Yellowbrick Versions Save

Deployed: Wednesday, November 14, 2018 Contributors: @rebeccabilbro, @bbengfort, @zjpoh, @Kautumn06, @ndanielsen, @drwaterman, @lwgray, @pdamodaran, @Juan0001, @abatula, @peterespinosa, @jlinGG, @rlshuhart, @archaeocharlie, @dschoenleber, @black-tea, @iguk1987, @mohfadhil, @lacanlale, @agodbehere, @sivu1, @gokriznastic

Major Changes: - Target module added for visualizing dependent variable in supervised models. - Added a prototype for a missing values visualizer to the contrib module. - BalancedBinningReference visualizer for thresholding unbalanced data (undocumented). - CVScores visualizer to instrument cross-validation. - FeatureCorrelation visualizer to compare relationship between a single independent variable and the target. - ICDM visualizer, intercluster distance mapping using projections similar to those used in pyLDAVis. - PrecisionRecallCurve visualizer showing the relationship of precision and recall in a threshold-based classifier. - Enhanced FeatureImportance for multi-target and multi-coefficient models (e.g probabilistic models) and allows stacked bar chart. - Adds option to plot PDF to ResidualsPlot histogram. - Adds document boundaries option to DispersionPlot and uses colored markers to depict class. - Added alpha parameter for opacity to the scatter plot visualizer. - Modify KElbowVisualizer to accept a list of k values. - ROCAUC bugfix to allow binary classifiers that only have a decision function. - TSNE bugfix so that title and size params are respected. - ConfusionMatrix bugfix to correct percentage displays adding to 100. - ResidualsPlot bugfix to ensure specified colors are both in histogram and scatterplot. - Fixed unicode decode error on Py2 compatible Windows using Hobbies corpus. - Require matplotlib 1.5.1 or matplotlib 2.0 (matplotlib 3.0 not supported yet). - Yellowbrick now depends on SciPy 1.0 and scikit-learn 0.20. - Deprecated percent and sample_weight arguments to ConfusionMatrix fit method.

Minor Changes: - Removed hardcoding of SilhouetteVisualizer axes dimensions. - Audit classifiers to ensure they conform to score API. - Fix for Manifold fit_transform bug. - Fixed Manifold import bug. - Started reworking datasets API for easier loading of examples. - Added Timer utility for keeping track of fit times. - Added slides to documentation for teachers teaching ML/Yellowbrick. - Added an FAQ to the documentation. - Manual legend drawing utility. - New examples notebooks for Regression and Clustering. - Example of interactive classification visualization using ipywidgets. - Example of using Yellowbrick with PyTorch. - Repairs to ROCAUC tests and binary/multiclass ROCAUC construction. - Rename tests/random.py to tests/rand.py to prevent NumPy errors. - Improves ROCAUC, KElbowVisualizer, and SilhouetteVisualizer documentation. - Fixed visual display bug in JointPlotVisualizer. - Fixed image in JointPlotVisualizer documentation. - Clear figure option to poof. - Fix color plotting error in residuals plot quick method. - Fixed bugs in KElbowVisualizer, FeatureImportance, Index, and Datasets documentation. - Use LGTM for code quality analysis (replacing Landscape). - Updated contributing docs for better PR workflow. - Submitted JOSS paper.

Deployed: Thursday, July 12, 2018 Contributors: @bbengfort, @ndanielsen, @rebeccabilbro, @lwgray, @RaulPL, @Kautumn06, @ariley1472, @ralle123, @thekylesaurus, @lumega, @pdamodaran, @lumega, @chrisfs, @mitevpi, @sayali-sonawane

Major Changes: - Added Support to ClassificationReport - @ariley1472 - We have an updated Image Gallery - @ralle123 - Improved performance of ParallelCoordinates Visualizer @thekylesaurus - Added Alpha Transparency to RadViz Visualizer @lumega - CVScores Visualizer - @pdamodaran - Added fast and alpha parameters to ParallelCoordinates visualizer @bbengfort - Make support an optional parameter for ClassificationReport @lwgray - Bug Fix for Usage of multidimensional arrays in FeatureImportance visualizer @rebeccabilbro - Deprecate ScatterVisualizer to contrib @bbengfort - Implements histogram alongside ResidualsPlot @bbengfort - Adds biplot to the PCADecomposition visualizer @RaulPL - Adds Datasaurus Dataset to show importance of visualizing data @lwgray - Add DispersionPlot Plot @lwgray

Minor Changes: - Fix grammar in tutorial.rst - @chrisfs - Added Note to tutorial indicating subtle differences when working in Jupyter notebook - @chrisfs - Update Issue template @bbengfort - Added Test to check for NLTK postag data availability - @sayali-sonawane - Clarify quick start documentation @mitevpi - Deprecated DecisionBoundary - Threshold Visualization aliases deprecated

Deployed: Thursday, May 17, 2018 Contributors: @bbengfort, @ndanielsen, @rebeccabilbro, @lwgray, @ianozsvald, @jtpio, @bharaniabhishek123, @RaulPL, @tabishsada, @Kautumn06, @NealHumphrey

Changes:

• New Feature! Manifold visualizers implement high-dimensional visualization for non-linear structural feature analysis.
• New Feature! There is now a model_selection module with LearningCurve and ValidationCurve visualizers.
• New Feature! The RFECV (recursive feature elimination) visualizer with cross-validation visualizes how removing the least performing features improves the overall model.
• New Feature! The VisualizerGrid is an implementation of the MultipleVisualizer that creates axes for each visualizer using plt.subplots, laying the visualizers out as a grid.
• New Feature! Added yellowbrick.datasets to load example datasets.
• New Experimental Feature! An experimental StatsModelsWrapper was added to yellowbrick.contrib.statsmodels that will allow user to use StatsModels estimators with visualizers.
• Enhancement! ClassificationReport documentation to include more details about how to interpret each of the metrics and compare the reports against each other.
• Enhancement! Modifies scoring mechanism for regressor visualizers to include the R2 value in the plot itself with the legend.
• Enhancement! Updated and renamed the ThreshViz to be defined as DiscriminationThreshold, implements a few more discrimination features such as F1 score, maximizing arguments and annotations.
• Enhancement! Update clustering visualizers and corresponding distortion_score to handle sparse matrices.
• Added code of conduct to meet the GitHub community guidelines as part of our contributing documentation.
• Added is_probabilistic type checker and converted the type checking tests to pytest.
• Added a contrib module and DecisionBoundaries visualizer has been moved to it until further work is completed.
• Numerous fixes and improvements to documentation and tests. Add academic citation example and Zenodo DOI to the Readme.

Bug Fixes

• Adds RandomVisualizer for testing and add it to the VisualizerGrid test cases.
• Fix / update tests in tests.test_classifier.test_class_prediction_error.py to remove hardcoded data.

Deprecation Warnings

• ScatterPlotVisualizer is being moved to contrib in 0.8
• DecisionBoundaryVisualizer is being moved to contrib in 0.8
• ThreshViz is renamed to DiscriminationThreshold.

NOTE: These deprecation warnings originally mentioned deprecation in 0.7, but their life was extended by an additional version.

Markdown for GitHub repo:

Deployed: Saturday, March 17, 2018 Contributors: @bbengfort, @ndanielsen, @rebeccabilbro, @lwgray, @Kautumn06, @georgerichardson, @pbs929, @Aylr, @gary-mayfield, @jkeung

Changes

• New Feature! The FeatureImportances Visualizer enables the user to visualize the most informative (relative and absolute) features in their model, plotting a bar graph of feature_importances_ or coef_ attributes.
• New Feature! The ExplainedVariance Visualizer produces a plot of the explained variance resulting from a dimensionality reduction to help identify the best tradeoff between number of dimensions and amount of information retained from the data.
• New Feature! The GridSearchVisualizer creates a color plot showing the best grid search scores across two parameters.
• New Feature! The ClassPredictionError Visualizer is a heatmap implementation of the class balance visualizer, which provides a way to quickly understand how successfully your classifier is predicting the correct classes.
• New Feature! The ThresholdVisualizer allows the user to visualize the bounds of precision, recall and queue rate at different thresholds for binary targets after a given number of trials.
• New MultiFeatureVisualizer helper class to provide base functionality for getting the names of features for use in plot annotation.
• Adds font size param to the confusion matrix to adjust its visibility.
• Add quick method to the confusion matrix
• Tests: In this version, we've switched from using nose to pytest. Image comparison tests have been added and the visual tests are updated to matplotlib 2.2.0. Test coverage has also been improved for a number of visualizers, including JointPlot, AlphaPlot, FreqDist, RadViz, ElbowPlot, SilhouettePlot, ConfusionMatrix, Rank1D, and Rank2D.
• Documentation updates, including discussion of Image Comparison Tests for contributors.

Bug Fixes:

• Fixes the resolve_colors function. You can now pass in a number of colors and a colormap and get back the correct number of colors.
• Fixes TSNEVisualizer Value Error when no classes are specified.
• Adds the circle back to RadViz! This visualizer has also been updated to ensure there's a visualization even when there are missing values
• Updated RocAuc to correctly check the number of classes
• Switch from converting structured arrays to ndarrays using np.copy instead of np.tolist to avoid NumPy deprecation warning.
• DataVisualizer updated to remove np.nan values and warn the user that nans are not plotted.
• ClassificationReport no longer has lines that run through the numbers, is more grid-like

Deprecation Warnings:

• ScatterPlotVisualizer is being moved to contrib in 0.7
• DecisionBoundaryVisualizer is being moved to contrib in 0.7

Deployed: Wednesday, August 9, 2017 Contributors: @bbengfort, @rebeccabilbro, @ndanielsen, @cjmorale, @JimStearns206, @pbs929, @jkeung

Changes

• Added VisualTestCase.
• New PCADecomposition Visualizer, which decomposes high dimensional data into two or three dimensions so that each instance can be plotted in a scatter plot.
• New and improved ROCAUC Visualizer, which now supports multiclass classification.
• Prototype Decision Boundary Visualizer, which is a bivariate data visualization algorithm that plots the decision boundaries of each class.
• Added Rank1D Visualizer, which is a one dimensional ranking of features that utilizes the Shapiro-Wilks ranking that takes into account only a single feature at a time (e.g. histogram analysis).
• Improved Prediction Error Plot with identity line, shared limits, and r squared.
• Updated FreqDist Visualizer to make word features a hyperparameter.
• Added normalization and scaling to Parallel Coordinates.
• Added Learning Curve Visualizer, which displays a learning curve based on the number of samples versus the training and cross validation scores to show how a model learns and improves with experience.
• Added data downloader module to the yellowbrick library.
• Complete overhaul of the yellowbrick documentation; categories of methods are located in separate pages to make it easier to read and contribute to the documentation.
• Added a new color palette inspired by ANN-generated colors

Bug Fixes:

• Repairs to PCA, RadViz, FreqDist unit tests
• Repair to matplotlib version check in JointPlot Visualizer

Update to the deployment docs and package on both Anaconda and PyPI.

• Deployed: Monday, May 22, 2017
• Contributors: @bbengfort, @jkeung

This release is an intermediate version bump in anticipation of the PyCon 2017 sprints.

The primary goals of this version were to (1) update the Yellowbrick dependencies (2) enhance the Yellowbrick documentation to help orient new users and contributors, and (3) make several small additions and upgrades (e.g. pulling the Yellowbrick utils into a standalone module).

We have updated the Scikit-Learn and SciPy dependencies from version 0.17.1 or later to 0.18 or later. This primarily entails moving from from sklearn.cross_validation import train_test_split to from sklearn.model_selection import train_test_split.

The updates to the documentation include new Quickstart and Installation guides as well as updates to the Contributors documentation, which is modeled on the Scikit-Learn contributing documentation.

This version also included upgrades to the KMeans visualizer, which now supports not only silhouette_score but also distortion_score and calinski_harabaz_score. The distortion_score computes the mean distortion of all samples as the sum of the squared distances between each observation and its closest centroid. This is the metric that K-Means attempts to minimize as it is fitting the model. The calinski_harabaz_score is defined as ratio between the within-cluster dispersion and the between-cluster dispersion.

Finally, this release includes a prototype of the VisualPipeline, which extends Scikit-Learn's Pipeline class, allowing multiple Visualizers to be chained or sequenced together.

Deployed: Monday, May 22, 2017 Contributors: @bbengfort, @rebeccabilbro, @ndanielsen

Changes

• Score and model visualizers now wrap estimators as proxies so that all methods on the estimator can be directly accessed from the visualizer
• Updated Scikit-learn dependency from >=0.17.1 to >=0.18
• Replaced sklearn.cross_validation with model_selection
• Updated SciPy dependency from >=0.17.1 to >=0.18
• ScoreVisualizer now subclasses ModelVisualizer; towards allowing both fitted and unfitted models passed to Visualizers
• Added CI tests for Python 3.6 compatibility
• Added new quickstart guide and install instructions
• Updates to the contributors documentation
• Added distortion_score and calinski_harabaz_score computations and visualizations to KMeans visualizer.
• Replaced the self.ax property on all of the individual draw methods with a new property on the Visualizer class that ensures all visualizers automatically have axes.
• Refactored the utils module into a package
• Continuing to update the docstrings to conform to Sphinx
• Added a prototype visual pipeline class that extends the Scikit-learn pipeline class to ensure that visualizers get called correctly.

Bug Fixes:

• Fixed title bug in Rank2D FeatureVisualizer

This release is the culmination of the Spring 2017 DDL Research Labs that focused on developing Yellowbrick as a community effort guided by a sprint/agile workflow. We added several more visualizers, did a lot of user testing and bug fixes, updated the documentation, and generally discovered how best to make Yellowbrick a friendly project to contribute to.

Notable in this release is the inclusion of two new feature visualizers that use few, simple dimensions to visualize features against the target. The JointPlotVisualizer graphs a scatter plot of two dimensions in the data set and plots a best fit line across it. The ScatterVisualizer also uses two features, but also colors the graph by the target variable, adding a third dimension to the visualization.

This release also adds support for clustering visualizations, namely the elbow method for selecting K, KElbowVisualizer and a visualization of cluster size and density using the SilhouetteVisualizer. The release also adds support for regularization analysis using the AlphaSelection visualizer. Both the text and classification modules were also improved with the inclusion of the PosTagVisualizer and the ConfusionMatrix visualizer respectively.

This release also added an Anaconda repository and distribution so that users can conda install yellowbrick. Even more notable, we got yellowbrick stickers! We've also updated the documentation to make it more friendly and a bit more visual; fixing the API rendering errors. All-in-all, this was a big release with a lot of contributions and we thank everyone that participated in the lab!

Deployed: Thursday, May 4, 2017 Contributors: @bbengfort, @rebeccabilbro, @ndanielsen, @mattandahalfew, @pdamodaran, @NealHumphrey, @jkeung, @balavenkatesan, @pbwitt, @morganmendis, @tuulihill

Changes

• Part of speech tags visualizer -- PosTagVisualizer.
• Alpha selection visualizer for regularized regression -- AlphaSelection
• Confusion Matrix Visualizer -- ConfusionMatrix
• Elbow method for selecting K vis -- KElbowVisualizer
• Silhouette score cluster visualization -- SilhouetteVisualizer
• Joint plot visualizer with best fit -- JointPlotVisualizer
• Scatter visualization of features -- ScatterVisualizer
• Added three more example datasets: mushroom, game, and bike share
• Contributor's documentation and style guide
• Maintainers listing and contacts
• Light/Dark background color selection utility
• Structured array detection utility
• Updated classification report to use colormesh
• Added anacondas packaging and distribution
• Refactoring of the regression, cluster, and classification modules
• Image based testing methodology
• Docstrings updated to a uniform style and rendering
• Submission of several more user studies

Intermediate sprint to demonstrate prototype implementations of text visualizers for NLP models. Primary contributions were the FreqDistVisualizer and the TSNEVisualizer.

The TSNEVisualizer displays a projection of a vectorized corpus in two dimensions using TSNE, a nonlinear dimensionality reduction method that is particularly well suited to embedding in two or three dimensions for visualization as a scatter plot. TSNE is widely used in text analysis to show clusters or groups of documents or utterances and their relative proximities.

The FreqDistVisualizer implements frequency distribution plot that tells us the frequency of each vocabulary item in the text. In general, it could count any kind of observable event. It is a distribution because it tells us how the total number of word tokens in the text are distributed across the vocabulary items.

Deployed: Wednesday, February 22, 2017 Contributors: @rebeccabilbro, @bbengfort

Changes

• TSNEVisualizer for 2D projections of vectorized documents
• FreqDistVisualizer for token frequency of text in a corpus
• Added the user testing evaluation to the documentation
• Created scikit-yb.org and host documentation there with RFD
• Created a sample corpus and text examples notebook
• Created a base class for text, TextVisualizer
• Model selection tutorial using Mushroom Dataset
• Created a text examples notebook but have not added to documentation.

Hardened the Yellowbrick API to elevate the idea of a Visualizer to a first principle. This included reconciling shifts in the development of the preliminary versions to the new API, formalizing Visualizer methods like draw() and finalize(), and adding utilities that revolve around Scikit-Learn. To that end we also performed administrative tasks like refreshing the documentation and preparing the repository for more and varied open source contributions.

Deployed: Friday, January 20, 2017 Contributors: @bbengfort , @rebeccabilbro, @StampedPassp0rt

Changes

• Converted Mkdocs documentation to Sphinx documentation
• Updated docstrings for all Visualizers and functions
• Created a DataVisualizer base class for dataset visualization
• Single call functions for simple visualizer interaction
• Added yellowbrick specific color sequences and palettes and env handling
• More robust examples with downloader from DDL host
• Better axes handling in visualizer, matplotlib/sklearn integration
• Added a finalize method to complete drawing before render
• Improved testing on real data sets from examples

Bugfixes

• Score visualizer renders in notebook but not in Python scripts.
• Tests updated to support new API