武毅
7 years ago
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/* 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::InferShapeContextBase *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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@ -0,0 +1,135 @@
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/* 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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename Place, typename T>
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class AucKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* inference = ctx.Input<Tensor>("Inference");
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auto* label = ctx.Input<Tensor>("Label");
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auto* auc = ctx.Output<Tensor>("AUC");
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float* auc_data = auc->mutable_data<float>(ctx.GetPlace());
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std::string curve = ctx.Attr<std::string>("curve");
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int num_thresholds = ctx.Attr<int>("num_thresholds");
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std::vector<float> thresholds_list;
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thresholds_list.reserve(num_thresholds);
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for (int i = 1; i < num_thresholds - 1; i++) {
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thresholds_list[i] = (float)i / (num_thresholds - 1);
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}
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const float kEpsilon = 1e-7;
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thresholds_list[0] = 0.0f - kEpsilon;
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thresholds_list[num_thresholds - 1] = 1.0f + kEpsilon;
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size_t num_samples = inference->numel();
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const T* inference_data = inference->data<T>();
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Tensor label_casted;
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label_casted.Resize(label->dims());
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bool* label_casted_data = label_casted.mutable_data<bool>(ctx.GetPlace());
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const int* label_data = label->data<int>();
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// cast label_data to bool
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for (size_t i = 0; i < num_samples; i++) {
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label_casted_data[i] = static_cast<bool>(label_data[i]);
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}
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// Create local tensor for storing the curve: TP, FN, TN, FP
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// TODO(typhoonzero): use eigen op to caculate these values.
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Tensor true_positive, false_positive, true_negative, false_negative;
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true_positive.Resize({num_thresholds});
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false_negative.Resize({num_thresholds});
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true_negative.Resize({num_thresholds});
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false_positive.Resize({num_thresholds});
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int* tp_data = true_positive.mutable_data<int>(ctx.GetPlace());
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int* fn_data = false_negative.mutable_data<int>(ctx.GetPlace());
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int* tn_data = true_negative.mutable_data<int>(ctx.GetPlace());
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int* fp_data = false_positive.mutable_data<int>(ctx.GetPlace());
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for (int idx_thresh = 0; idx_thresh < num_thresholds; idx_thresh++) {
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// caculate TP, FN, TN, FP for current thresh
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int tp = 0, fn = 0, tn = 0, fp = 0;
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for (size_t i = 0; i < num_samples; i++) {
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if (label_casted_data[i]) {
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if (inference_data[i] >= (thresholds_list[idx_thresh])) {
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tp++;
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} else {
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fn++;
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}
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} else {
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if (inference_data[i] >= (thresholds_list[idx_thresh])) {
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fp++;
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} else {
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tn++;
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}
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}
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}
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// store rates
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tp_data[idx_thresh] = tp;
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fn_data[idx_thresh] = fn;
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tn_data[idx_thresh] = tn;
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fp_data[idx_thresh] = fp;
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}
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// epsilon to avoid divide by zero.
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float epsilon = 1e-6;
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// Riemann sum to caculate auc.
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Tensor tp_rate, fp_rate, rec_rate;
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tp_rate.Resize({num_thresholds});
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fp_rate.Resize({num_thresholds});
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rec_rate.Resize({num_thresholds});
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float* tp_rate_data = tp_rate.mutable_data<float>(ctx.GetPlace());
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float* fp_rate_data = fp_rate.mutable_data<float>(ctx.GetPlace());
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float* rec_rate_data = rec_rate.mutable_data<float>(ctx.GetPlace());
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for (int i = 0; i < num_thresholds; i++) {
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tp_rate_data[i] =
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((float)tp_data[i] + epsilon) / (tp_data[i] + fn_data[i] + epsilon);
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fp_rate_data[i] = (float)fp_data[i] / (fp_data[i] + tn_data[i] + epsilon);
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rec_rate_data[i] =
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((float)tp_data[i] + epsilon) / (tp_data[i] + fp_data[i] + epsilon);
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}
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*auc_data = 0.0f;
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if (curve == "ROC") {
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for (int i = 0; i < num_thresholds - 1; i++) {
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auto dx = fp_rate_data[i] - fp_rate_data[i + 1];
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auto y = (tp_rate_data[i] + tp_rate_data[i + 1]) / 2.0f;
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*auc_data = *auc_data + dx * y;
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}
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} else if (curve == "PR") {
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for (int i = 1; i < num_thresholds; i++) {
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auto dx = tp_rate_data[i] - tp_rate_data[i - 1];
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auto y = (rec_rate_data[i] + rec_rate_data[i - 1]) / 2.0f;
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*auc_data = *auc_data + dx * y;
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}
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,66 @@
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import unittest
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import numpy as np
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from op_test import OpTest
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class TestAucOp(OpTest):
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def setUp(self):
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self.op_type = "auc"
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pred = np.random.random((128)).astype("float32")
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labels = np.random.randint(0, 2, (128, ))
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num_thresholds = 200
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self.inputs = {'Inference': pred, 'Label': labels}
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self.attrs = {'curve': 'ROC', 'num_thresholds': num_thresholds}
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# NOTE: sklearn use a different way to generate thresholds
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# which will cause the result differs slightly:
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# from sklearn.metrics import roc_curve, auc
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# fpr, tpr, thresholds = roc_curve(labels, pred)
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# auc_value = auc(fpr, tpr)
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# we caculate AUC again using numpy for testing
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kepsilon = 1e-7 # to account for floating point imprecisions
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thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
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for i in range(num_thresholds - 2)]
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thresholds = [0.0 - kepsilon] + thresholds + [1.0 + kepsilon]
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# caculate TP, FN, TN, FP count
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tp_list = np.ndarray((num_thresholds, ))
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fn_list = np.ndarray((num_thresholds, ))
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tn_list = np.ndarray((num_thresholds, ))
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fp_list = np.ndarray((num_thresholds, ))
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for idx_thresh, thresh in enumerate(thresholds):
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tp, fn, tn, fp = 0, 0, 0, 0
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for i, lbl in enumerate(labels):
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if lbl:
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if pred[i] >= thresh:
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tp += 1
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else:
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fn += 1
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else:
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if pred[i] >= thresh:
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fp += 1
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else:
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tn += 1
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tp_list[idx_thresh] = tp
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fn_list[idx_thresh] = fn
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tn_list[idx_thresh] = tn
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fp_list[idx_thresh] = fp
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epsilon = 1e-6
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tpr = (tp_list.astype("float32") + epsilon) / (
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tp_list + fn_list + epsilon)
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fpr = fp_list.astype("float32") / (fp_list + tn_list + epsilon)
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rec = (tp_list.astype("float32") + epsilon) / (
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tp_list + fp_list + epsilon)
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x = fpr[:num_thresholds - 1] - fpr[1:]
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y = (tpr[:num_thresholds - 1] + tpr[1:]) / 2.0
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auc_value = np.sum(x * y)
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self.outputs = {'AUC': auc_value}
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def test_check_output(self):
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self.check_output()
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if __name__ == "__main__":
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unittest.main()
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