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86 lines
2.9 KiB
86 lines
2.9 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#include "paddle/operators/auc_op.h"
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namespace paddle {
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namespace operators {
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class AucOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext *ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("Inference"),
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"Input of Inference must be initialized.");
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PADDLE_ENFORCE(ctx->HasInput("Label"),
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"Input of Label must be initialized.");
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auto inference_dim = ctx->GetInputDim("Inference");
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auto label_dim = ctx->GetInputDim("Label");
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PADDLE_ENFORCE_EQ(inference_dim, label_dim,
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"inference and label should have same shape");
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ctx->SetOutputDim("AUC", {1});
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ctx->ShareLoD("Inference", /*->*/ "AUC");
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}
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};
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class AucOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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AucOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("Inference",
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"A floating point tensor of arbitrary shape and whose values"
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"are in the range [0, 1].");
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AddInput("Label",
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"A tensor whose shape matches "
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"Inference. Will be cast to bool.");
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// TODO(typhoonzero): support weight input
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AddOutput("AUC",
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"A scalar representing the "
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"current area-under-curve.");
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AddAttr<std::string>("curve", "Curve type, can be 'ROC' or 'PR'.")
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.SetDefault("ROC");
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AddAttr<int>("num_thresholds",
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"The number of thresholds to use when discretizing the"
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" roc curve.")
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.SetDefault(200);
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AddComment(
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R"DOC(Computes the AUC according forward output and label.
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Best to use for binary classification evaluations.
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If input label contains values other than 0 and 1, it will be cast
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to bool.
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You can find the definations here:
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https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
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Possible curves are:
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- ROC: Receiver operating characteristic
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- PR: Precision Recall
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_WITHOUT_GRADIENT(auc, ops::AucOp, ops::AucOpMaker);
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REGISTER_OP_CPU_KERNEL(auc, ops::AucKernel<paddle::platform::CPUPlace, float>);
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