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GitHub - PAIR-code/what-if-tool: Source code/webpage/demos for the What-If Tool
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dir=\"auto\"\u003eWhat-If Tool\u003c/h1\u003e\u003ca id=\"user-content-what-if-tool\" class=\"anchor\" aria-label=\"Permalink: What-If Tool\" href=\"#what-if-tool\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"/PAIR-code/what-if-tool/blob/master/img/wit-smile-intro.png\"\u003e\u003cimg src=\"/PAIR-code/what-if-tool/raw/master/img/wit-smile-intro.png\" alt=\"What-If Tool Screenshot\" title=\"What-If Tool Screenshot\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe \u003ca href=\"https://pair-code.github.io/what-if-tool\" rel=\"nofollow\"\u003eWhat-If Tool\u003c/a\u003e (WIT) provides an easy-to-use interface for expanding\nunderstanding of a black-box classification or regression ML model.\nWith the plugin, you can perform inference on a large set of examples and\nimmediately visualize the results in a variety of ways.\nAdditionally, examples can be edited manually or programmatically and re-run\nthrough the model in order to see the results of the changes.\nIt contains tooling for investigating model performance and fairness over\nsubsets of a dataset.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe purpose of the tool is that give people a simple, intuitive, and powerful\nway to play with a trained ML model on a set of data through a visual interface\nwith absolutely no code required.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe tool can be accessed through TensorBoard or as an extension in a Jupyter\nor\n\u003ca href=\"https://colab.research.google.com/github/pair-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb\" rel=\"nofollow\"\u003eColab\u003c/a\u003e\nnotebook.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eI don’t want to read this document. Can I just play with a demo?\u003c/h2\u003e\u003ca id=\"user-content-i-dont-want-to-read-this-document-can-i-just-play-with-a-demo\" class=\"anchor\" aria-label=\"Permalink: I don’t want to read this document. Can I just play with a demo?\" href=\"#i-dont-want-to-read-this-document-can-i-just-play-with-a-demo\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCheck out the large set of web and colab demos in the\n\u003ca href=\"https://pair-code.github.io/what-if-tool/index.html#demos\" rel=\"nofollow\"\u003edemo section of the What-If Tool website\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo build the web demos yourself:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://pair-code.github.io/what-if-tool/uci.html\" rel=\"nofollow\"\u003eBinary classifier for UCI Census dataset salary prediction\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDataset: \u003ca href=\"https://archive.ics.uci.edu/ml/datasets/census+income\" rel=\"nofollow\"\u003eUCI Census\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eTask: Predict whether a person earns more or less than $50k based on their\ncensus information\u003c/li\u003e\n\u003cli\u003eTo build and run the demo from code:\n\u003ccode\u003ebazel run wit_dashboard/demo:demoserver\u003c/code\u003e\nthen navigate to \u003ccode\u003ehttp://localhost:6006/wit-dashboard/demo.html\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pair-code.github.io/what-if-tool/image.html\" rel=\"nofollow\"\u003eBinary classifier for smile detection in images\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDataset: \u003ca href=\"http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html\" rel=\"nofollow\"\u003eCelebA\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eTask: Predict whether the person in an image is smiling\u003c/li\u003e\n\u003cli\u003eTo build and run the demo from code:\n\u003ccode\u003ebazel run wit_dashboard/demo:imagedemoserver\u003c/code\u003e\nthen navigate to \u003ccode\u003ehttp://localhost:6006/wit-dashboard/image_demo.html\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pair-code.github.io/what-if-tool/iris.html\" rel=\"nofollow\"\u003eMulticlass classifier for Iris dataset\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDataset: \u003ca href=\"https://archive.ics.uci.edu/ml/datasets/iris\" rel=\"nofollow\"\u003eUCI Iris\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eTask: Predict which of three classes of iris flowers that a flower falls\ninto based on 4 measurements of the flower\u003c/li\u003e\n\u003cli\u003eTo build and run the demo from code:\n\u003ccode\u003ebazel run wit_dashboard/demo:irisdemoserver\u003c/code\u003e\nthen navigate to \u003ccode\u003ehttp://localhost:6006/wit-dashboard/iris_demo.html\u003c/code\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://pair-code.github.io/what-if-tool/age.html\" rel=\"nofollow\"\u003eRegression model for UCI Census dataset age prediction\u003c/a\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDataset: \u003ca href=\"https://archive.ics.uci.edu/ml/datasets/census+income\" rel=\"nofollow\"\u003eUCI Census\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eTask: Predict the age of a person based on their census information\u003c/li\u003e\n\u003cli\u003eTo build and run the demo from code:\n\u003ccode\u003ebazel run wit_dashboard/demo:agedemoserver\u003c/code\u003e\nthen navigate to \u003ccode\u003ehttp://localhost:6006/wit-dashboard/age_demo.html\u003c/code\u003e\u003c/li\u003e\n\u003cli\u003eThis demo model returns attribution values in addition to predictions (through the use of vanilla gradients)\nin order to demonstate how the tool can display attribution values from predictions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhat do I need to use it in a jupyter or colab notebook?\u003c/h2\u003e\u003ca id=\"user-content-what-do-i-need-to-use-it-in-a-jupyter-or-colab-notebook\" class=\"anchor\" aria-label=\"Permalink: What do I need to use it in a jupyter or colab notebook?\" href=\"#what-do-i-need-to-use-it-in-a-jupyter-or-colab-notebook\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYou can use the What-If Tool to analyze a classification or regression\n\u003ca href=\"https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator\" rel=\"nofollow\"\u003eTensorFlow Estimator\u003c/a\u003e\nthat takes TensorFlow Example or SequenceExample protos\n(data points) as inputs directly in a jupyter or colab notebook.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAdditionally, the What-If Tool can analyze\n\u003ca href=\"https://cloud.google.com/ml-engine/\" rel=\"nofollow\"\u003eAI Platform Prediction-hosted\u003c/a\u003e classification\nor regresssion models that take TensorFlow Example protos, SequenceExample protos,\nor raw JSON objects as inputs.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eYou can also use What-If Tool with a custom prediction function that takes\nTensorflow examples and produces predictions. In this mode, you can load any model\n(including non-TensorFlow models that don't use Example protos as inputs) as\nlong as your custom function's input and output specifications are correct.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWith either AI Platform models or a custom prediction function, the What-If Tool can\ndisplay and make use of attribution values for each input feature in relation to each\nprediction. See the below section on attribution values for more information.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf you want to train an ML model from a dataset and explore the dataset and\nmodel, check out the \u003ca href=\"https://colab.research.google.com/github/pair-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb\" rel=\"nofollow\"\u003eWhat_If_Tool_Notebook_Usage.ipynb notebook\u003c/a\u003e in colab, which starts from a CSV file,\nconverts the data to tf.Example protos, trains a classifier, and then uses the\nWhat-If Tool to show the classifier performance on the data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhat do I need to use it in TensorBoard?\u003c/h2\u003e\u003ca id=\"user-content-what-do-i-need-to-use-it-in-tensorboard\" class=\"anchor\" aria-label=\"Permalink: What do I need to use it in TensorBoard?\" href=\"#what-do-i-need-to-use-it-in-tensorboard\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA walkthrough of using the tool in TensorBoard, including a pretrained model and\ntest dataset, can be found on the\n\u003ca href=\"https://www.tensorflow.org/tensorboard/r2/what_if_tool\" rel=\"nofollow\"\u003eWhat-If Tool page on the TensorBoard website\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eTo use the tool in TensorBoard, only the following information needs to be provided:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe model server host and port, served using\n\u003ca href=\"https://github.com/tensorflow/serving\"\u003eTensorFlow Serving\u003c/a\u003e. The model can\nuse the TensorFlow Serving Classification, Regression, or Predict API.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eInformation on how to create a saved model with the \u003ccode\u003eEstimator\u003c/code\u003e API that\nwill use thse appropriate TensorFlow Serving Classification or Regression\nAPIs can be found in the \u003ca href=\"https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators\" rel=\"nofollow\"\u003esaved model documentation\u003c/a\u003e\nand in this \u003ca href=\"http://shzhangji.com/blog/2018/05/14/serve-tensorflow-estimator-with-savedmodel/\" rel=\"nofollow\"\u003ehelpful tutorial\u003c/a\u003e.\nModels that use these APIs are the simplest to use with the What-If Tool\nas they require no set-up in the tool beyond setting the model type.\u003c/li\u003e\n\u003cli\u003eIf the model uses the Predict API, the input must be serialized tf.Example\nor tf.SequenceExample protos and the output must be following:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFor classification models, the output must include a 2D float tensor\ncontaining a list of class probabilities for all possible class\nindices for each inferred example.\u003c/li\u003e\n\u003cli\u003eFor regression models, the output must include a float tensor\ncontaining a single regression score for each inferred example.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eThe What-If Tool queries the served model using the gRPC API, not the\nRESTful API. See the TensorFlow Serving\n\u003ca href=\"https://www.tensorflow.org/serving/docker\" rel=\"nofollow\"\u003edocker documentation\u003c/a\u003e for\nmore information on the two APIs. The docker image uses port 8500 for the\ngRPC API, so if using the docker approach, the port to specify in the\nWhat-If Tool will be 8500.\u003c/li\u003e\n\u003cli\u003eAlternatively, instead of querying a model hosted by TensorFlow Serving,\nyou can provide a python function for model prediction to the tool through\nthe \"--whatif-use-unsafe-custom-prediction\" runtime argument as\ndescribed in more detail below.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eA TFRecord file of tf.Examples or tf.SequenceExamples to perform inference on\nand the number of examples to load from the file.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eCan handle up to tens of thousands of examples. The exact amount depends\non the size of each example (how many features there are and how large the\nfeature values are).\u003c/li\u003e\n\u003cli\u003eThe file must be in the logdir provided to TensorBoard on startup.\nAlternatively, you can provide another directory to allow file loading\nfrom, through use of the -whatif-data-dir=PATH runtime parameter.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eAn indication if the model is a regression, binary classification or\nmulti-class classification model.\u003c/li\u003e\n\u003cli\u003eAn optional vocab file for the labels for a classification model. This file\nmaps the predicted class indices returned from the model prediction into class\nlabels. The text file contains one label per line, corresponding to the class\nindices returned by the model, starting with index 0.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIf this file is provided, then the dashboard will show the predicted\nlabels for a classification model. If not, it will show the predicted\nclass indices.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eAlternatively, the What-If Tool can be used to explore a dataset directly from\na CSV file. See the next section for details.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe information can be provided in the settings dialog screen, which pops up\nautomatically upon opening this tool and is accessible through the settings\nicon button in the top-right of the tool.\nThe information can also be provided directly through URL parameters.\nChanging the settings through the controls automatically updates the URL so that\nit can be shared with others for them to view the same data in the What-If Tool.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAll I have is a dataset. What can I do in TensorBoard? Where do I start?\u003c/h3\u003e\u003ca id=\"user-content-all-i-have-is-a-dataset-what-can-i-do-in-tensorboard-where-do-i-start\" class=\"anchor\" aria-label=\"Permalink: All I have is a dataset. What can I do in TensorBoard? Where do I start?\" href=\"#all-i-have-is-a-dataset-what-can-i-do-in-tensorboard-where-do-i-start\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eIf you just want to explore the information in a CSV file using the What-If Tool\nin TensorBoard, just set the path to the examples to the file (with a \".csv\"\nextension) and leave the inference address and model name fields blank.\nThe first line of the CSV file must contain column names. Each line after that\ncontains one example from the dataset, with values for each of the columns\ndefined on the first line. The pipe character (\"|\") deliminates separate feature\nvalues in a list of feature values for a given feature.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn order to make use of the model understanding features of the tool, you can\nhave columns in your dataset that contain the output from an ML model. If your\nfile has a column named \"predictions__probabilities\" with a pipe-delimited (\"|\") list of\nprobability scores (between 0 and 1), then the tool will treat those as the\noutput scores of a classification model. If your file has a numeric column named\n\"predictions\" then the tool will treat those as the output of a regression model. In\nthis way, the tool can be used to analyze any dataset and the results of any\nmodel run offline against the dataset. Note that in this mode, the examples\naren't editable as there is no way to get new inference results when an example\nchanges.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhat can it do?\u003c/h2\u003e\u003ca id=\"user-content-what-can-it-do\" class=\"anchor\" aria-label=\"Permalink: What can it do?\" href=\"#what-can-it-do\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDetails on the capabilities of the tool, including a guided walkthrough, can be\nfound on the \u003ca href=\"https://pair-code.github.io/what-if-tool\" rel=\"nofollow\"\u003eWhat-If Tool website\u003c/a\u003e.\nHere is a basic rundown of what it can do:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eVisualize a dataset of TensorFlow Example protos.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe main panel shows the dataset using \u003ca href=\"https://pair-code.github.io/facets\" rel=\"nofollow\"\u003eFacets Dive\u003c/a\u003e,\nwhere the examples can be organized/sliced/positioned/colored by any of the\ndataset’s features.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eThe examples can also be organized by the results of their inferences.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFor classification models, this includes inferred label, confidence of\ninferred label, and inference correctness.\u003c/li\u003e\n\u003cli\u003eFor regression models, this includes inferred score and amount of error\n(including absolute or squared error) in the inference.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eA selected example can be viewed in detail in the side panel, showing all\nfeature values for all features of that example.\u003c/li\u003e\n\u003cli\u003eFor examples that contain an encoded image in a bytes feature named\n\"image/encoded\", Facets Dive will create a thumbnail of the image to display\nthe point, and the full-size image is visible in the side panel for a\nselected example.\u003c/li\u003e\n\u003cli\u003eAggregate statistics for all loaded examples can be viewed in the side panel\nusing \u003ca href=\"https://pair-code.github.io/facets/\" rel=\"nofollow\"\u003eFacets Overview\u003c/a\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eVisualize the results of the inference\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eBy default, examples in the main panel are colored by their inference\nresults.\u003c/li\u003e\n\u003cli\u003eThe examples in the main panel can be organized into confusion matrices and\nother custom layouts to show the inference results faceted by a number of\ndifferent features, or faceted/positioned by inference result, allowing the\ncreation of small multiples of 1D and 2D histograms and scatter plots.\u003c/li\u003e\n\u003cli\u003eFor a selected example, detailed inference results (e.x. predicted classes\nand their confidence scores) are shown in the side panel.\u003c/li\u003e\n\u003cli\u003eIf the model returns attribution values in addition to predictions, they\nare displayed for each selected example, and the attribution values can be\nused to control custom layouts and as dimensions to slice the dataset on\nfor performance analysis.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eExplore counterfactual examples\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFor classification models, for any selected example, with one click you can\ncompare the example to the example most-similar to it but which is\nclassified as a different.\u003c/li\u003e\n\u003cli\u003eSimilarity is calculated based on the distribution of feature values across\nall loaded examples and similarity can be calculated using either L1 or L2\ndistance.\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eDistance is normalized between features by:\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eFor numeric features, use the distance between values divided by the\nstandard deviation of the values across all examples.\u003c/li\u003e\n\u003cli\u003eFor categorical features, the distance is 0 if the values are the same,\notherwise the distance is the probability that any two examples have\nthe same value for that feature across all examples.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eIn notebook mode, the tool also allows you to set a custom distance function\nusing set_custom_distance_fn in WitConfigBuilder, where that function is\nused to compute closest counterfactuals instead. As in the case with\ncustom_predict_fn, the custom distance function can be any python function.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eEdit a selected example in the browser and re-run inference and visualize the\ndifference in the inference results.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSee auto-generated partial dependence plots, which are plots that for every\nfeature show the change in inference results as that feature has its value\nchanged to different valid values for that feature.\u003c/li\u003e\n\u003cli\u003eEdit/add/remove any feature or feature value in the side panel and re-run\ninference on the edited datapoint. A history of the inference values of that\npoint as it is edited and re-inferred is shown.\u003c/li\u003e\n\u003cli\u003eFor examples that contain encoded images, upload your own image and re-run\ninference.\u003c/li\u003e\n\u003cli\u003eClone an existing example for editing/comparison.\u003c/li\u003e\n\u003cli\u003eRevert edits to an edited example.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eCompare the results of two models on the same input data.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eIf you provide two models to the tool during setup, it will run inference\nwith the provided data on both models and you can compare the results\nbetween the two models using all the features defined above.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIf using a binary classification model and your examples include a feature\nthat describes the true label, you can do the following:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSee the ROC curve and numeric confusion matrices in the side panel,\nincluding the point on the curve where your model lives, given the current\npositive classification threshold value.\u003c/li\u003e\n\u003cli\u003eSee separate ROC curves and numeric confusion matrices split out for subsets\nof the dataset, sliced of any feature or features of your dataset (i.e. by\ngender).\u003c/li\u003e\n\u003cli\u003eManually adjust the positive classification threshold (or thresholds, if\nslicing the dataset by a feature) and see the difference in inference\nresults, ROC curve position and confusion matrices immediately.\u003c/li\u003e\n\u003cli\u003eSet the positive classification thresholds with one click based on concepts\nsuch as the cost of a false positive vs a false negative and satisfying\nfairness measures such as equality of opportunity or demographic parity.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIf using a multi-class classification model and your examples include a\nfeature that describes the true label, you can do the following:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSee a confusion matrix in the side panel for all classifications and all\nclasses.\u003c/li\u003e\n\u003cli\u003eSee separate confusion matrices split out for subsets of the dataset, sliced\non any feature or features of your dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp dir=\"auto\"\u003eIf using a regression model and your examples include a feature that describes\nthe true label, you can do the following:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eSee the mean error, mean absolute error and mean squared error across the\ndataset.\u003c/li\u003e\n\u003cli\u003eSee separate calculations of those mean error calculations split out for\nsubsets of the dataset, sliced of any feature or features of your dataset.\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWho is it for?\u003c/h2\u003e\u003ca id=\"user-content-who-is-it-for\" class=\"anchor\" aria-label=\"Permalink: Who is it for?\" href=\"#who-is-it-for\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWe imagine WIT to be useful for a wide variety of users.\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eML researchers and model developers - Investigate your datasets and models and\nexplore inference results. Poke at the data and model to gain insights, for\ntasks such as debugging strange results and looking into ML fairness.\u003c/li\u003e\n\u003cli\u003eNon-technical stakeholders - Gain an understanding of the performance of a\nmodel on a dataset. Try it out with your own data.\u003c/li\u003e\n\u003cli\u003eLay users - Learn about machine learning by interactively playing with\ndatasets and models.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNotebook mode details\u003c/h2\u003e\u003ca id=\"user-content-notebook-mode-details\" class=\"anchor\" aria-label=\"Permalink: Notebook mode details\" href=\"#notebook-mode-details\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAs seen in the \u003ca href=\"https://colab.research.google.com/github/pair-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb\" rel=\"nofollow\"\u003eexample notebook\u003c/a\u003e,\ncreating the \u003ccode\u003eWitWidget\u003c/code\u003e object is what causes the What-If Tool to be displayed\nin an output cell. The \u003ccode\u003eWitWidget\u003c/code\u003e object takes a \u003ccode\u003eWitConfigBuilder\u003c/code\u003e object as a\nconstructor argument. The \u003ccode\u003eWitConfigBuilder\u003c/code\u003e object specifies the data and model\ninformation that the What-If Tool will use.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe WitConfigBuilder object takes a list of tf.Example or tf.SequenceExample\nprotos as a constructor argument. These protos will be shown in the tool and\ninferred in the specified model.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThe model to be used for inference by the tool can be specified in many ways:\u003c/p\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eAs a TensorFlow \u003ca href=\"https://www.tensorflow.org/guide/estimators\" rel=\"nofollow\"\u003eEstimator\u003c/a\u003e\nobject that is provided through the \u003ccode\u003eset_estimator_and_feature_spec\u003c/code\u003e method.\nIn this case the inference will be done inside the notebook using the\nprovided estimator.\u003c/li\u003e\n\u003cli\u003eAs a model hosted by \u003ca href=\"https://cloud.google.com/ml-engine/\" rel=\"nofollow\"\u003eAI Platform Prediction\u003c/a\u003e\nthrough the \u003ccode\u003eset_ai_platform_model\u003c/code\u003e method.\u003c/li\u003e\n\u003cli\u003eAs a custom prediction function provided through \u003ccode\u003eset_custom_predict_fn\u003c/code\u003e method.\nIn this case WIT will directly call the function for inference.\u003c/li\u003e\n\u003cli\u003eAs an endpoint for a model being served by \u003ca href=\"https://github.com/tensorflow/serving\"\u003eTensorFlow Serving\u003c/a\u003e,\nthrough the \u003ccode\u003eset_inference_address\u003c/code\u003e and \u003ccode\u003eset_model_name\u003c/code\u003e methods. In this case\nthe inference will be done on the model server specified. To query a model served\non host \"localhost\" on port 8888, named \"my_model\", you would set on your\nbuilder\n\u003ccode\u003ebuilder.set_inference_address('localhost:8888').set_model_name('my_model')\u003c/code\u003e.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"auto\"\u003eSee the documentation of \u003ca href=\"https://github.com/pair-code/what-if-tool/blob/master/witwidget/notebook/visualization.py\"\u003eWitConfigBuilder\u003c/a\u003e\nfor all options you can provide, including how to specify other model types\n(defaults to binary classification) and how to specify an optional second model\nto compare to the first model.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow can the What-If Tool use attribution values and other prediction-time information?\u003c/h3\u003e\u003ca id=\"user-content-how-can-the-what-if-tool-use-attribution-values-and-other-prediction-time-information\" class=\"anchor\" aria-label=\"Permalink: How can the What-If Tool use attribution values and other prediction-time information?\" href=\"#how-can-the-what-if-tool-use-attribution-values-and-other-prediction-time-information\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFeature attribution values are numeric values for each input feature to an ML model that\nindicate how much impact that feature value had on the model's prediction.\nThere are a variety of approaches to get feature attribution values for a predicts from an ML model,\nincluding \u003ca href=\"https://github.com/slundberg/shap\"\u003eSHAP\u003c/a\u003e,\n\u003ca href=\"https://github.com/ankurtaly/Integrated-Gradients\"\u003eIntegrated Gradients\u003c/a\u003e,\n\u003ca href=\"https://pair-code.github.io/saliency/\" rel=\"nofollow\"\u003eSmoothGrad\u003c/a\u003e, and more.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThey can be a powerful way of analyzing how a model reacts to certain input values beyond\njust simply studying the effect that changing individual feature values has on model\npredictions as is done with partial dependence plots.\nSome attribution techniques require access to a model internals, such as the gradient-based methods,\nwhereas others can be performed on black-box models. Regardless, the What-If Tool can visualize the\nresults of attribution methods in addition to the standard model prediction results.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThere are two ways to use the What-If Tool to visualize attribution values. If you have deployed a\nmodel to Cloud AI Platform with the explainability feature enabled, and provide this model to\nthe tool through the standard \u003ccode\u003eset_ai_platform_model\u003c/code\u003e method, then attribution values will\nautomatically be generated and visualized by the tool with no additional setup needed.\nIf you wish to view attribution values for a different model setup, this can be accomplished through\nuse of the custom prediction function.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAs described in the \u003ccode\u003eset_custom_predict_fn\u003c/code\u003e documentation in\n\u003ca href=\"https://github.com/pair-code/what-if-tool/blob/master/witwidget/notebook/visualization.py\"\u003eWitConfigBuilder\u003c/a\u003e, this method must return a list of the same size as the number of examples\nprovided to it, with each list entry representing the prediction-time information for that example.\nIn the case of a standard model with no attribution information, the list entry is just a number\n(in the case of a regression model), or a list of class probabilities (in the case of a classification model).\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHowever, if there is attribution or other prediction-time information, then the list entry\ncan instead be a dictionary, with the standard model prediction output under the \u003ccode\u003epredictions\u003c/code\u003e key. Attribution\ninformation can be returned under the \u003ccode\u003eattributions\u003c/code\u003e key and any other supplemental information under its own\ndescriptive key. The exact format of the attributions and other supplemental information can be found in the code\ndocumentation linked above.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIf attribution values are provided to the What-If Tool, they can be used in a number of ways. First, when selecting\na datapoint in the Datapoint Editor tab, the attribution values are displayed next to each feature value and the\nfeatures can be ordered by their attribution strength instead of alphabetically. Additionally, the feature values\nare colored by their attribution values for quick interpretation of attribution strengths.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eBeyond displaying the attribution values for the selected datapoint, the attribution values for each feature can be\nused in the tool in the same ways as any other feature of the datapoints. They can be used selected in the\ndatapoints visualization controls to use those values to create custom scatter plots and histograms.\nFor example, you can create a scatterplot showing the relationship between the attribution value of two different features,\nwith the datapoints colored by the predicted result from the model.\nThey can also be used in the Performance tab as a way to slice a dataset for comparing performance statistics of different slices.\nFor example, you can quickly compare the aggregate performance of a model on datapoints with low attribution of a\nspecified feature, against the datapoints with high attribution of that feature.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAny other supplemental information returned from a custom prediction function will appear in the tool as a\nfeature named after its key in the dictionary.\nThey can also be used in the same way, driving custom visualizations and as a dimension to slice when analyzing\naggregate model performance.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eWhen a datapoint is edited and the re-inferred through the model with the \"Run inference\" button, the attributions and\nother supplemental information is recalculated and updated in the tool.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor an example of returning attribution values from a custom prediction function (in this case using the SHAP library to\nget attributions), see the \u003ca href=\"https://colab.research.google.com/github/PAIR-code/what-if-tool/blob/master/WIT_COMPAS_with_SHAP.ipynb\" rel=\"nofollow\"\u003eWIT COMPAS with SHAP notebook\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow do I enable it for use in a Jupyter notebook?\u003c/h3\u003e\u003ca id=\"user-content-how-do-i-enable-it-for-use-in-a-jupyter-notebook\" class=\"anchor\" aria-label=\"Permalink: How do I enable it for use in a Jupyter notebook?\" href=\"#how-do-i-enable-it-for-use-in-a-jupyter-notebook\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFirst, install and enable WIT for Jupyter through the following commands:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-shell notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"pip install witwidget\njupyter nbextension install --py --symlink --sys-prefix witwidget\njupyter nbextension enable --py --sys-prefix witwidget\"\u003e\u003cpre\u003epip install witwidget\njupyter nbextension install --py --symlink --sys-prefix witwidget\njupyter nbextension \u003cspan class=\"pl-c1\"\u003eenable\u003c/span\u003e --py --sys-prefix witwidget\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThen, use it as seen at the bottom of the\n\u003ca href=\"/PAIR-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb\"\u003eWhat_If_Tool_Notebook_Usage.ipynb notebook\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow do I enable it for use in a Colab notebook?\u003c/h3\u003e\u003ca id=\"user-content-how-do-i-enable-it-for-use-in-a-colab-notebook\" class=\"anchor\" aria-label=\"Permalink: How do I enable it for use in a Colab notebook?\" href=\"#how-do-i-enable-it-for-use-in-a-colab-notebook\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eInstall the widget into the runtime of the notebook kernel by running a cell\ncontaining:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"!pip install witwidget\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e!pip install witwidget\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThen, use it as seen at the bottom of the\n\u003ca href=\"https://colab.research.google.com/github/pair-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb\" rel=\"nofollow\"\u003eWhat_If_Tool_Notebook_Usage.ipynb notebook\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow do I enable it for use in a JupyterLab or Cloud AI Platform notebook?\u003c/h3\u003e\u003ca id=\"user-content-how-do-i-enable-it-for-use-in-a-jupyterlab-or-cloud-ai-platform-notebook\" class=\"anchor\" aria-label=\"Permalink: How do I enable it for use in a JupyterLab or Cloud AI Platform notebook?\" href=\"#how-do-i-enable-it-for-use-in-a-jupyterlab-or-cloud-ai-platform-notebook\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eWIT has been tested in JupyterLab versions 1.x, 2.x, and 3.x.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eInstall and enable WIT for JupyterLab 3.x by running a cell containing:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"!pip install witwidget\n!jupyter labextension install wit-widget\n!jupyter labextension install @jupyter-widgets/jupyterlab-manager\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003e!pip install witwidget\n!jupyter labextension install wit-widget\n!jupyter labextension install @jupyter-widgets/jupyterlab-manager\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eNote that you may need to specify the correct version of jupyterlab-manager for\nyou JupyterLab version as per\n\u003ca href=\"https://www.npmjs.com/package/@jupyter-widgets/jupyterlab-manager\" rel=\"nofollow\"\u003ehttps://www.npmjs.com/package/@jupyter-widgets/jupyterlab-manager\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eNote that you may need to run \u003ccode\u003e!sudo jupyter labextension ...\u003c/code\u003e commands depending on your notebook setup.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUse of WIT after installation is the same as with the other notebook installations.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eCan I use a custom prediction function in TensorBoard?\u003c/h2\u003e\u003ca id=\"user-content-can-i-use-a-custom-prediction-function-in-tensorboard\" class=\"anchor\" aria-label=\"Permalink: Can I use a custom prediction function in TensorBoard?\" href=\"#can-i-use-a-custom-prediction-function-in-tensorboard\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eYes. You can do this by defining a python function named \u003ccode\u003ecustom_predict_fn\u003c/code\u003e\nwhich takes two arguments: a list of examples to preform inference on, and the\nserving bundle object which contains information about the model to query.\nThe function should return a list of results, one entry per example provided.\nFor regression models, the result is just a number. For classification models,\nthe result is a list of numbers, representing the class scores for each possible\nclass.\nHere is a minimal example that just returns random results:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-python notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"import random\n\n# The function name \u0026quot;custom_predict_fn\u0026quot; must be exact.\ndef custom_predict_fn(examples, serving_bundle):\n # Examples are a list of TFRecord objects, each object contains the features of each point.\n # serving_bundle is a dictionary that contains the setup information provided to the tool,\n # such as server address, model name, model version, etc.\n\n number_of_examples = len(examples)\n results = []\n for _ in range(number_of_examples):\n score = random.random()\n results.append([score, 1 - score]) # For binary classification\n # results.append(score) # For regression\n return results\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003eimport\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erandom\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e# The function name \"custom_predict_fn\" must be exact.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003edef\u003c/span\u003e \u003cspan class=\"pl-en\"\u003ecustom_predict_fn\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eexamples\u003c/span\u003e, \u003cspan class=\"pl-s1\"\u003eserving_bundle\u003c/span\u003e):\n \u003cspan class=\"pl-c\"\u003e# Examples are a list of TFRecord objects, each object contains the features of each point.\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e# serving_bundle is a dictionary that contains the setup information provided to the tool,\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e# such as server address, model name, model version, etc.\u003c/span\u003e\n\n \u003cspan class=\"pl-s1\"\u003enumber_of_examples\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003elen\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003eexamples\u003c/span\u003e)\n \u003cspan class=\"pl-s1\"\u003eresults\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e []\n \u003cspan class=\"pl-k\"\u003efor\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003e_\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003ein\u003c/span\u003e \u003cspan class=\"pl-en\"\u003erange\u003c/span\u003e(\u003cspan class=\"pl-s1\"\u003enumber_of_examples\u003c/span\u003e):\n \u003cspan class=\"pl-s1\"\u003escore\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003erandom\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003erandom\u003c/span\u003e()\n \u003cspan class=\"pl-s1\"\u003eresults\u003c/span\u003e.\u003cspan class=\"pl-en\"\u003eappend\u003c/span\u003e([\u003cspan class=\"pl-s1\"\u003escore\u003c/span\u003e, \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e-\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003escore\u003c/span\u003e]) \u003cspan class=\"pl-c\"\u003e# For binary classification\u003c/span\u003e\n \u003cspan class=\"pl-c\"\u003e# results.append(score) # For regression\u003c/span\u003e\n \u003cspan class=\"pl-k\"\u003ereturn\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eresults\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eDefine this function in a file you save to disk. For this example, let's assume\nthe file is saved as \u003ccode\u003e/tmp/my_custom_predict_function.py\u003c/code\u003e.\nThen the TensorBoard server with \u003ccode\u003etensorboard --whatif-use-unsafe-custom-prediction /tmp/my_custom_predict_function.py\u003c/code\u003e\nand the function should be invoked once you have set up your data and model in\nthe What-If Tool setup dialog.\nThe \u003ccode\u003eunsafe\u003c/code\u003e means that the function is not sandboxed, so make sure that\nyour function doesn't do anything destructive, such as accidently delete your\nexperiment data.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eHow can I help develop it?\u003c/h2\u003e\u003ca id=\"user-content-how-can-i-help-develop-it\" class=\"anchor\" aria-label=\"Permalink: How can I help develop it?\" href=\"#how-can-i-help-develop-it\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCheck out the \u003ca href=\"/PAIR-code/what-if-tool/blob/master/DEVELOPMENT.md\"\u003edevelopement guide\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eWhat's new in WIT?\u003c/h2\u003e\u003ca id=\"user-content-whats-new-in-wit\" class=\"anchor\" aria-label=\"Permalink: What's new in WIT?\" href=\"#whats-new-in-wit\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eCheck out the \u003ca href=\"/PAIR-code/what-if-tool/blob/master/RELEASE.md\"\u003erelease notes\u003c/a\u003e.\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"What-If Tool","anchor":"what-if-tool","htmlText":"What-If Tool"},{"level":2,"text":"I don’t want to read this document. Can I just play with a demo?","anchor":"i-dont-want-to-read-this-document-can-i-just-play-with-a-demo","htmlText":"I don’t want to read this document. Can I just play with a demo?"},{"level":2,"text":"What do I need to use it in a jupyter or colab notebook?","anchor":"what-do-i-need-to-use-it-in-a-jupyter-or-colab-notebook","htmlText":"What do I need to use it in a jupyter or colab notebook?"},{"level":2,"text":"What do I need to use it in TensorBoard?","anchor":"what-do-i-need-to-use-it-in-tensorboard","htmlText":"What do I need to use it in TensorBoard?"},{"level":3,"text":"All I have is a dataset. What can I do in TensorBoard? Where do I start?","anchor":"all-i-have-is-a-dataset-what-can-i-do-in-tensorboard-where-do-i-start","htmlText":"All I have is a dataset. What can I do in TensorBoard? Where do I start?"},{"level":2,"text":"What can it do?","anchor":"what-can-it-do","htmlText":"What can it do?"},{"level":2,"text":"Who is it for?","anchor":"who-is-it-for","htmlText":"Who is it for?"},{"level":2,"text":"Notebook mode details","anchor":"notebook-mode-details","htmlText":"Notebook mode details"},{"level":3,"text":"How can the What-If Tool use attribution values and other prediction-time information?","anchor":"how-can-the-what-if-tool-use-attribution-values-and-other-prediction-time-information","htmlText":"How can the What-If Tool use attribution values and other prediction-time information?"},{"level":3,"text":"How do I enable it for use in a Jupyter notebook?","anchor":"how-do-i-enable-it-for-use-in-a-jupyter-notebook","htmlText":"How do I enable it for use in a Jupyter notebook?"},{"level":3,"text":"How do I enable it for use in a Colab notebook?","anchor":"how-do-i-enable-it-for-use-in-a-colab-notebook","htmlText":"How do I enable it for use in a Colab notebook?"},{"level":3,"text":"How do I enable it for use in a JupyterLab or Cloud AI Platform notebook?","anchor":"how-do-i-enable-it-for-use-in-a-jupyterlab-or-cloud-ai-platform-notebook","htmlText":"How do I enable it for use in a JupyterLab or Cloud AI Platform notebook?"},{"level":2,"text":"Can I use a custom prediction function in TensorBoard?","anchor":"can-i-use-a-custom-prediction-function-in-tensorboard","htmlText":"Can I use a custom prediction function in TensorBoard?"},{"level":2,"text":"How can I help develop it?","anchor":"how-can-i-help-develop-it","htmlText":"How can I help develop it?"},{"level":2,"text":"What's new in WIT?","anchor":"whats-new-in-wit","htmlText":"What's new in WIT?"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fpair-code%2Fwhat-if-tool"}},{"displayName":"LICENSE","repoName":"what-if-tool","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2Fpair-code%2Fwhat-if-tool"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"blob_edit_unsaved_changes_storage":true,"accessible_code_button":true}}}}</script> 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4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="/PAIR-code/what-if-tool/blob/master/img/wit-smile-intro.png"><img src="/PAIR-code/what-if-tool/raw/master/img/wit-smile-intro.png" alt="What-If Tool Screenshot" title="What-If Tool Screenshot" style="max-width: 100%;"></a></p> <p dir="auto">The <a href="https://pair-code.github.io/what-if-tool" rel="nofollow">What-If Tool</a> (WIT) provides an easy-to-use interface for expanding understanding of a black-box classification or regression ML model. With the plugin, you can perform inference on a large set of examples and immediately visualize the results in a variety of ways. Additionally, examples can be edited manually or programmatically and re-run through the model in order to see the results of the changes. It contains tooling for investigating model performance and fairness over subsets of a dataset.</p> <p dir="auto">The purpose of the tool is that give people a simple, intuitive, and powerful way to play with a trained ML model on a set of data through a visual interface with absolutely no code required.</p> <p dir="auto">The tool can be accessed through TensorBoard or as an extension in a Jupyter or <a href="https://colab.research.google.com/github/pair-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb" rel="nofollow">Colab</a> notebook.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">I don’t want to read this document. Can I just play with a demo?</h2><a id="user-content-i-dont-want-to-read-this-document-can-i-just-play-with-a-demo" class="anchor" aria-label="Permalink: I don’t want to read this document. Can I just play with a demo?" href="#i-dont-want-to-read-this-document-can-i-just-play-with-a-demo"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Check out the large set of web and colab demos in the <a href="https://pair-code.github.io/what-if-tool/index.html#demos" rel="nofollow">demo section of the What-If Tool website</a>.</p> <p dir="auto">To build the web demos yourself:</p> <ul dir="auto"> <li><a href="https://pair-code.github.io/what-if-tool/uci.html" rel="nofollow">Binary classifier for UCI Census dataset salary prediction</a> <ul dir="auto"> <li>Dataset: <a href="https://archive.ics.uci.edu/ml/datasets/census+income" rel="nofollow">UCI Census</a></li> <li>Task: Predict whether a person earns more or less than $50k based on their census information</li> <li>To build and run the demo from code: <code>bazel run wit_dashboard/demo:demoserver</code> then navigate to <code>http://localhost:6006/wit-dashboard/demo.html</code></li> </ul> </li> <li><a href="https://pair-code.github.io/what-if-tool/image.html" rel="nofollow">Binary classifier for smile detection in images</a> <ul dir="auto"> <li>Dataset: <a href="http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html" rel="nofollow">CelebA</a></li> <li>Task: Predict whether the person in an image is smiling</li> <li>To build and run the demo from code: <code>bazel run wit_dashboard/demo:imagedemoserver</code> then navigate to <code>http://localhost:6006/wit-dashboard/image_demo.html</code></li> </ul> </li> <li><a href="https://pair-code.github.io/what-if-tool/iris.html" rel="nofollow">Multiclass classifier for Iris dataset</a> <ul dir="auto"> <li>Dataset: <a href="https://archive.ics.uci.edu/ml/datasets/iris" rel="nofollow">UCI Iris</a></li> <li>Task: Predict which of three classes of iris flowers that a flower falls into based on 4 measurements of the flower</li> <li>To build and run the demo from code: <code>bazel run wit_dashboard/demo:irisdemoserver</code> then navigate to <code>http://localhost:6006/wit-dashboard/iris_demo.html</code></li> </ul> </li> <li><a href="https://pair-code.github.io/what-if-tool/age.html" rel="nofollow">Regression model for UCI Census dataset age prediction</a> <ul dir="auto"> <li>Dataset: <a href="https://archive.ics.uci.edu/ml/datasets/census+income" rel="nofollow">UCI Census</a></li> <li>Task: Predict the age of a person based on their census information</li> <li>To build and run the demo from code: <code>bazel run wit_dashboard/demo:agedemoserver</code> then navigate to <code>http://localhost:6006/wit-dashboard/age_demo.html</code></li> <li>This demo model returns attribution values in addition to predictions (through the use of vanilla gradients) in order to demonstate how the tool can display attribution values from predictions.</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">What do I need to use it in a jupyter or colab notebook?</h2><a id="user-content-what-do-i-need-to-use-it-in-a-jupyter-or-colab-notebook" class="anchor" aria-label="Permalink: What do I need to use it in a jupyter or colab notebook?" href="#what-do-i-need-to-use-it-in-a-jupyter-or-colab-notebook"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">You can use the What-If Tool to analyze a classification or regression <a href="https://www.tensorflow.org/api_docs/python/tf/estimator/Estimator" rel="nofollow">TensorFlow Estimator</a> that takes TensorFlow Example or SequenceExample protos (data points) as inputs directly in a jupyter or colab notebook.</p> <p dir="auto">Additionally, the What-If Tool can analyze <a href="https://cloud.google.com/ml-engine/" rel="nofollow">AI Platform Prediction-hosted</a> classification or regresssion models that take TensorFlow Example protos, SequenceExample protos, or raw JSON objects as inputs.</p> <p dir="auto">You can also use What-If Tool with a custom prediction function that takes Tensorflow examples and produces predictions. In this mode, you can load any model (including non-TensorFlow models that don't use Example protos as inputs) as long as your custom function's input and output specifications are correct.</p> <p dir="auto">With either AI Platform models or a custom prediction function, the What-If Tool can display and make use of attribution values for each input feature in relation to each prediction. See the below section on attribution values for more information.</p> <p dir="auto">If you want to train an ML model from a dataset and explore the dataset and model, check out the <a href="https://colab.research.google.com/github/pair-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb" rel="nofollow">What_If_Tool_Notebook_Usage.ipynb notebook</a> in colab, which starts from a CSV file, converts the data to tf.Example protos, trains a classifier, and then uses the What-If Tool to show the classifier performance on the data.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">What do I need to use it in TensorBoard?</h2><a id="user-content-what-do-i-need-to-use-it-in-tensorboard" class="anchor" aria-label="Permalink: What do I need to use it in TensorBoard?" href="#what-do-i-need-to-use-it-in-tensorboard"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A walkthrough of using the tool in TensorBoard, including a pretrained model and test dataset, can be found on the <a href="https://www.tensorflow.org/tensorboard/r2/what_if_tool" rel="nofollow">What-If Tool page on the TensorBoard website</a>.</p> <p dir="auto">To use the tool in TensorBoard, only the following information needs to be provided:</p> <ul dir="auto"> <li>The model server host and port, served using <a href="https://github.com/tensorflow/serving">TensorFlow Serving</a>. The model can use the TensorFlow Serving Classification, Regression, or Predict API. <ul dir="auto"> <li>Information on how to create a saved model with the <code>Estimator</code> API that will use thse appropriate TensorFlow Serving Classification or Regression APIs can be found in the <a href="https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators" rel="nofollow">saved model documentation</a> and in this <a href="http://shzhangji.com/blog/2018/05/14/serve-tensorflow-estimator-with-savedmodel/" rel="nofollow">helpful tutorial</a>. Models that use these APIs are the simplest to use with the What-If Tool as they require no set-up in the tool beyond setting the model type.</li> <li>If the model uses the Predict API, the input must be serialized tf.Example or tf.SequenceExample protos and the output must be following: <ul dir="auto"> <li>For classification models, the output must include a 2D float tensor containing a list of class probabilities for all possible class indices for each inferred example.</li> <li>For regression models, the output must include a float tensor containing a single regression score for each inferred example.</li> </ul> </li> <li>The What-If Tool queries the served model using the gRPC API, not the RESTful API. See the TensorFlow Serving <a href="https://www.tensorflow.org/serving/docker" rel="nofollow">docker documentation</a> for more information on the two APIs. The docker image uses port 8500 for the gRPC API, so if using the docker approach, the port to specify in the What-If Tool will be 8500.</li> <li>Alternatively, instead of querying a model hosted by TensorFlow Serving, you can provide a python function for model prediction to the tool through the "--whatif-use-unsafe-custom-prediction" runtime argument as described in more detail below.</li> </ul> </li> <li>A TFRecord file of tf.Examples or tf.SequenceExamples to perform inference on and the number of examples to load from the file. <ul dir="auto"> <li>Can handle up to tens of thousands of examples. The exact amount depends on the size of each example (how many features there are and how large the feature values are).</li> <li>The file must be in the logdir provided to TensorBoard on startup. Alternatively, you can provide another directory to allow file loading from, through use of the -whatif-data-dir=PATH runtime parameter.</li> </ul> </li> <li>An indication if the model is a regression, binary classification or multi-class classification model.</li> <li>An optional vocab file for the labels for a classification model. This file maps the predicted class indices returned from the model prediction into class labels. The text file contains one label per line, corresponding to the class indices returned by the model, starting with index 0. <ul dir="auto"> <li>If this file is provided, then the dashboard will show the predicted labels for a classification model. If not, it will show the predicted class indices.</li> </ul> </li> </ul> <p dir="auto">Alternatively, the What-If Tool can be used to explore a dataset directly from a CSV file. See the next section for details.</p> <p dir="auto">The information can be provided in the settings dialog screen, which pops up automatically upon opening this tool and is accessible through the settings icon button in the top-right of the tool. The information can also be provided directly through URL parameters. Changing the settings through the controls automatically updates the URL so that it can be shared with others for them to view the same data in the What-If Tool.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">All I have is a dataset. What can I do in TensorBoard? Where do I start?</h3><a id="user-content-all-i-have-is-a-dataset-what-can-i-do-in-tensorboard-where-do-i-start" class="anchor" aria-label="Permalink: All I have is a dataset. What can I do in TensorBoard? Where do I start?" href="#all-i-have-is-a-dataset-what-can-i-do-in-tensorboard-where-do-i-start"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">If you just want to explore the information in a CSV file using the What-If Tool in TensorBoard, just set the path to the examples to the file (with a ".csv" extension) and leave the inference address and model name fields blank. The first line of the CSV file must contain column names. Each line after that contains one example from the dataset, with values for each of the columns defined on the first line. The pipe character ("|") deliminates separate feature values in a list of feature values for a given feature.</p> <p dir="auto">In order to make use of the model understanding features of the tool, you can have columns in your dataset that contain the output from an ML model. If your file has a column named "predictions__probabilities" with a pipe-delimited ("|") list of probability scores (between 0 and 1), then the tool will treat those as the output scores of a classification model. If your file has a numeric column named "predictions" then the tool will treat those as the output of a regression model. In this way, the tool can be used to analyze any dataset and the results of any model run offline against the dataset. Note that in this mode, the examples aren't editable as there is no way to get new inference results when an example changes.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">What can it do?</h2><a id="user-content-what-can-it-do" class="anchor" aria-label="Permalink: What can it do?" href="#what-can-it-do"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Details on the capabilities of the tool, including a guided walkthrough, can be found on the <a href="https://pair-code.github.io/what-if-tool" rel="nofollow">What-If Tool website</a>. Here is a basic rundown of what it can do:</p> <ul dir="auto"> <li> <p dir="auto">Visualize a dataset of TensorFlow Example protos.</p> <ul dir="auto"> <li>The main panel shows the dataset using <a href="https://pair-code.github.io/facets" rel="nofollow">Facets Dive</a>, where the examples can be organized/sliced/positioned/colored by any of the dataset’s features. <ul dir="auto"> <li>The examples can also be organized by the results of their inferences. <ul dir="auto"> <li>For classification models, this includes inferred label, confidence of inferred label, and inference correctness.</li> <li>For regression models, this includes inferred score and amount of error (including absolute or squared error) in the inference.</li> </ul> </li> </ul> </li> <li>A selected example can be viewed in detail in the side panel, showing all feature values for all features of that example.</li> <li>For examples that contain an encoded image in a bytes feature named "image/encoded", Facets Dive will create a thumbnail of the image to display the point, and the full-size image is visible in the side panel for a selected example.</li> <li>Aggregate statistics for all loaded examples can be viewed in the side panel using <a href="https://pair-code.github.io/facets/" rel="nofollow">Facets Overview</a>.</li> </ul> </li> <li> <p dir="auto">Visualize the results of the inference</p> <ul dir="auto"> <li>By default, examples in the main panel are colored by their inference results.</li> <li>The examples in the main panel can be organized into confusion matrices and other custom layouts to show the inference results faceted by a number of different features, or faceted/positioned by inference result, allowing the creation of small multiples of 1D and 2D histograms and scatter plots.</li> <li>For a selected example, detailed inference results (e.x. predicted classes and their confidence scores) are shown in the side panel.</li> <li>If the model returns attribution values in addition to predictions, they are displayed for each selected example, and the attribution values can be used to control custom layouts and as dimensions to slice the dataset on for performance analysis.</li> </ul> </li> <li> <p dir="auto">Explore counterfactual examples</p> <ul dir="auto"> <li>For classification models, for any selected example, with one click you can compare the example to the example most-similar to it but which is classified as a different.</li> <li>Similarity is calculated based on the distribution of feature values across all loaded examples and similarity can be calculated using either L1 or L2 distance. <ul dir="auto"> <li>Distance is normalized between features by: <ul dir="auto"> <li>For numeric features, use the distance between values divided by the standard deviation of the values across all examples.</li> <li>For categorical features, the distance is 0 if the values are the same, otherwise the distance is the probability that any two examples have the same value for that feature across all examples.</li> </ul> </li> </ul> </li> <li>In notebook mode, the tool also allows you to set a custom distance function using set_custom_distance_fn in WitConfigBuilder, where that function is used to compute closest counterfactuals instead. As in the case with custom_predict_fn, the custom distance function can be any python function.</li> </ul> </li> <li> <p dir="auto">Edit a selected example in the browser and re-run inference and visualize the difference in the inference results.</p> <ul dir="auto"> <li>See auto-generated partial dependence plots, which are plots that for every feature show the change in inference results as that feature has its value changed to different valid values for that feature.</li> <li>Edit/add/remove any feature or feature value in the side panel and re-run inference on the edited datapoint. A history of the inference values of that point as it is edited and re-inferred is shown.</li> <li>For examples that contain encoded images, upload your own image and re-run inference.</li> <li>Clone an existing example for editing/comparison.</li> <li>Revert edits to an edited example.</li> </ul> </li> <li> <p dir="auto">Compare the results of two models on the same input data.</p> <ul dir="auto"> <li>If you provide two models to the tool during setup, it will run inference with the provided data on both models and you can compare the results between the two models using all the features defined above.</li> </ul> </li> <li> <p dir="auto">If using a binary classification model and your examples include a feature that describes the true label, you can do the following:</p> <ul dir="auto"> <li>See the ROC curve and numeric confusion matrices in the side panel, including the point on the curve where your model lives, given the current positive classification threshold value.</li> <li>See separate ROC curves and numeric confusion matrices split out for subsets of the dataset, sliced of any feature or features of your dataset (i.e. by gender).</li> <li>Manually adjust the positive classification threshold (or thresholds, if slicing the dataset by a feature) and see the difference in inference results, ROC curve position and confusion matrices immediately.</li> <li>Set the positive classification thresholds with one click based on concepts such as the cost of a false positive vs a false negative and satisfying fairness measures such as equality of opportunity or demographic parity.</li> </ul> </li> <li> <p dir="auto">If using a multi-class classification model and your examples include a feature that describes the true label, you can do the following:</p> <ul dir="auto"> <li>See a confusion matrix in the side panel for all classifications and all classes.</li> <li>See separate confusion matrices split out for subsets of the dataset, sliced on any feature or features of your dataset.</li> </ul> </li> <li> <p dir="auto">If using a regression model and your examples include a feature that describes the true label, you can do the following:</p> <ul dir="auto"> <li>See the mean error, mean absolute error and mean squared error across the dataset.</li> <li>See separate calculations of those mean error calculations split out for subsets of the dataset, sliced of any feature or features of your dataset.</li> </ul> </li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Who is it for?</h2><a id="user-content-who-is-it-for" class="anchor" aria-label="Permalink: Who is it for?" href="#who-is-it-for"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">We imagine WIT to be useful for a wide variety of users.</p> <ul dir="auto"> <li>ML researchers and model developers - Investigate your datasets and models and explore inference results. Poke at the data and model to gain insights, for tasks such as debugging strange results and looking into ML fairness.</li> <li>Non-technical stakeholders - Gain an understanding of the performance of a model on a dataset. Try it out with your own data.</li> <li>Lay users - Learn about machine learning by interactively playing with datasets and models.</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Notebook mode details</h2><a id="user-content-notebook-mode-details" class="anchor" aria-label="Permalink: Notebook mode details" href="#notebook-mode-details"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">As seen in the <a href="https://colab.research.google.com/github/pair-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb" rel="nofollow">example notebook</a>, creating the <code>WitWidget</code> object is what causes the What-If Tool to be displayed in an output cell. The <code>WitWidget</code> object takes a <code>WitConfigBuilder</code> object as a constructor argument. The <code>WitConfigBuilder</code> object specifies the data and model information that the What-If Tool will use.</p> <p dir="auto">The WitConfigBuilder object takes a list of tf.Example or tf.SequenceExample protos as a constructor argument. These protos will be shown in the tool and inferred in the specified model.</p> <p dir="auto">The model to be used for inference by the tool can be specified in many ways:</p> <ul dir="auto"> <li>As a TensorFlow <a href="https://www.tensorflow.org/guide/estimators" rel="nofollow">Estimator</a> object that is provided through the <code>set_estimator_and_feature_spec</code> method. In this case the inference will be done inside the notebook using the provided estimator.</li> <li>As a model hosted by <a href="https://cloud.google.com/ml-engine/" rel="nofollow">AI Platform Prediction</a> through the <code>set_ai_platform_model</code> method.</li> <li>As a custom prediction function provided through <code>set_custom_predict_fn</code> method. In this case WIT will directly call the function for inference.</li> <li>As an endpoint for a model being served by <a href="https://github.com/tensorflow/serving">TensorFlow Serving</a>, through the <code>set_inference_address</code> and <code>set_model_name</code> methods. In this case the inference will be done on the model server specified. To query a model served on host "localhost" on port 8888, named "my_model", you would set on your builder <code>builder.set_inference_address('localhost:8888').set_model_name('my_model')</code>.</li> </ul> <p dir="auto">See the documentation of <a href="https://github.com/pair-code/what-if-tool/blob/master/witwidget/notebook/visualization.py">WitConfigBuilder</a> for all options you can provide, including how to specify other model types (defaults to binary classification) and how to specify an optional second model to compare to the first model.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How can the What-If Tool use attribution values and other prediction-time information?</h3><a id="user-content-how-can-the-what-if-tool-use-attribution-values-and-other-prediction-time-information" class="anchor" aria-label="Permalink: How can the What-If Tool use attribution values and other prediction-time information?" href="#how-can-the-what-if-tool-use-attribution-values-and-other-prediction-time-information"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Feature attribution values are numeric values for each input feature to an ML model that indicate how much impact that feature value had on the model's prediction. There are a variety of approaches to get feature attribution values for a predicts from an ML model, including <a href="https://github.com/slundberg/shap">SHAP</a>, <a href="https://github.com/ankurtaly/Integrated-Gradients">Integrated Gradients</a>, <a href="https://pair-code.github.io/saliency/" rel="nofollow">SmoothGrad</a>, and more.</p> <p dir="auto">They can be a powerful way of analyzing how a model reacts to certain input values beyond just simply studying the effect that changing individual feature values has on model predictions as is done with partial dependence plots. Some attribution techniques require access to a model internals, such as the gradient-based methods, whereas others can be performed on black-box models. Regardless, the What-If Tool can visualize the results of attribution methods in addition to the standard model prediction results.</p> <p dir="auto">There are two ways to use the What-If Tool to visualize attribution values. If you have deployed a model to Cloud AI Platform with the explainability feature enabled, and provide this model to the tool through the standard <code>set_ai_platform_model</code> method, then attribution values will automatically be generated and visualized by the tool with no additional setup needed. If you wish to view attribution values for a different model setup, this can be accomplished through use of the custom prediction function.</p> <p dir="auto">As described in the <code>set_custom_predict_fn</code> documentation in <a href="https://github.com/pair-code/what-if-tool/blob/master/witwidget/notebook/visualization.py">WitConfigBuilder</a>, this method must return a list of the same size as the number of examples provided to it, with each list entry representing the prediction-time information for that example. In the case of a standard model with no attribution information, the list entry is just a number (in the case of a regression model), or a list of class probabilities (in the case of a classification model).</p> <p dir="auto">However, if there is attribution or other prediction-time information, then the list entry can instead be a dictionary, with the standard model prediction output under the <code>predictions</code> key. Attribution information can be returned under the <code>attributions</code> key and any other supplemental information under its own descriptive key. The exact format of the attributions and other supplemental information can be found in the code documentation linked above.</p> <p dir="auto">If attribution values are provided to the What-If Tool, they can be used in a number of ways. First, when selecting a datapoint in the Datapoint Editor tab, the attribution values are displayed next to each feature value and the features can be ordered by their attribution strength instead of alphabetically. Additionally, the feature values are colored by their attribution values for quick interpretation of attribution strengths.</p> <p dir="auto">Beyond displaying the attribution values for the selected datapoint, the attribution values for each feature can be used in the tool in the same ways as any other feature of the datapoints. They can be used selected in the datapoints visualization controls to use those values to create custom scatter plots and histograms. For example, you can create a scatterplot showing the relationship between the attribution value of two different features, with the datapoints colored by the predicted result from the model. They can also be used in the Performance tab as a way to slice a dataset for comparing performance statistics of different slices. For example, you can quickly compare the aggregate performance of a model on datapoints with low attribution of a specified feature, against the datapoints with high attribution of that feature.</p> <p dir="auto">Any other supplemental information returned from a custom prediction function will appear in the tool as a feature named after its key in the dictionary. They can also be used in the same way, driving custom visualizations and as a dimension to slice when analyzing aggregate model performance.</p> <p dir="auto">When a datapoint is edited and the re-inferred through the model with the "Run inference" button, the attributions and other supplemental information is recalculated and updated in the tool.</p> <p dir="auto">For an example of returning attribution values from a custom prediction function (in this case using the SHAP library to get attributions), see the <a href="https://colab.research.google.com/github/PAIR-code/what-if-tool/blob/master/WIT_COMPAS_with_SHAP.ipynb" rel="nofollow">WIT COMPAS with SHAP notebook</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How do I enable it for use in a Jupyter notebook?</h3><a id="user-content-how-do-i-enable-it-for-use-in-a-jupyter-notebook" class="anchor" aria-label="Permalink: How do I enable it for use in a Jupyter notebook?" href="#how-do-i-enable-it-for-use-in-a-jupyter-notebook"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">First, install and enable WIT for Jupyter through the following commands:</p> <div class="highlight highlight-source-shell notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="pip install witwidget jupyter nbextension install --py --symlink --sys-prefix witwidget jupyter nbextension enable --py --sys-prefix witwidget"><pre>pip install witwidget jupyter nbextension install --py --symlink --sys-prefix witwidget jupyter nbextension <span class="pl-c1">enable</span> --py --sys-prefix witwidget</pre></div> <p dir="auto">Then, use it as seen at the bottom of the <a href="/PAIR-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb">What_If_Tool_Notebook_Usage.ipynb notebook</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How do I enable it for use in a Colab notebook?</h3><a id="user-content-how-do-i-enable-it-for-use-in-a-colab-notebook" class="anchor" aria-label="Permalink: How do I enable it for use in a Colab notebook?" href="#how-do-i-enable-it-for-use-in-a-colab-notebook"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Install the widget into the runtime of the notebook kernel by running a cell containing:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="!pip install witwidget"><pre class="notranslate"><code>!pip install witwidget </code></pre></div> <p dir="auto">Then, use it as seen at the bottom of the <a href="https://colab.research.google.com/github/pair-code/what-if-tool/blob/master/What_If_Tool_Notebook_Usage.ipynb" rel="nofollow">What_If_Tool_Notebook_Usage.ipynb notebook</a>.</p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">How do I enable it for use in a JupyterLab or Cloud AI Platform notebook?</h3><a id="user-content-how-do-i-enable-it-for-use-in-a-jupyterlab-or-cloud-ai-platform-notebook" class="anchor" aria-label="Permalink: How do I enable it for use in a JupyterLab or Cloud AI Platform notebook?" href="#how-do-i-enable-it-for-use-in-a-jupyterlab-or-cloud-ai-platform-notebook"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">WIT has been tested in JupyterLab versions 1.x, 2.x, and 3.x.</p> <p dir="auto">Install and enable WIT for JupyterLab 3.x by running a cell containing:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="!pip install witwidget !jupyter labextension install wit-widget !jupyter labextension install @jupyter-widgets/jupyterlab-manager"><pre class="notranslate"><code>!pip install witwidget !jupyter labextension install wit-widget !jupyter labextension install @jupyter-widgets/jupyterlab-manager </code></pre></div> <p dir="auto">Note that you may need to specify the correct version of jupyterlab-manager for you JupyterLab version as per <a href="https://www.npmjs.com/package/@jupyter-widgets/jupyterlab-manager" rel="nofollow">https://www.npmjs.com/package/@jupyter-widgets/jupyterlab-manager</a>.</p> <p dir="auto">Note that you may need to run <code>!sudo jupyter labextension ...</code> commands depending on your notebook setup.</p> <p dir="auto">Use of WIT after installation is the same as with the other notebook installations.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Can I use a custom prediction function in TensorBoard?</h2><a id="user-content-can-i-use-a-custom-prediction-function-in-tensorboard" class="anchor" aria-label="Permalink: Can I use a custom prediction function in TensorBoard?" href="#can-i-use-a-custom-prediction-function-in-tensorboard"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Yes. You can do this by defining a python function named <code>custom_predict_fn</code> which takes two arguments: a list of examples to preform inference on, and the serving bundle object which contains information about the model to query. The function should return a list of results, one entry per example provided. For regression models, the result is just a number. For classification models, the result is a list of numbers, representing the class scores for each possible class. Here is a minimal example that just returns random results:</p> <div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="import random # The function name "custom_predict_fn" must be exact. def custom_predict_fn(examples, serving_bundle): # Examples are a list of TFRecord objects, each object contains the features of each point. # serving_bundle is a dictionary that contains the setup information provided to the tool, # such as server address, model name, model version, etc. number_of_examples = len(examples) results = [] for _ in range(number_of_examples): score = random.random() results.append([score, 1 - score]) # For binary classification # results.append(score) # For regression return results"><pre><span class="pl-k">import</span> <span class="pl-s1">random</span> <span class="pl-c"># The function name "custom_predict_fn" must be exact.</span> <span class="pl-k">def</span> <span class="pl-en">custom_predict_fn</span>(<span class="pl-s1">examples</span>, <span class="pl-s1">serving_bundle</span>): <span class="pl-c"># Examples are a list of TFRecord objects, each object contains the features of each point.</span> <span class="pl-c"># serving_bundle is a dictionary that contains the setup information provided to the tool,</span> <span class="pl-c"># such as server address, model name, model version, etc.</span> <span class="pl-s1">number_of_examples</span> <span class="pl-c1">=</span> <span class="pl-en">len</span>(<span class="pl-s1">examples</span>) <span class="pl-s1">results</span> <span class="pl-c1">=</span> [] <span class="pl-k">for</span> <span class="pl-s1">_</span> <span class="pl-c1">in</span> <span class="pl-en">range</span>(<span class="pl-s1">number_of_examples</span>): <span class="pl-s1">score</span> <span class="pl-c1">=</span> <span class="pl-s1">random</span>.<span class="pl-en">random</span>() <span class="pl-s1">results</span>.<span class="pl-en">append</span>([<span class="pl-s1">score</span>, <span class="pl-c1">1</span> <span class="pl-c1">-</span> <span class="pl-s1">score</span>]) <span class="pl-c"># For binary classification</span> <span class="pl-c"># results.append(score) # For regression</span> <span class="pl-k">return</span> <span class="pl-s1">results</span></pre></div> <p dir="auto">Define this function in a file you save to disk. For this example, let's assume the file is saved as <code>/tmp/my_custom_predict_function.py</code>. Then the TensorBoard server with <code>tensorboard --whatif-use-unsafe-custom-prediction /tmp/my_custom_predict_function.py</code> and the function should be invoked once you have set up your data and model in the What-If Tool setup dialog. The <code>unsafe</code> means that the function is not sandboxed, so make sure that your function doesn't do anything destructive, such as accidently delete your experiment data.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">How can I help develop it?</h2><a id="user-content-how-can-i-help-develop-it" class="anchor" aria-label="Permalink: How can I help develop it?" href="#how-can-i-help-develop-it"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Check out the <a href="/PAIR-code/what-if-tool/blob/master/DEVELOPMENT.md">developement guide</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">What's new in WIT?</h2><a id="user-content-whats-new-in-wit" class="anchor" aria-label="Permalink: What's new in WIT?" href="#whats-new-in-wit"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Check out the <a href="/PAIR-code/what-if-tool/blob/master/RELEASE.md">release notes</a>.</p> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="Gct5zxsCpgpI8PLGDPUAHHoyt122M/ovTHKj4jNr6gzD++TG+zOEfXVJeKDlR6VCzpPeDYpx4dsMhIm7X/odCA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Source code/webpage/demos for the What-If Tool </p> <div class="my-3 d-flex flex-items-center"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" 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d="M1 7.775V2.75C1 1.784 1.784 1 2.75 1h5.025c.464 0 .91.184 1.238.513l6.25 6.25a1.75 1.75 0 0 1 0 2.474l-5.026 5.026a1.75 1.75 0 0 1-2.474 0l-6.25-6.25A1.752 1.752 0 0 1 1 7.775Zm1.5 0c0 .066.026.13.073.177l6.25 6.25a.25.25 0 0 0 .354 0l5.025-5.025a.25.25 0 0 0 0-.354l-6.25-6.25a.25.25 0 0 0-.177-.073H2.75a.25.25 0 0 0-.25.25ZM6 5a1 1 0 1 1 0 2 1 1 0 0 1 0-2Z"></path> </svg> <div class="ml-2 min-width-0"> <div class="d-flex"> <span class="css-truncate css-truncate-target text-bold mr-2" style="max-width: none;">Version 1.8.1</span> <span title="Label: Latest" data-view-component="true" class="Label Label--success flex-shrink-0"> Latest </span> </div> <div class="text-small color-fg-muted"><relative-time datetime="2021-10-12T17:36:36Z" class="no-wrap">Oct 12, 2021</relative-time></div> </div> </a> <div data-view-component="true" class="mt-3"> <a text="small" data-pjax="#repo-content-pjax-container" data-turbo-frame="repo-content-turbo-frame" href="/PAIR-code/what-if-tool/releases" 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