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119 lines
4.3 KiB
119 lines
4.3 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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/fluid/operators/metrics/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("Predict"),
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"Input of Out should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Label"),
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"Input of Label should not be null.");
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auto predict_width = ctx->GetInputDim("Predict")[1];
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if (ctx->IsRuntime()) {
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PADDLE_ENFORCE_LE(predict_width, 2,
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"Only support binary classification,"
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"prediction dims[1] should be 1 or 2");
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}
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auto predict_height = ctx->GetInputDim("Predict")[0];
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auto label_height = ctx->GetInputDim("Label")[0];
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if (ctx->IsRuntime()) {
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PADDLE_ENFORCE_EQ(predict_height, label_height,
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"Out and Label should have same height.");
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}
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int num_pred_buckets = ctx->Attrs().Get<int>("num_thresholds") + 1;
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int slide_steps = ctx->Attrs().Get<int>("slide_steps");
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PADDLE_ENFORCE_GE(num_pred_buckets, 1, "num_thresholds must larger than 1");
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PADDLE_ENFORCE_GE(slide_steps, 0, "slide_steps must be natural number");
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ctx->SetOutputDim("AUC", {1});
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slide_steps = slide_steps == 0 ? 1 : slide_steps;
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ctx->SetOutputDim("StatPosOut", {slide_steps, num_pred_buckets});
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ctx->SetOutputDim("StatNegOut", {slide_steps, num_pred_buckets});
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(
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OperatorWithKernel::IndicateVarDataType(ctx, "Predict"),
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platform::CPUPlace());
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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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void Make() override {
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AddInput("Predict",
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"A floating point 2D tensor with shape [batch_size, 2], values "
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"are in the range [0, 1]."
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"Typically, this tensor indicates the probability of each label");
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AddInput("Label",
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"A 2D int tensor indicating the label of the training data. "
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"shape: [batch_size, 1]");
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// TODO(typhoonzero): support weight input
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AddInput("StatPos", "Statistic value when label = 1");
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AddInput("StatNeg", "Statistic value when label = 0");
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AddOutput("AUC",
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"A scalar representing the "
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"current area-under-the-curve.");
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AddOutput("StatPosOut", "Statistic value when label = 1");
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AddOutput("StatNegOut", "Statistic value when label = 0");
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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>(
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"num_thresholds",
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"The number of thresholds to use when discretizing the roc curve.")
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.SetDefault((2 << 12) - 1);
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AddAttr<int>("slide_steps", "Use slide steps to calc batch auc.")
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.SetDefault(1);
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AddComment(R"DOC(
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Area Under The Curve (AUC) Operator.
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This implementation computes the AUC according to forward output and label.
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It is used very widely in binary classification evaluation. As a note:
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If input label contains values other than 0 and 1, it will be cast
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to bool. You can find the relevant definitions here:
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https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
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There are two types of possible curves:
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1. ROC: Receiver operating characteristic
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2. 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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