Merge pull request #5108 from zhouxiao-coder/pnp-evaluator

Pnp evaluator

paddle/operators/positive_negative_pair_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+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/positive_negative_pair_op.h"
+
+namespace paddle {
+namespace operators {
+
+class PositiveNegativePairOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(
+        ctx->HasInput("Score"),
+        "Input(Score) of PositiveNegativePairOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasInput("Label"),
+        "Input(Label) of PositiveNegativePairOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasInput("QueryID"),
+        "Input(QueryID) of PositiveNegativePairOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("PositivePair"),
+        "Output(PositivePair) of PositiveNegativePairOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("NegativePair"),
+        "Output(NegativePair) of PositiveNegativePairOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("NeutralPair"),
+        "Output(NeutralPair) of PositiveNegativePairOp should not be null.");
+    auto scalar_dim = framework::make_ddim({1});
+    if (ctx->HasInput("AccumulatePositivePair") ||
+        ctx->HasInput("AccumulateNegativePair") ||
+        ctx->HasInput("AccumulateNeutralPair")) {
+      PADDLE_ENFORCE(ctx->HasInput("AccumulatePositivePair") &&
+                         ctx->HasInput("AccumulateNegativePair") &&
+                         ctx->HasInput("AccumulateNeutralPair"),
+                     "All optional inputs(AccumulatePositivePair, "
+                     "AccumulateNegativePair, AccumulateNeutralPair) of "
+                     "PositiveNegativePairOp are required if one of them is "
+                     "specified.");
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("AccumulatePositivePair"), scalar_dim,
+                        "Shape of AccumulatePositivePair should be {1}.");
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("AccumulateNegativePair"), scalar_dim,
+                        "Shape of AccumulateNegativePair should be {1}.");
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("AccumulateNeutralPair"), scalar_dim,
+                        "Shape of AccumulateNeutralPair should be {1}.");
+    }
+
+    auto score_dim = ctx->GetInputDim("Score");
+    auto label_dim = ctx->GetInputDim("Label");
+    auto query_dim = ctx->GetInputDim("QueryID");
+    PADDLE_ENFORCE_EQ(score_dim.size(), 2, "Score should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(label_dim.size(), 2, "Label should be a 2-D tensor.");
+    PADDLE_ENFORCE_EQ(
+        label_dim[0], score_dim[0],
+        "Tensor Score and Label should have the same height (batch size).");
+    PADDLE_ENFORCE_EQ(label_dim[1], 1,
+                      "The width of Label should be 1, i.e. each item should "
+                      "have a scalar label.");
+    PADDLE_ENFORCE(query_dim == label_dim,
+                   "QueryID should have the same shape as Label.");
+    if (ctx->HasInput("Weight")) {
+      PADDLE_ENFORCE(ctx->GetInputDim("Weight") == label_dim,
+                     "Weight should have the same shape as Label.");
+    }
+    int column = ctx->Attrs().Get<int>("column");
+    auto depth = score_dim[1];
+    PADDLE_ENFORCE(column < depth && column >= -depth,
+                   "Attribute column should be in the range of [-%l, %l)",
+                   depth, depth);
+
+    ctx->SetOutputDim("PositivePair", scalar_dim);
+    ctx->SetOutputDim("NegativePair", scalar_dim);
+    ctx->SetOutputDim("NeutralPair", scalar_dim);
+  }
+
+ protected:
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::ToDataType(ctx.Input<Tensor>("Score")->type());
+  }
+};
+
+class PositiveNegativePairOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  PositiveNegativePairOpMaker(framework::OpProto *proto,
+                              framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Score",
+             "(Tensor, float) Model Score on an item (with "
+             "respect to QueryID). It's a 2-D tensor with shape [batch_size, "
+             "depth], where the column specified by the attribute \"column\" "
+             "is used as item score.");
+    AddInput("Label",
+             "(Tensor, float) Label of an item (with repsect to "
+             "QueryId). It's a 2-D tensor with shape [batch_size, 1].");
+    AddInput("QueryID",
+             "(Tensor, int64) Query ID that indicates the context. Its shape "
+             "should be the same as Label.");
+    AddInput(
+        "AccumulatePositivePair",
+        "(float) Optional. The accumulated number of positive pairs over a "
+        "stream of data. If provided, the output PositivePair will be "
+        "initialized with this number rather than 0. it won't be modified "
+        "in place.")
+        .AsDispensable();
+    AddInput(
+        "AccumulateNegativePair",
+        "(float) Optional. The accumulated number of negative pairs over a "
+        "stream of data. If provided, the output NegativePair will be "
+        "initialized with this number rather than 0. it won't be modified "
+        "in place.")
+        .AsDispensable();
+    AddInput("AccumulateNeutralPair",
+             "(float) Optional. The accumulated number of neutral pairs over a "
+             "stream of data. If provided, the output NeutralPair will be "
+             "initialized with this number rather than 0. it won't be modified "
+             "in place.")
+        .AsDispensable();
+    AddInput("Weight",
+             "(float) Optional. Weight of current item. If specified, its "
+             "shape should be the same as Label, and the meaning of the output "
+             "changes from numbers of pairs to the total sum of pairs' "
+             "weights. Weight of a pair of items is the average of their "
+             "weights.")
+        .AsDispensable();
+    AddOutput("PositivePair",
+              "(float) Number of positive pairs, i.e. the pairs of "
+              "items that are ranked correctly.");
+    AddOutput("NegativePair",
+              "(float) Number of negative pairs, i.e. the pairs of "
+              "items that are ranked incorrectly.");
+    AddOutput("NeutralPair",
+              "(float) Number of neutral pairs, i.e. the pairs of items "
+              "that have the same score.")
+        .AsDispensable();
+    AddAttr<int>(
+        "column",
+        "(int, default -1) The column position of Score used to rank items in "
+        "descending order. It must be in the range of [-rank(Score), "
+        "rank(Score)). "
+        "If `dim < 0`, the dim to reduce is `rank + dim`. "
+        "Noting that reducing on the first dim will make the LoD info lost.")
+        .SetDefault(0);
+    AddComment(R"DOC(
+        PositiveNegativePairOp can be used to evaluate Learning To Rank(LTR) 
+        model performance. 
+        Within some context, e.g. the "query", a LTR model generates scores
+        for a list of items, which gives a partial order of the items.
+        PositiveNegativePairOp takes a list of reference rank order 
+        (Input("Label")) and the model generated scores (Input(Score)) as 
+        inputs and counts the pairs that ranked correctly and incorrectly.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(positive_negative_pair,
+                             ops::PositiveNegativePairOp,
+                             ops::PositiveNegativePairOpMaker);
+REGISTER_OP_CPU_KERNEL(
+    positive_negative_pair,
+    ops::PositiveNegativePairKernel<paddle::platform::CPUPlace, float>,
+    ops::PositiveNegativePairKernel<paddle::platform::CPUPlace, double>);

paddle/operators/positive_negative_pair_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+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 <unordered_map>
+#include <vector>
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/utils/Logging.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename Place, typename T>
+class PositiveNegativePairKernel : public framework::OpKernel<T> {
+ public:
+  struct PredictionResult {
+    PredictionResult(T score, T label, T weight)
+        : score(score), label(label), weight(weight) {}
+    T score;
+    T label;
+    T weight;
+  };
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto score_t = context.Input<Tensor>("Score");
+    auto label_t = context.Input<Tensor>("Label");
+    auto query_t = context.Input<Tensor>("QueryID");
+    auto acc_positive_t = context.Input<Tensor>("AccumulatePositivePair");
+    auto acc_negative_t = context.Input<Tensor>("AccumulateNegativePair");
+    auto acc_neutral_t = context.Input<Tensor>("AccumulateNeutralPair");
+    auto positive_t = context.Output<Tensor>("PositivePair");
+    auto negative_t = context.Output<Tensor>("NegativePair");
+    auto neutral_t = context.Output<Tensor>("NeutralPair");
+    auto weight_t = context.Input<Tensor>("Weight");
+
+    auto score = score_t->data<T>();
+    auto label = label_t->data<T>();
+    auto query = query_t->data<int64_t>();
+    const T* weight = nullptr;
+    if (weight_t != nullptr) {
+      weight = weight_t->data<T>();
+    }
+    T* positive = positive_t->mutable_data<T>(context.GetPlace());
+    T* negative = negative_t->mutable_data<T>(context.GetPlace());
+    T* neutral = neutral_t->mutable_data<T>(context.GetPlace());
+
+    auto score_dim = score_t->dims();
+    auto batch_size = score_dim[0];
+    auto width = score_dim[1];
+    auto column = context.Attr<int32_t>("column");
+    if (column < 0) {
+      column += width;
+    }
+
+    // construct document instances for each query: Query => List[<score#0,
+    // label#0, weight#0>, ...]
+    std::unordered_map<int64_t, std::vector<PredictionResult>> predictions;
+    for (auto i = 0; i < batch_size; ++i) {
+      if (predictions.find(query[i]) == predictions.end()) {
+        predictions.emplace(
+            std::make_pair(query[i], std::vector<PredictionResult>()));
+      }
+      predictions[query[i]].emplace_back(score[i * width + column], label[i],
+                                         weight_t != nullptr ? weight[i] : 1.0);
+    }
+
+    // for each query, accumulate pair counts
+    T pos = 0, neg = 0, neu = 0;
+    if (acc_positive_t != nullptr && acc_negative_t != nullptr &&
+        acc_neutral_t != nullptr) {
+      pos = acc_positive_t->data<T>()[0];
+      neg = acc_negative_t->data<T>()[0];
+      neu = acc_neutral_t->data<T>()[0];
+    }
+    auto evaluate_one_list = [&pos, &neg,
+                              &neu](std::vector<PredictionResult> vec) {
+      for (auto ite1 = vec.begin(); ite1 != vec.end(); ++ite1) {
+        for (auto ite2 = ite1 + 1; ite2 != vec.end(); ++ite2) {
+          if (ite1->label == ite2->label) {  // labels are equal, ignore.
+            continue;
+          }
+          T w = (ite1->weight + ite2->weight) * 0.5;
+          if (ite1->score == ite2->score) {
+            neu += w;
+          }
+          (ite1->score - ite2->score) * (ite1->label - ite2->label) > 0.0
+              ? pos += w
+              : neg += w;
+        }
+      }
+    };
+    for (auto prediction : predictions) {
+      evaluate_one_list(prediction.second);
+    }
+    *positive = pos;
+    *negative = neg;
+    *neutral = neu;
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/framework/tests/test_positive_negative_pair_op.py

+import unittest
+import itertools
+import numpy as np
+from op_test import OpTest
+
+
+def py_pnpair_op(score, label, query, column=-1, weight=None):
+    # group by query id
+    predictions = {}
+    batch_size = label.shape[0]
+    if weight is None:
+        weight = np.ones(shape=(batch_size, 1)).astype('float32')
+    for s, l, q, w in zip(score, label, query, weight):
+        s, l, q, w = s[column], l[0], q[0], w[0]
+        if q not in predictions:
+            predictions[q] = []
+        predictions[q].append((s, l, w))
+
+    # accumulate statistics
+    pos, neg, neu = 0, 0, 0
+    for _, ranks in predictions.items():
+        for e1, e2 in itertools.combinations(ranks, 2):
+            s1, s2, l1, l2, w1, w2 = e1[0], e2[0], e1[1], e2[1], e1[2], e2[2]
+            w = (w1 + w2) * 0.5
+            if l1 == l2:
+                continue
+            if s1 == s2:
+                neu += w
+            elif (s1 - s2) * (l1 - l2) > 0:
+                pos += w
+            else:
+                neg += w
+
+    return np.array(pos).astype('float32'), np.array(neg).astype(
+        'float32'), np.array(neu).astype('float32')
+
+
+class TestPositiveNegativePairOp(OpTest):
+    def setUp(self):
+        self.op_type = 'positive_negative_pair'
+        batch_size = 20
+        max_query_id = 5
+        score = np.random.normal(size=(batch_size, 1)).astype('float32')
+        label = np.random.normal(size=(batch_size, 1)).astype('float32')
+        query = np.array(
+            [np.random.randint(max_query_id) for i in range(batch_size)])
+        query = np.reshape(query, newshape=(batch_size, 1)).astype('int64')
+
+        pos, neg, neu = py_pnpair_op(score, label, query)
+        self.inputs = {'Score': score, 'Label': label, 'QueryID': query}
+        self.attrs = {'column': -1}
+        self.outputs = {
+            'PositivePair': pos,
+            'NegativePair': neg,
+            'NeutralPair': neu
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestPositiveNegativePairOpAccumulateWeight(OpTest):
+    def setUp(self):
+        self.op_type = 'positive_negative_pair'
+        batch_size = 20
+        max_query_id = 5
+        max_random_num = 2 << 15
+        score_dim = 2
+        score = np.random.normal(size=(batch_size, 2)).astype('float32')
+        label = np.random.normal(size=(batch_size, 1)).astype('float32')
+        weight = np.random.normal(size=(batch_size, 1)).astype('float32')
+        query = np.array(
+            [np.random.randint(max_query_id) for i in range(batch_size)])
+        query = np.reshape(query, newshape=(batch_size, 1)).astype('int64')
+        acc_pos = np.reshape(
+            np.random.randint(max_random_num), newshape=(1)).astype('float32')
+        acc_neg = np.reshape(
+            np.random.randint(max_random_num), newshape=(1)).astype('float32')
+        acc_neu = np.reshape(
+            np.random.randint(max_random_num), newshape=(1)).astype('float32')
+        column = np.random.randint(score_dim)
+
+        pos, neg, neu = py_pnpair_op(
+            score, label, query, column=column, weight=weight)
+        self.inputs = {
+            'Score': score,
+            'Label': label,
+            'QueryID': query,
+            'AccumulatePositivePair': acc_pos,
+            'AccumulateNegativePair': acc_neg,
+            'AccumulateNeutralPair': acc_neu,
+            'Weight': weight
+        }
+        self.attrs = {'column': column}
+        self.outputs = {
+            'PositivePair': pos + acc_pos,
+            'NegativePair': neg + acc_neg,
+            'NeutralPair': neu + acc_neu
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == '__main__':
+    unittest.main()