{-# LANGUAGE DeriveGeneric #-}
{-# LANGUAGE DuplicateRecordFields #-}
{-# LANGUAGE NamedFieldPuns #-}
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE RecordWildCards #-}
{-# LANGUAGE StrictData #-}
{-# LANGUAGE NoImplicitPrelude #-}
{-# OPTIONS_GHC -fno-warn-unused-imports #-}
{-# OPTIONS_GHC -fno-warn-unused-matches #-}

-- Derived from AWS service descriptions, licensed under Apache 2.0.

-- |
-- Module      : Amazonka.Comprehend.Types.EntityRecognizerEvaluationMetrics
-- Copyright   : (c) 2013-2023 Brendan Hay
-- License     : Mozilla Public License, v. 2.0.
-- Maintainer  : Brendan Hay
-- Stability   : auto-generated
-- Portability : non-portable (GHC extensions)
module Amazonka.Comprehend.Types.EntityRecognizerEvaluationMetrics where

import qualified Amazonka.Core as Core
import qualified Amazonka.Core.Lens.Internal as Lens
import qualified Amazonka.Data as Data
import qualified Amazonka.Prelude as Prelude

-- | Detailed information about the accuracy of an entity recognizer.
--
-- /See:/ 'newEntityRecognizerEvaluationMetrics' smart constructor.
data EntityRecognizerEvaluationMetrics = EntityRecognizerEvaluationMetrics'
  { -- | A measure of how accurate the recognizer results are for the test data.
    -- It is derived from the @Precision@ and @Recall@ values. The @F1Score@ is
    -- the harmonic average of the two scores. For plain text entity recognizer
    -- models, the range is 0 to 100, where 100 is the best score. For
    -- PDF\/Word entity recognizer models, the range is 0 to 1, where 1 is the
    -- best score.
    EntityRecognizerEvaluationMetrics -> Maybe Double
f1Score :: Prelude.Maybe Prelude.Double,
    -- | A measure of the usefulness of the recognizer results in the test data.
    -- High precision means that the recognizer returned substantially more
    -- relevant results than irrelevant ones.
    EntityRecognizerEvaluationMetrics -> Maybe Double
precision :: Prelude.Maybe Prelude.Double,
    -- | A measure of how complete the recognizer results are for the test data.
    -- High recall means that the recognizer returned most of the relevant
    -- results.
    EntityRecognizerEvaluationMetrics -> Maybe Double
recall :: Prelude.Maybe Prelude.Double
  }
  deriving (EntityRecognizerEvaluationMetrics
-> EntityRecognizerEvaluationMetrics -> Bool
forall a. (a -> a -> Bool) -> (a -> a -> Bool) -> Eq a
/= :: EntityRecognizerEvaluationMetrics
-> EntityRecognizerEvaluationMetrics -> Bool
$c/= :: EntityRecognizerEvaluationMetrics
-> EntityRecognizerEvaluationMetrics -> Bool
== :: EntityRecognizerEvaluationMetrics
-> EntityRecognizerEvaluationMetrics -> Bool
$c== :: EntityRecognizerEvaluationMetrics
-> EntityRecognizerEvaluationMetrics -> Bool
Prelude.Eq, ReadPrec [EntityRecognizerEvaluationMetrics]
ReadPrec EntityRecognizerEvaluationMetrics
Int -> ReadS EntityRecognizerEvaluationMetrics
ReadS [EntityRecognizerEvaluationMetrics]
forall a.
(Int -> ReadS a)
-> ReadS [a] -> ReadPrec a -> ReadPrec [a] -> Read a
readListPrec :: ReadPrec [EntityRecognizerEvaluationMetrics]
$creadListPrec :: ReadPrec [EntityRecognizerEvaluationMetrics]
readPrec :: ReadPrec EntityRecognizerEvaluationMetrics
$creadPrec :: ReadPrec EntityRecognizerEvaluationMetrics
readList :: ReadS [EntityRecognizerEvaluationMetrics]
$creadList :: ReadS [EntityRecognizerEvaluationMetrics]
readsPrec :: Int -> ReadS EntityRecognizerEvaluationMetrics
$creadsPrec :: Int -> ReadS EntityRecognizerEvaluationMetrics
Prelude.Read, Int -> EntityRecognizerEvaluationMetrics -> ShowS
[EntityRecognizerEvaluationMetrics] -> ShowS
EntityRecognizerEvaluationMetrics -> String
forall a.
(Int -> a -> ShowS) -> (a -> String) -> ([a] -> ShowS) -> Show a
showList :: [EntityRecognizerEvaluationMetrics] -> ShowS
$cshowList :: [EntityRecognizerEvaluationMetrics] -> ShowS
show :: EntityRecognizerEvaluationMetrics -> String
$cshow :: EntityRecognizerEvaluationMetrics -> String
showsPrec :: Int -> EntityRecognizerEvaluationMetrics -> ShowS
$cshowsPrec :: Int -> EntityRecognizerEvaluationMetrics -> ShowS
Prelude.Show, forall x.
Rep EntityRecognizerEvaluationMetrics x
-> EntityRecognizerEvaluationMetrics
forall x.
EntityRecognizerEvaluationMetrics
-> Rep EntityRecognizerEvaluationMetrics x
forall a.
(forall x. a -> Rep a x) -> (forall x. Rep a x -> a) -> Generic a
$cto :: forall x.
Rep EntityRecognizerEvaluationMetrics x
-> EntityRecognizerEvaluationMetrics
$cfrom :: forall x.
EntityRecognizerEvaluationMetrics
-> Rep EntityRecognizerEvaluationMetrics x
Prelude.Generic)

-- |
-- Create a value of 'EntityRecognizerEvaluationMetrics' with all optional fields omitted.
--
-- Use <https://hackage.haskell.org/package/generic-lens generic-lens> or <https://hackage.haskell.org/package/optics optics> to modify other optional fields.
--
-- The following record fields are available, with the corresponding lenses provided
-- for backwards compatibility:
--
-- 'f1Score', 'entityRecognizerEvaluationMetrics_f1Score' - A measure of how accurate the recognizer results are for the test data.
-- It is derived from the @Precision@ and @Recall@ values. The @F1Score@ is
-- the harmonic average of the two scores. For plain text entity recognizer
-- models, the range is 0 to 100, where 100 is the best score. For
-- PDF\/Word entity recognizer models, the range is 0 to 1, where 1 is the
-- best score.
--
-- 'precision', 'entityRecognizerEvaluationMetrics_precision' - A measure of the usefulness of the recognizer results in the test data.
-- High precision means that the recognizer returned substantially more
-- relevant results than irrelevant ones.
--
-- 'recall', 'entityRecognizerEvaluationMetrics_recall' - A measure of how complete the recognizer results are for the test data.
-- High recall means that the recognizer returned most of the relevant
-- results.
newEntityRecognizerEvaluationMetrics ::
  EntityRecognizerEvaluationMetrics
newEntityRecognizerEvaluationMetrics :: EntityRecognizerEvaluationMetrics
newEntityRecognizerEvaluationMetrics =
  EntityRecognizerEvaluationMetrics'
    { $sel:f1Score:EntityRecognizerEvaluationMetrics' :: Maybe Double
f1Score =
        forall a. Maybe a
Prelude.Nothing,
      $sel:precision:EntityRecognizerEvaluationMetrics' :: Maybe Double
precision = forall a. Maybe a
Prelude.Nothing,
      $sel:recall:EntityRecognizerEvaluationMetrics' :: Maybe Double
recall = forall a. Maybe a
Prelude.Nothing
    }

-- | A measure of how accurate the recognizer results are for the test data.
-- It is derived from the @Precision@ and @Recall@ values. The @F1Score@ is
-- the harmonic average of the two scores. For plain text entity recognizer
-- models, the range is 0 to 100, where 100 is the best score. For
-- PDF\/Word entity recognizer models, the range is 0 to 1, where 1 is the
-- best score.
entityRecognizerEvaluationMetrics_f1Score :: Lens.Lens' EntityRecognizerEvaluationMetrics (Prelude.Maybe Prelude.Double)
entityRecognizerEvaluationMetrics_f1Score :: Lens' EntityRecognizerEvaluationMetrics (Maybe Double)
entityRecognizerEvaluationMetrics_f1Score = forall s a b t. (s -> a) -> (s -> b -> t) -> Lens s t a b
Lens.lens (\EntityRecognizerEvaluationMetrics' {Maybe Double
f1Score :: Maybe Double
$sel:f1Score:EntityRecognizerEvaluationMetrics' :: EntityRecognizerEvaluationMetrics -> Maybe Double
f1Score} -> Maybe Double
f1Score) (\s :: EntityRecognizerEvaluationMetrics
s@EntityRecognizerEvaluationMetrics' {} Maybe Double
a -> EntityRecognizerEvaluationMetrics
s {$sel:f1Score:EntityRecognizerEvaluationMetrics' :: Maybe Double
f1Score = Maybe Double
a} :: EntityRecognizerEvaluationMetrics)

-- | A measure of the usefulness of the recognizer results in the test data.
-- High precision means that the recognizer returned substantially more
-- relevant results than irrelevant ones.
entityRecognizerEvaluationMetrics_precision :: Lens.Lens' EntityRecognizerEvaluationMetrics (Prelude.Maybe Prelude.Double)
entityRecognizerEvaluationMetrics_precision :: Lens' EntityRecognizerEvaluationMetrics (Maybe Double)
entityRecognizerEvaluationMetrics_precision = forall s a b t. (s -> a) -> (s -> b -> t) -> Lens s t a b
Lens.lens (\EntityRecognizerEvaluationMetrics' {Maybe Double
precision :: Maybe Double
$sel:precision:EntityRecognizerEvaluationMetrics' :: EntityRecognizerEvaluationMetrics -> Maybe Double
precision} -> Maybe Double
precision) (\s :: EntityRecognizerEvaluationMetrics
s@EntityRecognizerEvaluationMetrics' {} Maybe Double
a -> EntityRecognizerEvaluationMetrics
s {$sel:precision:EntityRecognizerEvaluationMetrics' :: Maybe Double
precision = Maybe Double
a} :: EntityRecognizerEvaluationMetrics)

-- | A measure of how complete the recognizer results are for the test data.
-- High recall means that the recognizer returned most of the relevant
-- results.
entityRecognizerEvaluationMetrics_recall :: Lens.Lens' EntityRecognizerEvaluationMetrics (Prelude.Maybe Prelude.Double)
entityRecognizerEvaluationMetrics_recall :: Lens' EntityRecognizerEvaluationMetrics (Maybe Double)
entityRecognizerEvaluationMetrics_recall = forall s a b t. (s -> a) -> (s -> b -> t) -> Lens s t a b
Lens.lens (\EntityRecognizerEvaluationMetrics' {Maybe Double
recall :: Maybe Double
$sel:recall:EntityRecognizerEvaluationMetrics' :: EntityRecognizerEvaluationMetrics -> Maybe Double
recall} -> Maybe Double
recall) (\s :: EntityRecognizerEvaluationMetrics
s@EntityRecognizerEvaluationMetrics' {} Maybe Double
a -> EntityRecognizerEvaluationMetrics
s {$sel:recall:EntityRecognizerEvaluationMetrics' :: Maybe Double
recall = Maybe Double
a} :: EntityRecognizerEvaluationMetrics)

instance
  Data.FromJSON
    EntityRecognizerEvaluationMetrics
  where
  parseJSON :: Value -> Parser EntityRecognizerEvaluationMetrics
parseJSON =
    forall a. String -> (Object -> Parser a) -> Value -> Parser a
Data.withObject
      String
"EntityRecognizerEvaluationMetrics"
      ( \Object
x ->
          Maybe Double
-> Maybe Double
-> Maybe Double
-> EntityRecognizerEvaluationMetrics
EntityRecognizerEvaluationMetrics'
            forall (f :: * -> *) a b. Functor f => (a -> b) -> f a -> f b
Prelude.<$> (Object
x forall a. FromJSON a => Object -> Key -> Parser (Maybe a)
Data..:? Key
"F1Score")
            forall (f :: * -> *) a b. Applicative f => f (a -> b) -> f a -> f b
Prelude.<*> (Object
x forall a. FromJSON a => Object -> Key -> Parser (Maybe a)
Data..:? Key
"Precision")
            forall (f :: * -> *) a b. Applicative f => f (a -> b) -> f a -> f b
Prelude.<*> (Object
x forall a. FromJSON a => Object -> Key -> Parser (Maybe a)
Data..:? Key
"Recall")
      )

instance
  Prelude.Hashable
    EntityRecognizerEvaluationMetrics
  where
  hashWithSalt :: Int -> EntityRecognizerEvaluationMetrics -> Int
hashWithSalt
    Int
_salt
    EntityRecognizerEvaluationMetrics' {Maybe Double
recall :: Maybe Double
precision :: Maybe Double
f1Score :: Maybe Double
$sel:recall:EntityRecognizerEvaluationMetrics' :: EntityRecognizerEvaluationMetrics -> Maybe Double
$sel:precision:EntityRecognizerEvaluationMetrics' :: EntityRecognizerEvaluationMetrics -> Maybe Double
$sel:f1Score:EntityRecognizerEvaluationMetrics' :: EntityRecognizerEvaluationMetrics -> Maybe Double
..} =
      Int
_salt
        forall a. Hashable a => Int -> a -> Int
`Prelude.hashWithSalt` Maybe Double
f1Score
        forall a. Hashable a => Int -> a -> Int
`Prelude.hashWithSalt` Maybe Double
precision
        forall a. Hashable a => Int -> a -> Int
`Prelude.hashWithSalt` Maybe Double
recall

instance
  Prelude.NFData
    EntityRecognizerEvaluationMetrics
  where
  rnf :: EntityRecognizerEvaluationMetrics -> ()
rnf EntityRecognizerEvaluationMetrics' {Maybe Double
recall :: Maybe Double
precision :: Maybe Double
f1Score :: Maybe Double
$sel:recall:EntityRecognizerEvaluationMetrics' :: EntityRecognizerEvaluationMetrics -> Maybe Double
$sel:precision:EntityRecognizerEvaluationMetrics' :: EntityRecognizerEvaluationMetrics -> Maybe Double
$sel:f1Score:EntityRecognizerEvaluationMetrics' :: EntityRecognizerEvaluationMetrics -> Maybe Double
..} =
    forall a. NFData a => a -> ()
Prelude.rnf Maybe Double
f1Score
      seq :: forall a b. a -> b -> b
`Prelude.seq` forall a. NFData a => a -> ()
Prelude.rnf Maybe Double
precision
      seq :: forall a b. a -> b -> b
`Prelude.seq` forall a. NFData a => a -> ()
Prelude.rnf Maybe Double
recall