From 862a9da9b6b8dd5523b829278eb57648cd060e34 Mon Sep 17 00:00:00 2001 From: Adrian RC Date: Wed, 18 Dec 2024 13:39:43 -0800 Subject: [PATCH] Removes reference to Fairness Indicators plugin (#6966) There's a separate repo for this at https://github.com/tensorflow/fairness-indicators, and this is likely outdated. We'd like that other site to be the SoT for this plugin. Googlers see also b/384830200 for context. --- docs/_book.yaml | 2 - docs/fairness-indicators.md | 113 ---------------------------- docs/images/fairness-indicators.png | Bin 320296 -> 0 bytes 3 files changed, 115 deletions(-) delete mode 100644 docs/fairness-indicators.md delete mode 100644 docs/images/fairness-indicators.png diff --git a/docs/_book.yaml b/docs/_book.yaml index 01746e7e70..1b75f50b7a 100644 --- a/docs/_book.yaml +++ b/docs/_book.yaml @@ -29,8 +29,6 @@ upper_tabs: path: /tensorboard/tensorboard_projector_plugin - title: "What-If tool" path: /tensorboard/what_if_tool - - title: "Fairness Indicators" - path: /tensorboard/fairness-indicators - title: "Profiling tool" path: /tensorboard/tensorboard_profiling_keras - title: "TensorBoard in notebooks" diff --git a/docs/fairness-indicators.md b/docs/fairness-indicators.md deleted file mode 100644 index 53d504c22a..0000000000 --- a/docs/fairness-indicators.md +++ /dev/null @@ -1,113 +0,0 @@ -# Evaluating Models with the Fairness Indicators Dashboard [Beta] - -![Fairness Indicators](./images/fairness-indicators.png) - -Fairness Indicators for TensorBoard enables easy computation of -commonly-identified fairness metrics for _binary_ and _multiclass_ classifiers. -With the plugin, you can visualize fairness evaluations for your runs and easily -compare performance across groups. - -In particular, Fairness Indicators for TensorBoard allows you to evaluate and -visualize model performance, sliced across defined groups of users. Feel -confident about your results with confidence intervals and evaluations at -multiple thresholds. - -Many existing tools for evaluating fairness concerns don’t work well on large -scale datasets and models. At Google, it is important for us to have tools that -can work on billion-user systems. Fairness Indicators will allow you to evaluate -across any size of use case, in the TensorBoard environment or in -[Colab](https://github.com/tensorflow/fairness-indicators). - -## Requirements - -To install Fairness Indicators for TensorBoard, run: - -``` -python3 -m virtualenv ~/tensorboard_demo -source ~/tensorboard_demo/bin/activate -pip install --upgrade pip -pip install fairness_indicators -pip install tensorboard-plugin-fairness-indicators -``` - -## Demo - -If you want to test out Fairness Indicators in TensorBoard, you can download -sample TensorFlow Model Analysis evaluation results (eval_config.json, metrics -and plots files) and a `demo.py` utility from Google Cloud Platform, -[here](https://console.cloud.google.com/storage/browser/tensorboard_plugin_fairness_indicators/) -using following command. -``` -pip install gsutil -gsutil cp -r gs://tensorboard_plugin_fairness_indicators/ . -``` - -Navigate to directory containing downloaded files. -``` -cd tensorboard_plugin_fairness_indicators -``` - -This evaluation data is based on the -[Civil Comments dataset](https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification), -calculated using Tensorflow Model Analysis's -[model_eval_lib](https://github.com/tensorflow/model-analysis/blob/master/tensorflow_model_analysis/api/model_eval_lib.py) -library. It also contains a sample TensorBoard summary data file for reference. - - -The `demo.py` utility writes a TensorBoard summary data file, which will be read -by TensorBoard to render the Fairness Indicators dashboard (See the -[TensorBoard tutorial](https://github.com/tensorflow/tensorboard/blob/master/README.md) -for more information on summary data files). - -Flags to be used with the `demo.py` utility: - -- `--logdir`: Directory where TensorBoard will write the summary -- `--eval_result_output_dir`: Directory containing evaluation results - evaluated by TFMA (downloaded in last step) - -Run the `demo.py` utility to write the summary results in the log directory: - -`python demo.py --logdir=. --eval_result_output_dir=.` - -Run TensorBoard: - -Note: For this demo, please run TensorBoard from the same directory containing -all the downloaded files. - -`tensorboard --logdir=.` - -This will start a local instance. After the local instance is started, a link -will be displayed to the terminal. Open the link in your browser to view the -Fairness Indicators dashboard. - -### Demo Colab - -[Fairness_Indicators_TensorBoard_Plugin_Example_Colab.ipynb](https://github.com/tensorflow/fairness-indicators/blob/master/g3doc/tutorials/Fairness_Indicators_TensorBoard_Plugin_Example_Colab.ipynb) -contains an end-to-end demo to train and evaluate a model and visualize fairness evaluation -results in TensorBoard. - -## Usage - -To use the Fairness Indicators with your own data and evaluations: - -1. Train a new model and evaluate using - `tensorflow_model_analysis.run_model_analysis` or - `tensorflow_model_analysis.ExtractEvaluateAndWriteResult` API in - [model_eval_lib](https://github.com/tensorflow/model-analysis/blob/master/tensorflow_model_analysis/api/model_eval_lib.py). - For code snippets on how to do this, see the Fairness Indicators colab - [here](https://github.com/tensorflow/fairness-indicators). - -2. Write Fairness Indicators Summary using `tensorboard_plugin_fairness_indicators.summary_v2` API. - - ``` - writer = tf.summary.create_file_writer() - with writer.as_default(): - summary_v2.FairnessIndicators(, step=1) - writer.close() - ``` - -3. Run TensorBoard - - - `tensorboard --logdir=` - - Select the new evaluation run using the drop-down on the left side of - the dashboard to visualize results. diff --git a/docs/images/fairness-indicators.png b/docs/images/fairness-indicators.png deleted file mode 100644 index bb9593e736c9768e1333429be95ff28fba7fa90d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 320296 zcmeFYRal%)mpF)PaM#9yL*s$q?(Xgu+?~cPRI8NfabPBnSuy6lp0j6$l8#CkO~wS_HU{k~fDtUI>U! zlGdW4O46dDWJ=Bs7S^`r5D-$4$(rzLsw0Fsx+>CEfe2EF9g!+UvWWTO=-=s=5TwcS z`_s_HPDTrYZ16Cu^UElrDq+7jm;j?q&e6=3wYBk(xzyH+0xEOv*S+3(Tn@$r-+e(N zt1avhhicbFvwCWH0esS(i~LzkFT{852zD4}f6{(R|sF~HBJoD`t?HX=IFuu%ZIY%;fO zY;-cWXe@&oSL7vega@Qh$)#s4BSd{>UXFVAR1n`v6S;dbLO&*i>o7X}l{g<{e*!w% z*jQ#k|1>JMnQvd>E!t#^umUO22EEKh8IebgF?Ii{Rio`#6Kj{o71397PBJ^R~r)eYP$x%~x z2gPh|2-3G5nfo0>^Fp_M?F%fVIn6+~AcW)_AFb9f zh)DmSHZ0fA0AN<7Kg(ngmDUXX>Rm-BUPm%f^%4%UU?xpN!z;=F#3$?zeg3`E&tB_Q 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