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eval.py
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eval.py
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import utils
import copy
from prettytable import PrettyTable
"""
Set of classes and methods for computing and printing the results using the strict, boundaries and relaxed evaluation methods.
For more info about how to use them see tf_utils.py
"""
class printClasses():
def __init__(self):
self.t = PrettyTable(['Class', 'TP', 'FP', 'FN', 'Pr', 'Re', 'F1'])
def add(self, Class, TP, FP, FN, Pr, Re, F1):
if Class!="O":
self.t.add_row([Class, TP, FP, FN, Pr, Re, F1])
def print(self):
print(self.t)
def get_chunk_type(tok, idx_to_tag):
# method implemented in https://github.com/guillaumegenthial/sequence_tagging/blob/master/model/data_utils.py
"""
Args:
tok: id of token, ex 4
idx_to_tag: dictionary {4: "B-PER", ...}
Returns:
tuple: "B", "PER"
"""
tag_name = idx_to_tag[tok]
tag_class = tag_name.split('-')[0]
tag_type = tag_name.split('-')[-1]
return tag_class, tag_type
def get_chunks(seq, tags):
# method implemented in https://github.com/guillaumegenthial/sequence_tagging/blob/master/model/data_utils.py
"""Given a sequence of tags, group entities and their position
Args:
seq: [4, 4, 0, 0, ...] sequence of labels
tags: dict["O"] = 4
Returns:
list of (chunk_type, chunk_start, chunk_end)
Example:
seq = [4, 5, 0, 3]
tags = {"B-PER": 4, "I-PER": 5, "B-LOC": 3}
result = [("PER", 0, 2), ("LOC", 3, 4)]
"""
default = tags['O']
idx_to_tag = {idx: tag for tag, idx in tags.items()}
chunks = []
chunk_type, chunk_start = None, None
for i, tok in enumerate(seq):
# End of a chunk 1
if tok == default and chunk_type is not None:
# Add a chunk.
chunk = (chunk_type, chunk_start, i-1)
chunks.append(chunk)
chunk_type, chunk_start = None, None
# End of a chunk + start of a chunk!
elif tok != default:
tok_chunk_class, tok_chunk_type = get_chunk_type(tok, idx_to_tag)
if chunk_type is None:
chunk_type, chunk_start = tok_chunk_type, i
elif tok_chunk_type != chunk_type or tok_chunk_class == "B":
chunk = (chunk_type, chunk_start, i-1)
chunks.append(chunk)
chunk_type, chunk_start = tok_chunk_type, i
else:
pass
# end condition
if chunk_type is not None:
chunk = (chunk_type, chunk_start, len(seq)-1)
chunks.append(chunk)
return chunks
def relationChunks(relations, ners, relationTuple="boundaries_type"):
relationChunks = []
for rel in relations:
relation = rel[1]
left_chunk = ""
right_chunk = ""
for ner in ners:
if rel[0] >= ner[1] and rel[0] <= ner[2]:
# print (ner)
if relationTuple == "boundaries_type":
left_chunk = ner
elif relationTuple == "boundaries":
left_chunk = (ner[1], ner[2])
elif relationTuple == "type":
left_chunk = (ner[0])
if rel[2] >= ner[1] and rel[2] <= ner[2]:
# print (ner)
if relationTuple == "boundaries_type":
right_chunk = ner
elif relationTuple == "boundaries":
right_chunk = (ner[1], ner[2])
elif relationTuple == "type":
right_chunk = (ner[0])
if (left_chunk != "" and right_chunk != ""):
relationChunks.append((left_chunk, relation, right_chunk))
return relationChunks
def getTokenRelations(label_names, head_ids, token_ids):
relations = []
for labelLIdx in range(len(label_names)):
# print (predLabel)
labelL = label_names[labelLIdx]
headL = head_ids[labelLIdx]
tokenId = token_ids[labelLIdx]
for labelIdx in range(len(labelL)):
label = labelL[labelIdx]
head = headL[labelIdx]
# print (label)
# print ((tokenId)+" "+ label+ " " + head)
if label != "N":
# print (label)
relations.append((tokenId, label, head))
# print (tokenId,label,head)
return relations
def keepOnlyChunkBoundaries(ners):
nersNoBounds = []
ners = list(ners)
for ner in ners:
# ner[0]=None
# print (ner)
nersNoBounds.append((None, ner[1], ner[2]))
return nersNoBounds
class chunkEvaluator:
def __init__(self,config,ner_chunk_eval="boundaries_type",rel_chunk_eval="boundaries"):
self.nerSegmentationTags=config.dataset_set_bio_tags
self.NERset = config.dataset_set_ec_tags
self.RELset = config.dataset_set_relations
self.root_node=config.root_node
self.ner_chunk_eval=ner_chunk_eval
self.rel_chunk_eval=rel_chunk_eval
self.totals = 0
self.oks = 0
self.tpsNER = 0
self.fpsNER = 0
self.fnsNER = 0
self.tpsREL = 0
self.fpsREL = 0
self.fnsREL = 0
self.tpsClassesNER = dict.fromkeys(self.NERset, 0)
self.fpsClassesNER = dict.fromkeys(self.NERset, 0)
self.fnsClassesNER = dict.fromkeys(self.NERset, 0)
self.precisionNER = dict.fromkeys(self.NERset, 0)
self.recallNER = dict.fromkeys(self.NERset, 0)
self.f1NER = dict.fromkeys(self.NERset, 0)
self.tpsClassesREL = dict.fromkeys(self.RELset, 0)
self.fpsClassesREL = dict.fromkeys(self.RELset, 0)
self.fnsClassesREL = dict.fromkeys(self.RELset, 0)
self.precisionREL = dict.fromkeys(self.RELset, 0)
self.recallREL = dict.fromkeys(self.RELset, 0)
self.f1REL = dict.fromkeys(self.RELset, 0)
self.correct_predsNER, self.total_correctNER, self.total_predsNER = 0., 0., 0.
self.correct_predsREL, self.total_correctREL, self.total_predsREL = 0., 0., 0.
def add(self, pred_batchesNER, true_batchesNER, pred_batchesREL, true_batchesREL):
for batch_idx in range(len(pred_batchesNER)):
predNER = pred_batchesNER[batch_idx]
trueNER = true_batchesNER[batch_idx]
predRel = pred_batchesREL[batch_idx]
trueRel = true_batchesREL[batch_idx]
ptoken_ids, _, plabel_ids, phead_ids, plabel_names = utils.transformToInitialInput(
predRel, self.RELset)
_, _, tlabel_ids, thead_ids, tlabel_names = utils.transformToInitialInput(
trueRel, self.RELset)
trueRel = getTokenRelations(tlabel_names, thead_ids, ptoken_ids)
predRel = getTokenRelations(plabel_names, phead_ids, ptoken_ids)
tagsNER = utils.getSegmentationDict(self.nerSegmentationTags)#self.
if self.ner_chunk_eval == "boundaries_type":
lab_chunks = set(get_chunks(trueNER, tagsNER))
lab_pred_chunks = set(get_chunks(predNER, tagsNER))
elif self.ner_chunk_eval == "boundaries":
lab_chunks = set(keepOnlyChunkBoundaries(set(get_chunks(trueNER, tagsNER))))
lab_pred_chunks = set(keepOnlyChunkBoundaries(set(get_chunks(predNER, tagsNER))))
lab_chunks_list = list(lab_chunks)
lab_pred_chunks_list = list(lab_pred_chunks)
if self.ner_chunk_eval == "boundaries_type":
for lab_idx in range(len(lab_pred_chunks_list)):
if lab_pred_chunks_list[lab_idx] in lab_chunks_list:
# print (lab_pred_chunks_list[lab_idx][0])
self.tpsClassesNER[lab_pred_chunks_list[lab_idx][0]] += 1
else:
self.fpsClassesNER[lab_pred_chunks_list[lab_idx][0]] += 1
# fnsEntitiesNER+=1
for lab_idx in range(len(lab_chunks_list)):
if lab_chunks_list[lab_idx] not in lab_pred_chunks_list:
self.fnsClassesNER[lab_chunks_list[lab_idx][0]] += 1
elif self.ner_chunk_eval == "boundaries":
for lab_idx in range(len(lab_pred_chunks_list)):
if lab_pred_chunks_list[lab_idx] in lab_chunks_list:
# print (lab_pred_chunks_list[lab_idx][0])
self.tpsNER += 1
else:
self.fpsNER += 1
# fnsEntitiesNER+=1
for lab_idx in range(len(lab_chunks_list)):
if lab_chunks_list[lab_idx] not in lab_pred_chunks_list:
self.fnsNER += 1
if self.root_node==True:
lab_chunks_list_with_ROOT = copy.deepcopy(lab_chunks_list)
lab_chunks_list_with_ROOT.append((None,0, 0))
lab_pred_chunks_list_with_ROOT = copy.deepcopy(lab_pred_chunks_list)
lab_pred_chunks_list_with_ROOT.append((None,0, 0))
relTrue = set(relationChunks(trueRel, lab_chunks_list_with_ROOT, relationTuple=self.rel_chunk_eval))
relPred = set(relationChunks(predRel, lab_pred_chunks_list_with_ROOT, relationTuple=self.rel_chunk_eval))
else:
relTrue = set(relationChunks(trueRel, lab_chunks_list,relationTuple=self.rel_chunk_eval))
relPred = set(relationChunks(predRel, lab_pred_chunks_list,relationTuple=self.rel_chunk_eval))
relTrueList = list(relTrue) # trueRel#
# if (len(trueRel)!=len(relTrueList)):
# print ("warning")
relPredList = list(relPred) # predRel#
for lab_idx in range(len(relPredList)):
if relPredList[lab_idx] in relTrueList:
# print (lab_pred_chunks_list[lab_idx][0])
self.tpsClassesREL[relPredList[lab_idx][1]] += 1
# print (relPredList[lab_idx])
else:
self.fpsClassesREL[relPredList[lab_idx][1]] += 1
# fnsEntitiesNER+=1
for lab_idx in range(len(relTrueList)):
if relTrueList[lab_idx] not in relPredList:
self.fnsClassesREL[relTrueList[lab_idx][1]] += 1
self.correct_predsNER += len(lab_chunks & lab_pred_chunks)
self.total_predsNER += len(lab_pred_chunks)
self.total_correctNER += len(lab_chunks)
self.correct_predsREL += len(relTrue & relPred)
self.total_predsREL += len(relPred)
self.total_correctREL += len(relTrue)
def getResultsNER(self):
p = self.correct_predsNER / self.total_predsNER if self.correct_predsNER > 0 else 0
r = self.correct_predsNER / self.total_correctNER if self.correct_predsNER > 0 else 0
f1 = 2 * p * r / (p + r) if self.correct_predsNER > 0 else 0
print(self.correct_predsNER)
print(self.total_predsNER)
print(self.total_correctNER)
print(f1)
return f1
def getResultsREL(self):
p = self.correct_predsREL / self.total_predsREL if self.correct_predsREL > 0 else 0
r = self.correct_predsREL / self.total_correctREL if self.correct_predsREL > 0 else 0
f1 = 2 * p * r / (p + r) if self.correct_predsREL > 0 else 0
print(self.correct_predsREL)
print(self.total_predsREL)
print(self.total_correctREL)
print(f1)
return f1
def getPrecision(self, tps, fps):
if tps == 0:
return 0
else:
return tps / (tps + fps)
def getRecall(self, tps, fns):
if tps == 0:
return 0
else:
return tps / (tps + fns)
def getF1(self, tps, fps, fns):
if tps == 0:
return 0
else:
return 2 * self.getPrecision(tps, fps) * self.getRecall(tps, fns) / (
self.getPrecision(tps, fps) + self.getRecall(tps, fns))
def getChunkedOverallAvgF1(self):
return (self.getChunkedNERF1()+self.getChunkedRELF1())/2
def getChunkedOverallF1(self):
tpsNER=0
fnsNER=0
fpsNER=0
tpsREL=0
fnsREL=0
fpsREL=0
if self.ner_chunk_eval == "boundaries_type":
for label in self.NERset:
# if label != "O" :
tpsNER += self.tpsClassesNER[label]
fnsNER += self.fnsClassesNER[label]
fpsNER += self.fpsClassesNER[label]
elif self.ner_chunk_eval == "boundaries":
tpsNER=self.tpsNER
fnsNER = self.fnsNER
fpsNER = self.fpsNER
for label in self.RELset:
if label != "N":
tpsREL += self.tpsClassesREL[label]
fnsREL += self.fnsClassesREL[label]
fpsREL += self.fpsClassesREL[label]
return self.getF1(tpsNER+tpsREL, fpsNER+fpsREL, fnsNER+fnsREL)
def getOverallF1(self):
tpsNER=0
fnsNER=0
fpsNER=0
tpsREL=0
fnsREL=0
fpsREL=0
for label in self.NERset:
# if label != "O" :
tpsNER += self.tpsClassesNER[label]
fnsNER += self.fnsClassesNER[label]
fpsNER += self.fpsClassesNER[label]
for label in self.RELset:
if label != "N":
tpsREL += self.tpsClassesREL[label]
fnsREL += self.fnsClassesREL[label]
fpsREL += self.fpsClassesREL[label]
return self.getF1(tpsNER+tpsREL, fpsNER+fpsREL, fnsNER+fnsREL)
def getChunkedRELF1(self):
tpsREL=0
fnsREL=0
fpsREL=0
for label in self.RELset:
if label != "N":
tpsREL += self.tpsClassesREL[label]
fnsREL += self.fnsClassesREL[label]
fpsREL += self.fpsClassesREL[label]
return self.getF1(tpsREL, fpsREL, fnsREL)
def getChunkedNERF1(self):
tpsNER = 0
fnsNER = 0
fpsNER = 0
if self.ner_chunk_eval == "boundaries_type":
for label in self.NERset:
# if label != "O" :
tpsNER += self.tpsClassesNER[label]
fnsNER += self.fnsClassesNER[label]
fpsNER += self.fpsClassesNER[label]
elif self.ner_chunk_eval== "boundaries":
tpsNER =self.tpsNER
fnsNER = self.fnsNER
fpsNER = self.fpsNER
return self.getF1(tpsNER, fpsNER, fnsNER)
def getAccuracy(self):
return self.oks / self.totals
def printInfo(self):
printer = printClasses()
if self.ner_chunk_eval== "boundaries_type":
for label in self.NERset:
# if label != "O" :
self.tpsNER += self.tpsClassesNER[label]
self.fnsNER += self.fnsClassesNER[label]
self.fpsNER += self.fpsClassesNER[label]
printer.add(label, self.tpsClassesNER[label], self.fpsClassesNER[label], self.fnsClassesNER[label],
self.getPrecision(self.tpsClassesNER[label], self.fpsClassesNER[label]),
self.getRecall(self.tpsClassesNER[label], self.fnsClassesNER[label]),
self.getF1(self.tpsClassesNER[label], self.fpsClassesNER[label], self.fnsClassesNER[label]))
# print('%s TP: %d FP: %d FN: %d TN: %d precision: %f recall: %f F1: %f' % (label,self.tpsClasses[label],self.fpsClasses[label],self.fnsClasses[label],self.tnsClasses[label], self.precision[label], self.recall[label], self.f1[label]))
printer.add("-", "-", "-", "-",
"-", "-",
"-")
printer.add("Micro NER chunk", self.tpsNER, self.fpsNER, self.fnsNER,
self.getPrecision(self.tpsNER, self.fpsNER), self.getRecall(self.tpsNER, self.fnsNER),
self.getF1(self.tpsNER, self.fpsNER, self.fnsNER))
elif self.ner_chunk_eval== "boundaries":
printer.add("Micro NER chunk boundaries", self.tpsNER, self.fpsNER, self.fnsNER,
self.getPrecision(self.tpsNER, self.fpsNER), self.getRecall(self.tpsNER, self.fnsNER),
self.getF1(self.tpsNER, self.fpsNER, self.fnsNER))
printer.print()
printer = printClasses()
for label in self.RELset:
if label != "N":
self.tpsREL += self.tpsClassesREL[label]
self.fnsREL += self.fnsClassesREL[label]
self.fpsREL += self.fpsClassesREL[label]
printer.add(label, self.tpsClassesREL[label], self.fpsClassesREL[label], self.fnsClassesREL[label],
self.getPrecision(self.tpsClassesREL[label], self.fpsClassesREL[label]),
self.getRecall(self.tpsClassesREL[label], self.fnsClassesREL[label]),
self.getF1(self.tpsClassesREL[label], self.fpsClassesREL[label], self.fnsClassesREL[label]))
# print('%s TP: %d FP: %d FN: %d TN: %d precision: %f recall: %f F1: %f' % (label,self.tpsClasses[label],self.fpsClasses[label],self.fnsClasses[label],self.tnsClasses[label], self.precision[label], self.recall[label], self.f1[label]))
printer.add("-", "-", "-", "-",
"-", "-",
"-")
printer.add("Micro REL chunk", self.tpsREL, self.fpsREL, self.fnsREL,
self.getPrecision(self.tpsREL, self.fpsREL), self.getRecall(self.tpsREL, self.fnsREL),
self.getF1(self.tpsREL, self.fpsREL, self.fnsREL))
printer.print()
def getMaxOccurence(lst):
from collections import Counter
most_common, num_most_common = Counter(lst).most_common(1)[0] # 4, 6 times
return most_common
def classesToChunks(tokenClasses, chunks):
labeled_chunks = []
for chunk in chunks:
class_list = (tokenClasses[chunk[1]:chunk[2] + 1])
if chunk[0] in class_list:
labeled_chunks.append((chunk[0], chunk[1], chunk[2]))
else:
labeled_chunks.append((getMaxOccurence(class_list), chunk[1], chunk[2]))
# print (class_list)
return labeled_chunks
def listOfTagsToids(lstTags,tags):
lstids = []
for ner in lstTags:
lstids.append(tags.index(ner))
return lstids
def listOfIdsToTags(lst_ids,tags):
lstTags= []
for nerId in lst_ids:
lstTags.append(tags[nerId])
return lstTags
class relaxedChunkEvaluator:
def __init__(self,dataset_params,rel_chunk_eval="boundaries"):
self.nerSegmentationTags=dataset_params.dataset_set_bio_tags
self.NERset = dataset_params.dataset_set_ec_tags#utils.getNerSetACE04()
self.RELset = dataset_params.dataset_set_relations#reutils.getRelSetACE04()
#self.nerDict=dataset_params
# print (self.NERset)
self.rel_chunk_eval=rel_chunk_eval
self.totals = 0
self.oks = 0
self.tpsNER = 0
self.fpsNER = 0
self.fnsNER = 0
self.tpsREL = 0
self.fpsREL = 0
self.fnsREL = 0
self.tpsNERMacro = 0
self.fpsNERMacro = 0
self.fnsNERMacro = 0
self.tpsNERMacro_no_other = 0
self.fpsNERMacro_no_other = 0
self.fnsNERMacro_no_other = 0
self.tpsRELMacro = 0
self.fpsRELMacro = 0
self.fnsRELMacro = 0
self.NERF1Macro=0
self.NERF1Macro_no_other = 0
self.RELF1Macro = 0
self.OverallF1Macro = 0
self.OverallF1Macro_no_other = 0
self.tpsClassesNER = dict.fromkeys(self.NERset, 0)
self.fpsClassesNER = dict.fromkeys(self.NERset, 0)
self.fnsClassesNER = dict.fromkeys(self.NERset, 0)
self.precisionNER = dict.fromkeys(self.NERset, 0)
self.recallNER = dict.fromkeys(self.NERset, 0)
self.f1NER = dict.fromkeys(self.NERset, 0)
self.tpsClassesREL = dict.fromkeys(self.RELset, 0)
self.fpsClassesREL = dict.fromkeys(self.RELset, 0)
self.fnsClassesREL = dict.fromkeys(self.RELset, 0)
self.precisionREL = dict.fromkeys(self.RELset, 0)
self.recallREL = dict.fromkeys(self.RELset, 0)
self.f1REL = dict.fromkeys(self.RELset, 0)
self.correct_predsNER, self.total_correctNER, self.total_predsNER = 0., 0., 0.
self.correct_predsREL, self.total_correctREL, self.total_predsREL = 0., 0., 0.
def add(self, pred_batchesNER, true_batchesNER, pred_batchesREL, true_batchesREL,true_batchesBIONER):
for batch_idx in range(len(pred_batchesNER)):
predNER = pred_batchesNER[batch_idx]
trueNER = true_batchesNER[batch_idx]
predRel = pred_batchesREL[batch_idx]
trueRel = true_batchesREL[batch_idx]
trueBIONER=true_batchesBIONER[batch_idx]
ptoken_ids, _, plabel_ids, phead_ids, plabel_names = utils.transformToInitialInput(
predRel, self.RELset)
_, _, tlabel_ids, thead_ids, tlabel_names = utils.transformToInitialInput(
trueRel, self.RELset)
trueRel = getTokenRelations(tlabel_names, thead_ids, ptoken_ids)
predRel = getTokenRelations(plabel_names, phead_ids, ptoken_ids)
#print (self.NERset)
tagsNER = utils.getSegmentationDict(self.nerSegmentationTags)#self.
lab_chunks_ = set(get_chunks(listOfTagsToids(trueBIONER,self.nerSegmentationTags), tagsNER))
#lab_pred_chunks = set(get_chunks(predNER, tagsNER))
lab_chunks_list_ = list(lab_chunks_)
trueNER_tags=listOfIdsToTags(trueNER,self.NERset)
predNER_tags=listOfIdsToTags(predNER, self.NERset)
lab_chunks = set(classesToChunks(trueNER_tags, lab_chunks_list_))
lab_pred_chunks=set(classesToChunks(predNER_tags, lab_chunks_list_))
lab_chunks_list = list(lab_chunks)
lab_pred_chunks_list = list(lab_pred_chunks)
for lab_idx in range(len(lab_pred_chunks_list)):
if lab_pred_chunks_list[lab_idx] in lab_chunks_list:
# print (lab_pred_chunks_list[lab_idx][0])
self.tpsClassesNER[lab_pred_chunks_list[lab_idx][0]] += 1
else:
self.fpsClassesNER[lab_pred_chunks_list[lab_idx][0]] += 1
# fnsEntitiesNER+=1
for lab_idx in range(len(lab_chunks_list)):
if lab_chunks_list[lab_idx] not in lab_pred_chunks_list:
self.fnsClassesNER[lab_chunks_list[lab_idx][0]] += 1
relTrue = set(relationChunks(trueRel, lab_chunks_list,relationTuple=self.rel_chunk_eval))
relPred = set(relationChunks(predRel, lab_pred_chunks_list,relationTuple=self.rel_chunk_eval))
relTrueList = list(relTrue) # trueRel#
# if (len(trueRel)!=len(relTrueList)):
# print ("warning")
relPredList = list(relPred) # predRel#
#print("GOLD REL chunks:" + str(relTrueList))
#print("PRED REL chunks:" + str(relPredList))
for lab_idx in range(len(relPredList)):
if relPredList[lab_idx] in relTrueList:
# print (lab_pred_chunks_list[lab_idx][0])
self.tpsClassesREL[relPredList[lab_idx][1]] += 1
# print (relPredList[lab_idx])
else:
self.fpsClassesREL[relPredList[lab_idx][1]] += 1
# fnsEntitiesNER+=1
for lab_idx in range(len(relTrueList)):
if relTrueList[lab_idx] not in relPredList:
self.fnsClassesREL[relTrueList[lab_idx][1]] += 1
self.correct_predsNER += len(lab_chunks & lab_pred_chunks)
self.total_predsNER += len(lab_pred_chunks)
self.total_correctNER += len(lab_chunks)
self.correct_predsREL += len(relTrue & relPred)
self.total_predsREL += len(relPred)
self.total_correctREL += len(relTrue)
def getResultsNER(self):
p = self.correct_predsNER / self.total_predsNER if self.correct_predsNER > 0 else 0
r = self.correct_predsNER / self.total_correctNER if self.correct_predsNER > 0 else 0
f1 = 2 * p * r / (p + r) if self.correct_predsNER > 0 else 0
print(self.correct_predsNER)
print(self.total_predsNER)
print(self.total_correctNER)
print(f1)
return f1
def getResultsREL(self):
p = self.correct_predsREL / self.total_predsREL if self.correct_predsREL > 0 else 0
r = self.correct_predsREL / self.total_correctREL if self.correct_predsREL > 0 else 0
f1 = 2 * p * r / (p + r) if self.correct_predsREL > 0 else 0
print(self.correct_predsREL)
print(self.total_predsREL)
print(self.total_correctREL)
print(f1)
return f1
def getPrecision(self, tps, fps):
if tps == 0:
return 0
else:
return tps / (tps + fps)
def getRecall(self, tps, fns):
if tps == 0:
return 0
else:
return tps / (tps + fns)
def getF1(self, tps, fps, fns):
if tps == 0:
return 0
else:
return 2 * self.getPrecision(tps, fps) * self.getRecall(tps, fns) / (
self.getPrecision(tps, fps) + self.getRecall(tps, fns))
def getChunkedOverallF1(self):
tpsNER=0
fnsNER=0
fpsNER=0
tpsREL=0
fnsREL=0
fpsREL=0
for label in self.NERset:
# if label != "O" :
tpsNER += self.tpsClassesNER[label]
fnsNER += self.fnsClassesNER[label]
fpsNER += self.fpsClassesNER[label]
for label in self.RELset:
if label != "N":
tpsREL += self.tpsClassesREL[label]
fnsREL += self.fnsClassesREL[label]
fpsREL += self.fpsClassesREL[label]
return self.getF1(tpsNER+tpsREL, fpsNER+fpsREL, fnsNER+fnsREL)
def getOverallF1(self):
tpsNER=0
fnsNER=0
fpsNER=0
tpsREL=0
fnsREL=0
fpsREL=0
for label in self.NERset:
# if label != "O" :
tpsNER += self.tpsClassesNER[label]
fnsNER += self.fnsClassesNER[label]
fpsNER += self.fpsClassesNER[label]
for label in self.RELset:
if label != "N":
tpsREL += self.tpsClassesREL[label]
fnsREL += self.fnsClassesREL[label]
fpsREL += self.fpsClassesREL[label]
return self.getF1(tpsNER+tpsREL, fpsNER+fpsREL, fnsNER+fnsREL)
def getChunkedRELF1(self):
tpsREL=0
fnsREL=0
fpsREL=0
for label in self.RELset:
if label != "N":
tpsREL += self.tpsClassesREL[label]
fnsREL += self.fnsClassesREL[label]
fpsREL += self.fpsClassesREL[label]
return self.getF1(tpsREL, fpsREL, fnsREL)
def getChunkedNERF1(self):
tpsNER=0
fnsNER=0
fpsNER=0
for label in self.NERset:
# if label != "O" :
tpsNER += self.tpsClassesNER[label]
fnsNER += self.fnsClassesNER[label]
fpsNER += self.fpsClassesNER[label]
return self.getF1(tpsNER, fpsNER, fnsNER)
def getAccuracy(self):
return self.oks / self.totals
def getMacroF1scores(self):
return self.NERF1Macro,self.RELF1Macro,self.OverallF1Macro
def getMacroF1scoresNoOtherClass(self):
return self.NERF1Macro_no_other, self.RELF1Macro, self.OverallF1Macro_no_other
def computeInfoMacro(self,printScores=True):
printer = printClasses()
averageNERF1_no_Other=0
averageNERF1 = 0
averageNERrecall_no_Other = 0
averageNERrecall = 0
averageNERprecision_no_Other = 0
averageNERprecision = 0
for label in self.NERset:
if label != "O":
self.tpsNERMacro += self.tpsClassesNER[label]
self.fnsNERMacro += self.fnsClassesNER[label]
self.fpsNERMacro += self.fpsClassesNER[label]
f1_class=self.getF1(self.tpsClassesNER[label], self.fpsClassesNER[label], self.fnsClassesNER[label])
precision_class=self.getPrecision(self.tpsClassesNER[label], self.fpsClassesNER[label])
recall_class=self.getRecall(self.tpsClassesNER[label], self.fnsClassesNER[label])
if label!= "O" :
averageNERF1+=f1_class
averageNERrecall += recall_class
averageNERprecision += precision_class
if label!= "O" and label!= "Other":
averageNERF1_no_Other+=f1_class
averageNERrecall_no_Other += recall_class
averageNERprecision_no_Other += precision_class
if label != "O" and label != "Other":
self.tpsNERMacro_no_other += self.tpsClassesNER[label]
self.fnsNERMacro_no_other += self.fnsClassesNER[label]
self.fpsNERMacro_no_other += self.fpsClassesNER[label]
printer.add(label, self.tpsClassesNER[label], self.fpsClassesNER[label], self.fnsClassesNER[label],
precision_class,
recall_class,
f1_class)
# print('%s TP: %d FP: %d FN: %d TN: %d precision: %f recall: %f F1: %f' % (label,self.tpsClasses[label],self.fpsClasses[label],self.fnsClasses[label],self.tnsClasses[label], self.precision[label], self.recall[label], self.f1[label]))
printer.add("-", "-", "-", "-",
"-", "-",
"-")
averageNERrecall = averageNERrecall / (len(self.NERset) - 1)
averageNERprecision = averageNERprecision / (len(self.NERset) - 1)
averageNERF1 = averageNERF1 / (len(self.NERset) - 1)
if "other" in [x.lower() for x in self.NERset]:
averageNERprecision_no_Other=averageNERprecision_no_Other / (len(self.NERset) -2)
averageNERrecall_no_Other=averageNERrecall_no_Other / (len(self.NERset) -2)
averageNERF1_no_Other=averageNERF1_no_Other / (len(self.NERset) -2)
printer.add("Macro NER chunk RELAXED ^Other", self.tpsNERMacro_no_other, self.fpsNERMacro_no_other, self.fnsNERMacro_no_other,
averageNERprecision_no_Other, averageNERrecall_no_Other,
averageNERF1_no_Other)
else:
averageNERprecision_no_Other = averageNERprecision
averageNERrecall_no_Other = averageNERrecall
averageNERF1_no_Other = averageNERF1
printer.add("Macro NER chunk RELAXED", self.tpsNERMacro, self.fpsNERMacro, self.fnsNERMacro,
averageNERprecision, averageNERrecall,
averageNERF1)
if printScores ==True:
printer.print()
printer = printClasses()
averageRELF1 = 0
averageRELrecall = 0
averageRELprecision = 0
for label in self.RELset:
if label != "N":
self.tpsRELMacro += self.tpsClassesREL[label]
self.fnsRELMacro += self.fnsClassesREL[label]
self.fpsRELMacro += self.fpsClassesREL[label]
f1_class = self.getF1(self.tpsClassesREL[label], self.fpsClassesREL[label], self.fnsClassesREL[label])
precision_class = self.getPrecision(self.tpsClassesREL[label], self.fpsClassesREL[label])
recall_class = self.getRecall(self.tpsClassesREL[label], self.fnsClassesREL[label])
averageRELF1+=f1_class
averageRELrecall += recall_class
averageRELprecision += precision_class
printer.add(label, self.tpsClassesREL[label], self.fpsClassesREL[label], self.fnsClassesREL[label],
precision_class,
recall_class,
f1_class)
# print('%s TP: %d FP: %d FN: %d TN: %d precision: %f recall: %f F1: %f' % (label,self.tpsClasses[label],self.fpsClasses[label],self.fnsClasses[label],self.tnsClasses[label], self.precision[label], self.recall[label], self.f1[label]))
printer.add("-", "-", "-", "-",
"-", "-",
"-")
averageRELrecall=averageRELrecall/(len(self.RELset) - 1)
averageRELprecision=averageRELprecision/(len(self.RELset) - 1)
averageRELF1 =averageRELF1 /(len(self.RELset) - 1)
printer.add("Macro REL chunk RELAXED", self.tpsRELMacro, self.fpsRELMacro, self.fnsRELMacro,
averageRELprecision, averageRELrecall,
averageRELF1)
if printScores == True:
printer.print()
over_avg_f1 = (averageNERF1 + averageRELF1) / 2
over_avg_f1_no_other = (averageNERF1_no_Other + averageRELF1) / 2
t = PrettyTable(['Type','NER_F1', 'REL_F1', 'AVG_F1'])
t.add_row(['Overall', averageNERF1, averageRELF1, over_avg_f1])
if "other" in [x.lower() for x in self.NERset]:
t.add_row(['Overall ^Other', averageNERF1_no_Other, averageRELF1, over_avg_f1_no_other])
if printScores == True:
print (t)
self.NERF1Macro = averageNERF1
self.NERF1Macro_no_other = averageNERF1_no_Other
self.RELF1Macro = averageRELF1
self.OverallF1Macro = over_avg_f1