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add auc_op

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fix-typo
typhoonzero 8 years ago
parent 0be349496f
commit 4d988ed28e
2 changed files with 212 additions and 0 deletions
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80
paddle/operators/auc_op.cc
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/auc_op.h"
namespace paddle {
namespace operators {
class AccuracyOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Inference"),
"Input of Inference must be initialized.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input of Inference must be initialized.");
auto *inference = ctx.Input<framework::Tensor>("Inference");
auto *inference_prob = ctx.Input<framework::Tensor>("InferenceProb");
auto *label = ctx.Input<framework::Tensor>("Label");
PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label must be a vector");
PADDLE_ENFORCE_EQ(inference->dims()[0], label->dims()[0],
"inference size must be the same as label size");
PADDLE_ENFORCE_EQ(inference->dims(), inference_prob->dims());
ctx.Output<Tensor>("Accuracy")->Resize({1});
}
};
class AucOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AucOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Inference",
"Topk(indices) the network output, float value indicating "
"probabilities of classification");
AddInput("InferenceProb",
"Topk(values) the network output, float value indicating "
"probabilities of classification");
AddInput("Label", "Label of the training data");
// TODO(typhoonzero): support weight
AddOutput("AUC", "Area Under Curve caculations");
AddAttr<std::string>("curve", "Possible curves are ROC and PR")
.SetDefault("ROC");
AddAttr<int>("num_thresholds",
"The number of thresholds to use when discretizing the"
" roc curve.")
.SetDefault(200);
AddComment(
R"DOC(Computes the AUC according forward output and label.
You can find the definations here:
https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve
Possible curves are:
ROC: Receiver operating characteristic
PR: Precision Recall
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(auc, ops::AccuracyOp, ops::AccuracyOpMaker);
REGISTER_OP_CPU_KERNEL(auc, ops::AucKernel<paddle::platform::CPUPlace, float>);

132
paddle/operators/auc_op.h
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename Place, typename T>
class AccuracyKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* inference = ctx.Input<Tensor>("Inference");
auto* inference_prob = ctx.Input<Tensor>("InferenceProb");
auto* label = ctx.Input<Tensor>("Label");
auto* auc = ctx.Output<Tensor>("AUC");
float* auc_data = auc->mutable_data<float>(ctx.GetPlace());
std::string curve = ctx.Attr<std::string>("curve");
int num_thresholds = ctx.Attr<int>("num_thresholds");
std::vector<float> thresholds_list;
thresholds_list.reserve(num_thresholds);
for (int i = 1; i < num_thresholds - 1; i++) {
thresholds_list[i] = (float)i / (num_thresholds - 1);
}
const float kEpsilon = 1e-7;
thresholds_list[0] = 0.0f - kEpsilon;
thresholds_list[num_thresholds - 1] = 1.0f + kEpsilon;
const int* inference_data = inference->data<int>();
const T* inference_prob_data = inference->data<T>();
const T* label_data = label->data<T>();
size_t num_samples = inference->dims()[0];
size_t class_dim = inference->dims()[1];
// create local tensor for storing the curve: TP, FN, TN, FP
// TODO(typhoonzero): put these tensors in Scope
// TODO(typhoonzero): use op to caculate these values.
Tensor true_positive, false_positeve, true_negative, false_negative;
true_positive.Resize({num_thresholds});
false_negative.Resize({num_thresholds});
true_negative.Resize({num_thresholds});
false_positive.Resize({num_thresholds});
int* tp_data = true_positive.mutable_data<int>();
int* fn_data = false_negative.mutable_data<int>();
int* tn_data = true_negative.mutable_data<int>();
int* fp_data = false_positive.mutable_data<int>();
for (auto thresh = thresholds_list.begin(); thresh != thresholds_list.end();
thresh++) {
size_t idx_thresh = thresh - thresholds_list.begin();
// caculate TP, FN, TN, FP for current thresh
int tp, fn, tn, fp = 0;
for (size_t i = 0; i < num_samples; i++) {
for (size_t j = 0; j < class_dim; j++) {
if (inference_data[i * class_dim + j] == label_data[i]) {
if (inference_prob_data[i * class_dim + j] >= (*thresh)) {
tp++;
} else {
tn++;
}
} else {
if (inference_prob_data[i * class_dim + j] >= (*thresh)) {
fp++;
} else {
fn++;
}
}
}
}
// store rates
tp_data[idx_thresh] = tp;
fn_data[idx_thresh] = fn;
tn_data[idx_thresh] = tn;
fp_data[idx_thresh] = fp;
}
// epsilon to avoid divide by zero.
float epsilon = 1e-6;
// Riemann sum to caculate auc.
Tensor tp_rate, fp_rate, rec_rate;
tp_rate.Resize({num_thresholds});
fp_rate.Resize({num_thresholds});
rec_rate.Resize({num_thresholds});
float* tp_rate_data = tp_rate.mutable_data<float>();
float* fp_rate_data = fp_rate.mutable_data<float>();
float* rec_rate_data = rec_rate.mutable_data<float>();
for (int i = 0; i < num_thresholds; i++) {
tp_rate_data[i] = ((float)tp_data[i + epsilon) / (tp_data[i] + fn_data[i] + epsilon);
fp_rate_data[i] =
(float)fp_data[i] / (fp_data[i] + tn_data[i] + epsilon);
rec_rate_data[i] =
((float)tp_data[i] + epsilon) / (tp_data[i] + fp_data[i] + epsilon);
}
if (curve == "ROC") {
for (int i = 1; i < num_thresholds; i++) {
auto dx = fp_rate_data[i] - fp_rate_data[i - 1];
auto y = (tp_rate_data[i] + tp_rate_data[i - 1]) / 2.0f;
*auc_data = *auc_data + dx * y;
}
} else if (curve = "PR") {
for (int i = 1; i < num_thresholds; i++) {
auto dx = tp_rate_data[i] - tp_rate_data[i - 1];
auto y = (rec_rate_data[i] + rec_rate_data[i - 1]) / 2.0f;
*auc_data = *auc_data + dx * y;
}
}
}
};
} // namespace operators
} // namespace paddle
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