Add excluded_chunk_types to ChunkEvaluator

The chunks of types in excluded_chunk_types will not be counted in
ChunkEvaluator. This is useful for tasks such as SRL, in which chunks of
type V (verb) will not be taken into account in evaluation.

paddle/gserver/evaluators/ChunkEvaluator.cpp

@@ -12,6 +12,7 @@ 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 <set>
 #include <vector>
 
 #include "paddle/math/Vector.h"
@@ -72,6 +73,7 @@ class ChunkEvaluator : public Evaluator {
 
   std::vector<Segment> labelSegments_;
   std::vector<Segment> outputSegments_;
+  std::set<int> excludedChunkTypes_;
 
 public:
   virtual void init(const EvaluatorConfig& config) {
@@ -105,6 +107,10 @@ class ChunkEvaluator : public Evaluator {
     }
     CHECK(config.has_num_chunk_types()) << "Missing num_chunk_types in config";
     otherChunkType_ = numChunkTypes_ = config.num_chunk_types();
+
+    // the chunks of types in excludedChunkTypes_ will not be counted
+    auto& tmp = config.excluded_chunk_types();
+    excludedChunkTypes_.insert(tmp.begin(), tmp.end());
   }
 
   virtual void start() {
@@ -157,7 +163,8 @@ class ChunkEvaluator : public Evaluator {
     size_t i = 0, j = 0;
     while (i < outputSegments_.size() && j < labelSegments_.size()) {
       if (outputSegments_[i] == labelSegments_[j]) {
-        ++numCorrect_;
+        if (excludedChunkTypes_.count(outputSegments_[i].type) != 1)
+          ++numCorrect_;
       }
       if (outputSegments_[i].end < labelSegments_[j].end) {
         ++i;
@@ -168,8 +175,12 @@ class ChunkEvaluator : public Evaluator {
         ++j;
       }
     }
-    numLabelSegments_ += labelSegments_.size();
-    numOutputSegments_ += outputSegments_.size();
+    for (auto& segment : labelSegments_) {
+      if (excludedChunkTypes_.count(segment.type) != 1) ++numLabelSegments_;
+    }
+    for (auto& segment : outputSegments_) {
+      if (excludedChunkTypes_.count(segment.type) != 1) ++numOutputSegments_;
+    }
   }
 
   void getSegments(int* label, int length, std::vector<Segment>& segments) {

proto/ModelConfig.proto

@@ -433,8 +433,12 @@ message EvaluatorConfig {
   repeated string input_layers = 3;
 
   // Used by ChunkEvaluator
-  optional string chunk_scheme = 4; // one of "IOB", "IOE", "IOBES"
-  optional int32 num_chunk_types = 5; // number of chunk types other than "other"
+  // one of "IOB", "IOE", "IOBES"
+  optional string chunk_scheme = 4;
+  // number of chunk types other than "other"
+  optional int32 num_chunk_types = 5;
+  // chunk of these types are not counted
+  repeated int32 excluded_chunk_types = 12;
 
   // Used by PrecisionRecallEvaluator and ClassificationErrorEvaluator
   // For multi binary labels: true if output > classification_threshold
@@ -453,6 +457,8 @@ message EvaluatorConfig {
 
   // whether to delimit the sequence in the seq_text_printer
   optional bool delimited = 11 [default = true];
+
+  // NOTE: 12 has been occupied by excluded_chunk_types
 }
 
 message LinkConfig {

python/paddle/trainer/config_parser.py

@@ -1240,7 +1240,8 @@ def Evaluator(
         dict_file=None,
         result_file=None,
         num_results=None,
-        delimited=None, ):
+        delimited=None,
+        excluded_chunk_types=None, ):
     evaluator = g_config.model_config.evaluators.add()
     evaluator.type = type
     evaluator.name = MakeLayerNameInSubmodel(name)
@@ -1269,6 +1270,9 @@ def Evaluator(
     if delimited is not None:
         evaluator.delimited = delimited
 
+    if excluded_chunk_types:
+        evaluator.excluded_chunk_types.extend(excluded_chunk_types)
+
 
 class LayerBase(object):
     def __init__(

python/paddle/trainer_config_helpers/evaluators.py

@@ -57,19 +57,21 @@ def impl(method):
     return impl
 
 
-def evaluator_base(input,
-                   type,
-                   label=None,
-                   weight=None,
-                   name=None,
-                   chunk_scheme=None,
-                   num_chunk_types=None,
-                   classification_threshold=None,
-                   positive_label=None,
-                   dict_file=None,
-                   result_file=None,
-                   num_results=None,
-                   delimited=None):
+def evaluator_base(
+        input,
+        type,
+        label=None,
+        weight=None,
+        name=None,
+        chunk_scheme=None,
+        num_chunk_types=None,
+        classification_threshold=None,
+        positive_label=None,
+        dict_file=None,
+        result_file=None,
+        num_results=None,
+        delimited=None,
+        excluded_chunk_types=None, ):
     """
     Evaluator will evaluate the network status while training/testing.
 
@@ -127,7 +129,8 @@ def evaluator_base(input,
         positive_label=positive_label,
         dict_file=dict_file,
         result_file=result_file,
-        delimited=delimited)
+        delimited=delimited,
+        excluded_chunk_types=excluded_chunk_types, )
 
 
 @evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)
@@ -330,7 +333,8 @@ def chunk_evaluator(
         label,
         chunk_scheme,
         num_chunk_types,
-        name=None, ):
+        name=None,
+        excluded_chunk_types=None, ):
     """
     Chunk evaluator is used to evaluate segment labelling accuracy for a
     sequence. It calculates the chunk detection F1 score.
@@ -376,14 +380,17 @@ def chunk_evaluator(
     :param num_chunk_types: number of chunk types other than "other"
     :param name: The Evaluator name, it is optional.
     :type name: basename|None
+    :param excluded_chunk_types: chunks of these types are not considered
+    :type excluded_chunk_types: list of integer|[]
     """
     evaluator_base(
         name=name,
         type="chunk",
         input=input,
         label=label,
         chunk_scheme=chunk_scheme,
-        num_chunk_types=num_chunk_types)
+        num_chunk_types=num_chunk_types,
+        excluded_chunk_types=excluded_chunk_types, )
 
 
 @evaluator(EvaluatorAttribute.FOR_UTILS)