRIX: Predictions input should be Vector, but got %iD instead",
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predictions->rankOf());
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REQUIRE_TRUE(labels->isSameShape(predictions), 0, "CONFUSION_MATRIX: Labels and predictions should have equal shape");
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helpers::confusionFunctor(block.launchContext(), labels, predictions, weights, output);
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return sd::Status::OK;
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}
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DECLARE_SHAPE_FN(confusion_matrix) {
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auto labels = INPUT_VARIABLE(0);
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auto predictions = INPUT_VARIABLE(1);
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auto dtype = block.numD() ? D_ARG(0) : sd::DataType::INT64;
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int numClasses = 0;
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if (block.getIArguments()->size() > 0) {
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numClasses = INT_ARG(0);
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} else {
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auto* maxPredictionArr = predictions->reduceNumber(reduce::Max);
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int maxPrediction = maxPredictionArr->e<int>(0);
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delete maxPredictionArr;
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auto* maxLabelArr = labels->reduceNumber(reduce::Max);
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int maxLabel = maxLabelArr->e<int>(0);
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delete maxLabelArr;
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numClasses = (maxPrediction >= maxLabel) ? maxPrediction + 1 : maxLabel + 1;
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}
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std::array<sd::LongType, 2> shape = {{numClasses, numClasses}};
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auto newShape = ConstantShapeHelper::getInstance().createShapeInfo(dtype, 'c', 2, shape.data(),0);
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return SHAPELIST(newShape);
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}
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DECLARE_TYPES(confusion_matrix) {
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getOpDescriptor()
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->setAllowedInputTypes({ALL_INDICES})
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->setAllowedOutputTypes({ALL_INDICES});
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}
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} // namespace ops
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} // namespace sd
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#endif
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