auc_op

paddle/operators/auc_op.cc

@@ -28,15 +28,12 @@ class AucOp : public framework::OperatorWithKernel {
     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());
+    PADDLE_ENFORCE_EQ(inference->dims(), label->dims(),
+                      "inference should have same shape as label");
 
-    ctx.Output<Tensor>("Accuracy")->Resize({1});
+    ctx.Output<Tensor>("AUC")->Resize({1});
   }
 };
 
@@ -45,14 +42,15 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
   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");
+             "A floating point `Tensor` of arbitrary shape and whose values"
+             "are in the range `[0, 1]`.");
+    AddInput("Label",
+             "A `Tensor` whose shape matches "
+             "`Inference`. Will be cast to `bool`.");
+    // TODO(typhoonzero): support weight input
+    AddOutput("AUC",
+              "A scalar `Tensor` representing the "
+              "current area-under-curve.");
     AddAttr<std::string>("curve", "Possible curves are ROC and PR")
         .SetDefault("ROC");
     AddAttr<int>("num_thresholds",
@@ -62,12 +60,16 @@ class AucOpMaker : public framework::OpProtoAndCheckerMaker {
 
     AddComment(
         R"DOC(Computes the AUC according forward output and label.
+        Best to use for binary classification evaluations.
+        If `label` can be values other than 0 and 1, it will be cast
+        to bool.
+
         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
+        - ROC: Receiver operating characteristic
+        - PR: Precision Recall
         )DOC");
   }
 };

paddle/operators/auc_op.h

@@ -22,12 +22,15 @@ namespace operators {
 
 using Tensor = framework::Tensor;
 
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
 template <typename Place, typename T>
 class AucKernel : 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");
 
@@ -44,14 +47,20 @@ class AucKernel : public framework::OpKernel {
     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_prob->data<T>();
-    const T* label_data = label->data<T>();
+    size_t num_samples = inference->numel();
+
+    const T* inference_data = inference->data<T>();
+    Tensor label_casted;
+    label_casted.Resize(label->dims());
+    bool* label_casted_data = label_casted.mutable_data<bool>(ctx.GetPlace());
 
-    size_t num_samples = inference->dims()[0];
-    size_t class_dim = inference->dims()[1];
+    const int* label_data = label->data<int>();
+    // cast label_data to bool
+    for (size_t i = 0; i < num_samples; i++) {
+      label_casted_data[i] = static_cast<bool>(label_data[i]);
+    }
 
-    // create local tensor for storing the curve: TP, FN, TN, FP
+    // 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_positive, true_negative, false_negative;
@@ -72,19 +81,17 @@ class AucKernel : public framework::OpKernel {
       // 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++;
-            }
+        if (label_casted_data[i]) {
+          if (inference_data[i] >= (*thresh)) {
+            tp++;
+          } else {
+            tn++;
+          }
+        } else {
+          if (inference_data[i] >= (*thresh)) {
+            fp++;
           } else {
-            if (inference_prob_data[i * class_dim + j] >= (*thresh)) {
-              fp++;
-            } else {
-              fn++;
-            }
+            fn++;
           }
         }
       }