Merge branch 'master' into bootstrap
This commit is contained in:
commit
839ad5ce47
@ -1,4 +1,8 @@
|
||||
|
||||
## 1.22.0.0
|
||||
|
||||
* Add SegmentAccuracy
|
||||
|
||||
## 1.21.0.0
|
||||
|
||||
* Add Probabilistic-MultiLabel-F-measure
|
||||
|
||||
64
README.md
64
README.md
@ -2,7 +2,7 @@
|
||||
|
||||
GEval is a Haskell library and a stand-alone tool for evaluating the
|
||||
results of solutions to machine learning challenges as defined in the
|
||||
[Gonito](https://gonito.net) platform. Also could be used outside the
|
||||
[Gonito](https://gonito.net) platform. Also, could be used outside the
|
||||
context of Gonito.net challenges, assuming the test data is given in
|
||||
simple TSV (tab-separated values) files.
|
||||
|
||||
@ -14,6 +14,29 @@ The official repository is `git://gonito.net/geval`, browsable at
|
||||
|
||||
## Installing
|
||||
|
||||
### The easy way: just download the fully static GEval binary
|
||||
|
||||
(Assuming you have a 64-bit Linux.)
|
||||
|
||||
wget https://gonito.net/get/bin/geval
|
||||
chmod u+x geval
|
||||
./geval --help
|
||||
|
||||
#### On Windows
|
||||
|
||||
For Windows, you should use Windows PowerShell.
|
||||
|
||||
wget https://gonito.net/get/bin/geval
|
||||
|
||||
Next, you should go to the folder where you download `geval` and right-click to `geval` file.
|
||||
Go to `Properties` and in the section `Security` grant full access to the folder.
|
||||
|
||||
Or you should use `icacls "folder path to geval" /grant USER:<username>`
|
||||
|
||||
This is a fully static binary, it should work on any 64-bit Linux or 64-bit Windows.
|
||||
|
||||
### Build from scratch
|
||||
|
||||
You need [Haskell Stack](https://github.com/commercialhaskell/stack).
|
||||
You could install Stack with your package manager or with:
|
||||
|
||||
@ -36,6 +59,8 @@ order to run `geval` you need to either add `$HOME/.local/bin` to
|
||||
|
||||
PATH="$HOME/.local/bin" geval ...
|
||||
|
||||
In Windows you should add new global variable with name 'geval' and path should be the same as above.
|
||||
|
||||
### Troubleshooting
|
||||
|
||||
If you see a message like this:
|
||||
@ -64,15 +89,32 @@ In case the lzma package is not installed on your Linux, you need to run (assumi
|
||||
|
||||
sudo apt-get install pkg-config liblzma-dev libpq-dev libpcre3-dev
|
||||
|
||||
### Plan B — just download the GEval binary
|
||||
#### Windows issues
|
||||
|
||||
(Assuming you have a 64-bit Linux.)
|
||||
If you see this message on Windows during executing `stack test` command:
|
||||
|
||||
wget https://gonito.net/get/bin/geval
|
||||
chmod u+x geval
|
||||
./geval --help
|
||||
In the dependencies for geval-1.21.1.0:
|
||||
unix needed, but the stack configuration has no specified version
|
||||
In the dependencies for lzma-0.0.0.3:
|
||||
lzma-clib needed, but the stack configuration has no specified version
|
||||
|
||||
This is a fully static binary, it should work on any 64-bit Linux.
|
||||
You should replace `unix` with `unix-compat` in `geval.cabal` file,
|
||||
because `unix` package is not supported for Windows.
|
||||
|
||||
And you should add `lzma-clib-5.2.2` and `unix-compat-0.5.2` to section extra-deps in `stack.yaml` file.
|
||||
|
||||
If you see message about missing pkg-config on Windpws you should download two packages from the site:
|
||||
http://ftp.gnome.org/pub/gnome/binaries/win32/dependencies/
|
||||
These packages are:
|
||||
- pkg-config (the newest version)
|
||||
- gettext-runtime (the newest version)
|
||||
Extract `pkg-config.exe` file in Windows PATH
|
||||
Extract init.dll file from gettext-runtime
|
||||
|
||||
You should also download from http://ftp.gnome.org/pub/gnome/binaries/win32/glib/2.28 glib package
|
||||
and extract libglib-2.0-0.dll file.
|
||||
|
||||
All files you should put for example in `C:\MinGW\bin` directory.
|
||||
|
||||
## Quick tour
|
||||
|
||||
@ -189,7 +231,7 @@ But why were double quotes so problematic in German-English
|
||||
translation?! Well, look at the second-worst feature — `''`
|
||||
in the _output_! Oops, it seems like a very stupid mistake with
|
||||
post-processing was done and no double quote was correctly generated,
|
||||
which decreased the score a little bit for each sentence in which the
|
||||
which decreased the score a little for each sentence in which the
|
||||
quote was expected.
|
||||
|
||||
When I fixed this simple bug, the BLUE metric increased from 0.27358
|
||||
@ -502,9 +544,9 @@ submitted. The suggested way to do this is as follows:
|
||||
`test-A/expected.tsv` added. This branch should be accessible by
|
||||
Gonito platform, but should be kept “hidden” for regular users (or
|
||||
at least they should be kindly asked not to peek there). It is
|
||||
recommended (though not obligatory) that this branch contain all
|
||||
recommended (though not obligatory) that this branch contains all
|
||||
the source codes and data used to generate the train/dev/test sets.
|
||||
(Use [git-annex](https://git-annex.branchable.com/) if you have really big files there.)
|
||||
(Use [git-annex](https://git-annex.branchable.com/) if you have huge files there.)
|
||||
|
||||
Branch (1) should be the parent of the branch (2), for instance, the
|
||||
repo (for the toy “planets” challenge) could be created as follows:
|
||||
@ -567,7 +609,7 @@ be nice and commit also your source codes.
|
||||
git push mine master
|
||||
|
||||
Then let Gonito pull them and evaluate your results, either manually clicking
|
||||
"submit" at the Gonito web site or using `--submit` option (see below).
|
||||
"submit" at the Gonito website or using `--submit` option (see below).
|
||||
|
||||
### Submitting a solution to a Gonito platform with GEval
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
name: geval
|
||||
version: 1.21.1.0
|
||||
version: 1.22.0.0
|
||||
synopsis: Machine learning evaluation tools
|
||||
description: Please see README.md
|
||||
homepage: http://github.com/name/project
|
||||
|
||||
@ -4,11 +4,12 @@
|
||||
module GEval.Annotation
|
||||
(parseAnnotations, Annotation(..),
|
||||
parseObtainedAnnotations, ObtainedAnnotation(..),
|
||||
matchScore, intSetParser)
|
||||
matchScore, intSetParser, segmentAccuracy, parseSegmentAnnotations)
|
||||
where
|
||||
|
||||
import qualified Data.IntSet as IS
|
||||
import qualified Data.Text as T
|
||||
import Data.Set (intersection, fromList)
|
||||
|
||||
import Data.Attoparsec.Text
|
||||
import Data.Attoparsec.Combinator
|
||||
@ -17,11 +18,12 @@ import GEval.Common (sepByWhitespaces, (/.))
|
||||
import GEval.Probability
|
||||
import Data.Char
|
||||
import Data.Maybe (fromMaybe)
|
||||
import Data.Either (partitionEithers)
|
||||
|
||||
import GEval.PrecisionRecall(weightedMaxMatching)
|
||||
|
||||
data Annotation = Annotation T.Text IS.IntSet
|
||||
deriving (Eq, Show)
|
||||
deriving (Eq, Show, Ord)
|
||||
|
||||
data ObtainedAnnotation = ObtainedAnnotation Annotation Double
|
||||
deriving (Eq, Show)
|
||||
@ -52,6 +54,36 @@ obtainedAnnotationParser = do
|
||||
parseAnnotations :: T.Text -> Either String [Annotation]
|
||||
parseAnnotations t = parseOnly (annotationsParser <* endOfInput) t
|
||||
|
||||
parseSegmentAnnotations :: T.Text -> Either String [Annotation]
|
||||
parseSegmentAnnotations t = case parseAnnotationsWithColons t of
|
||||
Left m -> Left m
|
||||
Right annotations -> if areSegmentsDisjoint annotations
|
||||
then (Right annotations)
|
||||
else (Left "Overlapping segments")
|
||||
|
||||
areSegmentsDisjoint :: [Annotation] -> Bool
|
||||
areSegmentsDisjoint = areIntSetsDisjoint . map (\(Annotation _ s) -> s)
|
||||
|
||||
areIntSetsDisjoint :: [IS.IntSet] -> Bool
|
||||
areIntSetsDisjoint ss = snd $ foldr step (IS.empty, True) ss
|
||||
where step _ w@(_, False) = w
|
||||
step s (u, True) = (s `IS.union` u, s `IS.disjoint` u)
|
||||
|
||||
-- unfortunately, attoparsec does not seem to back-track properly
|
||||
-- so we need a special function if labels can contain colons
|
||||
parseAnnotationsWithColons :: T.Text -> Either String [Annotation]
|
||||
parseAnnotationsWithColons t = case partitionEithers (map parseAnnotationWithColons $ T.words t) of
|
||||
([], annotations) -> Right annotations
|
||||
((firstProblem:_), _) -> Left firstProblem
|
||||
|
||||
parseAnnotationWithColons :: T.Text -> Either String Annotation
|
||||
parseAnnotationWithColons t = if T.null label
|
||||
then Left "Colon expected"
|
||||
else case parseOnly (intSetParser <* endOfInput) position of
|
||||
Left m -> Left m
|
||||
Right s -> Right (Annotation (T.init label) s)
|
||||
where (label, position) = T.breakOnEnd ":" t
|
||||
|
||||
annotationsParser :: Parser [Annotation]
|
||||
annotationsParser = sepByWhitespaces annotationParser
|
||||
|
||||
@ -70,3 +102,7 @@ intervalParser = do
|
||||
startIx <- decimal
|
||||
endIx <- (string "-" *> decimal <|> pure startIx)
|
||||
pure $ IS.fromList [startIx..endIx]
|
||||
|
||||
segmentAccuracy :: [Annotation] -> [Annotation] -> Double
|
||||
segmentAccuracy expected output = (fromIntegral $ length matched) / (fromIntegral $ length expected)
|
||||
where matched = (fromList expected) `intersection` (fromList output)
|
||||
|
||||
@ -567,6 +567,11 @@ gevalCoreOnSources TokenAccuracy _ = gevalCoreWithoutInput SATokenAccuracy
|
||||
where
|
||||
hitsAndTotalsAgg = CC.foldl (\(h1, t1) (h2, t2) -> (h1 + h2, t1 + t2)) (0, 0)
|
||||
|
||||
gevalCoreOnSources SegmentAccuracy _ = gevalCoreWithoutInput SASegmentAccuracy
|
||||
averageC
|
||||
id
|
||||
noGraph
|
||||
|
||||
gevalCoreOnSources MultiLabelLogLoss _ = gevalCoreWithoutInput SAMultiLabelLogLoss
|
||||
averageC
|
||||
id
|
||||
|
||||
@ -298,6 +298,19 @@ in the expected file (but not in the output file).
|
||||
|
||||
|] ++ (commonReadmeMDContents testName)
|
||||
|
||||
readmeMDContents SegmentAccuracy testName = [i|
|
||||
Segment a sentence and tag with POS tags
|
||||
========================================
|
||||
|
||||
This is a sample, toy challenge for SegmentAccuracy.
|
||||
|
||||
For each sentence, give a sequence of POS tags, each one with
|
||||
its position (1-indexed). For instance, `N:1-10` means a nouns
|
||||
starting from the beginning (the first character) up to to the tenth
|
||||
character (inclusively).
|
||||
|
||||
|] ++ (commonReadmeMDContents testName)
|
||||
|
||||
readmeMDContents (ProbabilisticMultiLabelFMeasure beta) testName = readmeMDContents (MultiLabelFMeasure beta) testName
|
||||
readmeMDContents (MultiLabelFMeasure beta) testName = [i|
|
||||
Tag names and their component
|
||||
@ -474,6 +487,9 @@ B-firstname/JOHN I-surname/VON I-surname/NEUMANN John von Nueman
|
||||
trainContents TokenAccuracy = [hereLit|* V N I like cats
|
||||
* * V * N I can see the rainbow
|
||||
|]
|
||||
trainContents SegmentAccuracy = [hereLit|Art:1-3 N:5-11 V:12-13 A:15-19 The student's smart
|
||||
N:1-6 N:8-10 V:12-13 A:15-18 Mary's dog is nice
|
||||
|]
|
||||
trainContents (ProbabilisticMultiLabelFMeasure beta) = trainContents (MultiLabelFMeasure beta)
|
||||
trainContents (MultiLabelFMeasure _) = [hereLit|I know Mr John Smith person/3,4,5 first-name/4 surname/5
|
||||
Steven bloody Brown person/1,3 first-name/1 surname/3
|
||||
@ -541,6 +557,9 @@ Mr Jan Kowalski
|
||||
devInContents TokenAccuracy = [hereLit|The cats on the mat
|
||||
Ala has a cat
|
||||
|]
|
||||
devInContents SegmentAccuracy = [hereLit|John is smart
|
||||
Mary's intelligent
|
||||
|]
|
||||
devInContents (ProbabilisticMultiLabelFMeasure beta) = devInContents (MultiLabelFMeasure beta)
|
||||
devInContents (MultiLabelFMeasure _) = [hereLit|Jan Kowalski is here
|
||||
I see him
|
||||
@ -605,6 +624,9 @@ O B-firstname/JAN B-surname/KOWALSKI
|
||||
devExpectedContents TokenAccuracy = [hereLit|* N * * N
|
||||
N V * N
|
||||
|]
|
||||
devExpectedContents SegmentAccuracy = [hereLit|N:1-4 V:6-7 A:9-13
|
||||
N:1-4 V:6-7 A:9-19
|
||||
|]
|
||||
devExpectedContents (ProbabilisticMultiLabelFMeasure beta) = devExpectedContents (MultiLabelFMeasure beta)
|
||||
devExpectedContents (MultiLabelFMeasure _) = [hereLit|person/1,2 first-name/1 surname/2
|
||||
|
||||
@ -625,7 +647,8 @@ devExpectedContents _ = [hereLit|0.82
|
||||
|]
|
||||
|
||||
testInContents :: Metric -> String
|
||||
testInContents GLEU = testInContents BLEU
|
||||
testInContents GLEU = [hereLit|Alice has a black
|
||||
|]
|
||||
testInContents BLEU = [hereLit|ja jumala kutsui valkeuden päiväksi , ja pimeyden hän kutsui yöksi
|
||||
ja tuli ehtoo , ja tuli aamu , ensimmäinen päivä
|
||||
|]
|
||||
@ -673,6 +696,9 @@ No name here
|
||||
testInContents TokenAccuracy = [hereLit|I have cats
|
||||
I know
|
||||
|]
|
||||
testInContents SegmentAccuracy = [hereLit|Mary's cat is old
|
||||
John is young
|
||||
|]
|
||||
testInContents (ProbabilisticMultiLabelFMeasure beta) = testInContents (MultiLabelFMeasure beta)
|
||||
testInContents (MultiLabelFMeasure _) = [hereLit|John bloody Smith
|
||||
Nobody is there
|
||||
@ -691,7 +717,6 @@ testInContents _ = [hereLit|0.72 0 0.007
|
||||
|]
|
||||
|
||||
testExpectedContents :: Metric -> String
|
||||
testExpectedContents GLEU = testExpectedContents BLEU
|
||||
testExpectedContents BLEU = [hereLit|na ka huaina e te atua te marama ko te awatea , a ko te pouri i huaina e ia ko te po
|
||||
a ko te ahiahi , ko te ata , he ra kotahi
|
||||
|]
|
||||
@ -739,6 +764,9 @@ O O O
|
||||
testExpectedContents TokenAccuracy = [hereLit|* V N
|
||||
* V
|
||||
|]
|
||||
testExpectedContents SegmentAccuracy = [hereLit|N:1-6 N:8-10 V:12-13 A:15-17
|
||||
N:1-4 V:6-7 A:9-13
|
||||
|]
|
||||
testExpectedContents (ProbabilisticMultiLabelFMeasure beta) = testExpectedContents (MultiLabelFMeasure beta)
|
||||
testExpectedContents (MultiLabelFMeasure _) = [hereLit|person/1,3 first-name/1 surname/3
|
||||
|
||||
@ -754,10 +782,13 @@ bar:1/50,50,1000,1000
|
||||
testExpectedContents ClippEU = [hereLit|3/0,0,100,100/10
|
||||
1/10,10,1000,1000/10
|
||||
|]
|
||||
testExpectedContents GLEU = [hereLit|Alice has a black cat
|
||||
|]
|
||||
testExpectedContents _ = [hereLit|0.11
|
||||
17.2
|
||||
|]
|
||||
|
||||
|
||||
gitignoreContents :: String
|
||||
gitignoreContents = [hereLit|
|
||||
*~
|
||||
|
||||
@ -26,7 +26,7 @@ import Data.Attoparsec.Text (parseOnly)
|
||||
data Metric = RMSE | MSE | Pearson | Spearman | BLEU | GLEU | WER | Accuracy | ClippEU
|
||||
| FMeasure Double | MacroFMeasure Double | NMI
|
||||
| LogLossHashed Word32 | CharMatch | MAP | LogLoss | Likelihood
|
||||
| BIOF1 | BIOF1Labels | TokenAccuracy | LikelihoodHashed Word32 | MAE | SMAPE | MultiLabelFMeasure Double
|
||||
| BIOF1 | BIOF1Labels | TokenAccuracy | SegmentAccuracy | LikelihoodHashed Word32 | MAE | SMAPE | MultiLabelFMeasure Double
|
||||
| MultiLabelLogLoss | MultiLabelLikelihood
|
||||
| SoftFMeasure Double | ProbabilisticMultiLabelFMeasure Double | ProbabilisticSoftFMeasure Double | Soft2DFMeasure Double
|
||||
deriving (Eq)
|
||||
@ -67,6 +67,7 @@ instance Show Metric where
|
||||
show BIOF1 = "BIO-F1"
|
||||
show BIOF1Labels = "BIO-F1-Labels"
|
||||
show TokenAccuracy = "TokenAccuracy"
|
||||
show SegmentAccuracy = "SegmentAccuracy"
|
||||
show MAE = "MAE"
|
||||
show SMAPE = "SMAPE"
|
||||
show (MultiLabelFMeasure beta) = "MultiLabel-F" ++ (show beta)
|
||||
@ -118,6 +119,7 @@ instance Read Metric where
|
||||
readsPrec _ ('B':'I':'O':'-':'F':'1':'-':'L':'a':'b':'e':'l':'s':theRest) = [(BIOF1Labels, theRest)]
|
||||
readsPrec _ ('B':'I':'O':'-':'F':'1':theRest) = [(BIOF1, theRest)]
|
||||
readsPrec _ ('T':'o':'k':'e':'n':'A':'c':'c':'u':'r':'a':'c':'y':theRest) = [(TokenAccuracy, theRest)]
|
||||
readsPrec _ ('S':'e':'g':'m':'e':'n':'t':'A':'c':'c':'u':'r':'a':'c':'y':theRest) = [(SegmentAccuracy, theRest)]
|
||||
readsPrec _ ('M':'A':'E':theRest) = [(MAE, theRest)]
|
||||
readsPrec _ ('S':'M':'A':'P':'E':theRest) = [(SMAPE, theRest)]
|
||||
readsPrec _ ('M':'u':'l':'t':'i':'L':'a':'b':'e':'l':'-':'L':'o':'g':'L':'o':'s':'s':theRest) = [(MultiLabelLogLoss, theRest)]
|
||||
@ -154,6 +156,7 @@ getMetricOrdering Likelihood = TheHigherTheBetter
|
||||
getMetricOrdering BIOF1 = TheHigherTheBetter
|
||||
getMetricOrdering BIOF1Labels = TheHigherTheBetter
|
||||
getMetricOrdering TokenAccuracy = TheHigherTheBetter
|
||||
getMetricOrdering SegmentAccuracy = TheHigherTheBetter
|
||||
getMetricOrdering MAE = TheLowerTheBetter
|
||||
getMetricOrdering SMAPE = TheLowerTheBetter
|
||||
getMetricOrdering (MultiLabelFMeasure _) = TheHigherTheBetter
|
||||
|
||||
@ -33,7 +33,9 @@ import Data.Maybe (catMaybes)
|
||||
|
||||
import Control.Monad ((<=<))
|
||||
|
||||
import GEval.Annotation (Annotation, ObtainedAnnotation, parseAnnotations, parseObtainedAnnotations)
|
||||
import GEval.Annotation (Annotation, ObtainedAnnotation,
|
||||
parseAnnotations, parseObtainedAnnotations,
|
||||
parseSegmentAnnotations, segmentAccuracy)
|
||||
import GEval.Clippings (Clipping, ClippingSpec, LabeledClipping, lineClippingsParser, lineClippingSpecsParser, lineLabeledClippingsParser)
|
||||
import GEval.BIO (TaggedEntity, parseBioSequenceIntoEntities, parseBioSequenceIntoEntitiesWithoutNormalization)
|
||||
import GEval.LogLossHashed (parseWordSpecs, wordSpecToPair)
|
||||
@ -45,7 +47,7 @@ import GEval.ProbList (ProbList(..), parseIntoProbList, WordWithProb(..), countL
|
||||
singletons [d|data AMetric = ARMSE | AMSE | APearson | ASpearman | ABLEU | AGLEU | AWER | AAccuracy | AClippEU
|
||||
| AFMeasure | AMacroFMeasure | ANMI
|
||||
| ALogLossHashed | ACharMatch | AMAP | ALogLoss | ALikelihood
|
||||
| ABIOF1 | ABIOF1Labels | ATokenAccuracy | ALikelihoodHashed | AMAE | ASMAPE | AMultiLabelFMeasure
|
||||
| ABIOF1 | ABIOF1Labels | ATokenAccuracy | ASegmentAccuracy | ALikelihoodHashed | AMAE | ASMAPE | AMultiLabelFMeasure
|
||||
| AMultiLabelLogLoss | AMultiLabelLikelihood
|
||||
| ASoftFMeasure | AProbabilisticMultiLabelFMeasure | AProbabilisticSoftFMeasure | ASoft2DFMeasure
|
||||
deriving (Eq)
|
||||
@ -73,6 +75,7 @@ toHelper Likelihood = ALikelihood
|
||||
toHelper BIOF1 = ABIOF1
|
||||
toHelper BIOF1Labels = ABIOF1Labels
|
||||
toHelper TokenAccuracy = ATokenAccuracy
|
||||
toHelper SegmentAccuracy = ASegmentAccuracy
|
||||
toHelper (LikelihoodHashed _) = ALikelihoodHashed
|
||||
toHelper MAE = AMAE
|
||||
toHelper SMAPE = ASMAPE
|
||||
@ -114,6 +117,7 @@ type family ParsedExpectedType (t :: AMetric) :: * where
|
||||
ParsedExpectedType ABIOF1 = [TaggedEntity]
|
||||
ParsedExpectedType ABIOF1Labels = [TaggedEntity]
|
||||
ParsedExpectedType ATokenAccuracy = [Text]
|
||||
ParsedExpectedType ASegmentAccuracy = [Annotation]
|
||||
ParsedExpectedType AMAE = Double
|
||||
ParsedExpectedType ASMAPE = Double
|
||||
ParsedExpectedType AMultiLabelFMeasure = [Text]
|
||||
@ -146,6 +150,7 @@ expectedParser SALikelihood = doubleParser
|
||||
expectedParser SABIOF1 = parseBioSequenceIntoEntities
|
||||
expectedParser SABIOF1Labels = parseBioSequenceIntoEntitiesWithoutNormalization
|
||||
expectedParser SATokenAccuracy = intoWords
|
||||
expectedParser SASegmentAccuracy = parseSegmentAnnotations
|
||||
expectedParser SAMAE = doubleParser
|
||||
expectedParser SASMAPE = doubleParser
|
||||
expectedParser SAMultiLabelFMeasure = intoWords
|
||||
@ -190,6 +195,7 @@ outputParser SALikelihood = doubleParser
|
||||
outputParser SABIOF1 = parseBioSequenceIntoEntities
|
||||
outputParser SABIOF1Labels = parseBioSequenceIntoEntitiesWithoutNormalization
|
||||
outputParser SATokenAccuracy = intoWords
|
||||
outputParser SASegmentAccuracy = parseSegmentAnnotations
|
||||
outputParser SAMAE = doubleParser
|
||||
outputParser SASMAPE = doubleParser
|
||||
outputParser SAMultiLabelFMeasure = intoWords
|
||||
@ -244,6 +250,7 @@ itemStep SALikelihood = itemLogLossError
|
||||
itemStep SABIOF1 = uncurry gatherCountsForBIO
|
||||
itemStep SABIOF1Labels = uncurry gatherCountsForBIO
|
||||
itemStep SATokenAccuracy = countHitsAndTotals
|
||||
itemStep SASegmentAccuracy = uncurry segmentAccuracy
|
||||
itemStep SAMAE = itemAbsoluteError
|
||||
itemStep SASMAPE = smape
|
||||
itemStep SAMultiLabelFMeasure = getCounts (==)
|
||||
|
||||
@ -63,6 +63,7 @@ listOfAvailableMetrics = [RMSE,
|
||||
BIOF1,
|
||||
BIOF1Labels,
|
||||
TokenAccuracy,
|
||||
SegmentAccuracy,
|
||||
SoftFMeasure 1.0,
|
||||
SoftFMeasure 2.0,
|
||||
SoftFMeasure 0.25,
|
||||
@ -93,6 +94,8 @@ isMetricDescribed :: Metric -> Bool
|
||||
isMetricDescribed (SoftFMeasure _) = True
|
||||
isMetricDescribed (Soft2DFMeasure _) = True
|
||||
isMetricDescribed (ProbabilisticMultiLabelFMeasure _) = True
|
||||
isMetricDescribed GLEU = True
|
||||
isMetricDescribed SegmentAccuracy = True
|
||||
isMetricDescribed _ = False
|
||||
|
||||
getEvaluationSchemeDescription :: EvaluationScheme -> String
|
||||
@ -118,8 +121,26 @@ where calibration measures the quality of probabilities (how well they are calib
|
||||
if we have 10 items with probability 0.5 and 5 of them are correct, then the calibration
|
||||
is perfect.
|
||||
|]
|
||||
|
||||
|
||||
getMetricDescription GLEU =
|
||||
[i|For the GLEU score, we record all sub-sequences of
|
||||
1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then
|
||||
compute a recall, which is the ratio of the number of matching n-grams
|
||||
to the number of total n-grams in the target (ground truth) sequence,
|
||||
and a precision, which is the ratio of the number of matching n-grams
|
||||
to the number of total n-grams in the generated output sequence. Then
|
||||
GLEU score is simply the minimum of recall and precision. This GLEU
|
||||
score's range is always between 0 (no matches) and 1 (all match) and
|
||||
it is symmetrical when switching output and target. According to
|
||||
the article, GLEU score correlates quite well with the BLEU
|
||||
metric on a corpus level but does not have its drawbacks for our per
|
||||
sentence reward objective.
|
||||
see: https://arxiv.org/pdf/1609.08144.pdf
|
||||
|]
|
||||
getMetricDescription SegmentAccuracy =
|
||||
[i|Accuracy counted for segments, i.e. labels with positions.
|
||||
The percentage of labels in the ground truth retrieved in the actual output is returned.
|
||||
Accuracy is calculated separately for each item and then averaged.
|
||||
|]
|
||||
|
||||
outContents :: Metric -> String
|
||||
outContents (SoftFMeasure _) = [hereLit|inwords:1-4
|
||||
@ -132,6 +153,11 @@ outContents (ProbabilisticMultiLabelFMeasure _) = [hereLit|first-name/1:0.8 surn
|
||||
surname/1:0.4
|
||||
first-name/3:0.9
|
||||
|]
|
||||
outContents GLEU = [hereLit|Alice has a black
|
||||
|]
|
||||
outContents SegmentAccuracy = [hereLit|N:1-4 V:5-6 N:8-10 V:12-13 A:15-17
|
||||
N:1-4 V:6-7 A:9-13
|
||||
|]
|
||||
|
||||
expectedScore :: EvaluationScheme -> MetricValue
|
||||
expectedScore (EvaluationScheme (SoftFMeasure beta) [])
|
||||
@ -146,6 +172,10 @@ expectedScore (EvaluationScheme (ProbabilisticMultiLabelFMeasure beta) [])
|
||||
= let precision = 0.6569596940847289
|
||||
recall = 0.675
|
||||
in weightedHarmonicMean beta precision recall
|
||||
expectedScore (EvaluationScheme GLEU [])
|
||||
= 0.7142857142857143
|
||||
expectedScore (EvaluationScheme SegmentAccuracy [])
|
||||
= 0.875
|
||||
|
||||
helpMetricParameterMetricsList :: String
|
||||
helpMetricParameterMetricsList = intercalate ", " $ map (\s -> (show s) ++ (case extraInfo s of
|
||||
@ -194,7 +224,15 @@ the form LABEL:PAGE/X0,Y0,X1,Y1 where LABEL is any label, page is the page numbe
|
||||
formatDescription (ProbabilisticMultiLabelFMeasure _) = [hereLit|In each line a number of labels (entities) can be given. A label probability
|
||||
can be provided with a colon (e.g. "foo:0.7"). By default, 1.0 is assumed.
|
||||
|]
|
||||
|
||||
formatDescription GLEU = [hereLit|In each line a there is a space sparated sentence of words.
|
||||
|]
|
||||
formatDescription SegmentAccuracy = [hereLit|Labels can be any strings (without spaces), whereas is a list of
|
||||
1-based indexes or spans separated by commas (spans are inclusive
|
||||
ranges, e.g. "10-14"). For instance, "foo:bar:2,4-7,10" is a
|
||||
label "foo:bar" for positions 2, 4, 5, 6, 7 and 10. Note that no
|
||||
overlapping segments can be returned (evaluation will fail in
|
||||
such a case).
|
||||
|]
|
||||
|
||||
scoreExplanation :: EvaluationScheme -> Maybe String
|
||||
scoreExplanation (EvaluationScheme (SoftFMeasure _) [])
|
||||
@ -206,6 +244,17 @@ As far as the second item is concerned, the total area that covered by the outpu
|
||||
Hence, recall is 247500/902500=0.274 and precision - 247500/(20000+912000+240000)=0.211. Therefore, the F-score
|
||||
for the second item is 0.238 and the F-score for the whole set is (0 + 0.238)/2 = 0.119.|]
|
||||
scoreExplanation (EvaluationScheme (ProbabilisticMultiLabelFMeasure _) []) = Nothing
|
||||
scoreExplanation (EvaluationScheme GLEU [])
|
||||
= Just [hereLit|To find out GLEU score we first count number of tp (true positives) fp(false positives) and fn(false negatives).
|
||||
We have 4 matching unigrams ("Alice", "has", "a", "black") , 3 bigrams ("Alice has", "has a", "a black"), 2 trigrams ("Alice has a", "has a black") and 1 tetragram ("Alice has a black"),
|
||||
so tp=10. We have no fp, therefore fp=0. There are 4 fn - ("cat", "black cat", "a black cat", "has a black cat").
|
||||
Now we have to calculate precision and recall:
|
||||
Precision is tp / (tp+fp) = 10/(10+0) = 1,
|
||||
recall is tp / (tp+fn) = 10 / (10+4) = 10/14 =~ 0.71428...
|
||||
The GLEU score is min(precision,recall)=0.71428 |]
|
||||
scoreExplanation (EvaluationScheme SegmentAccuracy [])
|
||||
= Just [hereLit|Out of 4 segments in the expected output for the first item, 3 were retrieved correcly (accuracy is 3/4=0.75).
|
||||
The second item was retrieved perfectly (accuracy is 1.0). Hence, the average is (0.75+1.0)/2=0.875.|]
|
||||
|
||||
pasteLines :: String -> String -> String
|
||||
pasteLines a b = printf "%-35s %s\n" a b
|
||||
|
||||
@ -146,6 +146,9 @@ main = hspec $ do
|
||||
describe "TokenAccuracy" $ do
|
||||
it "simple example" $ do
|
||||
runGEvalTest "token-accuracy-simple" `shouldReturnAlmost` 0.5
|
||||
describe "SegmentAccuracy" $ do
|
||||
it "simple test" $ do
|
||||
runGEvalTest "segment-accuracy-simple" `shouldReturnAlmost` 0.4444444
|
||||
describe "precision count" $ do
|
||||
it "simple test" $ do
|
||||
precisionCount [["Alice", "has", "a", "cat" ]] ["Ala", "has", "cat"] `shouldBe` 2
|
||||
@ -342,6 +345,11 @@ main = hspec $ do
|
||||
it "just parse" $ do
|
||||
parseAnnotations "foo:3,7-10 baz:4-6" `shouldBe` Right [Annotation "foo" (IS.fromList [3,7,8,9,10]),
|
||||
Annotation "baz" (IS.fromList [4,5,6])]
|
||||
it "just parse wit colons" $ do
|
||||
parseSegmentAnnotations "foo:x:3,7-10 baz:4-6" `shouldBe` Right [Annotation "foo:x" (IS.fromList [3,7,8,9,10]),
|
||||
Annotation "baz" (IS.fromList [4,5,6])]
|
||||
it "just parse wit colons" $ do
|
||||
parseSegmentAnnotations "foo:x:3,7-10 baz:2-6" `shouldBe` Left "Overlapping segments"
|
||||
it "just parse 2" $ do
|
||||
parseAnnotations "inwords:1-3 indigits:5" `shouldBe` Right [Annotation "inwords" (IS.fromList [1,2,3]),
|
||||
Annotation "indigits" (IS.fromList [5])]
|
||||
|
||||
@ -0,0 +1,3 @@
|
||||
foo:0 baq:1-2 baz:3
|
||||
aaa:0-1
|
||||
xyz:0 bbb:x:1
|
||||
|
@ -0,0 +1 @@
|
||||
--metric SegmentAccuracy
|
||||
@ -0,0 +1,3 @@
|
||||
foo:0 bar:1-2 baz:3
|
||||
aaa:0-2
|
||||
xyz:0 bbb:x:1 ccc:x:2
|
||||
|
Loading…
Reference in New Issue
Block a user