Introduce :S flag (sorting words within a line)
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@ -492,6 +492,23 @@ gevalCoreOnSources CharMatch inputLineSource = helper inputLineSource
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gevalCoreOnSources (LogLossHashed nbOfBits) _ = helperLogLossHashed nbOfBits id
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gevalCoreOnSources (LikelihoodHashed nbOfBits) _ = helperLogLossHashed nbOfBits logLossToLikehood
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gevalCoreOnSources (Mean (MultiLabelFMeasure beta)) _
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= gevalCoreWithoutInputOnItemTargets (Right . intoWords)
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(Right . getWords)
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((fMeasureOnCounts beta) . (getCounts (==)))
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averageC
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id
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noGraph
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where
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-- repeated as below, as it will be refactored into dependent types soon anyway
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getWords (RawItemTarget t) = Prelude.map unpack $ selectByStandardThreshold $ parseIntoProbList t
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getWords (PartiallyParsedItemTarget ts) = Prelude.map unpack ts
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intoWords (RawItemTarget t) = Prelude.map unpack $ Data.Text.words t
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intoWords (PartiallyParsedItemTarget ts) = Prelude.map unpack ts
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gevalCoreOnSources (Mean _) _ = error $ "Mean/ meta-metric defined only for MultiLabel-F1 for the time being"
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-- only MultiLabel-F1 handled for JSONs for the time being...
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gevalCoreOnSources (MultiLabelFMeasure beta) _ = gevalCoreWithoutInputOnItemTargets (Right . intoWords)
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(Right . getWords)
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@ -55,6 +55,7 @@ createFile filePath contents = do
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writeFile filePath contents
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readmeMDContents :: Metric -> String -> String
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readmeMDContents (Mean metric) testName = readmeMDContents metric testName
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readmeMDContents GLEU testName = readmeMDContents BLEU testName
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readmeMDContents BLEU testName = [i|
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GEval sample machine translation challenge
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@ -413,6 +414,7 @@ configContents schemes precision testName = unwords (Prelude.map (\scheme -> ("-
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precisionOpt (Just p) = " --precision " ++ (show p)
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trainContents :: Metric -> String
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trainContents (Mean metric) = trainContents metric
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trainContents GLEU = trainContents BLEU
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trainContents BLEU = [hereLit|alussa loi jumala taivaan ja maan he mea hanga na te atua i te timatanga te rangi me te whenua
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ja maa oli autio ja tyhjä , ja pimeys oli syvyyden päällä a kahore he ahua o te whenua , i takoto kau ; he pouri ano a runga i te mata o te hohonu
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@ -510,6 +512,7 @@ trainContents _ = [hereLit|0.06 0.39 0 0.206
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|]
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devInContents :: Metric -> String
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devInContents (Mean metric) = devInContents metric
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devInContents GLEU = devInContents BLEU
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devInContents BLEU = [hereLit|ja jumala sanoi : " tulkoon valkeus " , ja valkeus tuli
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ja jumala näki , että valkeus oli hyvä ; ja jumala erotti valkeuden pimeydestä
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@ -577,6 +580,7 @@ devInContents _ = [hereLit|0.72 0 0.007
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|]
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devExpectedContents :: Metric -> String
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devExpectedContents (Mean metric) = devExpectedContents metric
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devExpectedContents GLEU = devExpectedContents BLEU
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devExpectedContents BLEU = [hereLit|a ka ki te atua , kia marama : na ka marama
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a ka kite te atua i te marama , he pai : a ka wehea e te atua te marama i te pouri
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@ -646,6 +650,7 @@ devExpectedContents _ = [hereLit|0.82
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|]
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testInContents :: Metric -> String
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testInContents (Mean metric) = testInContents metric
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testInContents GLEU = [hereLit|Alice has a black
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|]
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testInContents BLEU = [hereLit|ja jumala kutsui valkeuden päiväksi , ja pimeyden hän kutsui yöksi
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@ -716,6 +721,7 @@ testInContents _ = [hereLit|0.72 0 0.007
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|]
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testExpectedContents :: Metric -> String
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testExpectedContents (Mean metric) = testExpectedContents metric
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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
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a ko te ahiahi , ko te ata , he ra kotahi
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|]
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@ -6,8 +6,8 @@ import GEval.Metric
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import Text.Regex.PCRE.Heavy
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import Text.Regex.PCRE.Light.Base (Regex(..))
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import Data.Text (Text(..), concat, toLower, toUpper, pack, unpack)
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import Data.List (intercalate, break)
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import Data.Text (Text(..), concat, toLower, toUpper, pack, unpack, words, unwords)
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import Data.List (intercalate, break, sort)
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import Data.Either
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import Data.Maybe (fromMaybe)
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import qualified Data.ByteString.UTF8 as BSU
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@ -16,7 +16,7 @@ import qualified Data.ByteString.UTF8 as BSU
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data EvaluationScheme = EvaluationScheme Metric [PreprocessingOperation]
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deriving (Eq)
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data PreprocessingOperation = RegexpMatch Regex | LowerCasing | UpperCasing | SetName Text
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data PreprocessingOperation = RegexpMatch Regex | LowerCasing | UpperCasing | Sorting | SetName Text
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deriving (Eq)
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leftParameterBracket :: Char
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@ -39,6 +39,8 @@ readOps ('l':theRest) = (LowerCasing:ops, theRest')
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readOps ('u':theRest) = (UpperCasing:ops, theRest')
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where (ops, theRest') = readOps theRest
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readOps ('m':theRest) = handleParametrizedOp (RegexpMatch . (fromRight undefined) . ((flip compileM) []) . BSU.fromString) theRest
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readOps ('S':theRest) = (Sorting:ops, theRest')
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where (ops, theRest') = readOps theRest
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readOps ('N':theRest) = handleParametrizedOp (SetName . pack) theRest
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readOps s = ([], s)
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@ -70,6 +72,7 @@ instance Show PreprocessingOperation where
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show (RegexpMatch (Regex _ regexp)) = parametrizedOperation "m" (BSU.toString regexp)
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show LowerCasing = "l"
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show UpperCasing = "u"
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show Sorting = "S"
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show (SetName t) = parametrizedOperation "N" (unpack t)
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parametrizedOperation :: String -> String -> String
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@ -82,4 +85,5 @@ applyPreprocessingOperation :: PreprocessingOperation -> Text -> Text
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applyPreprocessingOperation (RegexpMatch regex) = Data.Text.concat . (map fst) . (scan regex)
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applyPreprocessingOperation LowerCasing = toLower
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applyPreprocessingOperation UpperCasing = toUpper
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applyPreprocessingOperation Sorting = Data.Text.unwords . sort . Data.Text.words
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applyPreprocessingOperation (SetName _) = id
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@ -28,7 +28,12 @@ data Metric = RMSE | MSE | Pearson | Spearman | BLEU | GLEU | WER | Accuracy | C
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| LogLossHashed Word32 | CharMatch | MAP | LogLoss | Likelihood
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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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| SoftFMeasure Double | ProbabilisticMultiLabelFMeasure Double
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| ProbabilisticSoftFMeasure Double | Soft2DFMeasure Double
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-- it would be better to avoid infinite recursion here
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-- `Mean (Mean BLEU)` is not useful, but as it would mean
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-- a larger refactor, we will postpone this
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| Mean Metric
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deriving (Eq)
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instance Show Metric where
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@ -73,8 +78,12 @@ instance Show Metric where
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show (MultiLabelFMeasure beta) = "MultiLabel-F" ++ (show beta)
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show MultiLabelLogLoss = "MultiLabel-Logloss"
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show MultiLabelLikelihood = "MultiLabel-Likelihood"
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show (Mean metric) = "Mean/" ++ (show metric)
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instance Read Metric where
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readsPrec p ('M':'e':'a':'n':'/':theRest) = case readsPrec p theRest of
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[(metric, theRest)] -> [(Mean metric, theRest)]
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_ -> []
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readsPrec _ ('R':'M':'S':'E':theRest) = [(RMSE, theRest)]
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readsPrec _ ('M':'S':'E':theRest) = [(MSE, theRest)]
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readsPrec _ ('P':'e':'a':'r':'s':'o':'n':theRest) = [(Pearson, theRest)]
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@ -162,6 +171,7 @@ getMetricOrdering SMAPE = TheLowerTheBetter
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getMetricOrdering (MultiLabelFMeasure _) = TheHigherTheBetter
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getMetricOrdering MultiLabelLogLoss = TheLowerTheBetter
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getMetricOrdering MultiLabelLikelihood = TheHigherTheBetter
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getMetricOrdering (Mean metric) = getMetricOrdering metric
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bestPossibleValue :: Metric -> MetricValue
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bestPossibleValue metric = case getMetricOrdering metric of
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@ -169,18 +179,21 @@ bestPossibleValue metric = case getMetricOrdering metric of
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TheHigherTheBetter -> 1.0
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fixedNumberOfColumnsInExpected :: Metric -> Bool
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fixedNumberOfColumnsInExpected (Mean metric) = fixedNumberOfColumnsInExpected metric
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fixedNumberOfColumnsInExpected MAP = False
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fixedNumberOfColumnsInExpected BLEU = False
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fixedNumberOfColumnsInExpected GLEU = False
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fixedNumberOfColumnsInExpected _ = True
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fixedNumberOfColumnsInInput :: Metric -> Bool
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fixedNumberOfColumnsInInput (Mean metric) = fixedNumberOfColumnsInInput metric
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fixedNumberOfColumnsInInput (SoftFMeasure _) = False
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fixedNumberOfColumnsInInput (ProbabilisticSoftFMeasure _) = False
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fixedNumberOfColumnsInInput (Soft2DFMeasure _) = False
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fixedNumberOfColumnsInInput _ = True
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perfectOutLineFromExpectedLine :: Metric -> Text -> Text
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perfectOutLineFromExpectedLine (Mean metric) t = perfectOutLineFromExpectedLine metric t
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perfectOutLineFromExpectedLine (LogLossHashed _) t = t <> ":1.0"
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perfectOutLineFromExpectedLine (LikelihoodHashed _) t = t <> ":1.0"
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perfectOutLineFromExpectedLine BLEU t = getFirstColumn t
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@ -48,6 +48,7 @@ listOfAvailableMetrics = [RMSE,
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MultiLabelFMeasure 1.0,
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MultiLabelFMeasure 2.0,
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MultiLabelFMeasure 0.25,
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Mean (MultiLabelFMeasure 1.0),
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ProbabilisticMultiLabelFMeasure 1.0,
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ProbabilisticMultiLabelFMeasure 2.0,
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ProbabilisticMultiLabelFMeasure 0.25,
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@ -127,6 +127,8 @@ main = hspec $ do
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runGEvalTest "accuracy-simple" `shouldReturnAlmost` 0.6
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it "with probs" $
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runGEvalTest "accuracy-probs" `shouldReturnAlmost` 0.4
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it "sorted" $
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runGEvalTest "accuracy-on-sorted" `shouldReturnAlmost` 0.75
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describe "F-measure" $ do
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it "simple example" $
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runGEvalTest "f-measure-simple" `shouldReturnAlmost` 0.57142857
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@ -326,6 +328,9 @@ main = hspec $ do
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runGEvalTest "multilabel-f1-with-probs" `shouldReturnAlmost` 0.615384615384615
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it "labels given with probs and numbers" $ do
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runGEvalTest "multilabel-f1-with-probs-and-numbers" `shouldReturnAlmost` 0.6666666666666
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describe "Mean/MultiLabel-F" $ do
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it "simple" $ do
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runGEvalTest "mean-multilabel-f1-simple" `shouldReturnAlmost` 0.5
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describe "MultiLabel-Likelihood" $ do
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it "simple" $ do
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runGEvalTest "multilabel-likelihood-simple" `shouldReturnAlmost` 0.115829218528827
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@ -0,0 +1,4 @@
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foo baz bar
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xyz aaa
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2 a:1 3
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1
test/accuracy-on-sorted/accuracy-on-sorted/config.txt
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1
test/accuracy-on-sorted/accuracy-on-sorted/config.txt
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@ -0,0 +1 @@
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--metric Accuracy:S
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@ -0,0 +1,4 @@
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bar baz foo
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xyz
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a:1 2 3
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@ -0,0 +1,4 @@
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foo bar baz
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uuu
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foo bar baz
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qqq aaa
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@ -0,0 +1 @@
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--metric Mean/MultiLabel-F1
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foo bar baz
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foo
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qqq qqq
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