Add SegmentAccuracy metric
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@ -4,11 +4,12 @@
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module GEval.Annotation
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(parseAnnotations, Annotation(..),
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parseObtainedAnnotations, ObtainedAnnotation(..),
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matchScore, intSetParser)
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matchScore, intSetParser, segmentAccuracy, parseSegmentAnnotations)
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where
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import qualified Data.IntSet as IS
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import qualified Data.Text as T
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import Data.Set (intersection, fromList)
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import Data.Attoparsec.Text
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import Data.Attoparsec.Combinator
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@ -17,11 +18,12 @@ import GEval.Common (sepByWhitespaces, (/.))
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import GEval.Probability
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import Data.Char
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import Data.Maybe (fromMaybe)
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import Data.Either (partitionEithers)
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import GEval.PrecisionRecall(weightedMaxMatching)
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data Annotation = Annotation T.Text IS.IntSet
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deriving (Eq, Show)
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deriving (Eq, Show, Ord)
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data ObtainedAnnotation = ObtainedAnnotation Annotation Double
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deriving (Eq, Show)
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@ -52,6 +54,36 @@ obtainedAnnotationParser = do
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parseAnnotations :: T.Text -> Either String [Annotation]
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parseAnnotations t = parseOnly (annotationsParser <* endOfInput) t
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parseSegmentAnnotations :: T.Text -> Either String [Annotation]
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parseSegmentAnnotations t = case parseAnnotationsWithColons t of
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Left m -> Left m
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Right annotations -> if areSegmentsDisjoint annotations
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then (Right annotations)
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else (Left "Overlapping segments")
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areSegmentsDisjoint :: [Annotation] -> Bool
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areSegmentsDisjoint = areIntSetsDisjoint . map (\(Annotation _ s) -> s)
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areIntSetsDisjoint :: [IS.IntSet] -> Bool
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areIntSetsDisjoint ss = snd $ foldr step (IS.empty, True) ss
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where step _ w@(_, False) = w
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step s (u, True) = (s `IS.union` u, s `IS.disjoint` u)
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-- unfortunately, attoparsec does not seem to back-track properly
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-- so we need a special function if labels can contain colons
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parseAnnotationsWithColons :: T.Text -> Either String [Annotation]
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parseAnnotationsWithColons t = case partitionEithers (map parseAnnotationWithColons $ T.words t) of
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([], annotations) -> Right annotations
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((firstProblem:_), _) -> Left firstProblem
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parseAnnotationWithColons :: T.Text -> Either String Annotation
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parseAnnotationWithColons t = if T.null label
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then Left "Colon expected"
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else case parseOnly (intSetParser <* endOfInput) position of
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Left m -> Left m
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Right s -> Right (Annotation (T.init label) s)
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where (label, position) = T.breakOnEnd ":" t
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annotationsParser :: Parser [Annotation]
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annotationsParser = sepByWhitespaces annotationParser
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@ -70,3 +102,7 @@ intervalParser = do
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startIx <- decimal
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endIx <- (string "-" *> decimal <|> pure startIx)
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pure $ IS.fromList [startIx..endIx]
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segmentAccuracy :: [Annotation] -> [Annotation] -> Double
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segmentAccuracy expected output = (fromIntegral $ length matched) / (fromIntegral $ length expected)
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where matched = (fromList expected) `intersection` (fromList output)
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@ -706,6 +706,13 @@ gevalCoreOnSources TokenAccuracy _ = gevalCoreWithoutInput intoTokens
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| otherwise = (h, t + 1)
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hitsAndTotalsAgg = CC.foldl (\(h1, t1) (h2, t2) -> (h1 + h2, t1 + t2)) (0, 0)
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gevalCoreOnSources SegmentAccuracy _ = gevalCoreWithoutInput parseSegmentAnnotations
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parseSegmentAnnotations
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(uncurry segmentAccuracy)
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averageC
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id
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noGraph
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gevalCoreOnSources MultiLabelLogLoss _ = gevalCoreWithoutInput intoWords
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(Right . parseIntoProbList)
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(uncurry countLogLossOnProbList)
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@ -297,6 +297,19 @@ in the expected file (but not in the output file).
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|] ++ (commonReadmeMDContents testName)
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readmeMDContents SegmentAccuracy testName = [i|
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Segment a sentence and tag with POS tags
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========================================
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This is a sample, toy challenge for SegmentAccuracy.
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For each sentence, give a sequence of POS tags, each one with
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its position (1-indexed). For instance, `N:1-10` means a nouns
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starting from the beginning (the first character) up to to the tenth
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character (inclusively).
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|] ++ (commonReadmeMDContents testName)
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readmeMDContents (ProbabilisticMultiLabelFMeasure beta) testName = readmeMDContents (MultiLabelFMeasure beta) testName
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readmeMDContents (MultiLabelFMeasure beta) testName = [i|
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Tag names and their component
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@ -473,6 +486,9 @@ B-firstname/JOHN I-surname/VON I-surname/NEUMANN John von Nueman
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trainContents TokenAccuracy = [hereLit|* V N I like cats
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* * V * N I can see the rainbow
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|]
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trainContents SegmentAccuracy = [hereLit|Art:1-3 N:5-11 V:12-13 A:15-19 The student's smart
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N:1-6 N:8-10 V:12-13 A:15-18 Mary's dog is nice
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|]
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trainContents (ProbabilisticMultiLabelFMeasure beta) = trainContents (MultiLabelFMeasure beta)
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trainContents (MultiLabelFMeasure _) = [hereLit|I know Mr John Smith person/3,4,5 first-name/4 surname/5
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Steven bloody Brown person/1,3 first-name/1 surname/3
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@ -540,6 +556,9 @@ Mr Jan Kowalski
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devInContents TokenAccuracy = [hereLit|The cats on the mat
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Ala has a cat
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|]
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devInContents SegmentAccuracy = [hereLit|John is smart
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Mary's intelligent
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|]
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devInContents (ProbabilisticMultiLabelFMeasure beta) = devInContents (MultiLabelFMeasure beta)
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devInContents (MultiLabelFMeasure _) = [hereLit|Jan Kowalski is here
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I see him
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@ -604,6 +623,9 @@ O B-firstname/JAN B-surname/KOWALSKI
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devExpectedContents TokenAccuracy = [hereLit|* N * * N
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N V * N
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|]
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devExpectedContents SegmentAccuracy = [hereLit|N:1-4 V:6-7 A:9-13
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N:1-4 V:6-7 A:9-19
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|]
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devExpectedContents (ProbabilisticMultiLabelFMeasure beta) = devExpectedContents (MultiLabelFMeasure beta)
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devExpectedContents (MultiLabelFMeasure _) = [hereLit|person/1,2 first-name/1 surname/2
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@ -673,6 +695,9 @@ No name here
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testInContents TokenAccuracy = [hereLit|I have cats
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I know
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|]
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testInContents SegmentAccuracy = [hereLit|Mary's cat is old
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John is young
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|]
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testInContents (ProbabilisticMultiLabelFMeasure beta) = testInContents (MultiLabelFMeasure beta)
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testInContents (MultiLabelFMeasure _) = [hereLit|John bloody Smith
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Nobody is there
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@ -738,6 +763,9 @@ O O O
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testExpectedContents TokenAccuracy = [hereLit|* V N
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* V
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|]
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testExpectedContents SegmentAccuracy = [hereLit|N:1-6 N:8-10 V:12-13 A:15-17
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N:1-4 V:6-7 A:9-13
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|]
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testExpectedContents (ProbabilisticMultiLabelFMeasure beta) = testExpectedContents (MultiLabelFMeasure beta)
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testExpectedContents (MultiLabelFMeasure _) = [hereLit|person/1,3 first-name/1 surname/3
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@ -26,7 +26,7 @@ import Data.Attoparsec.Text (parseOnly)
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data Metric = RMSE | MSE | Pearson | Spearman | BLEU | GLEU | WER | Accuracy | ClippEU
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| FMeasure Double | MacroFMeasure Double | NMI
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| LogLossHashed Word32 | CharMatch | MAP | LogLoss | Likelihood
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| BIOF1 | BIOF1Labels | TokenAccuracy | LikelihoodHashed Word32 | MAE | SMAPE | MultiLabelFMeasure Double
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| BIOF1 | BIOF1Labels | TokenAccuracy | SegmentAccuracy | LikelihoodHashed Word32 | MAE | SMAPE | MultiLabelFMeasure Double
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| MultiLabelLogLoss | MultiLabelLikelihood
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| SoftFMeasure Double | ProbabilisticMultiLabelFMeasure Double | ProbabilisticSoftFMeasure Double | Soft2DFMeasure Double
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deriving (Eq)
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@ -67,6 +67,7 @@ instance Show Metric where
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show BIOF1 = "BIO-F1"
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show BIOF1Labels = "BIO-F1-Labels"
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show TokenAccuracy = "TokenAccuracy"
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show SegmentAccuracy = "SegmentAccuracy"
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show MAE = "MAE"
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show SMAPE = "SMAPE"
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show (MultiLabelFMeasure beta) = "MultiLabel-F" ++ (show beta)
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@ -118,6 +119,7 @@ instance Read Metric where
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readsPrec _ ('B':'I':'O':'-':'F':'1':'-':'L':'a':'b':'e':'l':'s':theRest) = [(BIOF1Labels, theRest)]
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readsPrec _ ('B':'I':'O':'-':'F':'1':theRest) = [(BIOF1, theRest)]
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readsPrec _ ('T':'o':'k':'e':'n':'A':'c':'c':'u':'r':'a':'c':'y':theRest) = [(TokenAccuracy, theRest)]
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readsPrec _ ('S':'e':'g':'m':'e':'n':'t':'A':'c':'c':'u':'r':'a':'c':'y':theRest) = [(SegmentAccuracy, theRest)]
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readsPrec _ ('M':'A':'E':theRest) = [(MAE, theRest)]
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readsPrec _ ('S':'M':'A':'P':'E':theRest) = [(SMAPE, theRest)]
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readsPrec _ ('M':'u':'l':'t':'i':'L':'a':'b':'e':'l':'-':'L':'o':'g':'L':'o':'s':'s':theRest) = [(MultiLabelLogLoss, theRest)]
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@ -154,6 +156,7 @@ getMetricOrdering Likelihood = TheHigherTheBetter
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getMetricOrdering BIOF1 = TheHigherTheBetter
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getMetricOrdering BIOF1Labels = TheHigherTheBetter
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getMetricOrdering TokenAccuracy = TheHigherTheBetter
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getMetricOrdering SegmentAccuracy = TheHigherTheBetter
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getMetricOrdering MAE = TheLowerTheBetter
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getMetricOrdering SMAPE = TheLowerTheBetter
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getMetricOrdering (MultiLabelFMeasure _) = TheHigherTheBetter
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@ -63,6 +63,7 @@ listOfAvailableMetrics = [RMSE,
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BIOF1,
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BIOF1Labels,
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TokenAccuracy,
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SegmentAccuracy,
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SoftFMeasure 1.0,
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SoftFMeasure 2.0,
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SoftFMeasure 0.25,
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@ -94,6 +95,7 @@ isMetricDescribed (SoftFMeasure _) = True
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isMetricDescribed (Soft2DFMeasure _) = True
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isMetricDescribed (ProbabilisticMultiLabelFMeasure _) = True
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isMetricDescribed GLEU = True
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isMetricDescribed SegmentAccuracy = True
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isMetricDescribed _ = False
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getEvaluationSchemeDescription :: EvaluationScheme -> String
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@ -134,7 +136,11 @@ metric on a corpus level but does not have its drawbacks for our per
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sentence reward objective.
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see: https://arxiv.org/pdf/1609.08144.pdf
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|]
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getMetricDescription SegmentAccuracy =
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[i|Accuracy counted for segments, i.e. labels with positions.
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The percentage of labels in the ground truth retrieved in the actual output is returned.
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Accuracy is calculated separately for each item and then averaged.
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|]
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outContents :: Metric -> String
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outContents (SoftFMeasure _) = [hereLit|inwords:1-4
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@ -149,6 +155,9 @@ first-name/3:0.9
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|]
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outContents GLEU = [hereLit|Alice has a black
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|]
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outContents SegmentAccuracy = [hereLit|N:1-4 V:5-6 N:8-10 V:12-13 A:15-17
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N:1-4 V:6-7 A:9-13
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|]
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expectedScore :: EvaluationScheme -> MetricValue
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expectedScore (EvaluationScheme (SoftFMeasure beta) [])
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@ -165,6 +174,8 @@ expectedScore (EvaluationScheme (ProbabilisticMultiLabelFMeasure beta) [])
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in weightedHarmonicMean beta precision recall
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expectedScore (EvaluationScheme GLEU [])
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= 0.7142857142857143
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expectedScore (EvaluationScheme SegmentAccuracy [])
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= 0.875
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helpMetricParameterMetricsList :: String
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helpMetricParameterMetricsList = intercalate ", " $ map (\s -> (show s) ++ (case extraInfo s of
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@ -215,6 +226,13 @@ can be provided with a colon (e.g. "foo:0.7"). By default, 1.0 is assumed.
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|]
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formatDescription GLEU = [hereLit|In each line a there is a space sparated sentence of words.
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|]
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formatDescription SegmentAccuracy = [hereLit|Labels can be any strings (without spaces), whereas is a list of
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1-based indexes or spans separated by commas (spans are inclusive
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ranges, e.g. "10-14"). For instance, "foo:bar:2,4-7,10" is a
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label "foo:bar" for positions 2, 4, 5, 6, 7 and 10. Note that no
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overlapping segments can be returned (evaluation will fail in
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such a case).
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|]
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scoreExplanation :: EvaluationScheme -> Maybe String
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scoreExplanation (EvaluationScheme (SoftFMeasure _) [])
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@ -234,6 +252,9 @@ Now we have to calculate precision and recall:
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Precision is tp / (tp+fp) = 10/(10+0) = 1,
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recall is tp / (tp+fn) = 10 / (10+4) = 10/14 =~ 0.71428...
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The GLEU score is min(precision,recall)=0.71428 |]
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scoreExplanation (EvaluationScheme SegmentAccuracy [])
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= Just [hereLit|Out of 4 segments in the expected output for the first item, 3 were retrieved correcly (accuracy is 3/4=0.75).
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The second item was retrieved perfectly (accuracy is 1.0). Hence, the average is (0.75+1.0)/2=0.875.|]
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pasteLines :: String -> String -> String
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pasteLines a b = printf "%-35s %s\n" a b
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@ -146,6 +146,9 @@ main = hspec $ do
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describe "TokenAccuracy" $ do
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it "simple example" $ do
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runGEvalTest "token-accuracy-simple" `shouldReturnAlmost` 0.5
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describe "SegmentAccuracy" $ do
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it "simple test" $ do
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runGEvalTest "segment-accuracy-simple" `shouldReturnAlmost` 0.4444444
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describe "precision count" $ do
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it "simple test" $ do
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precisionCount [["Alice", "has", "a", "cat" ]] ["Ala", "has", "cat"] `shouldBe` 2
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@ -342,6 +345,11 @@ main = hspec $ do
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it "just parse" $ do
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parseAnnotations "foo:3,7-10 baz:4-6" `shouldBe` Right [Annotation "foo" (IS.fromList [3,7,8,9,10]),
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Annotation "baz" (IS.fromList [4,5,6])]
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it "just parse wit colons" $ do
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parseSegmentAnnotations "foo:x:3,7-10 baz:4-6" `shouldBe` Right [Annotation "foo:x" (IS.fromList [3,7,8,9,10]),
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Annotation "baz" (IS.fromList [4,5,6])]
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it "just parse wit colons" $ do
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parseSegmentAnnotations "foo:x:3,7-10 baz:2-6" `shouldBe` Left "Overlapping segments"
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it "just parse 2" $ do
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parseAnnotations "inwords:1-3 indigits:5" `shouldBe` Right [Annotation "inwords" (IS.fromList [1,2,3]),
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Annotation "indigits" (IS.fromList [5])]
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@ -0,0 +1,3 @@
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foo:0 baq:1-2 baz:3
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aaa:0-1
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xyz:0 bbb:x:1
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@ -0,0 +1 @@
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--metric SegmentAccuracy
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@ -0,0 +1,3 @@
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foo:0 bar:1-2 baz:3
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aaa:0-2
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xyz:0 bbb:x:1 ccc:x:2
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