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Status](https://travis-ci.org/spotify/noether.svg?branch=master)](https://travis-ci.org/spotify/noether)\n[![codecov.io](https://codecov.io/github/spotify/noether/coverage.svg?branch=master)](https://codecov.io/github/spotify/noether?branch=master)\n[![GitHub license](https://img.shields.io/github/license/spotify/noether.svg)](./LICENSE)\n[![Maven Central](https://img.shields.io/maven-central/v/com.spotify/noether-core_2.12.svg)](https://maven-badges.herokuapp.com/maven-central/com.spotify/noether-core_2.12)\n[![Scaladoc](https://img.shields.io/badge/scaladoc-latest-blue.svg)](https://spotify.github.io/noether/latest/api/com/spotify/noether/index.html)\n[![Scala Steward badge](https://img.shields.io/badge/Scala_Steward-helping-brightgreen.svg?style=flat\u0026logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAA4AAAAQCAMAAAARSr4IAAAAVFBMVEUAAACHjojlOy5NWlrKzcYRKjGFjIbp293YycuLa3pYY2LSqql4f3pCUFTgSjNodYRmcXUsPD/NTTbjRS+2jomhgnzNc223cGvZS0HaSD0XLjbaSjElhIr+AAAAAXRSTlMAQObYZgAAAHlJREFUCNdNyosOwyAIhWHAQS1Vt7a77/3fcxxdmv0xwmckutAR1nkm4ggbyEcg/wWmlGLDAA3oL50xi6fk5ffZ3E2E3QfZDCcCN2YtbEWZt+Drc6u6rlqv7Uk0LdKqqr5rk2UCRXOk0vmQKGfc94nOJyQjouF9H/wCc9gECEYfONoAAAAASUVORK5CYII=)](https://scala-steward.org)\n\n\u003e [Emmy Noether](https://en.wikipedia.org/wiki/Emmy_Noether) was a German mathematician known for her landmark contributions to abstract algebra and theoretical physics. \n\nNoether is a collection of Machine Learning tools targeted at the JVM and Scala.\nIt relies heavily on the [Algebird](https://github.com/twitter/algebird) library especially for Aggregators.\n\n# Aggregators\n\nAggregators enable creation of reusable and composable aggregation functions. Most Machine Learning loss functions and metrics can be \ndecomposed into a single aggregator.  This becomes useful when a model produces a set of predictions and one or more metrics are needed\nto be computed on this collection.\n\nBelow is an example for a binary classification task. Algebird's MultiAggregator can be used to combine multiple metrics into a \nsingle callable aggregator.\n\n```scala\nval multiAggregator =\n  MultiAggregator(AUC(ROC), AUC(PR), ClassificationReport(), BinaryConfusionMatrix())\n    .andThenPresent{case (roc, pr, report, cm) =\u003e\n      (roc, pr, report.accuracy, report.recall, report.precision, cm(1, 1), cm(0, 0))\n    }\n\nval predictions = List(Prediction(false, 0.1), Prediction(false, 0.6), Prediction(true, 0.9))\n\nprintln(multiAggregator(predictions))\n```\n\n## Prediction Object\n\nMost aggregators take a single parameterized class called Prediction as input to the aggregator. However the type of\nthe prediction object differ based on the aggregator. In the above example each binary classifier takes a prediction \nof type `Prediction[Boolean, Double]` where the first type is the label and the second in the predicted score.\n\nOther aggregators will takes slightly different types such as the Error Rate Aggregator which expects `Prediction[Int, List[Double]]`\nwhere the types are label and a list of scores.\n\n## Available Aggregators\n\nSee the docs on each aggregator for a more detailed walk-through on the functionality and the return objects.\n\n1. ConfusionMatrix\n    1.  Includes a special BinaryConfusionMatrix case to make composition easier with the other binary classification metrics.\n2. AUC\n    1. Supports both ROC and PR\n3. ClassificationReport\n    1. Returns a list of summary metrics for a binary classification problem.\n4. LogLoss\n    1. Available for multiclass. Returns the total log loss for the predictions.\n5. ErrorRateSummary\n    1. Available for multiclass. Returns the proportion of misclassified predictions.w      \n\n# Tensorflow Model Analysis Support \n\nNoether supports outputting metrics as TFX `metrics_for_slice` protobufs, which can be used in \nTFMA methods. This is available in the `noether-tfx` package:\n\n```scala\nlibraryDependencies += \"com.spotify\" %% \"noether-tfx\" % noetherVersion\n```\n\n```scala\nimport com.spotify.noether.tfx._\n\nval data = List(\n  (0, 0),\n  (0, 1),\n  (0, 0),\n  (1, 0),\n  (1, 1),\n  (1, 1),\n  (1, 1)\n).map { case (s, pred) =\u003e Prediction(pred, s) }\n\nval tfmaProto = ConfusionMatrix(Seq(0, 1)).asTfmaProto(data)\n```\n\n# License\n\nCopyright 2016-2018 Spotify AB.\n\nLicensed under the Apache License, Version 2.0: http://www.apache.org/licenses/LICENSE-2.0\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspotify%2Fnoether","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fspotify%2Fnoether","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspotify%2Fnoether/lists"}