Add chunk_eval_op

paddle/operators/chunk_eval_op.cc

+/* 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/chunk_eval_op.h"
+
+namespace paddle {
+namespace operators {
+
+class ChunkEvalOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Inference"),
+                   "Input(Inference) of ChunkEvalOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"),
+                   "Input(Label) of ChunkEvalOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Precision"),
+                   "Output(Precision) of ChunkEvalOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Recall"),
+                   "Output(Recall) of ChunkEvalOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("F1-Score"),
+                   "Output(F1-Score) of ChunkEvalOp should not be null.");
+
+    auto inference_dim = ctx->GetInputDim("Inference");
+    auto label_dim = ctx->GetInputDim("Label");
+
+    PADDLE_ENFORCE(inference_dim == label_dim,
+                   "Inference's shape must be the same as Label's shape.");
+
+    ctx->SetOutputDim("Precision", {1});
+    ctx->SetOutputDim("Recall", {1});
+    ctx->SetOutputDim("F1-Score", {1});
+  }
+
+  framework::DataType IndicateDataType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::DataType::FP32;
+  }
+};
+
+class ChunkEvalOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  ChunkEvalOpMaker(framework::OpProto *proto,
+                   framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("Inference",
+             "(Tensor, default: Tensor<int>) Predictions from the network.");
+    AddInput("Label", "(Tensor, default: Tensor<int>) Labels of the data.");
+    AddOutput(
+        "Precision",
+        "(float) The precision ratio of the predictions on current data.");
+    AddOutput("Recall",
+              "(float) The recall ratio of the predictions on current data.");
+    AddOutput("F1-Score",
+              "(float) The F1-Score of the predictions on current data.");
+    AddAttr<int>("num_chunk_types", "(int) The number of chunk type.");
+    AddAttr<std::string>("chunk_scheme",
+                         "(string, default IOB) The label scheme.")
+        .SetDefault("IOB");
+    AddAttr<std::vector<int>>(
+        "excluded_chunk_types",
+        "(list<int>) A list<int> indicating chunk types not to be counted.")
+        .SetDefault(std::vector<int>{});
+    AddComment(R"DOC(
+Chunk evaluator is used to evaluate segment labelling accuracy for a
+sequence. It calculates precision, recall and F1 scores for the chunk detection.
+To use chunk evaluator, several concepts need to be clarified firstly.
+[Chunk type] is the type of the whole chunk and a chunk consists of one or several words.  (For example in NER, ORG for organization name, PER for person name etc.)
+[Tag type] indicates the position of a word in a chunk. (B for begin, I for inside, E for end, S for single)
+We can name a label by combining tag type and chunk type. (ie. B-ORG for begining of an organization name)
+The construction of label dictionary should obey the following rules:
+- Use one of the listed labelling schemes. These schemes differ in ways indicating chunk boundry.
+
+    Scheme    Description
+    plain    Use the same label for the whole chunk.
+    IOB      Two labels for chunk type X, B-X for chunk begining and I-X for chunk inside.
+    IOE      Two labels for chunk type X, E-X for chunk ending and I-X for chunk inside.
+    IOBES    Four labels for chunk type X, B-X for chunk begining, I-X for chunk inside, E-X for chunk end and S-X for single word chunk.
+
+To make it clear, let's illustrate by an NER example.
+Assuming that there are three named entity types including ORG, PER and LOC which are called 'chunk type' here,
+if 'IOB' scheme were used, the label set will be extended to a set including B-ORG, I-ORG, B-PER, I-PER, B-LOC, I-LOC and O,
+in which B-ORG for begining of ORG and I-ORG for inside of ORG.
+Prefixes which are called 'tag type' here are added to chunk types and there are two tag types including B and I.
+Of course, the training data should be labeled accordingly.
+- Mapping is done correctly by the listed equations and assigning protocol.
+The following table are equations to extract tag type and chunk type from a label.
+
+    tagType = label % numTagType
+    chunkType = label / numTagType
+    otherChunkType = numChunkTypes
+
+The following table shows the mapping rule between tagType and tag type in each scheme.
+
+    Scheme Begin Inside End   Single
+    plain  0     -      -     -
+    IOB    0     1      -     -
+    IOE    -     0      1     -
+    IOBES  0     1      2     3
+
+Continue the NER example, and the label dict should look like this to satify above equations:
+
+    B-ORG  0
+    I-ORG  1
+    B-PER  2
+    I-PER  3
+    B-LOC  4
+    I-LOC  5
+    O      6
+
+In this example, chunkType has three values: 0 for ORG, 1 for PER, 2 for LOC, because the scheme is
+"IOB" so tagType has two values: 0 for B and 1 for I.
+Here we will use I-LOC to explain the above mapping rules in detail.
+For I-LOC, the label id is 5, so we can get tagType=1 and chunkType=2, which means I-LOC is a part of NER chunk LOC
+and the tag is I.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(chunk_eval, ops::ChunkEvalOp,
+                             ops::ChunkEvalOpMaker);
+REGISTER_OP_CPU_KERNEL(chunk_eval,
+                       ops::ChunkEvalKernel<paddle::platform::CPUPlace, float>);

paddle/operators/chunk_eval_op.h

+/* 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 <set>
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+template <typename Place, typename T>
+class ChunkEvalKernel : public framework::OpKernel<T> {
+ public:
+  struct Segment {
+    int begin;
+    int end;
+    int type;
+    bool operator==(const Segment& y) const {
+      return begin == y.begin && end == y.end && type == y.type;
+    }
+  };
+
+  void GetSegments(const int* label, int length, std::vector<Segment>& segments,
+                   int num_chunk_types, int num_tag_types, int other_chunk_type,
+                   int tag_begin, int tag_inside, int tag_end,
+                   int tag_single) const {
+    segments.clear();
+    segments.reserve(length);
+    int chunk_start = 0;
+    bool in_chunk = false;
+    int tag = -1;
+    int type = other_chunk_type;
+    for (int i = 0; i < length; ++i) {
+      int prev_tag = tag;
+      int prev_type = type;
+      PADDLE_ENFORCE_LE(label[i], num_chunk_types * num_tag_types);
+      tag = label[i] % num_tag_types;
+      type = label[i] / num_tag_types;
+      if (in_chunk && ChunkEnd(prev_tag, prev_type, tag, type, other_chunk_type,
+                               tag_begin, tag_inside, tag_end, tag_single)) {
+        Segment segment{
+            chunk_start,  // begin
+            i - 1,        // end
+            prev_type,
+        };
+        segments.push_back(segment);
+        in_chunk = false;
+      }
+      if (ChunkBegin(prev_tag, prev_type, tag, type, other_chunk_type,
+                     tag_begin, tag_inside, tag_end, tag_single)) {
+        chunk_start = i;
+        in_chunk = true;
+      }
+    }
+    if (in_chunk) {
+      Segment segment{
+          chunk_start,  // begin
+          length - 1,   // end
+          type,
+      };
+      segments.push_back(segment);
+    }
+  }
+
+  bool ChunkEnd(int prev_tag, int prev_type, int tag, int type,
+                int other_chunk_type, int tag_begin, int tag_inside,
+                int tag_end, int tag_single) const {
+    if (prev_type == other_chunk_type) return false;
+    if (type == other_chunk_type) return true;
+    if (type != prev_type) return true;
+    if (prev_tag == tag_begin) return tag == tag_begin || tag == tag_single;
+    if (prev_tag == tag_inside) return tag == tag_begin || tag == tag_single;
+    if (prev_tag == tag_end) return true;
+    if (prev_tag == tag_single) return true;
+    return false;
+  }
+
+  bool ChunkBegin(int prev_tag, int prev_type, int tag, int type,
+                  int other_chunk_type, int tag_begin, int tag_inside,
+                  int tag_end, int tag_single) const {
+    if (prev_type == other_chunk_type) return type != other_chunk_type;
+    if (type == other_chunk_type) return false;
+    if (type != prev_type) return true;
+    if (tag == tag_begin) return true;
+    if (tag == tag_inside) return prev_tag == tag_end || prev_tag == tag_single;
+    if (tag == tag_end) return prev_tag == tag_end || prev_tag == tag_single;
+    if (tag == tag_single) return true;
+    return false;
+  }
+
+  void Compute(const framework::ExecutionContext& context) const override {
+    // initialize to parse configurations
+    int num_chunk_types, num_tag_types;
+    int other_chunk_type;
+    int tag_begin, tag_inside, tag_end, tag_single;
+    std::vector<Segment> label_segments;
+    std::vector<Segment> output_segments;
+    std::set<int> excluded_chunk_types;
+    int64_t num_output_segments = 0;
+    int64_t num_label_segments = 0;
+    int64_t num_correct = 0;
+    if (context.Attr<std::string>("chunk_scheme") == "IOB") {
+      num_tag_types = 2;
+      tag_begin = 0;
+      tag_inside = 1;
+      tag_end = -1;
+      tag_single = -1;
+    } else if (context.Attr<std::string>("chunk_scheme") == "IOE") {
+      num_tag_types = 2;
+      tag_begin = -1;
+      tag_inside = 0;
+      tag_end = 1;
+      tag_single = -1;
+    } else if (context.Attr<std::string>("chunk_scheme") == "IOBES") {
+      num_tag_types = 4;
+      tag_begin = 0;
+      tag_inside = 1;
+      tag_end = 2;
+      tag_single = 3;
+    } else if (context.Attr<std::string>("chunk_scheme") == "plain") {
+      num_tag_types = 1;
+      tag_begin = -1;
+      tag_inside = -1;
+      tag_end = -1;
+      tag_single = -1;
+    } else {
+      PADDLE_THROW("Unknown chunk scheme.");
+    }
+    other_chunk_type = num_chunk_types = context.Attr<int>("num_chunk_types");
+    excluded_chunk_types.insert(
+        context.Attr<std::vector<int>>("excluded_chunk_types").begin(),
+        context.Attr<std::vector<int>>("excluded_chunk_types").end());
+
+    auto* inference = context.Input<LoDTensor>("Inference");
+    auto* label = context.Input<LoDTensor>("Label");
+    auto* precision = context.Output<Tensor>("Precision");
+    auto* recall = context.Output<Tensor>("Recall");
+    auto* f1 = context.Output<Tensor>("F1-Score");
+
+    const int* inference_data = inference->data<int>();
+    const int* label_data = label->data<int>();
+    T* precision_data = precision->mutable_data<T>(context.GetPlace());
+    T* racall_data = recall->mutable_data<T>(context.GetPlace());
+    T* f1_data = f1->mutable_data<T>(context.GetPlace());
+
+    auto lod = label->lod();
+    PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
+    PADDLE_ENFORCE(lod == inference->lod(),
+                   "LoD must be same between Inference and Label.");
+    int num_sequences = lod[0].size() - 1;
+    for (int i = 0; i < num_sequences; ++i) {
+      int seq_length = lod[0][i + 1] - lod[0][i];
+      EvalOneSeq(inference_data + lod[0][i], label_data + lod[0][i], seq_length,
+                 output_segments, label_segments, num_output_segments,
+                 num_label_segments, num_correct, num_chunk_types,
+                 num_tag_types, other_chunk_type, tag_begin, tag_inside,
+                 tag_end, tag_single, excluded_chunk_types);
+    }
+    *precision_data =
+        !num_output_segments ? 0 : (T)num_correct / num_output_segments;
+    *racall_data =
+        !num_label_segments ? 0 : (T)num_correct / num_label_segments;
+    *f1_data = !num_correct ? 0 : 2 * (*precision_data) * (*racall_data) /
+                                      ((*precision_data) + (*racall_data));
+  }
+
+  void EvalOneSeq(const int* output, const int* label, int length,
+                  std::vector<Segment>& output_segments,
+                  std::vector<Segment>& label_segments,
+                  int64_t& num_output_segments, int64_t& num_label_segments,
+                  int64_t& num_correct, int num_chunk_types, int num_tag_types,
+                  int other_chunk_type, int tag_begin, int tag_inside,
+                  int tag_end, int tag_single,
+                  const std::set<int>& excluded_chunk_types) const {
+    GetSegments(output, length, output_segments, num_chunk_types, num_tag_types,
+                other_chunk_type, tag_begin, tag_inside, tag_end, tag_single);
+    GetSegments(label, length, label_segments, num_chunk_types, num_tag_types,
+                other_chunk_type, tag_begin, tag_inside, tag_end, tag_single);
+    size_t i = 0, j = 0;
+    while (i < output_segments.size() && j < label_segments.size()) {
+      if (output_segments[i] == label_segments[j] &&
+          excluded_chunk_types.count(output_segments[i].type) != 1) {
+        ++num_correct;
+      }
+      if (output_segments[i].end < label_segments[j].end) {
+        ++i;
+      } else if (output_segments[i].end > label_segments[j].end) {
+        ++j;
+      } else {
+        ++i;
+        ++j;
+      }
+    }
+    for (auto& segment : label_segments) {
+      if (excluded_chunk_types.count(segment.type) != 1) ++num_label_segments;
+    }
+    for (auto& segment : output_segments) {
+      if (excluded_chunk_types.count(segment.type) != 1) ++num_output_segments;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

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

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class Segments(object):
+    def __init__(self, chunk_type, start_idx, end_idx):
+        self.chunk_type = chunk_type
+        self.start_idx = start_idx
+        self.end_idx = end_idx
+
+    def __str__(self):
+        return '(Segments: %s, %s, %s)' % (self.chunk_type, self.start_idx,
+                                           self.end_idx)
+
+    __repr__ = __str__
+
+
+class TestChunkEvalOp(OpTest):
+    num_sequences = 5
+    batch_size = 50
+
+    def parse_scheme(self):
+        if self.scheme == 'IOB':
+            self.num_tag_types = 2
+        elif self.scheme == 'IOE':
+            self.num_tag_types = 2
+
+    def fill_with_chunks(self, data, chunks):
+        for chunk in chunks:
+            if self.scheme == 'IOB':
+                data[chunk.start_idx] = chunk.chunk_type * self.num_tag_types
+                data[chunk.start_idx + 1:
+                     chunk.end_idx] = chunk.chunk_type * self.num_tag_types + (
+                         self.num_tag_types - 1)
+                data[chunk.end_idx] = chunk.chunk_type * self.num_tag_types + (
+                    self.num_tag_types - 1
+                ) if chunk.start_idx < chunk.end_idx else data[chunk.start_idx]
+            elif self.scheme == 'IOE':
+                data[chunk.start_idx:
+                     chunk.end_idx] = chunk.chunk_type * self.num_tag_types
+                data[chunk.end_idx] = chunk.chunk_type * self.num_tag_types + (
+                    self.num_tag_types - 1)
+
+    def rand_chunks(self, starts, num_chunks):
+        if num_chunks < 0:
+            num_chunks = np.random.randint(starts[-1])
+        chunks = []
+        # generate chunk beginnings
+        chunk_begins = sorted(
+            np.random.choice(
+                range(starts[-1]), num_chunks, replace=False))
+        seq_chunk_begins = []
+        begin_idx = 0
+        # divide chunks into sequences
+        for i in range(len(starts) - 1):
+            tmp_chunk_begins = []
+            while begin_idx < len(chunk_begins) and chunk_begins[
+                    begin_idx] < starts[i + 1]:
+                tmp_chunk_begins.append(chunk_begins[begin_idx])
+                begin_idx += 1
+            seq_chunk_begins.append(tmp_chunk_begins)
+        # generate chunk ends
+        chunk_ends = []
+        for i in range(len(seq_chunk_begins)):
+            for j in range(len(seq_chunk_begins[i])):
+                low = seq_chunk_begins[i][j]
+                high = seq_chunk_begins[i][j + 1] if j < len(seq_chunk_begins[
+                    i]) - 1 else starts[i + 1]
+                chunk_ends.append(np.random.randint(low, high))
+        # generate chunks
+        for chunk_pos in zip(chunk_begins, chunk_ends):
+            chunk_type = np.random.randint(self.num_chunk_types)
+            chunks.append(Segments(chunk_type, *chunk_pos))
+        return chunks
+
+    def gen_chunks(self, infer, label, starts):
+        chunks = self.rand_chunks(starts,
+                                  self.num_infer_chunks + self.num_label_chunks
+                                  - self.num_correct_chunks)
+        correct_chunks = np.random.choice(
+            range(len(chunks)), self.num_correct_chunks, replace=False)
+        infer_chunks = np.random.choice(
+            [x for x in range(len(chunks)) if x not in correct_chunks],
+            self.num_infer_chunks - self.num_correct_chunks,
+            replace=False)
+        infer_chunks = sorted(correct_chunks.tolist() + infer_chunks.tolist())
+        label_chunks = np.random.choice(
+            [x for x in range(len(chunks)) if x not in infer_chunks],
+            self.num_label_chunks - self.num_correct_chunks,
+            replace=False)
+        label_chunks = sorted(correct_chunks.tolist() + label_chunks.tolist())
+        self.fill_with_chunks(infer, [chunks[idx] for idx in infer_chunks])
+        self.fill_with_chunks(label, [chunks[idx] for idx in label_chunks])
+        # exclude types in excluded_chunk_types
+        if len(self.excluded_chunk_types) > 0:
+            for idx in correct_chunks:
+                if chunks[idx].chunk_type in self.excluded_chunk_types:
+                    self.num_correct_chunks -= 1
+            for idx in infer_chunks:
+                if chunks[idx].chunk_type in self.excluded_chunk_types:
+                    self.num_infer_chunks -= 1
+            for idx in label_chunks:
+                if chunks[idx].chunk_type in self.excluded_chunk_types:
+                    self.num_label_chunks -= 1
+        return self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks
+
+    def set_confs(self):
+        # Use the IOB scheme and labels with 2 chunk types
+        self.scheme = 'IOB'
+        self.num_chunk_types = 2
+        self.excluded_chunk_types = []
+        self.other_chunk_type = self.num_chunk_types
+        self.attrs = {
+            'num_chunk_types': self.num_chunk_types,
+            'chunk_scheme': self.scheme,
+            'excluded_chunk_types': self.excluded_chunk_types
+        }
+        self.parse_scheme()
+        self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = 4, 5, 9
+
+    def set_data(self):
+        infer = np.zeros((self.batch_size, )).astype("int32")
+        infer.fill(self.num_chunk_types * self.num_tag_types)
+        label = np.copy(infer)
+        starts = np.random.choice(
+            range(1, self.batch_size), self.num_sequences - 1,
+            replace=False).tolist()
+        starts.extend([0, self.batch_size])
+        starts = sorted(starts)
+        self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = self.gen_chunks(
+            infer, label, starts)
+        self.inputs = {
+            'Inference': (infer, [starts]),
+            'Label': (label, [starts])
+        }
+        precision = float(
+            self.num_correct_chunks
+        ) / self.num_infer_chunks if self.num_infer_chunks else 0
+        recall = float(self.num_correct_chunks
+                       ) / self.num_label_chunks if self.num_label_chunks else 0
+        f1 = float(2 * precision * recall) / (
+            precision + recall) if self.num_correct_chunks else 0
+        self.outputs = {
+            'Precision': [precision],
+            'Recall': [recall],
+            'F1-Score': [f1]
+        }
+
+    def setUp(self):
+        self.op_type = 'chunk_eval'
+        self.set_confs()
+        self.set_data()
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestChunkEvalOpWithExclude(TestChunkEvalOp):
+    def set_confs(self):
+        # Use the IOE scheme and labels with 3 chunk types
+        self.scheme = 'IOE'
+        self.num_chunk_types = 3
+        self.excluded_chunk_types = [1]
+        self.other_chunk_type = self.num_chunk_types
+        self.attrs = {
+            'num_chunk_types': self.num_chunk_types,
+            'chunk_scheme': self.scheme,
+            'excluded_chunk_types': self.excluded_chunk_types
+        }
+        self.parse_scheme()
+        self.num_correct_chunks, self.num_infer_chunks, self.num_label_chunks = 15, 18, 20
+
+
+if __name__ == '__main__':
+    unittest.main()