提交 d3e003be 编写于 作者: Y Yang yaming 提交者: GitHub

Merge pull request #2165 from pkuyym/fix-2078

Fix 2078
...@@ -16,6 +16,7 @@ limitations under the License. */ ...@@ -16,6 +16,7 @@ limitations under the License. */
#include <vector> #include <vector>
#include "paddle/math/Vector.h" #include "paddle/math/Vector.h"
#include "paddle/utils/StringUtil.h"
#include "Evaluator.h" #include "Evaluator.h"
...@@ -74,6 +75,7 @@ class ChunkEvaluator : public Evaluator { ...@@ -74,6 +75,7 @@ class ChunkEvaluator : public Evaluator {
std::vector<Segment> labelSegments_; std::vector<Segment> labelSegments_;
std::vector<Segment> outputSegments_; std::vector<Segment> outputSegments_;
std::set<int> excludedChunkTypes_; std::set<int> excludedChunkTypes_;
mutable std::unordered_map<std::string, real> values_;
public: public:
virtual void init(const EvaluatorConfig& config) { virtual void init(const EvaluatorConfig& config) {
...@@ -121,11 +123,9 @@ public: ...@@ -121,11 +123,9 @@ public:
} }
virtual void printStats(std::ostream& os) const { virtual void printStats(std::ostream& os) const {
double precision = (double)numCorrect_ / numOutputSegments_; storeLocalValues();
double recall = (double)numCorrect_ / numLabelSegments_; os << config_.name() << "=" << values_["F1-score"]
double f1 = << " true_chunks=" << numLabelSegments_
!numCorrect_ ? 0 : 2 * precision * recall / (precision + recall);
os << config_.name() << "=" << f1 << " true_chunks=" << numLabelSegments_
<< " result_chunks=" << numOutputSegments_ << " result_chunks=" << numOutputSegments_
<< " correct_chunks=" << numCorrect_; << " correct_chunks=" << numCorrect_;
} }
...@@ -243,6 +243,46 @@ public: ...@@ -243,6 +243,46 @@ public:
if (tag == tagSingle_) return true; if (tag == tagSingle_) return true;
return false; return false;
} }
// three metrics: precision, recall and F1-score
void getNames(std::vector<std::string>* names) {
storeLocalValues();
names->reserve(names->size() + values_.size());
for (auto it = values_.begin(); it != values_.end(); ++it) {
names->push_back(config_.name() + "." + it->first);
}
}
// get value by field name
real getValue(const std::string& name, Error* err) const {
storeLocalValues();
std::vector<std::string> buffers;
paddle::str::split(name, '.', &buffers);
auto it = values_.find(buffers.back());
if (it == values_.end()) { // not found
*err = Error("No such key %s", name.c_str());
return 0.0f;
}
return it->second;
}
// get type of evaluator
std::string getTypeImpl() const { return "chunk"; }
private:
void storeLocalValues() const {
CHECK_GE(numOutputSegments_, 0);
CHECK_GE(numLabelSegments_, 0);
double precision =
!numOutputSegments_ ? 0 : (double)numCorrect_ / numOutputSegments_;
double recall =
!numLabelSegments_ ? 0 : (double)numCorrect_ / numLabelSegments_;
values_["precision"] = precision;
values_["recall"] = recall;
values_["F1-score"] =
!numCorrect_ ? 0 : 2 * precision * recall / (precision + recall);
}
}; };
REGISTER_EVALUATOR(chunk, ChunkEvaluator); REGISTER_EVALUATOR(chunk, ChunkEvaluator);
......
...@@ -347,24 +347,47 @@ def chunk_evaluator( ...@@ -347,24 +347,47 @@ def chunk_evaluator(
excluded_chunk_types=None, ): excluded_chunk_types=None, ):
""" """
Chunk evaluator is used to evaluate segment labelling accuracy for a Chunk evaluator is used to evaluate segment labelling accuracy for a
sequence. It calculates the chunk detection F1 score. sequence. It calculates precision, recall and F1 scores for the chunk detection.
A chunk is correctly detected if its beginning, end and type are correct. To use chunk evaluator, several concepts need to be clarified firstly.
Other chunk type is ignored.
For each label in the label sequence, we have: * **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.)
.. code-block:: python * **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.
.. code-block:: text
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.
.. code-block:: text
tagType = label % numTagType tagType = label % numTagType
chunkType = label / numTagType chunkType = label / numTagType
otherChunkType = numChunkTypes otherChunkType = numChunkTypes
The total number of different labels is numTagType*numChunkTypes+1. The following table shows the mapping rule between tagType and tag type in each scheme.
We support 4 labelling scheme.
The tag type for each of the scheme is shown as follows:
.. code-block:: python .. code-block:: text
Scheme Begin Inside End Single Scheme Begin Inside End Single
plain 0 - - - plain 0 - - -
...@@ -372,7 +395,23 @@ def chunk_evaluator( ...@@ -372,7 +395,23 @@ def chunk_evaluator(
IOE - 0 1 - IOE - 0 1 -
IOBES 0 1 2 3 IOBES 0 1 2 3
'plain' means the whole chunk must contain exactly the same chunk label. Continue the NER example, and the label dict should look like this to satify above equations:
.. code-block:: text
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.
The simple usage is: The simple usage is:
...@@ -380,6 +419,7 @@ def chunk_evaluator( ...@@ -380,6 +419,7 @@ def chunk_evaluator(
eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types) eval = chunk_evaluator(input, label, chunk_scheme, num_chunk_types)
:param input: The input layers. :param input: The input layers.
:type input: LayerOutput :type input: LayerOutput
:param label: An input layer containing the ground truth label. :param label: An input layer containing the ground truth label.
......
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