Initial commit

.gitignore

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+*~
+/bazel-*
+**/__pycache__
+**/.ipynb_checkpoints/
+**/*.swp

AUTHORS

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+# This the official list of Bazel rules_closure authors for copyright purposes.
+# This file is distinct from the CONTRIBUTORS files.
+
+# See the latter for an explanation.
+# Names should be added to this file as:
+# Name or Organization <email address>
+# The email address is not required for organizations.
+
+Google Inc.

CONTRIBUTING.md

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+Want to contribute? Great! First, read this page (including the small print at the end).
+
+### Before you contribute
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+[Google Individual Contributor License Agreement]
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+(https://cla.developers.google.com/about/google-corporate).

CONTRIBUTORS

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+# People who have agreed to one of the CLAs and can contribute patches.
+# The AUTHORS file lists the copyright holders; this file
+# lists people.  For example, Google employees are listed here
+# but not in AUTHORS, because Google holds the copyright.
+#
+# https://developers.google.com/open-source/cla/individual
+# https://developers.google.com/open-source/cla/corporate
+#
+# Names should be added to this file as:
+#     Name <email address>
+
+James Wexler <jwexler@google.com>
+Jimbo Wilson <jimbo@google.com>
+Justine Tunney <jart@google.com>

LICENSE

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README.md

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+# Introduction
+
+The facets project contains two visualizations for understanding and analyzing machine learning datasets: Facets Overview and Facets Dive.
+
+The visualizations are implemented as [Polymer](https://www.polymer-project.org) web components, backed by [Typescript](https://www.typescriptlang.org) code and can be easily embedded into Jupyter notebooks or webpages.
+
+## Facets Overview
+
+![Overview visualization of UCI census data](/img/overview-census.png "Overview visualization of UCI census data -  Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml/datasets/Census+Income]. Irvine, CA: University of California, School of Information and Computer Science")
+
+Overview gives a high-level view of one or more data sets. It produces a visual feature-by-feature statistical analysis, and can also be used to compare statistics across two or more data sets. The tool can process both numeric and string features, including multiple instances of a number or string per feature.
+
+Overview can help uncover issues with datasets, including the following:
+
+* Unexpected feature values
+* Missing feature values for a large number of examples
+* Training/serving skew
+* Training/test/validation set skew
+
+Key aspects of the visualization are outlier detection and distribution comparison across multiple datasets.
+Interesting values (such as a high proportion of missing data, or very different distributions of a feature across multiple datasets) are highlighted in red.
+Features can be sorted by values of interest such as the number of missing values or the skew between the different datasets.
+
+Details about Overview usage can be found in its [README](./facets_overview/README.md).
+
+## Facets Dive
+
+![Dive visualization of UCI census data](/img/dive-census.png "Dive visualization of UCI census data -  Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml/datasets/Census+Income]. Irvine, CA: University of California, School of Information and Computer Science")
+
+Dive is a tool for interactively exploring up to tens of thousands of multidimensional data points, allowing users to seamlessly switch between a high-level overview and low-level details.
+Each example is a represented as single item in the visualization and the points can be positioned by faceting/bucketing in multiple dimensions by their feature values. Combining smooth animation and zooming with faceting and filtering, Dive makes it easy to spot patterns and outliers in complex data sets.
+
+Details about Dive usage can be found in its [README](./facets_dive/README.md).
+
+# Setup
+
+## Installation
+```
+git clone https://github.com/pairml/facets
+cd facets
+```
+
+## Enabling Usage in Jupyter Notebooks
+
+Pre-built versions of the visualization code can be found in the facets-dist directory.
+
+To enable use of these visualizations in Jupyter notebooks:
+1. Install the jupyter notebook software: http://jupyter.org/install.html
+2. Install the visualizations into Jupyter as an nbextension: ```jupyter nbextension install facets-dist/ --user``` (run from the facets top-level directory).
+
+## Building the Visualizations
+
+If you make code changes to the visualization and would like to rebuild them for use in Jupyter notebooks, follow these directions:
+1. Install bazel: https://bazel.build/
+2. Build the visualizations: ```bazel build facets:facets_jupyter```
+3. Move the resulting vulcanized html file into the facets-dist directory.
+4. Reinstall the facets-dist jupyter extension as in the previous section
+
+**Disclaimer: This is not an official Google product**